Population Well-Being Measures Help Explain Geographic Disparities In Life Expectancy At The County Level

Perspectives From Public Health Practitioners and Advocates on Community Development for Active Living: What are the Lasting Impacts?

PURPOSE

Evidence suggests differential impacts of community development, including gentrification and displacement. Public health practitioners and advocates are key stakeholders involved in the community development process related to active living, yet little is known about their perceptions of its impacts. We explored the perspectives of relevant leaders of public health departments and key community and advocacy organizations on community development, gentrification, and displacement.

APPROACH

Purposive key informant interviews.

SETTING

CDC State Physical Activity and Nutrition (SPAN) funding recipients.

PARTICIPANTS

CDC SPAN recipient leadership (n = 10 of 16) and advocacy organizations they partnered with (n = 7 of 16).

METHOD

Interviews were recorded, transcribed, coded, and thematically analyzed with direct quotes representing key themes.

RESULTS

Both groups felt community development held important benefits, specifically by creating healthy living opportunities, but also potentially leading to the displacement of long-time residents. Practitioners reported the benefits were for all community members, whereas advocates noted the benefits were seen in those with privilege, and the consequences were disproportionately seen in disadvantaged communities. Both mentioned the importance and difficulty of getting diverse representation for community engagement.

CONCLUSIONS

Learning how key stakeholders perceive and navigate the community development process can help inform recommendations for better equity in active living community improvements. More work is needed to further elucidate best practices for health and social equity in the community development process.

Filling in the white space: Spatial interpolation with Gaussian processes and social media data

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC's Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.) only allow for the estimation of annual averages for about 260 out of roughly U.S. 3,000 counties when a threshold of 300 responses per county is used. Using a relatively high threshold of 300 responses gives substantially higher convergent validity-higher correlations with health variables-than lower thresholds but covers a reduced and biased sample of the population. We present principled methods to interpolate spatial estimates and show that including large-scale geotagged social media data can increase interpolation accuracy. In this work, we focus on Gallup-reported life satisfaction, a widely-used measure of subjective well-being. We use Gaussian Processes (GP), a formal Bayesian model, to interpolate life satisfaction, which we optimally combine with estimates from low-count data. We interpolate over several spaces (geographic and socioeconomic) and extend these evaluations to the space created by variables encoding language frequencies of approximately 6 million geotagged Twitter users. We find that Twitter language use can serve as a rough aggregate measure of socioeconomic and cultural similarity, and improves upon estimates derived from a wide variety of socioeconomic, demographic, and geographic similarity measures. We show that applying Gaussian Processes to the limited Gallup data allows us to generate estimates for a much larger number of counties while maintaining the same level of convergent validity with external criteria (i.e., N = 1,133 vs. 2,954 counties). This work suggests that spatial coverage of psychological variables can be reliably extended through Bayesian techniques while maintaining out-of-sample prediction accuracy and that Twitter language adds important information about cultural similarity over and above traditional socio-demographic and geographic similarity measures. Finally, to facilitate the adoption of these methods, we have also open-sourced an online tool that researchers can freely use to interpolate their data across geographies.

Association of Population Well-Being With Cardiovascular Outcomes

Importance

Mortality from cardiovascular disease (CVD) varies across communities and is associated with known structural and population health factors. Still, a population's well-being, including sense of purpose, social relationships, financial security, and relationship to community, may be an important target to improve cardiovascular health.

Objective

To examine the association of population level measures of well-being with rates of CVD mortality in the US.

Design, Setting, and Participants

This cross-sectional study linked data from the Gallup National Health and Well-Being Index (WBI) survey to county-level rates of CVD mortality from the Centers for Disease Control and Prevention Atlas of Heart Disease and Stroke. Participants were respondents of the WBI survey, which was conducted by Gallup with randomly selected adults aged 18 years or older from 2015 to 2017. Data were analyzed from August 2022 to May 2023.

Main Outcomes and Measures

The primary outcome was the county-level rate of total CVD mortality; secondary outcomes were mortality rates for stroke, heart failure, coronary heart disease, acute myocardial infarction, and total heart disease. The association of population well-being (measured using a modified version of the WBI) with CVD mortality was assessed, and an analysis of whether the association was modified by county structural factors (Area Deprivation Index [ADI], income inequality, and urbanicity) and population health factors (percentages of the adult population who had hypertension, diabetes, or obesity; were currently smoking; and were physically inactive) was conducted. Population WBI and its ability to mediate the association of structural factors associated with CVD using structural equation models was also assessed.

Results

Well-being surveys were completed by 514 971 individuals (mean [SD] age 54.0 [19.2] years; 251 691 [48.9%] women; 379 521 [76.0%] White respondents) living in 3228 counties. Mortality rates for CVD decreased from a mean of 499.7 (range, 174.2-974.7) deaths per 100 000 persons in counties with the lowest quintile of population well-being to 438.6 (range, 110.1-850.4) deaths per 100 000 persons in counties with the highest quintile of population well-being. Secondary outcomes showed similar patterns. In the unadjusted model, the effect size (SE) of WBI on CVD mortality was -15.5 (1.5; P < .001), or a decrease of 15 deaths per 100 000 persons for each 1-point increase of population well-being. After adjusting for structural factors and structural plus population health factors, the association was attenuated but still significant, with an effect size (SE) of -7.3 (1.6; P < .001); for each 1-point increase in well-being, the total cardiovascular death rate decreased by 7.3 deaths per 100 000 persons. Secondary outcomes showed similar patterns, with mortality due to coronary heart disease and heart failure being significant in fully adjusted models. In mediation analyses, associations of income inequality and ADI with CVD mortality were all partly mediated by the modified population WBI.

Conclusions and Relevance

In this cross-sectional study assessing the association of well-being and cardiovascular outcomes, higher well-being, a measurable, modifiable, and meaningful outcome, was associated with lower CVD mortality, even after controlling for structural and cardiovascular-related population health factors, indicating that well-being may be a focus for advancing cardiovascular health.

Life Expectancy and Built Environments in the U.S.: A Multilevel Analysis

INTRODUCTION

The purpose of this study is to examine the associations between built environments and life expectancy across a gradient of urbanicity in the U.S.

METHODS

Census tract‒level estimates of life expectancy between 2010 and 2015, except for Maine and Wisconsin, from the U.S. Small-Area Life Expectancy Estimates Project were analyzed in 2022. Tract-level measures of the built environment included: food, alcohol, and tobacco outlets; walkability; park and green space; housing characteristics; and air pollution. Multilevel linear models for each of the 4 urbanicity types were fitted to evaluate the associations, adjusting for population and social characteristics.

RESULTS

Old housing (built before 1979) and air pollution were important built environment predictors of life expectancy disparities across all gradients of urbanicity. Convenience stores were negatively associated with life expectancy in all urbanicity types. Healthy food options were a positive predictor of life expectancy only in high-density urban areas. Park accessibility was associated with increased life expectancy in all areas, except rural areas. Green space in neighborhoods was positively associated with life expectancy in urban areas but showed an opposite association in rural areas.

CONCLUSIONS

After adjusting for key social characteristics, several built environment characteristics were salient risk factors for decreased life expectancy in the U.S., with some measures showing differential effects by urbanicity. Planning and policy efforts should be tailored to local contexts.

Geographical pattern of minerals and its association with health disparities in the USA

This study aimed to determine the common latent patterns of geographical distribution of health-related minerals across the USA and to evaluate the real-world cumulative effects of these patterns on overall population health. It was an ecological study using county-level data (3080 contiguous counties) on the concentrations of 14 minerals (i.e., aluminum, arsenic, calcium, copper, iron, lead, magnesium, manganese, mercury, phosphorus, selenium, sodium, titanium, zinc) in stream sediments (or surface soils), and the measurements of overall health including life expectancy at birth, age-specific mortality risks and cause-specific (summarized by 21 mutually exclusive groups) mortality rates. Latent class analysis (LCA) was employed to identify the common clusters of life expectancy-related minerals based on their concentration characteristics. Multivariate linear regression analyses were then conducted to examine the relationship between the LCA-derived clusters and the health measurements, with adjustment for potential confounding factors. Five minerals (i.e., arsenic, calcium, selenium, sodium and zinc) were associated with life expectancy and were analyzed in LCA. Three clusters were determined across the USA, the 'common' (n = 2056, 66.8%), 'infertile' (n = 739, 24.0%) and 'plentiful' (n = 285, 9.3%) clusters. Residents in counties with the 'infertile' profile were associated with the shortest life expectancy, highest mortality risks at all ages, and highest mortality rates for many reasons including the top five leading causes of death: cardiovascular diseases, neoplasms, neurological disorders, chronic respiratory conditions, and diabetes, urogenital, blood and endocrine diseases. Results remained statistically significant after confounding adjustment. Our study brings novel perspectives regarding environmental geochemistry to explain health disparities in the USA.

The Association Between Tobacco Retailer Outlet Density and Prevalence of Cigarette Smoking in Virginia

OBJECTIVE

We examine the association between tobacco retail outlet density and adult smoking prevalence at the county level in Virginia, controlling for spatial autocorrelations.

AIMS AND METHODS

Pooling data from 2020 County Health Rankings (compiled data from various sources including, but not limited to, the National Center for Health Statistics-Mortality Files, the Behavioral Risk Factor Surveillance System (BRFSS), and the American Community Survey) and Counter Tools, we conducted regression analyses that accounted for spatial autocorrelation (spatial lag models, LMlag) and adjusted for county-level access to healthcare, demographics, SES, environmental factors, risk conditions or behaviors, and population health measures.

RESULTS

Our estimates provide evidence that every increase of one tobacco retail outlet per 1000 persons was associated with 1.16 percentage points (95% CI: 0.80-1.52) higher smoking prevalence at the county level in Virginia after controlling for spatial autocorrelation. The effect of outlet density was largely explained by social determinants of health such as SES, risky conditions or behaviors, and environmental factors. We further noticed that the impact of social determinants of health were closely related and can be explained by indicators of population health (rates of mental distress (β = 1.49, 95% CI: 1.31-1.67) and physical inactivity (β = 0.07, 95% CI: 0.04-0.10).

CONCLUSIONS

Although higher tobacco outlet density was associated with an increase in county-level smoking prevalence, the impact of outlet density was largely explained by social determinants of health and mental illness. Improving well-being at the community level could be a promising strategy in future tobacco control policies.

IMPLICATION

The influence of tobacco outlet density seems to be explained by other social determinants of health and population level of mental or physical health. Thus, efforts to reduce tobacco use and consequent negative health effects should explore the impact of improving regional living standards. However, a sole focus on economic growth may not be sufficient, whereas a focus on such things as promoting work-life balance and improving overall well-being at the community level may be more.

Aggressive measures, rising inequalities, and mass formation during the COVID-19 crisis: An overview and proposed way forward

A series of aggressive restrictive measures were adopted around the world in 2020-2022 to attempt to prevent SARS-CoV-2 from spreading. However, it has become increasingly clear the most aggressive (lockdown) response strategies may involve negative side-effects such as a steep increase in poverty, hunger, and inequalities. Several economic, educational, and health repercussions have fallen disproportionately on children, students, young workers, and especially on groups with pre-existing inequalities such as low-income families, ethnic minorities, and women. This has led to a vicious cycle of rising inequalities and health issues. For example, educational and financial security decreased along with rising unemployment and loss of life purpose. Domestic violence surged due to dysfunctional families being forced to spend more time with each other. In the current narrative and scoping review, we describe macro-dynamics that are taking place because of aggressive public health policies and psychological tactics to influence public behavior, such as mass formation and crowd behavior. Coupled with the effect of inequalities, we describe how these factors can interact toward aggravating ripple effects. In light of evidence regarding the health, economic and social costs, that likely far outweigh potential benefits, the authors suggest that, first, where applicable, aggressive lockdown policies should be reversed and their re-adoption in the future should be avoided. If measures are needed, these should be non-disruptive. Second, it is important to assess dispassionately the damage done by aggressive measures and offer ways to alleviate the burden and long-term effects. Third, the structures in place that have led to counterproductive policies should be assessed and ways should be sought to optimize decision-making, such as counteracting groupthink and increasing the level of reflexivity. Finally, a package of scalable positive psychology interventions is suggested to counteract the damage done and improve humanity's prospects.

Life expectancy by county, race, and ethnicity in the USA, 2000-19: a systematic analysis of health disparities

BACKGROUND

There are large and persistent disparities in life expectancy among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically on a local scale is not well understood. This analysis estimated life expectancy for five racial-ethnic groups, in 3110 US counties over 20 years, to describe spatial-temporal variations in life expectancy and disparities between racial-ethnic groups.

METHODS

We applied novel small-area estimation models to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual sex-specific and age-specific mortality rates stratified by county and racial-ethnic group (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]) from 2000 to 2019. We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and then constructed abridged life tables to estimate life expectancy at birth.

FINDINGS

Between 2000 and 2019, trends in life expectancy differed among racial-ethnic groups and among counties. Nationally, there was an increase in life expectancy for people who were Black (change 3·9 years [95% uncertainty interval 3·8 to 4·0]; life expectancy in 2019 75·3 years [75·2 to 75·4]), API (2·9 years [2·7 to 3·0]; 85·7 years [85·3 to 86·0]), Latino (2·7 years [2·6 to 2·8]; 82·2 years [82·0 to 82·5]), and White (1·7 years [1·6 to 1·7]; 78·9 years [78·9 to 79·0]), but remained the same for the AIAN population (0·0 years [-0·3 to 0·4]; 73·1 years [71·5 to 74·8]). At the national level, the negative difference in life expectancy for the Black population compared with the White population decreased during this period, whereas the negative difference for the AIAN population compared with the White population increased; in both cases, these patterns were widespread among counties. The positive difference in life expectancy for the API and Latino populations compared with the White population increased at the national level from 2000 to 2019; however, this difference declined in a sizeable minority of counties (615 [42·0%] of 1465 counties) for the Latino population and in most counties (401 [60·2%] of 666 counties) for the API population. For all racial-ethnic groups, improvements in life expectancy were more widespread across counties and larger from 2000 to 2010 than from 2010 to 2019.

INTERPRETATION

Disparities in life expectancy among racial-ethnic groups are widespread and enduring. Local-level data are crucial to address the root causes of poor health and early death among disadvantaged groups in the USA, eliminate health disparities, and increase longevity for all.

FUNDING

National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Science Research, US National Institutes of Health.

Can a collective-impact initiative improve well-being in three US communities? Findings from a prospective repeated cross-sectional study

INTRODUCTION

Communities are seeking to learn if and how they can improve the well-being of their residents. We therefore examined the impact of a community-led, collective-impact initiative, deployed through Blue Zones Project by Sharecare, aimed at improving health and well-being in one set of US communities.

METHODS

We used data from cross-sectional surveys of the Well-Being Index (2010-2017) to assess how the Life Evaluation Index (LEI) in Hermosa Beach, Manhattan Beach and Redondo Beach in California (Beach Cities) changed over time and how this change compares with change for similar cities (Beach Cities-like) and for the USA as a whole. We examined types of interventions, perceived impacts, and relationships between intervention type and change in LEI.

RESULTS

The Beach Cities experienced greater increases in LEI than Beach Cities-like communities and the nation. The entire portfolio of interventions was positively associated with change in LEI in the Beach Cities (+1.12, p=0.012), with process-oriented interventions most closely associated with improvement.

CONCLUSIONS

Community-led collective action that leverages community engagement and activation, strategic use of programming and large-scale built-environment and policy change can improve health and well-being at scale.

Trends and geographical variation in population thriving, struggling and suffering across the USA, 2008-2017: a retrospective repeated cross-sectional study

OBJECTIVES

Well-being is a holistic, positively framed conception of health, integrating physical, emotional, social, financial, community and spiritual aspects of life. High well-being is an intrinsically worthy goal for individuals, communities and nations. Multiple measures of well-being exist, yet we lack information to identify benchmarks, geographical disparities and targets for intervention to improve population life evaluation in the USA.

DESIGN

Using data from the Gallup National Health and Well-Being Index, we conducted retrospective analyses of a series of cross-sectional samples.

SETTING/PARTICIPANTS

We summarised select well-being outcomes nationally for each year, and by county (n=599) over two time periods, 2008-2012 and 2013-2017.

MAIN OUTCOME MEASURES

We report percentages of people thriving, struggling and suffering using the Cantril Self-Anchoring Scale, percentages reporting high or low current life satisfaction, percentages reporting high or low future life optimism, and changes in these percentages over time.

RESULTS

Nationally, the percentage of people that report thriving increased from 48.9% in 2008 to 56.3% in 2017 (p<0.05). The percentage suffering was not significantly different over time, ranging from 4.4% to 3.2%. In 2013-2017, counties with the highest life evaluation had a mean 63.6% thriving and 2.3% suffering while counties with the lowest life evaluation had a mean 49.5% thriving and 6.5% suffering, with counties experiencing up to 10% suffering, threefold the national average. Changes in county-level life evaluation also varied. While counties with the greatest improvements experienced 10%-15% increase in the absolute percentage thriving or 3%-5% decrease in absolute percentage suffering, most counties experienced no change and some experienced declines in life evaluation.

CONCLUSIONS

The percentage of the US population thriving increased from 2008 to 2017 while the percentage suffering remained unchanged. Marked geographical variation exists indicating priority areas for intervention.

Exposure to Family Member Incarceration and Adult Well-being in the United States

Importance

More than half of the adult population in the United States has ever had a family member incarcerated, an experience more common among Black individuals. The impacts of family incarceration on well-being are not fully understood.

Objective

To assess the associations of incarceration of a family member with perceived well-being and differences in projected life expectancy.

Design, Setting, and Participants

This nationally representative cross-sectional study used data from the 2018 Family History of Incarceration Survey to examine how experiences of family member incarceration were associated with a holistic measure of well-being, including physical, mental, social, financial, and spiritual domains. Well-being was used to estimate change in life expectancy and was compared across varying levels of exposure to immediate and extended family member incarceration using logistic regression models to adjust for individual and household characteristics. Data were analyzed from October 2019 to April 2020.

Exposures

Respondents' history of family member incarceration, including immediate and extended family members.

Main Outcomes and Measures

The main outcome was self-reported life-evaluation, a measure of overall well-being from the 100 Million Healthier Lives Adult Well-being Assessment. Respondents were considered thriving with a current life satisfaction score of 7 or greater and a future life optimism score of 8 or greater, each on a scale of 0 to 10. Other outcomes included physical health, mental health, social support, financial well-being, and spiritual well-being, each measured with separate scales. Additionally, life expectancy projections were estimated using population-level correlations with the Life Evaluation Index. All percentages were weighted to more closely represent the US population.

Results

Of 2815 individuals included in analysis, 1472 (51.7%) were women, 1765 (62.8%) were non-Hispanic White, and 868 (31.5%) were aged 35 to 54 years. A total of 1806 respondents (45.0%) reported having an immediate family member who was incarcerated. Compared with respondents with no family incarceration, any family member incarceration was associated with lower well-being overall (thriving: 69.5% [95% CI, 65.0%-75.0%] vs 56.9% [95% CI, 53.9%-59.9%]) and in every individual domain (eg, physical thriving: 51.1% [95% CI, 46.2-56.0] vs 35.5% [95% CI, 32.6%-38.3%]) and with a mean (SE) estimated 2.6 (0.03) years shorter life expectancy. Among those with any family incarceration, Black respondents had a mean (SE) estimated 0.46 (0.04) fewer years of life expectancy compared with White respondents.

Conclusions and Relevance

These findings suggest that family member health and well-being may be an important avenue through which incarceration is associated with racial disparities in health and mortality. Decarceration efforts may improve population-level well-being and life expectancy by minimizing detrimental outcomes associated with incarceration among nonincarcerated family members.

Identifying characteristics of high-poverty counties in the United States with high well-being: an observational cross-sectional study

OBJECTIVE

To identify county characteristics associated with high versus low well-being among high-poverty counties.

DESIGN

Observational cross-sectional study at the county level to investigate the associations of 29 county characteristics with the odds of a high-poverty county reporting population well-being in the top quintile versus the bottom quintile of well-being in the USA. County characteristics representing key determinants of health were drawn from the Robert Wood Johnson Foundation County Health Rankings and Roadmaps population health model.

SETTING

Counties in the USA that are in the highest quartile of poverty rate.

MAIN OUTCOME MEASURE

Gallup-Sharecare Well-being Index, a comprehensive population-level measure of physical, mental and social health. Counties were classified as having a well-being index score in the top or bottom 20% of all counties in the USA.

RESULTS

Among 770 high-poverty counties, 72 were categorised as having high well-being and 311 as having low well-being. The high-well-being counties had a mean well-being score of 71.8 with a SD of 2.3, while the low-well-being counties had a mean well-being score of 60.2 with a SD of 2.8. Among the six domains of well-being, basic access, which includes access to housing and healthcare, and life evaluation, which includes life satisfaction and optimism, differed the most between high-being and low-well-being counties. Among 29 county characteristics tested, six were independently and significantly associated with high well-being (p<0.05). These were lower rates of preventable hospital stays, higher supply of primary care physicians, lower prevalence of smoking, lower physical inactivity, higher percentage of some college education and higher percentage of heavy drinkers.

CONCLUSIONS

Among 770 high-poverty counties, approximately 9% outperformed expectations, reporting a collective well-being score in the top 20% of all counties in the USA. High-poverty counties reporting high well-being differed from high-poverty counties reporting low well-being in several characteristics.

The American Heart Association 2030 Impact Goal: A Presidential Advisory From the American Heart Association

Each decade, the American Heart Association (AHA) develops an Impact Goal to guide its overall strategic direction and investments in its research, quality improvement, advocacy, and public health programs. Guided by the AHA’s new Mission Statement, to be a relentless force for a world of longer, healthier lives, the 2030 Impact Goal is anchored in an understanding that to achieve cardiovascular health for all, the AHA must include a broader vision of health and well-being and emphasize health equity. In the next decade, by 2030, the AHA will strive to equitably increase healthy life expectancy beyond current projections, with global and local collaborators, from 66 years of age to at least 68 years of age across the United States and from 64 years of age to at least 67 years of age worldwide. The AHA commits to developing additional targets for equity and well-being to accompany this overarching Impact Goal. To attain the 2030 Impact Goal, we recommend a thoughtful evaluation of interventions available to the public, patients, providers, healthcare delivery systems, communities, policy makers, and legislators. This presidential advisory summarizes the task force’s main considerations in determining the 2030 Impact Goal and the metrics to monitor progress. It describes the aspiration that these goals will be achieved by working with a diverse community of volunteers, patients, scientists, healthcare professionals, and partner organizations needed to ensure success.

Counseling Psychologists and Behavioral Health: Promoting Mental and Physical Health Outcomes

On the occasion of the 50th Anniversary of The Counseling Psychologist, we reflect on the many contributions that counseling psychologists have made and are poised to make in the areas of behavioral health and behavioral health care. We note that psychologists’ engagement in health promotion and prevention of behavioral, mental, and emotional disorders is consistent with counseling psychology values. We provide a concise review of theories that are widely applied in behavioral health contexts and discuss ways in which counseling psychologists may apply these theories to help ameliorate health disparities, empower communities to take control of their own health, and promote social justice. In addition, we highlight the need to create interdisciplinary partnerships to conduct culturally sensitive research on the bi-directional relationship between mental health and physical health. The article ends with wide-ranging implications and recommendations for theory development, research, training, practice, and advocacy.

Exposure to the US Criminal Legal System and Well-Being: A 2018 Cross-Sectional Study

Objectives. To assess the association between exposure to the US criminal legal system and well-being.Methods. We used data from the 2018 Family History of Incarceration Survey, a nationally representative cross-sectional study of family incarceration experience (n = 2815), which includes measures of participants' own criminal legal system exposure, including police stops, arrests, and incarceration. We measured well-being across 5 domains-physical, mental, social, spiritual, and overall life evaluation-and analyzed trends in well-being by criminal legal system exposure using logistic regression.Results. Exposure to police stops, arrests, and incarceration were each associated with lower well-being in every domain compared with those not exposed. Longer durations of incarceration and multiple incarcerations were associated with progressively lower well-being. Those who were stopped and frisked by the police had low well-being similar to that of those who had been incarcerated multiple times.Conclusions. Any exposure to police contact or incarceration is associated with lower well-being in every domain. More involved exposure is associated with even lower well-being.Public Health Implications. Jail diversion and broader criminal justice reform may improve population-level well-being by reducing police contact and incarceration.

Explaining the spatial variation in American life expectancy

Since 1980, average life expectancy in the United States has increased by roughly five years; however, in recent years it has been declining. At the same time, spatial variation in life expectancy has been growing. To explore reasons for this trend, some researchers have focused on morbidity factors, while others have focused on how mortality trends differ by personal characteristics. However, the effect community characteristics may play in expanding the spatial heterogeneity has not yet been fully explored. Using a spatial Durbin error model, we explore how community and demographic factors influence county-level life expectancy in 2014, controlling for life expectancy in 1980 and migration over time, and analyzing men and women separately. We find that community characteristics are important in determining life expectancy and that there may be a role for policy makers in addressing factors that are associated with lower life expectancy in some regions.

Associations between community well-being and hospitalisation rates: results from a cross-sectional study within six US states

OBJECTIVE

To evaluate the association between community well-being, a positively framed, multidimensional assessment of the health and quality of life of a geographic community, and hospitalisation rates.

DESIGN

Cross-sectional study SETTING: Zip codes within six US states (Florida, Iowa, Nebraska, New York, Pennsylvania and Utah) MAIN OUTCOME MEASURES: Our primary outcome was age-adjusted, all-cause hospitalisation rates in 2010; secondary outcomes included potentially preventable disease-specific hospitalisation rates, including cardiovascular-related, respiratory-related and cancer-related admissions. Our main independent variable was the Gallup-Sharecare Well-Being Index (WBI) and its domains (life evaluation, emotional health, work environment, physical health, healthy behaviours and basic access).

RESULTS

Zip codes with the highest quintile of well-being had 223 fewer hospitalisations per 100 000 (100k) residents than zip codes with the lowest well-being. In our final model, adjusted for WBI respondent age, sex, race/ethnicity and income, and zip code number of hospital beds, primary care physician density, hospital density and admission rates for two low-variation conditions, a 1 SD increase in WBI was associated with 5 fewer admissions/100k (95% CI 4.0 to 5.8; p<0.001). Results were similar for cardiovascular-related and respiratory-related admissions, but no association remained for cancer-related hospitalisation after adjustment. Patterns were similar for each of the WBI domains and all-cause hospitalisations.

CONCLUSION AND RELEVANCE

Community well-being is inversely associated with local hospitalisation rates. In addition to health and quality-of-life benefits, higher community well-being may also result in fewer unnecessary hospitalisations.

Putting 'place' in its place: Comparing place-based factors in interurban analyses of life expectancy in the United States

Extant interurban research in life expectancy greatly suffers from an underestimation of the role of place. Place is often conceptualized as a level of geography; this view ignores categorical differences between types of places. In addition, despite advances in theory and research that support their use, many important place-based factors remain under-utilized as control variables. We use multivariate analyses of life expectancy for the top and bottom quartiles of household income by sex in 148 US counties to compare the strengths of seventeen diverse variables. We find that cities' built, natural, and social environments play strong roles in life expectancy disparity among cities; many place-based variables consistently compare in strength to well-known control variables such as race, education, and behaviors. Furthermore, we find that place impacts men and women differently, even within the same income quartile. Indeed, some factors are associated with higher life expectancy in some demographic groups, and lower life expectancy in others. Researchers can protect against omitted variable bias when investigating public health outcomes by using a wider range of control variables. Researchers should also use better measures of place, and consider selecting specific cases to study.

Do pregnant women living in higher well-being populations in the USA experience lower risk of preterm delivery? A cross-sectional study

OBJECTIVE

To determine if preterm birth, defined as gestational age <37 weeks, is lower for women living in counties with higher well-being, after accounting for known individual risk factors.

DESIGN

Cross-sectional study of all US births in 2011.

PARTICIPANTS

We obtained birth data from the National Center for Health Statistics which included 3 938 985 individuals.

MAIN OUTCOMES MEASURES

Primary outcome measure was maternal risk of preterm delivery by county; primary independent variable was county-level well-being as measured by the Gallup-Sharecare Well-Being Index (WBI).

RESULTS

Women living in counties with higher population well-being had a lower rate of preterm delivery. The rate of preterm birth in counties in the lowest WBI quintile was 13.1%, while the rate of preterm birth in counties in the highest WBI quintile was 10.9%. In the model adjusted for maternal risk factors (age, race, Hispanic ethnicity, smoking status, timing of initiation of prenatal visits, multiparity, maternal insurance payer), the association was slightly attenuated with an absolute difference of 1.9% (95% CI 1.7% to 2.1%; p<0.001).

CONCLUSIONS

Pregnant women who live in areas with higher population well-being have lower risk of preterm birth, even after accounting for individual risk factors.

Patchwork of contrasting medication cultures across the USA

Health in the United States is markedly heterogeneous, with large disparities in disease incidence, treatment choices and health spending. Drug prescription is one major component of health care-reflecting the accuracy of diagnosis, the adherence to evidence-based guidelines, susceptibility to drug marketing and regulatory factors. Using medical claims data covering nearly half of the USA population, we have developed and validated a framework to compare prescription rates of 600 popular drugs in 2334 counties. Our approach uncovers geographically separated sub-Americas, where patients receive treatment for different diseases, and where physicians choose different drugs for the same disease. The geographical variation suggests influences of racial composition, state-level health care laws and wealth. Some regions consistently prefer more expensive drugs, even when they have not been proven more efficacious than cheaper alternatives. Our study underlines the benefit of aggregating massive information on medical practice into a summarized and actionable form.

Collective Well-Being to Improve Population Health Outcomes: An Actionable Conceptual Model and Review of the Literature

OBJECTIVES

To propose collective well-being as a holistic measure of the overall "health" of a community. To define collective well-being as a group-level construct measured across 5 domains (vitality, opportunity, connectedness, contribution, and inspiration) and introduce an actionable model that demonstrates how community characteristics affect collective well-being. To review the literature describing each domain's association with health outcomes and community characteristics' associations with collective well-being.

METHODS

We came to consensus on topics describing each component of our conceptual model. Because "well-being" is not indexed in MEDLINE, we performed topic-specific database searches and examined bibliographies of papers retrieved. We excluded articles that were limited to narrow subtopics or studies within small subpopulations. Preference was given to quasi-experimental or randomized studies, systematic reviews, or meta-analyses. Consensus was reached on inclusion or exclusion of all articles.

RESULTS

Reviewed literature supported each of the proposed domains as important aspects of collective well-being and as determinants of individual or community health. Evidence suggests a broad range of community characteristics support collective well-being.

CONCLUSIONS

The health and quality of life of a community may be improved by focusing efforts on community characteristics that support key aspects of well-being. Future work should develop a unified measure of collective well-being to evaluate the relative impact of specific efforts on the collective well-being of communities.

Trends in the contribution of major causes of death to the black-white life expectancy gap by US state

Life expectancy has increased in the United States over many decades. The difference in life expectancy between black and white Americans has also decreased, but some states have made much more progress towards racial equality than others. This paper describes the pattern of contributions of six major causes of death to the black-white life expectancy gap within US states and the District of Columbia between 1969 and 2013, and identifies states diverging from the overall pattern. Across multiple causes, the District of Columbia, Illinois, Wisconsin, and Michigan had the highest contributions to black-white inequality, while New York, Massachusetts, and Rhode Island had the lowest contributions and have either achieved or are the closest to achieving black-white equality in life expectancy.

Identifying county characteristics associated with resident well-being: A population based study

Background

Well-being is a positively-framed, holistic assessment of health and quality of life that is associated with longevity and better health outcomes. We aimed to identify county attributes that are independently associated with a comprehensive, multi-dimensional assessment of individual well-being.

Methods

We performed a cross-sectional study examining associations between 77 pre-specified county attributes and a multi-dimensional assessment of individual US residents’ well-being, captured by the Gallup-Sharecare Well-Being Index. Our cohort included 338,846 survey participants, randomly sampled from 3,118 US counties or county equivalents.

Findings

We identified twelve county-level factors that were independently associated with individual well-being scores. Together, these twelve factors explained 91% of the variance in individual well-being scores, and they represent four conceptually distinct categories: demographic (% black); social and economic (child poverty, education level [<high school, high school diploma/equivalent, college degree], household income, % divorced); clinical care (% eligible women obtaining mammography, preventable hospital stays per 100,000, number of federally qualified health centers); and physical environment (% commuting by bicycle and by public transit).

Conclusions

Twelve factors across social and economic, clinical care, and physical environmental county-level factors explained the majority of variation in resident well-being.

Emotional Well-Being and Public Health: Proposal for a Model National Initiative

In 2011, the National Prevention, Health Promotion, and Public Health Council named mental and emotional well-being as 1 of 7 priority areas for the National Prevention Strategy. In this article, we discuss emotional well-being as a scientific concept and its relevance to public health. We review evidence that supports the association between emotional well-being and health. We propose a national emotional well-being initiative and describe its 6 components: systematic measurement of emotional well-being, identification of the drivers of emotional well-being, formation of partnerships with diverse stakeholders, implementation and dissemination of evidence-based interventions to promote emotional well-being and its drivers, development of public health messaging, and identification of and strategies to address disparities in emotional well-being and its drivers. Finally, we discuss ways in which a national emotional well-being initiative would complement current public health efforts and the potential challenges to such an initiative.

The relationship between physical inactivity and mental wellbeing: Findings from a gamification-based community-wide physical activity intervention

Mental ill health accounts for 13 per cent of total global disease burden with predictions that depression alone will be the leading cause of disease burden globally by 2030. Poor mental health is consistently associated with deprivation, low income, unemployment, poor education, poorer physical health and increased health-risk behaviour. A plethora of research has examined the relationship between physical activity and mental wellbeing; however, the influence of community-wide gamification-based physical activity interventions on mental wellbeing, to the authors' knowledge, is yet to be explored. In view of this paucity of attention, the current study examined the relationship between physical activity and mental wellbeing pre/post a community-wide, gamification-based intervention. The findings revealed that increases in mental wellbeing were significantly greater for the least active prior to the intervention, and a strong, positive correlation between increase in physical activity and increase in mental wellbeing was observed.

Biopsychosocial predictors of perceived life expectancy in a national sample of older men and women

Perceived life expectancy (PLE) is predictive of mortality risk in older adults, but the factors that may contribute to mental conceptions of PLE are unknown. We aimed to describe the sociodemographic, biomedical, behavioral, and psychological predictors of self-reported PLE estimates among older English adults. Data were from 6662 adults aged 50–79 years in the population-based English Longitudinal Study of Ageing (cross-sectional sample from 2012/13). PLE was assessed in the face-to-face study interview (“What are the chances you will live to be age x or more?” where x = current age plus 10–15 years). Responses were categorized as ‘low’ (0–49%), ‘medium’ (50–74%), and ‘high’ (75–100%). Adjusted prevalence ratios (PRs) and 95% confidence intervals (CIs) for low vs. high PLE were estimated using population-weighted modified Poisson regression with robust error variance. Overall, 1208/6662 (18%) participants reported a low PLE, 2806/6662 (42%) reported a medium PLE, and 2648/6662 (40%) reported a high PLE. The predictors of reporting a low PLE included older age (PR = 1.64; 95% CI: 1.50–1.76 per 10 years), male sex (PR = 1.14; 95% CI: 1.02–1.26), being a smoker (PR = 1.39; 95% CI: 1.22–1.59 vs. never/former smoker), and having a diagnosis of cancer or diabetes. A low sense of control over life was associated with low PLE, as was low satisfaction with life and worse self-rated health. Those with a higher perceived social standing were less likely to report a low PLE (PR = 0.90; 95% CI: 0.87–0.93 per 10-point increase, out of 100). This study provides novel insight into potential influences on older adults’ expectations of their longevity, including aspects of psychological well-being. These results should be corroborated to better determine their implications for health-related decision-making, planning, and behavior among older adults.