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The Minder Sleep Attitudes Survey is an online study exploring public attitudes towards sleep habits and memory problems, aiming to improve understanding of their impact on physical and mental health.

Purpose of the Survey

The survey investigates how people perceive the relationship between sleep, memory, and conditions such as dementia. It seeks to understand public attitudes, knowledge, and beliefs about sleep habits and memory issues, which are linked to both physical and mental health outcomes. The findings will inform future research and public healthcare strategies related to sleep and memory management

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Minder Health Management - Sleep Attitudes Survey

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Sleep disturbances have been associated with a range of health issues, and when coupled with memory problems, they can profoundly impact the well-being of individuals with conditions such as dementia, as well as their caregivers. This survey aims to investigate public attitudes towards sleep habits and memory problems, both of which have significant implications for physical and mental health. Understanding these attitudes is crucial as they highlight the complex relationship between disturbed sleep and the progression of memory-related conditions. By exploring these attitudes and gathering this data, we aim to develop a deeper understanding of public perceptions and attitudes in these critical areas. The survey will consist of demographic questions, inquiries about participants’ general health or medical history, and questions related to attitudes and perceptions regarding sleep habits and memory problems.

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The place where children and young people grow up in England can make a difference to their health and care. CIRCLE Study by the Sea is a two and a half year research project to understand more about:

what community health and social care looks and feels like for children, young people and their families in seaside towns in England

what helps and protects children and young people’s health and care where they live

how local services can work together to support children and young people’s health and care in seaside towns

what difference this can make to children, young people and their families’ lives

Our research team is made up of universities, community health providers and local councils and we are supported by groups of local youth and parent/carer advisors and organisations in four seaside towns. The National Institute for Health and Care Research (NIHR) has provided funding for this work.

CIRCLE = Coastal/Care Integrated Responses & Children’s Lived Experiences

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CIRCLE study by the sea The place where children and young people grow up in England can make a difference to their health and care. CIRCLE Study by the Sea is a two and a half year research project to understand more about: what community health and social

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NIHR Research career development funding programmes

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NIHR Research career development funding programmes

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Allen Carr Easyway to Stop Smoking

Embedded Domestic Abuse Support Worker

Stroke Prevention Pilot

Postural Stability

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phresearch@kent.gov.uk

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Public Health Consultant

dino.motti@kent.gov.uk

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Data for Everyone's Health: Public Health Intelligence

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Data for Everyone's Health: Public Health Intelligence

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The Faculty of Public Health has published a new policy paper, Data for Everyone’s Health, which explores the challenges and opportunities in how public health intelligence can be harnessed to improve health and reduce inequalities across the UK.

The paper considers how the use of modern technologies, data, and analysis can be further developed to help deliver more effective, equitable, and timely public health interventions to protect and improve health.

It also identifies key barriers to progress, including fragmented access to data, limited workforce capacity, and the need for clearer pathways for cross-organisation collaboration.

Developed by the Faculty’s Public Health Information and Intelligence Special Interest Group, the paper sets out eight recommendations aimed at government departments, public health agencies, and professional bodies.

These include calls for stronger national leadership on public health intelligence, better data infrastructure, improved career pathways for public health intelligence professionals, and clearer legal frameworks to enable safe and effective data sharing.

The publication supports FPH’s strategic focus on prevention, equity, and systems leadership and underscores the importance of embedding analytical capability across the whole public health system.

By strengthening the role of data and modern technologies in public health practice, the recommendations aim to ensure that public health intelligence is fully utilised to population health.

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The Faculty of Public Health has published a new policy paper, Data for Everyone's Health, which explores the challenges and opportunities in how public health intelligence can be harnessed to improve health and reduce inequalities across the UK.

The paper considers how the use of modern technologies, data, and analysis can be further developed to help deliver more effective, equitable, and timely public health interventions to protect and improve health.

It also identifies key barriers to progress, including fragmented access to data, limited workforce capacity, and the need for clearer pathways for cross-organisation collaboration.

Developed by the Faculty's Public Health Information and Intelligence Special Interest Group, the paper sets out eight recommendations aimed at government departments, public health agencies, and professional bodies.

These include calls for stronger national leadership on public health intelligence, better data infrastructure, improved career pathways for public health intelligence professionals, and clearer legal frameworks to enable safe and effective data sharing.

The publication supports FPH's strategic focus on prevention, equity, and systems leadership and underscores the importance of embedding analytical capability across the whole public health system.

By strengthening the role of data and modern technologies in public health practice, the recommendations aim to ensure that public health intelligence is fully utilised to population health.
Read the policy paper

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Data for Everyone's Health: Public Health Intelligence

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The Faculty of Public Health has published a new policy paper, Data for Everyone's Health, which explores the challenges and opportunities in how public health intelligence can be harnessed to improve health and reduce inequalities across the UK.

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December Newsletter

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Housing Support Grant Evaluation - Summary Report

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Housing Support Grant Evaluation - Summary Report

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Housing Support Grant Evaluation Summary Report

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Housing Support Grant Evaluation - Full report

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Housing Support Grant Evaluation - Full report

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Housing Support Grant Evaluation Full Report

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Linked data analysis of learning disability health checks and emergency hospital admissions in the Kent Integrated Dataset

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Linked data analysis of learning disability health checks and emergency hospital admissions in the Kent Integrated Dataset

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Background

People with learning disabilities have higher rates of admitted patient care than the general population. This study explored emergency hospital admissions during 2018/19 in association with learning disability health check recording in general practice within the Kent Integrated Dataset during 2016/17 to 2018/19.

Methods

Multiple logistic regression evaluated the odds of emergency hospital admission by sex, age, deprivation, residence, risk score, long-term conditions, severe health needs and health check. During 2018/19, one or more emergency hospital admissions were recorded for 10.9% of the 5,759 persons recorded with learning disability.

Results

There were lower odds of emergency hospital admission in persons having had learning disability health check in the past 3 years even after adjustment.

Conclusions

Comparison to nationally representative research suggests a consistent finding of benefit from learning disability health check on indicators of unplanned care use, supporting the view that learning disability health checks facilitate the addressing of key health needs.

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Characteristics of patients with body mass index recorded within the Kent Integrated Dataset (KID)

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Characteristics of patients with body mass index recorded within the Kent Integrated Dataset (KID)

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Background Obesity is a significant health issue and key public health priority. This study explored body mass index (BMI) recording in general practice within the Kent Integrated Dataset.

Methods Using a sample aged 18–100 years, resident within Kent, who were alive and currently registered to a Kent general practice as of 6 August 2018 within the Kent Integrated Dataset. We identified the latest BMI from event records between 2015/2016 and 2017/2018. Recording was evaluated by sex, age, deprivation, hypertension, serious mental illness and multimorbidity.

Results Between 2015/2016 and 2017/2018 using the sample of 1 154 652 persons, BMI was recorded for 43.7% of the sample. Multiple logistic regression showed that BMI recording was higher in females, the middle age bands, persons living in the most deprived areas and within persons who were hypertensive, had serious mental illness or were multimorbid.

Conclusions Findings were aligned to previous research using nationally representative samples. Completeness of recording varied by age, sex, deprivation and comorbidity. Recording within general practice was aligned to chronic disease management. From a prevention perspective, earlier assessment and intervention for the management of excess weight within primary care may be an opportunity for avoiding increases in BMI trajectory. There may also be merit in recognising that the external disease agents that influence obesity can be controlled or reduced (obesogenic environment) from a national policy perspective. Such a perspective may also help reduce stigmatisation and the pressure around arguments that centre on personal responsibility for obesity.

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Verifying you are human. This may take a few seconds.

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Social gradients in health and social care costs: Analysis of linked electronic health records in Kent, UK

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Social gradients in health and social care costs: Analysis of linked electronic health records in Kent, UK

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Objectives

Research into the socio-economic patterning of health and social care costs in the UK has so far been limited to examining only particular aspects of healthcare. In this study, we explore the social gradients in overall healthcare and social care costs, as well as in the disaggregated costs by cost category.

Study design

We calculated the social gradient in health and social care costs by cost category using a linked electronic health record data set for Kent, a county in South East England. We performed a cross-sectional analysis on a sample of 323,401 residents in Kent older than 55 years to assess the impact of neighbourhood deprivation on mean annual per capita costs in 2016/17.

Methods

Patient-level costs were estimated from activity data for the financial year 2016/17 and were extracted alongside key patient characteristics. Mean costs were calculated for each area deprivation quintile based on the index of multiple deprivation of the neighbourhood (lower super output area) in which the patient lived. Cost subcategories were analysed across primary care, secondary care, social care, community care and mental health.

Results

The mean annual per capita cost increased with deprivation across each deprivation quintile, with a cost of £1205 in the most affluent quintile, compared with £1623 in the most deprived quintile, a 35% cost increase. Social gradients were found across all cost subcategories.

Conclusions

Health inequalities in the population older than 55 years in Kent are associated with health and social care costs of £109m, equivalent to 15% of the estimated total expenditure in this age group. Such significant costs suggest that appropriate interventions to reduce socio-economic inequalities have the potential to substantially improve population health and, depending on how much investment they require, may even result in cost savings.

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Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data

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Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data

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Background

Lung cancer has the poorest survival due to late diagnosis and there is no universal screening. Hence, early detection is crucial. Our objective was to develop a lung cancer risk prediction tool at a population level.

Methods

We used a large place-based linked data set from a local health system in southeast England which contained extensive information covering demographic, socioeconomic, lifestyle, health, and care service utilisation. We exploited the power of Machine Learning to derive risk scores using linear regression modelling. Tens of thousands of model runs were undertaken to identify attributes which predicted the risk of lung cancer.

Results

Initially, 16 attributes were identified. A final combination of seven attributes was chosen based on the number of cancers detected which formed the Kent & Medway lung cancer risk prediction tool. This was then compared with the criteria used in the wider Targeted Lung Health Checks programme. The prediction tool outperformed by detecting 822 cases compared to 581 by the lung check programme currently in operation.

Conclusion

We have demonstrated the useful application of Machine Learning in developing a risk score for lung cancer and discuss its clinical applicability.

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Abstract

Background

Lung cancer has the poorest survival due to late diagnosis and there is no universal screening. Hence, early detection is crucial. Our objective was to develop a lung cancer risk prediction tool at a population level.

Methods

We used a large place-based linked data set from a local health system in southeast England which contained extensive information covering demographic, socioeconomic, lifestyle, health, and care service utilisation. We exploited the power of Machine Learning to derive risk scores using linear regression modelling. Tens of thousands of model runs were undertaken to identify attributes which predicted the risk of lung cancer.

Results

Initially, 16 attributes were identified. A final combination of seven attributes was chosen based on the number of cancers detected which formed the Kent & Medway lung cancer risk prediction tool. This was then compared with the criteria used in the wider Targeted Lung Health Checks programme. The prediction tool outperformed by detecting 822 cases compared to 581 by the lung check programme currently in operation.

Conclusion

We have demonstrated the useful application of Machine Learning in developing a risk score for lung cancer and discuss its clinical applicability.

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Introduction

Lung cancer outcomes have improved only marginally over the last 40 years and remain poor in comparison to most other cancers-just 17.7% of women and 12.9% of men in the UK survive after diagnosis for 5 years or longer [ 7]. The lack of overt or specific symptoms in the early stages of lung cancer often leads to late presentations, resulting in delayed diagnosis and treatment [ 8]. However, early detection and diagnosis, followed by effective treatment, improves survival for nearly all cancer types. According to Cancer Research UK [ 9] "around 6 in 10 people with lung cancer survive their disease for 5 years or more, if diagnosed at the earliest stage. This falls to <1 in 10 people when lung cancer is diagnosed at the most advanced stage."

When diagnosed early, more treatment options are available for lung cancer, including surgical resections. If operable, primary treatment costs are largely attributable to surgical removal procedures. However, as the disease advances to Stages 3 and 4, the expenses associated with surgical interventions tend to decrease, whilst the costs related to systemic therapies escalate significantly. This shift in treatment modalities is primarily due to the diminished feasibility of surgical removal as the cancer becomes more widespread. Instead, systemic therapies become more pivotal at advanced stages, aiming to control tumour growth, alleviate symptoms and potentially prolong survival. Consequently, timely identification and detection of lung cancer can significantly alleviate the financial burden on the state, the insurer, patients, and their families. This includes mitigating the expenses associated with advanced-stage treatments, extended hospital stays, intensive therapies, and palliative care services [ 10].

In the quest for earlier diagnosis of lung cancer, in June 2023, the UK government announced plans for a new national targeted lung cancer screening programme, based on learning from existing Targeted Lung Health Check (TLHC) pilot sites. The programme, which is supported by a recommendation from the UK National Screening Committee, will invite patients aged between 55 and 74 who are current or former smokers for a lung health check, which may include a low-dose CT scan. In areas where the TLHC programme has been operating, early data suggests that approximately 76% of lung cancers are diagnosed at stages 1 and 2, which is a substantial improvement compared with usual pathways of care [ 11].

Artificial Intelligence (AI) is a new and rapidly evolving field where computers are taught to think like humans. Due to its enhanced accuracy, precision, and decision-support capabilities, AI has begun to be implemented in modern medicine. It is being used in two ways namely, physical and virtual. Physical applications of AI include robots that are automated to perform tasks such as caring for the elderly and others that assist in surgeries. Machine learning (ML) is a subfield of AI that deals with the virtual aspect. ML models can be trained to detect or predict occurrences of a health condition [ 12]. AI is suitable in the medical field as it has no concept of fatigue unlike doctors and therefore can process large number of images and data at any given time [ 13]. This requires a good prediction model to be designed which involves acquiring a large dataset for training the model. The bigger and more diverse the dataset is, better the results that can be expected from it [ 14]. However, researchers need to be aware that quality, curation, and expert annotation are vitally important while considering what data to include.

With the help of AI, we can make accurate assessments of one's risk of lung cancer. The detection or prediction of lung cancer serves as a prime illustration where the utilisation of AI is indispensable. This is due to the fact that lung cancer is a highly time-sensitive condition and early diagnosis can be difference between life and death. Risk factors associated with lifestyle choices can be used to provide profiles of potential risks. The objective of any risk prediction tool, such as the one described in this paper, is to identify a small fraction of the population in which a large proportion of the disease cases will occur [ 15].

Our study aims to address the challenge of delayed diagnosis of lung cancer by exploiting the processing power of AI. We developed a model for providing risk-based predictions of lung cancer based on an individual's lifestyle choices, family history and other clinical data. We had access to a large dataset consisting of 1.25 million adult residents across the Kent and Medway region called the Kent Integrated Dataset (KID) [ 22]. We harnessed the capabilities of ML to train the model in making risk predictions by extracting patterns from data records of residents who had been diagnosed and treated for lung cancer. Our objective was to find the best performing model among a group of ML models that gave accurate predictions of the risk of lung cancer.

Methods

The County of Kent

Kent County Council covers the largest population footprint of any other council in England with a population of 1.6 million [ 23]. It has an exceptional spread of affluence and extreme poverty. Before COVID, a life expectancy gap of almost 20 years already existed between the least and most deprived wards [ 24, 25].

Dataset description

Data for this study was taken from the KID [ 22], which contains a vast array of pseudonymised integrated health and care data. The data for KID are derived from various sources. Nearly 40% of the data is from secondary care, over ¼ from primary care and the rest are from a range of sources including community and mental health trust providers and other publicly available data at a spatial level. The KID is overseen by a steering group known as the Kent & Medway Shared Health and Care Analytics Board (SHcAB) that includes representatives of Kent County Council, local health commissioners and information governance leads. The SHcAB considers issues such as information governance, development of the dataset and applications for use of the data. The Kent and Medway data warehouse team provides day-to-day administration and project management. Access was granted to the first author by the SHcAB for the study duration through established due process. Patients can opt-out of contributing data to the KID by informing their GP surgery that they do not want their data to be shared with external organisations. It has to be appreciated that the data is not in the public domain and it is a pseudonymised person level data set for most of the variables. We established a project oversight group, supported by the Kent & Medway cancer alliance which included cancer clinicians, service managers, Public Health physicians, epidemiologists, and AI experts. Regular stakeholder engagement took place throughout the study involving patients and public representatives.

Data contained within the KID represented a 6-year longitudinal record of health and care data for residents for 2014-2019 which was 1,865,382. An initial exclusion for under 18s years was made ( n  = 599,866) which reduced the cohort to 1,265,516. We then removed a further 10,532 patients (0.8% of the total population), due to incomplete or missing records data (for example smoking status), which took the original cohort size down to 1,254,984. We used a set of pre-determined criteria to exclude the records with missing data. Given that recording of ethnicity is poor across the NHS, we did not use it as an exclusion criteria. We excluded records where data on one or more key variables relevant to our analysis were missing. These are: Pseudonymised Unique Patient ID, Smoking Status of the individual, GP Practice of Registration, Age, Gender, and valid Postcode. The final dataset contained a total of 1,254,984 patients, of which 6053 were diagnosed with primary lung cancer during this period and these were included within the scope of this investigation. The final dataset used in the analysis had no missing data on smoking status. The cohort selection (lung cancer cohort) was only made up of patients with primary malignant lung cancers, excluding benign tumours and metastases from other types of cancer. To ensure comprehensive capture of all patients meeting the criteria, we assessed both primary and secondary healthcare records using relevant SNOMED or ICD-10 codes, respectively. Patients with Lung Cancer included all confirmed diagnoses regardless of diagnosis of care setting, staging at the time of diagnosis, disease progression or onward treatment options and outcomes. Core dimensions of data used within this study are shown below:

Patient Demographics

Primary Care (Events, Consultations, Long term condition registers, Medications, Deaths)

Secondary Care (A&E, Inpatient Spells and Outpatients, Critical Care Bed Days)

Mental Health (Inpatient and Outpatient History)

Community Care (Contacts, Appointments, Minor Injuries Units and Walk In Centers)

Wider Health Determinants including Housing, Education, Employment, and Income.

Environmental Datasets-Pollution, Radon ground levels

We did not have information on all the above variables at an individual patient level. We had individual patient-level data on patient demographics, primary, secondary care, mental health and community clinical care activities. For the wider determinants of health including environmental factors, we applied spatial level data at the Lower layer Super Out Put area, a small geographical area in the UK with an average of 650 households to the patient level datasets.

Data access

All NHS organisations including general practices across Kent & Medway had entered into Joint data Controller arrangements, which includes a common process for safe, secure and lawful access to their data in the KID for population health analytics including work such as ours. This process is administered by a system wide oversight group representing the organisations, called the Kent & Medway Shared Health & Care Analytics Board. Patient-level consent would not apply in this context as the dataset is historical and fully pseudonymised and deidentified. Because of the above arrangements, access to the data in KID, its analysis and sharing of the findings, no ethical approval was required as per existing arrangements.

Data pre-processing

The dataset contained missing values mainly in the attribute named 'ethnicity' as shown in Table 1, despite a lot of work to try and capture ethnicity coding from various sources. We, therefore, excluded this from the model as we felt that it was not appropriate to try and use average value or synthetic data derivative, which is common practice. Other dataset attributes had no missing or outlier values from features, so no further transformations were made on the remainder of the datasets.

Table 1 Baseline characteristics of cohort groups.

The data attributes are grouped into life history, symptoms, diagnostics, treatment, and end-of-life care based on the stage at which the data are collected, as depicted in Fig. 1. To prepare the model for predicting patients' risk ratios, we extracted only the essential attributes from the dataset. These columns were selected based on their potential to provide valuable predictive information. We specifically focused on data concerning the pathways leading to the diagnosis of lung cancer as it held valuable insights regarding the associated causes and symptoms. Attributes related to cancer diagnosis or data related to 2-week wait urgent referrals, appointments to see an oncologist, Chest X-Rays and Low Dose Computerised Tomography scans for confirming diagnosis, treatment options such as chemotherapy and radiotherapy and mortality were omitted. These attributes were excluded from the dataset because they were deemed as non-predictive elements that did not offer significant insights into the associated risks of a positive diagnosis of lung cancer. We excluded the above diagnostics and treatment elements up to 12 months before the date of diagnosis.

The model uses only life history and symptoms as predictive elements for a lung cancer diagnosis. Diagnostic elements, treatment and end of life care features were omitted.

Relative risks (RR) were calculated for all the variables and were used to determine the important attributes and for categorisation. RR is the ratio of the incidence of an event occurring (Lung Cancer) with an exposure (e.g., smoking) versus the incidence of the same event occurring without the exposure. For example, the relative risk of developing lung cancer in smokers (the exposed group) versus non-smokers (non-exposed group) would be the probability of developing lung cancer for smokers divided by the probability of developing lung cancer for non-smokers. All characteristics of the individual datasets such as medications, events, tests, demographic qualities or wider determinant of health factors were tested, and risk-scored using this methodology. To reduce the number of categories we collapsed these into meaningful groupings, and these were informed by the higher relative risk of related variables. For instance, for respiratory disorders such as COPD and Asthma each of which have numerous diagnosis codes, these were built up into simple three-state options; Yes, No or Has Familial History. Other features, such as smoking history and activity with high dimensionality were ranked into similar groups by creating scores.

Model development

We used feature encoding to reduce the number of states and to simplify the complexity of model development and enhance performance. One-hot encoding and standard scaling was used for the feature encoding [ 26]. Given the need to develop a scalar response to risk scoring in order to aid prioritisation of patients at greatest risk of developing lung cancer within a screening pool, logistical and other categorical models were ruled out. Traditional linear regression was selected as an initial candidate model to detect lung cancers early and thereby improving outcomes over and above the current screening protocol for lung cancer in the UK.

This spans from extracting relevant data from the KID to comparing the number of lung cancer cases detected using the most successful model and the criteria used in the TLHC programme.

Model evaluation

The output of this model is not binary/logistical (with or without cancer) but a continuum of risk of developing the cancer. As we have stated within the dataset description section, the dataset we used also did not contain person-level information on all the variables included in the model. Hence, the traditional parameters to express the validity of a screening test such as sensitivity, specificity, positive predictive value, negative predictive value, area under the curve and likelihood ratios are not applicable. Instead, we rationalised that if the model is working most efficiently, we should be able to demonstrate more lung cancer cases being found within a screening pool in the population compared to that of the current screening pilots ongoing in England. In order to baseline our evaluation, therefore, we compared the output of the model against the current screening population for the TLHC [ 32] programme. Patients meeting the following three criteria will be invited for screening:

are over 55 but younger than 75 years old

are registered with an GP in the area the scheme is operating

have ever smoked, and this is recorded with the GP.

This number of cases found from the TLHC programme was then compared with the number of cases identified using the linear regression model using the top-performing combination of attributes.

Results

Selected characteristics of cohorts included in the study are shown in Table 1.

Relative risks for the attributes included in the model are presented in Table 2.

Table 2 Relative risks for the attributes and various levels of exposures included in the model.

In the attribute concerning family history of cancer, lung cancer is also included. Many attributes were associated with an increased risk of lung cancer and others a lower risk. As expected, key attributes showing a higher risk included older age, lack of physical activity, COPD, hypertension, other cancers and family history of other cancers, TB and family history of TB and financial status. Attributes associated with lower risk include intense physical activity, younger age, never smokers and higher socioeconomic status. As the results are from univariate linear regression the effect of confounding is apparent. For example, hypertension is associated with age.

The top ten combinations of attributes were selected which showed the best results in identifying lung cancers, out of many thousands of combinations (Table 3). The selected combinations contained attributes numbering from 7 to 11. The top performing combination included the following attributes: age; activity score; smoking score; any respiratory illness; hypertension; cancer; and Tuberculosis.

Table 3 Attributes included in the best performing models and cancer cases detected.

We needed to test the performance of the 7-attribute combination henceforth referred to as the Kent & Medway risk prediction tool with the TLHC eligibility criteria. By applying these three criteria to the 30% test population we identified on average 56,663 people (screening cohort) who will be eligible under the TLHC criteria. Among these there were 581 lung cancer cases recorded. We then applied the Kent & Medway risk prediction tool to the same 30% test population, and this predicted a lung cancer risk score for every individual. From this list, we identified the top 56,663 people and within this population 822 lung cancer cases were recorded. This was on average a benefit of 41.4% over and above the contemporaneous approach.

Discussion

Clinical utility of the work

The product of this work has immediate clinical implications and thus has the potential to improve patient care and resource utilization. As the model outperforms the standard wider TLHC eligibility criteria, this would help us to detect up to 40% more cancers. Currently, we are exploring how best to incorporate this as a screening and early diagnosis intervention. There are two options under consideration: provide a more comprehensive and refined screening model based on our risk tool compared to that of the THLC eligibility criteria; and the GP calculates the risk score for each patient during a consultation, similar to Framingham cardiac risk score [ 36] and use this for further action. Using the first option, we can further refine the risk group for screening there by increasing cancer detection and saving scarce cancer diagnostic and treatment resources. We intend to incorporate the tool into the management information system of the early cancer diagnosis team at the local hospital as a pilot and then to roll it out across a wider geographical area. The first author has already secured agreement in principle for this from the local cancer clinical and managerial leaders.

Strengths

We used a place-based linked data set entirely produced by a local health system whose primary use was for commissioning intelligence and health care planning purposes. It has the power of painting the entire picture of the population as it contains information from general practice, community health services, mental health services and hospital services. Furthermore, it included integrated spatial-level information on key socioeconomic factors and the extent of deprivation. This makes it a powerful repository to develop any risk prediction tool compared to tools that only rely on electronic patient clinical records [ 37]. Our data is complete compared to Callender et al. [ 38] where there are large number of missing values. We generated relative risks at a very granular level of detail in order to develop our aggregated sixteen attributes. We established a powerful partnership of cancer clinicians, Public Health physicians, epidemiologists, ML experts and leaders from the cancer alliance who were involved throughout from the inception of the project to its completion. This helped us to incorporate varying perspectives. Key stakeholders' views were constantly sought and acted upon during this work. These included regular meetings with the early diagnosis team, digital cancer alliance board, shared health and care analytics board and regional applied research consortium digital innovation group. Patients and the public are represented in most of these in order to ensure that there is support for this initiative.

Limitations

A few limitations of our study need to be acknowledged. All the seven variables included in the model had complete data although this does need to be treated with the following caution. For the activity score, we used the data at a population level i.e. lower layer super output area. This does not reflect the score for an individual-specific patient. Data on socio economic status was also only available at a spatial level. Although using data at geographical/spatial level gives us the advantage of complete data with no missing values, one needs to be cognisant of the limitations of this approach and the well-documented ecological fallacy [ 39]. Four of the variables in the final model were purely clinical conditions. These are: Any Respiratory Illness, Hypertension, Cancer, and Tuberculosis. It is extremely unlikely that such an important diagnosis will be left uncoded both in primary and secondary care. It is generally agreed that if such a clinical diagnosis does not appear on the patient record, the patient does not have the condition as it is not current practice to code that a patient does not have a condition. We recognise that this may not be universally true for all patients, but is unlikely to have a significant impact upon our longitudinal study results. Both for passive smoking and family history of cancer we assumed that if this information is not coded then the individual does not have that exposure although this may not be always accurate. As our analysis included over a million records any under/over assumption is likely to be random and will not have a major impact on the results. Ethnicity was not included in the model because the data was incomplete. In the future, we will ensure that ethnicity is included in further work. Data included in the study is only up to 2019.

We wish to acknowledge that we have not used traditional parameters to express the validity of a screening test as this approach is not applicable as explained in the model evaluation section. We have used a different approach to evaluate the model. It is the authors' belief that the approach adopted in this study still adds useful information to the literature as this method has been seldom applied. This needs to be borne in mind when interpreting the findings and developing any policy approach based upon our findings. Due to changes in commissioning arrangements, the KID was rendered static and data were not updated after 2019. We do not anticipate any weakening of the power of the prediction tool due to non-inclusion of more recent data. This study was undertaken in Kent & Medway in the southeast of England. Hence the question of generalisability across the United Kingdom needs to be considered. In our view, it is unlikely that the population and the strength of association between the attributes and lung cancer are so different elsewhere that the results will not be applicable. However, this may not be true for an international comparison. Another important limitation worthy of note is that applying similar machine-learning approaches using other databases with different characteristics may result in a less sensitive outcome. Hence, before our approach is adopted this needs to be tested on a much larger patient population under different settings.

Conclusion

In this paper, we have demonstrated the useful application of Machine Learning in developing a risk score for lung cancer using a large, place-based linked data set. We involved multidisciplinary stakeholders throughout this work, including patients and the public. Our risk prediction tool is superior to the eligibility criteria currently in use in the pilot sites for the TLHC Programme. This is a good example where local experts in fields as diverse as AI, ML, clinical oncology, Public Health and Epidemiology came together to produce an innovative solution to improve patient care and save scarce health care resources.

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Acknowledgements

We are grateful to the SHcAB for granting us permission to access and use the data. We acknowledge the support of Kent & Medway Cancer Alliance. We sincerely thank Dr Anjan Ghosh, Director of Public Health, Kent County Council for his support and encouragement.

Funding

The first and second authors received funding from the Kent and Medway Cancer Alliance to undertake the analysis.

Author information

Authors and Affiliations

1. Quantum Analytica, Berkshire, UK

David Howell & Ross Buttery

2. Surrey Heartlands Integrated Care System, Surrey, UK

David Howell

3. Public Health Medicine, Kent County Council, Maidstone, England, UK

Padmanabhan Badrinath & Abraham George

4. University of Cambridge, Cambridge, UK

Padmanabhan Badrinath

5. Kent and Medway Medical School, Kent, UK

Abraham George

6. Vellore Institute of Technology, Vellore, Tamil Nadu, India

Rithvik Hariprasad

7. Thames Valley Cancer Alliance, Reading, UK

Ian Vousden

8. NHS England - South East, Southampton, UK

Ian Vousden

9. Kent & Medway Cancer Alliance, Maidstone, UK

Tina George

10. Targeted Lung Health Checks, Sussex, UK

Tina George

11. NHS Sussex Integrated Care Board, Worthing, England, UK

Tina George

12. Cancer Research UK GP, London, UK

Tina George

13. Early Cancer Diagnosis and Cancer Health Inequalities, Kent and Medway Cancer Alliance, Maidstone, UK

Cathy Finnis

Contributions

All authors contributed to the publication according to the ICMJE guidelines for authorship. All authors read and approved the submitted version of the manuscript. Each author has agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Study concept and design: DH, RB, AG. Acquisition of the data: DH, RB, AG. Analysis and interpretation of data: DH, RB, AG, PB, RH, IV, CF, TG. Drafting of the manuscript: DH, RB, PB, AG, RH, IV, CF, TG. Statistical analysis: DH, RB, PB, AG, RH. Manuscript review and approval: DH, RB, PB, AG, RH, IV, CF, TG. Obtained funding: DH, IV.

Corresponding author

Correspondence to David Howell.

Ethics declarations

Competing interests

Two of the authors are directors of Quantum Analytica.

Ethics

Ethical approval was not required as this work was undertaken as part of the authors' job role and as a service activity to inform health care planning and delivery.

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Dr Tina George: The views expressed here are the professional views of the author and in no way represent the views of all the organisations this author has been associated with, at present or in the past.

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Howell, D., Buttery, R., Badrinath, P. et al. Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data. BJC Rep 1, 16 (2023). https://doi.org/10.1038/s44276-023-00019-5
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DOI: https://doi.org/10.1038/s44276-023-00019-5

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# data[].attributes.attachment_link.title
Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data - BJC Reports

# data[].attributes.attachment_link.description
Lung cancer has the poorest survival due to late diagnosis and there is no universal screening. Hence, early detection is crucial. Our objective was to develop a lung cancer risk prediction tool at a population level.

# data[].attributes.headline
Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups

# data[].attributes.subject
Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups

# data[].attributes.body
Introduction
Digital programmes in the newly created NHS integrated care boards (ICBs) in the United Kingdom mean that curation and linkage of anonymised patient data is underway in many areas for the first time. In Kent, Surrey and Sussex (KSS), in Southeast England, public health teams want to use these datasets to answer strategic population health questions, but public expectations around use of patient data are unknown.

Objectives
We aimed to engage with citizens of KSS to gather their views and expectations of data linkage and re-use, through deliberative discussions.

Methods
We held five 3-hour deliberative focus groups with 79 citizens of KSS, presenting information about potential uses of data, safeguards, and mechanisms for public involvement in governance and decision making about datasets. After each presentation, participants discussed their views in facilitated small groups which were recorded, transcribed and analysed thematically.

Results
The focus groups generated 15 themes representing participants' views on the benefits, risks and values for safeguarding linked data. Participants largely supported use of patient data to improve health service efficiency and resource management, preventative services and out of hospital care, joined-up services and information flows. Most participants expressed concerns about data accuracy, breaches and hacking, and worried about commercial use of data. They suggested that transparency of data usage through audit trails and clear information about accountability, ensuring data re-use does not perpetuate stigma and discrimination, ongoing, inclusive and valued involvement of the public in dataset decision-making, and a commitment to building trust, would meet their expectations for responsible data use.

Conclusions
Participants were largely favourable about the proposed uses of patient linked datasets but expected a commitment to transparency and public involvement. Findings were mapped to previous tenets of social license and can be used to inform ICB digital programme teams on how to proceed with use of linked datasets in a trustworthy and socially acceptable way.

# data[].attributes.attachment_link.authors[].name
Elizabeth Ford Brighton and Sussex Medical School, Brighton, UK

# data[].attributes.attachment_link.title
Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups. | International Journal of Population Data Science

# data[].attributes.attachment_link.description
IntroductionDigital programmes in the newly created NHS integrated care boards (ICBs) in the United Kingdom mean that curation and linkage of anonymised patient data is underway in many areas for the first time. In Kent, Surrey and Sussex (KSS), in Southeast England, public health teams want to use these datasets to answer strategic population health questions, but public expectations around use of patient data are unknown.
ObjectivesWe aimed to engage with citizens of KSS to gather their views and expectations of data linkage and re-use, through deliberative discussions.
MethodsWe held five 3-hour deliberative focus groups with 79 citizens of KSS, presenting information about potential uses of data, safeguards, and mechanisms for public involvement in governance and decision making about datasets. After each presentation, participants discussed their views in facilitated small groups which were recorded, transcribed and analysed thematically.
ResultsThe focus groups generated 15 themes representing participants’ views on the benefits, risks and values for safeguarding linked data. Participants largely supported use of patient data to improve health service efficiency and resource management, preventative services and out of hospital care, joined-up services and information flows. Most participants expressed concerns about data accuracy, breaches and hacking, and worried about commercial use of data. They suggested that transparency of data usage through audit trails and clear information about accountability, ensuring data re-use does not perpetuate stigma and discrimination, ongoing, inclusive and valued involvement of the public in dataset decision-making, and a commitment to building trust, would meet their expectations for responsible data use.
ConclusionsParticipants were largely favourable about the proposed uses of patient linked datasets but expected a commitment to transparency and public involvement. Findings were mapped to previous tenets of social license and can be used to inform ICB digital programme teams on how to proceed with use of linked datasets in a trustworthy and socially acceptable way.

# data[].attributes.headline
Embedding OR modelling as decision support in health capacity planning: insights from an evaluation

# data[].attributes.subject
Embedding OR modelling as decision support in health capacity planning: insights from an evaluation

# data[].attributes.body
Literature reviews over five decades have reported the paucity of examples of OR methods being routinely used to support decision-making in health and social care. This paper presents insights from an independent evaluation of a project intended to overcome some of the barriers to implementation by establishing a “community of practice” in Kent (England). The project itself was undertaken by practitioners, and had two main aims: providing training in system dynamics modelling to analysts, and making senior managers aware of the benefits of modelling. The findings largely confirmed previous studies, but also raised issues about style of training delivery and selection of problems to be modelled. Project leaders fully understood the barriers to embedding OR modelling skills, and made considerable efforts to avoid them, but nevertheless the main barrier, pressures on people’s time, remained an obstacle. The paper concludes with general reflections and advice.

# data[].attributes.attachment_link.title
Embedding OR modelling as decision support in health capacity planning: insights from an evaluation

# data[].attributes.attachment_link.description
Literature reviews over five decades have reported the paucity of examples of OR methods being routinely used to support decision-making in health and social care. This paper presents insights from…

# data[].attributes.headline
Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice

# data[].attributes.subject
Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice

# data[].attributes.body
In public health, the routine use of linear forecasting, which restricts our ability to understand the combined effects of different interventions, demographic changes and wider health determinants, and the lack of reliable estimates for intervention impacts have limited our ability to effectively model population needs. Hence, we adopted system dynamics modelling to forecast health and care needs, assuming no change in population behaviour or determinants, then generated a “Better Health” scenario to simulate the combined impact of thirteen interventions across cohorts defined by age groups and diagnosable conditions, including “no conditions”. Risk factors for the incidence of single conditions, progression toward complex needs and levels of morbidity including frailty were used to create the dynamics of the model. Incidence, prevalence and mortality for each cohort were projected over 25 years with “do nothing” and “Better Health” scenarios. The size of the “no conditions” cohort increased, and the other cohorts decreased in size. The impact of the interventions on life expectancy at birth and healthy life expectancy is significant, adding 5.1 and 5.0 years, respectively. We demonstrate the feasibility, applicability and utility of using system dynamics modelling to develop a robust case for change to invest in prevention that is acceptable to wider partners.

# data[].attributes.attachment_link.readable_text
Public Health Department, Kent County Council, Sessions House, County Road, Maidstone ME14 1XQ, Kent, UK

Whole Systems Partnership, 8 York Place, Knaresborough HG5 0AA, North Yorkshire, UK

Author to whom correspondence should be addressed.

1. Introduction

In any local health system, data and intelligence are essential for service planning and investment/disinvestment decision making for a defined population. This will invariably include forecasting demographics, health determinants, disease distribution and health status. At present, most attempts at forecasting the future health and care needs of local populations rely on linear extrapolations, which use a series of limited assumptions to estimate the likely burden of a specific health condition or demand for a service. These assumptions include trends in population change as well as in the condition or service under investigation [ 1]. This method of forecasting can be described as predictive analytics, where historical data are used to make predictions about future events [ 2]. Prevention is a key activity in public health, and this requires robust evidence to convince decision makers to invest in prevention where the gains may not be immediately apparent.

There have been previous attempts to use SDM "to align prevention efforts and maximise the effect of limited resources" [ 7]. A prevention impacts simulation model [ 8] was employed in the field of cardiovascular disease prevention to simulate the medium- and long-term impact of the various interventions. However, the simulation and the application of the SDM approach here were disease-specific. From a complex adaptive system perspective, population health needs are dynamic, and are shaped by socio-economic risk factors as well as the level of access to health and care services. Rutter et al. [ 9] describe the following properties of complex systems: emergence is defined as "properties of a complex system which cannot be directly predicted from the elements within it and are more than just the sum of its parts", feedback where "a change reinforces, or balances further change" and adaptation, which refers to "adjustments in behaviour in response to interventions". Such properties are the basis on which public health practice operates within a local health system. As such, it is essential that we move towards an approach that takes these complexities into account to help to answer the key questions in public health of what can be done and how it can be done in practice. Prescriptive analytics is the process of using data to determine an optimal course of action [ 2]. This would not only provide more accurate estimates of future health need but enable the system to better plan services and to ultimately reduce health inequalities. There are many evaluations of the use of SDM in health policy and planning; however, recent reviews [ 10, 11] in this area have highlighted the lack of research prior to 2013. Reviews also highlighted the importance of stakeholder involvement [ 12], which was highly valued in our study.

Cohort modelling using SDM is an accepted methodology in improving health policy making in complex systems, using qualitative and quantitative approaches. One such international example is the "Rethink health dynamics model" developed by the Rippel Foundation [ 13]. The model simulates a range of scenarios for a combination of preventive interventions, including reducing health risks and improving healthcare, on a defined US population over a 40-year period. This has generated evidence on the value of these interventions, which informs the planning and decision making, including investment in prevention. To our knowledge, such an approach has not been employed across multiple programme areas within a local health system in the United Kingdom to inform policy and decision making.

The Joint Strategic Needs Assessment (JSNA) uses a range of health indicators to identify the current health and care needs of the population and is a mandatory requirement for all local authority public health departments in England. Using the JSNA [ 14], local system leaders can work together to understand and agree on the needs of all local people, setting the priorities for collective action. Our aim is to demonstrate and apply the use of simulation modelling in the area of routine public health intelligence, analysis and inference. In this regard, our objective is to create a population cohort model using SDM to generate necessary evidence on the value of various preventive interventions for local priority setting within the current Kent JSNA development process and intelligence tools.

2. Materials and Methods

This study was carried out in the county of Kent, positioned in the southeast of England, with a diverse population of approximately 1.6 million [ 15] that varies considerably in terms of deprivation and ethnicity. Like other local areas in England, Kent exhibits wide health inequalities by geography and different vulnerable groups [ 16]. The model outputs were presented at the level of three sub-geographical regions, which aligned with existing commissioning boundaries-West Kent, East Kent and North Kent-and for this communication, we present selected examples from North Kent.

The prototype model was co-produced with the local council public health team, and was conceptualised, tested, populated and validated over a period of 9-12 months. Two parallel group model building workshops were run alongside each other, one for adults and one for children and young people (CYP). A series of three dedicated engagement sessions were carried out for each cohort and involved between 8 and 12 experts from across health and care settings as appropriate, as well as regular contact, dialogue and checking in with group participants in between sessions. These two groups were brought together at the Better Health Workshop in 2018. The model conceptualisation was socialised and developed, followed by scenario generation and testing, which was an iterative process. Stakeholders explored the key factors that influenced better health outcomes for population health within the Kent system. Variables, interactions and feedback loops were identified and informed the design of the causal loop diagram. We discussed key interventions impacting population health outcomes, identified cohorts of interest, selected relevant peer-reviewed evidence and agreed on appropriate data sources to input into the model. Data sources are described in Table S1 [ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Cohorts were based on the health or disease status of the individuals, and disease status is further broken down into individual long-term conditions (LTCs). The Kent County Council (KCC) senior team of public health specialists met to identify a combined scenario in which thirteen prevention/public health measures were achieved, including, for example, the rates of breastfeeding, the presence of adverse childhood experiences and the levels of smoking and obesity in the population. This has resulted in a 'Better Health' scenario being created that forecasts potential changes in the prevalence of a range of conditions, and, as a result, the prospects for increasing healthy life expectancy and the potential demand for health and care services. This exercise took place in January 2019 within days of the release of the NHS Long Term Plan [ 28] blueprint, in which many of the prevention strategies included in the model were heralded. This gave the public health specialists a 'real-time' opportunity to evidence the benefit of the Long Term Plan in our local context.

Population segmentation: Segmentation aims to categorise the population according to their health status, healthcare needs and priorities. According to this approach, groups of people share characteristics that influence the way they interact with health and care services. There is value in segmenting patients by need, complexity and severity of conditions. Segmentation was performed differently for children and adults. Segmentation for the CYP cohort was based on earlier work from the Derbyshire local health system [ 29]. Adult segmentation was based the work carried out by Outcome Based Health Care on behalf of NHS England [ 30].

For CYP, the population aged under 25 years was initially segmented into 8 cohorts and 6 age groups using a local person-level longitudinally linked population dataset known as the Kent Integrated Dataset (KID) [ 17]. The hierarchy for segmentation is illustrated in Figure 1. The eight cohorts for CYP were physical enduring, mental health enduring, learning disability, physical non-enduring, mental health non-enduring, autism and attention deficit hyperactivity disorder and no identified condition. This list is comprehensive and includes 100% of all people within the KID. These cohorts are described in Table S2.

For adults, the population was segmented using the English Longitudinal Study of Ageing (ELSA) [ 31] to gain insight about the progression of need and mortality. The hierarchy of segmentation is illustrated in Figure 2. The KID was also accessed in the same hierarchy to extract local prevalence and rates of access to health and care services.

The modelled adult population was segmented into five cohorts based on the presence of pre-defined health conditions or frailty. These cohorts are (1) severely frail, (2) single conditions with high/complex needs, (3) multiple conditions, (4) single conditions and (5) no conditions. In Figure 2, cohorts (2) and (3) are combined into "Multiple and Complex needs". These cohorts have an increasing progression of need, with cohort (5) as the lowest and cohort (1) as the highest need, and if an individual meets the requirements for more than one cohort, they are assigned to the highest need cohort. Cohort (1) includes those who are severely frail, which is defined as a score of 6 or more disabilities equivalent to moderate and severe frailty within the electronic frailty index [ 32]. Cohort (2) includes individuals with high-needs serious mental illness, severe learning disability, dementia or neurological conditions. Cohort (3) includes individuals with more than one of the following conditions: asthma, coronary heart disease, chronic obstructive pulmonary disease, type 2 diabetes, heart failure, stroke or moderate frailty. Cohort (4) includes individuals who have one of the conditions listed for cohort (3). Cohort (5) includes individuals who do not meet the requirements for cohorts (1-4). These cohorts are described in Table S3.

Model building: The model was split into two sections, CYP (under 18 years and under 25 years for selected health conditions) and adults (18 years and over). The CYP section and adult section have different structures, and the CYP section provides projected populations at age 18 years (and 25 years for selected health conditions), which form inputs to the adult section. The starting point for the model used the incidence, prevalence and mortality for each cohort in 2012 and projects forward to 2037. Initial prevalence as well as incidence and mortality for CYP and adults are shown in Tables S4-S10 [ 17, 19, 31]. This was calculated using local data analysis from the KID and nationally published longitudinal studies [ 17, 18, 19, 20] The approach used epidemiological information to estimate the contributions of changes in population-level risk factors relating to health and wellbeing where the impacts were mainly on the incidence of individual conditions and cohorts. Changes in the uptake of evidence-based interventions were subsequently applied and the impacts of these interventions were mainly measured using case fatality rates over time. The model scope incorporated additional risk factors relating to socioeconomic circumstances. Tables S11 and S12 [ 21, 31] provide details about the sources and methods that were used to accommodate socio-economic circumstances. We used socio-economic status as a proxy indicator of socioeconomic circumstances. This model examined the effects of changes in treatment uptake and risk factor trends on changes in cohort incidence, prevalence and mortality. It also explored the extent to which prevention strategies impact the incidence and mortality of cohorts.

The model estimated changes in incidence and deaths related to changes in adult risk factor levels in the population. The risk factors considered were cigarette smoking, total cholesterol (TC), systolic blood pressure (SBP), body mass index (BMI), physical inactivity and alcohol consumption, and these are listed in Table S13 [ 21]. The Health Survey for England was used to calculate trends in the prevalence (or mean values) of each risk factor. In both the CYP and adult sections of the model, two approaches to calculating relative risk reductions from changes in risk factors were used: the regression approach and change in the population attributable fraction (PAF). In the regression model for adults, the incidences of cohorts in 2012 (the start year) were multiplied by the absolute change in risk factor level and by a regression coefficient ('beta') quantifying the estimated relative change in cohort incidence and mortality that would result from a one-unit change in risk factor level. The regression (beta) coefficients used in these analyses for key risk factors are listed in Tables S14-S16 [ 33, 34, 35, 36]. A 'fixed gradient' approach was used to stabilise the estimates of risk factor change across the quintiles. Natural logarithms were used, as is conventional, in order to best describe the log-linear relationship between absolute changes in risk factor levels and relative change in incidence and mortality. The PAF approach can be interpreted as the proportion by which the incidence or mortality would be reduced if the exposure were eliminated. Worked examples for the two approaches are presented in Figures S1 and S2. Relative risks are displayed in Tables S17-S23 [ 37, 38, 39, 40, 41, 42, 43]. The CYP section included 5 interventions, as shown in Table 1, which were activated to test projected future impact. For each of the CYP cohorts, we estimated the proportions of incidence that were attributable to various treatments or interventions. We adopted the general approach of calculating the risk reduction from an intervention among a particular cohort by multiplying the change in the proportion of people exposed to a risk factor by the incidence rate and by the relative reduction due to the change in intervention or exposure. The approach to measuring the impact of interventions or risk factors for children was exactly the same as for adults using the PAF in most cases. The only difference was the application of a delay if the impact of an intervention in childhood occurs in adulthood. For example, the impact of changes in adverse childhood experience upon serious mental illness in adults is delayed by an average of 10 years. However, changes in smoking during pregnancy impact upon stillbirths immediately, similarly for breastfeeding upon child obesity.

The primary outcome measures of the model were cohort incidence, prevalence and deaths projected over the model timescale and the impacts of cohort incidence and prevalence on potential demand for health and wellbeing services. The calculation of the modelled impacts of change on incidence and mortality was based on utilising two well-studied relationships. The first is a change in risk factor against a relative change in incidence and mortality, and the second is changes in intervention uptake resulting in mortality reductions. Estimates in relative risk reduction for both relationships were derived from previous randomised controlled trials and meta-analyses, as shown in Tables S17-S23 [ 37, 38, 39, 40, 41, 42, 43]. The incidence and mortality benefits from the risk factor reduction in the population and the treatment and intervention benefits in patient groups were then summed. This summing used a cumulative approach rather than an additive approach [ 44] to avoid double-counting benefits in the same individual. This sum represents the changes in incidence and mortality 'explained' by policy changes made within the model.

Model structure: SDM was chosen for this project due to the complex interactions and dynamic nature of the system. An example of the causal loop diagrams used to investigate and visualise relationships in the system prior to model building is demonstrated in the Supplementary Materials (Figure S3). As the final SDM model has a total of 63 stocks, 170 flows and 869 converters, which generated 9024 variables including multiple element arrays and graphical functions, a simplified model structure is illustrated in Figure 3. The first five interventions in Table 1 apply to CYP section of the model and the others apply to the adult section. The left of the figure shows the CYP model structure and illustrates the movement of the population from birth through an aging chain (0-1, 2-4, 5-10, 11-15, 16-17 and 18-24 years) whilst also moving between different health cohorts, represented by the vertical arrows. The aging chain arrows represent the natural flow of the population from birth on to different age groups and flowing to the adult model at 18 and 25 years. The physical and mental enduring and LD cohorts move to the adult model at 25 years and progress to the same cohort group. For all other cohorts, they enter the adult model at 18 years and progress to the healthy cohort. Risk factors for CYP do carry a rate of risk across to the adult cohorts (e.g., child obesity and adult diabetes). The vertical arrows represent the progression or recovery of CYP who are flowing from different cohorts or health states over time (incidence). Adults flow from one cohort to another cohort without an aging chain, e.g., from healthy to a single condition. People flowing into or out of the geography are included in the model via net migration per cohort and people flowing out of a cohort due to death are represented by the red arrows. These rates of flow were determined by the data outlined in Tables S4-S12 [ 17, 19, 31]. Tables S2 and S3 outline the cohorts used in the model and illustrate the SD model structure in more detail.

Model calibration: To initiate the model with population cohorts through incidence, prevalence and mortality, we used various data sources, which are outlined in Tables S4-S12 [ 17, 19, 31]. Additionally, population-level risk factors were used to influence the impacts across cohorts, which are listed in Tables S17-S23 [ 37, 38, 39, 40, 41, 42, 43]. In the first instance, we used local data to initiate the model to consider differences in demographics and risk factors. The process of calibration involved importing baseline data and projections from various national sources including the Office for National Statistics and KCC housing forecast [ 18] to carry out validation of general outcome measures such as population and mortality. Locally, further validation took place to check against known outcome measures such as the Quality Outcome Framework [ 45] and health and care activity data.

The model validation process followed the framework outlined by Yarnoff et al. [ 46], and the five validation stages were undertaken in accordance with this. Face validation, involving assessment by subject matter experts (public health consultants from the locality), was achieved through model development in group model building workshops and through ongoing testing. This included continual one-way sensitivity analysis in order to validate input factors and the ranges of variables with their associated effect. Internal validation, verifying the model's code and calculations, involved a secondary modeller and analyst, who had not participated in the model development, reviewing model logic and calculations and evaluating the sensitivities. In cross validation, which compared the model output to other available models, we reviewed all of the available evidence of comparable models. Due to the novel nature and aims of this project, we were unable to find models with a similar magnitude and scope; however, individual sources of evidence were used in calibration and sensitivity testing. We encountered a similar challenge with external validation, which compares modelled outputs to surveillance data, and predictive validation, which compares modelled impacts to actual observations resulting from interventions. Although surveys were not available for the local health population and limited intervention and actual data could be retrieved, consensus amongst public health experts and healthcare providers along with the triangulation of academic literature was used where data were not available. Where appropriate, proxies for comparable regions or national average data were used in agreement with subject matter experts. Due to the complexity of the model and high number of variables, including graphical functions and arrayed elements, a small number of key prevalence percentages were selected for single output-level validation through discussion with subject matter experts and ongoing sensitivity testing throughout development. Similarly to Zhang et al., [ 47] relative deviation rate and average relative deviation rate were used to demonstrate the deviation between simulated outputs and surveillance data or externally modelled data (calculations for these are available in Figure S4). Single output and population validation results are shown in Figures S5 and S6 and Tables S24 and S25. The model represented the time trends in the population for CYP (0-17) and adults (18+) well when compared to ONS 2018 [ 48] population projections, with the largest average relative deviation of 1.12% ( Table S25 and Figure S6). Validation against external data sources was difficult because the base population of the model included major longitudinal studies. However, there was good agreement between modelled condition prevalence for CHD, COPD, stroke and diabetes compared to quality and outcomes framework (QoF) data ( Table S24 and Figure S5) [ 45]. The relative deviation for these variables ranges from 0.01% to 10.35%, and the average relative deviation ranges from 3.77% to 4.84%.

Sensitivity testing was based on Hekimoğlu and Barlas' behaviour sensitivity analysis algorithm [ 49]. The initial screening of key input factors was created during development, where sensitivities and ranges of input values, practical for public health planning and policy, were agreed on by experts. As noted above, sensitivities were further tested during internal validation. The regression model of behaviours was undertaken using ranges around selected input values (for example, input variables for healthy life expectancy at 18 are shown in Table S26), and five runs for each variable based on incremental steps were run through Stella Architect's model analysis tool (including all combinations). For the healthy life expectancy at 18 output, this resulted in 625 runs and the behaviour shown in Figure S7. However, selecting a dependent variable value that represents the behaviour did not fall within the examples of Hekimoğlu and Barlas' work [ 49]. Firstly, this model can not be defined as an inherently oscillating or tipping point. Secondly, our model is significantly more complex and has a far greater number of elements, including graphical interface variables and multiple arrays. The regression-dependent variable selected was based on the difference between the start and end values of the outputs, representing the change in health over the modelled period. The results of the regression in this example based on this calculation showed an R ² adjusted >80 and significance at p < 0.05 of all included variables.

The model was developed using a software platform known as Stella Architect developed by Isee Systems, which is accessible via Isee exchange [ 50]. Following calibration of the model, outputs were viewed and extracted.

Model interventions: Thirteen public health interventions were agreed on by Kent County Council public health professionals to achieve "Better Health" for their population. They were selected based on the latest published evidence and national policy [ 27] of these interventions in improving health. The interventions are listed in Table 1. The level of change and the target to be achieved were also agreed on by local professionals in the better health workshop by mutual consensus.

The CYP section of the model included interventions 1-5 in Table 1, and relative risk is shown in Table S27 [ 51, 52, 53, 54, 55, 56, 57]. The adult section of the model included interventions 6-13 in Table 1, which could be activated to test the projected future impact. For each cohort, we estimated the proportion of incidence and deaths that were attributable to various treatments or interventions. Data sources used to estimate the percentage at risk from the included interventions are displayed in Table S1 [ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. The general approach to calculating the risk reduction from an intervention among a particular cohort was to multiply the change in the proportion of people exposed to a risk factor by the incidence rate and by the relative reduction due to the change in intervention or exposure. Sources for current risk factors and treatment uptake are shown in Table S1 [ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Sources for estimates of treatment efficacy (relative risk reductions) are shown in Tables S17-S23 and S27 [ 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]. When multiple risk factors impacted simultaneously on incidence and mortality, they were jointly estimated by calculating cumulative risk reduction. Examples of the calculations to find treatment or incidence impacts, cumulative risk factor impact and proportional changes in incidence and mortality over time are shown in Figures S8-S11. This accounts for risk factor prevalence overlap but assumes independence of effects [ 44].

3. Results

We present the outputs of the model using North Kent as an example, which covers 22% of the Kent population and 27% of Kent County's land mass.

4. Discussion

Main Finding

We have described an SD simulation model for the population of Kent in southeast England, showing the impacts of a range of prevention interventions on life expectancy, the prevalence of long-term conditions, healthcare utilisation and cost. The model was initialised from 2012 and closely matches the historical data up till 2018. Of the 13 evidence-based prevention interventions that were simulated, 5 were applied to children and young people and 8 to the adult section. The application of the "Better Health" scenario in the model resulted in changes to the size of the four cohorts over the model period ( Figure 4). The size of the no-condition cohort increased, and the other three cohorts decreased in size. This shows the marginal benefit of the combined effect of the interventions across the course of life, at pace and scale. The impact of the interventions on both life expectancy at birth and healthy life expectancy is significant, adding 5.1 and 5.0 years, respectively ( Figure 5). This is significant from an individual perspective in terms of adding years to life and life to years, but the increase in the overall proportion and size of the healthy living population is moderated due to the dynamic properties of complex systems. Any improvement in the health status of the population leads to a productive workforce and its associated positive impact on the wider economy and society as a whole.

Figure 4. Population changes in the four cohorts due to no interventions or "Better Health" scenario. This shows the four cohorts and the difference in percentage of the population of that cohort at the end of the model period from the do nothing (no interventions) or "Better Health" scenarios.

Figure 5. Output of the model showing change in life expectancy and healthy life expectancy from birth due to no interventions or "Better Health" scenario.

Figure 6. Model output of non-elective admissions and accident and emergency attendance due to no interventions or "Better Health" scenario.

Using the modelling approach, we have also demonstrated the impact on healthcare utilisation in terms of emergency admissions and attendance at accident and emergency centres. Although the reduction in activity appears insignificant, the estimated accrued cost savings calculated using the unit price of activity over the model period is noteworthy, as for one area of Kent, it is GBP 7.8 million (GBP (Pound Sterling) 1 = USD (United States Dollar) 1.22) ( Figure 6). In the "Better Health" scenario, the modelling shows a significant reduction in most of the long-term conditions over the course of the model. All thirteen conditions except neurological conditions and moderate frailty show varying levels of reduction. Three conditions show a reduction well over 5% when compared to no interventions-stroke (15.04%), COPD (8.13%) and diabetes (6.66%). This demonstrates the robustness of the evidence base behind the included interventions ( Table 2).

The result of our study aligns with broader research in this area that uses SDM to address the complexities of preventing chronic diseases and other associated conditions. By creating "do nothing" versus "do something" scenarios, important distinctions are revealed, showing the long-term gains by investing in preventative actions [ 58]. To our knowledge, the systematic review by Wang et al. is the first attempt to evaluate the application of SDM to chronic diseases, which included 34 studies. Surprisingly, there were no studies from the UK, and the majority were from the USA. This represents a gap in the literature in the UK and the relevance of this research. Studies analysed differences between upstream and downstream prevention measures for chronic conditions. Upstream interventions include wider determinants such as improving income and community cohesion, whereas downstream interventions include behaviour-related interventions. Although only downstream interventions were found to significantly reduce chronic disease and mortality, the resources to fund them would need to be redirected from upstream allocations to meet these pressures. Upstream interventions, however, would reduce the prevalence of chronic illness but would have the added value of an increased impact on economic productivity. This demonstrated how SDM analyses health challenges as a whole, rather than taking a less reliable, simplistic view. Homer and Hirsh [ 7] explain the conditions that are best suited for the application of SDM to public health actions. They state that prevention models should incorporate all the elements of the ecological approach, incorporating disease outcomes, health and risk behaviour, environmental factors and health-related resources and delivery systems. There were notably very few examples of studies that simulated wider determinants, including employment, socioeconomic status and community cohesion, in the literature. This is one of the limitations of our model, as explained in the limitations section. Some studies focus on the qualitative process of engaging thought leaders and health planners in prioritising actions. Loyo et al. [ 59] demonstrated that SD modelling and local expertise were valuable tools in reprioritising community issues, obtaining community buy-in and determining the best use of community resources.

Further steps and future direction: The model provided the basis for conversations with health leaders, particularly in the North Kent system, where this needs-led approach to forecasting future demand became the subject of healthy debate. The approach was distinct from the extant 'big consultancy' solutions that projected future demand based on recent trends, sometimes also 'adding on' demographic changes, making the relationship between need and demand opaque. This led to a significant over-estimation of future demand, to the point that local plans to invest in community alternatives to inpatient care became unaffordable, thus undermining the confidence of local leaders in their ability to achieve a sustainable long-term solution. The use of the cohort model outputs formed the basis of a blended approach to demand forecasting that used trend analysis in the short term, gradually being replaced in a blended fashion using a needs-led approach. Cohort modelling is seen as complementary to population health management approaches [ 60] that are also based on segmentation but are designed to enable targeted interventions by professionals rather than strategic prospective modelling. Population health management represents the population segmented at a particular point in time. In SDM, the segmentation data are used to produce a dynamic projection of the population across segments and cohorts. Thus, both approaches complement each other. Going forward, investment is required to build up local research infrastructure to undertake evaluation studies in order to generate reliable evidence for model inputs. Currently, the cohort model does not include wider determinants. However, we are in the process of expanding the model by including wider determinants such as income, housing and education. This is likely to simulate much more pronounced health effects on the population than behavioural and healthcare determinants [ 61].

Strengths and limitations: SDM is a better approach than the traditional linear modelling and forecasting as it is able to deal with complex and dynamic systems and their interactions. For example, the draining of a stock through the application of incidence rates based on the presence of risk factors feeds back to reduce the absolute size of the stock and, therefore, the relationship between relative and absolute rates of flow, i.e., absolute rates of flow will reduce as a result of this feedback. These complex relationships are easily represented and calculated at each model time-step using the stock-flow characteristics of SDM, making these explicit and transparent to a model user. During the development of the model, we involved key stakeholders, including subject matter experts, in conceptualising the model, testing and developing the various inputs, and validated the selected scenarios. We also used validated, up-to-date local data to inform model assumptions and calibrate the model outputs.

In terms of the reliability of the results of the model, SDM is deterministic and, hence, the scenario run outputs will remain the same, assuming model inputs and assumptions remain constant, which is the case in our study.

Consideration of possible lag time and our approach: As the model is operating at a population level, the concept of considering lag at an individual patient level is not directly applicable. In the 'no intervention' scenario, disease incidence is routinely applied as an annual rate, which is converted into a monthly rate. Changes in risk factors in the Better Health scenario are applied to the incidence to increase or decrease this rate. Secondly, the specification of a timescale for each intervention is displayed in Table 1. This also affects disease incidence rates and is applied over the model timeframe.

Our model did not include an aging chain for the adult model and, hence, age-level assumptions could not be used. This led to the model being more generic. As set out in the Main Finding section, our initial model also did not incorporate wider determinants, and we are addressing all the identified limitations in the future version of the model, which is currently under development. Additionally, in regard to the sensitivity analysis performed, the significance of the input values on change in health over the model period are meaningful. However, the understanding of behaviour patterns and the ability to compute the simulation runs needed to test the model based on the behaviour sensitivity analysis algorithm [ 48] mentioned above require further study.

5. Conclusions

We have demonstrated the feasibility, applicability and utility of using system dynamics modelling to simulate the impacts of various preventive interventions on health status and healthcare utilisation in the local population. We created a "Better Health" scenario based on 13 interventions and were able to produce outputs through the model compared to the "no intervention" scenario. From the model conception stage to selecting interventions, we worked with stakeholders and subject matter experts, which further strengthened and added value to our approach. Through our modelling, we were able to demonstrate to the decision makers that investing in these prevention interventions will lead to an increase in the proportion of healthy people in the local population, a reduction in those with one or more health conditions and frailty, an increase in life expectancy, reduced urgent healthcare utilisation and reduced expenditure to the local health services, and will prevent the occurrence of many long-term conditions. If these results are scaled up to a wider geography, this could be potentially very significant. This modelling approach has helped us to have informed conversations backed by evidence with local healthcare leaders in our attempt to provide a realistic view of prevention impact on population health and reducing demand on local health services and cost.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/systems11050247/s1. The Supplementary Materials include Table S1: Data inputs and sources, Table S2: Children and young people cohort definitions, Table S3: Adult cohort definitions, Table S4: Prevalence of children and young people long-term conditions, Table S5: Prevalence of adult long-term conditions, Table S6: Adult percentage prevalence of single-condition long-term conditions within multiple and frail cohorts, Table S7: Incidence per 1000 people aged 18 and over, Table S8: Incidence and mortality rates per 1000 people aged 18 and over, Table S9: Cause of death percentage aged 50 and over, Table S10: ONS mortality by main cause of death, Table S11: Adult percentage prevalence of long-term conditions by social group, Table S12: Observed risk factor levels in 1999 and 2009 by social class, Table S13: Variable definitions for adult risk factors, Table S14: Beta coefficients for major risk factors: systolic blood pressure, Table S15: Beta coefficients for major risk factors: body mass index, Table S16: Beta coefficients for major risk factors: cholesterol, Table S17: Relative risk for underlying risk, incidence and mortality: smoking in adults, Table S18: Relative risk for underlying risk, incidence and mortality: physical inactivity in adults, Table S19: Relative risk for underlying risk, incidence and mortality: obesity and overweight in adults, Table S20: Relative risk for underlying risk, incidence and mortality: dementia in adults, Table S21: Relative risk for underlying risk, incidence and mortality: hypertension and hypercholesterolaemia in adults, Table S22: Relative risk reduction for CHD and stroke, Table S23: Relative risk for underlying risk, incidence and mortality: alcohol consumption in adults, Table S24: Single prevalence validation through relative deviation rates for Kent, Table S25:Population validation through relative deviation rates for Kent, Table S26: Sensitivity analysis testing ranges, Table S27: Relative risk for underlying risk, incidence: breastfeeding, smoking in pregnancy, child obesity, fuel poverty and ACE in Children and Young People, Figure S1: Estimation of risk factor changes using regression method, Figure S2: Estimation of incidence and mortality changes from risk factor changes using the PAF method, Figure S3: Causal Loop Diagram, Figure S4: Relative deviation, Figure S5: Visual single prevalence model validation for Kent, Figure S6: Visual population model validation for Kent, Figure S7: Sensitivity analysis variation for Healthy Life Expectancy (HLE) at 18, Figure S8: Model validation, Figure S4: Estimation of incidence and mortality changes from a specific treatment, Figure S9: Estimation of incidence changes from fuel poverty changes, Figure S10: Cumulative risk-reduction and Figure S11: Proportional change in cohort incidence and mortality rate over time.

Author Contributions

All authors have met authorship requirements. Specifically, all authors substantially contributed to manuscript development across the following inputs: conception and design (A.G. (Abraham George), A.G. (Alex Gray), P.B., C.H., P.T., P.L., D.S.), acquisition of data (A.G. (Abraham George)., A.G. (Alex Gray), C.H., P.T., P.L.), analysis (A.G. (Alex Gray), C.H., P.T., P.L.) and interpretation of data (A.G. (Abraham George), A.G. (Alex Gray), D.S., C.H., P.B., P.T., P.L.), drafting the article (A.G. (Abraham George), A.G. (Alex Gray), D.S., C.H., P.B., P.T., P.L.), data visualization (A.G. (Alex Gray), CH.) and reviewing the article and revising it critically (A.G. (Abraham George), A.G. (Alex Gray), D.S., C.H., P.B., P.T., P.L.) All authors have read and agreed to the published version of the manuscript.

Funding

This research received no specific external funding. Kent County Council commissioned the Whole Systems Partnership to develop a system modelling tool for the local Joint Strategic Needs Assessment.

Data Availability Statement

All data presented in this article were extracted from local and publicly available data sources.

Acknowledgments

The authors are grateful to Natalie Adams for her contributions in the development of the manuscript in its early stages.

Conflicts of Interest

A.G., C.T., P.T. and P.L. are part of the Whole Systems Partnership, who provide support for partnership development and system redesign in health and social care, and one of the approaches they use is systems dynamic modelling. A.G., D.S. and P.B. have no conflicts of interest to declare.

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George A, Badrinath P, Lacey P, Harwood C, Gray A, Turner P, Springer D. Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice. Systems. 2023; 11(5):247. https://doi.org/10.3390/systems11050247

Chicago/Turabian Style

George, Abraham, Padmanabhan Badrinath, Peter Lacey, Chris Harwood, Alex Gray, Paul Turner, and Davinia Springer. 2023. "Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice" Systems 11, no. 5: 247. https://doi.org/10.3390/systems11050247

George, A., Badrinath, P., Lacey, P., Harwood, C., Gray, A., Turner, P., & Springer, D. (2023). Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice. Systems, 11(5), 247. https://doi.org/10.3390/systems11050247

# data[].attributes.attachment_link.title
Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice

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In public health, the routine use of linear forecasting, which restricts our ability to understand the combined effects of different interventions, demographic changes and wider health determinants, and the lack of reliable estimates for intervention impacts have limited our ability to effectively model population needs.

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Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach

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Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach

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Objectives To develop, test, validate and implement a system dynamics model to simulate the pandemic progress and the impact of various interventions on viral spread, healthcare utilisation and demand in secondary care.

Design We adopted the system dynamics model incorporating susceptible, exposed, infection and recovery framework to simulate the progress of the pandemic and how the interventions for the COVID-19 response influence the outcomes with a focus on secondary care.

Setting This study was carried out covering all the local health systems in Southeast of England with a catchment population of six million with a specific focus on Kent and Medway system.

Participants Six local health systems in Southeast of England using Kent and Medway as a case study.

Interventions Short to medium ‘what if’ scenarios incorporating human behaviour, non-pharmaceutical interventions and medical interventions were tested using the model with regular and continuous feedback of the model results to the local health system leaders for monitoring, planning and rapid response as needed.

Main outcome measures Daily output from the model which included number infected in the population, hospital admissions needing COVID-19 care, occupied general beds, continuous positive airway pressure beds, intensive care beds, hospital discharge pathways and deaths.

Results We successfully implemented a regional series of models based on the local population needs which were used in healthcare planning as part of the pandemic response.

Conclusions In this study, we have demonstrated the utility of system dynamics modelling incorporating local intelligence and collaborative working during the pandemic to respond rapidly and take decisions to protect the population. This led to strengthened cooperation among partners and ensured that the local population healthcare needs were met.

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Verifying you are human. This may take a few seconds.

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Underserved groups access to reproductive services: A literature review of women aged 18–25 years

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Underserved groups access to reproductive services: A literature review of women aged 18–25 years

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The Covid-19 pandemic highlighted the need for several improvements for young women, in terms of accessing, funding, and strategies for the development of sexual and reproductive health care (SRH). Core recommendations were highlighted by the All-Party Parliamentary Group (APPG) Report (Johnson,2020) on sexual and reproductive Health in the UK. Key recommendations included that the choice of method and delivery of reproductive services for women is reducing. This was furthered globally by the World Health Organisation (WHO) (2023) who note a need for equity in access, supportive health monitoring and a focus on underserved groups.

This literature review was conducted using the established method of thematic analysis (Braun & Clarke,2006), and in parallel with the APPG report and WHO recommendations, consolidates the findings into three key themed areas: underserved women’s reproductive healthcare, cost effective contraception, young people’s access to and understanding of fertility and contraception services. Underserved communities such as young women from marginalised groups, people of colour (Messinis et al,2021), women who have sex with women (Burkill & Waterhouse2019) and women with disabilities (McCarthy,2011), experience inadequate and disparity of provision when accessing reproductive services. The review highlights limited high quality, recent, UK-based studies to further inform local governments, that considers the views and needs of young women 18–25 years when accessing reproductive services to implement change.

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Domestic violence and suicide in women under the care of mental health services in the UK, 2015–2021: a national observational study

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Domestic violence and suicide in women under the care of mental health services in the UK, 2015–2021: a national observational study

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Background

There is growing evidence of a link between domestic violence and suicidality. We present the first national analysis of women with mental disorders who died by suicide having experienced domestic violence. We aim to make recommendations to improve the safety of services, particularly for women who experience domestic violence.

Methods

We obtained data on women who died by suicide under the care of UK mental health services (2015–2021). We compared women who experienced domestic violence with women with no known history. Logistic regression identified associations between person and care characteristics and domestic violence.

Findings

We identified 630 women who experienced domestic violence, 26% of all women with mental disorders who died by suicide. These women were younger (median age 42 vs. 47), and more often unemployed (366, 64% v. 641, 39%; p0.002). Adverse life events were common (378, 66%), most often serious financial problems (104, 23% v. 163, 10%; p < 0.001) and/or loss of job, benefits, or housing (97, 20% v. 198, 13%; p < 0.001). Many had a diagnosis of PTSD (83, 13% v. 72, 4%), self-harm (500, 83% v. 1,077, 62%), and alcohol misuse (371, 63% v. 477, 27%). They were more likely to have a history of violence as a perpetrator (124, 22% v. 87, 5%; p < 0.001).

Interpretation

Many factors associated with suicide are also associated with domestic violence (e.g., unemployment, serious financial problems), suggesting intersecting disadvantages. Mental health clinicians have a responsibility to enquire about domestic violence and address its impacts as an integral part of suicide prevention.

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Improving and protecting health in England needs more than the NHS

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Improving and protecting health in England needs more than the NHS

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Improving and protecting health in England needs more than the NHS

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We welcome the Editors′1 call for a long-term strategy for a resilient health system
for England. However, the Editors do not seem to recognise that the system to protect
and improve the health of the population is led by local authorities and Public Health
England, not the National Health Service (NHS). Local authorities and Public Health
England lead communicable disease control and have led regional and local responses
to the pandemic. The NHS has not, because it has not been responsible for health protection
and health improvement since the 2012 Health and Social Care Act.

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Exploring freely accessible data describing wider determinants of health in England

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Exploring freely accessible data describing wider determinants of health in England

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Introduction
In England, life expectancy has stalled and significant decreases observed in certain geographical areas and populations. The cause of this involves complex dynamics between an individual's health, characteristics, lifestyle, and their wider environment known as the wider determinants of health which are key to good life expectancy, healthy life expectancy, and prevention of long-term medical conditions. Knowing the availability, breadth, features, and linkage potential of datasets relevant to wider determinants of health is important for exploring trends and associations for policy and public health planning.

Methods
A systematic mapping of internet content identified accessible datasets relevant to wider determinants of health in England with town level geographical granularity or lower. Search terms were used in search engines and chatbots to identify weblinks subsequently examined for eligible datasets.

Results
105 potential weblinks to datasets were identified. Of these, twenty-one weblinks were explored further after exclusion of those: not accessible or currently live (n = 13); duplicated across search engines (n = 17); providing information only (i.e. no raw data, n = 14); did not provide freely accessible data (n 3); were not relevant to wider determinants of health (n = 17); lacked geographical granularity (n = 26). Eighty-nine datasets of interest were compiled with sub-town level data aggregation. Approximately half (n = 47, 52%) were from the England and Wales census 2021, with the remaining sources including government bodies, public services, and research datasets. Datasets covered many valuable categories of wider determinants of health. Key data gaps included food consumption, social care data and community/voluntary services.

Conclusion
In England, access to data relevant to wider determinants of health is good and available at relatively small geographical resolution. Accessible datasets were identified and compiled within multiple categories of wider determinants of health as a useful data resource to explore wider determinants of health at place if linked to relevant health data or population studies.

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Centre for Health Services Studies, University of Kent, Canterbury, UK

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Data resource profile: Exploring freely accessible data describing wider determinants of health in England | International Journal of Population Data Science

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IntroductionIn England, life expectancy has stalled and significant decreases observed in certain geographical areas and populations. The cause of this involves complex dynamics between an individual’s health, characteristics, lifestyle, and their wider environment known as the wider determinants of health which are key to good life expectancy, healthy life expectancy, and prevention of long-term medical conditions. Knowing the availability, breadth, features, and linkage potential of datasets relevant to wider determinants of health is important for exploring trends and associations for policy and public health planning.
MethodsA systematic mapping of internet content identified accessible datasets relevant to wider determinants of health in England with town level geographical granularity or lower. Search terms were used in search engines and chatbots to identify weblinks subsequently examined for eligible datasets.
Results105 potential weblinks to datasets were identified. Of these, twenty-one weblinks were explored further after exclusion of those: not accessible or currently live (n = 13); duplicated across search engines (n = 17); providing information only (i.e. no raw data, n = 14); did not provide freely accessible data (n 3); were not relevant to wider determinants of health (n = 17); lacked geographical granularity (n = 26). Eighty-nine datasets of interest were compiled with sub-town level data aggregation. Approximately half (n = 47, 52%) were from the England and Wales census 2021, with the remaining sources including government bodies, public services, and research datasets. Datasets covered many valuable categories of wider determinants of health. Key data gaps included food consumption, social care data and community/voluntary services.
ConclusionIn England, access to data relevant to wider determinants of health is good and available at relatively small geographical resolution. Accessible datasets were identified and compiled within multiple categories of wider determinants of health as a useful data resource to explore wider determinants of health at place if linked to relevant health data or population studies.
Key Features This data resource profile describes a systematic mapping of freely accessible population data on wider determinants of health in England. To the authors knowledge this is the first comprehensive compilation of freely accessible data resources of this kind.
This data resource profile was created to support research into the mechanisms and impact of wider determinants on the health of populations in England but is applicable to research and populations studies wider than this.
Eighty-nine datasets were identified that may be of use to researchers in health and other population data fields. Datasets are held separately but many have the potential to be linked through common geographical area codes to other relevant datasets such as health data.
The datasets originate from multiple sources including government bodies, public services, and research datasets. They cover many key categories of wider determinants of health including socioeconomics, employment, housing, support services, and the environment. Key data gaps included food consumption, social care data and community/voluntary services.
The data resource profile in this article links directly to freely accessible datasets at the time of publication in the tables provided. The authors welcome contact from researchers for collaboration on exploring wider determinants of health through data use. Please contact Dr Melanie Rees-Roberts m.rees-roberts@kent.ac.uk

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A systematic review of economic evaluations of local authority commissioned preventative public health interventions in overweight and obesity, physical inactivity, alcohol and illicit drugs use and smoking cessation in the United Kingdom

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A systematic review of economic evaluations of local authority commissioned preventative public health interventions in overweight and obesity, physical inactivity, alcohol and illicit drugs use and smoking cessation in the United Kingdom

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Background: Since 2013, local authorities in England have been responsible for commissioning preventative public health interventions. The aim of this systematic review was to support commissioning by collating published data on economic evaluations and modelling of local authority commissioned public health preventative interventions in the UK.

Methods: Following the PRISMA protocol, we searched for economic evaluations of preventative intervention studies in four different areas: overweight and obesity, physical inactivity, alcohol and illicit drugs use and smoking cessation. The systematic review identified studies between January 1994 and February 2015, using five databases. We synthesized the studies to identify the key methods and examined results of the economic evaluations.

Results: The majority of the evaluations related to cost-effectiveness, rather than cost-benefit analyses or cost-utility analyses. These analyses found preventative interventions to be cost effective, though the context of the interventions differed between the studies.

Conclusions: Preventative public health interventions in general are cost-effective. There is a need for further studies to support justification of continued and/or increased funding for public health interventions. There is much variation between the types of economically evaluated preventative interventions in our review. Broader studies incorporating different contexts may help support funding for local authority-sponsored public health initiatives.

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A systematic review of economic evaluations of local authority commissioned preventative public health interventions in overweight and obesity, physical inactivity, alcohol and illicit drugs use and smoking cessation in the United Kingdom - PubMed

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Preventative public health interventions in general are cost-effective. There is a need for further studies to support justification of continued and/or increased funding for public health interventions. There is much variation between the types of economically evaluated preventative interventions i …

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Managing COVID-19 outbreaks in prisons – a brief review of literature and key lessons learn

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Managing COVID-19 outbreaks in prisons – a brief review of literature and key lessons learn

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Purpose

During the COVID-19 pandemic, prisons faced a unique challenge of preventing and managing outbreaks with minimal adverse impact. This study aims to describe the epidemiology of COVID-19 in prisons, identify lessons learnt and make recommendations.

Design/methodology/approach

The authors used the PubMed advanced search function using MeSH terms; (coronavirus, sars) AND (prisons) AND (disease outbreaks). The authors included original research reporting COVID-19 outbreaks in prisons. All other types and non-English publications were excluded. The authors used a structured data abstraction template to extract data systematically, and a second author independently abstracted data from 10% of the papers for quality assurance.

Findings

The search yielded 96 hits. The authors included 15 studies meeting the inclusion criteria. These studies were from four countries. Seven studies reported individual outbreaks. The mean and median number of inmates and staff were 1,765, 1,126 and 575, 510. The mean and median number of cases among inmates and staff were 584, 464, and 72, 77. The number of reported deaths varied from 0 to 11. The authors present the prison-specific hazards grouped under human factors, healthcare factors and environmental factors. The authors also summarise interventions deployed as either primary prevention interventions, such as vaccinations, or secondary prevention interventions, including screening and contact tracing.

Originality/value

This narrative review summarises the prison-specific hazards, which include movement of people in and out of the person, moving in new prisoners from other prisons, mixing of prisoners when transporting to courts, limited medical and isolation resources, crowded dormitories, shared lavatories, small communal facilities, poor ventilation and overcrowding. The interventions included limiting non-medical transfers into and out of the persons, assigning staff members to specific areas, encouraging face coverings among prisoners and staff and social isolation measures within the constraints of the prison setting. The interventions were adopted by prison authorities to contain and manage the outbreaks. Public Health and prison authorities need to be aware of the risk of further outbreaks of COVID-19 and other infectious diseases in these settings and implement key measures identified in this review to minimise adverse outcomes.

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September Newsletter

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This guide supports commissioners in embedding evaluation into the commissioning cycle for services and interventions.

It outlines how to plan for evaluation from the outset, ensure appropriate data governance, and how the Research Team can support the process.

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Evaluation Challenges in Local Authorities

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Evaluation Challenges in Local Authorities

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The evaluation process may look straight forward on paper. However, evaluations in local authorities often don’t tend to go the way you originally planned due to the nature of the working environment. Therefore, this webinar will help you to think about the challenges you might face so that you can work these into your plans and consider the best approach for your tasks.

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Evaluation Challenges in Local Authorities

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Why do we evaluate and when should we do it?

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Why do we evaluate and when should we do it?

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Why do we evaluate and when should we do it? Andy Jones

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CHSS Learning and Research Development Opportunities

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CHSS Learning and Research Development Opportunities

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CHSS / Centre for Health Services Studies

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Learning and Research Development Opportunities - CHSS / Centre for Health Services Studies - Research at Kent

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Local authority public health practice: a case study

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Local authority public health practice: a case study

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This article describes the Level 7 Systems Thinking Apprenticeship training completed by PH specialists Paul Clarke, Lin Guo and Nirosha Dissanayake, particularly around how they have applied their new skills in practice and key reflections from their experiences.

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Embedding systems thinking in local authority public health practice: a case study - A George, D Ahmed, L Guo, N Dissanayake, P Clarke, B Dalrymple, 2025

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Systems thinking is a well-established epistemology, often described in public health journals and articles, with growing recognition that a complex systems approach involving cross-boundary collaboration within and between organisations helps to better solve difficult public health challenges or ‘wicked issues’.1,2

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Curious about Health Research? Whether you're just starting out or an experienced professional—clinical or not—this wide range of flexible courses can help you build your role in the field.

Whatever your health professional background, the HealthUEA Academy for Professional Development offers a variety of educational courses to suit your individual learning needs.

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Take your place in our academy

Joining the HealthUEA Academy for Professional Development means becoming part of a dynamic professional community focused on advancing healthcare practice and education. You'll connect with leading experts in pharmacy, medicine, nursing, and other health disciplines, supported by state-of-the-art resources and a collaborative environment.

HealthUEA benefits from strong partnerships with local and national healthcare organisations. Our courses are shaped by real-world insights, with opportunities for professional placements, applied research projects, and networking with industry leaders to enhance your career prospects.

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Curious about Health Research? Whether you're just starting out or an experienced professional-clinical or not-our wide range of flexible courses can help you build your role in the field.

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Please scan the QR code below to join the collaborative platform

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Please scan the QR code below to join the collaborative platform

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Membership benefits and key features:

• Funding and grant information

• Find collaborators for your R&I programmes

• Webinars and training programmes

• Research and innovation resources

• Network with Health and Care, Academic, Research and Government organisations across Kent and Medway

Please note the email address to contact the KMRIC team is kmric-managers@lists.kent.ac.uk

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Kent and Medway Joint Research and Innovation Collaborative

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NIHR's Research Support Service Animation

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Sharing the consensus definition of research

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Sharing the consensus definition of research

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Sharing the consensus definition of research

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This e-learning module was developed by Kent County Council in collaboration with the Kent and Medway Integrated Care System (KMICS) as a tool to help those who need to carry out evaluations on interventions, systems or services now orin the future. It isthe agreed evaluation framework within the KMICS and will give anintroduction to the evaluation process and the approaches you may take.

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We’ve put some small files called cookies on your device to make our site work.

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The Population Health Impacts of Changes to the National Health Service Health Check Programme: A System Dynamics Modelling Approach in a Local Authority in England

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The Population Health Impacts of Changes to the National Health Service Health Check Programme: A System Dynamics Modelling Approach in a Local Authority in England

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Please read the latest publication by the Public Health team at Kent County Council describing the first ever local authority led system dynamics modelling of the NHS Health Checks programme.

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The Population Health Impacts of Changes to the National Health Service Health Check Programme: A System Dynamics Modelling Approach in a Local Authority in England

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Health checks aim to improve the health of the population by identifying individuals with risk factors earlier and intervening to prevent disease. The role of commissioners is to ensure health checks provide as much benefit as possible for taxpayer funds invested into them. As such, evidence of the potential impacts of different commissioning choices is beneficial in this decision-making process. System dynamics modelling can be used to provide this evidence by modelling the health check programme using a pre-existing cohort model of a given population. This modelling considers local data, literature findings, and stakeholder views, from which nine different scenarios of a local health check programme have been tested. These scenarios found that extending the duration of health checks to 20 years and improving treatment uptake for those with high blood pressure or high cholesterol reduced rates of cardiovascular disease, improved healthy life expectancy and reduced years lived in ill health. In contrast, improving attendance in the most deprived quintile of the population made very little change to the health of the population overall, although a larger effect was observed in the most deprived areas. These findings helped guide local commissioning decisions by showing the long-term impact of different health check scenarios.

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When designing policies and services, it is important to consider what is known about ‘what works’. In other words, it is important to look at the evidence in the literature.

Southward Council has put together this guide including several resources on finding evidence in the literature, and on assessing its quality.

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Working with evidence - Southwark Council

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This poster has been created as part of the NIHR-funded knowledge mobilisation for Public Health (KNOW-PH) work.The 8 Tips were developed at the Ideas Jam at the UK Knowledge Mobilisation Forum 2024. Many thanks to those who took part in the Ideas Jam for their input.Published under a CC BY 4.0

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This animated video is designed to be shown to research participants before they sign a GDPR form, allowing them to give informed consent to take part in medical research. It was created by the MRC Clinical Trials Unit at UCL, Penta and the Health Research Authority, with help from the Chiva Youth Committee and Penta Youth Trials Boards in Uganda, Zimbabwe, South Africa and the UK.

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The difference between research, quality improvement, service evaluation and audit in healthcare

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The difference between research, quality improvement, service evaluation and audit in healthcare

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This presentation offers a simple overview of the differences between a research study, quality improvement project, service evaluation and audit.

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The difference between research, quality improvement, service evaluation and audit in healthcare

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About 14% of UK 16-25-year-olds are not in education, employment, or training (NEET). The number of NEET young women is significantly growing. NEET young women have more mental health and social problems. There is little evidence about how to prevent this, although our recent research suggests that increasing hope can help. Hope can be defined as a mindset that is focused on meaningful goals. NEET young women especially struggle with hope. New solutions are needed to translate evidence about hope into practical support that young women can access and want to use. We worked with NEET young women, their families, and youth workers in coastal areas to create a structured talking and activity-based programme called HOPEFUL. The aim of HOPEFUL is to increase hope and help NEET young women to work towards their goals and spend time in meaningful activities that benefit their mental health. HOPEFUL has six sections. It can be completed flexibly within 16 weeks with the support of a youth-initiated mentor. This means that NEET young women choose someone whom they already know and trust (e.g., a relative or sports coach). Each mentor receives training, supervision, and a support manual for HOPEFUL. Our project first aims to check that it is possible to involve NEET young women and mentors in a trial of HOPEFUL. If so, our second aim is to do a large trial to test if HOPEFUL works and offers value for money. We will do these trials in coastal areas in Brighton & Hove, East Sussex, Kent, Medway, and Norfolk. For the first aim, we will do a small trial with 70 NEET young women to check they want to take part and can identify a mentor. All 70 will keep receiving usual support they already get (e.g., from social or youth services). Half will also be selected by chance to receive HOPEFUL. Those not selected will be given a HOPEFUL workbook after the trial. We will ask the young women to fill out surveys about their mental health, wellbeing, and activity at the start of the trial and again after 16 weeks. We will ask mentors how they have used HOPEFUL with the young women. We will monitor how many young women and mentors agree to take part, stay involved in the trial, and go on to complete HOPEFUL. We will interview 10 NEET young women and 10 mentors and ask what they liked and disliked about the trial. For the second aim, we will do a larger randomised controlled trial with 248 NEET young women, including those from the smaller trial. We will test how HOPEFUL compares to usual support at increasing hope, reducing depression, anxiety, loneliness, and improving wellbeing, life meaning, structured time use and help-seeking all of which lower the risk of developing serious mental health problems. We will collect information using questionnaires at the start of the trial, after 16 weeks, and after 12 months. We will also collect questionnaires from mentors and young women about relationship with each other during their time using HOPEFUL. We will also interview about 16 women and 16 mentors to explore their positive and negative experiences of HOPEFUL. We will work out the cost of delivering HOPEFUL and whether it is likely to lead to changes in employment, education, and support service use. This will allow us to assess value for money. After these trials, we will finalise HOPEFUL and make it freely available. We will share our results with the government and organisations working with NEET young women. The vision is that HOPEFUL can be used widely by community organisations to prevent mental ill-health and improve life chances for NEET young women. If you or your organisation is keen to take part in The Looking Forward Project, please contact phresearch@kent.gov.uk

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What is the Associate Principal Investigator Scheme?

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What is the Associate Principal Investigator Scheme?

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What is the Associate Principal Investigator Scheme?

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They provide support and funding to those delivering health and social care in the UK to help shine a light on how to make successful change happen.

They support testing and spreading of ideas, build capacity, skills and knowledge and share evidence to improve health and social care.

There aspiration is to create a virtuous circle, using what we know works on the ground to inform effective policymaking and vice versa.

View opportunities currently open for application, browse our fellowship programmes or view a full list of our funding programmes below.

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The Health Foundation is an independent charity committed to bringing about better health and health care for people in the UK. Find out more about us.

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Funding and support for innovators and entrepreneurs tackling identified healthcare problems in the NHS and wider health and social care system.

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Advanced Intervention Research Agency (ARIA)

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Advanced Intervention Research Agency (ARIA)

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ARIA is an R&D funding agency built to unlock scientific and technological breakthroughs that benefit everyone.

Many of society’s most important advances have stemmed from those with the foresight to pursue new capabilities that most believed to be unattainable.

ARIA empowers scientists and engineers with the resources and freedom to pursue those breakthroughs.

Many will fail to meet their target, but their efforts will inspire the next generation. Those that do succeed will generate massive social and economic returns.

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ARIA is an R&D funding agency built to unlock scientific and technological breakthroughs which could benefit everyone.

Many of society's most important advances have stemmed from those with the foresight to pursue new capabilities that most believed to be unattainable.

ARIA empowers scientists and engineers with the resources and freedom to pursue those breakthroughs.

Many will fail to meet their target, but their efforts will inspire the next generation. Those that do succeed will generate massive social and economic returns.

ARIA's Programme Directors are scientists and engineers with diverse fields of expertise and a range of experience across industry, academia and government.

Our success is shaped by one question: when the children of the UK grow up, how will their lives have been transformed by ARIA's work? Whether that's a life-changing technology or a burgeoning new industry, it should be obvious that ARIA played a catalytic role.

To get there, we'll need to relentlessly focus on long-term impact over easy wins. The journeys to the greatest science and technology breakthroughs have always combined a seemingly unattainable vision with resilience in the face of setbacks. We'll need to embrace experimentation and intentional changes of course. As we do, we'll generate new ideas and uncover new paths forward.

Above all: we'll need to support bold researchers with the foresight to look beyond what exists today to the breakthroughs we'll need tomorrow.

Follow us

Subscribe to our Substack where we will be posting updates on our progress.

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ARIA is an R&D funding agency built to unlock scientific and technological breakthroughs which could benefit everyone.

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The Nuffield Foundation funds research in areas such as Education, Welfare (Physical & Mental Health), and Justice.

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NIHR _ National Institute for Health and Care Research

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NIHR _ National Institute for Health and Care Research

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There are currently 17 open funding opportunities related to public health or prevention. Most calls close on 13th December 2024.

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Publication- Rapid Review Engagement and participation in research through the Community Engagement

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Publication- Rapid Review Engagement and participation in research through the Community Engagement

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Background

There is increasing recognition in the field of health and social care research that community-engaged methods can be used throughout the research process. Therefore, individuals from all backgrounds should be involved in research from the initial thoughts, plans and what constitute the conception and design of a study.

Method

A rapid review was undertaken and the results are presented as a narrative summary. The rapid search strategy was created to retrieve both published and unpublished studies. An initial search of CINHAL, MEDLINE, ERIC, SocIndex and British Education Index was   undertaken to identify articles on the topic.

Results

The themes we derived from the analysis of the published articles were: 1) Context of the individual and peers support. This theme, highlighted that partnership identity was important for individuals it brought clarity and understanding to their role and the expectations. 2) Creating inclusive environments for diverse communities. This themes emphasised that ambient learning environment should be enabled  and co-created by the tutors and community, delivering the training and particular use of reflective practice. 3) Incorporating recruitment and retention for diverse community participation, was depended upon the individuals experiences of taking part in research and research training.

Conclusions

The analysis suggests that partnerships, peer support and context of the individual are important facilitators of inclusion of diverse communities. The design of the studies, and recruitment approaches highlighted in the rapid review such as, flyers, word of mouth, attending health fairs, and partnering with nonprofit community based organisations to sponsor targeted recruitment events, led to an increase in diversity.

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Publication- Rapid Review Engagement and participation in research through the Community Engagement | Notion Publication- Rapid Review Engagement and participation in research through the Community Engagement.docx

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Publication- Rapid Review Engagement and participation in research through the Community Engagement | Notion

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Publication- Rapid Review Engagement and participation in research through the Community Engagement.docx

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Community Research Engagement Network (CREN): helping communities understand the benefits of engagement and participation in research.

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Community Research Engagement Network (CREN): helping communities understand the benefits of engagement and participation in research.

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Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.

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A new tool that blends your everyday work apps into one. It’s the all-in-one workspace for you and your team

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To subscribe to our mailing list and receive our newsletter and other important research related news and information please send the word 'subscribe' and your name and email address to phresearch@kent.gov.uk

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July Newsletter

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If you would like help with identifying the most suitable training for you, please complete the above form (which takes 2 minutes) and someone will contact you with a tailored programme of research training to meet your needs.

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October Newsletter

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NIHR Learn is a free online learning platform that allows anyone directly employed by either:

The NHS

A UK university

Another publicly funded organisation that conducts or supports clinical research

Courses are a mixture of online and face to face and include:

- Good Clinical Practice (GCP)

It’s for those with little or no experience of research.

The modules cover topics that will help you engage with research and research opportunities with confidence.

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National Institute for Health and Care Research | NIHR

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The nation’s largest funder of health and care research, providing the people, facilities and technology for research to thrive.

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August Newsletter

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The NHSE elearning for healthcare provides a number of research related training modules which have been developed in partnership with the NHS, 3rd sector and professional bodies and can be accessed for free, 24/7 by any health and care professional.

Online modules include:
- Literature Searching Skills
- Critical Appraisal
- Data Visualisation
- And many more

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Our elearning programmes are developed in partnership with the NHS, 3rd sector and professional bodies and can be accessed for free, 24/7 by health and care professionals.

To access the elearning please sign in.

To access the elearning please register and create an account.

Browse our catalogue to see the elearning programmes that are available.

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Research Practice in Health, Care and Community Settings 

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Research Practice in Health, Care and Community Settings 

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This resource is aimed at staff who are working in health, care and community settings, organisations or providers, such as social care or the local authority, who would like to know more about how to support research.

This resource can serve as an introduction to the standards involved in research such as how to manage your research records and collect good quality data. It has examples of types of wider settings research and examples of good practice with case studies of studies such as prisons, pharmacy and schools to help learners understand the process of research.

In the resource, learners will cover:

· The importance of research in health, care and community settings

· Distinguish between different types of research in health, care and community settings

· Understand the core legal and regulatory frameworks underpinning research in health, care and community research in the UK

· Describe the importance of research records and good quality data

· Understand the requirements of safety reporting.

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You can review our Privacy Policy, Cookie Policy, Terms and Conditions and Accessibility Statement

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Choosing a research question is an essential first step of both quantitative and qualitative research.  It can be challenging to turn your research ideas into logical questions which can be answered by research.

However, it is a skill which can be learnt, and this eLearning module aims to guide you in how to create good research questions.

This module is only available for KCC staff - please contact phresearch@kent.gov.uk if you are outside of KCC.

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For full access to courses you'll need to take a minute to create a new account for yourself on this web site. Each of the individual courses may also have a one-time "enrolment key", which you won't need until later.

Click on the button below to start the registration process.

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Research  Innovation- Definitions and Abbreviations - OTCGR180 v3.19.05.2022

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Planning to write a science article for publication? Use these tips from former judges of the prestigious Max Perutz Science Writing Award to ensure you write an engaging essay that communicates your ideas clearly and effectively.

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Planning to write a science article for publication? Use these tips from former judges of the prestigious Max Perutz Science Writing Award to ensure you write an engaging essay that communicates your ideas clearly and effectively.

Read

Learn what makes other writing good. By reading, you imbibe other writers' styles and techniques, mix them with your own abilities and creative stance, and end up with a style all of your own. Read Hemingway and Shakespeare. Read the Brontës and Virginia Woolf. Read features and news articles online and in newspapers and magazines.

Craft your words

The general rule for clear writing is: think about what you want to write before you want to write it and write it in the clearest way possible. Be sure to:

avoid excessive use of jargon: if you must use it, explain its meaning clearly

be original in your use of language and generally avoid clichés or slang (but remember that great writers do use clichés for particular effects, having thought about them carefully first)

avoid ugly, clumsy phrases and sentences all of the same length (never use a long word where a short word will do - If you can cut a word out, cut it out)

read your article through when you've finished it, and cut as many words as you can.

Start as you mean to go on

The beginning of your article is crucial. It must be a hook to grab the attention of the reader. If you haven't drawn in the reader during the first few sentences, you've lost them. Consider opening your article with:

an unusual, shocking or quirky fact

a fictional narrative that draws the reader in by placing them right in the middle of a scene that they can imagine (be sure to minimise irrelevant details - do not be flowery)

an intriguing question (how could a reader not resist finding out the answer by reading on?)

a quote, but not an overly long one (it's only a hook, not a walk-in wardrobe)

Or you could think of a completely original way to start. Whatever you choose, make sure your opening is relevant to the topic you're writing about, and not simply stuck on like a piece of lace.

Tell the story

So, what about the middle? As this exercise is writing about your research, the main part of your article should be exactly that.

Lead us through the story of the science

What do you actually do? What is the ultimate goal of the research? Is it controversial in any way? If so, you've got a tough job because justifying it is part of your mission. Essentially, why does your research matter?

Think about the ideas first

How do you explain the ideas when speaking to someone you know. Would they understand it? Would they even care? Is there an original way of explaining it? Do not pitch too high or include too much detail. Test it out on a friend.

Be careful with analogies

They must work. Avoid excessive metaphors and beware of mixing them. Be wary of using the word 'imagine'. If you are describing an analogy, the reader will imagine it without your instructing them to do it. Avoid anything too corny. For example, use puns with caution, except for headlines and subheadings, where puns can be effective.

Make it satisfying

The end of the article is important. It must leave the reader feeling satisfied: it's the end of the story even if it isn't the end of the research.

The ending can reflect ideas and themes within the article and is often a 'kicker', a kind of 'twist' which may be ironic or thoughtful, and make the reader want to know more. Read newspaper features for examples. Try not to end on a clichéd phrase.

Check your tone

Be careful to:

avoid making false claims about the value of your research or sensationalising it (for example, beware of saying that you are just a few years away from a 'breakthrough')

resist being melodramatic (have the confidence that your writing is good enough to convey the research in an accurate, yet entertaining, way)

shun a supercilious or self-righteous tone (are you making references to your being a scientist and how virtuous you are? If your article is good, we will know how your research is benefiting society and why).

Know your tools

Finally, and perhaps most crucially, do not neglect one of the most basic rules of style, which is to make sure you know how to use your tools. Know the importance of:

language - the flow of words and sentences, and the way you can use a black and white page to conjure up colourful ideas in a reader's mind

grammar and punctuation - they're the heart of the craft.

Do not make mistakes, because the reader may think you cannot use your tools, and not trust your finished product. Watch your commas. Understand the way sentences are constructed. Read a grammar book.

Break the rules

The last rule is very simple. You may break any of the rules mentioned here, although it helps to have understood them first. "It's not wise to violate rules until you know how to observe them," said T. S. Eliot. But when you know the rules, you can flout any convention and be as original and as crazy as you like.

You can take the words and use them as you would chemicals in a scientific experiment - mix them together and analyse the reaction. That is the beauty, the fun and the liberty of writing.

Read judges' feedback on previous entries to the Max Perutz Science Writing Award

Most of the articles submitted made good attempts at drawing the reader in with attention-grabbing first paragraphs. This is important because if you cannot get the reader's attention in the first place, they won't continue to read about why your research is important.

However, some articles failed to keep up with the tone they had introduced in the first paragraph, slipping back into using jargon or the kinds of phrases that you often find in scientific writing: 'signalling pathways', 'receptors', 'patient outcomes' etc. These are not phrases that are used in everyday language, so shouldn't be used without explanation.

Metaphors and similes are useful devices for getting complex scientific concepts across, and lots of last year's entrants used at least one device like this to explain an aspect of their research.

But remember to use metaphors and similes carefully, and make sure they aren't too specific (you want them to mean the same thing to each of your readers). Think twice about using an extended metaphor throughout an article - in some cases these worked really well, but in others they put people off.

Specifically, you should:

use the 'active' voice (do not write "I was able to see..." when you could write "I saw..." or "the cells were incubated" when you could write "I incubated the cells")

use first names rather than titles to help personalise the research

only include important and relevant information, as readers are restless and will stop reading if you include information that is important only to fellow scientists

avoid acronyms if at all possible - even if a protein is known as ABC2D among scientists, it is more appropriate and understandable to use a different name for a more general audience

use technical terms only sparingly and consistently, and provide explanations if necessary

be careful with punctuation (only use a semi-colon if you are sure it is appropriate and do not overuse commas)

break up your paragraphs, as it's hard work to read paragraphs of more than, say, 100 words (there's no hard and fast rule for word count in paragraphs or sentences but if in doubt, put in a break).

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Planning to write a science article for publication? Use these tips from former judges of the prestigious Max Perutz Science Writing Award to ensure you write an engaging essay that communicates your ideas clearly and effectively.

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Challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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Challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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We are pleased to announce another publication just released.  This is about raising the importance and possible solutions to encourage cross sectoral data linkage in England for population health improvement, particularly datasets outside the NHS and social care.

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Integrated Care Systems (ICSs) were formally established in England as legal entities with statutory powers and responsibilities in 2022 [ 1]. They are the latest in a succession of National Health Service (NHS) organisational restructuring exercises [ 2]. With boundaries normally based on hospital catchment populations, ICSs comprise of local partnerships between NHS organisations, local government authorities, voluntary, community or social enterprise (VCSE) sector organisations and other agencies [ 1], to provide more 'joined-up' services.

ICSs' primary aims extend well beyond health and social care service planning and delivery, to improving public and population health and addressing health inequalities in access, experience and outcomes [ 3]. Local authorities are central to this new NHS architecture and the effective implementation of the health inequalities agenda, embedding public health and prevention into their plans. They may work with partners, or commission VCSE sector organisations, to deliver a range of 'health determinant' services (e.g. education, housing and planning and waste collection) and facilities for people and businesses in a defined area. The importance of public health systems in protecting and improving a population's health was affirmed during the COVID-19 pandemic, with local areas mobilised to control infection, identify, reassure and support vulnerable communities, and maximise vaccination uptake [ 4].

This editorial explores impediments faced by local authority public health bodies in implementing the English health inequalities agenda, focussing on challenges to routine cross-sectoral data linkage, access, contemporariness, and use. In doing so, it first briefly describes cross-sectoral data linkage and its importance to the population health management (PHM) approach, before outlining health-related administrative data beyond health and social care, and discussing the types of datasets available to ICSs, and the challenges and potential solutions to cross-sectoral data linkage for local authority public health systems.

Cross-sectoral data linkage and its importance to PHM

PHM is a national programme introduced by NHS England that uses a data-driven methodology, bringing together data to identify specific populations that health and social care systems can prioritise for their services [ 5]. It gives an understanding of current and future health and care needs, including those arising from the impact of wider health determinants, enabling the design of more joined-up and sustainable services. For example, frequent users of accident and emergency departments can be targeted for preventative interventions to reduce acute admissions [ 5]. However, such data are not limited to that arising solely from the health and care sector.

Indeed, a population's care needs are premised on the availability of, access to, and use of integrated administrative data across multiple sectors, mirroring the complexity and dynamic effect of wider determinants, which account for 80 percent of a population's health [ 6]. The full data scope includes those from health care systems (e.g. primary care, emergency care and hospital services), local authority person-level data on education, housing, employment, welfare, the criminal justice services, and community-based preventative services delivered by the VCSE sector, such as social prescribing. Indeed, there is a growing demand for these datasets to be linked, with proven working examples centred around suicide and the criminal justice system [ 7], mental health, social care and education [ 8]. Only a few examples of active cross-sectoral data linkage exist in England. These include The Connected Bradford Whole System Data Linkage Accelerator [ 9], the Cambridge Child Health Informatics and Linked Data database [ 10], and the Dundee linked health and social care database [ 11]. However, it is not entirely clear whether these can be used for the full range of possible analytics, known as 'secondary uses', by their respective local ICSs. Guidance from NHS England lists approximately 34 secondary uses covering descriptive, diagnostic, predictive and prescriptive domains: risk stratification for early intervention and prevention (2), managing finances, quality and outcomes (14), planning, implementing and evaluating population health strategy (7), and undertaking research (11). Thus a variety of uses can be generated, ranging from an understanding of population segments associated with high intensity service use, re-identifying patients at risk of rehospitalization in need of multi-faceted care, and helping and evaluating the impact of new population health and preventative services, care pathways and programmes [ 12- 14], generating precise and robust analysis for effective planning and decision making, and answering questions requiring large sample sizes or detailed data on hard-to-reach populations [ 15].

Health-related administrative data beyond health and social care

Three categories of data have emerged of key importance for cross-sectoral data linkage:

1. Socio-economic determinants of health: Most of these are administrative datasets describing person-level public services delivered by local councils and the VCSE sector, organised in the context of key areas, such as education, employment, housing, social isolation and crime prevention [16].

2. Protected characteristics: Acknowledged in the 2010 Equality and Diversity Act [17], these data include age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, and sexual orientation. In many cases, individuals experience several of these characteristics simultaneously, known as intersectionality [18].

3. Inclusion health: Data referring to people who are socially excluded and typically experience multiple overlapping risk factors for poor health, such as poverty, violence and complex trauma. This includes people who experience homelessness, drug and alcohol dependence, vulnerable migrants, Gypsy, Roma and Traveller communities, sex workers, people in contact with the justice system and victims of modern slavery. People belonging to inclusion groups tend to have very poor health outcomes, often much worse than the general population and a lower average age of death, contributing considerably to increasing health inequalities.

Individuals and families may fall into one or more of these categories, resulting in complex, dynamic interrelationships. These can exacerbate existing population and health disparities, with their singular and collective magnitude changing over time. For example, severe and multiple disadvantaged [ 19] describes problems faced by adults involved in the homeless, substance misuse and criminal justice systems in England, with poverty almost universal and mental ill-health a common, complicating factor. In addressing this reality, a 'complexity and systems science' lens and associated research methods are being increasingly acknowledged in public and population health intervention research to understand 'real world' effects, beyond use of traditional approaches such as randomized controlled trials which tend to be more linear focused [ 20, 21].

Types of datasets available to ICSs

A great deal of data from the above three categories are high-level and anonymised in nature and mainly for surveillance purposes only, failing to achieve what in the business context has been referred to as a "transformational level of maturity" [ 22]. For such data, ICSs currently depend very much upon stand-alone sources, such as:

1. Propensity scoring tools: Used for socio-economic population segmentation (e.g. Index of Multiple Deprivation, CACI ACORN and MOSAIC [Experian]), these employ complex assumptions and apportionment formulae to describe socioeconomic rank by geography.

2. Large-scale surveys, such as Health Survey for England [] and the Census UK conducted every decade: These generate useful insights on person and population wellbeing and vulnerability not captured elsewhere but they are at best cross-sectional, measuring point prevalence and limited by their frequency and data anonymisation at source. This latter feature renders them impossible to be linked at the person level with, for example NHS datasets, to explore impact on health care and its demand in real time and over time.

3. National surveillance dashboards, such as Fingertips (https://fingertips.phe.org.uk/): These compile and collate the same anonymised datasets above and represent them on dashboards using statistical comparator tools to help councils and their ICSs to benchmark against each other on selected health indicators.

4. Shared care records: These are digital infrastructure programmes rolled out at the ICS (or grouped ICSs) level that create integrated patient-level information stored in one area, usually managed by a third-party digital infrastructure supplier. Care providers that typically contribute to an electronic shared care record include general practitioners (GPs), hospitals, community and mental health trusts and council social care. Their main purpose is for 'direct care', which means frontline clinicians can access real-time identifiable patient-level information via a secure dashboard at the point of care, enabling them to make informed decisions, not only in hospitals and GP surgeries but also in the community. ICSs are also increasingly usingthem now for PHM purposes, utilising specific dashboards designed for population health intelligence but these are for mainly risk stratification and surveillance purposes [24].

5. Research databases at the regional level: Administrative Data Research (ADR) UK is a partnership between the government and academia intended to transform the way researchers access public sector data to enable informed policy decisions to improve people's lives. More recently, Secure Data Environments (SDE), intended as the default way to access NHS health and social care data, are data storage and access platforms that allow approved users to access and analyse data without the data leaving their safe environment.

Challenges and potential solutions to cross-sectoral data linkage for local authority public health systems

Despite the clear need for, and merits of using, cross-sectoral linked data, the level and extent of data and digital infrastructure for PHM in England remains sub-optimal. To the authors' knowledge, none of UK or England national policies, guidance and frameworks adequately describe the relevance or the necessary process and steps for large-scale, cross-sectoral data linkage beyond NHS and social care.

Challenges

The strengths and limitations of the above-mentioned data categories vary. Discussing the ADR and SDE from the perspective of public interest research, Mourby et al highlighted the uncertainty surrounding legal powers to share and link data, data protection obligations, systemic delays and the historic public backlash [ 25]. From the viewpoint of local authority public health departments, there are a number of challenges affecting cross-sectoral data linkage and utilisation:

1. Unlike NHS and social care data, other datasets are periodically updated rather than contemporaneous and real-time. This is a disadvantage in respect to the shorter-term commissioning and planning cycles across local government and the NHS.

2. Aggregated or spatial data are more often available and accessible for data linkage to describe health characteristics for the vast majority of the population, compared with individual-level data that use a unique identifier normally requiring authorization for onward processing.

3. Dataset incompatibility occurs when different datasets from different periods (e.g. spatial- and person-level) are combined for analyses, potentially generating erroneous conclusions.

4. Cross-sectional rather than longitudinal surveys render extrapolating population health need projections problematic given significant time lags since data were collected. Equally, one-off, periodic collected data can also hinder an accurate understanding of morbidity, mortality and service and resource usage in a dynamically changing population (e.g. the socially excluded or at-risk populations), or among discrete cohorts of people.

5. In the context of national data protection legislation, Articles 6 and 9 of the 2018 UK General Data Protection Regulation (GDPR), together with the 2018 Data Protection Act, protect the processing of personal data (S2[1] DPA 2018), prohibiting the use of 'special category' or 'sensitive' data-i.e., data at the individual level-subject to additional privacy measures. Specifically, it forbids its use except in specific circumstances, such as health care management, reducing public health risks and historical research [26]. While the rationale behind these restrictions is understandable-to ensure privacy, confidentiality and prevent exploitation for commercial benefit-it generates a significant challenge for local organisations, particularly councils, for agreeing a common lawful basis for sharing and integrating person-level health determinants data with health and social care service data to understand and improve population health.

6. The Health Research Authority is an extension of the Department of Health and Social Care and advised by the Confidentiality Advisory Group (CAG), an independent body, on research uses, especially around confidential patient information (CPI). Undersection 251 of the NHS Act 2006 and its current regulations, the Health Service (Control of Patient Information) Regulations 2002 [27], a temporary, non-mandatory, authorization gateway is opened to permit the use of patients' medical information (e.g. for research), without their consent and without contravening the common law duty of confidentiality.

Provision exists for non-research applications, but they are likely to be more difficult to approve if local ICSs prioritise cross-sectoral data linkage for routine use. More specifically, guidance in England for integrating datasets outside NHS and adult social care, which normally do not contain NHS numbers, does not appear to exist. This creates uncertainty for a large number of non-NHS, non-social care organisations wishing to arrange new data flows to and from the NHS, particularly around the contractual arrangements that need to be in place and the use of CPI to trace NHS numbers within respective safe secure local data environment within the ICS infrastructure. Moreover, it fails to strike an optimal balance between the good of the majority and individual privacy concerns, with the latter routinely superseding the former. Given the increasing importance of the population health as well as complexity and systems science perspective in analytics and research, the scope of the current legislation and its interpretation limits requirements for routine, integrated, person-level data on health determinants, protected characteristics and inclusion health groups from non-NHS sources not seen as CPI.

Potential solutions

Extending and simplifying routine access and use of the NHS number for data linkage

To address existing data infrastructure limitations, access needs to be improved for broader use by local ICSs, such as tracking public health improvements over appropriate timelines (e.g. monthly or annually, as opposed to every decade like the Census). Although not a panacea [ 28], serious consideration should be given to using unique personal identifiers such as the NHS number, that can link information for each registered person in the population across multiple data sources. This can apply across all ICS organisations delivering statutory welfare services (e.g. housing, assisted bin collections, disability and other social security payments, weight management, smoking cessation and other health promotion services).

Improving the legal gateway for setting aside common law duty of confidentiality

Regulation 5 of Health Service (Control of Patient Information) Regulations 2002 describes the permission to process data for a range of medical purposes, broadly including preventative medicine, medical diagnosis, medical research, the provision of care and treatment and the management of health and adult social care services. This legislation could be amended to allow local ICPs with established joint data controller arrangements to process for a range of population health improvement purposes, including research involving the three categories of wider health-related administrative data mentioned above. This would allow greater autonomy and improved collaborative working for data access and analytics to mature ICSs. Secondly, greater autonomy and delegated powers should be given to ICPs for oversight and sanctioning routine data linkage and access where there is already a better understanding and awareness of the complex local data architecture and partnership working between local organisations. Given the increasing demand for data linkage, the centralised CAG may struggle with capacity to process and approve multiple requests for cross-sectoral linkage from various ICSs in England.

Reducing public suspicion and risk-averse organisational cultures

While incidents of data breaches are rare, high-profile examples (e.g. from universities, airlines, pension schemes) have increased public suspicion of the retention, use, and vulnerability of personal data [ 29]. This suspicion has understandably impacted organisational appetite for data sharing, with caution compelling ICP partners to adopt stricter controls over person-level, inter-organisational data distribution rather than embrace the data linkage and sharing necessary to address a population's needs.

The application of stricter controls by ICPs generates three further problems:

Full data integration is not possible, resulting in data analyses carried out at an aggregated or geographical level (e.g. Lower Layer Super Output Area [30], practice, electoral ward level), generating ecological fallacy.

Special category, person-level data collection is often duplicated from organisation to organisation in an ICP, particularly in general practice. Opportunities are thereby missed to make data collection more complementary, efficient and minimise waste.

Further opportunities are missed to test and improve the quality and completeness of special category data across multiple organisations, a key principle of the GDPR.

The COVID-19 pandemic demonstrated not only how efficient and secure access to linked data can support agile and responsive research [ 31], but also how data linkage is a crucial precondition to identifying unmet needs for essential healthcare services in a timely manner [ 32]. To unlock the full potential of available data for routine use, the general public must have confidence in how their data is protected and used. There is a greater need for local health systems to adopt a more proportionate approach for data linkage and access during non-crisis times for population health improvement, as well as during health protection crises like COVID-19.

While the elimination of data breaches is impossible, there is a need to test 'reasonable expectations' [ 33] (i.e., the extent to which a reasonable person should expect the processing of their data in particular circumstances). In this regard, ICPs-who have access to and wish to use available data-should be expected by central government to undertake intensive, regular, patient and public engagement exercises to explain the inherent and manifold public benefits of data sharing, integration and utilisation for secondary uses at the local level. This can improve and reinforce transparency and reassurance on how the latest safe and secure pseudonymisation and de-identification technology can protect privacy and confidentiality, strengthening confidence in how data linkage is achieved, for what purpose, and how it is managed and stored for safe access. Research examples exist (e.g. the RECOVERY trial) where datasets have been linked for trial participants while ensuring data security and quality [ 34].

Indeed, evidence of the testing of reasonable expectations around data sharing is a compulsory requirement of the CAG application under section 251. This public engagement may not necessarily avert a negative reaction to such data plans, or fully mitigate negative public perceptions regarding the risk of data breaches but would be helpful in building public trust and confidence in local organisations and enhance health information literacy. In those cases where opposition remains, individuals can be educated on the national data opt-out option, introduced in May 2018, enabling patients to opt out from the use of their health data for research or planning purposes [ 35]. Organisational data custodians, on the other hand, must be made informed partners in the data sharing endeavour, with individual and organisational needs subordinate to those of target populations.

Conclusion

Adopting a complexity and systems science perspective is becoming more crucial for public and population health analytics, including research. The UK, with Canada, Scandinavia and especially Australia [ 36], have led the way in making population-level, cross-sectoral data linkage more accessible, forging advances in data security, linkage efficiency, researcher-data custodian connections, workforce capacity and lived experience engagement [ 37]. However, more cross-sectoral data linkage is required to fulfil ICSs' statutory population health improvement expectations and those of their constituent member organisations, including local authority public health. There is no simple solution to addressing these challenges; they involve multiple data providers and regulators engaging in meaningful and difficult discussion of complex issues that have ethical, legal and societal implications. In the post-COVID-19 landscape, with the lessons learnt regarding routine data that is linked at the individual level, readily available, longitudinal and contemporaneous, there is no better time to advocate for the benefits of sharing data for public benefit. If these challenges are not addressed, the role of ICS partnerships to deliver timely population health improvements and reduce health inequalities will be significantly impeded.

Acknowledgements

No funding was received for this work.

Statement of conflicts of interest

The authors declare they have no conflicts of interest.

Disclaimer statement

RAP and MR are supported by the National Institute for Health Research (NIHR) Applied Research Collaboration Northwest London. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Ethics statement

Ethical approval is not required as this is an editorial expressing the authors' views based on their knowledge and experience. No new primary data was collected from patients or public.

Abbreviations

References

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11. Witham MD, Frost H, McMurdo M, Donnan PT, McGilchrist M. Construction of a linked health and social care database resource-lessons on process, content and culture. Inform Health Soc Care 2015; 40(3): 229-39. 10.3109/17538157.2014.892491 [Accessed 26th January 2024].
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12. Department of Health & Social Care. Guidance on the preparation of integrated care strategies. http://www.gov.uk/government/publications/guidance-on-the-preparation-of-integrated-care-strategies/guidance-on-the-preparation-of-integrated-care-strategies#:...:text=The%20purpose%20of%20the%20integrated,and%20wellbeing%20and%20reduce%20disparities. [Accessed 1st January 2023].

13. NHS England. What Good Looks Like framework for data and digital transformation. https://transform.england.nhs.uk/digitise-connect-transform/what-good-looks-like/what-good-looks-like-publication/. [Accessed 28th December 2022].

14. NHS England. Secondary Use Data Governance Tool (SUDGT); https://data.england.nhs.uk/sudgt/activities. [Accessed 21st December 2022].

15. Harron K, Dibben C, Boyd J, Hjern A, Azimaee M, Barreto ML, Goldstein H. Challenges in administrative data linkage for research. Big Data Soc. 2017; 4(2): 2053951717745678. https://doi.org/10.1177/2053951717745678 [Accessed 26th January 2024].
10.1177/2053951717745678

16. World Health Organization. Social determinants of health. https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1 [Accessed 26th January 2024].

17. UK Government. Equality Act 2010: guidance. 16 June 2015. https://www.gov.uk/guidance/equality-act-2010-guidance. [Accessed 29th December 2023].

18. Cooper B. Intersectionality. In: Disch L, Hawkesworth M eds, The Oxford Handbook of Feminist Theory. London: Oxford University Press; 2016. 10.1093/oxfordhb/9780199328581.013.20 [Accessed 26th January 2024].
10.1093/oxfordhb/9780199328581.013.20

19. Bramley G, Fitzpatrick S, with Edwards J, Ford D, Johnsen S, Sosenko F, Watkins D. Hard Edges: Mapping severe and multiple disadvantage. London: Lankelly Chase Foundation; 2015. https://lankellychase.org.uk/publication/hard-edges/.

20. Braithwaite J, Churruca K, Long JC, Ellis LA, Herkes J. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med. 2018; 16(63). https://doi.org/10.1186/s12916-018-1057-z.
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21. Rutter H, Savona N, Glonti K, Bibby J, Cummins S, Finegood DT, Greaves F, Harper L, Hawe P, Moore L, Petticrew M, Rehfuess E, Shiell A, Thomas J, White M. The need for a complex systems model of evidence for public health. The Lancet. 2017; 390(10112):P2602-2604. 10.1016/s0140-6736(17)31267-9. [Accessed 26th January 2024].
10.1016/s0140-6736(17)31267-9

22. Gartner. Gartner survey shows organisations are slow to advance in data and analytics, 5 February 2018. https://www.gartner.com/en/newsroom/press-releases/2018-02-05-gartner-survey-shows-organizations-are-slow-to-advance-in-data-and-analytics. [Accessed 24th August 2023].

23. Anon. Health Survey for England, 2021. 15 December 2022. https://www.gov.uk/government/statistics/health-survey-for-england-2021. [Accessed 6th December 2023].

24. Anon. Shared care records and digital health interoperability. 7 June 2022. https://www.theaccessgroup.com/en-gb/blog/hsc-shared-care-records/. [Accessed 26th January 2024].

25. Mourby MJ, Doidge J, Jones KH, Aidinlis S, Smith H, Bell J, Gilbert R, Dutey-Magni P, Kaye J. Health data linkage for UK public interest research: Key obstacles and solutions. Int J Popul Data Sci 2019; 4(1): 1093. https://doi.org/10.23889/ijpds.v4i1.1093.
10.23889/ijpds.v4i1.1093

26. Information Commissioner's Office. Special category data. https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/lawful-basis-for-processing/special-category-data/. [Accessed 2nd January 2023].

27. Anon. The Health Service (Control of Patient Information) Regulations 2002. No date. https://www.legislation.gov.uk/uksi/2002/1438/made. [Accessed 6th December 2023].

28. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A. The Swedish personal identity number: Possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol 2009; 24(11): 659-67. 10.1007/s10654-009-9350-y [Accessed 26th January 2024].
10.1007/s10654-009-9350-y

29. Scroxton A. Over a million opt out of NHS data-sharing. Computer Weekly, 24 August 2021. https://www.computerweekly.com/news/252505760/Over-a-million-opt-out-of-NHS-data-sharing. [Accessed 24th August 2023].

30. Office for National Statistics. Census 2021 geographies. Nd. https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeographies/census2021geographies. [Accessed 6th December 2023].

31. Harron K. Data linkage in medical research. BMJMED 2022;1:e000087. https://doi.org/10.1136/bmjmed-2021-000087.
10.1136/bmjmed-2021-000087

32. Schmidt AE, Abboud LA, Bogaert P. Making the case for strong health information systems during a pandemic and beyond. Arch Public Health 2021; 79(1): 13. https://doi.org/10.1186/s13690-021-00531-5.
10.1186/s13690-021-00531-5

33. UK Government. Sharing data in line with patients' reasonable expectations. http://www.gov.uk/government/publications/sharing-data-in-line-with-patients-reasonable-expectations. [Accessed 21st December 2022].

34. McCall B. Data, data all around. The Lancet Digital Health 2022;3:e284-285. https://doi.org/10.1016/S2589-7500(21)00063-7.
10.1016/S2589-7500(21)00063-7

35. Anon. National data opt-out. NHS 75 Digital, 5 July 2023. https://digital.nhs.uk/services/national-data-opt-out. [Accessed 23rd August 2023].

36. Smith M, Flack F. Data linkage in Australia: The first 50 years. Int J Environ Res Public Health 2021; 18(21): 11339. https://doi.org/10.3390%2Fijerph182111339.
10.3390%2Fijerph182111339

37. Pearce LA, Borschmann R, Young JT, Kinner SA. Advancing cross-sectoral data linkage to understand and address the health impacts of social exclusion: Challenges and potential solutions. International Journal of Population Data Science 2023;8(1):14. https://doi.org/10.23889/ijpds.v8i1.2116.
10.23889/ijpds.v8i1.2116

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Implementing the English health inequalities agenda: addressing challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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Integrated Care Systems (ICSs) were formally established in England as legal entities with statutory powers and responsibilities in 2022 [ 1]. They are the latest in a succession of National Health Service (NHS) organisational restructuring exercises [ 2].

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June Newsletter

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April Newsletter

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Research: What is it? Why is it important? And why should more people get involved?

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Research: What is it? Why is it important? And why should more people get involved?

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Watch the slideshow from the beginning to hear the content from this webinar delivered for the Kent and Medway Primary Care Research Partnership.

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Implementing the English health inequalities agenda: addressing challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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Implementing the English health inequalities agenda: addressing challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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Local authorities are central to the implementation of English Integrated Care Systems’health inequalities agendas, embedding public health into population health management planning.They work with partners to deliver a range of ‘health determinant’ services and facilities for peoplein a defined geographic area. This work is substantially premised on the use of cross-sectoral datathat is linked at the individual level, readily available, longitudinal and contemporaneous. However,multiple challenges exist to such data availability. This paper elaborates upon these challenges tolocal authority public health systems and their potential solutions.

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View of Implementing the English health inequalities agenda: addressing challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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Return to Article Details Implementing the English health inequalities agenda: addressing challenges to person-level, cross-sectoral data linkage, access and routine use for local authority public health

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Research and Innovation Manager

edyta.mccallum@kent.gov.uk

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Public Health Engagement Lead

davinia.springer@kent.gov.uk

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'Be Part of Research' is a platform for both lay people and professionals that makes it easy to understand what research is and does and find current research studies in specific clinical areas on its database.

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After being asked to take part in the NIHR-supported HEAL-COVID trial, Mercy Njoku believes the regular support she received from the research team has been crucial during her recovery from long COVID.

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Be Part of Research is a website run by the National Institute of Health and Care Research (NIHR) and is designed to help people find and volunteer for research studies taking place across the UK.

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Research Facilitator (Adult Social Care)

Olivia.Trapp@kent.gov.uk

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Research Programme Lead

cassidy.rowden@kent.gov.uk

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Understanding the difference between Clinical Health Research, Social Care Research and Public Health Research

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Understanding the difference between Clinical Health Research, Social Care Research and Public Health Research

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This guide has been designed to improve your basic understanding of:

- Clinical health research.
- Social care research.
- Public health research.

They have been structured to enable you to explore and compare key approaches and components in each area of research.  For those that have an NIHR Learn account, there is also a set of interactive flashcards which explain this information.

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Understanding the difference between Clinical Health Research, Public Health Research and Social Care Research pdf

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The open university has a number of free e-learning modules which are relevant to research, and would be useful in extending research skills, including:

- Understanding research with children and young people
- The power of infographics in research dissemination
- Becoming an ethical researcher
- Understanding different research perspectives
- Why use literature reviews in health and social care?

Courses vary in length and level, so if you are unsure of their relevance, please contact cassidy.rowden@kent.gov.uk to discuss the options.

Anyone can learn for free on OpenLearn, but signing-up will give you access to your personal learning profile and record of achievements that you earn while you study.

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Get inspired and learn something new today

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Earn a free digital badge or Statement of Participation as evidence of your learning to share with employers, friends and family.

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Get inspired and learn something new today free course icon Level level 1: introductory icon Badge icon free course icon Level level 1: introductory icon Badge icon free course icon Level level 2: intermediate icon Badge icon You can use OpenLearn to From 1 to 100 hours of learning - discover hundreds of free courses to inspire and inform everyone.

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In the past, research was about trying to find enough resources to cover a research topic. Today, the challenge is to find the best resources in a flood of information. The number of matches a search engine finds for a given query can be quite large, but sometimes knowing where to look for the strongest evidence is the first step to figuring out how to access it.

This presentation will provide information on using Google Scholar to find the best research for you.

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A short summary video explaining why research is so important.

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The NIHR Patient and Public Involvement (PPI) team are running online training sessions for researchers.

Sessions are free for staff from the NIHR, NHS, UK universities, public sector institutions and registered charities based in the UK.

Further details on the courses can be found on the above link but topics include:

- Building and maintaining a PPI group
- PPI Toolkit: Ways to involve the public in research
- Using PPI to help communicate your research
- Running successful focus groups for PPI
- Evaluating PPI
- Planning inclusive PPI

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Our Patient and Public Involvement (PPI) team are running online training sessions for researchers.

Sessions are free for students and staff from the NIHR, NHS, UK universities, public sector institutions and registered charities based in the UK. When registering, please make sure you use a work-associated email address e.g. .nhs.uk, .ac.uk etc.

Contact our PPI team if you have any questions.

You can also find a list of other training opportunities, including Good Clinical Practice and Edge training for CUH staff.

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Online PPI training for researchers - Cambridge Biomedical Research Centre

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Our Patient and Public Involvement (PPI) team are running online training sessions for researchers. Sessions are free for students and staff from the NIHR, NHS, UK universities, public sector institutions and registered charities based Read more

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CAVEAT The Community And Voluntary Organisation EvaluAtion Toolkit

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CAVEAT The Community And Voluntary Organisation EvaluAtion Toolkit

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CAVEAT is new, free, evaluation and impact toolkit that enables VSCE organisations demonstrate the impact of their services for older people.

Funded by the National Institute for Health and Care Research (NIHR) and developed by academics at the University of Kent and Sussex, the CAVEAT toolkit has been introduced in response to a local study following the COVID 19 pandemic which found that few VSCEs were confident in collecting and presenting the impact data required for funding applications, commissioner reports and ongoing quality monitoring and improvement.

The CAVEAT toolkit enables VSCEs who deliver support to older people to collect and present the data which demonstrates their impact with much more confidence,  boosting the prospects for future funding and enhancing service quality.

To find out more, watch the following video or head to the CAVEAT toolkit website above.

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CAVEAT is an easy to use toolkit to help you describe, measure and report key information about your organisation. It will help you to identify the difference you make to the lives of older people using your service; to your volunteers; and in your community. This is your impact. Information on impact is required when you apply for funding or when you want to demonstrate quality improvement.

The CAVEAT outcomes model is the main part of the toolkit.

It is made up of a 'menu' of outcomes. By selecting the outcomes that apply to the types of services you deliver will enable you to create a unique outcomes framework for your organisation. The toolkit also includes hints and tips and examples of the kind of data collection methods you can use to demonstrate impact. These include case studies, testimonials, surveys, and audit data, for example.

We have linked each outcome in the outcomes model to a carefully chosen range of tools or instruments which you could use to collect data to demonstrate achievement of the outcomes specified in your framework.

(This is an image of the outcome model, to access the interactive model please register with us)

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CAVEAT is an easy to use toolkit to help you describe, measure and report key information about your organisation. It will help you to identify the difference you make to the lives of older people using your service; to your volunteers; and in your community. This is your impact.

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See how the NIHR is harnessing the power of research and unlocking the answers to health and care challenges.

Learn more about health and social care research in the UK, why research is important and what the NIHR does.

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Delta has the following e-learning modules which would be useful for staff conducting research:

- Introduction to Critical Appraisal,
- Literature Searching
- IT skills including a Microsoft Forms module for creating questionnaires/surveys.

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For full access to courses you'll need to take a minute to create a new account for yourself on this web site. Each of the individual courses may also have a one-time "enrolment key", which you won't need until later.

Click on the button below to start the registration process.

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Not all research conducted within the UK requires review by an NHS Research Ethics Committee (REC), however, it is important to gain ethical approval if it is required.

This decision tool:

will help you to determine if your study requires a review by an NHS REC

will not tell you whether you need any other regulatory approvals and/or types of ethics review

At each stage of the decision tool you will be asked a series of questions. Read each question carefully and answer by selecting the 'YES' or 'NO' buttons.

To help you with terminology, a GLOSSARY button is available at the bottom every page. All links to individual glossary items or other websites appear in blue text and open in a new window.

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This guide has been developed as part of a National Institute for Health and Care Research (NIHR) Supraregional initiative by East of England, East Midlands and West Midlands Clinical Research Networks (CRN).

It provides a single space for the research delivery community to access key information to help engage and recruit in wider care and community settings and services.

The East of England, East Midlands and West Midlands CRN's were happy for us to share this information, but some of it might not be relevant to our geographical area.

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Knowledge and Library Services (KLS) provide a range of training modules for Public Health staff, which can be delivered in a group or one-to-one, via MS Teams.

Modules related to research include:

- Finding the evidence (AKA how to do a literature search)
- Introduction to critical appraisal
- Google is good-ish

If you, or your team, are interested in arranging a training session on one of the above please contact cassidy.rowden@kent.gov.uk to discuss.

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Information Skills Training for UKHSA Staff

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Knowledge and Library Services (KLS) provide a range of information skills training for UKHSA staff, which can be delivered in a group or one-to-one setting, via MS Teams. Information on Information Skills Training available to Local Authority Public Health Teams is available on our Support for Local Authority Public Health Teams pages.

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The UK Health Security Agency (UKHSA) Knowledge and Library Services Team (KLS) has produced  this  leaflet  to  support  people  working  in  public  health  teams  in  Local  Authorities (LAPHTs),  to  help  them  embed  evidence -

informed  decision making  in  their  daily  working practice.  The  leaflet  contains  details  about  library  services  available  to  LAPHTs,  electronic journal and database access, searching skills, critical appraisal, and reference management software.

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National Collaboration Centre for Methods and Tools

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National Collaboration Centre for Methods and Tools

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The National Collaborating Centre for Methods and Tools provides a series of online learning modules which are designed to support you in your journey toward evidence-informed decision making. Each module relates to one or more steps in the evidence-informed decision making process. Completed modules earn you a certificate of competence from the National Collaborating Centre for Methods and Tools.  There is also a Learning Needs Assessment for Evidence-Informed Decision Making to identify your strengths and learning needs.

E-learning modules include:

- Quantitative Research Designs 101
- Searching for Research Evidence in Public Health
- Critical Appraisal of Qualitative Studies

Anyone can sign up for a National Collaborating Centre for Methods and Tools account and access all modules for free.

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How the National Collaborating Centre for Methods and Tools' Learning Centre Supports Evidence-Informed Decision Making in Public Health

These online learning modules are designed to support you in your journey toward evidence-informed decision making. Each module relates to one or more steps in the evidence-informed decision making process. You will earn a certificate of competence from the National Collaborating Centre for Methods and Tools when you complete each of the ten certificate modules.
Unsure where to start?
Complete our Learning Needs Assessment for Evidence-Informed Decision Making to identify your strengths and learning needs. This assessment consists of 20 multiple-choice questions related to the evidence-informed decision making process and generates a list of recommended modules based on your results. Use this list to see which modules you should complete first.
Unsure if you have the time to complete the full modules?
Try using our shortened modules, Evidence-Informed Decision Making Essentials: Key Issues in Evidence-Informed Decision Making and Introduction to Evidence-Informed Decision Making for Managers. These modules give a brief introduction to the evidence-informed decision making process and can be completed in one hour. You will receive a statement of completion from the National Collaborating Centre for Methods and Tools when you complete these modules.

To access the modules, you will need to log-in with a free National Collaborating Centre for Methods and Tools account.

We value your feedback.
If you have completed one or more modules, please complete the brief feedback survey.

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Learning Centre | National Collaborating Centre for Methods and Tools

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These online learning modules are designed to support you in your journey toward evidence-informed decision making. Each module relates to one or more steps in the evidence-informed decision making process. You will earn a certificate of competence from the National Collaborating Centre for Methods and Tools when you complete each of the ten certificate modules.

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"Learning to learn is your most important capability since it provides the gateway to everything else you want to develop. How you learn is a key, if not the key life skill." Peter Honey (2016)

The Honey and Mumford Learning Styles Questionnaire has been used extensively within industry and academia for over 35 years. it is a highly cost-effective self-development instrument, designed to measure learning preferences in individuals aged 16+.

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Portfolio Lead

paige.hurlstone@kent.gov.uk

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Research Officer

kimberly.olive@kent.gov.uk