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Poverty in a city/county

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Index Scores

At Luko, cycling is in our DNA. As digital insurance specialists and committed bikers ourselves, we firmly believe that cyclists should be free to get from A to B, without having to worry about road quality, safety or bicycle theft. To delve into the topic further, we decided to investigate which cities around the world are improving their overall cycling conditions, as well as encouraging bicycle-usage as a healthy, sustainable mode of transport. We then ranked each location's efforts to determine the best cycling cities, as, after all, we believe that the road to future mobility is on two wheels.

To conduct the study, we first selected cities from around the world including traditional cycling cities, as well as some lesser-known locations improving their bicycle infrastructure. Then, we analysed each location for several factors which determine if a city is good for cycling or not. To begin, we looked at the percentage of bicycle users, as this is a huge indication of the overall cycling conditions, but also basic requirements such as safety, bicycle-related crime and road infrastructure. This research included the number of stolen bicycles, cycling fatalities and accidents, length of specialised cycling roads, road quality investments and more.
The table above reveals the best cities for cycling, where the higher the total score, the better the results. The legend below describes each of the factors within each category. You can filter each factor from highest to lowest and vice versa by clicking on the icon above each column. For a full explanation of how each factor was calculated, please see the methodology at the bottom of the page.
Next, since many casual cyclists are impacted by weather conditions, we determined the average hours of sunshine, millimetres of rainfall and number of extreme weather days to paint an overall picture of the climate in each city. Considering that the sharing economy is a billion-dollar industry, we wanted to include information about the boom in bike-sharing by looking into the volume of shared bicycles, as well as the number of sharing and rental stations in each city. Finally, to determine which locations go the extra mile for cyclists and their community, we included the popularity of special cycling-related events such as No Car Day and Critical Mass. We then ranked all factors to determine the best cycling cities overall.

Methodology

The Bicycle Cities Index analyses the conditions for cycling in 90 cities across the globe to determine if they are good for cyclists.

City Selection

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90 cities were selected for their willingness to invest and work on initiatives to improve cycling infrastructure and safety. The study does not reflect the best and worst cities for cycling, but rather evaluates the cycling climate for these 90 cities based on factors related to bike-users.

City Size: S stands for cities with less than 500,000 inhabitants, M for cities with between 500,000 and 999,999 inhabitants and L for cities with 1 million inhabitants and above.

Weather

Percentage Bicycle Usage.

Crime & Safety: Fatalities / 100,000 Cyclists, Accidents / 100,000 Cyclists, Bicycle Theft Score.

Infrastructure: Number of Bicycle Shops / 100,000 Cyclists, Specialised Roads & Road Quality Score, Investment & Infrastructure Quality Score.

Sharing: Number of Bicycle Sharing & Rental Stations / 100,000 Score, # Shared Bicycles / 100,000 Score.

Events: No Car Day, Critical Mass Score.

The study focuses on six main categories with the following factors that make a city cycling-friendly:

A weighted average was used for all of the factors in order to create the final scores for each category, for example the Weather Score was generated by analysing and aggregating the Hours of Sunshine, Rainfall and Extreme Weather Days of each city.

All of the information collected is based on the latest data available.

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Scoring

Scores are normalized such that 1 represents the lowest and 100 the highest value in the dataset, meaning that the higher the score, the better. However, for the factors Fatalities and Accidents / 100,00 cyclists, a lower value is better as it represents a higher safety rating for each city.

The equation for normalization is as follows: score = 10 * [x - min(X)] / [max(X) - min(X)]; or score inverted = 10 -10 * [x - min(X)] / [max(X) - min(X)] for inverted scores

The formula used for the weighted average, where n is the number of categories, and i is the i-th factor, is as follows: weighted_average: sum_(i=1)^n w_i*x_i, there w_i is the weight of column i, n - total number of columns used for the weighted average, x_i : i-th column.

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WEATHER

The Weather Score was calculated using an aggregated score, taking into account the total annual hours of sunshine, average annual precipitation in millimetres, and the number of weather days below 0 °C and over 30 °C in a city.
Sources: World Weather Online, Weather Base, Deutscher Wetter Dienst, other websites.

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BICYCLE USAGE

Percentage of people using bicycles in everyday life in each city.
Sources: Local statistical departments, Greenpeace, UN, Eco Mobility, The League of American Cyclists, and others.

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CRIME & SAFETY

Fatalities / 100,000 Cyclists

Deaths in bicycle accidents (includes deaths related to bicycle theft) per 100,000 cyclists in cities. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates.
Sources: OECD, local statistical departments. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Accidents / 100,000 Cyclists

An estimate of bicycle-related accidents that resulted in at least light injuries, per 100,000 cyclists. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates.
Sources: OECD, local statistical departments. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Bicycle Theft Score

Stolen bicycles / 100,000 cyclists

An estimate of stolen bicycle rate per 100,000 cyclists. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates. Sources: local statistical departments. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Homicide Rate

In order to account for low report rates of bicycle theft in some countries, the homicide rate was added as it is the most reported crime.
Source: World Bank.

A weighted average of the following subcategories collected in order to offset the low crime report rates in countries where crime is prevalent but underreported.

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INFRASTRUCTURE

Number of bicycle shops / 100,000 cyclists

Total number of bicycle shops within the city. Total number of cyclists estimated from bicycle usage rates as well as bicycle ownership rates.
Sources: the yellow pages, Google search engine result pages, Open Street Maps Overpass API responses. Bicycle ownership rate source: Oke, O., et al., Tracking global bicycle ownership patterns. Journal of Transport & Health (2015).

Specialised Roads and Road Quality

Bicycle roads length per population.
Sources: Open Street Maps Overpass API responses: km of ways (highways) tagged for bicycle usage (allowed and specific). Road Quality score. Source: World Economic Forum.

Investment and Infrastructure Quality

Infrastructure Investment Score.
Sources: World Bank LPI infrastructure score, German Institute of Economy (DIW): investment in infrastructure, Bertelsmann Stiftung.

Infrastructure Quality Score.
Sources: World Economic Forum Global Competitiveness Index Quality of Overall Infrastructure.

An average of the scored subcategories (country-level for international; investment and infrastructure: city level for German cities):

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SHARING

Number of bicycle sharing and rental stations / 100,000

An estimate of bicycle sharing and rental stations per 100,000 of population.
Sources: The yellow pages, google search engine result pages, Open Street Maps Overpass API responses.

Number of shared bicycles / 100,000

An estimate of shared bicycle fleet per 100,000 of population.
Sources: Local statistical departments, local bicycle sharing company websites, bicycle share map (http://bikes.oobrien.com).

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EVENTS

No Car Day

Score dependant on the existence of a car-free day, where motorists are encouraged to give up their car for one day. 1 - Has No Car Day. 0 - Does not have a No Car Day.

Critical Mass Score

Sources: local statistical departments, local websites, social media group pages (e.g. Facebook). Latest available event.

Size of local Critical Mass communities.
Sources: local Critical Mass event social media group size (e.g. Facebook).

Average of the subcategories: Size of Critical Mass events attendees.

Bicycle theft insurance as additional protection

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Luko, a Berlin-based InsurTech, has conducted research and published an index of the most cyclist-friendly cities in Germany and around the world.

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https://data.census.gov/all?q=Disability

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Census Bureau data search results for text query Disability.

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https://www.pewresearch.org/fact-tank/2023/02/08/for-valentines-day-5-facts-about-single-americans/

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377107/

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Abstract

The rising trend in pregnancy-related deaths during the past 2 decades in the United States stands out among other high-income countries where pregnancy-related deaths are declining. Cardiomyopathy and other cardiovascular conditions, hemorrhage, and other chronic medical conditions are all important causes of death. Unintentional death from violence, overdose, and self-harm are emerging causes that require medical and public health attention. Significant racial/ethnic inequities exist in pregnancy care with non-Hispanic black women incurring 3 to 4 times higher rates of pregnancy-related death than non-Hispanic white women. Varied terminology and lack of standardized methods for identifying maternal deaths in the United States have resulted in nuanced data collection and interpretation challenges. State maternal mortality review committees are important mechanisms for capturing and interpreting data on cause, timing, and preventability of maternal deaths. Importantly, a thorough standardized review of each maternal death leads to recommendations to prevent future pregnancy-associated deaths. Key interventions to improve maternal health outcomes include 1) integrating multidisciplinary care for women with high-risk comorbidities during preconception care, pregnancy, postpartum, and beyond; 2) addressing structural racism and the social determinants of health; 3) implementing hospital-wide safety bundles with team training and simulation; 4) providing patient education on early warning signs for medical complications of pregnancy; and 5) regionalizing maternal levels of care so that women with risk factors are supported when delivering at facilities with specialized care teams.

INTRODUCTION

Maternal death during pregnancy, childbirth, or postpartum is a tragedy with catastrophic impact on families and serves as an important indicator of the quality of a health system. The World Health Organization (WHO) has defined maternal mortality ratio (MMR) as "the number of maternal deaths per 100,000 live births, [where] maternal death is the death of a woman while pregnant or within 42 days of termination of pregnancy," regardless of whether the cause was related to or aggravated by pregnancy. ( 1) However, according to the WHO definition, maternal deaths do not include those from accidental or incidental causes. Although the MMR has been the most common indicator for international comparisons of maternal health, it does not specify the cause of death in relation to pregnancy.

The MMR in the United States has decreased drastically in the last century because of advances in surgical technique, safer anesthesia, antisepsis, and overall improved living conditions. Although the MMR dropped from 900 deaths per 100,000 live births in the 1900s to 12.7 in 2007, the US rate of MMR has seen a rise over the past several decades. ( 2) In 2014, complications during pregnancy, childbirth, and the postpartum period ranked as the 6th greatest cause of death among women aged 20 to 34 in the United States. ( 3) The MMR in the United States has more than doubled from 9.8 per 100,000 live births in 2000 to 21.5 in 2014, ( 4) and this trend stands out among high-income countries; maternal mortality has decreased in other high-income countries, such as Canada and the United Kingdom, during the same time period. According to the WHO, the MMR has fallen by 44% from 1990 to 2015 in low- and middle-income countries. ( 5)

When interpreting these trends, however, we must also account for improvements in data ascertainment. The addition of a pregnancy question on the US death certificate in 2003 coincided with increased mortality rate, which suggests improved detection and reporting as part of the story. ( 4) Increasing maternal mortality rates vary by state and have generated public attention on family planning availability and the growing prevalence of chronic medical conditions including obesity, diabetes, and heart disease. Delayed childbearing leading to more advanced age during pregnancy, higher cesarean delivery rates, the opioid epidemic, and fragmented and limited access to care during and after pregnancy have also been identified as potential contributors. ( 6)

In the United States, the racial/ethnic inequities in maternal deaths are troubling; most notably, non-Hispanic black women carry a 3- to 4-fold risk of pregnancy-related deaths compared with non-Hispanic white women. ( 7) While the medical and public health communities have made strides to reduce infant mortality with strategies such as the Safe to Sleep campaign, ( 8) it is time to focus on solutions to improve maternal health as well, particularly for those at highest risk for adverse outcomes. This review summarizes the data collection challenges, causes of maternal mortality and severe maternal morbidity, inequities in maternal health outcomes, and solutions to reduce maternal morbidity and mortality.

DEFINITIONS AND DATA COLLECTION CHALLENGES

In the United States, the Centers for Disease Control and Prevention (CDC) has put forward 3 classifications of pregnancy-associated death (death of a woman while pregnant or within 1 year of termination of pregnancy, irrespective of the cause)( 9):

1. Pregnancy-related: "The death of a woman while pregnant or within 1 year of termination of pregnancy, from any cause related to or aggravated by her pregnancy or its management, but not from accidental or incidental causes." (Example: the death of a woman from postpartum hemorrhage or amniotic fluid embolism).

2. Pregnancy-associated but not pregnancy-related: "The death of a woman while pregnant or within 1 year of termination of pregnancy due to a cause unrelated to pregnancy." (Example: the death of a pregnant woman from an earthquake).

3. Pregnancy-associated but undetermined if pregnancy-related: "The death of a woman while pregnant or within 1 year of termination of pregnancy from a cause that cannot be determined or conclusively categorized as either pregnancy-related or not pregnancy related." (Example: a woman with an unknown mental health history dies at 6 months postpartum from a self-inflicted cause).

The CDC manages the 2 national data sources of maternal deaths: 1) the National Vital Statistics System compiled annually by the National Center for Health Statistics (NCHS), and 2) the Pregnancy Mortality Surveillance System (PMSS), a flagship program run by the Division of Reproductive Health at the National Center for Chronic Disease Prevention and Health Promotion ( Table ). ( 10) The NCHS relies exclusively on International Classification of Diseases, 10th Revision (ICD-10) codes assigned to causes of death listed on maternal death certificates and publishes the maternal mortality rate, consistent with the WHO definition of maternal death. The PMSS relies on epidemiologists to classify deaths according to the aforementioned definitions of pregnancy-related and pregnancy-associated deaths and allocate the causes of death into 10 categories: hemorrhage, infection/sepsis, amniotic fluid embolism, thrombotic pulmonary or other embolism, hypertensive disorders of pregnancy, anesthesia complications, cerebrovascular accidents, cardiomyopathy, cardiovascular disease, and noncardiovascular medical conditions.

TABLE.

NATIONAL CENTER FOR HEALTH STATISTICS (NCHS)

PREGNANCY MORTALITY SURVEILLANCE SYSTEM (PMSS)

Measurement challenges include the limitations of ICD code accuracy and significant variation in the statewide implementation of the pregnancy checkbox on death certificates, which began in 2003, but was not fully implemented in all states until 2016. ( 10) Multiple studies have concluded that improvements in reporting and case ascertainment explain some of the recent increase in maternal mortality in the United States. ( 7)( 11)( 12)( 13) One study estimated that about 80% of the reported increase in maternal mortality between 2000 and 2014 could be attributed to improvements in data linkages and the pregnancy box. ( 10) Another study found that the addition of the checkbox may have increased case identification but also misclassification, particularly among women aged 40 years or older. ( 14) However, after correcting for improved ascertainment of maternal deaths from implementation of the pregnancy question, the adjusted average MMR across 48 US states is still estimated to have risen by 27% from 18.8 to 23.8 per 100,000 live births from 2000 to 2014 ( 4); the smaller adjusted increase in MMR is because of significant under-reporting during the early time point. ( 4) The MMR in Texas doubled from 2011 to 2014, suggesting gaps in data quality rather than a true doubling of maternal death rates. ( 13) The study team used an enhanced method with full review of medical records for identifying pregnancy-associated deaths and found that more than half of the obstetric-coded deaths were inaccurately labeled. ( 13) However, even accounting for these data collection challenges, the MMR in the United States has not decreased substantially in the recent decades, as it has in other high-income countries.

State-based maternal mortality review committees (MMRCs) are the gold standard in identifying and reviewing pregnancy-associated and pregnancy-related deaths because they are made of a multidisciplinary team that reviews all available data, including prenatal records, hospital records, and autopsy reports. ( 10) MMRCs are now functional in approximately two-thirds of states and are best positioned to classify deaths as preventable or not and to make recommendations to prevent similar deaths in the future. ( 15)( 16)( 17)( 18)( 19)( 20) The CDC has developed a standardized data collection system for state MMRCs called the Maternal Mortality Review Information Application (MMRIA). ( 21) The MMRIA is a publicly available set of standardized forms for abstracting data and recording MMRC decisions on 6 key questions: 1) Was the death pregnancy-related? 2) What was the cause of death? 3) Was the death preventable? 4) What were the factors that contributed to this death? 5) What are the recommendations and actions that address those contributing factors? 6) What is the anticipated impact of those actions if implemented?"( 21) MMRCs play a critical role in evaluating all information about maternal deaths to identify systems solutions to improve care delivery for those that are deemed preventable.

CAUSES OF MATERNAL MORTALITY AND MORBIDITY

The 2018 report from 9 state MMRCs concluded that around 50% of all pregnancy-related deaths were caused by hemorrhage, cardiovascular/coronary conditions, cardiomyopathy, or infection. For non-Hispanic black women, the most common underlying causes of death included preeclampsia, eclampsia, and embolism. For non-Hispanic white women, mental health conditions were the leading cause of death. ( 22)

The most recent CDC report on maternal mortality from May 2019 also identified cardiovascular conditions (including cardiomyopathy, myocardial infarction, and cerebrovascular accidents) as the cause for more than 33% of pregnancy-related deaths ( Fig 1 ). ( 22)( 23)( 24) From 2003 to 2012 there was a 25% increase in the number of women entering pregnancy with preexisting heart disease. Most of these women have congenital heart disease or valvular heart disease. However, the prevalence of cardiomyopathy and pulmonary hypertension increased significantly and demonstrated the highest in-hospital mortality, most often because of heart failure, arrhythmia, respiratory failure, shock, renal failure, and preeclampsia. ( 25) It is unclear why the incidence of cardiomyopathy is increasing, but it could be associated with increases in maternal age, multifetal pregnancies, or improved recognition.

In a study of the National Inpatient Sample from 2002 to 2013, the incidence of cardiogenic shock (the most extreme of cardiovascular disease associated with mortality) increased over 3-fold; the mortality rate for pregnant women with cardiogenic shock was 19% compared with 0.02% of women without cardiogenic shock. ( 26) More than 80% of pregnant and postpartum women with cardiogenic shock had peripartum cardiomyopathy, a pregnancy-associated diagnosis that has also increased during the same period. ( 27) Both acute and chronic renal failure were significantly associated with mortality in women with cardiogenic shock. ( 26) Commonly identified risk factors for cardiovascular death include increasing maternal age, obesity, and hypertensive disorders. ( 27)( 28) Strategies aimed at reducing modifiable chronic conditions such as obesity, hypertension, and diabetes in women of reproductive age may help reduce the incidence of cardiovascular disease and major adverse events. In addition, multidisciplinary care of pregnant women with cardiovascular disease, beginning with adequate risk assessment and evaluation for known and unknown cardiac disease is necessary to eliminate preventable maternal deaths. ( 29)( 30)( 31)

Obstetric hemorrhage was the cause of 11.5% of pregnancy-related deaths from 2011 to 2014 and is usually preventable. ( 32) Hemorrhage also accounts for the overwhelming majority of severe maternal morbidity, with blood product transfusions rising from 25 per 10,000 delivery hospitalizations in 1993 to 122 per 10,000 delivery hospitalizations in 2014. ( 24) This is a critical area for focused efforts to implement hospital-based national safety bundles, multidisciplinary team training, simulation, and reporting systems so that cases can be reviewed to ensure continuous improvement in safety and quality. ( 33) Successful implementation of state-wide bundles across 99 diverse hospitals in California has demonstrated reductions in severe morbidity rates from hemorrhage by 20.8%. ( 34) Some of the increase in prevalence of hemorrhage can be attributed to skyrocketing rates of cesarean delivery. Although cesarean can be a life-saving intervention, the US average annual cesarean delivery rate has risen from 23% in 1996 to 33% in 2011 without a corresponding reduction in maternal and neonatal morbidity or mortality. ( 35) Cesarean delivery is associated with increased maternal mortality and morbidity (particularly hemorrhage, infection, and thromboembolism) compared with vaginal birth and leads to future risks for abnormal placentation such as placenta previa and placenta accreta in subsequent pregnancies. The American College of Obstetricians and Gynecologists (ACOG) has taken steps to reduce the number of unnecessary cesarean deliveries by creating guidelines for the safe prevention of the primary cesarean. ( 35)

Another important emerging contributor to maternal death is self-harm (suicide or accidental overdose). In Colorado, 30% of the 211 maternal deaths over a 9-year study period were related to self-harm, with the majority occurring in the postpartum period. Prior psychiatric history and psychopharmacotherapy use during pregnancy were documented in over half of these women. ( 36) In Philadelphia, over a 4-year period, 49% of maternal deaths had nonmedical causes, including unintentional injury (overdose, motor vehicle crash, or other), homicide, and suicide; overdose composed 40% of nonmedical causes of maternal deaths. ( 37) The authors caution againstnarrowing the focus of maternal mortality on medical causes because nonmedical causes, particularly unintentional overdose, are important contributors to pregnancy-associated mortality. Mental illness, substance use, and intimate partner violence are common risk factors among women who died of both medical and nonmedical causes, reinforcing the importance of screening and providing interdisciplinary perinatal management of substance use disorders and psychobehavioral interventions. ( 37)

Severe maternal morbidity (SMM) is defined by the CDC as an index of 18 indicators of significant events (such as blood transfusion, hysterectomy, heart failure, eclampsia, respiratory distress, and sepsis) corresponding to ICD-10 diagnoses during delivery admission. ( 24) These indicators were chosen because they can be life-threatening, and are associated with short- or long-term morbidity, prolonged hospitalization, and high health care costs. ( 2) Many studies of smaller datasets use SMM as a surrogate for maternal mortality because SMM is thought to include the sentinel events that lead to significantly increased risk of death. SMM rates have increased 200% from 1993 to 2014, and this increase is primarily driven by increasing blood transfusions in response to postpartum hemorrhage. After removing blood transfusion, SMM has increased 20% over this period, with hysterectomy and temporary ventilatory support accounting for the next most common complications. ( 24)

The prevalence of reproductive-aged women with chronic conditions continues to rise, and pregnant women with multiple chronic conditions are at 276% higher risk of SMM and mortality than women with no chronic conditions. ( 38) Women with multiple chronic conditions are often older than women with a single chronic condition or no chronic conditions. Noncardiovascular medical conditions accounted for 14.3% of pregnancy-related deaths during the period from 2011 to 2015. ( 23)

Prepregnancy obesity has been associated with SMM and mortality in a cohort of women delivering in Washington State between 2004 and 2013, suggesting that the obesity epidemic was an important contributing factor to adverse maternal health outcomes. ( 39) An analysis of California births found that the incidence of SMM increased 65% from 2007 to 2014 and the prevalence of prepregnancy obesity, maternal age older than or equal to 35 years, and comorbid conditions also increased during the same period, but were estimated to contribute only 13% of the increasing morbidity. ( 40) Cesarean delivery was estimated to contribute 37% of the SMM in California during these 7 years. Increasing prevalence of maternal chronic medical conditions and cesarean delivery accounts for only half of the SMM, leaving room for investigation of other contributing factors. Although national efforts to reduce unnecessary cesarean deliveries may be promising interventions for reducing SMM, this study underscores the need to also address other contributing factors. ( 40)

In a study using delivery data from Washington State from 2000 to 2008, the rate of early-onset preeclampsia before 34 weeks' gestation increased by 33% and was associated with a 10-fold greater risk of maternal death compared with women without preeclampsia. ( 41) Preeclampsia is also associated with significantly higher rates of SMM, particularly from cardiovascular, respiratory, or renal failure. Hypertensive disease in pregnancy has long-term health implications, including a 2-fold increased risk for all-cause mortality before age 50 years, and increased mortality related to diabetes, ischemic heart disease, and stroke. These long-term sequelae occurred more frequently in the group of women who had 2 or more pregnancies complicated by hypertensive disease. ( 42) Identifying women at risk for cardiovascular disease during their childbearing years offers an opportunity to intervene and optimize long-term health.

Access to abortion care has been curtailed in multiple states because of legislative restrictions on physician and facility requirements, medication abortion restrictions, gestational age limits, funding cuts, mandatory waiting periods, and parental consent laws, which can place women at increased risk of harm if they attempt self-induced abortions. ( 43) Improved insurance coverage for abortion care through state Medicaid has been associated with 16% fewer cases of SMM, suggesting that increased coverage for abortion care reduces complications associated with pregnancy. ( 44)

INEQUITIES IN MATERNAL MORBIDITY AND MORTALITY

Inequities-differences that are systematic, avoidable, and unjust-in maternal health outcomes have persisted and are a cause for concern. Although multiple social conditions confer increased risk of adverse outcomes, the most prominent examples of inequities in maternal health in the United States are rooted in the social constructs of race and ethnicity. One conceptual model from Dr Elizabeth Howell demonstrates the ecosystem of factors (patient, community/neighborhood, provider, and systems) that contribute to adverse health outcomes throughout the continuum of reproductive health care, particularly for women of color. ( 45)

Black women experience maternal deaths at a rate 3 to 4 times that of white women in the United States, ( 2)( 7) regardless of age. ( 46) According to the CDC, the pregnancy-related mortality ratios between 2011 and 2014 were as follows: 12.4 deaths per 100,000 live births for white women, 40.0 deaths per 100,000 live births for black women, and 17.8 deaths per 100,000 live births for women of other races ( 32); this inequity remains unchanged in the 2019 report of pregnancy-related deaths from 2015. ( 23) The 2018 report of 9 MMRCs revealed that greater proportions of pregnancy-related deaths of all pregnancy-associated deaths occurred among non-Hispanic black women compared with white women ( Fig 2 ). ( 22)

Black and Native American women also experience increased complications from pregnancy compared with white women, regardless of socioeconomic status and comorbidities. ( 47) Using 7 state inpatient databases from 2008 to 2010, Creanga et al identified race/ethnicity as an important predictor of maternal morbidity. ( 48) Although other factors were also identified as significant predictors of SMM, such as age less than 20 years or more than or equal to 35 years, self-pay or Medicaid coverage for delivery, low socioeconomic status, and presence of chronic medical conditions, they did not fully explain the observed racial/ethnic disparities in SMM. ( 48) Using the National Inpatient Sample from 2012 to 2015, Admon et al also found that SMM during delivery hospitalization was higher among all racial/ethnic groups compared with non-Hispanic whites. ( 49) The most common SMM was blood transfusion, which accounted for nearly 75% of all morbidity across racial/ethnic groups. The incidence of SMM was highest among women with multiple chronic conditions and particularly among those of color, suggesting increased case morbidity. ( 49) Grobman et al found that racial/ethnic disparities in maternal morbidity persisted even after controlling for patient-level factors and hospital of delivery. ( 50) In addition, racial/ethnic disparities in obstetric care delivery exist. Non-Hispanic blacks, Hispanics, and Asians all had lower odds of labor induction than non-Hispanic whites. The odds of receiving an episiotomy was increased in Asians but decreased in non-Hispanic blacks and Hispanics compared with non-Hispanic whites, ( 50) indicating that there may be biases in care delivery. Women of color also experience pregnancy-related complications that are associated with higher rates of death, such as tuberculosis, ( 51) or experience higher case fatality with conditions such as ectopic pregnancy. ( 52)

The root cause of racial/ethnic inequities is the legacy of structural racism that permeates people's lived experiences, including experiences in the health care system. One important driver of inequities in maternal health is distrust in the health system as a result of historical and contemporary discrimination, which often manifests as lower prenatal care utilization and adherence with treatment plans. An analysis of more than 2,000 responses to the Listening to Mothers III survey found that more than 40% of participants reported communication challenges in prenatal care and 24% perceived discrimination during birth hospitalization, predominantly among black or Hispanic women and uninsured women. ( 53)

Another important driver of inequities is the variation in hospital quality of care during childbirth, in which women of color more commonly go to hospitals with higher risk-adjusted morbidity compared with the hospitals where white women go more commonly. ( 54) In an analysis of the black-white differences in hospital of delivery in New York City, Howell et al estimated that as much as 48% of the racial disparity in SMM could be attributed to differences in care quality at the hospital level. ( 55) A similar analysis among Hispanic women also showed that up to 37% of the ethnic disparity could be attributed to differences in care quality at the hospital level. ( 55)

Solutions to reduce inequities span all levels, from policies to address racism and social determinants of health to improving quality of care delivery and experience of care during childbirth, to mobilizing community-based organizations to support women before, during, and after pregnancy. Some examples include standardizing and improving quality of care in hospitals, particularly among facilities that have higher risk-adjusted morbidity rates and also care for a disproportionate number of women of color. ( 56) In addition, developing disparity dashboards to track outcomes among specific groups is important for monitoring, evaluation, and quality improvement. ( 57) Training on implicit bias is critical across the health care workforce to sensitize individuals to the role implicit bias may play in their interactions with patients. Lastly, there are innovative antenatal and postnatal care delivery models that are being evaluated as strategies to close disparities, such as group antenatal care and postpartum home visits with integrated interdisciplinary health teams. ( 58)

IMPROVING SAFETY AND QUALITY OF CHILDBIRTH CARE

With the increases in both maternal mortality and morbidity, there has been an increasing focus on quality of care at the hospital level in the days before and after childbirth. In addition to wide variation in SMM across hospitals, there is also substantial variation within hospitals. ( 56) The imperative to standardize and improve safety and quality is the foundation for reducing adverse maternal outcomes in the hospital setting. The most common mechanisms to achieve this end are 1) a focus on team communication and team training; 2) implementation of evidence-based safety bundles or toolkits to manage obstetric complications that are most likely to cause SMM and/or death; and 3) data-driven MMRCs that can provide specific recommendations for systems improvement to prevent future maternal deaths.

According to a review of sentinel events reported to the Joint Commission, failures in communication were the second leading root cause of SMM and maternal mortality and the leading root cause of perinatal deaths and injuries. ( 59) Key facilitators include leadership champions to build and support a culture of safety that encourages open communication among all team members and transparent, nonpunitive reporting of safety-critical events with a focus on systems improvement. ( 59) In addition, structured communication tools, such as safety huddles, safety checklists, "SBAR" (Situation, Background, Assessment, Recommendation) approach, and team training simulation such as TeamSTEPPS facilitate interprofessional communication and teamwork, particularly in acute situations that require coordination of resources and expertise. The maternal early warning triggers or criteria facilitate communication between bedside nurses and clinicians through increased clinical surveillance and responsiveness to patients with abnormal vital signs who may require prompt evaluation and treatment to prevent morbidity. ( 60)( 61)( 62)

Safety bundles and/or toolkits that facilitate adherence with evidence-based guidelines have been critical efforts to reduce maternal morbidity and mortality in the United States. At a national level, multiple professional organizations and stakeholders came together to form the National Partnership for Maternal Safety, which developed safety bundles for obstetric hemorrhage, severe hypertension in pregnancy, and peripartum venous thromboembolism. ( 63) Since the introduction of universal pneumatic compression devices at the time of cesarean delivery and rapid treatment of severe hypertension, there was a reduction in maternal deaths from postcesarean pulmonary embolism and in deaths and morbidity related to hypertensive disorders. (64)(65)(66)

The California Maternal Quality Care Collaborative (CMQCC) was developed as a public-private partnership and leveraged data from the Department of Public Health to support data-driven large-scale quality improvement initiatives to reduce maternal deaths and morbidity. ( 67) CMQCC-affiliated hospitals have demonstrated reductions in maternal morbidity from postpartum hemorrhage after implementing a comprehensive hemorrhage bundle compared with non-CMQCC-affiliated hospitals in California (20.8% vs 1.2%). ( 34)

With federal funding from the Health Resources and Services Administration Maternal Child Health Bureau, the Alliance for Innovation on Maternal Health (AIM) was formed and led by partners from the ACOG Council on Patient Safety and Women's Health Care and the National Partnership for Maternal Safety. ( 68) AIM is a state-based program that develops and provides implementation support for safety bundles. Each bundle has 4 components: readiness, recognition, response, and reporting and systems learning. ( 68) AIM operationalizes the bundle implementation and reporting systems through state-based teams, often organized as state perinatal quality collaboratives (PQCs). PQCs are defined as "state or multistate networks of multidisciplinary teams, working to improve measurable population outcomes for maternal and infant health by advancing evidence-informed clinical practices and processes using quality improvement principles."( 69) The anchoring structure of PQCs relies on the department of public health, state hospital association, and clinician leadership. Additional members may include the state MMRC, community health organizations, patient advocacy groups, risk management, payers, and purchasers. ( 69) A critical function of the PQCs is to develop and sustain a robust data collection system for maternal health indicators at the state level.

Adjunct health system solutions to improve maternal outcomes include developing an obstetrics hospitalist workforce that may be best positioned to respond to uncommon emergencies ( 4)( 70) and improve patient education at the time of discharge from the hospital. Although the obstetrics hospitalist workforce is growing across the United States, there remains wide variation regarding work models and scope of practice. ( 71) More data are needed to assess maternal outcomes in hospitals where a robust obstetrics hospitalist workforce manages intrapartum care. Similar to the integrated regional neonatal levels of care to improve perinatal mortality, maternal levels of care provide risk-appropriate care for those who would benefit from additional expertise and infrastructure because of preexisting comorbidities in pregnancy. ( 72) Implementation of regionalized maternal levels of care would allow for patients with high-risk conditions to be cared for at higher-volume hospitals with greater access to subspecialists. In addition, patient communication and education about danger signs, such as the POST-BIRTH tool, have been additional areas of focus to improve prompt recognition of symptoms and medical evaluation, especially once patients return home after childbirth. ( 73)

The 2018 report of 9 MMRCs found that more than 60% of pregnancy-related deaths were preventable and the leading factors contributing to death were patient/family factors (namely, lack of knowledge about early warning signs to seek care), provider factors (misdiagnosis or ineffective treatments), and factors related to systems of care (lack of coordination between providers) ( Fig 3 ). ( 22) Most importantly, the MMRCs provided recommendations to prevent future maternal deaths, such as adopting levels of maternal care, ( 72) improving and enforcing policies and procedures on obstetric hemorrhage, and addressing health equity. Specifically, a California review of pregnancy-related cardiovascular deaths identified both patient and provider contributing factors, particularly a delay in patients seeking care as well as in provider response, which suggests an opportunity to improve patient education and standardize response protocols. ( 28)

CURRENT HEALTH CARE POLICY TO ADDRESS MATERNAL MORTALITY

On December 21, 2018, the Preventing Maternal Deaths Act (HR 1318) was signed into law. This legislation allocates federal funding to support states in establishing and sustaining MMRCs. ( 74) One challenge will be standardizing data collection and reporting systems among the various MMRCs.

Other maternal health bills in Congress at this time are the Maternal Care Access and Reducing Emergencies (CARE) Act, the Rural Maternal and Obstetric Modernization of Services (MOMS) Act, and the Mothers and Offspring Mortality and Morbidity Awareness (MOMMA) Act. The CARE Act, introduced by California Senator Kamala Harris, focuses on dismantling structural racism through training programs around implicit bias for clinicians. The MOMS Act, introduced by North Dakota Senator Heidi Heitkamp, addresses the disparities in access to obstetric care for women in rural communities by creating regional networks and increasing workforce capacity in rural areas. The MOMMA Act, introduced by Illinois Representative Robin Kelly, seeks to expand Medicaid through 1 year after delivery, standardize data collection through the CDC, establish and enforce national emergency obstetric protocols, and improve culturally competent care. In addition, there are multiple bills at the state and federal levels around expanding Medicaid through 1 year after delivery and providing Medicaid reimbursement for doula services.

PREPREGNANCY AND INTERPREGNANCY HEALTH

Pregnancy conditions, such as hypertensive disease and gestational diabetes, are known risk factors for cardiovascular disease later in life and increased early mortality. ( 42)( 75) Other adverse pregnancy outcomes such as preterm labor and fetal growth restriction may also be associated with increased lifelong cardiovascular risk. ( 76) The postpartum period is a time of significant biological, psychological, and social transition with increased risk of complications, yet 10% to 40% of women do not attend any postpartum visit between 4 and 12 weeks. ( 77) During pregnancy and postpartum care, obstetricians have an opportunity to provide anticipatory guidance; arrange for appropriate follow-up for chronic health conditions, mental health, and substance use disorders; and identify and educate patients about early warning signs of SMM. According to the most recent CDC report, the majority of unintentional deaths occurred between 6 weeks and 1 year after delivery, highlighting the importance of the continuum of care that extends beyond the traditional 6-week postpartum period. ( 23) ACOG has revised recommendations for postpartum care to become an ongoing process of addressing recovery from birth, newborn care, psychosocial and sexual well-being, contraception, chronic disease management, health maintenance, and a transition to ongoing well-woman care, rather than a single encounter. ( 78) The prepregnancy and interpregnancy periods are windows of opportunity for implementing risk-reducing interventions for women with multiple medical conditions, mental health issues, or substance use disorders to optimize outcomes for future pregnancies and long-term well-being. Improving health systems to allow longitudinal continuous care from pregnancy and beyond is critical to reduce maternal mortality. ( 79)

CONCLUSION

Pregnancy-related deaths have been steadily rising in the United States and are not just a result of improved data acquisition. Cardiovascular conditions, obstetric hemorrhage, and self-harm or unintentional harm are important causes of pregnancy-related deaths; significant inequities exist between non-Hispanic black and non-Hispanic white women. The majority of pregnancy-related deaths are preventable. Implementation of safety bundles, team training, integrated multidisciplinary care for high-risk patients, risk-stratified levels of maternal care, improvements in communication between providers and patients regarding early warning signs, and addressing structural racism and the social determinants of health are all strategies for improving maternal safety, quality and equity. Health care policy to improve funding and resources for standardized, state-based review of pregnancy-related deaths are important steps to reverse rising rates and close persistent inequities in maternal morbidity and mortality.

​

1. In contrast to other high-income countries, the maternal death rate in the United States has been rising for over 20 years with persistent racial/ethnic inequities.

2. Initiatives to improve safety, quality, and equity of care during pregnancy, delivery, and beyond are essential to optimize maternal health outcomes.

1. Explain the definitions of pregnancy-related and pregnancy-associated deaths and the data challenges in the United States.

2. Recognize key contributors to rising maternal deaths and persistent inequities in the United States.

3. Identify key strategies and solutions for improving maternal health.

Know the effects on the fetus and/or newborn infant of maternal cardiac disease and its management.

Know the essentials of prenatal care, including risk assessment, perinatal referral, screening, and standard monitoring.

Know how maternal obesity may influence pregnancy and pregnancy outcome.

Know the components of pre- and periconceptional health care (including nutritional requirements during pregnancy) that influence pregnancy outcomes.

Know the issues in the organization of perinatal care (e.g., regionalization, transport, practice guidelines, benchmarking data, quality improvement).

Acknowledgments

AUTHOR DISCLOSURE Dr Collier is supported by the Reproductive Scientist Development Program (K12HD000849), the Eunice Kennedy Shriver National Institute of Child Health & Human Development, and Burroughs Wellcome Fund as part of the Reproductive Scientist Development Program. Dr Molina has disclosed no financial relationships relevant to this article. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.

ABBREVIATIONS

ACOG

American College of Obstetricians and Gynecologists

AIM

Alliance for Innovation on Maternal Health

CARE Act

Maternal Care Access and Reducing Emergencies Act

CDC

Centers for Disease Control and Prevention

CMQCC

California Maternal Quality Care Collaborative

ICD-10

International Classification of Diseases, 10th Revision

MMR

maternal mortality ratio

MMRC

maternal mortality review committee

MMRIA

Maternal Mortality Review Information Application

MOMMA Act

Mothers and Offspring Mortality and Morbidity Awareness Act

MOMS Act

Maternal and Obstetric Modernization of Services Act

NCHS

National Center for Health Statistics

PMSS

Pregnancy Mortality Surveillance System

PQC

perinatal quality collaborative

SMM

severe maternal morbidity

WHO

World Health Organization

References

5. World Health Organization. Trends in maternal mortality: 1990 to 2015: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. Geneva, Switzerland: World Health Organization; 2015 [ Google Scholar]

38. Admon LK, Winkelman TNA, Heisler M, Dalton VK. Obstetric outcomes and delivery-related health care utilization and costs among pregnant women with multiple chronic conditions. Prev Chronic Dis. 2018;15:E21. [[ PubMed] PMC free article] [ Google Scholar]

65. Gupta M, Greene N, Kilpatrick SJ. Timely treatment of severe maternal hypertension and reduction in severe maternal morbidity. Pregnancy Hypertens. 2018;14:55-58 [[ PubMed] Google Scholar]

78. American College of Obstetricians and Gynecologists. ACOG committee opinion no. 736: optimizing postpartum care. Obstet Gynecol. 2018;131:e140-e150 [[ PubMed] Google Scholar]

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Maternal Mortality in the United States: Updates on Trends, Causes, and Solutions

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The rising trend in pregnancy-related deaths during the past 2 decades in the United States stands out among other high-income countries where pregnancy-related deaths are declining. Cardiomyopathy and other cardiovascular conditions, hemorrhage, and other chronic medical conditions are all important causes of death.

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In the first year of the COVID-19 pandemic, global prevalence of anxiety and depression increased by a massive 25%, according to a scientific brief released by the World Health Organization (WHO) today. The brief also highlights who has been most affected and summarizes the effect of the pandemic on the availability of mental health services and how this has changed during the pandemic.

Concerns about potential increases in mental health conditions had already prompted 90% of countries surveyed to include mental health and psychosocial support in their COVID-19 response plans, but major gaps and concerns remain.

"The information we have now about the impact of COVID-19 on the world's mental health is just the tip of the iceberg," said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. "This is a wake-up call to all countries to pay more attention to mental health and do a better job of supporting their populations' mental health."
Multiple stress factors
One major explanation for the increase is the unprecedented stress caused by the social isolation resulting from the pandemic. Linked to this were constraints on people's ability to work, seek support from loved ones and engage in their communities.

Loneliness, fear of infection, suffering and death for oneself and for loved ones, grief after bereavement and financial worries have also all been cited as stressors leading to anxiety and depression. Among health workers, exhaustion has been a major trigger for suicidal thinking.
Young people and women worst hit
The brief, which is informed by a comprehensive review of existing evidence about the impact of COVID-19 on mental health and mental health services, and includes estimates from the latest Global Burden of Disease study, shows that the pandemic has affected the mental health of young people and that they are disproportionally at risk of suicidal and self-harming behaviours. It also indicates that women have been more severely impacted than men and that people with pre-existing physical health conditions, such as asthma, cancer and heart disease, were more likely to develop symptoms of mental disorders.

Data suggests that people with pre-existing mental disorders do not appear to be disproportionately vulnerable to COVID-19 infection. Yet, when these people do become infected, they are more likely to suffer hospitalization, severe illness and death compared with people without mental disorders. People with more severe mental disorders, such as psychoses, and young people with mental disorders, are particularly at risk.
Gaps in care
This increase in the prevalence of mental health problems has coincided with severe disruptions to mental health services, leaving huge gaps in care for those who need it most. For much of the pandemic, services for mental, neurological and substance use conditions were the most disrupted among all essential health services reported by WHO Member States. Many countries also reported major disruptions in life-saving services for mental health, including for suicide prevention.

By the end of 2021 the situation had somewhat improved but today too many people remain unable to get the care and support they need for both pre-existing and newly developed mental health conditions.

Unable to access face-to-face care, many people have sought support online, signaling an urgent need to make reliable and effective digital tools available and easily accessible. However, developing and deploying digital interventions remains a major challenge in resource-limited countries and settings.
WHO and country action
Since the early days of the pandemic, WHO and partners have worked to develop and disseminate resources in multiple languages and formats to help different groups cope with and respond to the mental health impacts of COVID-19. For example, WHO produced a story book for 6-11-year-olds, My Hero is You, now available in 142 languages and 61 multimedia adaptations, as well as a toolkit for supporting older adults available in 16 languages.

At the same time, the Organization has worked with partners, including other United Nations agencies, international nongovernmental organizations and the Red Cross and Red Crescent Societies, to lead an interagency mental health and psychosocial response to COVID-19. Throughout the pandemic, WHO has also worked to promote the integration of mental health and psychosocial support across and within all aspects of the global response.

WHO Member States have recognized the impact of COVID-19 on mental health and are taking action. WHO's most recent pulse survey on continuity of essential health services indicated that 90% of countries are working to provide mental health and psychosocial support to COVID-19 patients and responders alike. Moreover, at last year's World Health Assembly, countries emphasized the need to develop and strengthen mental health and psychosocial support services as part of strengthening preparedness, response and resilience to COVID-19 and future public health emergencies. They adopted the updated Comprehensive Mental Health Action Plan 2013-2030, which includes an indicator on preparedness for mental health and psychosocial support in public health emergencies.

Step up investment
However, this commitment to mental health needs to be accompanied by a global step up in investment. Unfortunately, the situation underscores a chronic global shortage of mental health resources that continues today. WHO's most recent Mental Health Atlas showed that in 2020, governments worldwide spent on average just over 2% of their health budgets on mental health and many low-income countries reported having fewer than 1 mental health worker per 100 000 people.

Dévora Kestel, Director of the Department of Mental Health and Substance Use at WHO, sums up the situation: "While the pandemic has generated interest in and concern for mental health, it has also revealed historical under-investment in mental health services. Countries must act urgently to ensure that mental health support is available to all."

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COVID-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide

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In the first year of the COVID-19 pandemic, global prevalence of anxiety and depression increased by a massive 25%, according to a scientific brief released by the World Health Organization (WHO) today.

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https://www.who.int/health-topics/maternal-health

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Every mother and newborn child has the right to a positive and safe birthing experience.

Maternal health can be promoted by:

Developing evidence-based policies and strategies;

Improving access to the full range of affordable, equitable and high-quality family planning and reproductive health services to increase the contraceptive use rate and reduce unwanted pregnancies; and

Improving the health and nutrition status of women of all ages, especially pregnant and nursing women.

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WHO is supporting countries to deliver integrated, evidence-based and cost-effective care for mothers and babies during pregnancy, childbirth and the postpartum period. Investing in health systems - especially in training midwives and in making emergency obstetric care available round-the-clock - is key to reducing maternal mortality.

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https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/

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Yet many see promise for artificial intelligence to help issues of bias in medical care

A new Pew Research Center survey explores public views on artificial intelligence (AI) in health and medicine - an area where Americans may increasingly encounter technologies that do things like screen for skin cancer and even monitor a patient's vital signs.

The survey finds that on a personal level, there's significant discomfort among Americans with the idea of AI being used in their own health care. Six-in-ten U.S. adults say they would feel uncomfortable if their own health care provider relied on artificial intelligence to do things like diagnose disease and recommend treatments; a significantly smaller share (39%) say they would feel comfortable with this.

One factor in these views: A majority of the public is unconvinced that the use of AI in health and medicine would improve health outcomes. The Pew Research Center survey, conducted Dec. 12-18, 2022, of 11,004 U.S. adults finds only 38% say AI being used to do things like diagnose disease and recommend treatments would lead to better health outcomes for patients generally, while 33% say it would lead to worse outcomes and 27% say it wouldn't make much difference.

These findings come as public attitudes toward AI continue to take shape, amid the ongoing adoption of AI technologies across industries and the accompanying national conversation about the benefits and risks that AI applications present for society. Read recent Center analyses for more on public awareness of AI in daily life and perceptions of how much advancement emerging AI applications represent for their fields.

Asked in more detail about how the use of artificial intelligence would impact health and medicine, Americans identify a mix of both positives and negatives.

On the positive side, a larger share of Americans think the use of AI in health and medicine would reduce rather than increase the number of mistakes made by health care providers (40% vs. 27%).

And among the majority of Americans who see a problem with racial and ethnic bias in health care, a much larger share say the problem of bias and unfair treatment would get better (51%) than worse (15%) if AI was used more to do things like diagnose disease and recommend treatments for patients.

But there is wide concern about AI's potential impact on the personal connection between a patient and health care provider: 57% say the use of artificial intelligence to do things like diagnose disease and recommend treatments would make the patient-provider relationship worse. Only 13% say it would be better.

The security of health records is also a source of some concern for Americans: 37% think using AI in health and medicine would make the security of patients' records worse, compared with 22% who think it would improve security.

Though Americans can identify a mix of pros and cons regarding the use of AI in health and medicine, caution remains a dominant theme in public views. When it comes to the pace of technological adoption, three-quarters of Americans say their greater concern is that health care providers will move too fast implementing AI in health and medicine before fully understanding the risks for patients; far fewer (23%) say they are more concerned that providers will move too slowly, missing opportunities to improve patients' health.

Concern over the pace of AI adoption in health care is widely shared across groups in the public, including those who are the most familiar with artificial intelligence technologies.

Younger adults, men, those with higher levels of education are more open to the use of AI in their own health care

There is more openness to the use of AI in a person's own health care among some demographic groups, but discomfort remains the predominant sentiment.

Among men, 46% say they would be comfortable with the use of AI in their own health care to do things like diagnose disease and recommend treatments, while 54% say they would be uncomfortable with this. Women express even more negative views: 66% say they would be uncomfortable with their provider relying on AI in their own care.

Those with higher levels of education and income, as well as younger adults, are more open to AI in their own health care than other groups. Still, in all cases, about half or more express discomfort with their own health care provider relying on AI.

Among those who say they have heard a lot about artificial intelligence, 50% are comfortable with the use of AI in their own health care; an equal share say they are uncomfortable with this. By comparison, majorities of those who have heard a little (63%) or nothing at all (70%) about AI say they would be uncomfortable with their own health care provider using AI.

At this stage of development, a modest share of Americans see AI delivering improvements for patient outcomes. Overall, 38% think that AI in health and medicine would lead to better overall outcomes for patients. Slightly fewer (33%) think it would lead to worse outcomes and 27% think it would not have much effect.

Men, younger adults, and those with higher levels of education are more positive about the impact of AI on patient outcomes than other groups, consistent with the patterns seen in personal comfort with AI in health care. For instance, 50% of those with a postgraduate degree think the use of AI to do things like diagnose disease and recommend treatments would lead to better health outcomes for patients; significantly fewer (26%) think it would lead to worse outcomes.

Americans who have heard a lot about AI are also more optimistic about the impact of AI in health and medicine for patient outcomes than those who are less familiar with artificial intelligence technology.

Four-in-ten Americans think AI in health and medicine would reduce the number of mistakes, though a majority say patient-provider relationships would suffer

Americans anticipate a range of positive and negative effects from the use of AI in health and medicine.

The public is generally optimistic about the potential impact of AI on medical errors. Four-in-ten Americans say AI would reduce the number of mistakes made by health care providers, while 27% think the use of AI would lead to more mistakes and 31% say there would not be much difference.

Many also see potential downsides from the use of AI in health and medicine. A greater share of Americans say that the use of AI would make the security of patients' health records worse (37%) than better (22%). And 57% of Americans expect a patient's personal relationship with their health care provider to deteriorate with the use of AI in health care settings.

The public is divided on the question of how it would impact the quality of care: 31% think using AI in health and medicine would make care for people like themselves better, while about as many (30%) say it would make care worse and 38% say it wouldn't make much difference.

Americans who are concerned about bias based on race and ethnicity in health and medicine are more optimistic than pessimistic about AI's potential impact on the issue

When it comes to bias and unfair treatment in health and medicine based on a patient's race or ethnicity, a majority of Americans say this is a major (35%) or minor (35%) problem; 28% say racial and ethnic bias is not a problem in health and medicine.

There are longstanding efforts by the federal government and across the health and medical care sectors to address racial and ethnic inequities in access to care and in health outcomes.

Black adults are especially likely to say that bias based on a patient's race or ethnicity is a major problem in health and medicine (64%). About four-in-ten Hispanic (42%) and English-speaking Asian adults (39%) also say this. A smaller share of White adults (27%) describe bias and unfair treatment related to a patient's race or ethnicity as a major problem in health and medicine.

On balance, those who see bias based on race or ethnicity as a problem in health and medicine think AI has potential to improve the situation. About half (51%) of those who see a problem think the increased use of AI in health care would help reduce bias and unfair treatment, compared with 15% who say the use of AI would make bias and unfair treatment worse. A third say the problem would stay about the same.

Among those who see a problem with bias in health and medicine, larger shares think the use of AI would make this issue better than worse among White (54% vs. 12%, respectively), Hispanic (50% vs. 19%) and English-speaking Asian (58% vs. 15%) adults. Views among Black adults also lean in a more positive than negative direction, but by a smaller margin (40% vs. 25%).

Note that for Asian adults, the Center estimates are representative of English speakers only. Asian adults with higher levels of English language proficiency tend to have higher levels of education and family income than Asian adults in the U.S. with lower levels of English language proficiency.

Asked for more details on their views about the impact of AI on bias in health and medicine, those who think it would improve the situation often explain their view by describing AI as more objective or dispassionate than humans. For instance, 36% say AI would improve racial and ethnic bias in medicine because it is more neutral and consistent than people and human prejudice is not involved. Another 28% explain their view by expressing the sense that AI is not biased toward a patient's characteristics. Examples of this sentiment include respondents who say AI would be blind to a patient's race or ethnicity and would not be biased toward their overall appearance.

Among those who think that the problem of bias in health and medicine would stay about the same with the use of AI, 28% say the main reason for this is because the people who design and train AI, or the data AI uses, are still biased. About one-in-ten (8%) in this group say that AI would not change the issue of bias because a human care provider would be primarily treating people even if AI was adopted, so no change would be expected.

Among those who believe AI will make bias and unfair treatment based on a patient's race or ethnicity worse, 28% explain their viewpoint by saying things like AI reflects human bias or that the data AI is trained on can reflect bias. Another reason given by 10% of this group is that AI would make the problem worse because human judgment is needed in medicine. These responses emphasized the importance of personalized care offered by providers and expressed the view that AI would not be able to replace this aspect of health care.

Americans' views on AI applications used in cancer screening, surgery and mental health support

The Center survey explores views on four specific applications of AI in health and medical care that are in use today or being developed for widespread use: AI-based tools for skin cancer screening; AI-driven robots that can perform parts of surgery; AI-based recommendations for pain management following surgery; and AI chatbots designed to support a person's mental health.

Public awareness of AI in health and medicine is still in the process of developing, yet even at this early stage, Americans make distinctions between the types of applications they are more and less open to. For instance, majorities say they would want AI-based skin cancer detection used in their own care and think this technology would improve the accuracy of diagnoses. By contrast, large shares of Americans say they would not want any of the three other AI-driven applications used in their own care.

For more on how Americans view the impact of these four developments read, " How Americans view emerging uses of artificial intelligence, including programs to generate text or art."

AI-based skin cancer screening

AI used for skin cancer detection can scan images of people's skin and flag areas that may be skin cancer for testing.

Nearly two-thirds of U.S. adults (65%) say that they would definitely or probably want AI to be used for their own skin cancer screening. Consistent with this view, about half (55%) believe that AI would make skin cancer diagnoses more accurate. Only 13% believe it would lead to less accurate diagnoses, while 30% think it wouldn't make much difference.

On the whole, Americans who are aware of this AI application view it as an advance for medical care: 52% describe it as a major advance while 27% call it a minor advance. Very few (7%) say it is not an advance for medical care.

Majorities of most major demographic groups say they would want AI to be used in their own screening for skin cancer, with men, younger adults, and those with higher education levels particularly enthused.

A larger majority of men (72%) than women (58%) say they would want AI to be used in their screening for skin cancer.

Black adults (57%) are somewhat less likely than White (65%) and Hispanic (69%) adults to say they would want AI used for skin cancer screening. Experts have raised questions about the accuracy of AI-based skin cancer systems for darker skin tones.

Younger adults are more open to using this form of AI than older adults, and those with a college degree are more likely to say they would want this than those without a college degree.

In addition, those who have heard at least a little about the use of AI in skin cancer screening are more likely than those who have heard nothing at all to say they would want this tool used in their own care (75% vs. 62%).

AI for pain management recommendations

AI is being used to help physicians prescribe pain medication. AI-based pain management systems are designed to minimize the chances of patients becoming addicted to or abusing medications; they use machine learning models to predict things like which patients are at high risk for severe pain and which patients could benefit from pain management techniques that do not involve opioids.

Asked to consider their own preferences for treatment of pain following surgery, 31% of Americans say they would want this kind of AI guiding their pain management treatment while two-thirds (67%) say they would not.

This reluctance is in line with people's beliefs about the effect of AI-based pain management recommendations. About a quarter (26%) of U.S. adults say that pain treatment would get better with AI, while a majority say either that this would make little difference (40%) or lead to worse pain care (32%).

Among those who say they've heard at least a little about this use of AI, fewer than half (30%) see it as a major advance for medical care, while another 37% call it a minor advance. By comparison, larger shares of those aware of AI-based skin cancer detection and AI-driven robots in surgery view these applications as major advances for medical care.

Those with some familiarity with AI-based pain management systems are more open to using AI in their own care plan. Of those who say they have heard at least a little about this, 47% say they would want AI-based recommendations used in their post-op pain treatment, compared with 51% who say they would not want this. By comparison, a large majority (72%) of those not familiar with this technology prior to the survey say they would not want this.

Demographic differences on this question are generally modest, with majorities of most groups saying they would not want AI to help decide their pain treatment program following a surgery.

Performing surgery with AI-driven robots

AI-driven robots are in development that could complete surgical procedures on their own, with full autonomy from human surgeons. These AI-based surgical robots are being tested to perform parts of complex surgical procedures and are expected to increase the precision and consistency of the surgical operation.

Americans are cautious toward the idea of surgical robots used in their own care: Four-in-ten say they would want AI-based robotics for their own surgery, compared with 59% who say they would not want this.

Still, Americans with at least some awareness of these AI-based surgical robots are, by and large, convinced they represent an advance for medical science: 56% of this group says it is a major advance and another 22% calls it a minor advance. (For more on how Americans view advances in artificial intelligence, read " How Americans view emerging uses of artificial intelligence, including programs to generate text or art.") Public familiarity with the idea of AI-based surgical robots is higher than for the three other health and medical applications included on the survey; 59% say they have heard at least a little about this development.

As with other AI applications included in the survey, those unfamiliar with AI-driven robots in surgery are especially likely to say they would not want them used in their own care (74% say this). Those who have heard of this use of AI before are evenly divided: 50% say they would want AI-driven robots to be used in their surgery, while 49% say they wouldn't want this.

Across demographic groups, men are more inclined than women to say they would want an AI-based robot for their own surgery (47% vs. 33%). And those with higher levels of education are more open to this technology than those with lower levels of education.

There is little difference between the views of older and younger adults on this: Majorities across age groups say they would not want an AI-based robot for their own surgery. This contrasts with preferences about other uses of AI in medical care in which younger adults are more likely than older adults to say they would want AI applications for skin cancer screening or pain management.

AI chatbots designed to support mental health

Chatbots aimed at supporting mental health use AI to offer mindfulness check-ins and "automated conversations" that may supplement or potentially provide an alternative to counseling or therapy offered by licensed health care professionals. Several chatbot platforms are available today. Some are touted as ways to support mental health wellness that are available on-demand and may appeal to those reluctant to seek in-person support or to those looking for more affordable options.

Public reactions to the idea of using an AI chatbot for mental health support are decidedly negative. About eight-in-ten U.S. adults (79%) say they would not want to use an AI chatbot if they were seeking mental health support; far fewer (20%) say they would want this.

In a further sign of caution toward AI chatbots for mental health support, 46% of U.S. adults say these AI chatbots should only be used by people who are also seeing a therapist; another 28% say they should not be available to people at all. Just 23% of Americans say that such chatbots should be available to people regardless of whether they are also seeing a therapist.

Large majorities of U.S. adults across demographic and educational groups lean away from using an AI chatbot for their own mental health support. Read the Appendix for details.

Even among Americans who say they have heard about these chatbots prior to the survey, 71% say they would not want to use one for their own mental health support.

And among those who have heard about these AI chatbots, relatively few (19%) consider these to be a major advance for mental health support; 36% call them a minor advance, while 25% say they are not an advance at all. Public opinion on this use of AI, as with many others, is still developing: 19% of those familiar with mental health chatbots say they're not sure if this application of AI represents an advance for mental health support.

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60% of Americans Would Be Uncomfortable With Provider Relying on AI in Their Own Health Care

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A new Pew Research Center survey explores public views on artificial intelligence (AI) in health and medicine - an area where Americans may increasingly encounter technologies that do things like screen for skin cancer and even monitor a patient's vital signs.

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Folk and Scientific Classifications of Animals and Plants (M790V1)

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This study contains folk classification of animals and plants. Each data set has two parts. The first part is a scientific classification of families into orders, classes and divisions. The second part is a set of 21 folk classifications matched to scientific classifications up to the rank of family.