# data[].attributes.headline
42. The proximity–access paradox in Medicaid administrative services: Transportation barriers and spatial inequalities in the United States. 

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42. The proximity–access paradox in Medicaid administrative services: Transportation barriers and spatial inequalities in the United States. 

# data[].attributes.body
Nie, X., Huang, Z., & Yu, G. (2027). The proximity–access paradox in Medicaid administrative services: Transportation barriers and spatial inequalities in the United States. Travel Behaviour and Society, 46, 101386. https://doi.org/10.1016/j.tbs.2026.101386

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41. Detección vehicular con YOLOv11L para estimación del factor de hora pico [Vehicle detection using YOLOv11L to estimate the rush-hour factor].

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41. Detección vehicular con YOLOv11L para estimación del factor de hora pico [Vehicle detection using YOLOv11L to estimate the rush-hour factor].

# data[].attributes.body
Tacuri-Rivas, M. A., Ordóñez-Fernández, J. L., Ordóñez-Barreto, H. N., & Naranjo-Zumba, K. E. (2026). Detección vehicular con YOLOv11L para estimación del factor de hora pico [Vehicle detection using YOLOv11L to estimate the rush-hour factor]. Ingenium et Potentia, 8(15), 117–132. https://doi.org/10.35381/i.p.v8i15.5199

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Detección vehicular con yolov11l para estimación del factor de hora pico

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Este estudio tuvo como objetivo evaluar la efectividad de un sistema automatizado de aforo vehicular basado en inteligencia artificial para la determinación del volumen y factor horario de máxima demanda. Metodológicamente, se implementó un sistema de visión por computadora utilizando el algoritmo YOLOv11l para la detección y clasificación vehicular, procesando secuencias de video de 12 horas diarias (06:00–18:00) durante 7 días consecutivos. Con respecto a los resultados, el VHMD registrado fue de 1,698 vehículos (Día 2, entre 17:00–18:00), con un FHP que varió entre 0.65 y 0.84 según el día analizado. En conclusión, el sistema de aforo vehicular automatizado constituye una herramienta confiable y eficiente para la determinación del volumen y factor horario de máxima demanda, superando las limitaciones de los métodos manuales tradicionales y proporcionando información detallada sobre los patrones de movilidad en diferentes días de la semana.

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Time, Not Distance: How Urban Congestion Shapes Health Access. / Es el tiempo, no la distancia: cómo la congestión urbana influye en el acceso a la salud. IADB-BID; 2026

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Time, Not Distance: How Urban Congestion Shapes Health Access. / Es el tiempo, no la distancia: cómo la congestión urbana influye en el acceso a la salud. IADB-BID; 2026

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Cuervo, L. G., Tejerina, L. R., & Goyeneche, L. (2026, August 5). IDB | Time, Not Distance: How Urban Congestion Shapes Health Access. Inter-American Development Bank.

Cuervo LG, Tejerina LR, Goyeneche L. BID | E s el tiempo, no la distancia: cómo la congestión urbana influye en el acceso a la salud [Internet]. Banco Interamericano de Desarrollo; 2026 [cited 2026 Aug 6]. Available from: https://www.iadb.org/es/blog/salud-nutricion-y-poblacion/es-el-tiempo-no-la-distancia-como-la-congestion-urbana-influye-en-el-acceso-la-salud

# data[].attributes.attachment_link.authors[].name
Luis Gabriel Cuervo and Luis Rodrigo Tejerina and Laura Goyeneche

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Time, Not Distance: How Urban Congestion Shapes Health Access

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Urban congestion can significantly affect access to health care. Travel-time data can help planners improve equity in access to essential services.

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Traffic Congestion, Health Services Accessibility, and Equity: A People-Centred, Data-Driven Approach to Urban and Health Planning. Test Cases for Acute and Chronic Care Accessibility in Cali, Colombia

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Traffic Congestion, Health Services Accessibility, and Equity: A People-Centred, Data-Driven Approach to Urban and Health Planning. Test Cases for Acute and Chronic Care Accessibility in Cali, Colombia

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Traffic Congestion, Health Services Accessibility, and Equity: A People-Centred, Data-Driven Approach to Urban and Health Planning. Test Cases for Acute and Chronic Care Accessibility in Cali, Colombia

Cuervo, L. G. (Author). 14 Apr 2026

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Universitat Autònoma de Barcelona Research Portal

# data[].attributes.attachment_link.authors[].name
Universitat Autònoma de Barcelona Research Portal

# data[].attributes.attachment_link.title
Traffic Congestion, Health Services Accessibility, and Equity: A People-Centred, Data-Driven Approach to Urban and Health Planning.

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Background: Traffic congestion restricts timely access to health services, worsening health inequities. Existing tools to measure or predict accessibility are often costly, highly specialised, and ill-suited to intersectoral decision-making. Advances in open georeferenced data (e.g. census, health service registries), big data, and participatory, people-centred approaches now allow the creation of policy-ready cartography and metrics to guide equitable service planning.

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40. Assessment of primary healthcare accessibility in rural areas considering vulnerability

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40. Assessment of primary healthcare accessibility in rural areas considering vulnerability

# data[].attributes.body
Castañeda-Torres, V., Pérez-Guzmán, S., & Gutiérrez-Gutiérrez, E. V. (2026). Assessment of primary healthcare accessibility in rural areas considering vulnerability. Socio-Economic Planning Sciences, 106, 102516. https://doi.org/10.1016/j.seps.2026.102516

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39. Inter-facility travel time and referral for emergency obstetric care in the 15 most populated cities of Nigeria: a spatial analysis. 

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39. Inter-facility travel time and referral for emergency obstetric care in the 15 most populated cities of Nigeria: a spatial analysis. 

# data[].attributes.body
Wong, K.L., Banke-Thomas, A., Olubodun, T. et al. Inter-facility travel time and referral for emergency obstetric care in the 15 most populated cities of Nigeria: a spatial analysis. BMC Glob. Public Health 4, 53 (2026). https://doi.org/10.1186/s44263-026-00285-8

# data[].attributes.attachment_link.title
Inter-facility travel time and referral for emergency obstetric care in the 15 most populated cities of Nigeria: a spatial analysis - BMC Global and Public Health

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Background Timely access to comprehensive emergency obstetric care (CEmOC) can be vital for ensuring maternal and newborn survival. However, pregnant women in need of CEmOC may not initially present at a CEmOC facility, thereby necessitating inter-facility referral. We assessed inter-facility travel time between potential referring non-CEmOC facilities and receiving CEmOC facilities in the 15 largest Nigerian cities. Methods Data was sourced from the 2018 Nigeria Health Facility Registry, with additional facilities verified in 2022. We applied Google Maps Platform’s internal Directions Application Programming Interface (API) to derive driving times from each 600m2 S2 cell to their respective nearest CEmOC facilities. Geographic coordinates of non-CEmOC facilities were mapped to S2 cells to retrieve travel time to public CEmOC facilities. Travel times were estimated from each S2 cell to the nearest CEmOC facilities by ownership (public and private) under peak traffic scenario (weekdays 18–20 h) and off-peak traffic scenario (weekends 01–03 h). Based on the shortest inter-facility travel time, each non-CEmOC facility was paired with a public CEmOC facility. Median travel time and percentage of non-CEmOC facilities located > 30 and > 60 min to the nearest public CEmOC facility were estimated. Sensitivity analysis comparing the API’s travel time estimates for randomly-selected 10% of non-CEmOC facilities with those from other methods was conducted. Results Altogether, 4,563 and 1,963 non-CEmOC and CEmOC facilities were included, respectively. Percentage of non-CEmOC facilities located > 30 min to the nearest public CEmOC was highest in Port Harcourt (51%) and lowest in Maiduguri (6%). All non-CEmOC facilities were located ≤ 60 min from the nearest public CEmOC facility in Aba, Owerri, and Ilorin. Median number of non-CEmOC facilities connected to a public CEmOC facility was 27, with five public CEmOC facilities connected to > 100 non-CEmOC facilities. For some non-CEmOC facilities, the nearest public CEmOC facilities are in a contiguous city or state. Conclusions Inter-facility referrals in large Nigerian cities show substantial variation in travel time and uneven referral loads, revealing critical pressure points that may delay timely access to CEmOC. Integrating travel time metrics into maternal health planning is essential for improving the efficiency, equity, and resilience of resource-constrained urban referral systems.

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37. Modeling Healthcare Accessibility with Endogenous Search Ranges: A Huff-Based Multi-Source Data Approach

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37. Modeling Healthcare Accessibility with Endogenous Search Ranges: A Huff-Based Multi-Source Data Approach

# data[].attributes.body
Chen, W., Mao, Y., Xu, T., Wang, Y., Huang, Z., Papageorgiou, M., & Zheng, P. (2026). Modeling Healthcare Accessibility with Endogenous Search Ranges: A Huff-Based Multi-Source Data Approach. Systems, 14(5), 571. https://doi.org/10.3390/systems14050571

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1. Introduction

Spatial accessibility to healthcare services is a central concern in urban planning and public health research, as it directly influences healthcare utilization, equity, and health outcomes. Among different healthcare facility types, tertiary hospitals exhibit distinctive service characteristics: they provide specialized care, attract patients across large spatial ranges, and operate within competitive and hierarchical hospital systems. Accurately capturing accessibility to such facilities therefore requires models that reflect both spatial opportunity structures and residents' healthcare-seeking behavior.

The Two-Step Floating Catchment Area (2SFCA) method and its variants have become widely used tools for measuring healthcare accessibility due to their intuitive structure and moderate data requirements [ 1, 2, 3]. Extensions incorporating distance decay, such as Gaussian or kernel-based 2SFCA models [ 4, 5], have improved realism by allowing service influence to decrease with travel cost. However, these approaches typically rely on exogenously defined catchment thresholds or globally fixed decay parameters [ 6, 7, 8], implicitly assuming that residents evaluate healthcare opportunities within uniform spatial or temporal limits.

Such assumptions are problematic in the context of tertiary hospitals. Unlike primary care facilities, tertiary hospitals do not operate within locally bounded catchments; patients often travel well beyond conventional thresholds when perceived service quality or specialization justifies longer journeys. Empirical studies consistently show substantial heterogeneity in healthcare travel behavior across space and population groups, suggesting that fixed catchment definitions are conceptually misaligned with actual decision processes.

To address these limitations, studies have incorporated probabilistic choice models, such as the Huff model [ 9], into accessibility analysis [ 10, 11]. These approaches allow facilities to compete probabilistically based on attractiveness and distance, thereby capturing the substitution effects among alternative facilities. However, in most existing integrations, probabilistic choice is used only to reweight facility influence, while the effective search range remains externally imposed or implicitly constrained. As a result, the fundamental question of how far residents are behaviorally willing to search remains unresolved.

This study argues that accessibility assessment should be grounded in observed travel behavior rather than arbitrary thresholds. To achieve this, we leverage large-scale taxi trajectory data to empirically calibrate the Huff model parameters from observed hospital choices and to construct realistic travel impedance matrices from actual trips.

Taxi data capture actual road-network conditions, including congestion and route choices. We construct a complete origin-destination travel time matrix between all residential townships/subdistricts and tertiary hospitals based on observed taxi trips. Building on these data-driven foundations, we propose a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework. The model supplies origin-specific choice probabilities that reflect competition among hospitals, calibrated from observed taxi trips, while the 2SFCA structure accounts for supply-demand ratios and cumulative opportunities without imposing exogenous catchment thresholds. The resulting accessibility measure captures both the attractiveness of hospitals and the actual travel burden experienced by residents, without imposing exogenous catchment thresholds.

The proposed framework is empirically validated using multi-source data from Ningbo, China: hospital bed capacity, township-level population, over 1.58 million taxi trajectories, and annual outpatient volumes from 16 tertiary hospitals. By comparing modeled accessibility outcomes with observed travel patterns and outpatient distributions, we provide behavioral evidence for the validity of our approach.

This study makes three main contributions: (1) empirical calibration of Huff model parameters from observed taxi trips; (2) construction of high-resolution travel impedance matrices from real-world trajectories; and (3) integration of these elements into a behavior-calibrated 2SFCA framework (BCEC-2SFCA) that generates endogenous search ranges.

It is important to emphasize that the proposed framework is specifically designed for high-order medical services-namely tertiary hospitals-which are characterized by strong inter-facility competition, large service areas, and prevalent cross-regional healthcare-seeking behavior. The endogenous search range mechanism is predicated on the assumption that patients actively compare and choose among multiple alternative facilities, a condition that does not hold for proximity-based services such as community health centers, where travel is typically constrained to a local catchment. The applicability of the BCEC-2SFCA model to primary care or other locally bounded service contexts should therefore be considered limited, and the conventional Gaussian 2SFCA or similar threshold-based methods may remain more appropriate in those settings.

2. Literature Review

Accessibility is a crucial concept for estimating the ease with which people can overcome spatial barriers to access services or resources. W. G. Hansen first defined it in 1959 as "the potential of opportunities for interaction," laying the foundation for accessibility research [ 12]. Since then, the concept has expanded to encompass transportation, temporal, and psychological dimensions, becoming a core metric in geography and urban planning [ 13].

The minimum distance method [ 19] and buffer analysis evaluate accessibility by calculating the straight-line distance to the nearest facility or setting a fixed service radius. These methods are simple and intuitive, often used for preliminary delineation of hospital service areas. However, neither considers the constraints of the actual transportation network, which may lead to discrepancies with real-world conditions.

Network analyses can answer a range of questions related to linear networks such as roads, railways, rivers, facilities and utilities. This spatial analysis technique uses network data (usually linear features such as roads, footpaths) to calculate distances between points or nodes on the network. This approach underpins the satellite navigation systems found in many cars. Common applications are route finding, route planning, identifying the closest facility by travel time or distance, calculation of service areas (e.g., areas within 10 min's walk of a bus stop), etc. There are various ways of parameterising the analysis based on typical road speeds, blockages, and minimising the use of smaller or remote parts of the network depending on the task [ 20].

The gravity model is one of the most representative and classic methods in spatial interaction and accessibility analysis. Originating from Newton's law of universal gravitation, it was first introduced into human geography and transportation planning by Zipf [ 21, 22], and later endowed with a rigorous statistical mechanics foundation by Wilson within the framework of entropy maximization, thereby evolving from an empirical formula into a systematic theory of spatial interaction [ 23]. In accessibility research, the fundamental principle of the gravity model is that the intensity of interaction between two locations (e.g., trip volume) is proportional to the "mass" of the origin (e.g., population size) and the "mass" of the destination (e.g., facility service capacity), and inversely proportional to a power of the spatial impedance (distance or time) between them. When applied to measure accessibility, the model is often used in its "potential model" form, which sums the service capacities of all medical facilities j weighted by a distance decay function for a given residential location i, thereby yielding a continuous accessibility score for that location. Compared with the cumulative opportunity method or the isochrone method, the gravity model offers the advantage of incorporating continuous distance decay, thus avoiding the issue of threshold discontinuity. In contrast to the two-step floating catchment area method, its traditional form typically does not explicitly address competition effects on the demand side. The two-step floating catchment area (2SFCA) method was first proposed by Radke and Mu (2000) [ 17], and the version modified by Luo and Wang (2003) [ 1] has been widely used. The 2SFCA method fully considers the influence of both supply-side and demand-side scales on accessibility and has become a primary research method for measuring spatial accessibility in the healthcare field. Extensions of the 2SFCA method by researchers can be summarized into four categories: (1) extensions of the distance decay function, (2) extensions of the catchment radius, (3) mode-of-transport-based extensions, and (4) extensions targeting demand or supply. The distance decay functions commonly used by researchers include a piecewise decay form that assigns different weights to different travel time intervals within the catchment area [ 24], the gravity decay model, the kernel density form [ 5], and the Gaussian form [ 4].

Luo et al. proposed an enhanced two-step floating catchment area (E2SFCA) method to address the assumption of uniform accessibility within the catchment area in the traditional 2SFCA method. This method assigns differentiated weights to different travel time intervals in both steps to reflect the distance decay effect [ 24]. The gravity-based 2SFCA method introduces the distance decay function from the well-established gravity model into the 2SFCA framework, thereby improving the discrete distance decay function of the E2SFCA method into a continuous function. The kernel density 2SFCA (KD2SFCA) method, proposed by Dai [ 5], incorporates a kernel density-based distance decay function within the 2SFCA catchment radius. The kernel density decay function is concave: when the distance is small, accessibility decays slowly with increasing distance; as the distance increases, the decay accelerates. Based on the 2SFCA method, Dai introduced a Gaussian function to continuously weight the distance decay within the catchment area and formally proposed the Gaussian 2SFCA method. Using the Detroit metropolitan area as the study region, Dai investigated the impact of racial residential segregation and spatial accessibility to healthcare facilities on the rate of late-stage breast cancer diagnosis, aiming to more accurately assess the spatial accessibility of medical facilities [ 4].

While the integration of Huff models into accessibility analysis has gained increasing attention, the calibration of Huff model parameters-the attractiveness elasticity α and the distance-decay coefficient β-remains a critical challenge. In most existing Huff-FCA studies, these parameters are either assumed based on prior literature (e.g., α = 1, β = 2) or determined through ad hoc sensitivity tests. However, because α and β govern the sensitivity of residents to facility attractiveness and travel impedance, respectively, arbitrarily setting their values risks producing accessibility estimates that deviate systematically from actual travel behavior.

In summary, three critical gaps in the existing Huff-based FCA literature are identified and addressed in this work. First, Huff parameters are typically assumed rather than empirically calibrated from local healthcare travel data. Second, impedance matrices are usually derived from idealized distances, failing to capture real-world travel conditions. Third, even with Huff probabilities, the effective service range remains constrained by exogenously imposed thresholds. This study addresses these gaps by integrating empirically calibrated Huff choice probabilities-derived from large-scale taxi trajectory data-into a 2SFCA framework to generate endogenous hospital search ranges, thereby eliminating reliance on both assumed behavioral parameters and exogenously imposed catchment thresholds. More precisely, the proposed BCEC-2SFCA framework makes three distinct contributions: (1) joint empirical calibration of α and β from observed taxi trips; (2) construction of an impedance matrix from actual travel times; and (3) a probabilistic 2SFCA structure in which catchment areas are endogenously generated by choice probabilities. The following section details the data and methods that operationalize this framework.

3. Methodology

3.1. Overview of the Framework

This study develops a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for tertiary hospitals by integrating a Huff-type probabilistic choice model with the supply-demand logic of the two-step floating catchment area (2SFCA) method. The framework is designed to replace exogenously specified service catchments with an endogenously generated search range, inferred from observed travel behavior, hospital attractiveness, and inter-hospital competition.

The methodological workflow consists of three components. First, hospital-oriented taxi trajectories are used to construct an empirical origin-destination impedance matrix based on observed travel time and travel distance. Second, the parameters of a Huff choice model are calibrated from revealed destination choices so that the probability of selecting each hospital reflects the joint effects of hospital attractiveness and travel impedance. Third, the estimated choice probabilities are embedded into a probabilistic 2SFCA structure to compute hospital-specific supply-demand ratios and origin-specific accessibility scores.

The conceptual difference from conventional 2SFCA is important. In standard Gaussian 2SFCA models, the analyst predefines the effective service range using a fixed threshold or a globally imposed decay function. In the proposed framework, by contrast, the effective service range is not fixed a priori. Instead, it emerges from the estimated choice probabilities across all hospitals. A hospital with stronger attractiveness or weaker competitive pressure can exert influence over a wider area, whereas a less attractive hospital faces a narrower effective reach. Therefore, the search range is treated as endogenous to the healthcare system, rather than externally imposed.

This section presents the theoretical specification of the BCEC-2SFCA framework, including the Huff probabilistic choice model, the derivation of endogenous search ranges, and the two-step accessibility formulation. The empirical implementation-data sources, matrix construction, and parameter calibration-is detailed separately in Section 4.

3.2. Endogenous Search Range Through Probabilistic Hospital Choice

3.2.1. Hospital Choice Model Based on Calibrated Huff-Type Probabilities

i = 1, 2, ..., n denote demand locations, represented by townships/subdistricts;

j = 1, 2, ..., m denote tertiary hospitals;

is the choice probability of the resident at demand point i for hospital j;

is the supply capacity of hospital j;

is the travel impedance from demand point i to hospital j, measured by observed travel time or travel distance;

is the service-capacity elasticity parameter;

is the impedance-decay parameter;

Equation (1) implies that the probability of choosing a hospital increases with hospital attractiveness and decreases with travel impedance. More importantly, it is normalized over all competing hospitals, so the attractiveness of any hospital is always evaluated relative to the alternatives available to the same origin. This allows inter-hospital competition to be represented directly within the accessibility model.

The term "endogenous search range" can now be defined rigorously. For any demand location i, the effective set of hospitals considered by residents is not determined by a fixed threshold such as 30 min or 20 km. Instead, it is generated by the set of hospitals with non-negligible choice probabilities:

where is a small behavioral relevance threshold. Under this definition, the search range varies across origins and hospitals according to the joint effects of attractiveness, impedance, and competition. Thus, range is an outcome of the model rather than an external assumption.

3.2.2. Parameter Calibration from Observed Trips

To avoid imposing arbitrary parameter values, α and β are calibrated using observed hospital-oriented taxi trips. Let denote the number of observed taxi trips from demand location i to hospital j, and let the observed hospital choice share be:

The Huff parameters are estimated by fitting the modeled choice probabilities in Equation (1) to the observed shares . This can be implemented using Log-centered transformation and Ordinary Least Squares (OLS) on the origin-hospital observations (See Section 4.2 for details).

The estimated parameters quantify two distinct behavioral mechanisms:

α captures the sensitivity of residents to hospital capacity, while β measures how quickly hospital attractiveness declines with increasing travel impedance. Because these parameters are estimated from observed mobility rather than borrowed from previous studies, the accessibility model becomes more behaviorally grounded and more context-specific.

3.3. Construction of the Empirical Impedance Matrix

The spatial impedance matrix is built from taxi trajectory data linking demand locations to tertiary hospitals. For each observed origin-hospital pair, travel time and travel distance are extracted from actual taxi trips. These observations reflect real road-network conditions, including congestion, route choice, and temporal variation, thereby providing a more realistic measure of healthcare-related travel cost than Euclidean distance or idealized network paths. The impedance matrix is constructed solely from observed taxi trips, providing complete coverage for all OD pairs.

3.4. Probabilistic 2SFCA Accessibility Model

The calibrated choice probabilities are then incorporated into a probabilistic 2SFCA framework.

3.4.1. Step 1: Hospital Supply-Demand Ratio

For each hospital j, the expected demand is defined as the population-weighted sum of origin-specific hospital choice probabilities:

where denote the population at demand location i. This expression replaces the conventional fixed-catchment demand aggregation. In standard 2SFCA, only demand within a predefined threshold contributes to hospital congestion. Here, all demand locations may contribute, but their contributions are weighted by the estimated probability of choosing that hospital. Consequently, the effective catchment of each hospital becomes behavior-dependent and hospital-specific.

The effective supply-demand ratio of hospital j is then given by:

A higher indicates that hospital j has greater effective service capacity relative to the demand it is expected to attract.

3.4.2. Step 2: Accessibility at Each Demand Location

The accessibility of origin i is defined as the expected supply-demand ratio across all hospitals, weighted by the hospital choice probabilities:

Equation (7) is the core accessibility measure used in this study. It retains the two-step logic of 2SFCA by accounting for both hospital congestion and demand-side access, but it eliminates the need for externally imposed catchment thresholds. Accessibility is instead determined by the endogenous hospital choice structure estimated from observed travel behavior.

3.5. Interpretation of the Endogenous Search Range

Under the proposed model, the effective influence range of a hospital is not fixed across space. For a large and attractive hospital, the term may offset higher travel impedance, allowing the hospital to retain meaningful choice probabilities over a wider area. Conversely, smaller or less attractive hospitals may have influence concentrated in nearby locations only. Likewise, for residents in peripheral areas with few competitive options, the dominant hospital may exhibit a broader effective search range than for residents in central areas with many alternatives.

Therefore, the endogenous search range is not represented by a single citywide threshold. Instead, it is embedded in the probability surface {P i j}, which varies across origins and facilities and is jointly shaped by attractiveness, impedance, and competition.

To provide a more intuitive geographical interpretation, the endogenous search range can be likened to a "probability cloud" surrounding each hospital, rather than a sharply bounded circle. In conventional threshold-based models, a hospital is either inside or outside a resident's search range depending on whether the travel cost falls below a fixed cutoff-a binary, all-or-nothing designation. In the BCEC-2SFCA framework, by contrast, every hospital exerts some degree of influence on every origin, but the strength of that influence decays continuously with travel impedance and is further modulated by the hospital's attractiveness relative to its competitors. A hospital with high attractiveness (e.g., a large tertiary center) may retain a non-negligible choice probability even for residents located 50 km away, meaning that it remains part of their effective search range. Conversely, a smaller hospital may fade into the background of the probability surface beyond a much shorter distance. The effective search range is therefore not a predefined geometric shape but an emergent property of the spatial pattern of choice probabilities-a gradient that captures both the friction of distance and the pull of service quality. This probabilistic, gradient-based conception of search range aligns more closely with the continuous nature of human spatial behavior than do conventional hard thresholds.

3.6. Standardization and Spatial Comparison

To facilitate spatial visualization and comparison across townships/subdistricts, the raw accessibility values Ai are standardized using max-normalization:

3.8. Methodological Contribution

The proposed method contributes to accessibility assessment in three ways. First, it estimates hospital choice sensitivities from observed trips rather than assuming parameter values. Second, it constructs the impedance matrix from real trajectory data rather than idealized distances. Third, it embeds these empirically estimated choice probabilities into a supply-demand accessibility framework-the BCEC-2SFCA-in which the effective hospital search range is generated endogenously. The resulting model is particularly suitable for tertiary hospital systems characterized by long-distance patient movement, strong facility competition, and heterogeneous travel behavior.

4. Data and Implementation

4.1. Data Processing and Initial Matrix Construction

Building on the theoretical framework presented in Section 3, this section describes the empirical implementation of the BCEC-2SFCA model. To implement the proposed model, this section details the research data and its processing workflow. The core objective is to construct four spatial impedance matrices for model calculation and parameter calibration: the trip frequency matrix , the observed choice probability matrix , the median travel time matrix , and the median travel distance matrix . The empirical analysis of this study is based on multifaceted data from Ningbo City, China.

4.1.1. Definition of the Study Area

This study focuses on the principal urban area of Ningbo, encompassing the districts of Haishu, Jiangbei, Yinzhou, Zhenhai, Beilun, and Fenghua ( Figure 1). The primary consideration for selecting this area as the research scope is that it concentrates the vast majority of tertiary hospital resources in Ningbo, where residents' healthcare travel behavior is intensive and exhibits typical patterns, effectively reflecting the service characteristics of tertiary hospitals. Ninghai County, Xiangshan County, Cixi City, Yuyao City, and the hospitals within their jurisdictions are temporarily excluded from this study due to their considerable distance from the main urban area and the fact that their healthcare travel patterns exhibit significant differences.

The six districts included in the study area have a combined permanent population of approximately 5.33 million as of 2024, with population densities varying considerably from the densely populated urban core (e.g., Haishu and Yinzhou) to the more sparsely populated peripheral districts (e.g., Fenghua and Beilun). The 22 tertiary hospitals in the study area collectively provide over 20,000 inpatient beds, with bed counts per hospital ranging from approximately 150 to 1900. The average road distance between a township/subdistrict centroid and its nearest tertiary hospital is approximately 9 km, while the distance to the nearest hospital exceeds 30 km in some peripheral areas, underscoring the substantial spatial variation in healthcare access across the region.

4.1.2. Data Sources and Preprocessing

Two primary categories of data are used: taxi trajectory data and hospital attribute data.

(1)

Taxi Trajectory Data: The dataset covers trips within Ningbo from 1 March to 30 November 2023. Each record includes timestamps, longitude/latitude coordinates for pick-up and drop-off locations, travel distance, and calculated trip duration.

(2)

Data processing aimed to identify and extract valid trips with hospitals as destinations suitable for travel behavior analysis. The workflow is illustrated in Figure 2. Key steps include:

From the raw data, all records containing terms such as "hospital" in the drop-off location field were extracted.

Trips with drop-off locations corresponding to each specific hospital (e.g., "The Affiliated First Hospital of Ningbo University") were extracted based on the hospital name and then aggregated for statistical analysis.

To eliminate recording errors and exceptional cases, trips meeting any of the following criteria were removed:

(a)

trips with a travel time of less than 1 min or greater than 180 min;

(b)

trips with a travel distance of less than 500 m or greater than 120 km;

The time and distance thresholds serve to exclude recording errors and data noise: the lower bounds (1 min, 500 m) remove likely mis-recorded drop-offs and short-distance positioning errors, while the upper bounds (180 min, 120 km) are set generously to retain legitimate long-distance healthcare-seeking trips-a defining characteristic of tertiary hospital access. Notably, only 0.03% of candidate trips exceeded 120 km, and only 0.32% were shorter than 500 m.

(c)

trips with an average speed outside the range of [5,100] km/h, to exclude trips with speeds clearly deviating from the typical range of urban road traffic;

(d)

trips with clearly anomalous coordinate information, to exclude cross-city trips and obvious GPS anomalies.

For spatial analysis, the 81 townships/subdistricts of Ningbo were used as demand units, with the centroid of each township/subdistrict representing a demand point i. Using ArcGIS spatial analysis tools (ArcGIS 10.8, Esri, Redlands, CA, USA), the pick-up point of each trip was matched to the township/subdistrict polygon in which it was located. These 81 townships and subdistricts constitute the finest administrative divisions within the six districts and serve as the demand units (i = 1, 2, ..., 81) throughout the subsequent accessibility analysis.

This process yielded 1,583,831 valid trip records, forming the foundation for subsequent analysis.

Figure 3 presents the distribution of taxi visit volumes to different hospitals. The black box in the figure indicates the central urban area of Ningbo, and each red circle represents a hospital. The larger the red circle, the greater the taxi visit volume to that hospital during the study period. As can be seen from the figure, most tertiary hospitals are concentrated in the central urban area, and the taxi visit volumes of these centrally located hospitals are generally high. In contrast, hospitals in peripheral areas (e.g., Zhenhai District, Jiangbei District, and Fenghua District) have relatively lower taxi visit volumes. It should be noted that this figure presents raw data on taxi visit intensity derived from trajectory data, which is a distinct concept from hospital annual outpatient volume. The relationship between taxi visit volume and outpatient volume isfurther analyzed later (see the subsection "Selection of Hospitals for Parameter Calibration" in Section 4.2.2), where a subset of hospitals exhibiting a strong linear correspondence with outpatient volume is identified for model calibration.

Figure 4 shows significant differences in taxi visit volumes across hospitals. The visit volume of The First Affiliated Hospital of Ningbo University is considerably higher than that of specialized institutions such as Ningbo Kangning Hospital. Moreover, taxi visits to each hospital are mainly concentrated in areas close to the hospital.

Figure 5 and Figure 6 present the distribution of trip volumes from Gulou Subdistrict in Haishu District to each hospital and the distribution of total trip volumes for all hospitals, respectively. Both figures clearly illustrate the variations in taxi visit volumes among the different hospitals.

4.1.3. Initial Spatiotemporal Matrices

Based on the cleaned data, four key matrices were constructed as the basis for parameter calibration and model computation:

The total number of trips during the observation period was counted for each "subdistrict i-hospital j" pair, forming a 81 × 22 matrix.

To transform trip counts into a probabilistic representation of residents' choice behavior, the observed choice probability was calculated based on the trip count matrix. For each subdistrict i, the observed probability of choosing hospital j is calculated as:

Preliminary analysis of the trip duration matrix revealed three important features:

(1)

Spatial Coverage Characteristics of the Data: the dataset provides complete coverage: for every township/subdistrict and hospital pair, observed taxi trips are available, ensuring a full origin-destination matrix.

(2)

Travel Time Distribution Characteristics: The median travel time for all valid OD pairs ranges from 3.9 to 110.5 min, with a mean of 33.2 min. Travel time interval analysis shows that the 15-30 min interval accounts for the highest proportion (31.1%), followed by the 30-45 min (29.2%) and 45-60 min (19.5%) intervals. This distribution aligns with the characteristic of tertiary hospitals serving large areas and involving medium-to-long-distance travel.

(3)

Travel Distance Distribution Characteristics: The median travel distance for all valid OD pairs ranges from 1.4 km to 87.4 km, with a mean of 22.6 km. Travel distance interval analysis shows that the 10-20 km interval accounts for the highest proportion (26.2%), followed by the 0-10 km (25.5%) and 20-30 km (18.1%) intervals. This distribution also aligns with the characteristic of tertiary hospitals serving large areas and involving medium-to-long-distance travel.

4.2. Behavioral Parameter Calibration

4.2.1. Theoretical Derivation: Log-Centering Transformation of the Huff Model

To calibrate the service capacity elasticity coefficient α and the impedance decay coefficient β in the Huff model, this study follows the classic approach of Yang et al. (2012) [ 35], adopting the log-centering transformation proposed by Nakanishi and Cooper (1982) [ 40] to convert the nonlinear Huff model into a linear estimable form, and then using Ordinary Least Squares (OLS) for parameter estimation. It should be noted that this calibration is performed exclusively using travel time impedance; the resulting parameters () capture residents' actual sensitivity to time cost. In the subsequent BCEC-2SFCA model based on travel distance, these same parameters are retained to maintain behavioral consistency, thereby focusing the comparison on the sensitivity of accessibility outcomes to different impedance specifications.

For each demand point and hospital , the standard Huff model is expressed as:

where represents the theoretical probability of choosing hospital from demand point , is hospital service capacity, is the healthcare travel time from to , α and β are parameters to be estimated.

To eliminate , we compute geometric means over all hospitals j for each origin i:

Subtracting (15) from (14) eliminates the origin-specific constant :

Finally, including an error term , the estimable linear regression model is:

where:

Equation (17) is the linear regression model obtained through the log-centering transformation, and its parameters α and β can be estimated via ordinary least squares (OLS).

4.2.2. Empirical Implementation: Parameter Estimation Based on Observed Data

The theoretical derivation in Section 4.2.1 provides a linear regression framework for estimating the Huff model parameters. This subsection applies this framework to the empirical data, using the filtered set of 14 hospitals that exhibit a strong linear relationship between taxi-visit volumes and annual outpatient volumes (as described in subsection "Selection of Hospitals for Parameter Calibration"). The estimation is based on the observed taxi trips to these hospitals, which provide revealed preferences for healthcare destinations.

Selection of Hospitals for Parameter Calibration

To ensure that the observed taxi-based choice probabilities reasonably reflect overall healthcare-seeking behavior, we first identify a subset of hospitals for which taxi-visit volumes exhibit a strong linear relationship with annual outpatient volumes. This screening step mitigates the potential bias arising from using taxi data as a proxy for the general population, as taxi trips may not fully represent all transportation modes.

Among the 22 tertiary hospitals in the study area, annual outpatient volume data are available for 16 hospitals. For these 16 hospitals, we perform a linear regression of the natural logarithm of taxi-visit counts on the natural logarithm of outpatient volumes. The regression is specified as:

To identify hospitals where taxi-visit volumes deviate substantially from the pattern suggested by outpatient volumes, we compute the studentized residuals (also known as internally studentized residuals) for each hospital. The studentized residual measures the difference between the observed and predicted log-taxi visits, scaled by an estimate of its standard deviation. Hospitals with an absolute studentized residual greater than 1.5 are considered outliers and are excluded from the calibration sample. This threshold is chosen as a conservative criterion to remove observations that are moderately influential while retaining the majority of the data.

Applying this criterion, two hospitals are identified as outliers, both of them have substantially lower taxi-visit volumes than would be expected given their outpatient volumes, possibly due to their suburban locations, specialized patient populations, or different modal shares. These two hospitals are therefore removed from the calibration set.

The remaining 14 hospitals exhibit a strong linear relationship between taxi visits and outpatient volumes. The Pearson correlation coefficient between the original (untransformed) taxi visits and outpatient volumes is 0.846, and the correlation on the log-scale is 0.898. These high correlations indicate that, for this subset, the relative magnitudes of taxi flows closely mirror actual service volumes. By calibrating the Huff model parameters on this subset, we effectively use the hospitals where the taxi proxy is most reliable as a "training set" to estimate residents' sensitivity to travel time and hospital size. This approach reduces the influence of proxy-induced bias while still leveraging the rich spatial detail of taxi trajectory data. It is important to note, however, that the calibrated parameters are best interpreted as reflecting the behavior of the population segments that are reasonably well captured by taxi trips; generalizing to all patients or all hospitals should be done with caution. Future work could integrate additional data sources (e.g., public transit smart card data, mobile phone signaling) to obtain a more comprehensive representation of healthcare travel behavior.

Figure 7 presents the scatter plots of taxi visit counts against outpatient volumes for the 16 hospitals with available data, distinguishing the 14 retained hospitals (blue) and the two excluded outliers (red), together with the fitted regression lines based on the retained sample; the left panel shows the original scale, and the right panel shows the log-transformed scale.

Correspondence Between Theoretical Variables and Empirical Data/Adjustments

Table 1 summarizes how the theoretical variables in the Huff model are operationalized using the available data. For the 14 retained hospitals, the supply capacity is measured by the number of beds, and the travel time is obtained from the taxi-based origin-destination matrix. The observed choice probability is derived from the taxi trip counts.

Practical Adjustments for Geometric Mean Calculation

For the 14 retained hospitals, we compute the following geometric means for each origin i:

Construction of the Actual Regression Variables

Based on the adjustments above, the actual regression variables are constructed as follows:

These variables are computed for every observed OD pair (i,j) among the 14 retained hospitals. A total of 1134 observations are obtained.

Sample Selection and Regression Execution

Parameters are estimated using Ordinary Least Squares (OLS). To control for potential correlation among multiple OD pairs from the same demand point, standard errors are clustered at the demand point level.

This empirical implementation preserves the core idea of the theoretical framework while accommodating the practical constraints of the observed data. The parameter estimation results will reflect the strength of influence of service capacity and travel time on hospital choice in real healthcare travel behavior.

Summary of Regression Data

A total of 1134 observations (all OD pairs from the 81 townships/subdistricts to the 14 hospitals) were used for parameter calibration. For each observation, the dependent variable and independent variables and were constructed as described above. Table 2 presents descriptive statistics for these variables. The means of , and are zero, confirming the validity of the log-centering transformation. The standard deviations (1.69 for , 0.51 for , and 0.38 for ) indicate sufficient variation for parameter identification. The Pearson correlation between and is 0.12, suggesting no multicollinearity concerns.

Additionally, Table 3 summarizes the geometric means of observed probabilities and travel times at the township level.

4.2.3. Parameter Estimation Criteria

The goodness-of-fit and parameter significance of the regression model are assessed using the following criteria:

1.

Goodness-of-fit (R ²): Measures the model's ability to explain the variation in observed data. R ² values range from 0 to 1, with values closer to 1 indicating better model fit.

2.

Parameter Significance Test: The significance level (p-value) of each parameter is assessed via t-test. When p-value < 0.05, the parameter is considered statistically significantly different from zero at the 95% confidence level.

3.

Confidence Intervals: The 95% confidence interval for each parameter is calculated to assess the precision of parameter estimates.

4.

Variance Inflation Factor (VIF): Tests for multicollinearity among independent variables. VIF values less than 10 indicate no serious multicollinearity.

5.

Residual Diagnostics: Tests for normality, homoscedasticity, and independence of residuals to ensure that the basic assumptions of the regression model are met.

4.2.4. Calibration Results

The OLS regression based on the 1134 OD pairs from the 14 retained hospitals yields the parameter estimates shown in Table 4. The estimated service capacity elasticity coefficient is 1.1758 (p < 0.001), indicating that a 1% increase in hospital bed count leads to an average increase of approximately 1.18% in the probability of selecting that hospital. The estimated time decay coefficient is 2.9608 (p < 0.001), indicating that a 1% increase in travel time reduces the selection probability by approximately 2.96%, reflecting Ningbo residents' high sensitivity to healthcare travel time costs. The model exhibits good explanatory power (), indicating that 66.5% of the variation in observed trip counts can be explained by hospital service capacity and travel time, and with extremely high statistical significance.

To contextualize the estimated time-decay coefficient, it is useful to compare = 2.96 with values reported in related empirical studies. Jin et al. [ 30] varied the travel-time impedance coefficient between 1.2 and 2.2 in a sensitivity analysis of food accessibility, suggesting that values in this range are considered behaviorally plausible for daily consumption trips. Yue et al. [ 35] calibrated a retail Huff model using taxi GPS trajectories in Wuhan, China, obtaining a distance-decay coefficient of = 1.73 for shopping trips. Liang et al. [ 38] calibrated a dynamic Huff model with mobile phone location data across multiple U.S. cities and found substantial variation across business formats, with reaching 1.83 for certain supermarket brands. In the commuting context, Wang [ 41] estimated a distance friction coefficient of = 1.85 for employment accessibility in Chicago by matching gravity-model predicted commuting distances to observed trip lengths.

The = 2.96 obtained in the present study is notably higher than these estimates from retail, commuting, and daily consumption contexts. This discrepancy likely reflects the distinctive nature of tertiary hospital choice: unlike shopping or commuting trips, healthcare-seeking behavior involves higher stakes, less frequent decision-making, and greater sensitivity to the perceived time burden. When seeking specialized tertiary care, patients appear willing to accept long travel distances only when the anticipated medical benefit substantially outweighs the time cost, resulting in a steeper distance-decay gradient. The comparably high estimated here thus reinforces the behavioral specificity of healthcare destination choice and underscores the importance of context-specific parameter calibration.

4.2.5. Discussion of Methodological Limitations

Although taxi trajectory data provide unprecedented spatiotemporal granularity for healthcare travel behavior research, the use of this data for parameter calibration in this study has several limitations:

The dataset is limited to taxi trips and does not include other transportation modes (e.g., public transit, private cars, walking). This may restrict the applicability of the calibrated parameters in areas where other transportation modes dominate, particularly in regions with low taxi usage or high taxi fares.

Taxi passengers do not fully represent the entire population. The exclusion of non-taxi users' healthcare-seeking behavior may introduce systematic bias, especially when analyzing healthcare accessibility for low-income groups, elderly populations, or specific communities. To mitigate this concern, the Huff model parameters were calibrated exclusively on hospitals for which taxi visit volumes exhibited a strong linear relationship with total outpatient volumes, thereby ensuring that the relative distribution of taxi trips closely mirrors the overall pattern of healthcare demand. Nevertheless, the absolute level of accessibility for populations reliant on public transit or ambulances may still differ from the estimates presented here. Future work could integrate multi-source data (e.g., public transit smart card records, mobile phone signaling, and ambulance dispatch logs) to obtain a more comprehensive representation of healthcare travel behavior across all demographic groups.

Although this study uses nine months of taxi data (March-November 2023), it cannot capture seasonal variations throughout the year or healthcare travel patterns during special periods (e.g., epidemic outbreaks).

Despite these limitations, using taxi data to calibrate healthcare accessibility models offers significant advantages: 1. providing large-sample revealed behavior rather than self-reported data; 2. capturing actual travel times under real road network conditions rather than theoretical distances; 3. reflecting residents' actual choice behavior rather than hypothetical preferences. Future research could integrate multi-source data (e.g., public transit smart card data, mobile phone signaling data) to obtain a more comprehensive representation of healthcare travel behavior.

At this point, the BCEC-2SFCA framework proposed in this study is fully computable. This model will serve as the foundation for the empirical analysis in Chapter 5, where it will be employed to evaluate the healthcare accessibility of tertiary hospitals in Ningbo City. A comparative analysis with the conventional Gaussian 2SFCA method will subsequently be conducted to validate the advantages of the proposed framework in capturing residents' actual medical travel behavior.

5. Results and Analysis

5.1. Accessibility Spatial Pattern

Using the calibrated Huff model parameters () and the observed travel time matrix and travel distance matrix derived from taxi trajectories, we computed the healthcare accessibility index for each of the 81 townships/subdistricts in Ningbo, allowing a comparison of accessibility results under two impedance measures: travel time and travel distance. This section presents the spatial distribution of the normalized accessibility scores and compares the results with those obtained from the conventional Gaussian two-step floating catchment area (Gaussian 2SFCA) method. The comparison highlights how the incorporation of empirically calibrated choice probabilities and real-world travel times reshapes the accessibility landscape, revealing competitive effects in hospital-dense areas and moderating the accessibility gap between central and peripheral regions.

The BCEC-2SFCA model proposed in this study and the traditional Gaussian 2SFCA based on a fixed threshold exhibit systematic differences in characterizing the spatial pattern of accessibility to tertiary hospitals in Ningbo ( Figure 8).

The results calculated by the BCEC-2SFCA model reveal significant spatial heterogeneity in the accessibility of tertiary hospitals in Ningbo. High-accessibility areas are highly concentrated in central urban districts such as Haishu, Jiangbei, and Yinzhou, forming accessibility peak zones centered around key hospitals like First Affiliated Hospital of Ningbo University (Yuehu and Waitan campuses) and Ningbo Medical Center Lihuili Hospital. This region has a high density of hospital resources, where residents' choice sets overlap significantly, leading to pronounced competitive effects and consequently higher effective accessibility calculated by the model. Areas with lower accessibility are primarily distributed in Fenghua District and Beilun District.

A comparison between the BCEC-2SFCA model (travel time) and the BCEC-2SFCA model (travel distance) reveals that the accessibility distributions of the two models are generally similar, with the main differences located in the western part of Haishu District and the northern part of Fenghua District. To further assess the robustness of the proposed framework, we examined the consistency between accessibility estimates derived from travel time and travel distance. The two sets of accessibility scores exhibit a strong positive correlation, with a Pearson correlation coefficient of 0.845 (p < 0.001) and a Spearman rank correlation coefficient of 0.817 (p < 0.001). This strong agreement indicates that the behavioral core of the model-the Huff probability structure-is not unduly sensitive to the choice of impedance metric. Whether travel time or travel distance is used as the measure of spatial separation, the resulting accessibility patterns remain remarkably consistent, underscoring the stability and reliability of the BCEC-2SFCA approach.

Crucially, even in peripheral areas, the minimum standardized accessibility calculated by the BCEC-2SFCA model is 0.31, with no residential points being absolutely inaccessible (value of 0). This reflects the reality of residents' healthcare-seeking behavior: even those in outer suburbs have a possibility (albeit low probability) of choosing to travel to major tertiary hospitals in the urban center.

In contrast, the spatial pattern presented by the Gaussian 2SFCA model with a fixed catchment size is markedly different. In this study, the fixed threshold of 45 min was selected for the Gaussian 2SFCA method. This choice was guided by two complementary considerations. First, based on the complete origin-destination travel time matrix, 98.15% of all observed hospital-oriented taxi trips fell within 45 min, confirming that this threshold captures the vast majority of actual healthcare-seeking travel behavior. Second, following a maximum-minimum distance principle, we computed for each of the 81 townships/subdistricts the travel time to its nearest tertiary hospital. The maximum of these nearest-hospital travel times across all townships was 40.29 min. Any threshold below this value would leave at least one township with no hospital within range. The adopted threshold of 45 min exceeds this lower bound, thereby ensuring complete spatial coverage of the study area while remaining firmly grounded in the observed travel time distribution. However, even with this adjustment, the disparities in the model results clearly reveal the systematic spatial bias inherent in traditional fixed-threshold models. The Gaussian 2SFCA model exhibits the following issues:

1.

Overestimation of accessibility in areas proximate and at intermediate distances to hospitals: Within close and intermediate proximity to tertiary hospitals, the Gaussian 2SFCA model overestimates accessibility. This is primarily attributed to the relatively slow decay of the Gaussian decay function over short distances. Furthermore, the longer the preset exogenous fixed threshold, the larger the zone of slow decay becomes, thereby amplifying this effect. Additionally, the Gaussian 2SFCA model fails to capture the "choice overload" and competitive effects resulting from the high concentration of hospitals in central urban areas. In the BCEC-2SFCA model, although residents in these areas face a multitude of high-quality hospitals, the choice weights derived from the Huff model concentrate their selections on a few nearest or most attractive hospitals. The influence of other neighboring hospitals is diluted due to competition, consequently lowering the comprehensive accessibility score. In contrast, the Gaussian model simply performs a linear summation of the influence of all hospitals within a 45 min range, overlooking the concentration of resident decision-making in contexts of abundant choice.

2.

Underestimation of accessibility in areas distant from hospitals: The Gaussian 2SFCA model significantly underestimates accessibility in regions farther from hospitals. This represents the primary bias identified in this study. On one hand, due to its fixed hard cutoff, the Gaussian model completely disregards the long-distance travel behavior of residents in peripheral areas seeking quality medical resources. Conversely, the BCEC-2SFCA model does not set a fixed threshold, this incorporates large core hospitals, which, despite being distant, still have a probability of being selected, into the effective choice set, thereby assigning more realistic accessibility values to these areas. On the other hand, the Gaussian decay function decays very rapidly over intermediate distances, resulting in extremely low weights for hospitals at long distances. Consequently, the Gaussian model produces a substantial accessibility disparity between central and peripheral areas.

5.2. Analysis of Accessibility Ranking Changes

To quantitatively assess the differences in evaluation results between the BCEC-2SFCA Model (Travel Time) and Gaussian 2SFCA Model, this section further compares the accessibility rankings of each region under both models. Changes in ranking directly reflect how the relative assessment of regional healthcare resource accessibility is systematically reconfigured when residents' behavioral decisions are incorporated.

Among the 81 townships/subdistricts, the accessibility rankings derived from the two models exhibit a substantial reconfiguration. Overall, 37 areas (45.7%) experienced an increase in rank under the BCEC-2SFCA model compared to the Gaussian 2SFCA model, indicating that their relative level of healthcare resource accessibility was reassessed as higher after incorporating residents' behavioral decisions. Conversely, 43 areas (53.1%) experienced a decrease in rank, with these areas predominantly concentrated in the hospital-dense central city and inner suburbs. Rankings for only 1 area (1.2%) remained unchanged, highlighting the widespread corrective effect of the behaviorally endogenous model on the assessment outcomes.

Figure 9 provides a visual comparison of the accessibility rankings for all townships/subdistricts under the two models using a scatter plot. Regarding the magnitude of change, the ranking shifts are more pronounced than those observed in preliminary analyses. The average increase for areas that rose in rank was 16.14 places, while the average decrease for areas that fell in rank was 13.88 places. These figures indicate that the BCEC-2SFCA model not only reorders many areas but also does so with considerable magnitude, particularly upgrading peripheral locations and downgrading some central ones.

To further quantify the linear relationship between the accessibility rankings derived from the two models, we performed an ordinary least squares regression using the Gaussian 2SFCA rankings as the independent variable (x) and the BCEC-2SFCA model rankings as the dependent variable (y). The fitted linear equation is:

The slope of 0.6575, being substantially less than 1, indicates a notable compression effect: the BCEC-2SFCA model tends to assign relatively lower rankings (i.e., worse positions) to many of the top-ranked areas under the Gaussian model, while assigning relatively higher rankings (i.e., better positions) to many of the lower-ranked areas. This aligns with the earlier observation that the BCEC-2SFCA model moderates extreme rankings, narrowing the accessibility gap between central and peripheral regions. The positive intercept of 14.0435 implies that, on average, the BCEC-2SFCA rankings are shifted upward (i.e., numerically larger, meaning less favorable) relative to the Gaussian rankings, especially for areas with very low Gaussian rankings (near the top). Conversely, for areas with very poor Gaussian rankings (large x values), the BCEC-2SFCA rankings become comparatively better than the linear trend would predict. The R ² value of 0.4323 demonstrates a moderate linear association, confirming that the reconfiguration induced by the BCEC-2SFCA model is systematic, though with substantial scatter reflecting the model's nuanced adjustments.

These regression results reinforce the conclusion that the BCEC-2SFCA model corrects the systematic biases of the fixed-threshold Gaussian model by moderating extreme rankings-downgrading overestimated central areas and upgrading underestimated peripheral regions-while still preserving a recognizable overall spatial order.

To further illustrate the spatial pattern of the adjustments introduced by the BCEC-2SFCA model, Figure 10 presents a difference map of the standardized accessibility scores, computed as BCEC-2SFCA minus Gaussian 2SFCA for each township/subdistrict. Green shades indicate negative differences (BCEC < Gaussian), representing areas where the BCEC-2SFCA model assigns lower accessibility than the Gaussian benchmark. These areas are predominantly concentrated in the central urban districts of Haishu, Jiangbei, and Yinzhou, reflecting the competition effect that moderates accessibility in hospital-dense locations. In contrast, yellow, orange, and red shades indicate positive differences (BCEC > Gaussian), representing areas where the BCEC-2SFCA model yields higher accessibility. These positive adjustments are most pronounced in the peripheral townships of Fenghua, Beilun, and Zhenhai districts, confirming that the endogenous search range mechanism substantially elevates accessibility estimates in outlying areas. The spatial divergence revealed by the difference map underscores the extent to which the BCEC-2SFCA framework corrects the systematic overestimation of central accessibility and underestimation of peripheral accessibility inherent in fixed-threshold Gaussian models.

5.3. Implications for Planning and Policy

The model differences lead directly to divergent assessments of whether regional medical resources are adequate, carrying clear policy implications.

1.

Re-evaluating "Service Blind Spots": Although the Gaussian 2SFCA model did not generate "service blind spots" (areas with zero accessibility) in this study, its fixed threshold and hard cutoff still assume that residents in peripheral areas can only access services from a very limited number of hospitals. Consequently, the accessibility level in these areas remains extremely low. In contrast, under the BCEC-2SFCA model, the accessibility disparity between peripheral and central areas is significantly narrowed. For example, in certain townships or subdistricts of Fenghua District and Beilun District, the accessibility scores under the Gaussian model are extremely low, even approaching zero. However, under the BCEC model, they still maintain accessibility scores of approximately 0.3-0.4 and exhibit non-negligible choice probabilities for major hospitals in the city center. This finding implies that, for regions identified by traditional methods as severely resource-deficient, policy priorities should shift away from simply constructing new hospitals to fill absolute gaps (which may be economically inefficient and ineffective), towards strategies aimed at reducing the actual barriers residents face in accessing existing high-quality resources located farther away. For instance, establishing a direct express bus route connecting the western townships of Fenghua District to the Ningbo First Hospital area could substantially reduce travel time for residents in these peripheral communities, thereby improving effective accessibility without requiring new hospital construction. Similarly, expanding telemedicine services and streamlining referral pathways between community health centers in Beilun's eastern townships and major tertiary hospitals could mitigate the burden of long-distance travel.

2.

Transformation in Resource Allocation Assessment: In the central districts of Haishu, Jiangbei, and Yinzhou, the BCEC-2SFCA model assigns lower accessibility scores than the Gaussian model for many townships, despite their proximity to multiple hospitals. This downward adjustment reflects the competition effect captured by the Huff choice probabilities: when many attractive hospitals are available, residents' selections concentrate on a few preferred facilities, diluting the effective demand at neighboring hospitals. This finding implies that the marginal benefit of constructing new large hospitals in the urban core may be overestimated. Instead, resource investment should focus on systemic integration and functional differentiation-for instance, strengthening specialized departments at existing hospitals to reduce redundant competition and improve overall service efficiency.

3.

Methodological Implications: The systematic differences between the BCEC-2SFCA and Gaussian 2SFCA rankings-particularly the fact that the average rank adjustment for certain townships exceeds 10 positions-indicate that when assessing the accessibility of high-level, wide-coverage service facilities, the primary source of measurement error is not data precision, but erroneous a priori assumptions regarding residents' behavioral search ranges. Adopting endogenous, behavior-based choice models to define the search range can fundamentally avoid the systematic spatial bias (overestimating the periphery, underestimating the center) caused by exogenous, rigid thresholds, providing a scientific basis for more equitable and effective spatial planning of healthcare.

In summary, the comparative analysis in this chapter demonstrates that, compared to the traditional Gaussian 2SFCA model, the BCEC-2SFCA framework generates a continuously varying and behaviorally plausible spatial picture of accessibility. It not only eliminates unrealistic "service blind spots" but also reveals competitive effects in resource-concentrated areas, providing a more reliable analytical foundation for precisely identifying shortcomings in the spatial allocation of medical resources and formulating differentiated improvement strategies.

6. Model Validation Using Hospital Outpatient Volume

To objectively evaluate the predictive capability of the proposed BCEC-2SFCA model in reproducing residents' hospital choice distribution, this chapter employs annual hospital outpatient volume-a dataset independent of the model construction process-for validation. By comparing predicted shares with actual outpatient shares, we quantify the predictive accuracy of different models (the travel time-based BCEC-2SFCA model and the Gaussian 2SFCA method) and examine their stability across hospital types.

6.3. Validation Results

This section validates the predictive accuracy of the BCEC-2SFCA model against the Gaussian 2SFCA model using annual outpatient volume data from 16 tertiary hospitals in Ningbo for which such data are available. The actual outpatient share is computed by Equation (27); the predicted shares and are obtained from Equation (28) and Equation (29), respectively. Four metrics are employed: Pearson correlation coefficient, Spearman rank correlation coefficient, root mean square error (RMSE), and mean absolute percentage error (MAPE).

Table 5 summarizes the validation results. The BCEC-2SFCA model achieves a Pearson correlation of 0.5682 (p = 0.0216) and a Spearman rank correlation of 0.5265 (p = 0.0362), indicating a moderately strong and statistically significant monotonic relationship with the actual outpatient shares. In contrast, the Gaussian 2SFCA model yields much weaker correlations: Pearson 0.1163 (p = 0.6679) and Spearman 0.0529 (p = 0.8456). This demonstrates that the BCEC-2SFCA model is considerably more capable of replicating the relative order of hospital attractiveness and the competitive effects captured by real-world choice behavior.

Regarding prediction errors, the BCEC-2SFCA model yields a lower MAPE (32.17% vs. 42.12%), suggesting that its empirically calibrated choice probabilities better capture the relative distribution of patient demand across hospitals. However, its RMSE (0.02700) is slightly higher than that of the Gaussian 2SFCA model (0.02211), indicating that while the BCEC-2SFCA model excels at replicating the rank order of hospital attractiveness, it does not uniformly achieve lower absolute errors across all facilities. This trade-off underscores that the model's primary strength lies in capturing competitive effects and relative attractiveness rather than minimizing aggregate absolute prediction error.

Overall, the validation results confirm that the proposed BCEC-2SFCA framework, with its empirically calibrated parameters and behaviorally derived choice probabilities, provides a more plausible representation of residents' hospital-seeking behavior in terms of ranking and spatial competition than the conventional fixed-threshold Gaussian 2SFCA model. However, this improved behavioral realism does not translate into uniformly superior absolute predictive accuracy. The framework is therefore particularly suited for evaluating the relative accessibility of high-order medical services, where capturing competition and patient choice is more critical than minimizing absolute prediction error.

To further examine whether model performance varies by hospital type, a supplementary subgroup analysis was conducted. The 16 hospitals with available outpatient data were divided into two groups based on the median number of beds: larger hospitals (≥1014 beds, N = 8) and smaller hospitals (<1014 beds, N = 8). For the larger hospitals, the BCEC-2SFCA model achieved a Pearson correlation of 0.192 and a Spearman rank correlation of 0.262, compared with 0.007 and −0.214 for the Gaussian model. The RMSE and MAPE for BCEC-2SFCA were 0.0219 and 23.9%, respectively, versus 0.0311 and 34.9% for the Gaussian model. For the smaller hospitals, BCEC-2SFCA yielded a Pearson correlation of 0.458 and a Spearman rank correlation of 0.548, whereas the Gaussian model produced values of −0.103 and 0.024. In this subgroup, BCEC-2SFCA had a lower MAPE (31.9% vs. 34.5%) but a slightly higher RMSE (0.0270 vs. 0.0242).

Although the reduced sample size in each subgroup precludes definitive statistical inference (none of the correlations reached conventional significance levels), the pattern of results is consistent with the full-sample findings: the BCEC-2SFCA model consistently outperforms the Gaussian benchmark in capturing the relative attractiveness of hospitals, regardless of hospital scale. The absolute prediction errors remain modest for larger hospitals but increase for smaller ones-a challenge common to both models and likely attributable to the greater volatility and lower absolute volumes of outpatient visits at smaller facilities. This exploratory analysis suggests that the BCEC-2SFCA framework maintains its behavioral advantage across heterogeneous hospital types, while also highlighting the inherent difficulty of predicting demand for smaller, lower-volume hospitals. Future work with larger validation samples could more rigorously assess the boundary conditions of the proposed approach.

6.4. Robustness Check: Model Calibration with All 22 Tertiary Hospitals

To assess the sensitivity of the parameter estimates to the composition of the calibration sample, we repeated the log-centered OLS calibration using all 22 tertiary hospitals located within the study area (1782 OD pairs; 81 townships × 22 hospitals). The estimated parameters were = 0.9586 (p < 0.001) and = 3.0685 (p < 0.001), with = 0.620. These values are comparable in magnitude and statistical significance to the estimates obtained from the 14-hospital subset used in the main analysis ( = 1.1758, = 2.9608, = 0.665).

Using the alternatively calibrated parameters, we recomputed the BCEC-2SFCA accessibility scores and the predicted outpatient shares for the 16 hospitals with available outpatient data. The resulting validation metrics are as follows: Pearson's r = 0.505 (p = 0.046), Spearman's ρ = 0.541 (p = 0.030), RMSE = 0.0254, and MAPE = 28.20%. For comparison, the 14-hospital calibration yielded Pearson's r = 0.568, Spearman's ρ = 0.527, RMSE = 0.027, and MAPE = 32.17%. The performance of the BCEC-2SFCA model under the 22-hospital calibration remains very close to that of the main calibration, and both substantially outperform the Gaussian 2SFCA benchmark (Pearson's r = 0.116, Spearman's ρ = 0.053, RMSE = 0.022, MAPE = 42.12%).

The slight variations in parameter estimates and validation metrics are expected, as the 14-hospital subset was selected to maximize the reliability of the taxi-based proxy. Nevertheless, the consistency of the results across the two calibration samples confirms that the core findings of this study are not sensitive to the specific composition of the calibration sample.

7. Discussion

The validation results presented in this study highlight a trade-off: the BCEC-2SFCA model excels at capturing the rank order of hospital attractiveness () and relative demand distribution (MAPE = 32.17%), but its RMSE (0.02700) is slightly higher than that of the Gaussian model (0.02211). This indicates that the model's primary strength lies in replicating competitive dynamics rather than minimizing absolute prediction error.

The strong correlation between travel-time and travel-distance accessibility () further supports the robustness of the approach, suggesting that the model's behavioral core is stable across different impedance specifications. This consistency enhances confidence in its applicability to diverse data contexts.

The findings of this study can be situated within the broader literature on Huff-based FCA methods. Compared to Jin and Lu [ 30], who applied a multi-mode Huff-2SFCA to food accessibility, the present study extends the Huff-FCA framework to the healthcare domain while additionally calibrating the Huff parameters from observed travel behavior rather than relying on sensitivity tests. Relative to Subal et al. [ 31], who incorporated a Gaussian distance-decay function into a Huff-3SFCA model, our approach eliminates the need for predefined decay parameters entirely by letting the probability surface emerge from empirical data. The generalized flow-based 2SFCA of Chen et al. [ 34] shares with our work the use of taxi trajectory data; however, their method still relies on adaptive catchment delineation, whereas BCEC-2SFCA generates endogenous search ranges probabilistically without any spatial cutoff. These comparisons highlight that the key advancement of BCEC-2SFCA lies not in any single component but in the systematic integration of empirical calibration, real-world impedance, and endogenous catchment generation into a unified accessibility framework.

From a theoretical standpoint, this study contributes to the accessibility literature by demonstrating that the conventional dichotomy between "accessible" and "inaccessible"-embedded in all threshold-based 2SFCA variants-can be replaced with a continuous, competition-sensitive probability gradient. This shift from binary to probabilistic accessibility aligns the methodological framework more closely with the continuous nature of spatial behavior described in classical geography and with discrete choice theory in transportation research. Furthermore, the empirical calibration of Huff parameters from passive mobility data suggests a pathway toward behaviorally grounded accessibility models that reduce reliance on stated-preference surveys or ad hoc parameter assumptions, thereby enhancing the replicability and context-specificity of accessibility assessments.

Nevertheless, several limitations warrant acknowledgment. First, the use of taxi trajectory data does not capture all modes of healthcare travel; future work should integrate multi-source mobility data such as public transit smart card records or mobile phone signaling. Second, hospital attractiveness is represented solely by bed count, ignoring specialty mix, reputation, or insurance acceptance. Incorporating such attributes could refine the choice probabilities. Third, due to the unavailability of patient origin-destination flow data from hospital records-a common constraint in healthcare accessibility research-direct validation against actual patient flow patterns at the subdistrict level was not feasible. Nevertheless, the taxi trajectory data used for model calibration provide a large-scale proxy of revealed healthcare travel behavior, and the outpatient volume data offer an independent, facility-level benchmark for external validation. Fourth, the relatively small number of hospitals with available outpatient data (N = 16) precluded meaningful subsample analyses by region or hospital scale. Such analyses would require a substantially larger sample to retain statistical power within each subgroup. Future work could revisit these validation dimensions should more granular patient flow data or expanded outpatient datasets become available.

From a policy perspective, the findings suggest that resource allocation strategies should account for the competitive dynamics revealed by the BCEC-2SFCA model. In central urban areas, where accessibility is already high, further expansion of hospital capacity may yield diminishing returns due to competition; instead, efforts could focus on service integration and differentiation. In peripheral areas, improving transport connections to existing high-quality hospitals may be more effective than building new facilities, as residents already demonstrate a willingness to travel longer distances when necessary.

8. Conclusions

This study proposed a data-driven BCEC-2SFCA framework for assessing spatial accessibility to tertiary hospitals, using taxi trajectory data to empirically calibrate the Huff model parameters and to construct realistic travel impedance matrices. The framework was applied to Ningbo, China, with data from 22 tertiary hospitals, 81 townships/subdistricts, and over 1.58 million taxi trips. The main findings are as follows:

Empirically grounded parameters: The calibrated service capacity elasticity () and time decay coefficient () reflect residents' strong preference for larger hospitals and high sensitivity to travel time, providing a behaviorally realistic foundation for accessibility modeling.

Competition effects and spatial equity: The BCEC-2SFCA model captures competition among hospitals, moderating accessibility estimates in hospital-dense central areas while avoiding the underestimation of accessibility in peripheral regions that plagues fixed-threshold models. This yields a continuously varying and equitable spatial distribution.

Predictive performance: Validation against annual outpatient volumes shows that the BCEC-2SFCA model excels at capturing the relative order of hospital attractiveness (Spearman ρ = 0.527) and relative demand distribution (MAPE = 32.17%), though its RMSE (0.02700) is slightly higher than that of the Gaussian model (0.02211). This trade-off underscores the model's strength in replicating competitive dynamics rather than minimizing absolute prediction error.

Policy implications: The model highlights the need for differentiated strategies: in central areas, optimizing existing resources rather than expanding capacity; in peripheral areas, improving transport connectivity to overcome spatial barriers. The use of passive mobility data offers a scalable approach to dynamically monitor healthcare-seeking behavior.

In conclusion, the BCEC-2SFCA framework integrates empirically calibrated behavioral parameters and real-world travel data to produce more realistic and policy-relevant accessibility assessments. It should be reiterated that this framework is tailored specifically to high-order medical services such as tertiary hospitals, where facility competition and patient choice are central to travel behavior. For primary care or other services characterized by localized catchments, traditional threshold-based models may be more appropriate.

Future work could proceed in several directions. First, integrating additional mobility data sources-such as public transit smart card records, mobile phone signaling, and ambulance dispatch logs-would enable the calibration of mode-specific impedance matrices and better capture the healthcare travel behavior of underrepresented groups, including the elderly and low-income populations. Second, hospital attractiveness could be represented through a multi-dimensional index incorporating specialty mix, physician expertise, patient satisfaction, and insurance acceptance, rather than bed count alone. Third, the temporal dynamics of healthcare accessibility could be explored by extending the BCEC-2SFCA framework to account for diurnal and seasonal variations in traffic conditions and hospital service availability. Fourth, the generalizability of the framework should be tested in other urban contexts with different spatial configurations of healthcare resources, as well as for other types of high-order public services such as regional cultural centers and specialized elderly care facilities. Finally, coupling the BCEC-2SFCA model with spatial optimization algorithms could provide decision-support tools for equitable and efficient healthcare resource allocation.

Author Contributions

Conceptualization, W.C., Y.W., Z.H. and P.Z.; methodology, W.C., Z.H., M.P. and P.Z.; software, W.C., Y.M. and T.X.; validation, W.C., Y.M., T.X. and P.Z.; formal analysis, W.C., Y.M., T.X. and Z.H.; investigation, W.C., Y.M. and Z.H.; resources, P.Z.; data curation, W.C. and Y.M.; writing-original draft preparation, W.C., Y.M. and Z.H.; writing-review and editing, Y.W., M.P. and P.Z.; visualization, W.C.; supervision, M.P. and P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by the National Natural Science Foundation of China (52272334, 61074142), Key R&D Program of Zhejiang Province (2024C01180), Ningbo Internation-al Science and Technology Cooperation Project (2023H020), EC H2020 Project (690713) and National Key Research and Development Program of China (2017YFE0194700).

Data Availability Statement

The data are available from the corresponding author on reasonable request.

Acknowledgments

We would like to thank the National "111" Centre on Safety and Intelligent Operation of Sea Bridges (D21013), the Zhejiang 2011 Collaborative Innovation Center for Port Economy, and the Donghai Academy of Ningbo University for the financial support in publishing this paper. The authors would like to thank the K.C. Wong Magna Fund in Ningbo University for sponsorship.

Conflicts of Interest

The authors declare no conflicts of interest.

References

1. Cuervo, L.G.; Villamizar, C.J.; Osorio, L.; Ospina, M.B.; Cuervo, D.E.; Cuervo, D.; Bula, M.O.; Zapata, P.; Owens, N.J.; Hatcher-Roberts, J.; et al. Dynamic measurements of geographical accessibility considering traffic congestion using open data: A cross-sectional assessment for haemodialysis services in Cali, Colombia. Lancet Reg. Health-Am. 2024, 34, 100752. [Google Scholar] [CrossRef] [PubMed]

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Modeling Healthcare Accessibility with Endogenous Search Ranges: A Huff-Based Multi-Source Data Approach

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This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters (α=1.1758, β=2.9608) based on observed hospital choices and construct travel time and distance matrices from observed trips.

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38. Segregación residencial y movilidad en zonas de pobreza en Santiago de Cali (Colombia)[Residential segregation and mobility in poverty stricken areas of Santiago de Cali (Colombia)] 

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38. Segregación residencial y movilidad en zonas de pobreza en Santiago de Cali (Colombia)[Residential segregation and mobility in poverty stricken areas of Santiago de Cali (Colombia)] 

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Vivas-Pacheco, H; López, V.; Arroyo, S.J.; Jaramillo, C., (2025). Segregación residencial y movilidad en zonas de pobreza en Santiago de Cali. Panorama Económico, 33(1), 9-26. DOI: https://doi.org/10.32997/pe-2025-4979 The published version was released on May 14, 2014, not as dated

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Segregación residencial y movilidad en zonas de pobreza en Santiago de Cali (Colombia)

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Objetivos : El estudio examina la relación entre la segregación residencial y la movilidad urbana en Santiago de Cali, con énfasis en comunidades en situación de pobreza caracterizadas por su pertenencia al SISBEN IV. Se busca comprender cómo la estructura socioespacial condiciona la accesibilidad a oportunidades urbanas y profundiza desigualdades históricas. Métodos : Se emplearon índices locales de segregación y técnicas de análisis espacial sobre información sociodemográfica a nivel intraurbano. Complementariamente, se aplicó una encuesta presencial a usuarios del SISBEN para explorar percepciones, hábitos de viaje y barreras de movilidad, integrando enfoques cuantitativos y cualitativos. Resultados : Los resultados evidencian una marcada segregación socioespacial. La población vulnerable se concentra en sectores periféricos con baja accesibilidad a servicios y centralidades urbanas. Las mujeres jefas de hogar y personas afrodescendientes enfrentan mayores dificultades para desplazarse cotidianamente. Las percepciones muestran alta insatisfacción con el transporte público, infraestructura deficiente para peatones y ciclistas, y costos diarios significativos. La mayoría de encuestados considera el acceso a servicios básicos relativamente fácil, aunque con limitaciones notables para servicios especializados de salud. Conclusión : La movilidad urbana en Cali refleja desigualdades estructurales que reproducen patrones históricos de exclusión. Superarlas requiere políticas públicas que integren planificación urbana, transporte inclusivo y accesibilidad universal. Se recomienda mejorar la infraestructura vial, fortalecer la red de transporte público y reducir brechas territoriales en el acceso a servicios esenciales.

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Doctoral thesis document - AMORE Project

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Doctoral thesis document - AMORE Project

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Cuervo LG. Traffic Congestion, Health Services Accessibility, and Equity: A People-Centred, Data-Driven Approach to Urban and Health Planning. Test Cases for Acute and Chronic Care Accessibility in Cali, Colombia. [online]. Universitat Autònoma de Barcelona, 2026-01-14. DOI:10.6084/m9.figshare.32099494 [Published: 15 May 2026]. [online <https://hdl.handle.net/10803/697498>].

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Traffic Congestion, Health Services Accessibility, and Equity: A People-Centred, Data-Driven Approach to Urban and Health Planning. Test Cases for Acute and Chronic Care Accessibility in Cali, Colombia - TDX (Tesis Doctorals en Xarxa)

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Institutional repository that preserves and disseminates the academic and scientific output of the institution.

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Conduciendo hacia la equidad en salud - Café Virtual 2026 de SIHI-LAC-ALACISS-CIDEIM 

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Conduciendo hacia la equidad en salud - Café Virtual 2026 de SIHI-LAC-ALACISS-CIDEIM 

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https://youtu.be/ojryoh3iyxA?si=9E_ad0KpgTH_vycw&t=394

- En este Café Virtual abrimos un espacio de conversación para reflexionar sobre cómo avanzar hacia la equidad en salud mediante enfoques integrales que conectan la salud con la movilidad, el urbanismo y el acceso a servicios esenciales. En esta sesión exploramos el Proyecto de Análisis con Macrodatos para Orientar Resultados en Equidad (AMORE) 🚗🌍, una iniciativa que propone nuevas formas de entender y abordar las desigualdades en salud desde perspectivas intersectoriales y centradas en las personas.

🚦🏥 ¿Cómo alinear las agendas de #urbanismo, #movilidad y #salud para avanzar hacia la #saluduniversal en ciudades congestionadas? En ciudades congestionadas, la equidad en salud se juega en el tiempo de traslado.

EN 20 minutos muestro cómo, con la participación de usuarios del conocimiento y otras partes interesadas, fue posible:
✔️ Medir la accesibilidad con enfoque de equidad
✔️ Identificar brechas reales en el acceso a servicios
✔️ Modelar la ubicación estratégica de nuevos servicios para optimizar la accesibilidad urbana

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Café Virtual 2026   Conduciendo hacia la equidad en salud

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36. Optimización Industrial del Agendamiento y Flujo de Usuarios Mediante el Uso de Aplicaciones de Análisis de Datos y Automatización: Caso de Estudio en una Clínica de Rehabilitación Electrodiagnóstica 

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36. Optimización Industrial del Agendamiento y Flujo de Usuarios Mediante el Uso de Aplicaciones de Análisis de Datos y Automatización: Caso de Estudio en una Clínica de Rehabilitación Electrodiagnóstica 

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Romero Acosta, A. S. (2026). Optimización Industrial del Agendamiento y Flujo de Usuarios Mediante el Uso de Aplicaciones de Análisis de Datos y Automatización: Caso de Estudio en una Clínica de Rehabilitación Electrodiagnóstica [Corporación Universitaria del Meta UNIMETA]. https://repositorio.unimeta.edu.co/handle/unimeta/1326

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Please wait while we check if you are a Human

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Doctoral defense dissertation / Disertación y defensa doctoral

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Doctoral defense dissertation / Disertación y defensa doctoral

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🌐 Subtitles (CC) are available—use YouTube’s auto-translate to follow in your preferred language.

https://www.youtube.com/watch?v=GmVq8p_fr7Q

[1:39] Introductions | Introducciones | Introduções

[6:10] Dissertation | Disertación | Dissertação

[46:18] Defence – Q&A | Defensa – Preguntas y respuestas | Defesa – Perguntas e respostas

[1:45:42] Tribunal’s Decision | Decisión del tribunal | Decisão do júri

Key messages

Accessibility can—and should—be measured accounting for traffic congestion, and from an equity perspective.

Accessibility can be acted upon. For example, it can be improved by strategically locating services to address service deserts in segregated cities.

Measurement and prediction can inform policy. Leveraging open and big data enables continuous monitoring and evidence-informed decision-making.

The methodology enables an integrated approach to accessibility, bringing sectors together around people-centred, unifying metrics.

The approach is transferable and can be applied to other services, modes of transport, and urban contexts.

Thesis by compendium of three publications - Tesis por compendio de tres publicaciones:

Art 1 - Tertiary emergency care 2022. Dynamic Accessibility by Car to Tertiary Care Emergency Services in Cali, Colombia, in 2020: Cross-Sectional Equity Analyses Using Travel-Time Big Data from a Google API. BMJ Open 12, no. 9 (September 1, 2022): e062178. https://doi.org/10.1136/bmjopen-2022-...

Art 2 - Haemodialysis 2024. Dynamic measurements of geographical accessibility considering traffic congestion using open data: a cross-sectional assessment for haemodialysis services in Cali, Colombia. The Lancet Regional Health – Americas, Volume 34, 100752. https://doi.org/10.1016/j.lana.2024.1...

Art 3 - Radiotherapy 2024. Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020. Int J Equity Health 23, 161 (2024). https://doi.org/10.1186/s12939-024-02...

Complementary articles Editorial Dynamic Accessibility by Car to Tertiary Care Emergency Services in Cali, Colombia, in 2020: Cross-Sectional Equity Analyses Using Travel-Time Big Data from a Google API. BMJ Open 12, no. 9 (September 1, 2022): e062178. https://doi.org/10.1136/bmjopen-2022-... Protocol Dynamic geographical accessibility assessments to improve health equity: protocol for a test case in Cali, Colombia. F1000Research 2022, 11:1394 (https://doi.org/10.12688/f1000researc...

WHO Case Study Driving towards equity: The AMORE Project. Included in WHO's case studies collection: https://www.who.int/teams/social-dete...

PDF Driving towards equity: the AMORE project from https://cdn.who.int/media/docs/defaul...

Stakeholder assessment Improving spatial accessibility to health care services in Cali, Colombia: stakeholder assessment of an innovative platform. Rev Panam Salud Publica. 2024;48:e69.

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Doctoral Dissertation and Defence - Luis Gabriel Cuervo - AMORE Project - Driving toward equity

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Vídeo por Laura Viviana Molina Benavides describiendo el proyecto AMORE

https://youtu.be/kuvCsRYjN-4

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Video síntesis e integración del proyecto AMORE

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AMORE Project - doctoral thesis defence - Defensa de tesis doctoral del Proyecto AMORE. 

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AMORE Project - doctoral thesis defence - Defensa de tesis doctoral del Proyecto AMORE. 

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DOI: 10.6084/m9.figshare.32095171

Accessibility can—and should—be measured accounting for traffic congestion, and from an equity perspective.

Accessibility can be acted upon. For example, it can be improved by strategically locating services to address service deserts in segregated cities.

Measurement and prediction can inform policy. Leveraging open and big data enables continuous monitoring and evidence-informed decision-making.

The methodology enables an integrated approach to accessibility, bringing sectors together around people-centred, unifying metrics.

The approach is transferable and can be applied to other services, modes of transport, and urban contexts.

Dissertation recording: https://youtu.be/GmVq8p_fr7Q

Thesis by compendium of three publications - Tesis por compendio de tres publicaciones:

Art 3 - Radiotherapy

Art 2 - Haemodialysis

Art 1 - Tertiary emergency care 2022

Complementary articles

Editorial

Protocol

WHO Case Study

Stakeholder assessment

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Doctoral Dissertation and Defence - Luis Gabriel Cuervo - AMORE Project - Driving toward equity

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14. Abstracts of the 17th World Congress on Public Health, Rome, May 2-6,  2023

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14. Abstracts of the 17th World Congress on Public Health, Rome, May 2-6,  2023

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Cuervo LG, Villamizar CJ, Jaramillo C, Martinez E. “Equitable Accessibility to Health Services Using Timely Measurements.” Population Medicine 5, no. Supplement (April 26, 2023): Pg 268. 17th World Congress on Public Health. https://doi.org/10.18332/popmed/164377

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Introduction:
The links between traffic congestion, accessibility to health services, and equity show how traffic congestion impacts accessibility among different populations. It can also be used to improve health services and land use planning. The study proposes an approach to measuring, analysing, and communicating accessibility to health services in terms of travel time, as opposed to traditional travel time analyses.

Methodology:
We studied the entire city of Cali (2.258 million), assessing accessibility by car to urgent care (tertiary care emergencies) and frequent ambulatory care (haemodialysis and radiation therapy) in July and November 2020. The study is the cocreation of an interdisciplinary multisectoral group involving authorities, academia and other civil society, service providers and beneficiaries.

Results:
Services are far from where most of the population lives, especially some of the most vulnerable. These people pay more to access essential health services. New analyses will indicate potential solutions and their potential impact.

Discussion:
The study used a person-centred design to address the needs of different stakeholders. Studies like this can expand to other services, transportation means, and locations. They allow for integrating health equity with urban planning and enable participatory evidence- informed decision-making.

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Equitable accessibility to health services using timely measurements

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Introduction: The links between traffic congestion, accessibility to health services, and equity show how traffic congestion impacts accessibility among different populations. It can also be used to improve health services and land use planning. The study proposes an approach to measuring,...

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24. Challenges of Environmental and Health Policies: a dialogue among scientists and policy makers. 

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24. Challenges of Environmental and Health Policies: a dialogue among scientists and policy makers. 

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Eliana Martínez Herrera represented the AMORE Project at this event. Recording link pending.

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Project's Timeline - Cronología del proyecto

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Project's Timeline - Cronología del proyecto

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https://padlet.com/Proyecto_AMORE_Project/Timeline

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4. Deaths from traffic injuries and social inequality in Colombia, 2019. Rev Panam Salud Publica. 2023;47:e121

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4. Deaths from traffic injuries and social inequality in Colombia, 2019. Rev Panam Salud Publica. 2023;47:e121

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Murillo-Hoyos J, García-Moreno LM, Tinjacá N, Jaramillo C. Mortalidad por lesiones de tránsito y desigualdades sociales en Colombia, 2019 [Deaths from traffic injuries and social inequality in Colombia, 2019 / Mortalidade por acidentes de trânsito e desigualdades sociais na Colômbia, 2019]. Rev Panam Salud Publica. 2023 Aug 31;47:e121. Spanish. doi: 10.26633/RPSP.2023.121. PMID: 37654795; PMCID: PMC10464641.

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doi: 10.26633/RPSP.2023.121.

Affiliations

¹ Escuela de Ingeniería Civil y Geomática Universidad del Valle Cali Colombia Escuela de Ingeniería Civil y Geomática. Universidad del Valle, Cali, Colombia.

² Organización Panamericana de la Salud Bogotá Colombia Organización Panamericana de la Salud. Bogotá, Colombia.

Free PMC article

Free PMC article

doi: 10.26633/RPSP.2023.121.

Affiliations

¹ Escuela de Ingeniería Civil y Geomática Universidad del Valle Cali Colombia Escuela de Ingeniería Civil y Geomática. Universidad del Valle, Cali, Colombia.

² Organización Panamericana de la Salud Bogotá Colombia Organización Panamericana de la Salud. Bogotá, Colombia.

Objective: To evaluate the association between social inequalities and deaths from traffic injuries in Colombia in 2019.

Methods: This ecological study evaluated the association between social inequalities and deaths from traffic injuries among users of the road transport system in Colombia in 2019, based on secondary information sources, using the department level as the administrative and geographic unit of study. A descriptive statistical analysis of health indicators and equity stratifiers was performed. Absolute and relative measures were used to determine social inequality gaps.

Results: In 2019, 6 580 people died from road traffic injuries in Colombia. The majority of them (82%) were men. The most critical user condition was being a motorcyclist. The age group with the most victims was approximately 30 years old. Departments with populations between 500 000 and 2 000 000 were the most represented. The most critical equity stratifier was the number of registered motorcycles per 100 000 population. Significant inequality gaps between departments were observed.

Conclusions: Inequalities in deaths from road traffic injuries in Colombia were observed. Policies and actions should focus on helping to reduce identified inequities, resulting in better quality of life, well-being, and health for the population.

Objetivo.: Evaluar la asociación entre las desigualdades sociales y la mortalidad por lesiones de tránsito en Colombia durante 2019.

Métodos.: En este estudio ecológico se evaluó la asociación entre las desigualdades sociales y la mortalidad por lesiones de tránsito de las personas usuarias del sistema de transporte terrestre carretero en Colombia durante 2019, con base en fuentes secundarias de información, a nivel de departamento como unidad administrativa y geográfica de estudio. Se hizo un análisis estadístico descriptivo tanto del indicador de salud como de los estratificadores de equidad y se utilizaron medidas absolutas y relativas para determinar las brechas de desigualdad social.

Resultados.: En 2019 murieron en Colombia 6 580 personas por lesiones de tránsito, la mayoría de las cuales (82%) eran hombres. La condición de usuario más crítica fue la de motociclista. El grupo etario con más víctimas tenía aproximadamente 30 años. Los departamentos con población entre 500 000 y 2 000 000 de habitantes tuvieron la más alta participación. El estratificador de equidad con la condición más crítica de desigualdad fue el número de motocicletas registradas por cada 100 000 habitantes. Se evidenciaron brechas de desigualdad importantes entre los departamentos.

Conclusiones.: Se reconocieron desigualdades de la mortalidad por lesiones de tránsito en Colombia. Se deben orientar políticas y actuaciones que contribuyan a la disminución de las inequidades identificadas, lo que redunda en la calidad de vida, bienestar y salud de los ciudadanos.

Objetivo: Avaliar a relação entre desigualdades sociais e mortalidade por acidentes de trânsito na Colômbia no ano de 2019.

Métodos: Este estudo ecológico avaliou a relação entre desigualdades sociais e mortalidade por acidentes de trânsito entre usuários do sistema de transporte rodoviário na Colômbia em 2019, com base em fontes secundárias de informação e departamentos como unidades administrativas e geográficas do estudo. Foi feita uma análise estatística descritiva do indicador de saúde e dos estratificadores de equidade, e foram utilizadas medidas absolutas e relativas para determinar as lacunas de desigualdade social.

Resultados: Em 2019, 6 580 pessoas morreram na Colômbia em decorrência de acidentes de trânsito, em sua maioria homens (82%). A categoria de usuário mais afetada foi a de motociclistas, e a faixa etária com o maior número de vítimas girava em torno dos 30 anos. Departamentos com população entre 500 mil e 2 milhões de habitantes tiveram a maior participação. O estratificador de equidade com a condição mais crítica de desigualdade foi o número de motocicletas registradas por 100 mil habitantes. Foram evidenciadas lacunas significativas de desigualdade entre os departamentos.

Conclusões: Foram reconhecidas desigualdades na mortalidade por acidentes de trânsito na Colômbia. É preciso implementar políticas e ações que contribuam para a redução das desigualdades identificadas, o que resultará em qualidade de vida, bem-estar e saúde para os cidadãos.

Keywords: Colombia; Socioeconomic factors; accidents, traffic; equity stratifiers; inequality, social; mortality.

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References

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Grants and funding

Este estudio recibió apoyo financiero de la representación de la Organización Panamericana de la Salud en Colombia.

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national center for biotechnology information

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[Deaths from traffic injuries and social inequality in Colombia, 2019Mortalidade por acidentes de trânsito e desigualdades sociais na Colômbia, 2019] - PubMed

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Inequalities in deaths from road traffic injuries in Colombia were observed. Policies and actions should focus on helping to reduce identified inequities, resulting in better quality of life, well-being, and health for the population.

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Premios 2025 de la Academia Nacional de Medicina de Colombia a la investigación científica

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Mención de Honor - Academia Nacional de Medicina de Colombia Premios 2025 a la investigación científica 

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Mención de Honor - Academia Nacional de Medicina de Colombia Premios 2025 a la investigación científica 

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Proyecto AMORE - Conduciendo hacia la equidad. https://www.youtube.com/live/r7JKa5Jyq4k?si=cOPF8gqgvftAArL-&t=4713

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34. Travel time prediction from sparse open data. (2026-02-11) International Journal of Geographical Information Science (Behind paywall).

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34. Travel time prediction from sparse open data. (2026-02-11) International Journal of Geographical Information Science (Behind paywall).

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Boeing, G., & Zhou, Y. (2026-02-11). Travel time prediction from sparse open data. International Journal of Geographical Information Science, 1–17. https://doi.org/10.1080/13658816.2026.2628193

Open access preprint: https://osf.io/preprints/socarxiv/qepc6_v1

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Fabio Y. Moraes, Maria Thereza M. Starling, Vanessa F Bratti, Daniel Del Rosso, Gustavo A. Viani, Mauricio F. da Silva, Arthur A. Rosa, Marcus S. Castilho, Gustavo N. Marta, Theodoros Tsakiridis, Edward Christopher Dee, Cecília F.P.M. de Sousa, Samir A. Hanna, Silmara R. Segala, Andre G. Gouveia. Geographical and Sociodemographic Disparities in Access to Radiotherapy in Brazil: A Nationwide Cross-Sectional Study (2017–2022). International Journal of Radiation Oncology, Biology, Physics (2026-02-14), doi: https://doi.org/10.1016/j.ijrobp.2026.01.035

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35. Geographical and Sociodemographic Disparities in Access to Radiotherapy in Brazil: A Nationwide Cross-Sectional Study (2017–2022)

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35. Geographical and Sociodemographic Disparities in Access to Radiotherapy in Brazil: A Nationwide Cross-Sectional Study (2017–2022)

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Geographical and Sociodemographic Disparities in Access to Radiotherapy in Brazil: A Nationwide Cross-Sectional Study (2017–2022). International Journal of Radiation Oncology, Biology, Physics (2026-02-14), doi: https://doi.org/10.1016/j.ijrobp.2026.01.035

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19. Accesibilidad espacial a la infraestructura crítica en situaciones de emergencia. OPS/OMS. Quito Oct 16, 2024.

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19. Accesibilidad espacial a la infraestructura crítica en situaciones de emergencia. OPS/OMS. Quito Oct 16, 2024.

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Jaramillo, Ciro. “Accesibilidad espacial a la infraestructura crítica en situaciones de emergencia.” Presented at the Primera Reunión Interregional sobre el Fortalecimiento de Capacidades de Preparación ante Emergencias en Salud y Desastres en Ciudades y Entornos Urbanos - OPS/OMS, Quito, Ecuador, October 16, 2024. https://www.paho.org/es/eventos/primera-reunion-interregional-sobre-fortalecimiento-capacidades-preparacion-ante.

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28. Evaluación de la accesibilidad espacial a servicios de Diálisis en el Servicio de Salud Metropolitano Sur Oriente

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28. Evaluación de la accesibilidad espacial a servicios de Diálisis en el Servicio de Salud Metropolitano Sur Oriente

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Portales Orellana, F., Fuenzalida Díaz, M., Núñez Vargas, D., & Suárez Gajardo, F. (2025). Evaluación de la accesibilidad espacial a servicios de Diálisis en el Servicio de Salud Metropolitano Sur Oriente. Revista Chilena De Salud Pública, 29, e75004. https://doi.org/10.5354/0719-5281.2025.75004

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Evaluación de la accesibilidad espacial a servicios de Diálisis en el Servicio de Salud Metropolitano Sur Oriente

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Evaluación de la accesibilidad espacial a servicios de Diálisis en el Servicio de Salud Metropolitano Sur Oriente

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Introducción: El objetivo principal fue evaluar la accesibilidad a los servicios de diálisis en el área urbana del Servicio de Salud Metropolitano Sur Oriente de Chile (SSMSO), utilizando el modelo de acceso basado en agentes racionales (RAAM).
Materiales y métodos: Se calculó la oferta y demanda de servicios mediante un proceso de geocodificación de direcciones y posterior agregación territorial a nivel de unidades vecinales con ayuda de la librería de análisis espacial ‘Geopandas’ de Python. A continuación, se estimaron tiempos de transporte en automóvil a través del motor de ruteo R5py para finalmente ejecutar el modelo.
Resultados: Los valores estandarizados del modelo RAAM nos permitieron identificar ciertas unidades vecinales que presentan altos costos de accesibilidad, con valores entre un 13% y 19% sobre el promedio del área de estudio en los dos escenarios que se evaluaron (26 y 13 minutos, respectivamente).
Discusión: Los sectores con mayores dificultades para acceder a este tipo de servicio se encuentran distribuidos en las comunas de La Florida, San Ramón, La Granja y la Pintana. Por otro lado, la comuna de Puente Alto en general presenta mejores indicadores. Estos resultados pueden apoyar la toma decisiones en materia de acceso potencial a servicios de diálisis, las cuales se pueden sustentar en distintas configuraciones de oferta y demanda. El método empleado puede ser replicado para identificar brechas de accesibilidad a escala local en servicios de urgencias o especialidades médicas.

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26. Data analytics model of the accessibility-vulnerability relationship: risk scenarios in urban areas. 

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26. Data analytics model of the accessibility-vulnerability relationship: risk scenarios in urban areas. 

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Sánchez-Grajales, J., Bucheli, V., & Jaramillo, C. (2025). Data analytics model of the accessibility-vulnerability relationship: risk scenarios in urban areas. [Modelo de analítica de datos de la relación accesibilidad-vulnerabilidad: Escenarios de riesgo en ciudades] Revista Facultad De Ingenieria, 34(73), 1-16. doi:https://doi.org/10.19053/01211129.v34.n73.2025.18085

Link: https://www.proquest.com/scholarly-journals/data-analytics-model-accessibility-vulnerability/docview/3283613013/se-2?accountid=15292

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33. Geospatial Modelling Reveals Flood-Driven Inequities in Nutrition-Sensitive Healthcare Access across Kampala, Uganda 2025. (Preprint)

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33. Geospatial Modelling Reveals Flood-Driven Inequities in Nutrition-Sensitive Healthcare Access across Kampala, Uganda 2025. (Preprint)

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Lubbers, R., Weitkamp, G., Hierink, F., Ray, N., Oporia, F., Businge, O., & Biesma, R. (2025). Geospatial modelling reveals flood-driven inequities in nutrition-sensitive healthcare access across Kampala, Uganda. Research Square. https://doi.org/10.21203/rs.3.rs-8055572/v1

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Geospatial modelling reveals flood-driven inequities in nutrition-sensitive healthcare access across Kampala, Uganda

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BackgroundUrban floods slow or sever travel to healthcare facilities, reducing timely access to maternal and child healthcare in Kampala. We estimated flood-related changes in walking access to all public and private-not-for-profit facilities and to hospitals.MethodsWe…

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23. Honorary mention from Colombia's National Academy of Medicine - Mención de Honor de la Academia Nacional de Medicina de Colombia

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23. Honorary mention from Colombia's National Academy of Medicine - Mención de Honor de la Academia Nacional de Medicina de Colombia

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El proyecto AMORE recibe Mención de Honor en la ceremonia de entrega de los Premios Científicos de la Academia Nacional de Medicina de Colombia 2025.

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El proyecto AMORE recibe Mención de Honor en la ceremonia de entrega de los Premios Científicos de la Academia Nacional de Medicina de Colombia 2025.

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🗓 Miércoles, 10 de diciembre 🕟 16:30 h

▶️ En vivo por YouTube:

https://www.youtube.com/live/r7JKa5Jyq4k?si=cOPF8gqgvftAArL-&t=4713

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ENTREGA PREMIOS A LA INVESTIGACIÓN CIENTÍFICA - ACADEMIA NACIONAL DE MEDICINA

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The World Health Organization's case studies showcasing how cities around the world are implementing its Strategic Approach to Urban Health 🌆🌍

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The World Health Organization's case studies showcasing how cities around the world are implementing its Strategic Approach to Urban Health 🌆🌍

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Driving towards equity: The AMORE Project. Included in WHO's case studies collection: https://www.who.int/teams/social-determinants-of-health/urban-health/strategic-approach/case-studies

Download the PDF of "Driving towards equity: the AMORE project" at https://cdn.who.int/media/docs/default-source/urban-health-documents/formatted-014-driving-toward-equity.pdf

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WHO’s primary role is to direct international health within the United Nations’ system and to lead partners in global health responses.

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30. Incorporating Travel Purposes into the Dynamic Accessibility Evaluation for Elderly Public Transport Based on End-to-End Travel.

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30. Incorporating Travel Purposes into the Dynamic Accessibility Evaluation for Elderly Public Transport Based on End-to-End Travel.

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Qian, H.; Sun, Z.; Zhang, Y.; Zhang, T.; Chen, Y. Incorporating Travel Purposes into the Dynamic Accessibility Evaluation for Elderly Public Transport Based on End-to-End Travel. Journal of Transport Geography 2026, 130, 104457, https://doi.org/10.1016/j.jtrangeo.2025.104457

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32. Geospatial Analysis of Emergency Healthcare Accessibility: Bridging Urban–Rural Disparities in Romania. 

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32. Geospatial Analysis of Emergency Healthcare Accessibility: Bridging Urban–Rural Disparities in Romania. 

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Cazacu, C.; Dumitrache, L.; Mareci, A.; Cioclu, A.; Petre, A.-A.; Cazacu, C.; Dumitrache, L.; Mareci, A.; Cioclu, A.; Petre, A.-A. Geospatial Analysis of Emergency Healthcare Accessibility: Bridging Urban–Rural Disparities in Romania. Land 2025, 14, doi:10.3390/land14122321.

"recent studies stipulate that the discussion should be oriented more towards the capacity to reach different services, with accessibility being influenced based on the hierarchy, importance, number and territorial display of facilities."

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Geospatial Analysis of Emergency Healthcare Accessibility: Bridging Urban–Rural Disparities in Romania

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Romania faces significant disparities in access to emergency healthcare. This study employs a national geospatial analysis and network-based GIS modelling to assess current spatial accessibility to emergency care services and to develop a scenario-based spatial model for Romania, aiming to improve access to these services. This study is the first to design such a scenario and to do so at a national level. To improve access to the emergency network, this study proposes establishing additional emergency units at designated hospitals. Integrating national-scale data into the Network Analyst tools, we identify critical gaps in emergency service coverage, with current estimates indicating that approximately 65% of the rural population lives in areas with limited or no access to emergency care. Travel time to the nearest emergency care unit exceeds 120 min in remote rural regions, highlighting a severe spatial inequity in healthcare provision. The results show that in our scenario, the total proportion of the population that has to travel for more than 30 min to the nearest emergency care unit has dropped from almost 60.28% to 35.63% when travelling in a private car, and from 72.58% to 46.08% when travelling in an ambulance. In fact, the total number of inhabitants reaching an emergency facility within 30 min increases by more than 4.7 million when travelling by private car and by more than 5 million when travelling by ambulance. These findings underscore the importance of spatial planning in the development of healthcare infrastructure and provide quantitative, spatially explicit evidence to support infrastructure planning and policy decisions aimed at reducing the urban–rural healthcare divide. This study contributes to the broader discourse on spatial justice and health equity, offering local or national authorities a basis for developing improved healthcare policies to improve coverage and reduce the urban–rural healthcare divide.

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31. Using explainable artificial intelligence for mapping health vulnerability: Interaction-based analysis of multiple sources of data in Latin America

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31. Using explainable artificial intelligence for mapping health vulnerability: Interaction-based analysis of multiple sources of data in Latin America

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Arias Tapia, S.A., López, A.S. & Jadán-Guerrero, J. Using explainable artificial intelligence for mapping health vulnerability: Interaction-based analysis of multiple sources of data in Latin America. Environ Sci Pollut Res (2025-11-21). https://doi.org/10.1007/s11356-025-37051-6

📊🌐 The study introduces a composite Interaction Index capturing ethnic diversity, scarcity of medical access, and altitude, and uses machine learning, clustering, and decision trees to identify “clusters of vulnerability” that may be invisible in traditional analyses. These insights help guide equitable, AI-enhanced decision-making for public health across diverse geographies.

🏙️🚦 Importantly, the authors cite the AMORE Project as a key example of a replicable, open-data, people-centred methodology for evaluating how congestion, spatial segregation, and dynamic accessibility influence health equity in urban settings — and for supporting actionable public policy design.

🔍💡 This cross-fertilisation of approaches — combining explainable AI, open data, and equity-driven spatial analysis — strengthens a growing body of work committed to designing fairer, more resilient urban and regional health systems.

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Using explainable artificial intelligence for mapping health vulnerability: Interaction-based analysis of multiple sources of data in Latin America - Environmental Science and Pollution Research

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Even as there are extensive genetic linkages across Latin America, local health risk is influenced by a host of interdependent factors that include (a) ethnic heterogeneity, (b) geographical isolation, and (c) disproportionate access to healthcare. The article presents a new explainable artificial intelligence (XAI) model for mapping and interpreting health vulnerability, combining several open-access datasets, such as disease prevalence, medical supply, and genetic ancestry profiles. We introduce a compound Interaction Index, as the product of ethnic diversity (E), inverted medical access (1 − M), and altitude (A), to quantify compounded structural and biological risk factors. Applying supervised learning models (F1 = 0.596 for SVM, F1 = 0.571 for gradient boosting, and logistic regression), in combination with unsupervised clustering and interpretable classification trees, we detect the high-risk regions with high diversity, low access, and mid-to-high altitude. This transparent and scalable methodology for equitable public health planning illuminates such ‘clusters of vulnerability’ which might remain hidden amid aggregate data.

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27.  The Development of a New Location-Based Accessibility Measure Based on GPS Data. Sensors, 25(20), 6274. https://doi.org/10.3390/s25206274

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27.  The Development of a New Location-Based Accessibility Measure Based on GPS Data. Sensors, 25(20), 6274. https://doi.org/10.3390/s25206274

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New citation highlights our project’s ability to measure accessibility gradients related to traffic congestion: Liu, F., Yasar, A., Cui, J., Janssens, D., Wets, G., & Cools, M. (2025). The Development of a New Location-Based Accessibility Measure Based on GPS Data. Sensors, 25(20), 6274. https://doi.org/10.3390/s25206274

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Transportation Research Institute (IMOB), Hasselt University, 3500 Hasselt, Belgium

School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Chongqing Research Institute of HIT, Chongqing 401100, China

LEMA, University of Liège, 4000 Liège, Belgium

Author to whom correspondence should be addressed.

1. Introduction

1.1. Problem Statement

With the continuing urbanisation of the world's population and economic growth of cities, spatial urban areas are expanding, and new communities and activity locations are becoming decentralised. However, existing transport networks have not developed at the same pace as urban growth, generating isolated pockets of areas that are difficult to reach by the transport systems [ 1]. It is thus important to conduct a timely examination of the changing land-use structure and travel conditions and accurately evaluate accessibility. This helps identify poorly reachable areas and informs efforts to improve the accessibility of these places as well as the accessibility of the city as a whole [ 2].

Location-based potential measures have been widely applied to analyse the accessibility of an urban area [ 3, 4, 5]. These measures use an impedance function, typically the negative exponential function (NEF) (i.e., = , where t is the travel time and k is a controlling parameter), to characterise the declining attractiveness of activities in a zone to a destination as the average travel time between these two places increases, in order to take into account the effects of travel times on people's perceptions of activity attractiveness.

Nevertheless, the potential measures only consider a single time point (i.e., the average travel time) while neglecting detailed travel time distributions, resulting in the same measure to an activity place for two study zones that share identical mean travel times but differ in travel time distributions (e.g., the standard deviations). According to NEF, for individual trips, the number of changes in the attractiveness of activities resulting from the changes in travel times is non-linearly distributed; the longer the time, the more diminishing the attractiveness and the lower the accessibility. This non-linear relationship implies that the actual value of accessibility is more decided by trips with shorter travel times than those featuring longer times. The zones displaying varied travel time distributions should thus be assigned different measures. For zones with smaller deviations, the distribution of travel times is more centred around the mean travel time, generating fewer trips with shorter times, leading to a relatively higher level of diminishing activity attractiveness and, consequently, a lower level of accessibility. In contrast, regarding zones with larger deviations, travel times are more dispersed, causing more trips with shorter times, resulting in a lower level of reduction in attractiveness and, consequently, a higher level of accessibility (see further elaboration in the Section The New Measure).

In order to characterise the differences in accessibility for varied travel time distributions, in this study, we extended the potential measure to incorporate travel time distributions into the impedance function based on GPS data of urban vehicles. Compared with existing location-based measures (LBMs), the new method offers several key advantages:

It constructs a new impedance function by integrating the probability distribution of travel times, considering both the mean and variation in individual travel times. By combining travel time distributions, the new measure accommodates the effect of each individual trip, whereas existing measures only consider the effect of the mean travel time.

Given that traffic conditions in an urban road network are highly divergent and travel times are stochastic (even within the same period of the day), the proposed method captures the statistical fluctuations of travel times and provides a more accurate and realistic assessment of network accessibility.

In many cities worldwide, GPS devices are installed in taxis and in many other urban vehicles-such as private cars, buses and trucks, generating massive GPS data and enabling the extraction of travel time distributions. This makes the approach cost-effective, timely-updated and easily transferrable to other cities.

The remainder of this paper is organised as follows. Section 1.2 describes the state-of-the-art accessibility research, while Section 2 introduces the data and explains the proposed method. Section 3 presents a case study, and Section 4 ends this paper with major discussions and conclusions.

1.2. State-of-the-Art Accessibility Research

1.2.1. Accessibility

Accessibility is defined as the ease and extent to which land-use and transport systems enable individuals to reach activities and destinations using certain transport modes, e.g., the number of jobs accessible within 30 min by car [ 6]. It not only considers travel conditions (e.g., travel times or distances) but also the distribution of land-use and activity locations across the transport network [ 7].

Various methods have been developed for measuring accessibility, including infrastructure-based [ 6], person-based [ 8], location-based [ 9], and utility-based [ 10] approaches. Particularly, location-based measures (LBMs) analyse accessibility from a locational perspective, and are mostly applied owning to their good operability and interpretation [ 11]. Three major categories of LBMs exist, consisting of contour (cumulative opportunity), potential (gravity), and 2-step floating catchment area (2SFCA) measures. A contour measure represents the total number of activities that can be reached from the study zone within a predefined travel time threshold [ 4]. In contrast, a potential measure uses an impedance function to reduce the attractiveness of activities to the study zone, as the travel time between the study and activity zones increases [ 5]. Furthermore, 2SFCA incorporates the competitive relationship between activity supply and demand, accounting for the fact that access to opportunities is affected not only by geographical proximity and travel times, but also by the competition of many people trying to access the same opportunity [ 12, 13].

Traditionally, all the above measures are derived through a static analysis; travel time is calculated as the average travel time per day, obtained from travel surveys or static sensors, while the attractiveness of activities is quantified as the total number or size of activities within activity zones [ 6, 14]. Consequently, the resulting measures remain fixed throughout the day and fail to capture the dynamic nature of accessibility, which fluctuates across different time periods due to variations in human activity patterns, traffic conditions and activity availability (e.g., shop opening and closing hours). This lack of temporal differentiation renders the measures inadequate for reflecting actual accessibility at varied times of the day.

1.2.2. Dynamic Accessibility

With advancements in information and communication technologies, it has become technically and economically feasible to collect and process large amounts of mobility data (e.g., GPS, mobile phone and social media data) and activity data (e.g., crowdsourced information on business establishment) [ 15]. These data facilitate the extraction of detailed insights into human activity patterns, service demand and traffic conditions, presenting great opportunities to refine conventional accessibility measures. Based on such data, time-dependent dynamic location-based measures (D-LBMs) have been developed to capture temporal variations in accessibility across different time periods of the day [ 3, 5]. For example, Järv et al. [ 16] incorporated the temporal dynamics of activities and travel-such as grocery store opening hours, time-dependent traffic conditions and travel times-to compute hourly food accessibility. By comparing these hourly measures with conventional static analyses (e.g., using average daily travel times), they found that static measures typically overestimate people's access to potential opportunities. Similarly, Hu and Downs [ 17] presented a framework for measuring and visualising hourly job accessibility, accounting for temporal fluctuations in job supply, worker demand and travel times. Furthermore, Cuervo et al. [ 18] classified traffic congestion into nine levels, from free-flow (level 1) to peak traffic (level 9), and examined accessibility to medical services at each level. Their results revealed substantial variations; accessibility during peak traffic was 53% lower than under free-flow conditions.

Nevertheless, although D-LBMs account for inter-period variability (e.g., each hour or each congestion level), they still assume that travel times within each period are deterministic, and use the average travel time for accessibility calculation. However, given the stochastic nature of travel demand and traffic conditions in urban road networks, travel times even within the same period are not consistent but subject to variability [ 19, 20]. Travel time variability has been particularly addressed in reliability-based location-based measures (R-LBMs), which integrate travel time reliability-the probability of reaching an activity location within a specified time budget-into accessibility analysis [ 21]. The rationality is that, variability, as a form of travel-time uncertainty, affects how people perceives facilities and therefore should be included in accessibility studies [ 22]. However, despite the integration of variability, R-LBMs basically still rely on a single time point (e.g., a specific percentile of the travel time distribution) for accessibility estimations, without fully considering the entire distribution of travel times across the study period.

1.2.3. Limitations of Current Accessibility Measures

Both D-LBMs and R-LBMs have proven their value and feasibility in capturing temporal variations, providing deeper insights and more advanced approaches for accessibility analysis [ 23, 24, 25]. Nevertheless, these measures compute accessibility using either the mean or a specific percentile of travel time distributions between study and activity zones, assuming that all the travellers experience the same travel time during the analysed period. Neither measure considers the detailed travel time distribution. According to literature [ 4, 6, 9], different travel times influence activity attractiveness to varied degrees. For instance, based on NEF, a negative non-linear relationship exists; the longer the (average) travel time, the more the activity attractiveness and accessibility diminish. Thus, accessibility should be differentiated not only across locations and time periods but also among individuals' trips and perceptions shaped by the varied travel times. Researchers have indeed questioned the underlying assumption of LBMs that all individuals at a given location have equal awareness of activity destinations [ 26, 27, 28]. This assumption overlooks the diverse experiences and perceptions of individual travellers. Existing measures, particularly D-LBMs, strive to overcome this weakness by splitting a day into short periods and use average travel times for each period. However, they still assume homogeneous travel times and accessibility within each period and therefore only partially resolve the problem. Consequently, a method that models individual trips and explicitly accounts for travellers' heterogeneous experiences remains lacking.

The growing availability of GPS data provides accurate routes and travel times for many people, offering detailed spatial and temporal information and near real-time traffic conditions [ 29]. This makes it possible to analyse accessibility at the level of individual trips. In this study, we incorporate travel time distributions into the calculation of LBMs and derive accessibility based on individual travel times (trips), using taxi GPS data. The key differences between the new and existing measures lie in how they utilise travel times and model the effects of these times. The new measure aggregates the effect of every trip and duration across a period, while existing measures typically analyse the effect of the average duration or another single percentile of the travel time distribution. As a result, the new approach produces accessibility estimates that are more sensitive to individuals' diverse views and practices, even for the same zone pairs within the same time period.

2. Materials and Methods

2.1. Data

The GPS data were collected from all licenced taxis in Harbin, the capital of Heilongjiang province in China, totalling 16,000 vehicles. Data were recorded every 30 s during the day and every 2 min at night, generating 1.6 GB of data and 24 million GPS points each day. According to the data, each taxi completes an average of 30 passenger trips per day, resulting in a total of 0.48 m passenger trips. By comparison, Harbin has approximately 1 million private cars, which collectively produce an estimated 2.41 million trips daily, assuming that each car generates an average of 2.41 trips per day [ 30]. Consequently, taxi passenger trips account for 17% of the total personal travel undertaken in the urban area daily. This highlights the significant role of taxis in meeting urban travel demand for private trips. Further information on the data can be referred to in the paper [ 30].

For this study, GPS data collected between July and September 2016 were used. The dataset includes variables such as taxi vehicle IDs, GPS coordinates, recording times and status messages indicating whether passengers were on board. A digital map of the road network, obtained from the Baidu Map Open Platform [ 31], was also utilised. This dataset provides the coordinates and classifications of all activity locations (16,625 in total, spanning 16 types) across the urban area. The classification of activity types is described in Table A1 in Appendix A, and this study performs accessibility analysis regarding the integration of all these types.

2.2. Methodology

The proposed method consists of four main steps (See Figure 1): (i) pre-processing GPS data and extracting passenger trips, (ii) constructing passenger travel patterns and identifying high-density residential zones, (iii) calculating accessibility for each residential zone using both the new and conventional measures, and (iv) identifying zones with the lowest levels of accessibility.

2.2.2. Travel Pattern Construction and High-Density Residential Zone Identification

The entire urban area is divided into GridX × GridY disjoint zones using a grid-based method. Each zone is denoted as (i = 1,..., GridX × GridY) or z(ix, iy) (ix = 1,..., GridX; iy = 1,..., GridY), and each zone pair from to is referred to as or - . The temporal dimension of trips is classified into different time periods (i.e., TimeP) within a day (i.e., Day) and further distinguished by day type (i.e., DayT). Based on this spatial and temporal division, a passenger travel pattern matrix OD(, , TimeP, Day, DayT) is constructed, with each matrix element representing all the trips that originate from , end in , and start within TimeP on Day of type DayT.

From the matrix OD, the average number of trips per day that either originate in in the morning () or end in at night () is calculated for all days of type DayT. Zones with both and exceeding a threshold are identified as high-density residential areas and used as the study zones. Simultaneously, all activity locations in the city are assigned to zones based on their geographic positions, with zones containing at least one activity location forming the activity zones.

To ensure accurate passenger travel times, two parameters and are defined to filter trips that may involve spatial detours (e.g., due to taxi sharing) or temporal extensions (e.g., when taxis stop for an extended period during a trip). Trips with circuity exceeding or travel times longer than are considered to involve potential spatial detours or temporal extensions and are removed from OD. The travel times of the remaining trips are then used for subsequent accessibility computation.

2.2.3. Accessibility Computation

Based on the obtained study and activity zones as well as passenger travel times, accessibility is computed using both the new and traditional measures. Table A2 in Appendix A summarises all the major variables used in the computation process.

The New Measure

Assume that across all observed trips for , there are n discrete travel times (t ₁, ..., ) with corresponding probabilities (p ₁, ...,), such that p ₁ + ... + = 1. Based on Formula (4), the impedance function , describing the average effect across all the times, is given as

Here, and represent the minimum and maximum travel times for , respectively, and reflects the impact of travel times t. The new measure integrates the existing measure with the probability distribution (t) of travel times for all trips between each pair of study and activity zones. The key difference lies in the impedance function; for , only accounts for the effect (and accessibility) of the mean travel time , ignoring individual times. In contrast, calculates the effect for each individual travel time and uses to represent the average of across all the times.

Given that = ( Figure 3) yields higher values for shorter times, achieves a higher accessibility level than z ₁. This is evidenced by the average of (i.e., , equal to ) over all trips, calculated as 0.06 for z ₁ ( Figure 2b) and 0.08 for ( Figure 2 d), with k = 0.1. This demonstrates that, under the NEF framework, accessibility is more influenced by trips with shorter travel times, as shorter times lead to less diminishing attractiveness and higher accessibility.

Figure 2e depicts the travel time distribution from a third zone z ₃ to , with the same mean ( = 30 min) but an even larger standard deviation ( = 15). This generates even more trips with shorter times, further increasing accessibility to 0.12 ( Figure 2 f).

For all the three zones, the existing measure = 0.05, which is smaller than for each zone. The differences are −0.01, −0.03, and −0.07, corresponding to underestimations of accessibility of these zone (pairs) by 17%, 38%, and 58%, respectively. This highlights that underestimates accessibility more significantly as increases, revealing that greater variability in travel times results in higher accessibility underestimated by the existing measure.

2.2.4. Zones with the Lowest Level of Accessibility Detection

4. Discussion

Achieving equitable transport accessibility and a balanced distribution of urban services is one of the primary objectives of transport managers and urban planners [ 5]. As cities grow and populations expand, accessibility challenges become increasingly critical, necessitating more advanced and accurate analysis methods [ 1]. To address this challenge, we have developed a novel approach to measure accessibility to various urban services by car. Compared to traditional methods, this new approach is more sensitive to traffic conditions and travel time distributions, providing a more objective representation of accessibility. Additionally, the continuous generation of GPS data from urban vehicles enables timely updates of the derived results, allowing these results to keep pace with rapid urban land-use changes, population growth, and evolving mobility patterns.

4.1. Major Differences Between the New and Existing Measures

When the proposed method was applied to the study city, a certain level of deviation was observed between the new measure, , and existing measures, and . Specifically, compared to , underestimates accessibility across all the study zones by an average of 17%, whereas overestimates accessibility by 360%. Moreover, under- or overestimation varies across zones, leading to discrepancies between the new ranking, , and existing rankings, and . The geographic characteristics and underlying causes for these discrepancies also differ. The differences between and are primarily influenced by traffic conditions and travel time variations. Most zones with a lower rank are located in or around the urban centre, , where a high concertation of activities is established (See Figure 7 c). In these zones (e.g., ), travel times between and activity zones -particularly those within -are generally short due to proximity but exhibit high variability due to congestion. As a result, travel time distributions show large deviations (), skewness (), and/or ratios ( = /). The larger travel time deviations in result in more trips with shorter times (relative to the mean), leading to a lower reduction in attractiveness and, therefore, a higher level of accessibility represented by and a higher rank by (compared to and ). In contrast, zones with a higher rank are predominantly found in suburban areas farther from . These zones (e.g., ) typically experience longer travel times between and (especially within ) but with less variability (e.g., due to reduced congestion in suburban areas). Under such conditions, travel time distributions have larger values but relatively smaller , and , meaning that travel times are more concentrated around the mean travel time. This results in fewer trips with shorter travel times, leading to a greater reduction in activity attractiveness and, consequently, a lower level of accessibility as reflected by and a lower rank by (compared to and ).

The differences between and are more closely related to the spatial distribution of activities within and outside the Area(,T) (places reached from within T) (See Figure 8 c). In zones with a larger number of activities located outside Area(,T), tends to be larger, resulting in a higher and a lower . Conversely, in zones with fewer activities outside Area(,T), is relatively smaller, leading to a lower and a correspondingly higher .

The above discrepancies highlight the inaccuracies of the existing measures and and underscore the advantages of adopting the new measure , particularly in urban environments characterised by heavy congestion surrounding and uneven activity distributions outside . The higher the congestion levels and the more uneven the activity distributions, the larger the disparities between the existing and new measures. In the experimental city, the minimum and maximum differences between and range from −10 to 12, while those between and span from −18 to 18. Across all the 103 study zones, this ranking variation translates into relative percentage changes: a shift of −10% to +12% between and , and −17% to +17% between and .

4.2. Potential Applications of the New Method

The proposed method facilitates systematic analysis of accessibility across urban road networks and the identification of zones experiencing significant (vehicle-based) accessibility problems. It can also be applied to assess the impact of implemented land-use or transport policies on accessibility by comparing GPS-derived measures before and after their adoption. Across these applications, the new measure provides a more accurate reflection of actual network performance by considering average travel times and detailed travel time distributions. This allows for more precise accessibility evaluations and problem identification, aiding in the design of policies better aligned with real-world traffic conditions, thereby enhancing network reachability and reducing inequities.

This is particularly relevant in the post-COVID era. During and after the pandemic, the rise in remote work has reduced commuting trips while increasing home-centred activities and travel [ 36, 37]. Mobility patterns have shifted from work-centric destinations to local areas near homes, making the re-evaluation of road network accessibility, particularly in local areas, an essential step [ 38]. In this context, the proposed measure is especially valuable for identifying areas with poor accessibility and assisting governments in addressing these shifts, ultimately improving reachability for local communities.

4.3. Future Research Avenues

Several avenues for future research remain open. The first concerns parameter sensitivity analysis. This method relies on a set of parameters, and the experimental results are influenced by their specified values. Future research should systematically explore how variations in these parameter values would affect the results in order to provide more precise guidance on optimal parameter selection. Particularly, for this analysis, we simply adopted a fixed threshold ( = 20%) to identify zones with the lowest accessibility and compared these zones detected by the new and existing measures. For subsequent research, applying more refined data analysis techniques-such as clustering [ 30]-would allow for a multi-level classification of zones by accessibility, enabling more precise identification of the lowest-ranking zones. We also recommend externally validating these lowest-ranking zones against observed accessibility conditions and examining the policy implications of any identified problems.

Additionally, the parameter k captures the effect of travel times between zones on people's behaviour [ 11]; the higher the value, the greater the decreasing effect of travel times. Future work could explore alternative values of k beyond the current setting. While varying k changes the absolute accessibility measures of each zone pair and zone, it may also alter zone rankings. Increasing k generally reduces overall attractiveness across all zones, but the reduction in is smaller for zones with larger deviations. Those zones tend to generate more short trips, and a higher k amplifies the influence of short trips, which mitigates their attractiveness loss. As a result, as k increases, decreases for all zones, but zones with larger deviations experience a relatively smaller decrease and therefore rise in zone rankings ().

Second, future research should examine alternative probability distributions. In the case study, travel times for each zone pair were assumed to follow a normal distribution. However, in a more congested urban network, travel time distributions can be more right-skewed; functions such as lognormal or gamma distributions may better represent these conditions. These skewed distributions tend to increase the average travel time (), which would likely amplify the underestimation of accessibility by the traditional function () relative to the new function ().

Third, activity attractiveness in each activity zone was quantified based solely on the total number of activities of all types, without considering other attributes such as sizes or specific activity types. Since the primary differences between the new and existing measures stem from the impedance functions ( and ), this study focused on examining the differences resulted from these functions. The method to quantify activity attractiveness is unlikely to significantly impact the compared results, as all the accessibility measures use the same attractiveness values. Nevertheless, this quantification method could be refined by including additional attributes (e.g., sizes or types) or by weighting attributes to reflect activities' relative importance [ 15].

Fourth, when deriving Formula (7) to characterise the relationship between Δ and travel time distributions, we used three key variables: , and . However, other factors-such as the difference between the mean and median-likely also affect this relationship. Future work could explore these additional variables to create a more precise regression model.

Fifth, our analysis concentrated in the weekday morning rush hour (7:00-9:00 AM), and compared against two established dynamic measures- and -all derived from this same period. Future research could shrink the temporal period to a shorter (e.g., one-hour) interval, instead of the full two-hour window used here. Given that traffic conditions remain relatively consistent throughout the rush hour and travel time variability exists within each shorter interval, we expect any differences between the new and existing measures to mirror the trends observed in our current findings. Beyond adjusting the time window, the proposed method offers broader applicability to other periods like afternoons, evenings, or weekends. However-given the large differences in travel patterns and traffic conditions-applying it to these periods would yield distinct accessibility values and zonal rankings compared to our current results.

Lastly, future research should explore the use of expanded data sources. This study demonstrates the utility and benefits of the new approach using GPS data from taxis. However, this data source has inherent limitations; for example, high-density residential areas with accessibility issues may be overlooked if they receive limited taxi trips. This limitation could be mitigated by incorporating GPS data from other urban vehicles, such as ride-hailing services, private cars, buses and trucks [ 39], and from smartphones carried by individuals while walking or using public transport. Given the heavy reliance on public transit in major cities, applying the proposed method to bus GPS data is particularly crucial for accurately assessing and improving accessibility gaps. Additionally, for this proof of concept, we used the 2016 taxi dataset. Urban land-use and population have changed considerably since then-especially after the COVID-19 pandemic-so future work should apply this method to more recent data and compare the results. Combining these varied data sources will strengthen the model's robustness, enabling more precise identification of contemporary accessibility challenges-not just across road networks, but also within public transit and pedestrian pathways [ 40]. Alongside expanding the data, we also plan to improve visualisation in future work. Using a more geographically oriented tool-such as ColorBrewer 2 ( https://colorbrewer2.org/) (accessed on 4 May 2025)-would better display zones and their accessibility variations.

Author Contributions

Conceptualisation, F.L.; methodology, F.L.; tools, J.C.; validation, M.C.; formal analysis, F.L.; investigation, A.Y.; resources, J.C.; data curation, J.C.; writing-original draft preparation, F.L.; writing-review and editing, M.C.; visualisation, A.Y.; supervision, D.J.; project administration, G.W.; funding acquisition, A.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the RAISE project under the new Horizon Europe framework, grant number 101058479.

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

The corresponding author will provide the information supporting the research study's conclusions upon reasonable request.

Conflicts of Interest

The author declares no conflicts of interest.

References

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Figure 1. Overall structure of the proposed method. Note: OD (, , TimeP, Day, DayT) denotes the passenger travel pattern matrix, where and are the origin and destination zones and TimeP, Day and DayT specify the temporal attributes (time period, calendar day, and day type). , and denote the new, existing potential, and contour measures for study zone , while , and indicate the zones with the lowest , and values, respectively.

Figure 2. An example of varied accessibility for zone pairs with different travel time distributions. Note: In ( a, c, e), the x-axis denotes travel times, std is the standard deviation, and the blue curves and red lines represent the fitted normal distributions and values of . In ( b, d, f), the x-axis denotes values of = , and the red and orange lines indicate = and , respectively.

Figure 4. Distribution of average speeds in each half an hour of a weekday. Note: the x-axis denotes the time of the day, and the y-axis is the average speed of trips over half-hour intervals.

Figure 6. Travel time distributions and values of and of two actual zone pairs. Note: In ( a, c), the x-axis denotes travel times, std is the standard deviation, and the blue curves and red lines represent the fitted normal distributions and values of . In ( b, d), the x-axis denotes values of , and the red and orange lines indicate and , respectively.

Figure 7. Geographic distributions of study zones with ( a), ( b) and Δ ( c), respectively. Note: In ( a, b), the large filled red, small filled yellow and green circles represent zones with and ranks of 1-20, 21-50 and 51-103, respectively. In ( c), the large filled purple, small filled blue and large filled orange circles represent zones with Δ ≤ −3, −2 ≤ Δ ≤ 2, and Δ ≥ 3, respectively. In all the figures, the zones enclosed in purple rectangles are included in but not in ; while those enclosed in orange rectangles are in but not in . The large unfilled purple oval outlines .

Figure 8. Geographic distributions of study zones with ( a), Δ ( b) and activity zones ( c). Note: In ( a), the large filled red, small filled yellow and green circles represent zones with ranks of 1-20, 21-50 and 51-103, respectively. The zones enclosed in purple rectangles are included in but not in ; while those enclosed in orange rectangles are in but not in . In ( b), the large filled purple, small filled blue and large filled orange circles represent zones with Δ ≤ −3, −2 ≤ Δ ≤ 2, and Δ ≥ 3, respectively. In ( c), the filled red circles denote activity zones, with the radius being proportional to the number of activities. Red circles enclosed by purple polygons outline activities in the northwest, southwest and south. The two black dash circles represent Area(,T) for z(23,25) and z(30,20). In ( b, c), the zones enclosed by purple and orange rectangles are the exemplified zones z(23,25) and z(30,20), featuring a lower and higher rank, respectively. In all the figures, the large unfilled purple oval outlines .

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Liu F, Yasar A, Cui J, Janssens D, Wets G, Cools M. The Development of a New Location-Based Accessibility Measure Based on GPS Data. Sensors. 2025; 25(20):6274. https://doi.org/10.3390/s25206274

Chicago/Turabian Style

Liu, Feng, Ansar Yasar, Jianxun Cui, Davy Janssens, Geert Wets, and Mario Cools. 2025. "The Development of a New Location-Based Accessibility Measure Based on GPS Data" Sensors 25, no. 20: 6274. https://doi.org/10.3390/s25206274

Liu, F., Yasar, A., Cui, J., Janssens, D., Wets, G., & Cools, M. (2025). The Development of a New Location-Based Accessibility Measure Based on GPS Data. Sensors, 25(20), 6274. https://doi.org/10.3390/s25206274

# data[].attributes.attachment_link.title
The Development of a New Location-Based Accessibility Measure Based on GPS Data

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Accessibility is a key dimension for sustainable transport network management and planning. However, conventional location-based accessibility measures typically rely on average travel times as the sole temporal metric, neglecting detailed travel time distributions. Consequently, these methods yield identical accessibility values for study zones with the same mean travel time but different travel time variations.

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29. Big Data in Modelling Geographical Accessibility to Healthcare: A Scoping Review Protocol. BMJ Open 2025, 15, e101567, doi:10.1136/bmjopen-2025-101567.

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29. Big Data in Modelling Geographical Accessibility to Healthcare: A Scoping Review Protocol. BMJ Open 2025, 15, e101567, doi:10.1136/bmjopen-2025-101567.

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Njogu, A.; Libertini, L.; Avahoundjè, E.M.; Grovogui, F.M.; Ba, O.A.; Ray, N.; Beňová, L.; Macharia, P.M. Big Data in Modelling Geographical Accessibility to Healthcare: A Scoping Review Protocol. BMJ Open 2025, 15, e101567, doi:10.1136/bmjopen-2025-101567.

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This 48-second video presents the AMORE project’s goals and strategies for addressing inequities in access to critical services in congested urban areas. The project uses a participatory approach, integrating open and big data to provide evidence for shaping urban futures. Successfully tested in Cali, Colombia, the results were shared by local contributors with an audience of interest holders, including regional leaders, service users and providers, and NGOs, at the forum "Positioning Cali at the forefront of the World Health Organization (WHO) Strategic Approach to Urban Health" at the XXVI International Health Research Symposium, XXVI Health Research Symposium organized by Universidad del Valle.

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Shaping Cali's future. Forum: Positioning Cali at the forefront of the WHO’s Strategic Approach to Urban Health. XXVI International Health Research Symposium - Univalle. Sept 2024. 

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Shaping Cali's future. Forum: Positioning Cali at the forefront of the WHO’s Strategic Approach to Urban Health. XXVI International Health Research Symposium - Univalle. Sept 2024. 

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https://youtu.be/sCwNeD2iFdI

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21. Sara M. Valencia presenta en el evento "Inteligencia Artificial en Salud: de la Promesa a la adopción en el sistema de salud", destacando oportunidades y potenciales desarrollos para fortalecer la planificación urbana de servicios de salud.

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21. Sara M. Valencia presenta en el evento "Inteligencia Artificial en Salud: de la Promesa a la adopción en el sistema de salud", destacando oportunidades y potenciales desarrollos para fortalecer la planificación urbana de servicios de salud.

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Valencia Cadavid, S.M. IA en Salud: de la Promesa a la Adopción en el sistema de salud. Presented at the IA en Salud: de la Promesa a la adopción en el sistema de salud, Medellín, Colombia, 2025-05-28. Disponible en: https://www.youtube.com/live/aYsONOtUiRk?si=q2beptTCe2WA1f0i&t=1228

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25.b Special communication: Improving the Reporting on Health Equity in Observational Research (STROBE-Equity)Extension Checklist and Elaboration. AMA Netw OpenPublished Online: September 3, 20252025;8;(9):e2532512. doi:10.1001/jamanetworkopen.2025.32512

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25.b Special communication: Improving the Reporting on Health Equity in Observational Research (STROBE-Equity)Extension Checklist and Elaboration. AMA Netw OpenPublished Online: September 3, 20252025;8;(9):e2532512. doi:10.1001/jamanetworkopen.2025.32512

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https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2838494

Dewidar, Omar, Larissa Shamseer, G. J. Melendez-Torres, et al. “Improving the Reporting on Health Equity in Observational Research (STROBE-Equity): Extension Checklist and Elaboration.” JAMA Network Open 8, no. 9 (2025): e2532512. https://doi.org/10.1001/jamanetworkopen.2025.32512.

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25a. Improving the Reporting on Health Equity in Observational Research (STROBE-Equity): Extension Checklist and Elaboration. BMJ 390 (September 2025): e083882. https://doi.org/10.1136/bmj-2024-083882.

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25a. Improving the Reporting on Health Equity in Observational Research (STROBE-Equity): Extension Checklist and Elaboration. BMJ 390 (September 2025): e083882. https://doi.org/10.1136/bmj-2024-083882.

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"Therefore, addressing health inequities in research, especially observational studies, is essential for ensuring that knowledge users have access to high quality data that can guide equitable healthcare interventions. This aim aligns with the global agenda of leaving no one behind—achieving universal health coverage and the sustainable development goals—where generating data on approaches to reach populations experiencing inequities, or measurement at disaggregated levels, is fundamental.”

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Improving the reporting on health equity in observational research (STROBE-Equity): extension checklist and elaboration

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Data on health equity to inform societally relevant evidence based decisions and policy making are lacking in the research literature. Observational studies have the potential to provide data on health equity. Yet, guidance on how to report health equity data and considerations in observational research is inadequate. The STROBE-Equity extension addresses this gap by providing a structured set of criteria to enhance the reporting of health equity data and considerations. #### Summary points Health equity indicators are frequently collected in observational studies (defined in box 1), making them an important source of information for population health. The STROBE (STrengthening the Reporting of OBservational studies in Epidemiology)1 statement is considered the current ideal approach for reporting observational studies; however, the consideration of health equity is outside its scope.2 Health equity is achieved when everyone has an equal and fair opportunity to attain optimal health.3 Evidence consistently shows that certain groups typically experience barriers to achieving health equity arising from unequal access to resources and opportunities.45 These barriers are exacerbated by structural inequities that manifest as uneven distribution of power and privilege, disadvantaging certain population groups based on their living and working …

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23. AI applications in disaster governance with health approach: A scoping review. Arch Public Health 83, 218 (26 Aug 2025). https://doi.org/10.1186/s13690-025-01712-2

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23. AI applications in disaster governance with health approach: A scoping review. Arch Public Health 83, 218 (26 Aug 2025). https://doi.org/10.1186/s13690-025-01712-2

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Kolivand, P., Azari, S., Bakhtiari, A. et al. AI applications in disaster governance with health approach: A scoping review. Arch Public Health 83, 218 (2025). https://doi.org/10.1186/s13690-025-01712-2

“Comprehensive disaster databases and platforms like the AMORE system contribute to evaluating the accessibility and equity of emergency services through visual and statistical tools”

” Such approaches enhance institutional legitimacy and enable governments to act with authority while maintaining the trust and cooperation of affected populations”

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Systematic Review

Open access

Published:

Pirhossein Kolivand 1,2,3,

Samad Azari 2,4,

Ahad Bakhtiari 5,6,7,

Peyman Namdar 8,

Seyed Mohammad Ayyoubzadeh 9,10,

Soheila Rajaie 1,6 &

...

Maryam Ramezani 5,6,7

Show authors
Archives of Public Health 83, Article number: 218 (2025) Cite this article

Abstract

Introduction

The increasing frequency and severity of disasters worldwide underscore the urgent need for robust systems that facilitate effective information sharing and decision-making. This study explores the current and potential applications of artificial intelligence (AI) in disaster governance, with a particular focus on health. By examining the transformative capabilities of AI, the study aims to provide practical insights to inform both national and international disaster management policies.

Methods

A scoping review methodology was adopted to investigate the role of AI in disaster management. Systematic searches were conducted in PubMed, Scopus, and Web of Science databases, covering the period from 2000 to 2024. The search strategy employed keywords related to artificial intelligence, disaster management, governance, and health.

Findings

The review identified three core themes where AI enhances disaster governance: governance functions, by improving policy mechanisms, legitimacy, and health system resilience; information-based strategies, through real-time data, predictive analytics, and modeling; and operational processes, by strengthening logistics, communication, and social media management. Together, these applications improve preparedness and response capacity.

Conclusions

This study provides a structured framework for integrating artificial intelligence into disaster governance with a health-oriented approach. By synthesizing evidence across three thematic domains-governance functions, information-based strategies, and operational processes-it highlights how AI can enhance decision-making, strengthen system resilience, and enable more coordinated and equitable disaster responses. These findings offer practical guidance for policymakers and health professionals to develop adaptive, data-driven strategies in the face of increasing global disaster risks.

Peer Review reports

Text box 1. Contributions to the literature

* While numerous studies have explored AI-driven disaster governance with a health-centered approach, no prior research has systematically synthesized these findings to establish an integrated framework. This review consolidates and critically evaluates existing studies, providing a structured analysis of how AI contributes to health-focused disaster governance, with particular emphasis on resilience and policy adaptability.

* Previous research has largely examined AI applications in discrete disaster phases-such as early warning systems, response coordination, and post-crisis recovery-without addressing their cohesive implementation across the entire disaster cycle. This study advances the field by synthesizing diverse perspectives into a unified framework that integrates predictive analytics, algorithmic decision-making, and operational coordination within disaster governance.

* The findings generate novel insights for policymakers and healthcare professionals, illustrating how AI can enhance governance legitimacy, enable anticipatory disaster response strategies, promote equitable interventions, and ensure long-term resilience in public health systems.

Introduction

The rising frequency and severity of disasters-from natural hazards to global pandemics-pose escalating challenges to traditional governance systems. These mechanisms often fall short in managing the complex, rapidly evolving nature of modern crises, particularly due to limitations in processing multi-source, real-time data, which leads to critical gaps in preparedness, response, and recovery [ 1, 2]. Health outcomes are a vital component of disaster governance, not only because of the immediate threat to lives and infrastructure, but also due to their long-term effects on public health, mental well-being, education, social cohesion, and economic stability [ 3, 4, 5, 6]. Addressing such multifaceted challenges requires the adoption of intelligent, technology-driven strategies. AI-based tools-including crowd simulation systems-are increasingly used to optimize evacuation routes and reduce casualties in high-risk environments [ 7]. In the field of Environmental Health Risk Management (EHRM), technologies such as Big Data Analytics (BDA) have improved operational efficiency and enabled multi-sector collaboration during emergencies [ 8, 9]. For instance, Social Big Data (SBD) platforms provide real-time, high-resolution information that enhances situational awareness and supports timely, evidence-based decision-making [ 10].

Artificial Intelligence (AI) has emerged as a transformative force in disaster governance by automating complex cognitive processes such as forecasting, triage, and strategic planning. Techniques like machine learning (ML) and generative AI-including systems like ChatGPT-have been applied to a wide range of functions, from early warning systems to post-crisis recovery modeling [ 11, 12]. These technologies can, for example, analyze data from wearable sensors or electronic health records to detect medical emergencies in real time, a capability particularly valuable in resource-limited, disaster-affected regions [ 13]. Similarly, predictive AI models can anticipate hazards such as floods, wildfires, and landslides by analyzing meteorological and geospatial data, enabling proactive risk mitigation [ 7, 14]. AI also contributes to situational awareness by analyzing drone footage, satellite imagery, and social media streams, allowingemergency services to respond more rapidly and effectively [ 13, 15]. Recent advancements underscore the increasing prioritization of digital information governance during public health emergencies. Machine learning techniques-particularly graph-based analytic frameworks-have demonstrated substantial utility in identifying and controlling the dissemination of misinformation, thereby enhancing situational clarity and communication efficacy under volatile conditions [ 16]. In parallel, a growing body of scholarship has drawn attention to the inherent risks associated with AI integration in healthcare, including algorithmic bias, system unpredictability, and the lack of coherent regulatory oversight. These concerns highlight the urgent need for resilient and ethically grounded governance mechanisms capable of safeguarding equitable health outcomes in disaster contexts [ 6].

Despite the growing body of research on the application of artificial intelligence (AI) in crisis management, most previous reviews have focused on isolated stages of the disaster cycle or on narrowly defined technical functions. A subset of studies has examined language models in early warning systems [ 17], while others have analyzed the use of intelligent agents in emergency evacuation simulations [ 18], and some have assessed damage prediction using multimodal data sources [ 19]. In the health domain, systematic reviews have shown that AI-based triage-particularly using models such as XGBoost and deep neural networks-offers higher accuracy than traditional methods [ 20, 21]. Additionally, several investigations have explored reinforcement learning in designing resilient cities [ 22] and AI-driven frameworks for infrastructure safety and emergency response [ 23, 24].

This study conducts a systematic analysis of empirical articles across three domains-governance mechanisms, data-driven strategies, and operational systems-in order to propose a conceptual framework that demonstrates how AI can enhance inclusive decision-making, strengthen health system resilience, and reinforce institutional coherence in crisis governance.

Method

Search strategy

This scoping review was conducted following the six-stage framework proposed by Arksey and O'Malley [ 27], which includes: (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) charting the data, (5) collating, summarizing, and reporting the results, and (6) expert consultation. This methodological approach was selected to comprehensively explore the broad and interdisciplinary applications of artificial intelligence (AI) in disaster governance, with a particular emphasis on health. In contrast to systematic reviews or meta-analyses, which typically address narrowly defined research questions or quantitative data synthesis, the scoping review methodology enables mapping a wide range of concepts and identifying knowledge gaps in emerging fields such as AI in disaster management.

1. 1.
Research Question: The primary research question guiding this review was: "What are the applications of AI in disaster governance with a health-oriented approach?" This question served as the foundation for search strategy design and inclusion criteria, ensuring the review remained focused on the intersection of AI, health, and disaster governance.

2. 2.
Search Strategy: A systematic literature search was performed in three major electronic databases: PubMed, Scopus, and Web of Science. The search strategy employed a combination of keywords. Boolean operators (AND, OR) were applied to effectively combine terms. The complete list of search queries for each database is provided in Table 1.

3. 3.
Inclusion and Exclusion Criteria: The following criteria were used to determine study eligibility:

Inclusion criteria: Peer-reviewed articles published in English between 2000 and 2024 that addressed the application of AI in disaster governance, with a particular focus on health-related outcomes, and were available in full text.

Exclusion criteria: Studies were excluded if they did not clearly link AI to both disaster governance and health, or if full-text versions were not accessible.

1. 4.
Screening and Quality Assessment: Two reviewers (MR and AB) independently screened titles and abstracts against the eligibility criteria. Full texts of potentially relevant articles were subsequently reviewed. Discrepancies during the screening process were resolved through discussion, and a third reviewer was consulted when consensus could not be reached, ensuring methodological rigor and consistency.

2. 5.
Data Extraction and Charting: A structured data extraction form was used to capture key variables. The extracted data were then coded and grouped into thematic domains to facilitate synthesis. These categories informed the development of a conceptual framework mapping AI applications within the broader context of disaster governance and health systems.

3. 6.
Expert Consultation: To enhance the validity and relevance of the findings, results were refined through internal discussions within the research team and consultations with a limited number of external experts in disaster management, public health, and artificial intelligence. This iterative process ensured that the synthesis accurately reflected current practices and emerging opportunities in the field.

Table 1 Search queries used for target databases

Results

This section presents the findings of the scoping review in three main stages: (1) search and selection process, (2) development of a conceptual framework, and (3) thematic synthesis of key AI applications in disaster governance. Each part provides a structured understanding of how artificial intelligence is being used-and can further be utilized-in managing disasters with a focus on health and governance.

Study selection process

Fig. 1 illustrates the systematic approach employed for identifying and selecting relevant studies in this scoping review. It outlines the sequential phases, including the initial database search, duplicate removal, title and abstract screening, full-text evaluation, and final selection based on predefined inclusion criteria.

Flow chart of the search strategy

The initial database search yielded 974 records. After removing 223 duplicates entries, a total of 751 unique articles remained for title and abstract screening. Based on the predefined inclusion and exclusion criteria, 591 articles were excluded at this stage due to irrelevance or failure to meet the criteria.

Subsequently, 160 articles were selected for full-text review. Of these, 71 studies were excluded after detailed assessment due to either insufficient methodological quality or lack of clear relevance to AI applications in disaster governance within a health context. Ultimately, 89 studies were included in the final synthesis and analysis. (Fig. 1 and Appendix 1 for details of included studies).

The application of AI in disaster governance

To address the complexities of disaster governance, this study proposes a comprehensive framework that categorizes the critical dimensions of AI-driven solutions. As shown in Fig. 2, the framework serves as a foundational model for understanding how AI technologies, such as predictive analytics, Geographic Information Systems (GIS), machine learning models, and the Internet of Things (IoT), contribute to disaster preparedness, response, and recovery efforts.

The framework itself highlights several key dimensions, including goals, health-related disasters, governance areas, disaster management tools, and infrastructures. Following this conceptual framework, the findings are explored in three primary thematic domains: (1) governance functions and elements, (2) information-based approaches, and (3) process and operational methods. These domains encapsulate the diverse applications of AI in enhancing disaster governance with a health-oriented focus.

Integrated framework for AI applications in disaster governance

The framework developed in this study offers a structured and multidimensional approach to understanding the roles and potential of AI in disaster governance, particularly from a health-focused perspective. It is organized into five interconnected dimensions: strategic goals, health-related disaster scenarios, governance areas, disaster management tools and analysis, and infrastructures. Each dimension reflects how AI technologies contribute to strengthening the effectiveness, equity, and resilience of disaster response systems.

Conceptual framework: existing knowledge and future directions

Taken together, these five dimensions offer a comprehensive view of how AI can be systematically leveraged to enhance disaster governance across all phases of crisis management. By synthesizing insights from diverse domains, the framework not only fills existing conceptual gaps in the literature but also provides practical guidance for policymakers, health professionals, and technology developers engaged in disaster risk reduction and emergency preparedness.

Strategic goals of AI in disaster governance

At the core of the framework lies a set of overarching goals that define the intended outcomes of AI integration. These include enhancing preparedness, ensuring timely and effective response, supporting sustainable recovery, and improving long-term resilience. AI-driven approaches can support sustainability by aligning with global agendas such as the Sustainable Development Goals (notably Goal 12), while also facilitating the development of smart cities and securing livelihoods in vulnerable settings. Additionally, the framework highlights AI's potential to strengthen health and social welfare systems by improving access to care, enabling faster crisis response, and maintaining societal stability during and after disasters. Enhancing the performance of governance-particularly through better communication, broader coverage, and more adaptive decision-making-is also emphasized as a key objective.

AI in health-related disaster scenarios

A central focus of the framework is the application of AI in health-related disasters. AI technologies can support various aspects of health crisis management, including epidemic detection, trauma response, and health system optimization. These capabilities are particularly relevant in the context of a wide range of disasters such as earthquakes, floods, wildfires, pandemics, and psychological crises. AI tools can analyze diverse datasets-from environmental sensors to healthcare records-to detect hazards early, allocate resources more efficiently, and manage field operations more effectively. Furthermore, AI can help mitigate the broader social impacts of disasters, including emotional distress, erosion of public trust, and the spread of misinformation, thereby reinforcing public health and safety.

Governance areas and policy functions

The framework categorizes AI applications across key governance domains, underscoring their role in strategic decision-making and equitable service delivery. In the domain of policymaking, AI facilitates the development of evidence-based strategies and real-time decision support systems. It also plays a critical role in addressing social equity by identifying vulnerable populations, monitoring disparities, and ensuring fair distribution of aid and resources. Financial governance benefits from AI tools that support risk modeling, economic impact analysis, and resource prioritization. Additionally, the integration of AI into information governance enables timely risk identification, alert systems, and continuous monitoring, all of which are essential for proactive disaster management. Social governance functions, such as media regulation, public sentiment analysis, and community engagement, are also enhanced through AI-enabled tools.

Disaster management tools and analytical capabilities

One of the most tangible contributions of AI lies in its ability to enhance disaster management operations through advanced tools and analytics. Predictive models, Geographic Information Systems (GIS), the Internet of Things (IoT), and real-time data analytics significantly improve situational awareness and operational decision-making. These technologies allow for the monitoring of spatial and temporal trends, identification of emerging risks, and optimization of humanitarian logistics. The framework emphasizes the role of AI in strengthening institutional capacities for recovery, reconstruction, and environmental protection. Additionally, AI supports the analysis of needs in affected regions, facilitates damage assessment, and contributes to the development of adaptive protocols for crisis management.

Infrastructure and technological ecosystems

Finally, the effective implementation of AI in disaster governance depends on the availability and integration of supportive infrastructure. This includes physical devices such as sensors, surveillance systems, and data-collection tools, as well as digital platforms for big data processing and cloud computing. The framework underscores the importance of robust information and communication technologies (ICT), which enable rapid data sharing and decision support across agencies and jurisdictions. It also addresses the legal and ethical aspects of AI deployment-particularly in terms of data governance, privacy, and accountability-which must be considered to ensure responsible use of emerging technologies.

Thematic analysis of AI applications

The effective application of Artificial Intelligence (AI) in disaster governance requires a multidimensional understanding of how intelligent technologies interact with institutional structures, data ecosystems, and operational practices. This thematic framework synthesizes the core domains through which AI enhances resilience, coordination, and responsiveness in crisis settings. The first domain examines governance foundations-highlighting system architecture, institutional legitimacy, policy frameworks, and healthcare integration-demonstrating how AI supports transparency, trust-building, and adaptive capacity within formal disaster management systems. The second domain focuses on information-based approaches, emphasizing the role of AI-powered analytical tools, digital platforms, economic modeling, and data governance in transforming reactive systems into predictive, real-time decision environments. Finally, the third domain addresses operational and process-oriented strategies, encompassing social dynamics, logistical infrastructures, collaborative coordination, and digital public engagement. Together, these thematic strands offer a holistic perspective on how AI technologies not only automate and optimize disaster response mechanisms but also reshape the structural, informational, and human components of governance in the face of increasingly complex and uncertain emergencies.

Governance functions and elements

Governance functions and institutional mechanisms are foundational to disaster management systems, serving to organize, coordinate, and sustain responses across local, national, and transnational levels. In an era of digital transformation, the integration of Artificial Intelligence (AI), big data, and Internet of Things (IoT) technologies into governance structures redefines how states and institutions prepare for, respond to, and recover from disasters. This category explores the core pillars of disaster governance, including system architecture, legitimacy and authority, public policy management, and healthcare systems. These pillars support adaptive, transparent, and data-driven decision-making processes, ensuring that government actors can enhance service delivery, maintain public trust, and promote institutional resilience-particularly in times of public health emergencies and complex crises. Each subcomponent emphasizes how AI-enabled technologies interact with institutional capacity to reinforce coordination, communication, risk regulation, and equitable access to services.

System architecture

A foundational shift in disaster management architecture has emerged through the concept of AI-infused risk thinking-a systemic framework that embeds artificial intelligence within conventional risk management structures. This approach strengthens overall system safety, facilitates behavioral regulation, and addresses ethical and legal considerations in emergency planning [ 25]. Additionally, incorporating indigenous expertise within institutional governance structures helps legitimize integrated risk mitigation models and supports sustainable development goals, thereby reinforcing both social and governmental capacities for disaster response [ 28, 29].

Modern disaster management increasingly relies on layered technological architectures that enable real-time information exchange, advanced analytics, and predictive modeling. Innovations in big data, cloud computing, and the Internet of Things (IoT) have led to the development of dynamic crisis information management systems, which allow decision-makers to draw on historical disaster patterns and localized datasets to forecast health-related impacts and orchestrate timely interventions [ 30]. Environmental sensors, once used solely for early warning systems, now also contribute to post-disaster spatial and physiological data collection, further enhancing situational awareness and response accuracy [ 2].

The COVID-19 pandemic highlighted the critical role of legal and institutional structures in shaping the digital architecture of disaster response. Regulatory frameworks, such as those implemented in Hainan Province, enabled secure big data operations, clarified AI accountability, and enforced data protection-essential elements of a robust system architecture [ 33]. In clinical emergencies, the application of AI has enhanced the operational efficiency of acute care systems, although ethical and adoption-related challenges remain [ 12].

Integrated architectures also extend to healthcare disaster response. The convergence of big data, cloud platforms, and IoT improves triage systems, resource distribution, and inter-agency coordination. However, the expansion of these capabilities necessitates robust data privacy protections and comprehensive oversight models [ 30]. Scholars emphasize the need for long-term AI education strategies and the institutionalization of professional training programs to ensure future experts can effectively design, manage, and operate these complex systems [ 34].

As governments expand digital infrastructure and enhance data-driven governance, researchers and technologists are increasingly empowered to build AI-based applications that reinforce systemic risk mitigation, improve disaster preparedness, and elevate the structural integrity of national and regional disaster management architectures [ 35].

Enhancing legitimacy and authority

The integration of AI technologies into disaster management has not only improved operational efficiency but has also reshaped the foundations of institutional legitimacy and governmental authority during crises [ 36]. While these advancements bolster rapid response capabilities, they also demand robust governance structures to uphold public trust, ethical accountability, and transparent decision-making.

Public policy management

Healthcare systems

Artificial Intelligence (AI) has emerged as a foundational technology in managing pandemics and health-related disasters by streamlining virus detection, diagnostics, drug development, and public health surveillance. Tools such as image recognition, deep learning algorithms, and facial recognition systems have optimized epidemiological tracking and healthcare resource allocation, significantly enhancing emergency preparedness during global crises like COVID-19 [ 61]. The pandemic accelerated the deployment of IoT-enabled humanitarian aid frameworks that support contactless suspect detection, remote patient monitoring, relief supply logistics, surveillance, and automated disinfection processes-collectively reducing transmission risks and improving the operational efficiency of healthcare systems [ 62].

Beyond clinical settings, AI applications extend to aerial and satellite imaging for post-disaster assessment. Despite their value in documenting damage zones, conventional imaging systems often encounter delays in data processing and distribution. Synthetic Aperture Radar (SAR), with its capacity to generate high-resolution imagery under adverse weather and cloud cover, improves situational awareness and reliability in damage evaluation. Recent integration of Deep Learning (DL) algorithms into SAR analysis workflows has further automated interpretation processes, enabling more accurate damage mapping and more effective post-disaster recovery planning [ 50].

Information based approaches

Information-based approaches constitute a critical pillar of AI-enabled disaster governance by enabling proactive, data-driven responses across all phases of crisis management. Leveraging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Geographic Information Systems (GIS), and big data platforms, these approaches enhance the accuracy, speed, and scalability of disaster preparedness, response, and recovery. They support dynamic risk assessment, early warning systems, real-time monitoring, and strategic resource allocation, thereby shifting crisis governance from reactive decision-making to anticipatory action. This section is organized into four subdomains: (1) analytical tools that process and interpret diverse data streams; (2) intelligent systems and digital platforms that integrate multiple technologies for operational efficiency; (3) economic and financial modeling for resource optimization and policy resilience; and (4) data governance frameworks that ensure security, interoperability, and informed decision-making. Together, these components enable equitable, efficient, and context-sensitive disaster response-particularly within health system infrastructures.

Analysis and tools

System and platform tools and applications

Novel systems combining real-time context recognition with emergency notifications have been developed to assess individual health risks, composed of components such as emergency, interaction, and context-aware managers [ 77]. Further studies have emphasized the importance of early detection systems in forecasting healthcare system strain [ 78]. In the context of environmental hazards, AI-driven predictive models are increasingly used, including neuro-fuzzy systems for urban flood forecasting based on rainfall patterns [ 79], machine learning algorithms to improve pandemic preparedness [ 80], and GNSS data applications for tsunami prediction [ 8]. Within the industrial sector, expert systems are implemented in refinery emergencies to ensure protocol optimization and personnel safety [ 81]. Tools like the Smart Medical Assistant for Disasters (SMAD), which incorporates deep learning-based sentiment analysis, provide integrated psychological and physical support to affected populations [ 82].

The convergence of Big Data Analytics (BDA) and the Internet of Things (IoT) has enabled real-time data acquisition during disasters, which informs policy decisions such as agricultural zoning in flood-prone areas [ 26, 83]. IoT tools like Node-RED, InfluxDB, and Grafana enhance situational awareness, particularly in fire emergencies, with future integration of machine learning expected to optimize these systems further [ 84]. Mobile applications like WISER, enhanced by machine learning, assist responders in identifying hazardous chemical agents based on symptoms [ 50, 85], while fire-specific decision support systems enable real-time crisis resolution [ 86]. Geographic Information Systems (GIS) have proven essential in planning road traffic emergency response across all disaster phases [ 87].

Economic and financial analysis

Simulation-based studies further indicate that data-driven infrastructure planning can substantially reduce energy consumption during post-disaster recovery phases, contributing to long-term economic sustainability and resilience [ 42]. In parallel, governments increasingly employ tax preference policies to stabilize financial markets and facilitate economic recovery in disaster-stricken regions. By modernizing tax administration systems with big data analytics, authorities can enhance epidemic surveillance, response capacity, and ensure broader macro-financial stability [ 92].

Moreover, the establishment of cross-provincial emergency logistics frameworks and centralized transportation hubs has proven essential in maintaining continuity of essential services in regions vulnerable to epidemic outbreaks. These logistics systems not only reduce economic disruptions but also bolster the resilience and adaptability of supply chains [ 92].

Data governance

Geographic Information Systems (GIS) enhance emergency responsiveness by delivering location-based intelligence, whereas Expert Systems (ES) integrate real-time field data and remote sensing to enable proactive disaster assessments and safety interventions [ 95]. Sensor-enabled data acquisition systems further support emergency preparedness, particularly in medical and traffic-related incidents, by linking live monitoring inputs with healthcare databases for early warning and rapid response [ 96].

Traditionally, impact assessments and post-disaster recovery estimates relied heavily on manual reports and insurance documentation. Today, AI-powered big data analytics offer automated, accurate, and scalable alternatives, streamlining the recovery planning process [ 35]. These innovations are especially critical in low- and middle-income countries (LMICs), where crises often stem from intersecting human and environmental vulnerabilities. In such contexts, AI-enhanced ICT tools generate localized insights, improving both resilience and operational efficiency. Future research should focus on tailoring data governance frameworks to specific disaster types, regional dynamics, and evolving situational demands [ 97].

The digital transformation of communication systems has redefined information dissemination during crises. While social media platforms like Twitter now serve as real-time disaster communication hubs, legacy systems often prioritize text-based data over multimedia formats. Updated data governance policies advocate for the integration of diverse content types, promoting dynamic interaction between the public and emergency services [ 68]. In parallel, scientific and governmental websites remain reliable sources of validated, though less frequent, information updates [ 4].

Process and operational methods

This category addresses the operational, logistical, and human-centered dimensions of disaster governance, focusing on how artificial intelligence (AI) facilitates coordinated actions across different sectors and communities. It encompasses four key pillars: social analysis and governance, social media analysis and communication strategies, supply chain management and logistics, and collaborative operational coordination. Together, these elements frame how AI can be employed not only to enhance situational awareness and optimize emergency response logistics, but also to foster inclusive and resilient governance structures through digital participation, real-time data analysis, and cross-institutional integration. By examining these operational mechanisms, this section highlights how technology-supported interventions can bridge gaps in preparedness, mitigate cascading failures, and promote equity and efficiency in disaster recovery efforts.

Social analysis and governance

Artificial Intelligence (AI) has become a critical component of modern disaster response frameworks, significantly enhancing crisis prediction, automated decision-making, and large-scale data analysis. In health-related emergencies, AI and machine learning (ML) models are widely utilized to forecast medical outcomes for high-risk populations, including displaced elderly individuals [ 100] and immigrants, whose vulnerability stems from unstable housing and limited access to healthcare services [ 101].

Furthermore, lockdowns intensified mental health concerns, with AI studies identifying shifts in the prevalence of eating disorders, substance abuse, psychosis, and suicidality in emergency department records before and after the pandemic [ 109]. Strengthening disaster resilience in such contexts requires the application of AI-driven social media analytics, which facilitate public access to digital mental health resources and support adaptive recovery initiatives [ 5].

Social media analysis and governance

Social media platforms have become indispensable tools in emergency response, offering authorities real-time insights into public sentiment, misinformation trends, and crisis-related discourse. Platforms such as Twitter facilitate the extraction of crisis-specific information, enabling decision-makers to assess community reactions and prioritize urgent needs effectively [ 5]. Advanced semantic analysis technologies-such as Social Big Data (SBD) and Emergency Named Entity Recognition (ENER)-support the identification of both explicit and implicit emergency-related content, thereby enhancing the precision of response strategies and intervention efforts [ 10].

Social media has also emerged as a vital communication conduit during emergencies, providing the public with direct access to official updates from crisis management agencies while also enabling grassroots participation in relief operations [ 46]. Historians and digital researchers study these online interactions to derive behavioral insights that inform future crisis preparedness strategies. These findings can help governments and policymakers refine crisis communication protocols, thereby improving public trust, compliance, and community resilience [ 4].

Emotional analytics of social media content further illuminate the psychological impact of crises. For example, analyses of Weibo blog posts during disease outbreaks have revealed patterns in emotional progression, illustrating how continuous exposure to crisis narratives can influence public sentiment over time [ 49]. Concurrently, IoT-integrated platforms that monitor patient behavior, emerging health threats, and system anomalies in real time serve to reinforce early warning mechanisms and support proactive disaster mitigation [ 4].

Effective governance of social media ecosystems is critical to ensuring accurate and responsible dissemination of information. Studies examining media communication models during public emergencies highlight how uncontrolled information flow can increase social entropy, reinforcing the need for structured media oversight and engagement strategies [ 110]. Researchers advocate for improved journalistic standards, including enhanced content verification, higher-quality reporting, and the integration of AI-powered platforms to strengthen crisis communication. Closer collaboration between official and independent media outlets is also essential for fostering transparency and trust in emergency information delivery [ 110].

Supply chain management and logistics

Efficient supply chain management is a fundamental pillar in humanitarian disaster response, enabling authorities to forecast demand, allocate resources efficiently, and ensure timely distribution of critical supplies [ 98]. The World Health Organization (WHO) proposes a structured three-phase strategy for improving disaster logistics: (1) assessing vulnerability based on population demographics, particularly age distribution; (2) estimating disease transmission and case numbers using predictive modeling; and (3) determining essential resource needs in advance [ 93]. These steps help mitigate shortages, reduce delays, and ensure operational continuity during emergencies.

Technological advancements have revolutionized disaster logistics. The integration of Electronic Product Code (EPC) with Internet of Things (IoT) systems provides real-time visibility across the supply chain, especially useful during post-disaster municipal solid waste (MSW) management [ 111]. Automated systems equipped with Radio Frequency Identification (RFID) technology facilitate inventory control, track expired or missing supplies, and optimize warehouse organization, reducing inefficiencies and preventing stockouts or waste [ 112].

Recent developments in IoT-based emergency logistics systems, combining real-time monitoring, analytics, and automation, enhance situational awareness and streamline operations [ 112]. Workflow automation tools and digital financing frameworks have been introduced to improve fund allocation speed and transparency, strengthening long-term disaster resilience [ 112]. These innovations contribute to the creation of adaptive, data-driven supply networks capable of meeting the unpredictable demands of modern disasters.

Collaborative operational coordination

Disaster and risk management require efficient interagency collaboration and data-driven coordination to ensure timely and effective responses. The Internet of Things (IoT) serves as a foundational infrastructure for early warning systems and rapid response strategies, with Community Sensing and Response (CSR) systems enabling real-time monitoring and informed decision-making [ 114]. While IoT enhances automated crisis detection, human-centered elements-such as public education, risk awareness, and coordinated emergency management-remain crucial to ensuring successful technology integration into disaster response frameworks. The effective deployment of IoT solutions also depends on international collaboration, standardized interoperability protocols, and comprehensive training for end users, all of which enhance disaster preparedness and operational resilience [ 114].

While ethical, legal, and technical challenges remain in applying AI to disaster governance, this review emphasizes practical use cases and highlights the need for further interdisciplinary research to address these emerging concerns comprehensively.

Conclusions

In the face of increasing global disasters, the integration of artificial intelligence (AI) into disaster governance presents a paradigm shift in how crises are managed, mitigated, and overcome. By systematically examining the potential of AI technologies, this paper highlights actionable insights that address both theoretical gaps and practical challenges in disaster preparedness, response, and recovery. Building on recent advancements in AI, the study offers a forward-looking perspective that aligns technological innovations with sustainable and equitable disaster management practices.

This study highlights the transformative role of artificial intelligence (AI) in disaster governance, presenting a conceptual framework that synthesizes existing knowledge and addresses key gaps in understanding and application. By categorizing AI applications into predictive analytics, decision-making, health system resilience, and social governance, the study provides actionable insights for advancing both theoretical understanding and practical implementation in disaster management.

Technologies such as Geographic Information Systems (GIS), machine learning models, and IoT-driven big data are shown to revolutionize disaster preparedness, response, and recovery by enabling real-time data integration, resource optimization, and enhanced situational awareness.

The research underscores the importance of leveraging AI to promote long-term resilience and equity in health governance systems. By systematically analyzing AI capabilities, the study charts a strategic roadmap for policymakers and researchers, fostering collaboration at national and international levels to tackle socioeconomic disparities and strengthen disaster resilience globally. Findings are categorized into three domains: governance functions and elements, information-based approaches, and process and operational methods. Governance functions emphasize enhancing government legitimacy, improving public policy mechanisms, and reinforcing healthcare system resilience. Information-based approaches focus on predictive analytics and real-time data integration to enable proactive disaster strategies, while process and operational methods address supply chain logistics, collaborative communication, and social media governance to facilitate coordinated responses and build societal resilience.

The study contributes to the field by unifying fragmented insights into a coherent framework, emphasizing innovative strategies that redefine disaster governance. Practical implications include fostering sound governance, strategic planning, and the adoption of AI technologies to mitigate disaster impacts, enhance societal resilience, and ensure equitable health outcomes. Future research should explore ethical, regulatory, and technical barriers to AI implementation and provide empirical studies to validate the conceptual framework presented.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

DL:

Deep learning

AI:

Artificial intelligence

ML:

Machine learning

IoT:

Internet of things

GIS:

Geographic information system

References

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Acknowledgements

This study was a part of the research project that was conducted at the Research Center for Emergency and Disaster Resilience, Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran.

Funding

This study was supported by the Research Center for Emergency and Disaster Resilience, Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran.

Author information

Authors and Affiliations

1. Research Center for Emergency and Disaster Resilience, Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran

Pirhossein Kolivand & Soheila Rajaie

2. Research Center for Health Management in Mass Gathering, Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran

Pirhossein Kolivand & Samad Azari

3. Department of Health Economics, Faculty of Medicine, Shahed University, Tehran, Iran

Pirhossein Kolivand

4. Hospital Management Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran

Samad Azari

5. Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran

Ahad Bakhtiari & Maryam Ramezani

6. Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Ahad Bakhtiari, Soheila Rajaie & Maryam Ramezani

7. Iranian Research Network for Social Determinants of Health (IRNSDH), Tehran, Iran

Ahad Bakhtiari & Maryam Ramezani

8. Department of Emergency Medicine, Metabolic Disease Research Center, Qazvin University of Medical Sciences, Qazvin, Iran

Peyman Namdar

9. Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran

Seyed Mohammad Ayyoubzadeh

10. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Seyed Mohammad Ayyoubzadeh

Contributions

"MR and PK conceived the study. MR wrote the main manuscript text and prepared figures. SA provided feedback on the result and manuscript. MR and AB categorized AI applications independently and created descriptions by synthesizing the extracted information. SA, PN, SMA, SR, and AB edited the manuscript. All authors reviewed the manuscript."

Corresponding author

Correspondence to Maryam Ramezani.

Ethics declarations

Ethics approval and consent to participate

This study received ethical approval from the Research Ethics Committees of Education, Research, and Technology Division of Iranian Red Crescent Society (Approval ID: IR.RCS.REC.1404.001).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Kolivand, P., Azari, S., Bakhtiari, A. et al. AI applications in disaster governance with health approach: A scoping review. Arch Public Health 83, 218 (2025). https://doi.org/10.1186/s13690-025-01712-2
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DOI: https://doi.org/10.1186/s13690-025-01712-2

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AI applications in disaster governance with health approach: A scoping review - Archives of Public Health

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Introduction The increasing frequency and severity of disasters worldwide underscore the urgent need for robust systems that facilitate effective information sharing and decision-making. This study explores the current and potential applications of artificial intelligence (AI) in disaster governance, with a particular focus on health.

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22. Jornada Línea de Investigación LIESU GEPI: Salud urbana a diez años de su creación. 28 de agosto de 2025. Universidad de Antioquia, Facultad Nacional de Salud Pública Héctor Abad Gómez. Presentación: "Equitable Access to Health Services Cali,Colombia (AMORE Project)" 

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Recording: https://youtu.be/44mcRs5S6f0

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Extractos del testimonio de Felipe Piquero. Qué distingue al proyecto desde la perspectiva como usuario de servicios ambulatorios de uso frecuente - multilingual automatic subtitles available. Mayo 2025.

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Extractos del testimonio de Felipe Piquero. Qué distingue al proyecto desde la perspectiva como usuario de servicios ambulatorios de uso frecuente - multilingual automatic subtitles available. Mayo 2025.

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¿Qué distingue al proyecto desde la perspectiva de un usuario frecuente de servicios ambulatorios?

https://youtu.be/7N5LvRF-ul0

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2025 Testimonio Felipe Piquero Extractos

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7. Co-producing Space-time Accessibility: a Transdisciplinary Approach to Enhancing Transportation Justice. May 28, 2024. 

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7. Co-producing Space-time Accessibility: a Transdisciplinary Approach to Enhancing Transportation Justice. May 28, 2024. 

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Orjuela, Juan Pablo and Schwanen, Tim and Giraldo, Diego and Morales, Natalia and Ruda, Nidia, Co-producing Space-time Accessibility: a Transdisciplinary Approach to Enhancing Transportation Justice (February 20, 2024). Available at SSRN: http://dx.doi.org/10.2139/ssrn.4733010

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Co-producing Space-time Accessibility: a Transdisciplinary Approach to Enhancing Transportation Justice

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Accessibility analysis has long been used to gauge inequalities and inequities in people’s ability to reach employment, education, greenspaces, and other activi

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22. Analyzing equity in transport planning using the 15-minute city approach: a case study of Oslo City, Norway.  Transportation Research Part A: Policy and Practice (August 7, 2025)

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22. Analyzing equity in transport planning using the 15-minute city approach: a case study of Oslo City, Norway.  Transportation Research Part A: Policy and Practice (August 7, 2025)

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Pira S, Hansson L. 𝘈𝘯𝘢𝘭𝘺𝘻𝘪𝘯𝘨 𝘦𝘲𝘶𝘪𝘵𝘺 𝘪𝘯 𝘵𝘳𝘢𝘯𝘴𝘱𝘰𝘳𝘵 𝘱𝘭𝘢𝘯𝘯𝘪𝘯𝘨 𝘶𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 15-𝘮𝘪𝘯𝘶𝘵𝘦 𝘤𝘪𝘵𝘺 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩: 𝘈 𝘤𝘢𝘴𝘦 𝘴𝘵𝘶𝘥𝘺 𝘰𝘧 𝘖𝘴𝘭𝘰 𝘊𝘪𝘵𝘺, 𝘕𝘰𝘳𝘸𝘢𝘺. 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗿𝘁 𝗔: 𝗣𝗼𝗹𝗶𝗰𝘆 𝗮𝗻𝗱 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 (2025). 🔗 Read the article here https://doi.org/10.1016/j.tra.2025.104633

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21. Optimizing Pandemic Mitigation and Advisory Solutions using Random Forest Classifier. IEEE. 2025 - 7th International Conference on Inventive Material Science and Applications (ICIMA)

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21. Optimizing Pandemic Mitigation and Advisory Solutions using Random Forest Classifier. IEEE. 2025 - 7th International Conference on Inventive Material Science and Applications (ICIMA)

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Nandhini T, Kumar PSP, MoheshKumar G, Dhivyadharshini S, Dhanush MM, Pradeepkumar K. Optimizing Pandemic Mitigation and Advisory Solutions using Random Forest Classifier. In: 2025 7th International Conference on Inventive Material Science and Applications (ICIMA) [Internet]. Namakkal, India: IEEE; 2025 [cited 2025 Jul 20]. p. 1156–62. Available from: https://ieeexplore.ieee.org/document/11073988

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Optimizing Pandemic Mitigation and Advisory Solutions using Random Forest Classifier

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This study aims at developing an optimized pandemic mitigation and advisory solution by using a Random Forest Classifier that will help predict the outcomes of the disease accurately and give the targeted recommendations. The proposed model is compared with a Naive Bayes model in terms of precision, accuracy and recall. This study is conducted on two groups. Group 1 includes the proposed Random Forest Classifier with 26 samples, while Group 2 includes the Naive Bayes model with 26 samples. The dataset has symptoms, demographics, and environmental factors. The metrics used to evaluate the models include precision, accuracy, F1-score and recall, which are set at a confidence interval of 89.2 %. The random forest classifier showed better performance compared to the Naive Bayes model. Accuracy of Random Forest Classifier ranged between 85.8 % to 89.2 %, while the Naive Bayes model had varied accuracy between 79.8 % and 83.9 %. The high-risk cases in the Random Forest model are regularly predicted with precision and recall. According to feature importance, symptoms and demographics are good predictors for developing mitigation strategies. The significance is well below 0.05 (p < 0.05). Finally, the Random Forest Classifier performed better than the Naive Bayes model in predicting disease outcomes and giving efficient pandemic advisory solutions. The results thus are confirmed: machine learning can significantly enhance decision-making during pandemics by making real-time predictions and personalized recommendations that can be effective for mitigation.

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20. Spatial Accessibility to Healthcare Facilities: GIS-Based Public–Private Comparative Analysis Using Floating Catchment Methods. July 1, 2025. https://doi.org/10.3390/ijgi14070253

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20. Spatial Accessibility to Healthcare Facilities: GIS-Based Public–Private Comparative Analysis Using Floating Catchment Methods. July 1, 2025. https://doi.org/10.3390/ijgi14070253

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Pérez-Fernández, Onel, and Gregorio Rosario Michel. “Spatial Accessibility to Healthcare Facilities: GIS-Based Public–Private Comparative Analysis Using Floating Catchment Methods.” ISPRS International Journal of Geo-Information 14, no. 7 (July 2025): 253. https://doi.org/10.3390/ijgi14070253.

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by

Dr. Onel Pérez-Fernández is a full-time professor at the University of Panama, with over 16 years [...]

Gregorio Rosario Michel is sub-director of R&D at the Dominican Refinery of Petroleum. He received a [...]

3,4 1,2,* and

Escuela de Geografía, Departamento de Cartografía, Universidad de Panamá, Panamá City 0824, Panama

Grupo de Investigación en Ciencia de Datos Geoespaciales (GICDGE), Centro Regional Universitario de Veraguas, Universidad de Panamá, Santiago de Veraguas 8007, Panama

Escuela de Agrimensura, Universidad Nacional Pedro Henríquez Ureña (UNPHU), Santo Domingo 10602, Dominican Republic

Escuela de Agrimensura, Facultad de Ingeniería y Arquitectura, Universidad Autónoma de Santo Domingo (UASD), Ciudad Universitaria, Santo Domingo 10103, Dominican Republic

Author to whom correspondence should be addressed.

1. Introduction

Access to healthcare facilities is a fundamental right for all citizens [ 1]. The World Health Organization (WHO) defines healthcare facilities as essential for delivering high-quality, safe, and climate-resilient care, crucial for achieving universal health coverage. These facilities, such as hospitals and clinics, are considered the core of any health system [ 2]. From the urban infrastructure perspective, healthcare facilities contribute to the physical and social well-being of the population [ 3, 4]. The spatial distribution of these services influences not only health outcomes but also territorial cohesion, urban equity, and residents' quality of life [ 5, 6]. As cities expand, ensuring that all residents have equitable access to healthcare services becomes a significant concern for policymakers and urban planners. Therefore, measuring and evaluating geographic accessibility to healthcare facilities through GIS-based approaches becomes a relevant tool for identifying underserved areas and guiding public investment decisions [ 7, 8, 9].

Accessibility is defined as the ability of residents to reach medical facilities within a reasonable travel time, enabling quality care and disease prevention [ 10, 11]. Some authors suggest that accessibility encompasses both the availability and acceptability of medical facilities [ 1, 12]. According to Luo and Wang (2003) [ 13], accessibility refers to the ease with which people can reach healthcare service locations from a given point. Moreover, the spatial distribution of healthcare professionals and the residences of patients affects access to healthcare services due to uneven spatial distribution [ 13, 14]. For instance, Le et al. (2022) [ 15] evaluated healthcare accessibility, using distance-based models that consider elements such as street networks and the distribution of healthcare facilities.

1.1. Literature Review

Previous studies have examined spatial accessibility to healthcare services in detail through geospatial tools. These investigations have encompassed a range of healthcare facilities, from hospitals and clinics [ 1, 15, 16], oncology care centers [ 17, 18], physiotherapy services [ 10], ambulatory care [ 7], and emergency healthcare facilities [ 19, 20]. Additionally, other authors have analyzed diagnostic and COVID-19 vaccination sites [ 18, 21, 22]. A recurring conclusion of these studies is the persistence of spatial inequalities in healthcare access, with greater emphasis on urban and peri-urban areas. For instance, Xiong et al. (2022) [ 1] explored the accessibility, availability, and affordability of primary healthcare in Hong Kong through spatial analysis. The educational level and economic income were social determinants influencing access to primary healthcare. The study demonstrated that, although the accessibility and availability of primary care services are adequate, affordability-related difficulties persist for specific population groups. Wang and Onega (2015) [ 18] proposed a research framework for analyzing the accessibility to oncological care, focusing on measuring geographic and demographic disparities in access to cancer centers. Their methodology employed advanced models based on Geographic Information Systems (GIS) to quantify accessibility, considering both spatial and socioeconomic factors, and multilevel models to examine the relationship between access, service utilization, and cancer-specific mortality outcomes.

The literature identifies multiple methodological approaches for measuring spatial accessibility, primarily within the context of GIS. Systematic reviews, such as those conducted by various authors [ 14, 16], highlight the role of these tools in disease mapping, territorial planning, and the distribution of healthcare services. One of the most widely used methods is the Two-Step Floating Catchment Area (2SFCA), which integrates supply and demand factors within a determined analysis radius [ 1, 8, 11, 16, 23]. Variants such as the Enhanced Two-Step Floating Catchment Area (E2SFCA) model incorporate distance decay functions for a more realistic representation [ 9, 22, 24], while the Three-Step Floating Catchment Area (3SFCA) method adds additional criteria to reflect the competition between providers or the population distribution [ 10, 25, 26]. Although the 3SFCA method has been recognized for its ability to incorporate provider competition and improved demand weighting [ 26, 27], its application requires data on patient behavior, healthcare provider capacity, and competition parameters, which are often unavailable in developing country contexts. In this study, we focused on the 2SFCA and E2SFCA methods, which have been widely validated and remain suitable for evaluating spatial accessibility in data-limited environments, such as Panama. Future research could consider applying 3SFCA once such detailed inputs become available.

While extensive research has been conducted in high-income urban areas in developed countries, there is still a lack of investigation in most developing nations' settings, such as Latin America. For example, some of these studies have been conducted in large cities, such as Hong Kong [ 1], New Zealand [ 12], Chicago [ 13], and Florida [ 21], where the data availability is relatively high. In this regard, some research focuses on analyzing healthcare accessibility in rural areas using the Floating Catchment Area method [ 10, 27]. Mahuve et al. (2022) [ 28] applied an enhanced version of the E2SFCA method targeted to a rural area of Tanzania. Wang (2011) [ 29] used the 2SFCA method to determine healthcare accessibility among various ethnic groups in Toronto. These analyses have provided methodological insights. But a significant gap remains in understanding accessibility patterns in Latin American countries, particularly in rural areas with limited healthcare and population data availability. To address this, the present study applies geospatial analysis methods to assess healthcare accessibility in the Santiago de Veraguas district. Santiago de Veraguas is a mid-sized city in Panama, with a population of 109,605 inhabitants distributed across 16 districts. Therefore, this study contributes new insights from a Latin American perspective, which is a region that remains underrepresented mainly in the spatial health research literature.

1.2. Research Questions and Objective

This study proposes using the 2SFCA and E2SFCA models, which are among the most widely employed methods for measuring population accessibility to healthcare facilities. Unlike previous studies focused on large urban areas, our research offers a comparative assessment of the accessibility to public and private healthcare facilities, providing support for territorial planning in resource-limited regions. The primary objective of this study is to evaluate the geographic accessibility patterns of the population in the Santiago de Veraguas district, Panama, to both public and private healthcare facilities. In this regard, this paper addresses the following main research questions to guide our work:

What are the spatial patterns of accessibility to public and private healthcare facilities in the Santiago de Veraguas district?

How do geospatial accessibility differences in service coverage between public and private sectors influence the identification of underserved areas?

In this study, we implement a network analysis approach with ArcGIS Pro 3.4.3 software tools to calculate accessibility to healthcare facilities based on different travel time intervals. The results reveal better accessibility to public healthcare facilities and a higher concentration of services in the central area of the studied region.

Spatial accessibility studies of healthcare facilities in developing countries are crucial, as many of these nations lack territorial analyses of the distribution and quality of healthcare facilities. Governmental and private entities can use this research as a reference to support decision-making processes when planning the location of new healthcare facilities.

The paper is structured in the following way. First, Section 2 presents the research method undertaken in this study. Section 3 presents our results on the evaluation of the geographic accessibility patterns of the population in the Santiago de Veraguas district, Panama. In Section 4, we discuss our findings. Finally, Section 5 closes the paper by presenting our main conclusions.

2. Materials and Methods

2.1. Study Area

We selected the city of Santiago district as the study area due to its strategic role as the capital of the province of Veraguas, Panama ( Figure 1). According to the latest census by the Institute of Statistics and Census (2020) [ 30], the city has a population of 109,605 inhabitants distributed across 16 districts, with a population density of 113 inhabitants per square kilometer. The service sector primarily constitutes the base of the economy. Major establishments include restaurants, shopping centers, pharmacies, and clinics that provide medical healthcare to the local population. The Pan-American Highway crosses the city from east to west, supplemented by a network of secondary roads facilitating intra-urban mobility. The Panamanian healthcare system classifies medical facilities as either public or private institutions. Most of the population accesses services at the Social Security Hospital and the Luis Chicho Fábrega Hospital. This research selected the Santiago district as the study site based on two primary considerations: its significant role as the administrative and service center for the country's central region and the imperative to assess healthcare accessibility patterns within developing nation urban environments.

2.2. Datasets

The research was conducted through the integration of three complementary geospatial datasets. The first dataset consisted of census information on population settlements obtained from the Statistics and Census Bureau of the Republic of Panama. This data, corresponding to the year 2020, detailed the spatial distribution of 289 populated places within the Santiago district and its total population of 109,605 inhabitants, which were georeferenced to represent the potential demand for healthcare facilities.

The second dataset, comprising the geographic locations of healthcare facilities (both public and private), was downloaded from OpenStreetMap (OSM) using the Overpass Turbo query tool version 0.7.62.4 [ 31]. This platform enabled us to extract point features classified under tags such as "amenity = hospital", "amenity = clinic", and "amenity = healthcare = center", which are commonly used in the OSM schema to identify medical institutions. The "hospital" and "healthcare center" amenities represent the public healthcare facilities. Meanwhile, the "clinic" amenities represent the private healthcare facilities. The OSM dataset included the name, category, and precise geographic coordinates of each facility. The number of physicians in each healthcare facility was also included, based on a field visit and a consultation of the database from the regional office of the Ministry of Health and Social Welfare in the Santiago de Veraguas district. A validation process was conducted to clean the data, remove duplicates, and verify that all facilities were within the boundaries of the Santiago district. The resulting georeferenced layer was used for subsequent spatial accessibility and cluster analyses ( Figure 1 a).

This study also incorporated a third dataset to integrate healthcare facilities' supply and demand data, consisting of the OpenStreetMap street network ( Figure 1b). In order to facilitate geospatial accessibility analysis, a connectivity network was built using its edges and nodes. This network allowed for the modeling of possible transit from residential areas to medical care centers ( Figure 1 b).

2.3. Methods

In this subsection, we introduce the methods used to undertake this research. In order to evaluate accessibility indexes to healthcare facilities, the 2SFCA and the enhanced E2SFCA methods were implemented. The 2SFCA method was used with fixed catchment areas and equal weighting to estimate the accessibility index. This method may not accurately reflect actual demand behavior because it assumes that all healthcare facilities are equally accessible, regardless of travel time and distance. To address this limitation, we additionally incorporated the E2SFCA method. This method uses a cost function that penalizes healthcare facilities located farther from the patient's place of residence. By using both methods, our research captures accessibility index differences to healthcare facilities in the study area, considering both fixed catchment areas and weighted travel time estimations [ 19, 32, 33, 34].

2.3.1. Data Gathering and Preparation

The development of accessibility models relied on three essential elements: (1) point locations of the healthcare facilities (offer of health services), (2) point locations of the geographical distribution of the population (demand of health services), and (3) a street network that simulates the communication channel between the offer and demand of health services. To optimize spatial analysis, we aggregated population data into hexagons, reducing the number of points used as demand sites in the accessibility index calculation. The hexagon size was determined following the methodology proposed by Barros, Moya-Gomez, and Gutierrez (2019) [ 35], resulting in a side length of 1000 m. Studies highlighting the efficiency of this geometry in spatial pattern analysis support its selection [ 17, 36, 37, 38, 39, 40]. The choice of hexagons as the spatial unit was guided by methodological advantages: hexagonal cells reduce edge effects and directional bias, as each cell has six equidistant neighbors; the distance between the centroid of a hexagon and the neighboring centroids is the same in all directions; all neighboring hexagons have the same spatial relationship to the central hexagon; its smoother shape helps to better visualize gradual spatial changes [ 37, 38, 39, 41], unlike square grids, which vary in distance and adjacency [ 42]. This geometry allows consistent spatial interaction modeling and uniform area comparison. In contrast, administrative units such as census tracks are often irregular in size, which can introduce bias in accessibility measurements. The use of a regular hexagonal grid ensured statistical consistency across the study area and is increasingly used in spatial epidemiology and urban planning studies [ 43, 44]. In addition to the hexagon-based model, we incorporated the street network as a key element in developing accessibility indexes. We constructed a street network using street segments and nodes extracted from OpenStreetMap via the Overpass Turbo online application version 0.7.62.4. The street network dataset included road type (highway) and direction (one-way) attributes. But it had limited availability of speed limit data (maximum speed). To enrich the network dataset, we purposefully assigned speed limits to each street segment based on each street hierarchy, according to Panama's national traffic regulations (e.g., 40 km/h for residential roads, 80 km/h for primary roads).

The network was structured as a directed graph to preserve flow restrictions and was integrated into ArcGIS Pro 3.4.3 using the Network Analyst toolbox. The travel time for each segment was estimated based on assigned speed limits and segment length; accessibility analyses were performed using time as travel cost impedances to simulate automobile accessibility.

2.3.2. Accessibility Indexes

In this study, the accessibility indexes were calculated using the 2SFCA and E2SFCA models. These models are widely applied in academia to measure population accessibility to healthcare facilities. Both models considered travel time metrics and coverage areas to evaluate geographic accessibility patterns in the Santiago of Veraguas district for public and private healthcare facilities. We established comparisons between different facility types. The accessibility models were applied to all healthcare facilities and then separately to public and private facilities. The difference between the accessibility to public and private healthcare facilities in Panama is stark. The government separates the country's healthcare system into two distinct parts. Many people, especially those without private insurance or from low-income families, rely on public services for their healthcare needs. In contrast, private services cater to a smaller, higher-income group. When we examined the accessibility of these services, we uncovered spatial inequities in how different groups access them. We developed a comprehensive accessibility model that covers all healthcare options. This approach mirrors real-life situations where patients choose care from either sector based on urgency or location. This helps inform better urban and health planning strategies. It also answers our second research question by identifying underserved areas, regardless of their administrative classification, which reinforces the policy relevance of our findings.

The implementation used ArcGIS Pro 3.2, specifically the Network Analyst module and its service area tools. The 2SFCA method defines the service areas of healthcare facilities based on a predefined travel time threshold, considering physician availability relative to surrounding demand.

The 2SFCA model is structured in two steps to obtain the accessibility index, considering the relationship between available healthcare facilities and the spatial distribution of the population. According to Kuai and Zhao (2017) [ 45] and Hashtarkhani (2024) [ 46], the procedure is as follows:

Step 1: For each healthcare center, identify the population covered within a predefined travel time threshold. The healthcare-to-demand ratio is calculated using the following equation:

where P k represents the population at location k within the coverage area j (d kj ≤ d ₀), S j is the capacity of the healthcare center at location j, and d kj is the travel time between k and j.

Step 2: For each demand location i, identify all healthcare centers j within the specified travel time threshold (d ₀) and sum the healthcare-to-demand ratios (R j) obtained in Step 1.

where A i F represents the accessibility at demand location i based on the 2SFCA method. R j represents the healthcare-to-demand ratio for healthcare centers j within the service areas of demand site i (ie, d ji ≤ d ₀) and d ij is the travel time threshold between i and j.

To implement the 2SFCA model, we used the locations of public and private healthcare centers and the number of physicians providing services. The potential demand was represented by centroids containing population data for settlements in the Santiago district. A 15 min automobile travel threshold was applied, considering speed limit data from the street network. Previous accessibility studies of healthcare centers have demonstrated the validity of using a 15 min threshold [ 1, 7, 15]. Furthermore, the 15 min city concept has been developed in academic research as a criterion for evaluating urban sustainability and resilience [ 47].

The E2SFCA model incorporates three influence zones defined by different travel time intervals (10, 15, and 20 min). This enhancement accounts for the decline in accessibility with distance using a Gaussian function that weights access according to the influence zone. The selection of these intervals is based on previous studies on healthcare service accessibility [ 15, 33]. The E2SFCA method utilized travel time intervals of 10, 15, and 20 min, chosen based on the geographic and urban characteristics of the Santiago district, a medium-sized city featuring short travel distances. Using longer intervals, like 30 min, would have exceeded the spatial extent of the district and resulted in an overestimation of service areas.

The inclusion of a 15 min interval aligns with recommendations in the literature that suggest this threshold is a reasonable standard for urban healthcare access [ 48, 49], especially for primary or general outpatient services. The 10, 15, and 20 min intervals provide finer granularity to assess potential inequalities in spatial accessibility, capturing both core and fringe areas of coverage. All other parameters are consistent with those used in the 2SFCA model. According to Luo and Qi (2009) [ 33] and Hashtarkhani (2024) [ 46], the E2SFCA calculation procedure is as follows:

Step 1: For each healthcare center j, generate three service areas based on 10, 15, and 20 min travel times. All population centroids (k) within each service area (D r) are identified, and the healthcare-to-demand ratio (R j) is calculated using the following equation:

where P k represents the demand at location k within the service areas of j (d kjD r), S j is the number of healthcare services at location j, d kj is the travel time between k and j. D r is the rth catchment zone (r{1,2,3}) within the service areas. W r represents the weight for each zone, calculated using the Gaussian function to account for distance decay in accessibility to healthcare center j.

Step 2: For each population centroid i, identify all healthcare centers j within 10, 15, and 20 min travel times and sum the weighted healthcare-to-demand ratios R j calculated in Step 1.

where A i F represents accessibility at demand location i, R j is the healthcare-to-demand ratio at location j within the service areas of i (ie, d kj D r), and d ij is the travel time between i and j. The same Gaussian functions from Step 1 were applied to account for distance decay.

2.3.3. Spatial Clustering Analysis

To spatially interpret the results obtained from accessibility indexes calculated using the 2SFCA and E2SFCA methods, spatial analysis techniques were applied to identify significant clustering patterns. First, the Getis-Ord Gi* statistic was employed, which enables the detection of significant concentrations of high or low accessibility values, namely, the identification of hot spots (high accessibility clusters) and cold spots (low accessibility clusters) through the analysis of z-scores and statistical significance levels [ 50, 51]. This tool proves helpful in highlighting critical areas where accessibility is considerably low and, therefore, requires priority attention in territorial planning.

Complementarily, the Anselin Local Moran's I local autocorrelation statistic, also known as LISA (Local Indicators of Spatial Association), was applied to examine whether extreme values identified in the accessibility index are spatially correlated with their immediate neighbors [ 21, 52, 53]. The Moran I index allows for distinguishing between clusters of high accessibility surrounded by similar areas (high-high), low accessibility surrounded by low accessibility (low-low), or atypical spatial patterns (high-low, low-high). It also provides a more detailed interpretation of territorial structure [ 54]. The combination of both statistical analyses, combined with the 2SFCA and E2SFCA results, provides evidence of the unequal distribution of geographic accessibility, facilitating the identification of gaps and the targeting of more equitable public policies [ 54, 55].

3. Results

3.1. Service Areas

The service area analysis of the public and private healthcare facilities in the Santiago de Veraguas district considered travel times of 10, 15, and 20 min. The study evaluated three scenarios, all medical facilities in the district, exclusively public, and private healthcare facilities, independently.

The spatial distribution analysis of all healthcare facilities ( Figure 2 a) revealed that the central districts (Santiago Cabecera, San Martín de Porres, Nuevo Santiago, Rodrigo Luque, and Canto del Llano) demonstrated optimal coverage within a 10 min travel time. The 15 min threshold analysis indicated that the eastern and western zones (districts Nuevo Santiago and Carlos Santana) required longer travel times. In contrast, districts such as Ponuga, La Raya de Santamaría, and Los Algarrobos, located in the eastern and southern zones, required a travel time of more than 20 min to be within the service areas of a healthcare center.

The analysis of public healthcare facilities ( Figure 2b) showed spatial distribution patterns similar to those observed for all healthcare facilities, with a notable variation in the Urracá district. Conversely, the service areas of private healthcare facilities ( Figure 2 c) exhibited lower coverage, particularly in the southern district, where coverage was nonexistent.

3.2. Comparative Analysis of Spatial Accessibility Models

Initially, the 2SFCA accessibility index was calculated for all healthcare facilities, followed by separate calculations based on the type of healthcare facilities (public or private).

Table 1 shows the overall values of accessibility indexes in the Santiago de Veraguas district obtained with both accessibility models. According to the 2SFCA model, public and private healthcare facilities show different spatial patterns. The public healthcare facilities showed index values ranging from 0.00 to 23.36 (mean = 11.58; median = 11.82; SD = 7.77), suggesting wider and more evenly distributed accessibility.

In contrast, the private facilities had a more skewed distribution (range: 0.00-12.89; mean = 5.6; median = 0.5; SD = 6.2), reflecting concentration in central urban zones. This disparity suggests an inequity in access, particularly in rural or remote areas.

The E2SFCA accessibility index showed distinct spatial patterns. For the public healthcare facilities, the index values ranged from 0.0 to 24.2, with a mean of 9.1, a median of 9.5, and a standard deviation of 6.0, indicating broad and relatively even coverage across the facilities. In contrast, the index values for the private healthcare facilities ranged from 0.0 to 6.8, with a mean of 2.9, a median of 3.4, and a standard deviation of 2.6, indicating a more uneven distribution concentrated in central urban areas. These results suggest that public services are more evenly distributed across the study area, while private healthcare services are clustered in high-accessibility zones, with limited coverage in peripheral regions. When comparing the statistics of both models, public healthcare facilities have higher accessibility values (mean and median) than private centers. The coefficients of variation indicate moderate dispersion for public healthcare facilities (67% concerning the mean). In contrast, the accessibility to private centers shows that the relative variability is very high, reaching over 100% of the mean. In summary, the coefficient of variation shows that the accessibility to public healthcare facilities is more homogeneous than that recorded for private centers, regardless of the method used.

The analysis of all of the healthcare facilities ( Figure 3 a) revealed greater coverage and service availability in the district's central zone, encompassing the districts of Santiago, Canto del Llano, San Martín de Porres, and Rodrigo Luque; this zone exhibited the highest accessibility indices. These districts have a higher population density and, therefore, a greater demand for healthcare services. Additionally, this area hosts most healthcare centers and private clinics. These districts also host most of the public services and businesses. In contrast, the northeastern, eastern, and southern districts exhibited limited accessibility indices, particularly in the districts of La Peña, Santa María, and Santiago Sur, where the healthcare facilities' locations were more than 15 min away by car. The poor road infrastructure in these districts could be one of the reasons for this low accessibility to healthcare facilities. These zones mainly have a rural population. As a result, citizens living there struggle to access healthcare services, particularly private healthcare facilities.

The independent analysis of the public and private centers ( Figure 3b,c) revealed higher accessibility in the central zone. However, the values were significantly lower compared to the indices obtained for all of the healthcare facilities ( Figure 3 a). The accessibility analysis for the private healthcare facilities demonstrated that the indices were lower than those obtained for the public healthcare facilities, maintaining higher accessibility patterns in the centrally located districts. Additionally, the analysis revealed low accessibility to private healthcare facilities in the southern district.

Regarding the E2SFCA index, the analysis reflected an improvement in accessibility across most of the Santiago district when considering all of the healthcare facilities, with indices ranging from 16 to 25 physicians per 10,000 inhabitants. This improvement was particularly notable in the districts of the central zone (Santiago Cabecera, San Martín de Porres, Los Algarrobos, Canto del Llano, and Urracá). Additionally, the analysis identified better accessibility in the southern district, including the districts of Ponuga and Santiago Sur ( Figure 4 a).

On the other hand, the accessibility index was lower when the E2SFCA algorithm was applied exclusively to the public healthcare facilities. The analysis revealed a similar distribution pattern for all healthcare facilities ( Figure 4b). In the case of private healthcare facilities, the accessibility indices were low, with values ranging from 5 to 10 physicians per 10,000 inhabitants. The accessibility in the southern districts remained low ( Figure 4 c).

Figure 5 illustrates the distribution of the 2SFCA and E2SFCA accessibility indices calculated for public and private healthcare facilities. The central districts exhibited the highest values in both models, while the southern districts had better access to public healthcare facilities. The E2SFCA model reflected higher coverage indices in the southern zone for public healthcare facilities without affecting coverage in the central zone ( Figure 5 b).

3.3. Spatial Pattern Analysis

Figure 6 presents the cluster formation identified through Getis-Ord Gi* and Moran's I analysis. This analysis compared the accessibility indexes calculated using the 2SFCA-E2SFCA models for all healthcare facilities. In the spatial distribution of the 2SFCA model, the district's central zone exhibited high-accessibility clusters (hot spots). In contrast, the southern districts (Ponuga, Santiago Sur) displayed clusters with low accessibility levels ( Figure 6 a).

The E2SFCA model revealed distinct spatial patterns, with a reduction in the size of the central cluster and the emergence of two new clusters in the southern districts ( Figure 6b). The comparison between the 2SFCA and E2SFCA models ( Figure 6 c) demonstrated that the central districts maintained their hot spot status. At the same time, Ponuga and part of Santiago Sur transitioned from cold spots to hot spots.

The Local Moran's I analysis ( Figure 6d,e) represented the distribution of clusters and outliers detected in the Santiago district. The accessibility values obtained from the 2SFCA model showed a high-accessibility cluster in the central districts (Santiago Cabecera, San Martín de Porres, Canto del Llano, and Rodrigo Luque). Conversely, the indices obtained with the E2SFCA model indicated the presence of two clusters: one in the central zone and another in the district of Ponuga ( Figure 6e). Figure 6 f illustrates the transitions in cluster types recorded with both models, maintaining the central zone's clustering while the southern area transitioned from low to high values.

4. Discussion

This study evaluated the geographic accessibility patterns of the Santiago of Veraguas district population to public and private healthcare facilities by applying spatial accessibility models to all healthcare facilities and then differentiating between public and private facilities. Impedance factors informed this analysis, including the travel time through the road network and permitted speed limits. This study employed the 2SFCA and E2SFCA spatial accessibility indexes to compare the availability of accessible healthcare facilities for the population. Using both methods enabled a more comprehensive evaluation of healthcare accessibility in Santiago de Veraguas by incorporating different assumptions. The E2SFCA method, with its distance decay functions, offers a more refined perspective that may reveal patterns missed by the traditional 2SFCA. This comparison underscores how assumptions shape the spatial distribution of accessibility.

The findings reveal that the central districts of the study region exhibited the highest accessibility indexes, which aligns with previous research indicating more favorable access in urban areas compared to peripheral zones [ 4, 25, 46]. The central and northern districts-where accessibility is higher-coincide with the historic urban core and the city's primary economic corridors, where institutional healthcare supply is concentrated.

When analyzing the districts with the lowest coverage, it was found that their access was limited to public healthcare facilities, as the distance to private healthcare facilities exceeded 20 min of travel time. The southern sector of the Santiago de Veraguas district, which exhibits the lowest accessibility indexes in both the public and private healthcare systems, is characterized as a rural area with a low population density and a lack of private healthcare facilities. The unequal distribution of these facilities generates areas with limited accessibility in the south. The results obtained for the southern region of the district suggest the necessity to create new public or private healthcare facilities or the expansion of existing facilities to ensure equitable access to healthcare services.

When comparing the accessibility indexes between the public and private healthcare facilities, the results suggest that the public healthcare facilities provide better coverage. The dispersal of health posts-at least one in each district capital-as opposed to the concentration of private clinics in the district centers is probably the cause of this phenomenon. In addition, the public healthcare system includes two hospitals with greater physician availability in the Santiago de Veraguas districts. This significantly enhances their capacity to deliver healthcare facilities and increases their influence in the accessibility model.

Townships located in the district's center have easier access to private healthcare facilities than those in the south. This trend emphasizes how inhabitants in outlying areas must drive farther to the center area, home to most private healthcare. Peripheral communities' limited access to healthcare services may result in subpar treatment, which could lower the standard of living for the local people. Rural areas should be given priority when building and allocating healthcare facilities because they have less infrastructure and fewer doctors to meet demand.

Various researchers have conducted accessibility studies in other Latin American cities, where territorial inequalities strongly influence healthcare accessibility. Cortes (2021) [ 55] found an unequal distribution of accessibility to healthcare services in Chile associated with socioeconomic factors. Cuervo et al. (2022) [ 56] in Cali, Colombia, identified significant accessibility inequalities related to socioeconomic segregation and the concentration of services in high-income areas. In their work, Bascuñán and Quezada (2016) [ 57] suggest that regions with lower access to medical facilities tend to have populations with lower incomes. These studies support the interpretation that, in Santiago, the extended reach of public services may reflect state-driven equity strategies. In contrast, private services follow market logic, clustering in higher-income urban sectors. Although socioeconomic variables were not considered in our research, the inclusion of such demographic indicators in future analyses would allow for a better understanding of how structural factors influence healthcare accessibility.

This research might be relevant for the Republic of Panama as well as the scientific community from other developing countries in Latin America, the Caribbean region, and around the world. To the best of our knowledge, this work represents the most comprehensive spatial accessibility study of healthcare facilities in the Republic of Panama and any other Central American country and the Caribbean region ever undertaken. Therefore, the results of the spatial accessibility study to healthcare facilities offer a roadmap to support decision-makers in planning future allocations of healthcare facilities, which contributes to reducing the gap to health services in underserved areas of developing countries. The results of this study may also be helpful for international agencies and private investors that are supporting developing countries such as the Republic of Panama to enhance their access to healthcare services and to implement future strategies to achieve Sustainable Development Goal (SDG) 3, also known as "Good Health and Well-being".

There are some limitations to our research approach. First, socioeconomic and demographic data availability in developing countries remains a challenge, particularly regarding healthcare statistics for private facilities. Concerning population data, the most detailed level of data disaggregation available corresponds to districts, preventing the analyses at the neighborhood, block, or parcel level. Using census data gathered into hexagonal units helped to reduce this restriction and enable more accurate calculations of the accessibility index.

Another major drawback is the omission of important factors, including the availability of private vehicles, public transit, medical specialties, and the percentage of insured and uninsured populations. The results, limited by the absence of these variables, should be interpreted within the methodological framework of this study. Moreover, it is important to highlight that the dataset used in this study has a low temporal resolution. In particular, the population, infrastructure, and road network datasets only represented static snapshots of the current settings in the Santiago de Veraguas district. This restricts the study's ability to assess temporal variations in accessibility to healthcare facilities-such as changing conditions during rush hours or seasonal fluctuations.

Future studies should consider integrating time-sensitive data to improve the dynamic analysis of spatial access to healthcare services. For example, historical or real-time traffic data could be incorporated. Furthermore, adding data on floating populations from social media or cell phone records may increase the precision of spatial analysis. In addition, exploring the application of machine learning algorithms that include socioeconomic and infrastructure variables in accessibility modeling is suggested.

5. Conclusions

In this paper, we report on the implementation of the 2SFCA and E2SFCA models to obtain the patterns of spatial accessibility to public and private healthcare facilities in the Santiago de Veraguas district. For practical reasons, the 2SFCA and E2SFCA models were implemented to calculate accessibility indexes for each type of healthcare facility, using the travel time through the street network and catchment areas between the supply and demand locations of healthcare facility services, and, thus, answered the following research question: what are the patterns of spatial accessibility to public and private healthcare facilities in the Santiago de Veraguas district?

Our overall results revealed that public healthcare facilities (23.4) present a higher level of spatial accessibility than private healthcare facilities (12.9). Our findings also showed that public healthcare facilities offer broader and more balanced spatial coverage across both urban and peripheral zones. In contrast, private healthcare facilities are mainly concentrated in central urban areas, resulting in more fragmented accessibility patterns.

This paper also aimed to identify how geospatial accessibility differences in service coverage between public and private sectors influence the identification of underserved areas. Our study also reveals that some areas with lower private healthcare service coverage are located in the peripheral zone of the study area. The results also show that public healthcare facilities cover a greater portion of the territory; however, underserved areas are still found. Planners can use these differences as a reference to address areas with low accessibility to healthcare facilities. Furthermore, the spatial distribution of the accessibility indices obtained from the 2SFCA and E2SFCA models was analyzed using Getis-Ord Gi* and Local Moran statistics to identify high- and low-accessibility spatial clusters. According to this study, the central portion of the district has the best coverage; the southern zones show the lowest accessibility indexes.

The results of this research are particularly relevant for legislators, urban designers, and government agencies, as they contribute to increasing fairness in access to healthcare. New healthcare facilities should be established in areas with low accessibility or the number of physicians should be increased in existing facilities.

This study also opens new avenues for research. One approach is to incorporate historical traffic data into the network using big data sources, such as TomTom or the Google Maps API. Another line of research could be the use of GTFS files, which store information on travel times and transit schedules. This approach would enable the assessment of accessibility to services by public transport and allow dynamic temporal analyses throughout the day, facilitating the evaluation of accessibility variations according to traffic patterns and public transport availability.

Finally, this methodology can be applied to cities in developing countries to evaluate and compare access to healthcare services, incorporating socioeconomic variables into the analyses.

Author Contributions

The authors' individual contributions are specified as followed: Concep-tualization, Onel Pérez-Fernández; methodology, Onel Pérez-Fernández and Gregorio Rosario Michel; software, Onel Pérez-Fernández; validation, Onel Pérez-Fernández and Gregorio Rosario Michel; formal analysis, Onel Pérez-Fernández and Gregorio Rosario Michel; data curation, Onel Pérez-Fernández and Gregorio Rosario Michel; writing-original draft preparation, Onel Pérez-Fernández and Gregorio Rosario Michel; writing-review and editing, Onel Pérez-Fernández and Gregorio Rosario Michel; visualization, Onel Pérez-Fernández and Gregorio Rosario Michel. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Research System (SNI) and the National Secretariat for Science, Technology, and Innovation (SENACYT) of the Republic of Panama.

Data Availability Statement

Data is contained within the article.

Acknowledgments

We thank the National Research System (SNI) and the National Secretariat for Science, Technology, and Innovation (SENACYT) of the Republic of Panama for providing the re-sources that funded this study. We also thank the Ministry of Health and private clinics for facilitating the data necessary to develop this research. We thank the support of the technical and administrative personnel at the Grupo de Investigación en Ciencia de Datos Geoespaciales (GICDGE), Centro Regional Universitario de Veraguas, Universidad de Panamá, and Universidad Nacional Pedro Henríquez Ureña (UNPHU), Dominic Republic. We are very grateful for their encouragement and valuable insights in the development of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. ( a) Study area and healthcare facilities. ( b) Street network.

Figure 5. 2SFCA and E2SFCA accessibility index for public and private healthcare facilities: ( a) 2SFCA model, ( b) E2SFCA model.

Figure 6. Spatial accessibility analysis using Getis-Ord Gi* and Local Moran: ( a) Getis-Ord Gi* 2SFCA, ( b) Getis-Ord Gi* E2SFCA, and ( c) differences between Getis-Ord Gi* 2SFCA and E2SFCA. ( d) Local Moran 2SFCA, ( e) Local Moran E2SFCA, ( f) differences between Local Moran 2SFCA and E2SFCA.

Table 1. Overall values of accessibility indexes in Santiago de Veraguas district.

Healthcare

2SFCA

E2SFCA

Mean

SD

C.V.*

Median

Min

Max

Mean

SD

C.V.*

Median

Min

Max

Public

11.6

7.8

67.1

11.8

0.0

23.4

9.1

6.0

66.0

9.5

0.0

24.2

Private

5.7

6.2

109.3

0.6

0.0

12.9

2.9

2.6

89.6

3.4

0.0

6.8

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# data[].attributes.attachment_link.title
Spatial Accessibility to Healthcare Facilities: GIS-Based Public-Private Comparative Analysis Using Floating Catchment Methods

# data[].attributes.attachment_link.description
Healthcare accessibility is among the most critical challenges affecting millions, reflecting profound geospatial disparities in Latin America. This study aims to evaluate healthcare service geospatial accessibility patterns, comparing the geospatial coverage between public and private healthcare facilities in Santiago district, Panama.

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IntalinC LAC 2025, bulletin of the  Latin American branch of the International Network for Transport and Accessibility in Low Income Communities.

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IntalinC LAC 2025, bulletin of the  Latin American branch of the International Network for Transport and Accessibility in Low Income Communities.

# data[].attributes.body
Featuring several accessibility studies, including two published by the AMORE Project.

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Transport Policy. Received 29 January 2025, Revised 22 May 2025, Accepted 27 May 2025, Available online 31 May 2025, Version of Record 6 June 2025. 

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24. “Preparing for an Agentic Era of Human-Machine Transportation Systems: Opportunities, Challenges, and Policy Recommendations.” https://doi.org/10.1016/j.tranpol.2025.05.030.

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Yu, Jiangbo. “Preparing for an Agentic Era of Human-Machine Transportation Systems: Opportunities, Challenges, and Policy Recommendations.” Transport Policy 171 (September 1, 2025): 78–97. https://doi.org/10.1016/j.tranpol.2025.05.030.

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Felipe Piquero - Parte 2. El enfoque centrado en las personas permitió la comunicación efectiva. Mayo 2025.

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Felipe Piquero - Parte 2. El enfoque centrado en las personas permitió la comunicación efectiva. Mayo 2025.

# data[].attributes.body
Felipe Piquero Villegas habla de su participación en este proyecto centrado en las necesidades de las personas y cómo la diversidad del equipo facilitó comunicar efectivamente a las partes interesadas y a quienes impulsan acciones y políticas para la salud. https://youtu.be/XDbPoZPYnnc

# data[].attributes.attachment_link.title
Felipe Piquero - Parte 2. El enfoque centrado en las personas permitió la comunicación efectiva

# data[].attributes.headline
Felipe Piquero, representante de pacientes (hemodiálisis, radioterapia, y urgencias de alta complejidad) habla del impacto de los traslados para los usuarios, las familias, los aseguradores y proveedores. Mayo 2025.

# data[].attributes.subject
Felipe Piquero, representante de pacientes (hemodiálisis, radioterapia, y urgencias de alta complejidad) habla del impacto de los traslados para los usuarios, las familias, los aseguradores y proveedores. Mayo 2025.

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Felipe presenta desde su experiencia vivida, su valoración del Proyecto AMORE como usuario de los servicios, conocedor de la legislación sanitaria, y como autor. 8 de mayo de 2025. https://youtu.be/3T4hCrYEC70

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Felipe Piquero Villegas: el impacto del tiempo de traslado para los usuarios de servicios de salud.

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Testimonio de la Dra. Cuervo, especialista en salud laboral y discapacidad. Mayo 2025.

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Testimonio de la Dra. Cuervo, especialista en salud laboral y discapacidad. Mayo 2025.

# data[].attributes.body
Dos contribuciones clave:

1. Involucra los sectores y actores, incorporando sus aportes para que la información sea útil

2. Apunta a reducir las ausencias prolongadas por tratamientos médicos

10 de mayo de 2025 https://youtu.be/7ikyjxKIxr4

# data[].attributes.attachment_link.title
Diana E. Cuervo, experta en derecho y salud laboral habla del Proyecto AMORE

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18. “Desigualdades en la accesibilidad espacial al servicio de hemodiálisis considerando el tráfico en el área urbana de Cali, noviembre 2020.” Feb 2025. Available at https://hdl.handle.net/10893/33520.

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18. “Desigualdades en la accesibilidad espacial al servicio de hemodiálisis considerando el tráfico en el área urbana de Cali, noviembre 2020.” Feb 2025. Available at https://hdl.handle.net/10893/33520.

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Paredes Mondragón, Carol Viviana. “Desigualdades en la accesibilidad espacial al servicio de hemodiálisis considerando el tráfico en el área urbana de Cali, noviembre 2020.” Universidad del Valle, Facultad de Salud, Escuela de Salud Pública, Cali, Colombia, 2025. Available at https://hdl.handle.net/10893/33520.

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Biblioteca Digital Universidad del Valle

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Biblioteca Digital Universidad del Valle

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En la Biblioteca Digital Universidad del Valle están depositados materiales en formato digital fruto de la producción científica o académica, de esta manera permite almacenar, difundir y preservar información de vital importancia.

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19. "Inequities in Spatial Access to Childbirth Care in the Grand Conakry Conurbation, Guinea.” Nature Cities, April 23, 2025, 1–12. https://doi.org/10.1038/s44284-025-00220-2.

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19. "Inequities in Spatial Access to Childbirth Care in the Grand Conakry Conurbation, Guinea.” Nature Cities, April 23, 2025, 1–12. https://doi.org/10.1038/s44284-025-00220-2.

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Grovogui, Fassou Mathias, Nafissatou Dioubate, Hawa Manet, Pépé Kpogomou, Tamba Mina Millimouno, Aline Semaan, Lenka Beňová, Alexandre Delamou, and Peter M. Macharia. “Inequities in Spatial Access to Childbirth Care in the Grand Conakry Conurbation, Guinea.” Nature Cities, April 23, 2025, 1–12. https://doi.org/10.1038/s44284-025-00220-2.

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Inequities in spatial access to childbirth care in the Grand Conakry conurbation, Guinea - Nature Cities

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Grovogui et al. demonstrate major disparities in Grand Conakry, Guinea, in geographic access to healthcare facilities providing childbirth care, driven by the skewed spatial distribution of health facilities, heavy traffic and socio-economic disadvantage

# data[].attributes.headline
Guía de usuario de la plataforma AMORE. Marzo 2025.

# data[].attributes.subject
Guía de usuario de la plataforma AMORE. Marzo 2025.

# data[].attributes.body
Encuentre la información del proyecto en https://youtu.be/zcza6Aq2FaI

In English: A brief introduction to using the interactive AMORE Platform.

https://youtu.be/8oeGm1498AM

# data[].attributes.body
English: https://oncodaily.com/insight/amore-247880

En español (traducción automática) https://oncodaily-com.translate.goog/insight/amore-247880?_x_tr_sl=en&_x_tr_tl=es&_x_tr_hl=en&_x_tr_pto=wapp

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Oncodaily - Oncology News, Insights, Stories

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AMORE Project: Using Big Data to Improve Cancer Care Accessibility in Urban Areas - OncoDaily

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AMORE Project: Using Big Data to Improve Cancer Care Accessibility in Urban Areas / Alfredo Polo, Ángela Suárez, cancer, Carmen Juliana Villamizar, Daniel

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13. User's response before implementing the road infrastructure improvement  project in the Gatot Subroto Area of Semarang City.” Pondasi 29, no. 1 (July 31, 2024): 65–83. https://doi.org/10.30659/pondasi.v29i1.37556. 

# data[].attributes.subject
13. User's response before implementing the road infrastructure improvement  project in the Gatot Subroto Area of Semarang City.” Pondasi 29, no. 1 (July 31, 2024): 65–83. https://doi.org/10.30659/pondasi.v29i1.37556. 

# data[].attributes.body
GPT-4.o translated.

Hapsoro Adhi, T., Pratikso, & Mudiyono, R. (2024). Road Users’ Response Before Implementing the Road Infrastructure Improvement Project in the Gatot Subroto Area of Semarang City. PONDASI Journal, 29(1), 65-81. Universitas Islam Sultan Agung.

This study aims to identify key factors that significantly influence the success of the road improvement program in the Gatot Subroto area of Semarang City. Data were collected through surveys and in-depth interviews with 100 respondents from diverse age groups and occupational backgrounds. The collected data were analyzed quantitatively using SPSS and qualitatively to provide insights for addressing the research objectives.

The study identifies six key factors that are predicted to significantly impact the success of the planned road improvement project in Semarang City’s Gatot Subroto area:

1. User Satisfaction (Regression Coefficient: 0.233)

2. Travel Time (Regression Coefficient: 0.078)

3. Traffic Safety (Regression Coefficient: 0.180)

4. Traffic Congestion (Regression Coefficient: 0.163)

5. Accessibility (Regression Coefficient: 0.551)

6. Environmental Impact (Regression Coefficient: 0.472)

Findings from the field study confirm that these variables significantly correlate with the success rate of the road improvement project in the Gatot Subroto area, which has important implications for more effective transportation infrastructure planning.

# data[].attributes.attachment_link.authors[].name
Tunggul Hapsoro, Pratikso Pratikso, Rachmat Mudiyono

# data[].attributes.attachment_link.title
RESPON PENGGUNA JALAN SEBELUM PELAKSANAAN PROYEK PENINGKATAN INFRASTRUKTUR JALAN DI KAWASAN GATOT SUBROTO KOTA SEMARANG | Hapsoro | Pondasi

# data[].attributes.attachment_link.description
RESPON PENGGUNA JALAN SEBELUM PELAKSANAAN PROYEK PENINGKATAN INFRASTRUKTUR JALAN DI KAWASAN GATOT SUBROTO KOTA SEMARANG

# data[].attributes.headline
Radiotherapy travel times baseline vs prediction Cali Colombia, July 6-12, 2020.  [2m 40s video]

# data[].attributes.subject
Radiotherapy travel times baseline vs prediction Cali Colombia, July 6-12, 2020.  [2m 40s video]

# data[].attributes.body
Supplementary Material 6: Cuervo et al. “Improving Accessibility to Radiotherapy Services in Cali, Colombia: Cross-Sectional Equity Analyses Using Open Data and Big Data Travel Times from 2020.” International Journal for Equity in Health 23, no. 1 (August 15, 2024): 161. https://doi.org/10.1186/s12939-024-02211-6

Supplementary material 6: [Improving Radiotherapy Accessibility—AMORE Project. https://youtu.be/lQsGmbGWyZI

Narrated by Dr. Ángela Suárez

# data[].attributes.attachment_link.title
Radiotherapy travel times baseline vs prediction Cali Colombia, July 6-12, 2020.  [3-min video]

# data[].attributes.headline
Enhancing Health Services Accessibility in Congested Urban Areas with an Equity Focus. 2025

# data[].attributes.subject
Enhancing Health Services Accessibility in Congested Urban Areas with an Equity Focus. 2025

# data[].attributes.body
Cuervo, LG, Bula MO, Cuervo D, Echeverri DH, Rocha TAH, Jaramillo C, Martínez Herrera E, Miranda JJ, Ospina MB, Tejerina L, Valencia SM, Vivas H. “Enhancing Health Services Accessibility in Congested Urban Areas with an Equity Focus: A Case Study in Cali Leveraging People-Centered Approaches and Open Data to Support Public Policy and Engagement.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, January 29, 2025. https://doi.org/10.2139/ssrn.5117085.

# data[].attributes.attachment_link.readable_text
46 Pages Posted: 18 Feb 2025

See all articles by Luis Gabriel Cuervo

Universitat Autònoma de Barcelona - Department of Paediatrics, Obstetrics & Gynaecology and Preventative Medicine; Academia Nacional de Medicina de Colombia

Independent Researcher

IQuartil SAS

Fundación para el Desarrollo Integral del Pacífico

Pan-American Health Organization

Universidad del Valle - School of Civil and Geomatic Engineering

Universidad de Antioquia - National Faculty of Public Health

Universidad Peruana Cayetano Heredia

Queen's University - Faculty of Health Sciences

Inter-American Development Bank (IDB)

Universidad Nacional de Colombia

Universidad del Valle

Date Written: January 29, 2025

Abstract

Traffic congestion presents a significant challenge in urban areas but is poorly understood. Longer travel times impede access to essential health services, impacting health outcomes, quality of care, and out-of-pocket health expenses. This report presents three case studies from Cali, Colombia, demonstrating that prolonged travel times impact service accessibility, particularly in the city's outskirts and the eastern district of Aguablanca. One study examined emergency tertiary care, where delays can affect prognosis, while the other two focused on hemodialysis and radiotherapy, both of which require repeated visits and can lead to financial hardship. Findings suggest strategically placing new services can significantly enhance equity, reduce travel burdens, and provide valuable urban and healthcare planning insights. Moreover, this approach enables monitoring and comprehensive analysis of multiple healthcare facilities within geographic sectors, presenting data in accessible formats for diverse stakeholders. The findings align with WHO's urban health strategy and UN-HABITAT recommendations to integrate health and urban planning. Unlike traditional approaches emphasizing origin-based solutions-such as increasing housing density near existing services or modifying transportation networks-this study highlights destination-based strategies, bringing healthcare closer to communities in need. Results indicate that strategically locating new services can achieve accessibility levels comparable to low-traffic scenarios, provided congestion remains within projected parameters. This underscores the potential for targeted interventions to bridge accessibility gaps. These findings are now part of the scientific foundation for Cali's urban planning efforts. They have been incorporated into the city's 2024-2027 Development Plan and land use strategies, offering policymakers actionable insights for improving health equity in congested metropolitan areas. Additionally, the report provides practical tools, metrics, and data-driven reporting to enhance communication among decision-makers across sectors. Our research introduces new metrics and methodologies, expanding the field of urban health planning. The synergy between public health professionals, urban planners, and mobility experts fosters a proactive, multidisciplinary approach that aligns with WHO's 2024 Strategic Approach to Urban Health and international policy frameworks. The participatory, intersectoral methodology and developed tools can be adapted for other urban contexts facing traffic-related accessibility challenges. With the growing availability of big data on travel times and open, georeferenced mobility data, this approach can be feasibly replicated and refined in diverse settings. Furthermore, the methodology remains scalable and transferable across regions by linking research with policy impact through clear, communicable metrics.

Keywords: Health services accessibility, Geographic information systems, Urban planning, Health services planning, Accessibility estimates, Accessibility to health services, Dynamic travel information, Health equity, Land Use, Colombia, Cali, Colombia, Participatory research methodologies, Intersectoral strategy, Intersectoral action, Governance, Partnerships, Evidence, Research for health, Mobility, Travel times

JEL Classification: I14, I18, R52, R41, O18

Suggested Citation

Cuervo Amore, Luis Gabriel and Bula, Maria Olga and Cuervo, Daniel and Echeverri, Daniel and Hernandes Rocha, Thiago Augusto and JARAMILLO, CIRO and Martinez Herrera, Eliana and Miranda, J. Jaime and Ospina, Maria Beatriz and Tejerina, Luis and Valencia, Sara Marcela and Vivas, Harvy, Enhancing Health Services Accessibility in Congested Urban Areas with an Equity Focus: A Case Study in Cali Leveraging People-Centered Approaches and Open Data to Support Public Policy and Engagement (January 29, 2025). Available at SSRN: https://ssrn.com/abstract=5117085 or http://dx.doi.org/10.2139/ssrn.5117085

# data[].attributes.attachment_link.keywords[]
enhancing health services accessibility in congested urban areas with an equity focus: a case study in cali leveraging people-centered approaches and open data to support public policy and engagement

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Enhancing Health Services Accessibility in Congested Urban Areas with an Equity Focus: A Case Study in Cali Leveraging People-Centered Approaches and Open Data to Support Public Policy and Engagement

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Traffic congestion presents a significant challenge in urban areas but is poorly understood. Longer travel times impede access to essential health service

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Policy Brief available in Spanish and English

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1. Acciones para cerrar las brechas de accesibilidad en barrios populares [Actions to close accessibility gaps in popular neighbourhoods] - Populab Policy Brief PB.04 

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1. Acciones para cerrar las brechas de accesibilidad en barrios populares [Actions to close accessibility gaps in popular neighbourhoods] - Populab Policy Brief PB.04 

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English: https://drive.google.com/file/d/1ZFjKo3bc_fk-bx0i3bl27Ymdw9p-Zg87/view

Español: https://drive.google.com/file/d/1cPAcp19jBd590wE_6hXSNgY68KlCOdZG/view?usp=sharing

Gallego Méndez, Jorge, Lina M. García-Moreno, Lina María Franco-Calderón, Jackeline Murillo-Hoyos, and Ciro Jaramillo Molina, Ciro. “PB.04 Acciones Para Cerrar Las Brechas de Accesibilidad En Barrios Populares [Actions to Close Accessibility Gaps in Popular Neighbourhoods].” In Laboratorio de Barrios Populares - Policy Briefs: translated by Laura Corredor. Laboratorio de Barrios Populares - Policy Briefs, PB.04. Cali, Colombia: Populab-GREAT-Universidad del Valle, 2023. https://populab.correounivalle.edu.co/policy-briefs.

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Using the AMORE interactive platform. March 2025.

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Using the AMORE interactive platform. March 2025.

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An introduction to using the interactive AMORE Platform.

https://youtu.be/8oeGm1498AM

Guía de usuario de la plataforma AMORE. En español https://youtu.be/zcza6Aq2FaI

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20. Consortium for Sustainable Urbanization - Green Cities Lecture: Cali, Colombia. Feb 5, 2025.

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20. Consortium for Sustainable Urbanization - Green Cities Lecture: Cali, Colombia. Feb 5, 2025.

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The presentation focuses on how collaboration is developed and the application of a people-centered approach.

Traffic congestion reduces accessibility to critical services that impact health. However, establishing the links between traffic congestion, accessibility to essential infrastructure, and health equity has remained elusive to intersectoral efforts and public policy. The proponent discusses using relatable metrics and knowledge exchange approaches that capitalize on data sciences and available georeferenced data. This innovative approach demonstrates how urban planning can align with sustainable development goals to address complex challenges. https://youtu.be/zh3KMHgpMvo?si=142DFkWkJFD-nQqA

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The Consortium for Sustainable Urbanism's Lecture on Green Cities. Feb 5, 2025.

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The Consortium for Sustainable Urbanism's Lecture on Green Cities. Feb 5, 2025.

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Invitation and registration https://csu.global/events/green-cities-cali-colombia/

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Five years of The AMORE Project. Feb 2025.  

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Five years of The AMORE Project. Feb 2025.  

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https://youtu.be/ZdQgosvaTUE?si=Ux5c3BD8jfcMktxw

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This is The AMORE Project - Fifth Anniversary

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Este vídeo destaca cinco logros del proyecto AMORE en 2024: Dic 2024. 

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Este vídeo destaca cinco logros del proyecto AMORE en 2024: Dic 2024. 

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1. Incidencia en el ordenamiento territorial y plan de desarrollo de Cali

2. Los actores que moldean los servicios y la ciudad, recibieron los resultados

3. Difusión y debate cientifico

4. Somos referencia como trabajo intersectorial e interdisciplinario

5. Nuevos indicadores y líneas de investigación que responden a las posibilidades y necesidades ciudadanas.

https://youtu.be/MN5Gm-H-ZPs

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17. Movilidad Urbana y Percepción Ciudadana en Cali: Retos y Oportunidades para la Equidad Territorial.

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17. Movilidad Urbana y Percepción Ciudadana en Cali: Retos y Oportunidades para la Equidad Territorial.

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Jaramillo Molina, Ciro, Harvy Vivas Pacheco, Arroyo Mina, Alvaro José Benedetti Arévalo, and Vanessa Fernanda López Rojas. “Capitulo V. Movilidad Urbana y Percepción Ciudadana en Cali: Retos y Oportunidades para la Equidad Territorial.” In Transformando a Cali en una Ciudad-Región Próspera: Un Análisis Basado en Datos, edited by Alvaro José Benedetti Arévalo and José Santiago Arroyo Mina, 151–90. Cali, Colombia: Departamento Administrativo de Planeación – Fundación Universidad del Valle, 2024.

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Nota en el periódico El Tiempo, noviembre 14 de 2024. 

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Nota en el periódico El Tiempo, noviembre 14 de 2024. 

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https://www.eltiempo.com/salud/el-costo-de-transitar-la-ciudad-enfermo-modelos-de-datos-le-apuntan-a-solucionar-esta-problematica-3399702

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El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática

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Un trabajo conjunto con investigadores nacionales e internacionales evidencia cómo la inequidad en términos de accesibilidad a servicios ambulatorios se amplifica por la congestión del tráfico.

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Alcaldía Distrital de Santiago de Cali, Fundación Universidad del Valle, and Departamento Administrativo de Planeación Distrital. Construyendo Ciudad desde el análisis de Políticas Públicas: Aportes desde la Academia al Plan de Desarrollo Distrital de Santiago de Cali 2024-2027 “Cali, capital Pacífica de Colombia.” Cali, Colombia: Fundación Universiad del Valle, 2024. https://www.cali.gov.co/publicaciones/180363/plan-de-desarrollo-distrital-2024---2027-cali-capital-pacifica-de-colombia/.

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9. Construyendo Ciudad desde el análisis de Políticas Públicas: Aportes desde la Academia al Plan de Desarrollo Distrital de Santiago de Cali 2024-2027 “Cali, capital Pacífica de Colombia” Abril 2024. [Building the City from the analysis of Public Policies: Contributions from the Academy to the District Development Plan of Santiago de Cali 2024-2027 “Cali, the Pacific capital of Colombia.]

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9. Construyendo Ciudad desde el análisis de Políticas Públicas: Aportes desde la Academia al Plan de Desarrollo Distrital de Santiago de Cali 2024-2027 “Cali, capital Pacífica de Colombia” Abril 2024. [Building the City from the analysis of Public Policies: Contributions from the Academy to the District Development Plan of Santiago de Cali 2024-2027 “Cali, the Pacific capital of Colombia.]

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Plan de Desarrollo Distrital 2024 - 2027 “Cali, capital pacífica de Colombia”

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Nuestro Plan de Desarrollo es la hoja de ruta para revivir a Cali durante estos 4 años que iniciaron a partir del pasado 1 de enero

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16. Using crowd-sourced traffic data and open-source tools for urban congestion analysis.Transportation Research Interdisciplinary Perspectives. DOI: 10.1016/j.trip.2024.101261

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16. Using crowd-sourced traffic data and open-source tools for urban congestion analysis.Transportation Research Interdisciplinary Perspectives. DOI: 10.1016/j.trip.2024.101261

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Volume 28, November 2024, 101261. https://doi.org/10.1016/j.trip.2024.101261

Alkaabi, Khaula, Mohsin Raza, Esra Qasemi, Hafsah Alderei, Mazoun Alderei, and Sharina Almheiri. “Using Crowd-Sourced Traffic Data and Open-Source Tools for Urban Congestion Analysis.” Transportation Research Interdisciplinary Perspectives 28 (November 1, 2024): 101261. https://doi.org/10.1016/j.trip.2024.101261.

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What the AMORE Project offers, described in 45 seconds. Sept 2024. 

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What the AMORE Project offers, described in 45 seconds. Sept 2024. 

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Dozens of volunteer stakeholders, experts, and key informants have collaborated through the AMORE Project to provide practical solutions that address inequalities amplified by traffic congestion, which limits access to essential services in heavily congested cities. The proof of concept was completed in Cali, Colombia. https://youtu.be/vEdk8Jl0yXA

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AMORE Project: The Problems It Solves and Its Approach - in Under 45 Seconds

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https://www.youtube.com/@Proyecto_AMORE/videos

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accessibility equity "health equity" "geographical accessibility" "urban health" "urban planning" "land use"

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We are a participatory intersectoral research project aiming to advance the Sustainable Development Goals by addressing equity in accessibility to essential health services. We provide an approach and evidence to inform public policy and urban and health services planning. We seek social appropriation of knowledge, dialogue, and engagement with stakeholders.

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Perspectivas y recomendaciones de las partes interesadas. Foro "Posicionemos a Cali a la vanguardia del enfoque estratégico de salud urbana de la OMS", durante el XXVI Simposio Internacional de Investigaciones en Salud - Univalle.  Sep 19, 2024

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Perspectivas y recomendaciones de las partes interesadas. Foro "Posicionemos a Cali a la vanguardia del enfoque estratégico de salud urbana de la OMS", durante el XXVI Simposio Internacional de Investigaciones en Salud - Univalle.  Sep 19, 2024

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https://youtu.be/XPdkpY2WaWY

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Opinan actores interesados. Foro Posicionemos a Cali a la vanguardia del enfoque estratégico de OMS

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Lo que ofrece el Proyecto AMORE, en 45 segundos. Oct 2024.

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Lo que ofrece el Proyecto AMORE, en 45 segundos. Oct 2024.

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https://youtu.be/nCm5dV1yEPQ

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Proyecto AMORE: Qué Problemas Resuelve y Su Enfoque -  en 45 Segundos

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Transformando el futuro de Cali. Foro Posicionemos a Cali a la vanguadia del enfoque estratégico de salud urbana de la OMS. XXVI Simposio internacional de investigaciones en salud - Univalle

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Transformando el futuro de Cali. Foro Posicionemos a Cali a la vanguadia del enfoque estratégico de salud urbana de la OMS. XXVI Simposio internacional de investigaciones en salud - Univalle

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En 48 segundos se comunican objetivos y abordajes del proyecto AMORE para resolver inequidades en la accesibilidad a servicios críticos, usando un enfoque participativo que integra datos abiertos y grandes datos. https://youtu.be/I3qJ6a9DQXo

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Proyecto AMORE - transformando el futuro de Cali

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Perspectivas de investigadores, en 3 minutos. Foro "Posicionemos a Cali a la vanguardia del enfoque estratégico de salud urbana de la OMS", durante el XXVI Simposio Internacional de Investigaciones en Salud - Univalle. Sept 2024

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Perspectivas de investigadores, en 3 minutos. Foro "Posicionemos a Cali a la vanguardia del enfoque estratégico de salud urbana de la OMS", durante el XXVI Simposio Internacional de Investigaciones en Salud - Univalle. Sept 2024

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Presentaciones por los profesores Eliana Martínez Herrera, Lyda Osorio, Daniel Elías Cuartas Arroyave, y Ciro Jaramillo Molina. Producción: Laura V. Molina.

https://youtu.be/gjO5DN-Zwc0

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Enfoque estratégico de salud urbana de OMS - Hablan los investigadores

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English/Spanish El Proyecto AMORE: contrarrestando el efecto de la congestión del tráfico en el traslado a los servicios de salud, cuando el tiempo de traslado es crítico

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The AMORE Project: counteracting the effect of traffic congestion on travel to health care services when travel time is critical [El Proyecto AMORE: contrarrestando el efecto de la congestión del tráfico en el traslado a los servicios de salud, cuando el tiempo de traslado es crítico]. Sep 2024

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The AMORE Project: counteracting the effect of traffic congestion on travel to health care services when travel time is critical [El Proyecto AMORE: contrarrestando el efecto de la congestión del tráfico en el traslado a los servicios de salud, cuando el tiempo de traslado es crítico]. Sep 2024

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Blog Facultad de Ingeniería de la Universidad del Valle, Cali, Colombia. Incluye testimonios de pacientes y otros grupos interesados en mejorar la accesibilidad con un enfoque de equidad https://ingenieriainforma.blogspot.com/2024/10/the-amore-project-counteracting-effect.html

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El Proyecto AMORE: contrarrestando el efecto de la congestión del tráfico en el traslado a los servicios de salud, cuando el tiempo de traslado es crítico

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Read the article in English here . Luis Gabriel Cuervo, investigador principal del proyecto AMORE. Crédito: Jhon Gamboa, Oficina de Comunica…

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Foro: Posicionemos a Cali a la vanguardia del enfoque estratégico de salud urbana de la OMS. 24 de septiembre de 2024. 

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Foro: Posicionemos a Cali a la vanguardia del enfoque estratégico de salud urbana de la OMS. 24 de septiembre de 2024. 

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Liderado por Lyda Osorio y Ciro Jaramillo. Moderado por Eliana Martínez-Herrera y Laura V. Molina. Universidad del Valle, XXVI Simposio Internacional de Investigaciones en Salud. Con la participación de decisores de la política pública, asesores, prestadores de servicios, pacientes, ONGs y la comunidad académica. https://www.univalle.edu.co/salud/cali-a-la-vanguardia-del-enfoque-estrategico-de-salud-urbana-de-la-oms/

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15. (Preprint for publication from 2025-04-23). Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea. Research Square 16 Sep 2024.  DOI/10.21203/rs.3.rs-4926298/v1

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15. (Preprint for publication from 2025-04-23). Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea. Research Square 16 Sep 2024.  DOI/10.21203/rs.3.rs-4926298/v1

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Fassou Mathias GROVOGUI, Nafissatou Dioubate, Hawa Manet et al. Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study, 16 September 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4926298/v1]

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Geographical accessibility and inequalities in access to childbirth care in the Grand Conakry metropolitan area, Guinea: a spatial modelling study

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We modelled geographic accessibility, coverage, and wealth-based inequalities for childbirth care in Grand Conakry, Guinea. We assembled boundaries, health facilities, wealth index, road network, elevation, landcover, and geo-traced travel speeds. Using least-cost path algorithm, we computed t…

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Devolución de resultados del proyecto AMORE a la ciudad. XXVI Simposio de investigaciones en salud de la Universidad del Valle en Cali. 

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Devolución de resultados del proyecto AMORE a la ciudad. XXVI Simposio de investigaciones en salud de la Universidad del Valle en Cali. 

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La Prof. Lyda Osorio presenta el resultado integrado del proyecto AMORE ante actores clave, en el XXVI Simposio de investigaciones en salud Univalle, en Cali, Colombia. Octubre 18 de 2024. https://youtu.be/JNS4F-y9Zhs

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20240918 Lyda Osorio presenta el Proyecto AMORE en el XXVI Simposio de Investigaciones en Salud Univ

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La congestión vehicular reduce el acceso a servicios de tratamiento del cáncer en Cali, Colombia. Ago 2024.

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La congestión vehicular reduce el acceso a servicios de tratamiento del cáncer en Cali, Colombia. Ago 2024.

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“Mejorando la accesibilidad a los servicios de radioterapia en Cali, Colombia: análisis transversal de equidad utilizando datos abiertos y macrodatos (big data) de tiempos de viaje de 2020.” International Journal for Equity in Health (2024) Lea el artículo completo en: https://doi.org/10.1186/s12939-024-02... Acceda el estudio, la información complementaria y la plataforma AMORE en https://padlet.com/Proyecto_AMORE_Pro... https://www.iquartil.net/proyectoAMORE/ ó escanee el código QR al final del vídeo https://youtu.be/8lC1JqywAwk

https://youtu.be/lG1QI1ruAGU

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La congestión vehicular reduce el acceso a servicios de tratamiento del cáncer en Cali, Colombia.

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Traffic congestion reduces access to cancer treatment services in Cali, Colombia. Video complementing https://doi.org/10.1186/s12939-024-02211-6. Aug 2024.

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Traffic congestion reduces access to cancer treatment services in Cali, Colombia. Video complementing https://doi.org/10.1186/s12939-024-02211-6. Aug 2024.

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https://youtu.be/DyHJpMynvKo

https://youtu.be/r0W-GmTtnkk

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Traffic congestion reduces access to cancer treatment services in Cali, Colombia

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https://www.univalle.edu.co/salud/el-costo-de-transitar-enfermo-la-ciudad-modelos-de-datos-le-apuntan-a-solucionar-esta-problematica

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El costo de transitar enfermo la ciudad: modelos de datos le apuntan a solucionar esta problemática - Universidad del Valle / Cali, Colombia

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Ciro Jaramillo Molina, investigador, profesor de la Escuela de Ingeniería Civil y Geomática y codirector de la tesis doctoral que dio como resultado el Proyecto AMORE. Crédito: Édgar Bejarano, Oficina de Comunicaciones Facultad de Ingeniería. La ubicación de entidades prestadoras de servicios de sal…

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https://www.calicomovamos.org.co/post/el-costo-de-transitar-la-ciudad-enfermo

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El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática

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El proyecto AMORE (Análisis con Macrodatos para Orientar Resultados de Equidad), iniciativa de su investigación doctoral en Metodología.

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Improving spatial accessibility to health care services in Cali, Colombia: stakeholder assessment of an innovative platform. August 2024

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Improving spatial accessibility to health care services in Cali, Colombia: stakeholder assessment of an innovative platform. August 2024

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Villamizar Jaimes CJ, Cuervo LG, Martinez Herrera E, Cash-Gibson L, Jaramillo C, Benach J. Improving spatial accessibility to health care services in Cali, Colombia: stakeholder assessment of an innovative platform. Rev Panam Salud Publica. 2024;48:e69. https://doi.org/10.26633/RPSP.2024.69 https://iris.paho.org/handle/10665.2/61109

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Improving spatial accessibility to health care services in Cali, Colombia: stakeholder assessment of an innovative platform | Pan American Journal of Public Health

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Objectives To (i) ascertain stakeholders’ perceptions of the contextual factors and resources necessary to successfully implement the AMORE platform, a tool that provides accessibility assessments for health care services, considering factors such travel time and traffic conditions, and (ii) identify potential barriers to and facilitators for enhancing spatial accessibility to health care services within the Colombian urban context.

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Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020

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Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020

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Cuervo, L.G., Villamizar, C.J., Cuervo, D. et al. Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020. Int J Equity Health 23, 161 (2024). https://doi.org/10.1186/s12939-024-02211-6

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Research

Open access

Published:

Show authors

International Journal for Equity in Health 23, Article number: 161 (2024) Cite this article

Abstract

In this study, we evaluated and forecasted the cumulative opportunities for residents to access radiotherapy services in Cali, Colombia, while accounting for traffic congestion, using a new people-centred methodology with an equity focus. Furthermore, we identified 1-2 optimal locations where new services would maximise accessibility. We utilised open data and publicly available big data. Cali is one of South America's cities most impacted by traffic congestion.

Methodology: Using a people-centred approach, we tested a web-based digital platform developed through an iterative participatory design. The platform integrates open data, including the location of radiotherapy services, the disaggregated sociodemographic microdata for the population and places of residence, and big data for travel times from Google Distance Matrix API. We used genetic algorithms to identify optimal locations for new services. We predicted accessibility cumulative opportunities (ACO) for traffic ranging from peak congestion to free-flow conditions with hourly assessments for 6-12 July 2020 and 23-29 November 2020. The interactive digital platform is openly available.

Primary and secondary outcomes: We present descriptive statistics and population distribution heatmaps based on 20-min accessibility cumulative opportunities (ACO) isochrones for car journeys. There is no set national or international standard for these travel time thresholds. Most key informants found the 20-min threshold reasonable. These isochrones connect the population-weighted centroid of the traffic analysis zone at the place of residence to the corresponding zone of the radiotherapy service with the shortest travel time under varying traffic conditions ranging from free-flow to peak-traffic congestion levels. Additionally, we conducted a time-series bivariate analysis to assess geographical accessibility based on economic stratum. We identify 1-2 optimal locations where new services would maximize the 20-min ACO during peak-traffic congestion.

Results: Traffic congestion significantly diminished accessibility to radiotherapy services, particularly affecting vulnerable populations. For instance, urban 20-min ACO by car dropped from 91% of Cali's urban population within a 20-min journey to the service during free-flow traffic to 31% during peak traffic for the week of 6-12 July 2020. Percentages represent the population within a 20-min journey by car from their residence to a radiotherapy service. Specific ethnic groups, individuals with lower educational attainment, and residents on the outskirts of Cali experienced disproportionate effects, with accessibility decreasing to 11% during peak traffic compared to 81% during free-flow traffic for low-income households. We predict that strategically adding sufficient services in 1-2 locations in eastern Cali would notably enhance accessibility and reduce inequities. The recommended locations for new services remained consistent in both of our measurements.

These findings underscore the significance of prioritising equity and comprehensive care in healthcare accessibility. They also offer a practical approach to optimising service locations to mitigate disparities. Expanding this approach to encompass other transportation modes, services, and cities, or updating measurements, is feasible and affordable. The new approach and data are particularly relevant for planning authorities and urban development actors.

What we know on this topic

The benefits of setting up new hospitals and health services in areas that are accessible and convenient to patients are well known, particularly for vulnerable populations. Moreover, people increasingly prefer to visit healthcare sites within a short trip where they spend their daily lives rather than embarking on long journeys or sitting in traffic. Direct and indirect costs associated with long journeys impact adherence, clinical outcomes, and patients' and their family's quality of life and economy. Having conveniently located health services contributes to spatial justice and health equity.

This report expands on our studies for haemodialysis and tertiary care emergency services. Those studies assessed the feasibility of generating dynamic geographical accessibility measurements by integrating open data and big data in participatory processes with stakeholders while incorporating an equity perspective. Our previous findings also identified eastern Cali as the priority area where new services would optimise accessibility. However, the recommended sectors varied as the distribution of haemodialysis and tertiary care emergency services differs from radiotherapy services.

What this study adds

This study introduces an innovative methodology and tool codesigned with stakeholders and data end users to be applied by the public and private sectors to strategically plan the establishment of new healthcare sites, focusing on urban accessibility to critical services such as radiotherapy. In addition to the participatory approach, the tool relies on relatable cartography and descriptive statistics to effectively communicate assessments and predictions regarding urban accessibility to radiotherapy services in Cali. It identifies optimal locations for new radiotherapy services, considering factors such as traffic congestion, and offers crucial data-driven insights for enhanced health services planning. This study tests a practical approach involving relevant stakeholders in response to numerous calls from leaders and intersectoral organisations advocating for participatory intersectoral actions to align the urban and health agendas by focusing on equity and social justice.

How might this study affect research, practice, or policy?

This study supports a fundamental shift in how accessibility measurements for health services are approached when traffic congestion is a factor. This approach equips advocates and stakeholders to participate in urban and health services planning with accurate and relatable data, fostering intersectoral and multistakeholder collaborations. The study also offers an interactive public platform for testing data and scenarios.

Governments, local authorities, and health/insurance companies benefit from incorporating these measurements into their health and urban planning policies. Civic organisations, government, and patient groups can explore how to monitor and link traffic congestion with equity and accessibility. This integration can improve overall accessibility, efficiency, and public participation. Moreover, the study can influence policies prioritising public health by underscoring the critical role of addressing traffic congestion in ensuring equitable access to health services. It sheds light on the impact of travel times as barriers to adherence and access to health services, particularly in conjunction with appointment opportunities, authorisations, and the fair allocation of subsidies.

Background

A research project in Cali's informal settlements of the western peripheral Commune 18 produced qualitative data highlighting the problems people face when commuting to ambulatory cancer and dialysis services they require several times weekly [ 39, 40, 41]. These patients and their families can face financial hardship because of the high costs, direct and indirect (e.g., lost wages, missed opportunities) [ 42, 43, 44, 45, 46, 47, 48, 49] (Supplementary Material 1).

Data and insights are needed to understand the factors contributing to poor spatial (geographical) accessibility to radiotherapy services and to address health equity and social justice [ 2, 58, 59, 60, 61]. Measuring the effects of traffic congestion on accessibility among populations enables data-driven approaches to address underlying issues. These include land-use policies or other factors contributing to market failure and service scarcity in underserved areas, including revenue, availability of specialists, or insecurity. Individual ability to pay is not directly linked to service payments in Colombia because health insurance schemes make most payments but need to cover transportation systematically [ 62]. A systematic review assessing the use of geographical information systems for radiotherapy planning found no studies providing an equity perspective that considered traffic congestion in low or middle-income countries, part of a multistakeholder coalition, or following a participatory people-centred approach [ 63]. We found no other studies co-designed with end users and stakeholders providing dynamic spatiotemporal accessibility measurements and predictions for health services [ 1]. Participation of beneficiaries and health services users in health services research promotes trust, effectiveness, community rights and several other benefits [ 37, 64].

Transportation subsidies are increasingly available in Colombia to treat high-cost diseases like cancer. However, these subsidies can perpetuate inequities because they need to account for factors that define out-of-pocket and indirect costs, such as travel time or distance. Furthermore, implementing these subsidies in 2020 was not systematic, leaving disadvantaged populations facing accessibility challenges [ 42, 65, 66, 67, 68]. Poor geographical accessibility to cancer treatments diminishes the quality of care and is associated with poor clinical outcomes [ 56, 63, 69, 70, 71, 72, 73].

Methods

Study population and setting

Cali, a tropical city nested in a valley, lies between the western Andean mountains and the west bank of the Cauca River. Some of its neighbourhoods cover the western Andean slopes, including those along the road leading to the Pacific in northwest Cali on its westernmost border. Cali is the only major Colombian city with access to the Pacific c﻿oast (Fig. 1). This study focused on the urban population of Cali, as estimated for 2020 after updating the recorded 2018 national census microdata, as described in the research protocol.

Radiotherapy services

In 2020, Cali registered five radiotherapy facilities in the Colombian Special Registry of Health Services Providers (REPS in Spanish) [ 86]. Radiotherapy services are located in four TAZ along the north-south corridor on the lower part of the western Andean slopes and foothills, which are well-off areas of the city; two services at Imbanaco Medical Center are in the same TAZ. Four services are on the city's west and one, Fundación Valle de Lilí, in the south, all in well-off areas (Fig. 1).

However, some high-income residents live in villas and mansions in southern Cali, and high-income housing hosts 9% of the population, including low-income resident homeworkers; almost half of the population lives in low-income areas, mainly in the city's east and peripheric areas (Fig. 2) [ 80]. According to REPS, these radiotherapy facilities have extended hours from Monday to Saturday, providing crucial healthcare services.

Study design

This cross-sectional study used the AMORE web-based platform to integrate big data of travel times with georeferenced microdata from the adjusted 2018 national census, TAZs, and the geographic locations of radiotherapy services [ 12, 38, 89, 90]. The data sources integrated into the platform included the 2018 National Census Data for Cali, obtained from the public official databases of the Colombian National Department of Statistics (DANE in Spanish); [ 91, 92] administrative divisions of Cali obtained from the Colombian IDESC Geoportal, Traffic Analysis Zones (TAZs), and census block sectors [ 93, 94]; Google's Distance Matrix API; and information about the five radiation therapy services in Cali. The study's complete list of variables can be found in the protocol and previous reports [ 12, 36, 38].

Statistical analysis

Our report provides frequencies and bivariate analyses to represent cumulative opportunities as a measure of accessibility [ 79]. Critical variables for this report are travel time from the population weighed centroid of each residential TAZ to that of the TAZ hosting the radiotherapy service with the shortest journey time and the housing and sociodemographic characteristics of the population, as recorded in the Colombian national census microdata [ 38, 96]. The AMORE web-based platform displays population absolute and relative distribution (percentages) per travel time threshold measured for each of the nine traffic congestion levels, ranging from free-flow to peak traffic congestion [ 12]. We use isochrones or choropleths to represent the shortest median journey time required to reach a radiotherapy service at each hour of the day throughout the week. Univariate and bivariate analyses are described in the protocol and reports [ 12, 36, 38].

Results / Outcomes

Participants

Table 1 Relative figures: situational analysis and predicted 20-minute ACO by car to radiotherapy in Cali in July and November 2020

Table 2 Population data: situational analysis and predicted 20-minute ACO by car to radiotherapy in Cali in July and November 2020

Baseline situational analyses

Effect of traffic variations (6-12 July vs 23-29 November 2020)

In July, Afro-descendants and the smaller Palenque community experienced the lowest accessibility rates, with 15.2% and 11%, respectively. In contrast, Islander and 'Other' populations (including whites and mestizos) fared significantly better, with accessibility rates of 40% and 33.4%, respectively. An earlier report showed that the small Palenque population is concentrated in a few neighbourhoods [ 12]. Because most of this population lives near the 20-minute ACO threshold borders, accessibility improved greatly when traffic congestion eased in November, reaching an accessibility rate of 78.8%, followed by indigenous communities at 67.5% (Table 2).

We observed an age-related gradient in accessibility, with older populations enjoying better access. Interestingly, all age groups benefited similarly from the traffic congestion relief (Tables 1 and 2).

Effects of the addition of new radiotherapy providers

We analysed what peak-traffic accessibility would look like if new radiotherapy facilities were optimally located in 1-2 TAZs. Optimisation analyses for urban accessibility during peak traffic hours consistently pointed to adding new services in the eastern sector of La Base (Figs. 8 and 11). This finding was consistent for both measurement periods. Simulating the addition of this new service in July increased predicted urban accessibility from 30.8% to 61.6%. This addition expanded the 20-minute ACO to include 695,502 more inhabitants (Table 2).

The evidence, drawn from measurements in July and November, shows that introducing new radiotherapy services in recommended locations would improve accessibility, particularly in eastern Cali, and reduce inequities (Figs. 9, 10, 11 and 12). Moreover, the data also shows that it is better to add more sites. In July, the increase from adding two sites instead of one was 21.0%. In November, although lower by 7.9% (178,426), this increase still improves ACO for a large population (Supplementary Material 7 and 8).

Other analyses

Comparing July and November 2020

We measured the gradients by which traffic fluctuations from free flow to peak traffic affect populations per housing economic stratum (Fig. 7). We found that the negative impact of traffic congestion is more remarkable for the lower-income stratum groups. We found a small high-income population living in villas in the city's southern outskirts that remains beyond the 20-minute ACO.

Discussion

The city's layout and demography have changed since the city was concentrated around the corridor where services are located, and our findings offer a justification for updating land-use and health services plans. The new data provide insights for an equity-focused and data-driven approach that supports aligning the urban and health agenda [ 60, 99, 100, 101]. Our latest data indicates that populations in the peripheral areas and the densely populated city's east must invest disproportionately when commuting to radiotherapy services. This should challenge traditional urban and planning approaches and support actions to enhance social justice by focusing on health equity [ 60, 71, 96].

If decision-makers add services in a single area, La Base or its surroundings emerge as the optimal location to maximise accessibility (Figs. 8 and 11). These services must have the capacity to meet the high demand from the populations covered in their catchment area and remain accessible to all radiotherapy patients. Expanding capacity in the east could alleviate demand pressure on existing centres, freeing up resources to accommodate the needs of metropolitan areas and neighbouring communities.

Our findings emphasise the need to monitor demand, building on open and big data integration opportunities [ 30]. Epidemiological and economic studies could shape the services and capacities of new and existing radiotherapy facilities. Understanding current and predicted radiotherapy needs and expected trends would benefit such deliberations.

Another practical application is calculating a fairer distribution of subsidies by adjusting them according to DSTAM. These measurements could be used to prioritise the organisation of mobility services for patients in outlying urban areas, and extended to cover metropolitan areas [ 63, 112].

Given the substantial expected demand that strategically located services could have, our findings underscore the urgent need for additional radiotherapy services accessible to all residents. Our data provides insights and valuable parameters for health services planning and advocating for re-evaluating Cali land-use policies.

Key results

This study provides a valuable analysis of accessibility opportunities within the Colombian health system. It also sheds light on a practical estimation of traffic congestion's impact on equity and accessibility. It is informative to stakeholders from diverse sectors (e.g., health, urban planning, smart city, economy) with a stake in urban and health services planning [ 27, 37, 53, 61, 100, 101].

Interpretation

These findings empower citizens to participate in data-driven dialogues with land-use and health services planners. The overarching goal is to facilitate necessary improvements, bridging the health equity gap and ensuring universal access to healthcare [ 116].

Care models for bustling cities should incorporate quality aspects of health services like travel times. This analysis is an opportunity to recognise that comprehensive care for radiotherapy patients is inherently linked to other services, including psychological support, rehabilitation therapies, nutritional guidance, pain management, or palliative care. Therefore, to ensure that patients can access services without suffering hardship, planners must organise to offer integral care within reach, including chemotherapy and other ambulatory services that can be combined with radiotherapy [ 52, 53, 55, 56, 57, 87, 104]. Future research and smart governance could monitor and optimise the distribution of treatments with an integral perspective to minimise travel and service fragmentation and to provide continuity with established integral cancer treatment teams [ 55, 104]. A refined version of the AMORE Platform could be used to monitor progress [ 51, 60]. Local governments could adopt strategic approaches and enhanced indicators for accessibility in cities with heavy traffic [ 53, 60].

Radiotherapy services are concentrated in the area from the north-to-south corridor of Cali to the west end of the valley where the city lies, leaving eastern Cali uncovered (Figs. 1 and 2). Restrictions that prevent patients from accessing services available in every sector could further reduce accessibility. For instance, the Fundación Valle de Lilí radiotherapy service, in the southernmost part of Cali, is the sole provider for the densely populated southern areas of the city. Suppose health maintenance organisations or insurers (EPS/EAPB, in Spanish) do not contract with providers close to patients or assign patients to distant services. In that case, patients won't benefit from services in their proximity. We consider that the low-accessibility and inequity gradients stress the urgency of implementing reforms to address the fragmentation of health service delivery.

Colombian law requires the treatment of emergencies throughout the health system, reducing fragmentation. However, such a law needs to be put in place for ambulatory services for high-cost conditions, like radiotherapy; therefore, patients might be restricted from accessing conveniently placed IPSs [ 12, 118]. Colombia is undergoing a health reform, and assessments like this could provide insights for policy change, including free choice for radiotherapy providers.

Generalizability

The availability of travel-time big data allows for assessing various transportation modes. This approach applies to a broad spectrum of services, provided georeferenced data is available.

Notably, international policies and mandates are accelerating digitisation and datafication trends in many countries [ 119]. This study's approach is transferable and can be replicated in other settings with traffic congestion, provided the essential data is available. Using population-weighed TAZ centroids instead of blocks reduces the overall costs associated with travel-time data while allowing for precise estimates [ 3, 36, 38, 89]. Like Colombia, other countries can harness available georeferenced open data of their population and services and integrate it with travel-time big data and TAZs to estimate ACOs [ 119, 120]. Travel-time big data is available from various providers. The resulting assessments could inform land-use and health services planning and contribute to aligning radiotherapy and other cancer treatments with universal health coverage [ 29, 121].

The public and private sectors can also adopt and refine this methodology by integrating information about population epidemiology, insurance providers, and payment agreements with health providers. These approaches are more complex and require investing in technology and participatory methods for stakeholder engagement. For example, integrating additional data might help identify the service with the shortest journey and availability for a specific appointment time. It might also guide insurers' prioritisation of providers to expand their coverage or reduce fragmentation. When patients can access services in every sector offering them, accessibility will reach the levels we measured. As we reported in this study, the benefits of obtaining more precise and sophisticated assessments should be carefully weighed against the required resources, considering the high-level evaluation feasible with reduced funding and in a short time.

Our methodology can be adapted to assess other transportation modes, services, and geographical areas. Such replication might involve downloading travel times for additional transport modes, adjusting traffic congestion models, population centroids within traffic analysis zones (TAZs), and refining predictive models [ 38].

Limitations

This study seeks to identify the radiotherapy service with the shortest travel time. However, patients in Colombia are assigned to a health services provider institution (IPS in Spanish) under contract with the health promotion entity (EPS, in Spanish) or Benefits Plan Administration Company (EAPB) responsible for providing services to them [ 118]. It may result in reported accessibility rates exceeding the accessibility individuals experience, which will be lower when EPS affiliation limits a patient's access to the services offering the shortest travel times. We had no access to georeferenced data on EPS/EAPB coverage for populations; therefore, our findings show cumulative opportunities instead of actual accessibility. This fragmentation introduces a potential bias in our findings.

This report does not consider the characteristics of individual radiotherapy services, their capacity, or which ones offer specialised brachytherapy services, for example. It also does not integrate service agreements or georeferenced patient information. Therefore, our assessment errs on being optimistic; it reflects those populations that, even in the best-case scenario, would be beyond the assessed travel-time threshold. It points out that addressing these issues may involve actions on different fronts, from adding services to addressing health services fragmentation to enable access to those services with the shortest journeys and providing an integral approach to cancer treatment where radiotherapy services are located. Nevertheless, it is possible to adjust for specific services by activating or deactivating services considered in the accessibility assessments in the AMORE Platform. For faster response, the published AMORE platform is set to identify locations that optimise accessibility with peak traffic [ 98].

Radiotherapy services are provided to the broader metropolitan area and neighbouring cities; this study focused on urban Cali. While the approach's efficiency relies on using open and big data, the accuracy of the findings depends upon the accuracy of these publicly available data sources. Inaccuracies in sources may render some estimations or conclusions inaccurate. We did not include cost-effectiveness analyses or accurate demand and capacity estimates for new services.

Conclusions

This study advocates for using DGAMs to monitor accessibility in cities where traffic congestion might be amplifying inequities and as conditions, infrastructure and populations change [ 122]. It also demonstrates that predictions suitable for integrating health with urban and territorial planning can be made using open data. The engagement of local stakeholders improved the platform and the pertinence of the analysis options and placed the focus on the needs of data end users and beneficiaries.

The study pragmatically approached the singularities of bustling urban settings with a holistic approach considering the stakeholders, tensions, and challenges of territorial and health services planning. It measures accessibility per traffic congestion gradients with an equity perspective. It offers a simple and robust approach, provides baseline assessments, and predicts the impact of interventions building on readily accessible open data and accurate travel time big data. The data and analyses used to reach the conclusions presented here are drawn from multiple research tools and methodologies, including cross-sectional epidemiological study, participatory research, and AI and data sciences. In using this approach, we have aimed to provide an integrative perspective built on a robust and diverse evidence base.

The potential of improving accessibility to radiotherapy services for many patients stresses the urgency of acting on these data. We recommend prioritising new integrated radiotherapy or cancer care services in Cali's densely populated east, considering locations that maximise accessibility and health equity. Subsequent updates and evaluations will determine if adherence to integrated knowledge translation and social appropriation of knowledge principles lead to participatory urban and health services planning. They might also establish if services in eastern Cali will increase service referrals from the broader metropolitan area and neighbouring municipalities where radiotherapy is in short supply.

Other information

Health services planners could further research the effects of people-centred participatory approaches with multistakeholder engagement to seek stakeholder engagement in shaping urban and health services planning and social justice. The AMORE Project interactive interface is publicly available, and stakeholders can assess diverse scenarios and parameters [ 21, 24, 35, 38, 74, 124].

Availability of data and materials

Data sources are listed in the text, the protocol, and the project's websites.

Datasets are available at reasonable request.

References

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Acknowledgements

We sincerely thank the dedicated teams at Cuenta de Alto Costo, Colombia, for their invaluable assistance in addressing our data requests and inquiries regarding radiotherapy service demand in Cali. Special appreciation goes to Dr. Lizbeth Alexandra Acuña Merchán, the Executive Director, for her exemplary diligence in following up on our requests. Their cooperation and support have significantly enriched our research endeavour. We acknowledge the invaluable contribution of the non-profit private foundation ProPacífico teams implementing the City Cancer Challenge in Cali. We extend our heartfelt thanks to their Executive Director, Maria Isabel Ulloa, and Health Project Director, Daniel Humberto Echeverry, for their exceptional leadership in encouraging their teams to contribute to this report. Their dedication and support have been instrumental in enhancing the quality and impact of our work. We thank City Cancer Challenge (C/Can) for contributing to this report. We especially thank Isabel Mestres, Chief Executive Officer, and Meritxell Mallafré-Larrosa, Technical Manager and Research Lead, for their instrumental roles in facilitating their teams' contributions. Throughout this project we received valuable historical background and insights on Cali's urban development from Arq. Enrique Sinisterra O'Byrne. For this manuscript we also received insights from Arq. Francisco Ramírez. Louisa Stüwe, Alice M. Vasconcellos, and Cristina Cuervo Rengifo reviewed the translated abstracts.
Additional AMORE Project collaborators:
Freddy Enrique Agredo Lemos, Juan Camilo Arboleda, German Avila Rodriguez, Alberto Concha-Eastman, Ingrid Faber, Oscar H. Franco, Crhistian Camilo Garcia Altamirano, Rodrigo Guerrero Velasco, Déborah Itriago, Edith Alejandra Martin, Fernando Rafael Martinez Arambula, Maria Fernanda Merino Juarez, Jackeline Murillo-Hoyos, Ana Ortigoza, Lyda Osorio, David Paredes-Zapata, Luis Fernando Pinilla, Oscar Rojas Rentería, Myriam Rosero Hernández, María Fernanda Tobar-Blandón

Disclaimer

The depiction of boundaries on included maps does not imply the expression of any opinion of publishers concerning the legal status of any country, territory, jurisdiction or area or its authorities. This map is provided without any warranty, either express or implied. The maps we present in this publication were generated using open-source mapping services. Specifically, for Figs. 1 and 8, we utilised the Google Maps™ mapping service, incorporating map data sourced from ©2024 Google LLC. For Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 and supplementary material graphics, we used ©2023 Microsoft Corporation's resources, including Office 365 apps, Microsoft Bing and Microsoft Power Bi™.

The views expressed in this article do not necessarily reflect the decisions or policies of the author's employers, including PAHO/WHO and Egis Consulting. Reproductions of this article should not imply that PAHO/WHO, Egis Consulting, or any other employers endorse this research or any specific organisation, service, or product. This project is done within the context of the doctoral programme in Biomedical Research Methodology and Public Health at the Autonomous University of Barcelona. Only the corresponding author will include it as part of his doctoral work.

Funding

This study received no external funding.

Author information

Author notes

Eliana Martínez-Herrera and Ciro Jaramillo are senior authors.

Authors and Affiliations

1. Departamento de Pediatría, de Obstetricia y Ginecología y de Medicina Preventiva y Salud Pública. Facultad de Medicina - Edificio M, Universitat Autònoma de Barcelona, Campus Universitario UAB, 08193, Bellaterra, (Cerdanyola del Vallès) Cataluña, Spain

Luis Gabriel Cuervo

2. Academia Nacional de Medicina de Colombia, Cra. 7ª # 69-11, 110231, Bogotá, Colombia

Luis Gabriel Cuervo

3. Johns Hopkins Bloomberg School of Public Health, Wolfe Street Building, W1015, Baltimore, MD, 21205, USA

Carmen Juliana Villamizar & Ángela Suárez

4. IQuartil SAS, Cra 13A # 107A-47, 110111, Bogotá, Colombia

Daniel Cuervo & Pablo Zapata

5. Department of Public Health Sciences, Faculty of Health Sciences, Queen's University, Carruthers Hall 204, Kingston, ON, K7L 3N6, Canada

Maria B. Ospina

6. Universidad Nacional de Colombia, Ave Cra. 30 # 45-03, 111321, Bogotá, Colombia

Sara Marcela Valencia

7. Facultad de Medicina, Universidad de Antioquia, Cra. 51D # 62-29, Medellín, Antioquia, 050010, Colombia

Sara Marcela Valencia

8. Technical Cooperation and Capacity Development, City Cancer Challenge Foundation, 9 Rue du Commerce, Geneva, 1204, Switzerland

Alfredo Polo

9. Independent Researcher, 110221, Bogotá, Colombia

Maria O. Bula

10. CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Av. Armendáriz 445 - Miraflores, 15074, Lima, Peru

J. Jaime Miranda

11. Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, 2006, Australia

J. Jaime Miranda

12. Universidad del Valle, Cali, Valle del Cauca, 760032, Colombia

Gynna Millan

13. Junta Nacional de Calificación de Invalidez [National Disability Board of Colombia], 110111, Bogotá, Colombia

Diana Elizabeth Cuervo

14. Independent Content and Communications Consultant, Fairfax, VA, 22032, USA

Nancy J. Owens

15. Patient Advocate and Author of an Autopathography, 110231, Bogotá, Colombia

Felipe Piquero

16. WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment for Health Equity, Bruyère Research Institute, University of Ottawa, Ottawa, ON, K1N 5C8, Canada

Janet Hatcher-Roberts

17. Independent Consultant On Emergency Medicine and Humanitarian Response, 110111, Bogotá, Colombia

Gabriel Dario Paredes

18. Regional Director, City Cancer Challenge Foundation, 110111, Bogotá, Colombia

María Fernanda Navarro

19. ProPacífico, Calle 35 Norte #6A Bis - 100, 760046, Cali, Valle del Cauca, Colombia

Ingrid Liliana Minotta & Carmen Palta

20. National Faculty of Public Health, Universidad de Antioquia, Cl. 62 #52-59, La Candelaria, 050010, Medellín, Antioquia, Colombia

Eliana Martínez-Herrera

21. JHU-UPF Public Policy Center, Departament de Ciències Polítiques I Socials, Universitat Pompeu Fabra (UPF), Barcelona School of Management (UPF-BSM), Barcelona, Cataluña, Spain

Eliana Martínez-Herrera

22. School of Civil and Geomatic Engineering of the Universidad del Valle, Cali, Valle del Cauca, 760032, Colombia

Ciro Jaramillo

Consortia

on behalf of the AMORE Project Collaboration

Freddy Enrique Agredo Lemos

, Juan Camilo Arboleda

, German Avila Rodriguez

, Alberto Concha-Eastman

, Ingrid Faber

, Oscar H. Franco

, Crhistian Camilo Garcia Altamirano

, Rodrigo Guerrero Velasco

, Déborah Itriago

, Edith Alejandra Martin

, Fernando Rafael Martinez Arambula

, Maria Fernanda Merino Juarez

, Jackeline Murillo-Hoyos

, Ana Ortigoza

, Lyda Osorio

, David Paredes-Zapata

, Luis Fernando Pinilla

, Oscar Rojas Rentería

, Myriam Rosero Hernández

& María Fernanda Tobar-Blandón

Contributions

All authors approved the final draft submitted and agreed to be accountable for all aspects of this manuscript. Conceptualisation contributions: LGC, DC, JHR, and CJ. Methodological contributions: LGC, CJV, CJM, EMH, DC. Software: LGC, DC, PZ. Validation: LGC, DC, PZ, and CJ. Formal Analysis: LGC, DC, CJM, JM, AP. Investigation: LGC, EMH, CJM, MOB, GM, SMV, AP, MFN, ILM, CP. Resources: LGC and DC. Data curation: LGC, CJV, PZ, and CJ. Original draft writers: LGC, CJV, CJM, EMH. Draft review and editing: LGC, CJV, CJM, EMH, DC, PZ, MBO, SMV, AP, AS, MOB, JM, GM, DEC, NJO, FP, JHR, GDP, MFN, ILM, CP. Visualizations: LGC, CJV, DC, PZ, AS, CJM; Supervision: LGC, CJV, EMH, and CJM. Project administration: LGC and CJV. Funding acquisition: LGC.

Corresponding author

Correspondence to Luis Gabriel Cuervo.

Ethics declarations

Ethics approval and consent to participate

This observational study integrates anonymised coded secondary data sources from publicly available open records. Colombia's census microdata used in this study and the location of approved selected health services obtained from REPS are publicly available [ 126, 127].

The study protocol does not involve human subjects research and is considered "without risk" per Colombian law. The Commission on Ethics in Animal and Human Experimentation (CEEAH) of the Universidad del Valle in Cali and the Vice-Rector for Research, Universitat Autònoma de Barcelona (Ref: CEEAH-6100) cleared this project [ 38].

Competing interests

All authors and collaborators completed the ICMJE uniform disclosure form and declared no financial support from any organisation for the submitted work. IQuartil SAS provided technical support to develop the AMORE Platform and was subsidised for consulting services by LGC. PZ received consulting fees and time from IQuartil SAS for the AMORE Platform development. DC is a partner at IQuartil SAS and a sibling to LGC. LGC contributed to this work in his own time and capacity. CJ and EMH are LGC's thesis directors. LGC, CJV, and AS contributed personal time to Driving for Equity, a proposal aligned with the AMORE Project and a finalist at WHO's 2023 LEAD Innovation Challenge. JM declared that his hiring institutions received the following grants to support other work projects: Alliance for Health Policy and Systems Research (HQHSR1206660), Bloomberg Philanthropies (grant 46129, via University of North Carolina at Chapel Hill School of Public Health), FONDECYT via CIENCIACTIVA/CONCYTEC, British Council, British Embassy and the Newton-Paulet Fund (223-2018, 224-2018), DFID/MRC/Wellcome Global Health Trials (MR/M007405/1), Fogarty International Center (R21TW009982, D71TW010877, R21TW011740), Grand Challenges Canada (0335-04), International Development Research Center Canada (IDRC 106887, 108167), Inter-American Institute for Global Change Research (IAI CRN3036), National Cancer Institute (1P20CA217231), National Heart, Lung and Blood Institute (HHSN268200900033C, 5U01HL114180, 1UM1HL134590), National Institute of Mental Health (1U19MH098780), Swiss National Science Foundation (40P740-160366), UKRI BBSRC (BB/T009004/1), UKRI EPSRC (EP/V043102/1), UKRI MRC (MR/P008984/1, MR/P024408/1, MR/P02386X/1), Wellcome (074833/Z/04/Z, 093541/Z/10/Z, 103994/Z/14/Z, 107435/Z/15/Z, 205177/Z/16/Z, 214185/Z/18/Z, 218743/Z/19/Z) and the World Diabetes Foundation (WDF15-1224). He has also held a contract unrelated to this work with Health Action International. JM participated in these unpaid data safety and monitoring and advisory boards: DSMB, Nigeria Sodium Study (NaSS); Trial Steering Committee, INTEnsive care bundle with blood pressure Reduction in Acute Cerebral haemorrhage Trial (INTERACT 3); International Advisory Board, Latin American Brain Health institute (BrainLat), Universidad Adolfo Ibáñez (Chile); Consultative Board, Programa de Gastronomía, Facultad de Estudios Interdisciplinarios, Pontificia Universidad Católica del Perú; Advisory Board InterAmerican Heart Foundation (IAHF). JM participates in the following unpaid groups: Independent Group of Scientists (IGS), 2023 Global Sustainable Development Report, United Nations; Scientific Expert Committee, Global Data Collaborative for CV Population Health, World Health Federation, Microsoft, and Novartis Foundation; Scientific and Technical Advisory Committee for the Alliance for Health Policy and Systems Research, World Health Organization; WHO Technical Advisory Group on NCD-related Research and Innovation, Noncommunicable Diseases Department, World Health Organization; Advisory Scientific Committee, Instituto de Investigación Nutricional (Peru). NJO received consulting fees from Cochrane Complementary Medicine, Vivli, and Cochrane. JHR has leadership roles unrelated to this area of study. She sits on the Canadian Medical Foundation and the Pegasus Institute boards. JHR is the co-director of the WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment for Health Equity at the Brùyere Institute of the University of Ottawa. All other authors have nothing to declare.

The opinions conveyed in this article do not necessarily reflect the decisions or policies of the author's employers, including PAHO/WHO. Any reproductions of this article must avoid implying endorsement by PAHO/WHO or other employers for this research or any particular organisation, service, or product. This project was conducted within the context of the doctoral programme in Biomedical Research Methodology and Public Health at the Autonomous University of Barcelona.

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https://padlet.com/Proyecto_AMORE_Project/Collaborators. AMORE stands for 'Análisis espacial con Macrodatos para Orientar Resultados en Equidad,' which translates to 'Spatial Analysis with Big Data to Guide Equity Outcomes' in English.

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Cuervo, L.G., Villamizar, C.J., Cuervo, D. et al. Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020. Int J Equity Health 23, 161 (2024). https://doi.org/10.1186/s12939-024-02211-6
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# data[].attributes.attachment_link.title
Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020 - International Journal for Equity in Health

# data[].attributes.attachment_link.description
In this study, we evaluated and forecasted the cumulative opportunities for residents to access radiotherapy services in Cali, Colombia, while accounting for traffic congestion, using a new people-centred methodology with an equity focus. Furthermore, we identified 1-2 optimal locations where new services would maximise accessibility. We utilised open data and publicly available big data.

# data[].attributes.headline
Dr. Diana Cuervo gathers stakeholder perspectives on equitable improvement of accessibility to essential health services in Cali. Aug 2024

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Dr. Diana Cuervo gathers stakeholder perspectives on equitable improvement of accessibility to essential health services in Cali. Aug 2024

# data[].attributes.body
Perspectives of a service user, health epidemiologist, expert in health equity, researcher in the organization of health systems, applied economics, and law, and occupational health. https://youtu.be/PjhLUdwEosw

# data[].attributes.attachment_link.title
Amore project an analysis from all sectors

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La Dra. Diana Cuervo recoge perspectivas de actores centrales al mejoramiento equitativo de la accesibilidad a los servicios esenciales de salud de Cali. Agosto 2024.

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La Dra. Diana Cuervo recoge perspectivas de actores centrales al mejoramiento equitativo de la accesibilidad a los servicios esenciales de salud de Cali. Agosto 2024.

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Perspectivas de usuario de servicio, epidemióloga salubrista experta en equidad en salud, investigadora en la organización de los sistemas de salud, economía aplicada, legislación y salud ocupacional. https://youtu.be/PjhLUdwEosw

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Proyecto Amore: Un análisis desde todos los sectores

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14. How can multi-source heterogeneous data contribute to assessing urban transportation carrying capacity? Environmental Impact Assessment Review. 108, (2024), 107602

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14. How can multi-source heterogeneous data contribute to assessing urban transportation carrying capacity? Environmental Impact Assessment Review. 108, (2024), 107602

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Wei, Xiaoxuan, Liyin Shen, Xiaoyun Du, Zhenchuan Yang, Zhenhua Guo, and Qiaorong Yin. “How Can Multi-Source Heterogeneous Data Contribute to Assessing Urban Transportation Carrying Capacity?” Environmental Impact Assessment Review 108 (September 1, 2024): 107602. https://doi.org/10.1016/j.eiar.2024.107602

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3. Improving social justice in observational studies. International Journal for Equity in Health 22, no. 1 (2023)

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3. Improving social justice in observational studies. International Journal for Equity in Health 22, no. 1 (2023)

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Funnell, S., J. Jull, L. Mbuagbaw, V. Welch, O. Dewidar, X. Wang, M. Lesperance, et al. “Improving Social Justice in Observational Studies: Protocol for the Development of a Global and Indigenous STROBE-Equity Reporting Guideline.” International Journal for Equity in Health 22, no. 1 (2023). https://doi.org/10.1186/s12939-023-01854-1.

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Improving social justice in observational studies: protocol for the development of a global and Indigenous STROBE-equity reporting guideline - International Journal for Equity in Health

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Background Addressing persistent and pervasive health inequities is a global moral imperative, which has been highlighted and magnified by the societal and health impacts of the COVID-19 pandemic. Observational studies can aid our understanding of the impact of health and structural oppression based on the intersection of gender, race, ethnicity, age and other factors, as they frequently collect this data. However, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline, does not provide guidance related to reporting of health equity. The goal of this project is to develop a STROBE-Equity reporting guideline extension. Methods We assembled a diverse team across multiple domains, including gender, age, ethnicity, Indigenous background, disciplines, geographies, lived experience of health inequity and decision-making organizations. Using an inclusive, integrated knowledge translation approach, we will implement a five-phase plan which will include: (1) assessing the reporting of health equity in published observational studies, (2) seeking wide international feedback on items to improve reporting of health equity, (3) establishing consensus amongst knowledge users and researchers, (4) evaluating in partnership with Indigenous contributors the relevance to Indigenous peoples who have globally experienced the oppressive legacy of colonization, and (5) widely disseminating and seeking endorsement from relevant knowledge users. We will seek input from external collaborators using social media, mailing lists and other communication channels. Discussion Achieving global imperatives such as the Sustainable Development Goals (e.g., SDG 10 Reduced inequalities, SDG 3 Good health and wellbeing) requires advancing health equity in research. The implementation of the STROBE-Equity guidelines will enable a better awareness and understanding of health inequities through better reporting. We will broadly disseminate the reporting guideline with tools to enable adoption and use by journal editors, authors, and funding agencies, using diverse strategies tailored to specific audiences.

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12. How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households. Journal of Geographical Sciences. 34, no. 6 (June 8, 2024): 1195–1227.

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12. How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households. Journal of Geographical Sciences. 34, no. 6 (June 8, 2024): 1195–1227.

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Wang, F., and Y. Wang. “How Geographic Accessibility and Rural Governance Mitigate the Impact of Multiple Risks on Rural Households’ Well-Being: Evidence from the Dabie Mountains in China.” Journal of Geographical Sciences 34, no. 6 (2024): 1195–1227. https://doi.org/10.1007/s11442-024-2245-8.

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Abstract

Despite previous studies on the interconnectedness of livelihood risks and human well-being, limited emphasis has been placed on the influence of geographic accessibility and rural governance on the well-being of rural households. Furthermore, the interplay between geographic accessibility, rural governance, and livelihood risk remains inadequately explored. Based on 522 household samples collected in the Dabie Mountains and ordinary least squares regression, this study examines the influence of multiple risks on the subjective well-being of rural households by investigating the moderating roles played by geographic accessibility and rural governance. The results show that (1) multiple risk factors have a significant negative effect on rural households' well-being (β=−0.219, p<0.01); (2) geographic accessibility has a weak positive effect on rural households' well-being (β=0.064, p<0.1) compared to rural governance, which plays a larger positive role (β=0.228, p<0.01); and (3) geographic accessibility has a significant moderating effect on the relationship between multiple risks and rural households' well-being, decreasing the adverse impact of multiple risks on households' well-being. Our findings suggest that geographic accessibility and rural governance have positive implications for enhancing the well-being of rural households. The findings provide policy insight into mitigating livelihood risks and their negative impacts on household well-being in mountainous regions worldwide.

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Authors and Affiliations

1. Department of Land Resources Management, School of Public Administration, China University of Geosciences, Wuhan, 430074, China

Feifan Wang & Ying Wang

2. Key Laboratory of the Ministry of Natural Resources for Legal Research, Wuhan, 430074, China

Feifan Wang & Ying Wang

Corresponding author

Correspondence to Ying Wang.

Additional information

Foundation: National Natural Science Foundation of China, No.42371315, No.41901213.

Author: Wang Feifan (1999-), Master Candidate, E-mail: feifanwang5703@cug.edu.cn

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Wang, F., Wang, Y. How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households' well-being: Evidence from the Dabie Mountains in China. J. Geogr. Sci. 34, 1195-1227 (2024). https://doi.org/10.1007/s11442-024-2245-8
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How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households' well-being: Evidence from the Dabie Mountains in China - Journal of Geographical Sciences

# data[].attributes.attachment_link.description
Despite previous studies on the interconnectedness of livelihood risks and human well-being, limited emphasis has been placed on the influence of geographic accessibility and rural governance on the well-being of rural households. Furthermore, the interplay between geographic accessibility, rural governance, and livelihood risk remains inadequately explored.

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@Proyecto_AMORE liderado por investigadores de @UABBarcelona @eicg_univalle @SaludPublicaUV @UdeA @i_quartil utilizan macrodatos para abordar problemática de acceso a la salud en Cali, que afecta principalmente a los barrios con menor accesibilidad  
👉Lee https://t.co/BuXmQZzkEH

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El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática

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@Proyecto_AMORE liderado por investigadores de @UABBarcelona @eicg_univalle @SaludPublicaUV @UdeA @i_quartil utilizan macrodatos para abordar problemática de acceso a la salud en Cali, que afecta principalmente a los barrios con menor accesibilidad  
👉Lee https://t.co/BuXmQZzkEH https://t.co/8hrghgw1Pr

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@Proyecto_AMORE liderado por investigadores de @UABBarcelona @eicg_univalle @SaludPublicaUV @UdeA @i_quartil utilizan macrodatos para abordar problemática de acceso a la salud en Cali, que afecta principalmente a los barrios con menor accesibilidad
👉Lee https://t.co/BuXmQZzkEH pic.twitter.com/8hrghgw1Pr
— Facultad de Ingeniería Univalle (@ingenieriauv) May 29, 2024

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El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática

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@Proyecto_AMORE liderado por investigadores de @UABBarcelona @eicg_univalle @SaludPublicaUV @UdeA @i_quartil utilizan macrodatos para abordar problemática de acceso a la salud en Cali, que afecta principalmente a los barrios con menor accesibilidad  
👉Lee https://t.co/BuXmQZzkEH https://t.co/8hrghgw1Pr

# data[].attributes.attachment_link.metadata.attachment_props.original_text
@Proyecto_AMORE liderado por investigadores de @UABBarcelona @eicg_univalle @SaludPublicaUV @UdeA @i_quartil utilizan macrodatos para abordar problemática de acceso a la salud en Cali, que afecta principalmente a los barrios con menor accesibilidad  
👉Lee https://t.co/BuXmQZzkEH https://t.co/8hrghgw1Pr

# data[].attributes.attachment_link.metadata.attachment_props.text
@Proyecto_AMORE liderado por investigadores de @UABBarcelona @eicg_univalle @SaludPublicaUV @UdeA @i_quartil utilizan macrodatos para abordar problemática de acceso a la salud en Cali, que afecta principalmente a los barrios con menor accesibilidad  
👉Lee https://t.co/BuXmQZzkEH

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El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática

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Blog Ingeniería Informa 2024-05-29 El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática. [The cost of driving in the city sick: data models aim to solve this problem]

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Blog Ingeniería Informa 2024-05-29 El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática. [The cost of driving in the city sick: data models aim to solve this problem]

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Español: https://ingenieriainforma.blogspot.com/2024/05/el-costo-de-transitar-la-ciudad-enfermo.html

English: https://ingenieriainforma.blogspot.com/2024/05/the-cost-of-driving-in-city-sick-data.html

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El costo de transitar la ciudad enfermo: modelos de datos le apuntan a solucionar esta problemática

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Ciro Jaramillo Molina, investigador, profesor de la Escuela de Ingeniería Civil y Geomática y codirector de la tesis doctoral que dio como r…

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11. Measuring geographic access to emergency obstetric care. Geospatial Health 2024; 19:1266

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11. Measuring geographic access to emergency obstetric care. Geospatial Health 2024; 19:1266

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Macharia, Peter M., Kerry L. M. Wong, Lenka Beňová, Jia Wang, Prestige Tatenda Makanga, Nicolas Ray, and Aduragbemi Banke-Thomas. “Measuring Geographic Access to Emergency Obstetric Care: A Comparison of Travel Time Estimates Modelled Using Google Maps Directions API and AccessMod in Three Nigerian Conurbations.” Geospatial Health 19, no. 1 (May 27, 2024). https://doi.org/10.4081/gh.2024.1266.

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Population and Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

School of Computing and Mathematical Sciences, University of Greenwich, London, United Kingdom.

Surveying and Geomatics Department, Midlands State University Faculty of the Built Environment, Gweru, Midlands, Zimbabwe; Climate, Environment and Health Department, Centre for Sexual Health and HIV/AIDS Research, Harare, Zimbabwe; Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, Zimbabwe.

GeoHealth Group, Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland.

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom; School of Human Sciences, University of Greenwich, London, United Kingdom; Maternal and Reproductive Health Research Collective, Surulere, Lagos, Nigeria.

Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API's in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

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Citation / Copyright

# data[].attributes.attachment_link.title
Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations

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Population and Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

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10. Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates. The Lancet Global Health: 2024; 19(5):e848-58 

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10. Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates. The Lancet Global Health: 2024; 19(5):e848-58 

# data[].attributes.body
Banke-Thomas, Aduragbemi, Kerry L. M. Wong, Tope Olubodun, Peter M. Macharia, Narayanan Sundararajan, Yash Shah, Gautam Prasad, et al. “Geographical Accessibility to Functional Emergency Obstetric Care Facilities in Urban Nigeria Using Closer-to-Reality Travel Time Estimates: A Population-Based Spatial Analysis.” The Lancet Global Health 12, no. 5 (May 1, 2024): e848–58. https://doi.org/10.1016/S2214-109X(24)00045-7.

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www.thelancet.com needs to review the security of your connection before proceeding.

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Dynamic Measurements of Geographical Accessibility Considering Traffic Congestion Using Open Data: A Cross-Sectional Assessment for Haemodialysis Services in Cali, Colombia. May 2024

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Dynamic Measurements of Geographical Accessibility Considering Traffic Congestion Using Open Data: A Cross-Sectional Assessment for Haemodialysis Services in Cali, Colombia. May 2024

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Dynamic measurements of geographical accessibility considering traffic congestion using open data: a cross-sectional assessment for haemodialysis services in Cali, Colombia. The Lancet Regional Health – Americas, Volume 34, 100752. https://doi.org/10.1016/j.lana.2024.100752

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Dynamic measurements of geographical accessibility considering traffic congestion using open data: a cross-sectional assessment for haemodialysis services in Cali, Colombia

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Establishing haemodialysis services in targeted areas has significant potential benefits.
By increasing accessibility, it would enhance urban health and equity.

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Abstracts of the 17th World Congress on Public Health, Rome, May 2-6,  2023

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Abstracts of the 17th World Congress on Public Health, Rome, May 2-6,  2023

# data[].attributes.body
Cuervo LG, Villamizar CJ, Jaramillo C, Martinez E. “Equitable Accessibility to Health Services Using Timely Measurements.” Population Medicine 5, no. Supplement (April 26, 2023): Pg 268. 17th World Congress on Public Health. https://doi.org/10.18332/popmed/164377

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Introduction:
The links between traffic congestion, accessibility to health services, and equity show how traffic congestion impacts accessibility among different populations. It can also be used to improve health services and land use planning. The study proposes an approach to measuring, analysing, and communicating accessibility to health services in terms of travel time, as opposed to traditional travel time analyses.

Methodology:
We studied the entire city of Cali (2.258 million), assessing accessibility by car to urgent care (tertiary care emergencies) and frequent ambulatory care (haemodialysis and radiation therapy) in July and November 2020. The study is the cocreation of an interdisciplinary multisectoral group involving authorities, academia and other civil society, service providers and beneficiaries.

Results:
Services are far from where most of the population lives, especially some of the most vulnerable. These people pay more to access essential health services. New analyses will indicate potential solutions and their potential impact.

Discussion:
The study used a person-centred design to address the needs of different stakeholders. Studies like this can expand to other services, transportation means, and locations. They allow for integrating health equity with urban planning and enable participatory evidence- informed decision-making.

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Equitable accessibility to health services using timely measurements

# data[].attributes.attachment_link.description
Introduction: The links between traffic congestion, accessibility to health services, and equity show how traffic congestion impacts accessibility among different populations. It can also be used to improve health services and land use planning. The study proposes an approach to measuring,...

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14. 17th World Congress on Public Health

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14. 17th World Congress on Public Health

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Abstract to Page 268

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Improving Accessibility to Radiotherapy Services in Cali, Colombia: Cross-Sectional Equity Analyses Using Open Data and Big Data Travel Times from 2020.

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Improving Accessibility to Radiotherapy Services in Cali, Colombia: Cross-Sectional Equity Analyses Using Open Data and Big Data Travel Times from 2020.

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Cuervo LG, Villamizar CJ, Cuervo Daniel, Zapata P, Ospina MB, Valencia SM, Polo A, Suárez Á, Bula MO, Miranda JJ, Millan GF, Cuervo DE, Owens NJ, Piquero F, Hatcher-Roberts J, Paredes, GD, Navarro MF, Minotta IL, Palta CR, Martinez-Herrera Eliana, Jaramillo C. Improving Accessibility to Radiotherapy Services in Cali, Colombia: Cross-Sectional Equity Analyses Using Open Data and Big Data Travel Times from 2020. (February 10, 2024). Available at SSRN: https://ssrn.com/abstract=4722439 or http://dx.doi.org/10.2139/ssrn.4722439

Also at https://www.researchsquare.com/article/rs-4138973/v1

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Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders - Communications Medicine

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8. Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders. Feb 2024. Communications Medicine (Nature). 2024;4 (1);1-9. 

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8. Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders. Feb 2024. Communications Medicine (Nature). 2024;4 (1);1-9. 

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Song, J., Ramírez, M.C., Okano, J.T. et al. Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders. Commun Med 4, 26 (2024). https://doi.org/10.1038/s43856-024-00441-x

https://www.nature.com/articles/s43856-024-00441-x

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Abstract

Background

Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas.

Methods

We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters.

Results

We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p  = 5.67E-05). We find nine clusters/hotspots of inpatients.

Conclusions

Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

Plain language summary

The frequencies of mental disorders vary by geographic region. Investigating such variations may lead to more equitable access to mental healthcare and to scientific discoveries that reveal specific localized factors that contribute to the causes of mental illness. This study examined the frequency of three disorders with a major impact on public health - schizophrenia, bipolar disorder, and major depressive disorder - by analyzing electronic health records from a hospital providing comprehensive mental health care for a large region in Colombia. We show that individuals receiving outpatient care mainly live relatively near the facility. Those receiving inpatient care live throughout the region, but cluster in a few scattered locations. Future research could lead to strategies for more equitable provision of mental healthcare in Colombia and identify environmental or genetic factors that affect the likelihood that someone will develop one of these disorders.

Introduction

Geographical variation in the distribution of mood and psychotic disorders has been observed, and extensively studied, in upper-income countries (UICs)1,2,3,4,5. These observed patterns may reflect inequities in the geographic accessibility of mental healthcare. Such inequities were first observed, and quantified, in a study published in 18521. This, and subsequent studies3,5,6,7 used statistical models to identify a relationship between mental healthcare utilization and distance. Specifically, these studies identified the phenomenon termed distance-decay, whereby the utilization of a healthcare facility declines in direct proportion to the distance (or travel-time8) to the facility1,2. Studies in UICs have also indicated that geographical variations in the observed frequency of particular mental health disorders and outcomes may reflect localized social, environmental, and genetic factors associated with treatment utilization or disease risk9,10,11,12,13,14,15.

The longstanding availability in UICs of population registries and other public databases has provided crucial resources that have enabled geospatial investigation of treatment for mental health disorders, as exemplified by the several studies referenced above. A lack of comparable data repositories has traditionally limited the opportunities for conducting such research in low- and middle-income countries (LMICs). Recently, however, electronic health record (EHR) databases have become available in some LMICs and enable population-level investigations of individuals who have sought treatment for these disorders. As we describe here for a middle-income country, Colombia, analyses of such databases may provide insights that could not be obtained using previously available forms of data.

The main sources of population-level data on mental health disorders in Colombia have been four National Mental Health Surveys, conducted, most recently, in 201516,17. The 2015 National Survey is an observational cross-sectional household-survey conducted in a nationally representative sample of 15,351 individuals aged seven and above, which obtained interview and self-report data. It has provided information on a wide range of topics (e.g., the prevalence of mental health and substance use disorders and on perceptions of access to care) that are important for our understanding of mental health disorders in Colombia and for efforts to improve the provision of mental healthcare.

Several critical types of information, however, are not available from the National Survey, but may be obtained by analyzing EHR databases; these include the distribution of the most severe and acute presentations of mental health disorders (the National Survey obtained little data on psychotic disorders), and the actual utilization of mental healthcare services. Additionally, as the National Survey sampled sparsely in any given location, its data cannot be used for geospatial investigation of mental health disorders. In contrast, as we demonstrate here, analyses utilizing the EHR database of the Clínica San Juan de Dios Manizales (CSJDM), provide a detailed picture of geospatial variation in treated mental illness across the entire department (state) of Caldas.

The CSJDM provides comprehensive mental healthcare for the one million inhabitants of Caldas. Located in the metropolitan municipality of Manizales, it provides mental health services to all individuals seeking care, regardless of health insurance status or other socioeconomic factors. We have identified and characterized geographic variation in the number of individuals who have sought treatment for the mood and psychotic disorders that most commonly result in psychiatric hospitalizations (bipolar disorder [BPD], schizophrenia [SCZ], and major depressive disorder [MDD]) and quantified differences, over the department, in geographic accessibility to mental healthcare, considering both individuals whose illness required inpatient care as well as those who were treated only as outpatients. We did so by combining diagnostic code data from the CSJDM EHR for the years 2005-2018, with publicly available geospatial data.

We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20%, an effect that is primarily driven by outpatients with MDD. Further, we identify nine hotspots where inpatient cases cluster. Notably, one hotspot comprises a nearly six-fold overrepresentation of inpatient BPD cases and crosses the administrative boundaries of two municipalities.

The results of the geospatial analyses that we performed can inform the design of programs aimed at reducing inequities in access to mental healthcare for mood and psychotic disorders. Additionally, they suggest the need for future studies aimed at identifying mechanisms for the geographical variations in disease incidence that we observed across the department of Caldas.

Methods

Inclusion criteria

We employed a retrospective cross-sectional design. Since 2005 the CSJDM has maintained EHRs on all outpatient visits and inpatient admissions. We utilized three forms of structured EHR data covering 2005-2018: demographic information, residential address, and diagnostic codes using the International Statistical Classification of Diseases and Related Health Problems: 10th Revision (ICD-10)18. The Institutional Review Boards (IRBs) at CSJDM and UCLA provided approval for the study, which involved uploading a version of the EHR database with patient names, dates (of birth and healthcare service), and with ID numbers removed, to a HIPAA-compliant Amazon Web Services server. Individual informed consent was not required for this study since only anonymized data were used. Gender was obtained through self-report and used as recorded in the EHR.

Using the EHRs for all adult patients 18-90 years, residing anywhere in Caldas, we focused on the 16,376 individuals who were assigned a diagnosis, at their first visit of BPD (diagnostic code: F31), SCZ (diagnostic code: F20), or MDD (diagnostic codes: F32 or F33). We used the initial diagnoses to eliminate possible bias introduced by right censoring of the diagnostic data, and did not use the longitudinal, repeated measures aspect of the EHR data in these analyses. In prior work, we have shown that diagnostic codes recorded in this EHR are highly accurate compared to diagnoses obtained by manual chart review by a psychiatrist19. We stratified analyses based on hospitalization status.

Geocoding workflow

We identified the subset of diagnosed individuals with available residential addresses ( N = 16,308). We assigned longitude and latitude coordinates to each patient's address using the Spanish language setting of OpenCage's geocoder through the R package opencage20. As input, OpenCage takes an address as free text. Prior to geocoding, we formatted addresses according to the following procedures. Most addresses ( N = 11,745) fit within a grid pattern that is typical throughout Colombia, comprised of parallel streets called "calles" and perpendicular streets called "carreras". We formatted addresses of this type according to the pattern "Calle [X], Carrera [Y], [Municipality], Caldas", where X is the street number for the "calle", and Y is the street number for the "carrera". The remaining addresses ( N  = 4563) were situated in small rural areas called "veredas" which have few roads. For these addresses, we took the name of the vereda or other address entry and appended the name of the municipality and department.

OpenCage provides a list of potential longitude and latitude coordinates with corresponding accuracy confidence scores. If multiple potential matches were returned, the most relevant was selected, based on the OpenStreetMap data21 relevance criteria for Colombian addresses. We excluded addresses with OpenCage confidence scores lower than 6. Using this threshold for confidence scores ensured that all addresses included in the study were within a 7.5 km bounding box measured diagonally and yielded geographic coordinates for 16,295 of the 16,308 patient addresses; all analyses reported here focus on these 16,295 patients.

Table 1 provides a summary of the dataset stratified by diagnosis (BPD, SCZ, or MDD), gender, and hospitalization status. All individuals had the location of their residence georeferenced to a 7.5 × 7.5 km bounding box; for 71% of them we could define the location of their residence to a 0.5 × 0.5 km bounding box.

Table 1 Summary of the dataset.

Calculating the incidence rate of mood and psychotic disorders

We first calculated the annual incidence per 100,000 individuals of newly diagnosed patients (both in- and outpatients) for each of the 27 municipalities within the department. For each year between 2005-2018, we divided the number of newly diagnosed patients in that specific year in each municipality by the estimated number of residents of that municipality in that year. The number of residents in a municipality was estimated using the WordPop database22, which includes gridded spatial demographic estimates. For most municipalities, the annual incidence of newly diagnosed cases was very low; we therefore conducted all our analyses based on the 14-year aggregate of the annual incidence. We subsequently referred to this aggregated value as the incidence rate and report this rate per 100,000 individuals.

We also calculated the incidence rate for each diagnosis, separately for in- and outpatients, at a more fine-grained geographic level unconstrained by municipality boundaries. To make these calculations, we imposed a grid (with a cell size of 5 km by 5 km) on the map of Caldas and counted the number of residents in each cell. Following the definition of the incidence rate at the level of the municipality, the incidence rate at the cellular level was defined as the number of newly diagnosed cases in the cell (between 2005 and 2018), divided by the total number of residents in that cell in the year 2018, and report this rate per 100,000. To visualize incidence rates, we generated maps using the R package ggplot2 v3.1.1 and shapefiles from the National Statistics Department of Colombia (DANE)23.

Calculating a friction-surface, and a geographic accessibility, map

To quantify the geographic accessibility of mental healthcare for the population of Caldas, and to identify a relationship between the distribution of cases and the travel-time to the CSJDM, we constructed a friction surface map and subsequently a geographic accessibility map. We first plotted maps of Caldas using shapefiles from the National Statistics Department of Colombia (DANE)23. We then used the software package AccessMod5 v5.6.024 to construct the friction surface map. This map quantifies the minimum amount of time that it takes to traverse one square kilometer, assuming driving time using a motorized vehicle on roads and walking time if no roads exist, while accounting for elevation changes and ground cover. To construct the map we combined geospatial data on topography25, land cover26, water bodies27 and road networks23 in the following stages: (1) We aggregated the topographic data to match the resolution (100 meters by 100 meters) of the WorldPop data. (2) We merged the land cover data, with data on roads and inland (permanent) water bodies, and with the aggregated topographic data. (3) We calculated travel-times using AccessMod, basing the estimated travel-time for each pixel on the quickest mode of transportation; Supplementary Table 1 lists the average speeds used to compute travel time, stratified by mode of transportation (see also Supplementary Material Section C in ref. 28). We then generated a geographic accessibility map by plotting the geographic location of the CSJDM on the friction surface map and computing the travel-time to the CSJDM from the centroid of each one square kilometer grid cell. We employed the option for anisotropic analysis, which uses the elevation data to modify travel speeds for walking.

The incidence rate of mood and psychotic disorder in relation to the travel-time to the CSJDM

To make these calculations, we modeled the expected number of cases by travel-time in hours, accounting for the number of individuals in each grid cell of the geographic accessibility map. We used a zero-inflated negative binomial regression from the R package pscl v1.5.5, a procedure that accounts for both the large number of grid units with zero cases and over-dispersion of cases. Normal 95% confidence intervals and two-sided p-values were calculated for each relative risk (RR) based on the distribution generated from 1200 bootstraps using the R package boot v1.3-20. We performed this computation for inpatients and outpatients separately, considering: (i) all three diagnoses together (i.e., two comparisons), and (ii) the three diagnoses separately (i.e., six comparisons). We applied Bonferroni thresholds to correct for eight independent tests, with a significance threshold of 0.00625 (0.05/8). To evaluate possible heterogeneity in the observed relative risk by gender, we conducted sensitivity analyses, including in the model gender, and a gender by travel-time interaction.

Identification of hotspots for residence of inpatients

To identify possible hotspots of residences of inpatients, we used the gridded map of Caldas showing the incidence rate of cases (for BPD, SCZ, and MDD combined, and separately). We then applied Kulldorff's Spatial Scan Statistic29, implemented in the software package SaTScan v9.630. SaTScan uses a circular window to scan a gridded surface. The window varies in size from one that encompasses a single grid cell to one that contains a predefined maximum population size. Use of a varying window size enables identification of hotspots unconstrained by municipality administrative boundaries. We used a cell size of 5 km by 5 km and set the maximum window size to encompass 25% of Caldas' population. SaTScan identifies hotspots by maximizing a likelihood ratio function, and assesses their statistical significance using a one-sided Monte Carlo hypothesis procedure29. To account for multiple testing for four different tests, we applied a Bonferroni correction with a significance threshold of 0.0125 (0.05/4). As a sensitivity analysis, we also performed separate hotspot analyses based on gender. We selected 25% as the maximum hotspot size as it is frequently considered the largest window with potential future utility for policymaking31. To evaluate the effect of this threshold, we conducted a sensitivity analysis exploring, for each diagnosis, how the identified hotspots changed in size/location when varying the maximum allowable size of the reported cluster32.

We also employed SaTScan to quantify and visualize the uncertainty in defining the borders of hotspots, using Oliveira's F function33. This function assigns an intensity value, ranging from zero to one, to each grid cell surrounding a hotspot, reflecting the probability that the grid cell belongs to the hotspot. To visualize hotspots and the uncertainty in their borders, we used QGIS v3.1034. The Oliveira's F values were colored in categories split by Jenk's natural breaks35,36, a setting in QGIS that enables better visualization of potential borders.

Results

Inequities in the geographic accessibility of mental healthcare in Caldas

The Central Cordillera of the Andes Mountains divides Caldas into Eastern and Western sections, with the Western side containing the capital city, Manizales, and most towns (Fig. 1a, b). This division is evident in the geographic accessibility map (Fig. 1c), which shows the locations for which the CSJDM is the most inaccessible (travel-time (driving) of greater than 5 h, orange-red colored areas); virtually all these sites lie east of the Central Cordillera. While these relatively inaccessible locations, some of which are more than 10 h in travel-time from CSJDM, are geographically sizable, they include a relatively small proportion of the department's population. Most of the population can access the hospital with less than 1 h of travel, while 90% can access it within approximately 4 h (Fig. 1d, see also Supplementary Table 2).

The maps show main roads and towns in Caldas23 ( a), population size estimates for each 5 km x 5 km grid as specified in the WorldPop database22 ( b), travel-time to Clínica San Juan de Dios Manizales (CSJDM, in hours) from anywhere in the department ( c) and the percentage of the population of Caldas that can reach the CSJDM as a function of travel-time ( d).

Geographic variation in incidence rates

The pattern of geographic variation in incidence rates, differs substantially between inpatients and outpatients (Figs. 2 and 3). For outpatients, by far the highest incidence rate is in the municipality (Manizales) which includes the CSJDM (Fig. 3). Notably, we found that the observed pattern for outpatients reflects strong distance-decay (as measured here by travel-times8); for every 1-h increase in travel-time to the CSJDM, the number of expected outpatients decreases by 20% (RR = 0.80, 95% confidence interval (0.71, 0.89), p = 5.67E-05, zero-inflated negative binomial model, Supplementary Table 3).

a The incidence rate for each municipality for patients whose first visit to the hospital was as an inpatient (per 100,000). The blue dot on the maps indicates the location of the Clínica San Juan de Dios Manizales (CSJDM). b Hotspots for all inpatients with bipolar disorder (BPD), major depressive disorder (MDD), or schizophrenia (SCZ). The statistically significant locations are indicated by circles: circle size indicates cluster size and color codes correspond to different values of relative risk. The grid units are colored by Oliveira's F values.

a The incidence rate for each municipality of inpatients stratified by diagnosis of bipolar disorder (BPD), major depressive disorder (MDD), and schizophrenia (SCZ) (per 100,000). The blue dot on the maps indicates the location of the Clínica San Juan de Dios Manizales (CSJDM). b Hotspots stratified by diagnosis. The statistically significant locations are indicated by circles: circle size indicates cluster size and color codes correspond to different values of relative risk. The grid units are colored by Oliveira's F values.

For inpatients, by contrast, we found no significant distance-decay, either for the incidence rate overall (i.e., for BPD, SCZ, and MDD combined) or for that of any of the three diagnoses considered separately (Supplementary Table 3. The overall incidence rate (Fig. 2a) is highest in a municipality, Aranzazu, located ~55 km from the CSJDM. This pattern reflects the high concentration of inpatient mood disorders (BPD and MDD) cases from this municipality compared to the department, overall (Fig. 3a): the incidence rate of inpatients with BPD is nearly fourfold higher (862/100,000 vs. 219/100,000), and the incidence rate of inpatients with MDD is more than 1.5 times higher (364/100,000 vs. 235/100,000). For MDD this incidence rate is nearly as high as that in Manizales (387/100,000). For SCZ (Fig. 3a) the incidence rate is highest in three neighboring municipalities located ~111 km from the hospital, where it is about twofold higher than in the department, overall (93/100,000 vs. 45/100,000). We found no significant interactions between travel-time and gender.

Identification of hotspots for inpatients with mood and psychotic disorders

We identified nine distinct hotspots: eight hotspots were identified for the broad category of all inpatients with either BPD, SCZ, or MDD (Clusters 1-8, Table 2, and Figs. 2b, 3b), while one hotspot was unique to inpatients with BPD (Cluster 9). Seven of the nine hotspots were also found in our diagnosis-specific analyses (Clusters 1-6, 9, Table 2, and Figs 2b, 3b); i.e., they were hotspots for inpatients with either BPD, SCZ, or MDD.

Table 2 Hotspots for residences of inpatients.

In our diagnosis-specific analyses, we identified seven hotspots in total (Clusters 1-6 and Cluster 9, Table 2, Fig. 3b), all of which show a significant overrepresentation of inpatient BPD cases, and three of which (Clusters 1, 2, and 5) cross municipality boundaries. Cluster 2 is also a hotspot for MDD inpatients, while Clusters 1, 3, and 4 are hotspots for inpatients across all three diagnoses. The hotspot that includes the grid cells around the hospital (Cluster 1) displays the highest likelihood ratio for each of the three diagnoses, as well as for overall cases of MDD inpatients. This observation reflects the fact that many more patients reside in this metropolitan area than in any other region of the department; in particular, this hotspot is home to 1476 inpatients with MDD (60% of MDD inpatients in the department) and shows the greatest overrepresentation of this diagnosis (RR = 5.47, p = 1.00E-17). Other hotspots, however, show patterns of overrepresentation that appear to differentiate them by diagnosis. Cluster 9 (Fig. 3b) is specific to BPD and is not identified in the cross-diagnostic analyses.

Cluster 2 denotes the hotspot comprising the most extreme overrepresentation of inpatient BPD cases (RR = 5.83, p = 1.00E-17, Table 2 and Fig. 3b). It is the largest of the BPD hotspots in terms of geographical extent (covering four grid units: ~20 square kilometers) and crosses the administrative boundaries of two municipalities (Aranzazu, the municipality with the highest incidence rate of BPD [Fig. 3a/b] and Filadelfia); it is located about 1.5 h driving time from the hospital. This hotspot displays an extremely high average annual incidence of BPD hospitalizations (862/100,000) and also shows an overrepresentation of MDD but not of SCZ hospitalizations; it may therefore constitute a hotspot specific for inpatients with mood disorder diagnoses. Cluster 3, by contrast, appears to be a hotspot for inpatients overall, as evidenced by the overrepresentation of all three diagnoses, despite being located 5 h from the hospital. While the total number of cases of SCZ from this location is relatively small ( N = 85 inpatients, N=13 with SCZ), the nearly nine-fold overrepresentation of SCZ cases is particularly striking (RR = 8.98, p = 2.11E-05, Table 2 and Fig. 3b).

Finally, we conducted a sensitivity analysis to explore the impact on the identified hotspots of varying the maximum allowable size of the reported cluster. The hotspots for MDD and SCZ are insensitive to the maximum scanning window size parameter as we observed nearly identical hotspots, irrespective of the parameters used (Supplementary Fig. 1). For BPD, when we increased the maximum reported cluster size above 40% of the population, Cluster 2 disappears. This is a consequence of the process by which SaTScan pre-computes the most likely hotspot centered at each grid, and then iteratively selects non-overlapping hotspots with the highest likelihood ratio; at settings above 40% the hotspot centered at Cluster 2 includes the area of Cluster 1. Since Cluster 1 has a higher likelihood ratio, Cluster 2 cannot be selected. The overall consistency of the hotspot patterns in these sensitivity analyses indicates the robustness of our findings. Our findings are also consistent when analyzed separately by gender; no new hotspots emerged, and we identify the same clusters described in Table 2.

Discussion

Increasing the equitable availability and utilization of mental healthcare services is a major international development objective. Reducing inequities in the geographic accessibility of such services is critical to achieving this objective37,38,39,40,41,42 and, in turn, depends on detailed knowledge regarding variations in the geospatial distribution of mental health disorders and elucidation of the factors responsible for such variations. Within Latin America, geospatial methodology has been used to quantify geographic accessibility to different forms of healthcare39,43, but not to mental healthcare.

Our results show the inequitable geographic accessibility of specialty mental healthcare services in the department of Caldas, Colombia; while most of its population can access services at the CSJDM with a travel time of <1 h drive, a substantial proportion resides at >4 h drive distant from the facility. Our geospatial analyses of CSJDM EHR data reveal an important impact of this inequity, as utilization of outpatient care for mood and psychotic disorders demonstrates significant distance-decay in relation to the CSJDM. This observation aligns with findings from studies in UICs1,2,3,4,5, which have shown that most people will travel only short distances to obtain such care. The Government of Colombia recently enacted a strategy of increasing the geographical accessibility of mental healthcare by embedding it within primary care clinics distributed throughout the country44,45. Future geospatial investigations, including construction of geographic accessibility maps for primary care facilities and analyses of mental health treatment data from the EHRs of these facilities, could play a valuable role in evaluating the impact of this strategy.

Factors other than geographic accessibility, however, appear to be the main determinants of inpatient treatment for mood and psychotic disorders, which, as has also been found in UICs46, does not demonstrate significant distance-decay. Instead, utilization of such intensive treatment clusters significantly around nine hotspots, widely dispersed across the department of Caldas. This finding indicates that locally important sociodemographic, environmental, or genetic factors influence geospatial variation in risk, treatment-seeking behavior, or treatment accessibility for clinically severe presentations of these disorders.

Elucidating the above factors is a priority for future research aimed at reducing inequities in mental healthcare in this region, and an important early step will be to explain the approximately five-fold overrepresentation of BPD relative to SCZ in the CSJDM EHR database. Such an extreme excess of BPD among individuals hospitalized for mental disorders, has not, to our knowledge, been observed in studies of inpatients in other countries, where BPD and SCZ usually occur at roughly equal frequencies47,48,49. This excess is particularly striking in the observed BPD hotspots; for example, the hotspot at Cluster 2 displays an incidence for this disorder which may be the highest yet reported worldwide48,49,50. Systematic investigation of each of the hotspots, incorporating several additional types of data, may identify specific factors, contributing to these patterns.

Levels of socioeconomic inequality in Latin America, overall, are among the highest worldwide51, and have been associated with extreme disparities in utilization of mental health services52. Recent studies in Brazil and Peru have used geospatial analyses to document the relationship between inequalities in factors such as education levels, income, employment, and migration and geographic variation in the frequency of mental illness parameters (suicide and depressive symptomatology, respectively)53,54. By incorporating information on sociodemographic factors that is available at a detailed level throughout Colombia (e.g., each residence has an assigned socioeconomic status indicator, from 1 [lowest] to 6 [highest], which determines utility rates39), future studies, using sources such as Colombia's National Department of Statistics census microdata23, could provide a more comprehensive equity perspective by integrating disaggregated georeferenced population sociodemographic open data.

Among the environmental factors that may contribute to geographic variation in risks of mental disorders, exposure to violent conflict, and forced displacement associated with such exposure are particularly relevant in Colombia; its decades long history of internal armed conflict led to the forced displacement of nearly 6 million people between the 1960s and the 2000s, one of the highest rates of any country worldwide55,56. Previous studies in Colombia have linked exposure to violent conflict to poor health outcomes generally56, and increased rates of mental health disorders, specifically55,57. Georeferenced resources, such as those aggregated by the government58, can be used to test this hypothesis.

An additional possibility, which can be investigated, is that genetic variants have contributed to the generation of hotspots for mood and psychotic disorders and to the overall patterns of geographic variation in their frequencies that we observe in Caldas. The demographic history of its population suggests this possibility. The predominant population of Caldas ("The Paisa") is an example of a genetic isolate that has expanded rapidly subsequent to a series of migration-related bottlenecks59,60, from population founders of indigenous, African, and European ancestry61. Such rapid expansion from a bottleneck creates the opportunity for even deleterious variants to attain sizable frequencies - and thereby exert a substantial effect on disease risk - in a highly localized manner62. Because Caldas is part of a larger region in which the Paisa predominate ("The Paisa region"), extension of geospatial investigations to the EHR databases of healthcare facilities in neighboring departments provides an opportunity to determine the potential impact of such variants through well-powered genetic studies.

Finally, it is important to consider the possible impact of factors that may influence treatment-seeking behavior for mood and psychotic disorders but for which relevant data are currently unavailable and may be difficult to obtain on a sufficiently large scale to permit their use in detailed geospatial analyses. For example, several studies have suggested that stigma and the fear of discrimination may be important barriers to utilization of mental healthcare, in Colombia, but have focused on outpatient treatment63, excluded individuals living with psychotic disorders17 or were based on qualitative analyses of very small samples64.

We discuss here several limitations of this study. First, for simplicity sake our geospatial analyses have focused only on patients' clinical presentations and diagnoses at their first visit, while a major strength of the EHR database is that it enables longitudinal investigations of variations in patient trajectories; many patients experience changes in the severity of their illness (e.g., their initial visit is as an outpatient, but they subsequently have at least one inpatient admission) or in diagnosis (e.g., they initially are diagnosed with MDD but subsequently experience manic or hypomanic episodes which leads to a change in diagnosis to BPD). Future analyses that incorporate information on such changes may reveal geographic variations that differ from those reported here. Second, as the size of the inpatient population at CSJDM is smaller than that of the outpatient population, it is possible that the lack of significant distance-decay in the former group reflects a lack of power to detect modest effects; future studies that include additional catchment areas may find some degree of distance-decay for these patients. Third, our estimates of geographic accessibility of treatment are based on average travel speeds using different surfaces, and do not account for potential seasonal or daytime variations. As more granular information becomes available (e.g., through dynamic road use databases), it may be possible to incorporate such variations as has been accomplished in Colombia for geospatial investigations of tertiary emergency healthcare39. Other directions for future work include the development of more sophisticated geostatistical models that could have policy relevance. For example, SaTScan maximizes the likelihood ratio and selects the most likely hotspot sequentially rather than jointly, which may not always lead to the selection of the most precise global pattern of hospots.

It is increasingly recognized that population-level investigations in low- and middle-income countries must be a priority for mental health research - both to reduce health inequities and to increase opportunities for transformative scientific discoveries65. The work reported here extends geospatial investigation of mood and psychotic disorders to a middle-income country that has recently experienced extensive and traumatic social disruptions. Despite such disruptions, Colombia has established infrastructure, such as the CSJDM EHR database, that constitute extraordinary resources for population mental health studies. Future studies can make further use of these resources to contribute to our understanding of sociodemographic, environmental and genetic risk factors of mental illness.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Acknowledgements

This work was supported by R01MH113078 (C.E.B., C.L.J. and N.B.F.), R01MH123157 (L.M.O.L., C.L.J. and N.B.F.), R00MH116115 (L.M.O.L.) and R56AI152759 (S.B. and J.T.O.). We thank Anjene Musick for valuable feedback on the manuscript.

Author information

Author notes

These authors jointly supervised this work: Loes M. Olde Loohuis; Sally Blower.

Authors and Affiliations

1. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

Janet Song, Susan K. Service, Juan de la Hoz, Ana M. Díaz-Zuluaga, Terri Teshiba, Carrie E. Bearden, Nelson B. Freimer & Loes M. Olde Loohuis

2. Department of Mental Health and Human Behavior, University of Caldas, Manizales, Colombia

Mauricio Castaño Ramírez, Cristian Gallago & Alexandra Valderrama Sánchez

3. Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

Justin T. Okano & Sally Blower

4. Department of Psychiatry, University of Antioquía, Medellín, Colombia

Cristian Vargas Upegui, Alejandro Arias & Carlos Lopez Jaramillo

5. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

Chiara Sabatti

6. Department of Psychiatry, University of Pennsylvania School of Medicine and the Penn-CHOP Lifespan Brain Institute, Philadelphia, PA, USA

Ruben C. Gur

7. Department of Psychiatry, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA

Javier I. Escobar

8. Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA

Victor I. Reus

Contributions

Conceptualization: C.E.B., N.B.F., L.M.O.L., and S.B.; Methodology: J.S., J.T.O., S.K.S., C.E.B., N.F., L.M.O.L., and S.B.; Software: J.S., J.d.l.H., C.G., and A.A.; Formal analysis: J.S. and S.K.S.; Investigation: M.C.R., J.T.O., A.M.D.Z., C.V.U., A.A., and T.T.; Data curation: J.S., J.d.l.H., C.G., and A.A.; Writing Original Draft: J.S., N.B.F., L.M.O.L., and S.B.; Writing - review & editing: J.S., M.C.R., J.T.O., S.K.S., J.d.l.H., A.M.D.Z., C.V.U., C.G., A.A., A.V.S., T.T., C.S., R.C.G., C.E.B., J.I.E., V.I.R., C.L.J., N.B.F., L.M.O.L., and S.B.; Visualization: J.S., J.T.O., and S.B.; Supervision: C.L.J., N.B.F., L.M.O.L., and S.B.; Project Administration: C.E.B., C.L.J., N.B.F., L.M.O.L., and S.B.; Funding acquisition: C.E.B., C.L.J., L.M.O.L., and S.B.

Corresponding author

Correspondence to Loes M. Olde Loohuis.

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The authors declare no competing interests.

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Communications Medicine thanks Luis Gabriel Cuervo, Winfred Dotse-Gborgbortsi, Angela Suárez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Song, J., Ramírez, M.C., Okano, J.T. et al. Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders. Commun Med 4, 26 (2024). https://doi.org/10.1038/s43856-024-00441-x
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DOI: https://doi.org/10.1038/s43856-024-00441-x

# data[].attributes.attachment_link.title
Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders - Communications Medicine

# data[].attributes.attachment_link.description
Song et al. conduct a geospatial analysis of variations in incidence of mood and psychotic disorders and access to care in Caldas, Colombia. They show that many patients requiring only outpatient care live too far away to access it and identify hotspots of more severely ill patients, highlighting the need for targeted interventions.

# data[].attributes.body
Miao, Y., Dai, T., & Song, J. (2023). Assessing the effects of the large‐scale road construction on the ethnic disparities of accessibility in Tibet from 2010 to 2020. Growth and Change, 54(3), 754-770. https://doi.org/10.1111/grow.12668

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6. Developing policy-ready digital dashboards of geospatial access to emergency obstetric care. Dic 2023 - Health and Technology. 2024;14(1):69-80

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6. Developing policy-ready digital dashboards of geospatial access to emergency obstetric care. Dic 2023 - Health and Technology. 2024;14(1):69-80

# data[].attributes.body
Wang, J., Wong, K.L.M., Olubodun, T. et al. Developing policy-ready digital dashboards of geospatial access to emergency obstetric care: a survey of policymakers and researchers in sub-Saharan Africa. Health Technol. 14, 69–80 (2024). https://doi.org/10.1007/s12553-023-00793-9

# data[].attributes.attachment_link.readable_text
Developing policy-ready digital dashboards of geospatial access to emergency obstetric care: a survey of policymakers and researchers in sub-Saharan Africa

Jia Wang 1 na1,

Kerry L. M. Wong 2 na1,

Tope Olubodun 3,

Uchenna Gwacham-Anisiobi 4,

Olakunmi Ogunyemi 5,

Bosede B. Afolabi 6,7,

Peter M. Macharia 8,9,

Prestige Tatenda Makanga 10,

Ibukun-Oluwa Omolade Abejirinde 11,12,

Lenka Beňová 13 &

...

Aduragbemi Banke-Thomas orcid.org/0000-0002-4449-0131 2,9,14

Show authors Cite this article

Abstract

Background

Dashboards are increasingly being used in sub-Saharan Africa (SSA) to support health policymaking and governance. However, their use has been mostly limited to routine care, not emergency services like emergency obstetric care (EmOC). To ensure a fit-for-purpose dashboard, we conducted an online survey with policymakers and researchers to understand key considerations needed for developing a policy-ready dashboard of geospatial access to EmOC in SSA.

Methods

Questionnaires targeting both stakeholder groups were pre-tested and disseminated in English, French, and Portuguese across SSA. We collected data on participants' awareness of concern areas for geographic accessibility of EmOC and existing technological resources used for planning of EmOC services, the dynamic dashboard features preferences, and the dashboard's potential to tackle lack of geographic access to EmOC. Questions were asked as multiple-choice, Likert-scale, or open-ended. Descriptive statistics were used to summarise findings using frequencies or proportions. Free-text responses were recoded into themes where applicable.

Results

Among the 206 participants (88 policymakers and 118 researchers), 90% reported that rural areas and 23% that urban areas in their countries were affected by issues of geographic accessibility to EmOC. Five percent of policymakers and 38% of researchers were aware of the use of maps of EmOC facilities to guide planning of EmOC facility location. Regarding dashboard design, most visual components such as location of EmOC facilities had almost universal desirability; however, there were some exceptions. Nearly 70% of policymakers considered the socio-economic status of the population and households relevant to the dashboard. The desirability for a heatmap showing travel time to care was lower among policymakers (53%) than researchers (72%). Nearly 90% of participants considered three to four data updates per year or less frequent updates adequate for the dashboard. The potential usability of a dynamic dashboard was high amongst both policymakers (60%) and researchers (82%).

Conclusion

This study provides key considerations for developing a policy-ready dashboard for EmOC geographical accessibility in SSA. Efforts should now be targeted at establishing robust estimation of geographical accessibility metrics, integrated with existing health system data, and developing and maintaining the dashboard with up-to-date data to maximise impact in these settings.

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Article Open access

Dohyeong Kim, Malabika Sarker & Priyanka Vyas

Article Open access

Joshua Wells, Robert Grant, ... Reem Kayyali

1 Introduction

Globally, about 295,000 women lose their lives due to complications of pregnancy and childbirth annually, with 66% of these deaths occurring in sub-Saharan Africa (SSA) [ 1]. Similarly, there are about two million stillbirths annually, and 58% of these deaths occur in SSA [ 2]. These deaths are mostly associated with complications of pregnancy and childbirth, such as bleeding, high blood pressure, infection, and abortion, which can be worsened by events happening between the onset of the complication and the time when the woman receives care [ 3]. However, it has long been established that delays in travel to access emergency obstetric care (EmOC) can lead to deaths of mothers and their unborn child(ren). Indeed, access to EmOC reduces maternal deaths by 15-50% and stillbirths by 45-75% [ 4]. EmOC is a package of nine clinical and surgical evidence-based interventions including parenteral antibiotics, uterotonic drugs, parenteral anticonvulsants, manual removal of placenta, removal of retained products of conception, assisted vaginal delivery, neonatal resuscitation, blood transfusion and caesarean section [ 5]. Before women needing EmOC can access care, they need to travel from the community or be referred from other facilities to health facilities with EmOC capacity. Delays in reaching such health facilities have significant negative impacts on pregnancy outcomes of mothers and newborns [ 6, 7, 8, 9].

For policymakers and other pertinent stakeholders of health systems, addressing access barriers which pregnant women with obstetric emergencies face is an ethical obligation [ 10] and very much in line with achieving universal health coverage (UHC), which is a key component of the Sustainable Development Goal (SDG) 3 to reduce maternal mortality to 70 per 100,000 live births globally by 2030 [ 11, 12]. To support the EmOC service planning function of policymakers, including deciding location of new facilities, upgrading the existing facilities, or siting of ambulance services in SSA, researchers have mainly used modelled approaches to assess geographical accessibility [ 13]. However, modelled approaches, which are typically static and report one moment in time (depending on available data, this time might not be recent), do not reflect the dynamic reality of travel which may be affected by traffic, terrain difficulties, and weather conditions [ 14, 15]. For example, in Dhaka, Bangladesh, ignoring variability in traffic conditions results in as much as a three-fold overestimation of geographic accessibility to emergency healthcare [ 16]. In another study in Lagos, Nigeria, mean errors of > 45 min were estimated when modelled travel time was compared to actual travel time [ 14, 15].

Policymakers require closer-to-reality and up-to-date travel time estimates to be able to comprehensively assess geographic accessibility within their jurisdiction, which will translate to allowing them to effectively address the issue of EmOC access, as has been shown in Lagos, Nigeria [ 17]. To address this need, a new effort to develop an innovative policy-ready dynamic dashboard using closer-to-reality travel time estimates to EmOC in SSA is being championed as part of the 'On Tackling In-transit delays for Mothers in Emergency' (OnTIME) project [ 18]. Dashboards such as this have been used mostly in high-income countries and increasingly in low- and middle-income countries (LMICs) to support health policymaking. However, their use has been mostly limited to geographical accessibility to routine care, not emergency services like EmOC [ 19, 20, 21, 22]. During the 2019 Coronavirus Disease pandemic, policy dashboards were put forward and ultimately aided the pandemic response of governments and international stakeholders [ 23, 24]. Dashboards allow a "visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance" [ 25]. If available, such visual display would make recognising areas of poor EmOC geographical accessibility easier for policymakers and facilitate evidence-based policy response [ 17, 26]. To guide the development of such fit-for-purpose dashboard, guarantee its uptake, and promote ownership, it is essential to gather user needs at the development stage from individuals who will use the dashboard [ 27, 28, 29] As such, our objective in this study was to survey policymakers and researchers on considerations needed for developing a policy-ready dashboard of geospatial access to EmOC in SSA.

2 Methods

2.1 Study design

We conducted an online survey of policymakers and researchers involved in health service planning, including for EmOC services in SSA.

2.2 Survey questionnaire

The survey questionnaire was initially developed in English and translated into French and Portuguese by health professionals who are native speakers to capture at least one official language for every country in SSA. Two versions of the survey were designed, one for policymakers and the other for researchers, and were made available online via Mentimeter (Mentimeter, Stockholm, Sweden). Separate versions were deemed necessary to ensure that the tool was pitched at the level of understanding of technical jargon matching each stakeholder group, with more simplified language used in the tool targeting policymakers. The online questionnaire was piloted with target participants, resulting in minor language changes being made to the tool before it was socialised to prospective participants.

The survey started with a demographic section that collected information about the participants' profession and the sub-Saharan African country in which they primarily worked as policymakers or conducted research on as researchers. The questionnaire consisted of three substantive sections collecting data on participants' awareness of areas of concern of EmOC geographic accessibility and existing tools, dynamic online dashboard features preferences among participants and perceptions of the potential of dashboard applicability to tackle geographic access to EmOC. Across the three substantive sections, questions were structured in different formats, including multiple-choice, Likert-scale, and open-ended text. In the first section, we asked the participants to describe the areas in their country of work that were affected by poor geographic access to EmOC services and existing technological resources used for planning of EmOC services. The second section pertained to the dashboard design features that they deemed relevant and desirable. For this section, we enquired about preference for a pre-set list of dashboard visual components including a base geomap showing EmOC service points, a display of various travel barriers, a heatmap showing travel time, the socio-economic status of the population and households, carparks at EmOC facilities, and public transport access points. These visual components were based on insight garnered from previous research [ 17]. We also asked participants for their opinion on the spatial granularity at which EmOC accessibility indicators and metrics are to be presented for maximum relevance and what modelling scenarios in the dashboard would help support policymaking and improve geographic access to EmOC services. Lastly, we collected information on the frequency of updates. The final section of the survey tool focused on the participant's perception of the future potential and usability of a dynamic dashboard in tackling geographic access to EmOC service. A copy of the questionnaire for the policymakers and researchers in all languages used for the survey is available in Additional file 1.

2.3 Recruitment of survey participants and data collection

The inclusion criteria for the survey were: policymakers currently working on EmOC service planning in SSA, and researchers interested in EmOC service planning in SSA, as established through their recent publication history and responses to screening questions while filling the online questionnaire. Using a quota sampling to ensure all countries in the region were represented [ 30], we targeted between two to four participants per country in SSA (48 countries in the sub-region, totalling 92-184 participants). Researchers were recruited through contact email addresses provided in published research on accessing critical maternal health services in SSA. Policymakers were recruited through targeted emails to national Ministries of Health from the listservs of the African Centres for Disease Control and Prevention and the West African Network of Emerging Leaders in Health Policy and Systems Research. We also distributed the survey through colleagues at the WHO's Partnership for Maternal, Newborn and Child Health. Data collection for the online survey commenced on 1 st August 2022 and continued until 30 th November 2022. The emails inviting participants to the study include a link to the informed consent sheet (containing the participant information sheet and the certificate of consent), which participants must complete before accessing the online survey. The survey took approximately 20 min to complete.

2.4 Data analysis

Responses were exported from the online data collection portal as Microsoft Excel sheets. Translation of survey responses completed in French and Portuguese to English were carried out before data analysis. Subsequently, data cleaning was performed and the extent of missingness reported. Corresponding to the three substantive sections of the questionnaire, we conducted a descriptive analysis to summarise the data by stakeholder group (overall, policymakers, researchers); multiple-choice and Likert-scale questions are reported as frequencies and percentages and are visually presented as maps or bar charts. We compared the proportion of policymakers who gave certain responses with those of researchers. Each response in the participants' free text responses were individually reviewed, coded as one of the original categories provided, where applicable, or recoded as a new category, following deliberations by co-authors who led the data analysis.

3 Results

3.1 Participants' demographics

A total of 206 participants (118 researchers and 88 policymakers) took part in the survey. The commonest age group was 31 to 40 years old (42%). Of the 206 participants, 188 reported the SSA countries where they worked including 32 respondents (15%) who worked in Nigeria, 25 (13%) in Cameroon, and another 14 (7%) each in Kenya or Uganda. Altogether, 34 countries were represented on the survey (Fig. 1). Twenty participants did not specify their country of work.

3.2 Participants' awareness of areas affected by poor EmOC geographic accessibility and related technological tools to improve access

Of the 153 participants who responded to this question, over 90% described areas affected by EmOC access issues as being characterised by rurality, including other similar characteristics, such as being hard-to-reach or sparsely populated. In comparison, 23% of participants described areas affected by EmOC access issues as typified by urbanicity, including peri-urban areas and urban peripheries. A higher percentage of policymakers (29%) had concern for urban areas compared to researchers (19%) (Fig. 2).

Disaggregated by country, respondents from the majority of represented countries in SSA only or mostly mentioned rural areas as areas affected by poor geographic access to EmOC. However, a relatively larger share of respondents working in the Democratic Republic of Congo (DR Congo) (five of seven respondents), Ghana (three of five respondents), and Nigeria (nine of 24 respondents) expressed concern about geographic access to EmOC in urban areas alone or both urban and rural areas. On the other hand, in Liberia, Ethiopia, Tanzania, for instance, no participants (at least five in each country) expressed concerns for geographic access to EmOC in urban areas (Fig. 3).

Forty-three percent of participants reported knowing at least one technological tool being used to deal with poor geographic access to EmOC in the countries where they worked - 38% of policymakers and 50% of researchers. Further, when asked about whether they were aware of the use of static digital maps of EmOC facilities to help guide service planning, 21% of the participants responded positively (5% among policymakers and 38% among researchers).

3.3 Dashboard design and functionality preferences

Regarding visual components desired by participants, the most highly regarded visual components were a map showing EmOC facilities' locations (80% answered "somewhat relevant" and "absolutely essential") and the display of various travel barriers (75%). When disaggregated by respondent type, there was consensus on desirability of several visual components of interest for policymakers and researchers, including a map of EmOC facilities (policymakers: 76%; researchers: 83%), travel barriers (e.g., difficult terrain and congested roads) (policymakers: 75%; researchers: 75%), public transport access points (policymakers: 64%; researchers: 57%), and carpark provided at EmOC facilities (policymakers: 29%; researchers: 25%). However, there were some notable differences. Almost 70% of policymakers considered the socio-economic status of the population and households relevant to the dashboard. The desirability for heatmap showing travel time was lower among policymakers (53%) when compared to researchers (72%), with a greater proportion of researchers (29%) feeling neutral about such heatmaps than policymakers (19%) (Fig. 4).

Most participants considered the facility catchment area of the buffer area as the most relevant spatial scale to display accessibility measures (91%). Both policymakers and researchers were largely in agreement with the levels of relevance of different spatial scales, except for the first administrative subdivision, which was more highly regarded among policymakers (53%) than researchers (22%) (Table 1).

Table 1 Dashboard design and functionality preferences among survey respondents

Nearly 90% of participants considered three to four data updates per year or less frequent updates per year adequate for the dashboard, while 3% of researchers and no policymakers considered real-time updates relevant for the dashboard (Table 1).

When asked if 24/7 ambulance availability and various facility upgrade modelled scenarios on the dashboard might help with decision-making to improve geographic access to EmOC services, over 85% of respondents answered positively. About 50% of respondents also suggested other modelled scenarios they considered useful, with the most common being alternative human resource for health (HRH) scenarios - e.g., more skilled health personnel and other allied health personnel (Table 1).

3.4 Perception of the potential usability of the dashboard to tackle geographic access to EmOC

The potential usability of a dynamic dashboard such as the one proposed was high amongst all respondents (72% among non-missing responses). Disaggregated by stakeholder group, the potential usability of the dashboard amongst researchers was 82%, while amongst researchers was 60% (Table 2).

Table 2 Perception on the potentials use of an online dashboard to tackle geographic access to EmOC services

Two-thirds of the policymakers (67%) believed that the dashboard would add value and benefits to ministries of health and equivalent government health authorities, with 27% indicating that the dashboard will also benefit the work of other government departments and offices (including scientific offices, regional planning, road construction departments, etc.). On the other hand, researchers tended more to think that the dashboard would be of use to researchers, research institutes, and academic institutes (Table 2).

4 Discussion

We set out in this study to conduct a survey with policymakers and researchers on considerations needed for developing a policy-ready dashboard of geospatial access to EmOC in SSA. Of all the 206 respondents who took part in the survey, 90% reported that rural areas and 23% that urban areas in their countries were affected by issues of geographic accessibility to EmOC. Five percent of policymakers and 38% of researchers and 5% of policymakers were aware of the use of maps of EmOC facilities to guide planning of EmOC facility location. Regarding dashboard design, most visual components such as location of EmOC facilities had almost universal desirability; however, the desirability of socio-economic status of the population and households relevant and a heatmap showing travel time to care differed between policymakers and researchers. Nearly 90% of participants considered three to four data updates per year or fewer adequate for the dashboard. The potential usability of a dynamic dashboard was high amongst both policymakers (60%) and researchers (82%).

Participants almost unanimously thought that rural areas are affected by lack of EmOC access. About a quarter expressed urban areas are also affected by EmOC geographical accessibility issues and were mainly respondents from/working in DR Congo, Ghana, and Nigeria. When compared, policymakers tended more to be concerned about EmOC geographical accessibility in urban areas than researchers. The consensus on rural areas as areas of concerns is not surprising. Indeed, evidence in the literature shows that women in these areas have limited access to EmOC [ 31]. Though, recent evidence shows that women in urban and peri-urban areas also experience difficult access [ 32] and, in the largest SSA metropolis (Lagos), have higher odds of poor pregnancy outcomes for themselves and their newborns than those in rural areas [ 7, 9]. In our survey, since the countries from where a larger share of participants deemed urban as a problem are also countries that house two of the most-populated megacities (population > 10 million) in SSA [ 33], this observation might be because these are stakeholders who are already experiencing some of the impacts of urbanisation. It might also explain why policymakers tended more to be concerned about EmOC geographical accessibility in urban areas, as many typically live and work in such settings. Indeed, the challenges with access to healthcare in fast-growing, urbanising countries in SSA, including Ghana and Nigeria, have been flagged by researchers, who advocated for a need to rethink health systems more broadly in the context of urbanisation [ 34].

Participants reported some current use of digital technology to understand and address EmOC access issues. It is worth noting that the use of static maps showing facility locations was more frequently reported by researchers than policymakers. This is despite the fact that policymakers are the ones who need such evidence to support/guide EmOC service planning. As authors, we are aware of only two published studies reporting dashboards focused on geographical accessibility to health services in LMICs. There is one focused on primary health care in the Ifanadiana district of Tanzania called Lalana ( https://research.pivot-dashboard.org/) [ 22] and another one which is concentrated on emergency healthcare services in Cali, Colombia called the AMORE Project ( https://www.iquartil.net/proyectoamore/) [ 35]. There are probably other dashboards that are not publicly available. However, the 5% v. 38% awareness rate amongst policymakers and researchers that we observed in our survey suggests a significant disconnect between the creation of digital tools which is typically developed by researchers and their institutionalisation for supporting EmOC access policy actions. In a recently published qualitative study, policymakers specifically highlighted that digital tools that are dynamic and reflective of reality are more useful for decision-making [ 36]. We argue that these tools also need to be available beyond single districts of entire countries, as is the case with the two dashboards mentioned earlier, to realise their policy-influencing potential fully.

According to our survey respondents, the most highly desired visual component for a dynamic dashboard was a map showing facility locations. This desire is a legitimate need, as it is a fundamental requirement of a dashboard focused on EmOC access. The socio-economic status of populations and households was also reported as a desirable feature, especially among policymakers, indicating the interest in understanding access from the equity perspective, which is very much in line with the emphasis of the global community. Travel barriers showing difficult terrain and congested roads were also visual components of interest. We noted that compared to researchers, policymakers tended more to feel neutral about the relevance of a heatmap showing travel time. In static form, a heatmap based on closer-to-reality travel time estimates which showed congested roads was used to identify hotspots for EmOC geographical accessibility in Lagos, Nigeria [ 17]. The potential of these features may need to be brought to the attention of policymakers.

In addition, despite the premise of the survey being an online interactive dynamic dashboard showing EmOC access, the majority of survey participants considered three to four updates per year as adequate instead of more frequent updates (e.g., monthly) and no policymakers considered real-time updates as relevant. Bearing in mind the need to ensure optimum use of resources while assuring value for money of interventions such as this, which are aimed at improving outcomes for mothers and their newborns [ 37], this consideration will be critical for the sustainability of such dashboards. It also probably reflects that EmOC service planning from a supply side is not a real-time issue for policymakers. A dashboard updated three to four times a year or even less frequently, to aid in medium and long-term health policy planning is highly achievable. It minimises the need to collect primary data to update the inputs for the dashboard and efforts to re-run backend algorithms needed to keep the dashboard relevant for the times. In addition, most respondents wanted the dynamic dashboard to have the capacity to include modelled scenarios that offering comparison to the current situation in order to generate outputs to support better service planning and provision. In particular, the modelling scenarios of various locations and levels of ambulance availability and functionality of lower-tier health facilities, which could be upgraded for EmOC provision, were of interest to policymakers and researchers. For the latter, the functionality of EmOC facilities is known to vary even in settings such as urban areas where comprehensive EmOC is expected [ 38, 39, 40]. Ultimately, for such modelling features to work, there is a need to ensure that the dashboard is interlinked with other databases [ 41]. A feature allowing modelling various provision scenarios such as HRH, cost of service provision, or service outcomes could extend the utility of the dashboard for service planning and allow it to be more informative for policymakers in their efforts to identify areas of inequalities in healthcare utilisation, achieve cost-efficiency in provision of maternal and newborn health services and reduce maternal and neonatal morbidity and mortality [ 42, 43]. There are also other potential gains of a dashboard like this in SSA countries, including improving the routine health management information systems and enhancing accountability/stewardship of health systems and policymakers toward the population, as was the case with the routine immunisation dashboard implemented in Nigeria [ 21].

A key strength of the survey is that we covered almost all counties of SSA with 34 out of 48 countries in the sub-region included, representing 97% of the total population across the entire SSA region. However, there were some countries from which we could not secure any participant despite our best effort to disseminate the survey across all countries in SSA. We enlisted the major potential dashboard visual components in the survey but were unable to provide the participants with all the possibilities. Despite following up with an open-ended question in order to gauge other responses, respondents with little prior exposure to related tools were probably less able to provide further responses. Despite this, our findings provide critical insights that now need to be taken forward in the design and development of a user-friendly, context-specific, and customisable dashboard with the potential to inform and guide policymaking to address inequities in EmOC geographical accessibility in the sub-region.

In terms of the implication of our study findings for practice and policy, there is an appetite in government to improve EmOC geographical accessibility for women. However, efforts to do this are rarely data-driven [ 44, 45]. Dashboards like the one proposed in this study which will be built on up-to-date data on health facility functionality and closer-to-reality travel time [ 46] provide this much-needed evidence to support policymaking. The potential usability of similar dashboards may even extend beyond health service provision and the health sector in general and can benefit other crucial components of EmOC access, including road construction and urban and regional planning. However, currently, algorithms of navigation platforms which have the capacity to estimate closer-to-reality travel times, such as Google Maps ( https://maps.google.com) developed by Google, Waze ( https://waze.com) developed by Waze Mobile, Here WeGo ( https://wego.here.com/) developed by HERE Global B.V., and the open-source, World Bank supported platform, OpenTraffic ( http://opentraffic.io/) are more reflective of the situation in more urbanised settings compared to rural ones [ 15]. To put this in context, the team that worked on Lalana, which was done in a rural district, had to map over 100,000 buildings and 23,000 km of footpaths [ 22]. More work is needed to grow the functionality of the platforms and any resulting dashboards for rural areas, as these remain priority areas of inequities requiring attention. To ensure that when such dashboards become available, they are used, we have captured critical insights from policymakers and researchers that allow us to co-design a policy-ready dashboard study. At the minimum, a policy-ready dashboard should provide data on facility locations, the socio-economic status of populations and households, and travel barriers showing difficult terrain and congested roads. Its utility will be optimised if it is connected to other databases, and it can be updated quarterly. Some of the considerations highlighted by policymakers and researchers from their health system lens might be relevant for corollary dashboards at a facility-level and for demand-side dashboards for service users such as pregnant women.

In terms of future research, there is a need to test strategies aimed at augmenting uptake, utilisation, and fidelity of these dashboards amongst stakeholders when they are implemented [ 47] while also capturing impact stories on how it is making a difference in the functionality of service planners and policymakers and most ultimately in the lives of women and their newborns. This will stimulate more evidence-based planning and policymaking in SSA.

5 Conclusion

This study has provided vital considerations for developing a policy-ready dashboard for EmOC geographical accessibility in SSA. Insights gathered from the survey informed by two groups of stakeholders take us one step closer to the 'utopia' of geographical accessibility of maternal health services, where geographical information systems built on better data for better analytics will be available to support policymaking, as was imagined by researchers in this field previously [ 26]. Efforts now need to be placed on establishing partnerships that ensure the capture of closer-to-reality travel time, integration with existing health system data, and the development and maintenance of such policy-ready dashboards for EmOC geographical accessibility. If well implemented, the tool has all potential to contribute to critical policy decisions that will be lifesaving for mothers and their newborns.

Availability of data and materials

Data used in this study are available from the authors upon reasonable request.

Abbreviations

EmOC:

Emergency Obstetric Care

HRH:

Human Resource for Health

LMIC:

Low- and Middle-Income Country

OnTIME :

On Tackling In-transit delays for Mothers in Emergency

SDG:

Sustainable Development Goal

SSA:

Sub-Saharan Africa

UHC:

Universal Health Coverage

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Acknowledgements

We appreciate colleagues at the African Centres for Disease Control and Prevention, the West African Network of Emerging Leaders in Health Policy and Systems Research, the World Health Organization's PMNCH for Women's Children's and Adolescents' Health, Google, government representatives, and colleagues on Twitter and LinkedIn who helped in circulating the study questionnaire. We are particularly grateful to the policymakers and researchers who took time out of their busy schedules to participate in the survey that informed this study.

Funding

This study was funded by the Ideas Fund (Award 14650) from the University of Greenwich.

Author information

Author notes

Jia Wang and Kerry L. M. Wong are first authors.

Authors and Affiliations

1. School of Computing & Mathematical Sciences, University of Greenwich, London, UK

Jia Wang

2. Faculty of Epidemiology and Population Health, London, School of Hygiene and Tropical Medicine , London, UK

Kerry L. M. Wong & Aduragbemi Banke-Thomas

3. Department of Community Medicine and Primary Care, Federal Medical Centre Abeokuta, Abeokuta, Ogun, Nigeria

Tope Olubodun

4. Nuffield Department of Population Health, University of Oxford, Oxford, UK

Uchenna Gwacham-Anisiobi

5. Lagos State Ministry of Health, Ikeja, Lagos, Nigeria

Olakunmi Ogunyemi

6. Department of Obstetrics and Gynaecology, College of Medicine of the University of Lagos, Lagos, Nigeria

Bosede B. Afolabi

7. Maternal and Reproductive Health Research Collective, Lagos, Nigeria

Bosede B. Afolabi

8. Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya

Peter M. Macharia

9. Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK

Peter M. Macharia & Aduragbemi Banke-Thomas

10. Surveying and Geomatics Department, Midlands State University, Faculty of Science and Technology, Gweru, Midlands, Zimbabwe

Prestige Tatenda Makanga

11. Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

Ibukun-Oluwa Omolade Abejirinde

12. Women`s College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Canada

Ibukun-Oluwa Omolade Abejirinde

13. Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium

Lenka Beňová

14. School of Human Sciences, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK

Aduragbemi Banke-Thomas

Contributions

AB-T conceptualised the study. AB-T and PMM conducted the literature review that informed the study. AB-T, JW, KLMW, and PMM developed the tool for the study. All authors were involved in the recruitment of participants for the survey. AB-T, JW, KLMW, TO, UG-A, and OO were involved in the data cleaning and recoding of the data. AB-T, JW, and KLMW conducted the analysis and prepared the first draft of the manuscript. All authors were involved in the preparation of subsequent drafts. In addition, all authors approved the final version.

Corresponding author

Correspondence to Aduragbemi Banke-Thomas.

Ethics declarations

Ethical approval and consent to paticipant

As this was research involving human participants, it was performed in accordance with the Declaration of Helsinki. Ethical approval was obtained from the University of Greenwich Research and Ethics Committee (UREC/21.4.7.8). Informed consent was obtained from all participants before recruitment.

Consent for publication

Not applicable.

Competing interest

None declared.

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Wang, J., Wong, K.L.M., Olubodun, T. et al. Developing policy-ready digital dashboards of geospatial access to emergency obstetric care: a survey of policymakers and researchers in sub-Saharan Africa. Health Technol. (2023). https://doi.org/10.1007/s12553-023-00793-9
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# data[].attributes.attachment_link.title
Developing policy-ready digital dashboards of geospatial access to emergency obstetric care: a survey of policymakers and researchers in sub-Saharan Africa - Health and Technology

# data[].attributes.attachment_link.description
Background Dashboards are increasingly being used in sub-Saharan Africa (SSA) to support health policymaking and governance. However, their use has been mostly limited to routine care, not emergency services like emergency obstetric care (EmOC). To ensure a fit-for-purpose dashboard, we conducted an online survey with policymakers and researchers to understand key considerations needed for developing a policy-ready dashboard of geospatial access to EmOC in SSA.

# data[].attributes.headline
5. A Geospatial Database of Close-to-Reality Travel Times to Obstetric Emergency Care in 15 Nigerian Conurbations. Scientific Data. 2023;10:736

# data[].attributes.subject
5. A Geospatial Database of Close-to-Reality Travel Times to Obstetric Emergency Care in 15 Nigerian Conurbations. Scientific Data. 2023;10:736

# data[].attributes.body
Macharia, Peter M., Kerry L. M. Wong, Tope Olubodun, Lenka Beňová, Charlotte Stanton, Narayanan Sundararajan, Yash Shah, et al. “A Geospatial Database of Close-to-Reality Travel Times to Obstetric Emergency Care in 15 Nigerian Conurbations.” Scientific Data 10 (October 23, 2023): 736. https://doi.org/10.1038/s41597-023-02651-9.

# data[].attributes.attachment_link.readable_text
Abstract

Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing EmOC is essential for improving pregnancy outcomes. Current understanding of travel time to care is inadequate in many urban areas of Africa, where short distances obscure long travel times and travel times can vary by time of day and road conditions. Here, we describe a database of travel times to comprehensive EmOC facilities in the 15 most populated extended urban areas of Nigeria. The travel times from cells of approximately 0.6 × 0.6 km to facilities were derived from Google Maps Platform's internal Directions Application Programming Interface, which incorporates traffic considerations to provide closer-to-reality travel time estimates. Computations were done to the first, second and third nearest public or private facilities. Travel time for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility.

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Background & Summary

Evidence shows that timely access to emergency obstetric care (EmOC) provided by skilled health personnel, can reduce maternal deaths by 15-50% and intrapartum stillbirths by 45-75%1. EmOC is a set of nine clinical and surgical evidence-based interventions consisting of parenteral antibiotics, uterotonic drugs, parenteral anticonvulsants, manual removal of placenta, removal of retained products of conception, assisted vaginal delivery, neonatal resuscitation (altogether known as basic EmOC), blood transfusion and caesarean section (CS). The first seven interventions are collectively known as basic EmOC and the full set of nine are referred to as comprehensive EmOC. Globally, the met need for EmOC is only 45% with significant differences between low- and middle-income countries (LMICs) and high-income countries2. Basic EmOC is typically available in lower level health facilities such as primary health care centres and clinics while comprehensive EmOC tends to be provided only in public secondary and tertiary facilities and a number of private hospitals.

However, before a pregnant woman with an obstetric emergency can access EmOC, she needs to travel to a health facility which can provide EmOC. This makes pertinent the questions of where the service is located (availability), the distance and time it takes to reach such services (accessibility). For those in urban areas, where multiple service locations are typical, authors have suggested that both availability and accessibility (referred to when combined as spatial accessibility) should be considered simultaneously. As such, there is a clear need to understand spatial accessibility between the location where a need for obstetric emergency services arises and possible locations with comprehensive EmOC facilities9,10. Whether the woman accesses care through direct self-referral or institutional referral having previously been to another health facility11, the approach or method to estimate travel time in urban areas needs to consider complex factors such as traffic patterns and security concerns (e.g., during night-time12). Consequently, the approach adopted to estimate travel time has important implications for spatial metrics derived, and the subsequent policy decisions.

In literature and specifically in the context of LMICs, there are a range of approaches and methods that have been used to estimate spatial access to healthcare8,13. These primarily include modelled approaches and self-reported travel times8,13 varying from simple Euclidean distances14,15 to more complex cost distance algorithms16. However, the suitability of these methods is directly dependent on how well the models can be parametrised to accurately represent the dynamics of the journey between where the women is and the location of the EmOC facilities. This model parametrisation is achieved through data on the health-seeking behaviour and preferences of pregnant women and an accurate representation of road networks, land use, topography, and travel barriers.

Health-seeking data define parameters such as choices of pregnant women on whether to seek care, choices they make as to which facilities they trust to provide good-quality care, financial affordability, the means of transport and corresponding travel speeds for different road network classes and landcover categories12,17. However, there is a lack of such observational data on health-seeking behaviour to parametrise the models for the majority of the LMICs18. This challenge is more complicated in crowded, urban settings in Africa, where data availability is particularly limited. In urban areas, short distances may at times be obfuscated by long travel times, due to the variabilities and disruptions that might occur because of traffic congestion19, weather conditions, road accidents, police checkpoints, time of the day and day of the week when travelling occurs9. As a result, many models rely on average or plausibility-based speeds and modes of transport that are simplistic and underestimate travel times20,21,22,23. Thus, in a bid to improve access to EmOC services, policymakers often rely on inaccurate information or fail to rely on it because of the approximations.

Beyond the complexities of estimating travel times in African urban settings, population growth justifies the focus on urban Africa. Two-thirds of the world's population will live in urban areas by 2050 with a significant proportion of these additional 2.5 billion urban residents concentrating in Africa and tripling its urban population24. This is happening at a time when the use of health facilities for childbirth is near universal (>90%)3 in African urban conurbations (a city area containing many people, formed by various towns growing and merging ( https://dictionary.cambridge.org/dictionary/english/conurbation)). Yet, emerging evidence shows that the odds of maternal death and stillbirth are significantly higher in urban areas compared to rural areas. Consequently, spatial accessibility to EmOC in African urban settings has never been more crucial if we are to avert maternal and stillbirths and reach the 2030 Sustainable Development Goals (SDG)28 on maternal and newborn deaths.

Not only is Africa urbanising rapidly, but it also has the highest number of maternal deaths (69% of all 287,000 maternal deaths globally) and still births (49% of the 1.9 million stillbirths globally). The highest risk of maternal death and stillbirth is at the time of childbirth, from causes including severe bleeding, hypertensive disorders, obstructed labour, and sepsis30,31. Most of the 287,000 maternal and 1.9 million stillbirths32 can be prevented if women are able to access adequate care during pregnancy and give birth in facilities able to manage complications comprehensively30,32.

Therefore, understanding travel to EmOC in the African continent and in particular, urban and peri-urban areas is particularly important. There is an urgent need for estimates of travel time to EmOC facilities in African urban areas to account for on-the-ground realities9. The majority of previous and current travel time analyses16,22,33,34,35 have, however, not considered the time of the day (peak and non-peak hours), day of the week (weekend and weekday), weather and variations during transit to an EmOC facility. The current spatial accessibility models have mainly considered travel time to the nearest facility ignoring the alternatives such as the second and third closest facilities that a pregnant woman might prefer or choose36. Evidence shows women may bypass the nearest facility influenced by factors such as cost and quality of care. Further, many of these analyses have only considered spatial accessibility to facilities in the public sector, ignoring the significant role played by the private health sector in expanding health service delivery in Africa23.

Given the need for updated and robust spatial access metrics, we developed and in this article describe a novel database capturing realistic estimates of travel time representing a significant improvement over earlier, comparable initiatives. The estimates capture travel time between cells (the size of each cell is approximately 0.6 square kilometres) and locations of EmOC facilities in the 15 most populated urban conurbations in Nigeria. These travel times were generated using Google Maps Platform's internal Directions API ( https://developers.google.com/maps/documentation/directions/overview) which uses machine Learning models that incorporates real-time traffic conditions along with historical traffic patterns and road network data to predict travel times38. The database focuses on Nigeria because it will have the third largest absolute increase in the size of the urban population, globally (highest increase in the urban population in Africa by 205024) nearly doubling its urban dwellers between 2018 and 205024. Additionally, of all African countries, Nigeria contributes the most to global maternal deaths (82,000) and stillbirths (182,307), globally30,32.

Methods

Overview

We followed a four-step process (Fig. 1) in developing this database incorporating closer-to-reality travel times in the 15 most populated Nigerian urban conurbations. It entailed i) selecting a subset of urban conurbations (the study areas) within Nigeria, ii) outlining the urban conurbation boundaries, iii) assembly, cleaning, and geocoding of hospitals capable of comprehensive EmOC services, and iv) applying the algorithm to estimate travel times between residence and service provider locations.

The methodological framework used to compute travel time in urban Nigeria. The framework includes selection of urban conurbations, delineation of urban conurbation boundaries, assembly and cleaning of health facilities, estimation of travel time and analysis based on various factors.

Geographic scope and delineation of urban conurbation boundaries

The 15 urban conurbations included are Aba, Abuja, Benin City, Ibadan, Ilorin, Jos, Kaduna, Kano, Lagos, Maiduguri, Onitsha, Owerri, Port Harcourt, Uyo, and Warri. The inclusion criterion was based on a population threshold. That is, either the urban conurbation had an estimated population of least one million in 2022 or its population is projected to reach the same threshold by 203024,39 (Table 1), the SDG target year28. The justification for focusing on these conurbations is in recognition that they constitute areas where the effect of urbanisation and increasing population density are most acutely felt41. The maps of the selected urban areas are shown in the Supplementary file.

Table 1 Details of the urban conurbations included in the analysis.

The population dataset for each urban agglomeration was derived from the world city population39. The population data of the urban conurbations is based on the 2018 World Urbanisation Prospects by the United Nations Population Division24. In the prospectus, urban agglomeration incorporates the population in a city plus the population in the suburban areas lying outside but being adjacent to the city boundaries. The urban populations were extrapolated using a robust approach incorporating an average annual rate of change between urban-rural ratios that follows a logistic model and conforms to the worldwide observed pattern of urbanisation24.

There is no standard definition of urban conurbation boundaries, therefore, we used bespoke methods to delineate the geographic extents of each urban conurbation using existing layers of geospatial data. The ancillary data layers of population distribution showing counts of people42 and the Global Human Settlement Layer (GHSL) layer showing the degree of urbanisation43 were overlaid on Google Maps and Nigerian Local Government Area (LGA) boundaries40. The simultaneous spatial overlay of urban cells, agglomeration of people and built areas that intersected with an LGA formed the urban conurbation boundary. Provided part of the LGA was occupied by the spatial overlap of the proxies, the entire LGA was considered part of an urban area for policy relevance by the Federal Ministry of Health and state-level planners. Where necessary, in this study, several LGAs were merged to form the geographic extent of the urban conurbation. The defined geographical extent of the Ibadan urban conurbation in Oyo state is shown as an example in Fig. 2 while the maps of the other urban conurbation are shown in the Supplementary file.

Ibadan urban area in Oyo state, Nigeria. Eleven Local Government Areas (LGAs) boundaries used to define Ibadan urban area spatially overlaid with population density ranging from low (yellow) to high density (dark blue).

Geocoded database of health facilities capable of comprehensive EmOC services

Our main interest was to collate and geocode all health facilities from both public and non-public sectors capable of providing comprehensive EmOC for each of the 15 selected urban conurbations in Nigeria. We proxied and defined the availability of comprehensive EmOC services as any health facility that can offer CS services. The base list of health facilities was assembled from The Nigeria Health Facility Registry - NHFR (last updated in 2018)44. The NHFR contained information on the state, LGA, level of health facility, ownership, operation status, registration status, licence status, contact details and geographic location (latitude and longitude) of each facility. However, the NHFR did not have data on service availability including specific services such as CS, blood transfusion and operation hours (24 hours or not).

To determine if a health facility was capable of EmOC services (defined in this database by proxy of CS service availability since capacity for providing many other EmOC interventions is usually subsumed in capacity of facilities for CS45), we employed a range of approaches. For all urban conurbations, the main approach used was on-ground verification, in which research assistants visited the health facilities and administered short questionnaires to collect information on the availability of CS as well as hours of operation. In addition, when possible, additional verification of service availability in some facilities was done using the facility's website or online health facility registries and by personal communication with physicians in the respective urban conurbations as well as phone calls to the respective health facilities. Further, specifically for Lagos, the Lagos State Health Facilities Monitoring and Accreditation Agency and the Lagos State Health Scheme databases which provided information on service availability and the state health insurance provider status, respectively, were used to complement our definition of service availability in health facilities.

The research assistants who conducted the on-ground verification of service availability were recruited from each of the 15 urban conurbations and included fifth- and sixth-year medical students, nurses, and medical doctors. The research assistants were trained on the administration of the questionnaire which contained questions on the alternative name of the facility, whether the health facility could provide a CS and its operating hours and the collection of the geolocation data of the health facility. Only tertiary, secondary facilities and privately owned primary facilities from the NHFR list were considered; public primary health centres and maternity homes were excluded. At each health facility, the research assistant acquired the necessary information from a senior medical staff e.g., a medical doctor or senior nurse. With the aid of a Geolocator mobile application, the geographic coordinates of each facility were collected while standing at the entrance of the health facility. A picture of the health facility showing the signpost was also taken.

After the removal of duplicates and harmonization, the final list of 2,021 functional health facilities contained attributes on facility code and name, location details (LGA name, latitude, and longitude) and ownership status. The list is publicly available46. Assembly was done using MS Excel (Microsoft, Redmond, USA).

Computation of travel time

The travel time between approximately every 0.6 × 0.6 km cell (S2 level 14 cell ( https://s2geometry.io/devguide/s2cell_hierarchy.html) (origin) - the resolution selected to balance between accuracy and computation needed for analysis - to locations of assembled EmOC facilities (destination) was computed using Google Maps Platform's internal Directions API ( https://developers.google.com/maps/documentation/directions/overview) for each urban conurbation. The S2 library defines a framework for decomposing the unit sphere into a hierarchy of cells where each cell is a quadrilateral bounded by four geodesics ( https://s2geometry.io/devguide/s2cell_hierarchy.html). There are 30 levels of the S2 Cells, with the smallest at level 30. From each S2 cell, the API retrieved travel time from the cells to the destination by incorporating estimates of traffic to predict closer-to-reality travel time ( https://developers.google.com/maps/documentation/directions/overview). This can help provide more realistic estimates given certain routes may be more affected by weather, accidents, or periodic traffic patterns at different time of the day. The equivalent external APIs providing such historical traffic data have been recently used to compute travel time to healthcare in LMIC settings19,47.

The API retrieved a driving travel time only, given that women in emergency conditions are likely to use a mode of transport that involved driving48 unless there is a part of the journey that is not motorable, where walking was assumed. Driving times were estimated from the centre of the S2 cell to the facility, given a particular day and time of the week. The retrieval call was done for every hour of the week: that is from Sunday at 12 am, 1 am, 2 am,..., and up to Saturday at 11 pm. The API calls were made in January 2023. This led to 168 travel times (7 days in a week × 24 hours per day) from each S2 cell to neighbouring facilities. For each S2 cell - facility pair, the median travel time was extracted by looking at the periodic distribution. That is, we extracted eight traffic scenarios (weekday/1-3 am, weekday/6-8 am, weekday/1-3 pm, weekday/6-8 pm, weekend/1-3 am, weekend/6-8 am, weekend/1-3 pm, weekend/6-8 pm). For example, for the "weekend/1-3 am" scenario, the median of the following six travel times was used: Sunday 1 am, 2 am, 3 am and Saturday 1 am, 2 am, and 3 am.

From the retrieved travel times, the following summaries were computed per S2 cell: i) travel times to the first, second, and third nearest facilities, recognising that pregnant women even in an emergency do not always travel to their nearest facility12 and ii) the count of the number of health facilities within 15, 30, and 60 minutes of each S2 cell, keeping in mind that poor pregnancy outcomes like stillbirths and maternal deaths have been reported after self-referral and institutional referral respectively at these lower travel time benchmarks in urban African conurbations26,27. The nearest facilities were defined as the ones that required the shortest travel time to get to. These estimates were disaggregated by facility ownership (public, private and a combination of both).

Data Records

The database of public and private EmOC facilities46 and that of travel time49 resulting from the described process have been made publicly and freely available through the Figshare repositories46,49. The travel time database includes data from 15 urban conurbations in Nigeria. Each data record represents a travel time estimate from 0.6 km square cell to a specific comprehensive EmOC facility (private, public or either) at a particular time of the day and week. The database has 16 descriptive variables summarised in Table 2. The World Geodetic System 1984 (WGS84) coordinate system was adopted for the presentation of any geographical coordinates, at six decimal places.

Table 2 Description of variables in the travel time database.

Technical Validation

We validated the functionality and capacity of the health facility to perform comprehensive EmOC services as elaborated under the assembly of a geocoded database of health facilities capable of EmOC services46. To validate the extracted travel time from the internal API, we randomly picked locations in each of the conurbation and manually extracted corresponding travel (particular day of the week and time of the day) from Google Maps, using the typical travel time on the front end of Google Maps, as we have done previously50.

Usage Notes

Closer-to-reality estimates of travel time, using the Google Maps external Directions API or manually extracting travel time from Google Maps, have been used to guide stakeholders to comprehensively explore geographical inequities in healthcare access including EmOC within urban settings and inform policy and targeted planning47,50. Such results when presented in a dynamic way have been deemed particularly valuable by policymakers51. The provided travel time data sets can be summarized by aggregating at any subnational region of choice for example at the LGA, ward or urban conurbation-level using any statistical or any geospatial mapping software via the Zonal statistics functionalities.

The geographic coordinates can be used to link the database with existing population distribution data52, compute spatial coverage estimates and other socio-demographic datasets such as the relative wealth index53. While the dataset does not refer to any residential addresses, it can be spatially linked with external household surveys to derive their travel times. The database, however, contains driving as the only mode of transport for a very specific selection of health facilities and cannot be generalised.

Eight traffic scenarios were considered during data generation. However, for data summary purposes, in this data descriptor, we refer to peak as Friday at 6 p.m. (the departure day and time with the longest travel time across all the generated travel time scenarios) and non-peak as Sunday at 4 a.m. (the departure day and time with the shortest travel time across all the generated travel time scenarios).

The estimates are available at 0.6 by 0.6 km grids, a spatial resolution that balanced our computational power and efficiency and allows more granular understanding of accessibility. This is an improvement in global and regional studies that have used courser grids.

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Acknowledgements

The authors wish to acknowledge the Nigerian Federal Ministry of Health, state-level ministries of health, research assistants (fifth- and sixth-year medical students from the University of Ilorin, University of Benin, University of Jos, University of Ibadan, Nnamdi Azikiwe University, University of Uyo, University of Lagos and Chukwuemeka Odumegwu Ojukwu University; a nurse from Bingham University Teaching Hospital; medical doctors from Ahmadu Bello Teaching Hospital and Lagos University Teaching Hospital; and research assistants from the states of Abia, Borno, Kano, Port Harcourt, Imo, Delta and the FCT) who participated in the health facilities validation exercise, from 10 th May 2022 to 9 th August 2022. The Phase I of the OnTIME project led by AB-T was funded by Google. AB-T and BA are funded by Bill and Melinda Gates Foundation (Investment ID: INV-032911). PMM was supported by Newton International Fellowship (Number NIF/R1/201418) of the Royal Society and acknowledges the support of the Wellcome Trust to the Kenya Major Overseas Programme (Number 203077). LB was funded in part by the Research Foundation-Flanders (FWO) as part of her Senior Postdoctoral Fellowship. UGA is funded by a joint Clarendon/Balliol college/Nuffield Department of Population Health DPhil scholarship.

Author information

Authors and Affiliations

1. Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium

Peter M. Macharia & Lenka Beňová

2. Population & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya

Peter M. Macharia

3. Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK

Peter M. Macharia

4. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

Kerry L. M. Wong & Aduragbemi Banke-Thomas

5. Department of Community Medicine and Primary Care, Federal Medical Centre Abeokuta, Abeokuta, Ogun, Nigeria

Tope Olubodun

6. Google LLC, California, USA

Charlotte Stanton, Narayanan Sundararajan, Yash Shah, Gautam Prasad, Mansi Kansal, Swapnil Vispute & Tomer Shekel

7. Nuffield Department of Population Health, University of Oxford, Oxford, UK

Uchenna Gwacham-Anisiobi

8. Lagos State Ministry of Health, Ikeja, Lagos, Nigeria

Olakunmi Ogunyemi

9. School of Computing & Mathematical Sciences, University of Greenwich, London, UK

Jia Wang

10. Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

Ibukun-Oluwa Omolade Abejirinde

11. Women's College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Canada

Ibukun-Oluwa Omolade Abejirinde

12. Surveying and Geomatics Department, Midlands State University Faculty of Science and Technology, Gweru, Midlands, Zimbabwe

Prestige Tatenda Makanga

13. Climate and Health Division, Centre for Sexual Health and HIV/AIDS Research, Harare, Zimbabwe

Prestige Tatenda Makanga

14. Maternal and Reproductive Health Research Collective, Lagos, Nigeria

Bosede B. Afolabi & Aduragbemi Banke-Thomas

15. Department of Obstetrics and Gynaecology, College of Medicine of the University of Lagos, Lagos, Nigeria

Bosede B. Afolabi

16. School of Human Sciences, University of Greenwich, London, UK

Aduragbemi Banke-Thomas

Contributions

P.M.M. - conceptualization, methodology, original manuscript drafting, K.L.M.W. - conceptualization, methodology, review of the manuscript, T.O. - assembly and geocoding of health facility, review of the manuscript, L.B. - review of the manuscript, C.S. - extraction of travel times and review of the manuscript, N.S. - extraction of travel times and review of the manuscript, M.K. - extraction of travel times and review of the manuscript, Y.S. - extraction of travel times and review of the manuscript, G.P. - extraction of travel times and review of the manuscript, T.S. - extraction of travel times and review of the manuscript, S.V. - extraction of travel times and review of the manuscript, U.G.A. - assembly and geocoding of health facility, review of the manuscript, O.O. - assembly and geocoding of health facility, review of the manuscript, J.W. - review of the manuscript, I.O.O.A. - review of the manuscript, P.T.M. - review of the manuscript, B.B.A. - review of the manuscript, A.B.-T. - conceptualization, methodology, review of the manuscript.

Corresponding authors

Correspondence to Charlotte Stanton or Aduragbemi Banke-Thomas.

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Competing interests

CS, MK, YS, GP, TS and SV are employees of Google LLC, which makes the Google Maps Platform. All other authors declare no competing interests.

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Macharia, P.M., Wong, K.L.M., Olubodun, T. et al. A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbations. Sci Data 10, 736 (2023). https://doi.org/10.1038/s41597-023-02651-9
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DOI: https://doi.org/10.1038/s41597-023-02651-9

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A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbations - Scientific Data

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Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing EmOC is essential for improving pregnancy outcomes. Current understanding of travel time to care is inadequate in many urban areas of Africa, where short distances obscure long travel times and travel times can vary by time of day and road conditions.

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Comentario de orden por parte del Dr. Juan Manuel Anaya respecto al Proyecto AMORE durante la ceremonia de ingreso de  Luis Gabriel Cuervo, 3 de noviembre de 2022

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Comentario de orden por parte del Dr. Juan Manuel Anaya respecto al Proyecto AMORE durante la ceremonia de ingreso de  Luis Gabriel Cuervo, 3 de noviembre de 2022

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“una elegante y útil prueba de concepto para mejorar la equidad en salud reduciendo los tiempos de viaje a los servicios mediante la colaboración intersectorial” Juan Manuel Anaya, Académico y coordinador del Foco de Ciencias de la Vida y la Salud de la Misión Internacional de Sabios de Colombia.

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SESIÓN ACADÉMICA: CEREMONIA DE INGRESO ACADÉMICOS CORRESPONDIENTES

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