Quantifying and explaining variation in life expectancy at census tract, county, and state levels in the United States

The Ecology of Economic Distress and Life Expectancy

Objectives

To determine whether life expectancy (LE) changes between 2000 and 2019 were associated with race, rural status, local economic prosperity, and changes in local economic prosperity, at the county level.

Methods

Between 12/1/22 and 2/28/23, we conducted a retrospective analysis of 2000 and 2019 data from 3,123 United States counties. For Total, White, and Black populations, we compared LE changes for counties across the rural-urban continuum, the local economic prosperity continuum, and for counties in which local economic prosperity dramatically improved or declined.

Results

In both years, overall, across the rural-urban continuum, and for all studied populations, LE decreased with each progression from the most to least prosperous quintile (all p < 0.001); improving county prosperity between 2000-2019 was associated with greater LE gains (p < 0.001 for all).

Conclusion

At the county level, race, rurality, and local economic distress were all associated with LE; improvements in local economic conditions were associated with accelerated LE. Policymakers should appreciate the health externalities of investing in areas experiencing poor economic prosperity if their goal is to improve population health.

Where Does the Black-White Life Expectancy Gap Come From? The Deadly Consequences of Residential Segregation

The disparity in life expectancy between white and black Americans exceeds five years for men and three years for women. While prior research has investigated the roles of healthcare, health behaviors, biological risk, socioeconomic status, and life course effects on black mortality, the literature on the geographic origins of the gap is more limited. This study examines how the black-white life expectancy gap varies across counties and how much of the national gap is attributable to within-county racial inequality versus differences between counties. The estimates suggest that over 90% of the national gap can be attributed to within-county factors. Using a quasi-experimental research design, I find that black-white residential segregation increases the gap by approximately 16 years for men and five years for women. The segregation effect loads heavily on causes of death associated with access to and quality of healthcare; safety and violence; and public health measures. Residential segregation does not appear to operate through health behaviors or individual-level factors, but instead acts primarily through institutional mechanisms. Efforts to address racial disparities in mortality should focus on reducing racial residential segregation or reducing inequalities in the mechanisms through which residential segregation acts: public services, employment opportunities, and community resources.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US

INTRODUCTION

Neighborhoods are complex and multi-faceted. Analytic strategies used to model neighborhoods should reflect this complexity, with the potential to better understand how neighborhood characteristics together impact health. We used latent profile analysis (LPA) to derive a residential neighborhood typology applicable for census tracts across the US.

METHODS

From tract-level 2015-2019 American Community Survey (ACS) five-year estimates, we selected five indicators that represent four neighborhood domains: demographic composition, commuting, socioeconomic composition, and built environment. We compared model fit statistics for up to eight profiles to identify the optimal number of latent profiles of the selected neighborhood indicators for the entire US. We then examined differences in national tract-level 2019 prevalence estimates of physical and mental health derived from CDC's PLACES dataset between derived profiles using one-way analysis of variance (ANOVA).

RESULTS

The 6-profile LPA model was the optimal categorization of neighborhood profiles based on model fit statistics and interpretability. Neighborhood types were distinguished most by demographic composition, followed by commuting and built environment domains. Neighborhood profiles were associated with meaningful differences in the prevalence of health outcomes. Specifically, tracts characterized as "Less educated non-immigrant racial and ethnic minority active transiters" (n = 3,132, 4%) had the highest poor health prevalence (Mean poor physical health: 18.6 %, SD: 4.30; Mean poor mental health: 19.6 %, SD: 3.85), whereas tracts characterized as "More educated metro/micropolitans" (n = 15, 250, 21%) had the lowest prevalence of poor mental and physical health (Mean poor physical health: 10.6 %, SD: 2.41; Mean poor mental health: 12.4 %, SD: 2.67; p < 0.001).

CONCLUSION

LPA can be used to derive meaningful and standardized profiles of tracts sensitive to the spatial patterning of social and built conditions, with observed differences in mental and physical health by neighborhood type in the US.

Neighborhoods And Health: Interventions At The Neighborhood Level Could Help Advance Health Equity

Housing is tied to neighborhoods. Therefore, to understand how housing affects health and health equity, the role of neighborhood environments must be considered. This article is a critical review of the relationship between neighborhoods and health. We discuss inequality among US neighborhoods and the roots of that inequality. We then explore the ways in which neighborhood environments may shape health, review the evidence about these effects, and discuss policy responses. Many studies document an association between neighborhoods and physical and mental health, and a few studies suggest that some of these relationships are causal. Thus, the evidence suggests that interventions at the neighborhood scale can potentially help advance health equity. Further research on the long-term impacts of neighborhoods on health and more rigorous studies of the impact of particular neighborhood interventions are needed. To advance health equity, policy makers also need to better understand the institutional arrangements and social policies that have created neighborhood inequality and pursue innovative approaches to changing them.

Addressing health equity in pediatric surgical care in the United States- Progress and challenges

There are notable inequities in health outcomes for children based on their social determinants of health (SDOH), including where they are born and live, their primary language, their race and ethnicity, socioeconomic status, and more. These health inequities are not restricted to resource limited settings; here we highlight three broad topics that are relevant to pediatric surgeons in the United States (US): access to care and disparities, and examples of inequities in firearm-related injuries and appendicitis. Most of our patients will at some point require operative interventions, yet there can be significant challenges in accessing this care and navigating our health systems, particularly around complex perioperative care. There are significant opportunities to improve equitable care by helping patients navigate our health systems and connecting them with additional resources, including screening for primary care services. Firearm-related injuries are now the leading cause of death in children in the US, with significant associated morbidity for non-fatal injuries. There are notable inequities in the risk of injury and types of injuries experienced by children based on their SDOH. Appendicitis is one of the most common pathologies managed by pediatric surgeons, with similar inequities in the rates of perforated appendicitis based on a child's SDOH. For both issues, addressing the inequities our patients experience requires moving upstream and working towards prevention. Key opportunities include better research and data to understand the drivers for observed inequities, multidisciplinary collaboration, community engagement, and public health advocacy among others. As a profession, we have a responsibility to work to address the health inequities our patients experience.

Structural Factors and Racial/Ethnic Inequities in Travel Times to Acute Care Hospitals in the Rural US South, 2007-2018

Policy Points Policymakers should invest in programs to support rural health systems, with a more targeted focus on spatial accessibility and racial and ethnic equity, not only total supply or nearest facility measures. Health plan network adequacy standards should address spatial access to nearest and second nearest hospital care and incorporate equity standards for Black and Latinx rural communities. Black and Latinx rural residents contend with inequities in spatial access to hospital care, which arise from fundamental structural inequities in spatial allocation of economic opportunity in rural communities of color. Long-term policy solutions including reparations are needed to address these underlying processes.

CONTEXT

The growing rate of rural hospital closures elicits concerns about declining access to hospital-based care. Our research objectives were as follows: 1) characterize the change in rural hospital supply in the US South between 2007 and 2018, accounting for health system closures, mergers, and conversions; 2) quantify spatial accessibility (in 2018) for populations most at risk for adverse outcomes following hospital closure-Black and Latinx rural communities; and 3) use multilevel modeling to examine relationships between structural factors and disparities in spatial access to care.

METHODS

To calculate spatial access, we estimated the network travel distance and time between the census tract-level population-weighted centroids to the nearest and second nearest operating hospital in the years 2007 and 2018. Thereafter, to describe the demographic and health system characteristics of places in relation to spatial accessibility to hospital-based care in 2018, we estimated three-level (tract, county, state-level) generalized linear models.

FINDINGS

We found that 72 (10%) rural counties in the South had ≥1 hospital closure between 2007 and 2018, and nearly half of closure counties (33) lost their last remaining hospital to closure. Net of closures, mergers, and conversions meant hospital supply declined from 783 to 653. Overall, 49.1% of rural tracts experienced worsened spatial access to their nearest hospital, whereas smaller proportions experienced improved (32.4%) or unchanged (18.5%) access between 2007 and 2018. Tracts located within closure counties had longer travel times to the nearest acute care hospital compared with tracts in nonclosure counties. Moreover, rural tracts within Southern states with more concentrated commercial health insurance markets had shorter travel times to access the second nearest hospital.

CONCLUSIONS

Rural places affected by rural hospital closures have greater travel burdens for acute care. Across the rural South, racial/ethnic inequities in spatial access to acute care are most pronounced when travel times to the second nearest open acute care hospital are accounted for.

Association between Low Urban Neighborhood Greenness and Hypertensive Disorders of Pregnancy

OBJECTIVE

Urban neighborhood greenness is associated with greater cardiovascular health in the general population, and with better pregnancy and neonatal outcomes. Hypertension in pregnancy is a leading cause of maternal mortality and long-term cardiovascular morbidity and mortality in women. We sought to examine the association between greenness and hypertensive disorders of pregnancy.

STUDY DESIGN

This study is a secondary analysis of a prospective cohort study of 1,943 women who received prenatal care from December 2013 to December 2016 at a single, urban, and tertiary academic medical center in Philadelphia, PA. Greenness measure was quantified via residential tree canopy cover within circumferential buffers of 100- and 500-meter radii around participants' homes. Associations between greenness and hypertensive disorders of pregnancy (defined as gestational hypertension or preeclampsia) were estimated by using multilevel logistic regression accounting for maternal sociodemographic information (race-ethnicity, insurance status, and age) medical history (diabetes, body mass index, smoking history, and parity), neighborhood deprivation index, and including 1,225 Philadelphia residents for whom key exposure and outcome data were available.

RESULTS

At baseline, the participants' mean (SD) age was 27.5 (5.9) years, (range: 14-44 years). The majority of participants were non-Hispanic Black (857, 70.2%). Participants with less residential tree canopy cover were significantly more likely to have hypertensive disorders of pregnancy. The multivariable-adjusted odds ratio of hypertensive disorders of pregnancy among participants with less than 10% compared with those with greater than 30% tree canopy cover was 2.14 (95% confidence interval [CI]: 1.11-4.15) within 100-meter buffer.

CONCLUSION

In our cohort, greenness was associated with lower hypertensive disorders of pregnancy odds. Our findings add to evidence that greenness may confer health benefits and warrant further investigations in identifying whether there is a causal pathway through which greenness may be protective against hypertensive disorders of pregnancy.

KEY POINTS

· Low residential tree canopy is associated with increased risk of hypertensive disorders of pregnancy. · 100-meter buffers are most sensitive in identifying associations between tree canopy and HDP risk. · The role of greenness against hypertensive disorders of pregnancy should be further studied experimentally.

The Association Between Census Tract Healthy Food Accessibility and Life Expectancy in the United States

Accessibility of healthy food is an important predictor for several health outcomes, but its association with life expectancy is unclear. We evaluated the association between U.S. Department of Agriculture's Food Research Atlas measures of healthy food accessibility and life expectancy at birth across contiguous U.S. census tracts using spatial modeling analysis. Both income and healthy food accessibility were associated with life expectancy at birth, as indicated by shorter life expectancy in low-income census tracts when comparing tracts with similar healthy food accessibility level, and in low-access tracts when comparing tracts with similar income level. Compared to high-income/high-access census tracts, life expectancy at birth was lower in high-income/low-access (- 0.33 years; 95% confidence interval - 0.42, - 0.28), low-income/high-access (- 1.45 years; - 1.52, - 1.38), and low-income/low-access (- 2.29 years; - 2.38, - 2.21) tracts after adjusting for socio-demographic characteristics and incorporating vehicle availability. Effective interventions to increase healthy food accessibility may improve life expectancy.

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.

Investigating Health Disparities Associated With Multisystem Inflammatory Syndrome in Children After SARS-CoV-2 Infection

BACKGROUND

Multisystem inflammatory syndrome in children (MIS-C) is a postinfectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related complication that has disproportionately affected racial/ethnic minority children. We conducted a pilot study to investigate risk factors for MIS-C aiming to understand MIS-C disparities.

METHODS

This case-control study included MIS-C cases and SARS-CoV-2-positive outpatient controls less than 18 years old frequency-matched 4:1 to cases by age group and site. Patients hospitalized with MIS-C were admitted between March 16 and October 2, 2020, across 17 pediatric hospitals. We evaluated race, ethnicity, social vulnerability index (SVI), insurance status, weight-for-age and underlying medical conditions as risk factors using mixed effects multivariable logistic regression.

RESULTS

We compared 241 MIS-C cases with 817 outpatient SARS-CoV-2-positive at-risk controls. Cases and controls had similar sex, age and U.S. census region distribution. MIS-C patients were more frequently previously healthy, non-Hispanic Black, residing in higher SVI areas, and in the 95th percentile or higher for weight-for-age. In the multivariable analysis, the likelihood of MIS-C was higher among non-Hispanic Black children [adjusted odds ratio (aOR): 2.07; 95% CI: 1.23-3.48]. Additionally, SVI in the 2nd and 3rd tertiles (aOR: 1.88; 95% CI: 1.18-2.97 and aOR: 2.03; 95% CI: 1.19-3.47, respectively) were independent factors along with being previously healthy (aOR: 1.64; 95% CI: 1.18-2.28).

CONCLUSIONS

In this study, non-Hispanic Black children were more likely to develop MIS-C after adjustment for sociodemographic factors, underlying medical conditions, and weight-for-age. Investigation of the potential contribution of immunologic, environmental, and other factors is warranted.

Heterogeneity in Disparities in Life Expectancy Across US Metropolitan Areas

BACKGROUND

Life expectancy in the United States has declined since 2014 but characterization of disparities within and across metropolitan areas of the country is lacking.

METHODS

Using census tract-level life expectancy from the 2010 to 2015 US Small-area Life Expectancy Estimates Project, we calculate 10 measures of total and income-based disparities in life expectancy at birth, age 25, and age 65 within and across 377 metropolitan statistical areas (MSAs) of the United States.

RESULTS

We found wide heterogeneity in disparities in life expectancy at birth across MSAs and regions: MSAs in the West show the narrowest disparities (absolute disparity: 8.7 years, relative disparity: 1.1), while MSAs in the South (absolute disparity: 9.1 years, relative disparity: 1.1) and Midwest (absolute disparity: 9.8 years, relative disparity: 1.1) have the widest life expectancy disparities. We also observed greater variability in life expectancy across MSAs for lower income census tracts (coefficient of variation [CoV] 3.7 for first vs. tenth decile of income) than for higher income census tracts (CoV 2.3). Finally, we found that a series of MSA-level variables, including larger MSAs and greater proportion college graduates, predicted wider life expectancy disparities for all age groups.

CONCLUSIONS

Sociodemographic and policy factors likely help explain variation in life expectancy disparities within and across metro areas.

Association of Neighborhood Conditions and Resources for Children With Life Expectancy at Birth in the US

Importance

To address inequities in life expectancy, we must understand the associations of modifiable socioeconomic and structural factors with life expectancy. However, the association of limited neighborhood resources and deleterious physical conditions with life expectancy is not well understood.

Objective

To evaluate the association of community social and economic conditions and resources for children with life expectancy at birth.

Design, Setting, and Participants

This cross-sectional study examined neighborhood child opportunity and life expectancy using data from residents of 65 662 US Census tracts in 2015. The analysis was conducted from July 6 to October 1, 2021.

Exposures

Neighborhood conditions and resources for children in 2015.

Main Outcomes and Measures

The primary outcome was life expectancy at birth at the Census tract level based on data from the US Small-Area Life Expectancy Estimates Project (January 1, 2010, to December 31, 2015). Neighborhood conditions and resources for children were quantified by Census tract Child Opportunity Index (COI) 2.0 scores for 2015. This index captures community conditions associated with children's health and long-term outcomes categorized into 5 levels, from very low to very high opportunity. It includes 29 indicators in 3 domains: education, health and environment, and social and economic factors. Mixed-effects and simple linear regression models were used to estimate the associations between standardized COI scores (composite and domain-specific) and life expectancy.

Results

The study included residents from 65 662 of 73 057 US Census tracts (89.9%). Life expectancy at birth across Census tracts ranged from 56.3 years to 93.6 years (mean [SD], 78.2 [4.0] years). Life expectancy in Census tracts with very low COI scores was lower than life expectancy in Census tracts with very high COI scores (-7.06 years [95% CI, -7.13 to -6.99 years]). Stepwise associations were observed between COI scores and life expectancy. For each domain, life expectancy was shortest in Census tracts with very low compared with very high COI scores (education: β = -2.02 years [95% CI, -2.12 to -1.92 years]); health and environment: β = -2.30 years [95% CI, -2.41 to -2.20 years]; social and economic: β = -4.16 years [95% CI, -4.26 to -4.06 years]). The models accounted for 41% to 54% of variability in life expectancy at birth (R2 = 0.41-0.54).

Conclusions and Relevance

In this study, neighborhood conditions and resources for children were significantly associated with life expectancy at birth, accounting for substantial variability in life expectancy at the Census tract level. These findings suggest that community resources and conditions are important targets for antipoverty interventions and policies to improve life expectancy and address health inequities.

Intraurban socioeconomic inequalities in life expectancy: a population-based cross-sectional analysis in the city of Córdoba, Argentina (2015-2018)

OBJECTIVES

To evaluate variability in life expectancy at birth in small areas, describe the spatial pattern of life expectancy, and examine associations between small-area socioeconomic characteristics and life expectancy in a mid-sized city of a middle-income country.

DESIGN

Cross-sectional, using data from death registries (2015-2018) and socioeconomic characteristics data from the 2010 national population census.

PARTICIPANTS/SETTING

40 898 death records in 99 small areas of the city of Córdoba, Argentina. We summarised variability in life expectancy at birth by using the difference between the 90th and 10th percentile of the distribution of life expectancy across small areas (P90-P10 gap) and evaluated associations with small-area socioeconomic characteristics by calculating a Slope Index of Inequality in linear regression.

PRIMARY OUTCOME

Life expectancy at birth.

RESULTS

The median life expectancy at birth was 80.3 years in women (P90-P10 gap=3.2 years) and 75.1 years in men (P90-P10 gap=4.6 years). We found higher life expectancies in the core and northwest parts of the city, especially among women. We found positive associations between life expectancy and better small-area socioeconomic characteristics, especially among men. Mean differences in life expectancy between the highest versus the lowest decile of area characteristics in men (women) were 3.03 (2.58), 3.52 (2.56) and 2.97 (2.31) years for % adults with high school education or above, % persons aged 15-17 attending school, and % households with water inside the dwelling, respectively. Lower values of % overcrowded households and unemployment rate were associated with longer life expectancy: mean differences comparing the lowest versus the highest decile were 3.03 and 2.73 in men and 2.57 and 2.34 years in women, respectively.

CONCLUSION

Life expectancy is substantially heterogeneous and patterned by socioeconomic characteristics in a mid-sized city of a middle-income country, suggesting that small-area inequities in life expectancy are not limited to large cities or high-income countries.

Structural competency in emergency medical education: A scoping review and operational framework

Objectives

Existing curricula and recommendations on the incorporation of structural competency and vulnerability into medical education have not provided clear guidance on how best to do so within emergency medicine (EM). The goal of this scoping review and consensus building process was to provide a comprehensive overview of structural competency, link structural competency to educational and patient care outcomes, and identify existing gaps in the literature to inform curricular implementation and future research in EM.

Methods

A scoping review focused on structural competency and vulnerability following Arksey and O'Malley's six-step framework was performed in concurrence with a multistep consensus process culminating in the 2021 SAEM Consensus Conference. Feedback was incorporated in developing a framework for a national structural competency curriculum in EM.

Results

A literature search identified 291 articles that underwent initial screening. Of these, 51 were determined to be relevant to EM education. The papers consistently conceptualized structural competency as an interdisciplinary framework that requires learners and educators to consider historical power and privilege to develop a professional commitment to justice. However, the papers varied in their operationalization, and no consensus existed on how to observe or measure the effects of structural competency on learners or patients. None of the studies examined the structural constraints of the learners studied.

Conclusions

Findings emphasize the need for training structurally competent physicians via national structural competency curricula focusing on standardized core competency proficiencies. Moreover, the findings highlight the need to assess the impact of such curricula on patient outcomes and learners' knowledge, attitudes, and clinical care delivery. The framework aims to standardize EM education while highlighting the need for further research in how structural competency interventions would translate to an ED setting and affect patient outcomes and experiences.

Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States

IMPORTANCE

Prior studies on the association between fine particulate matter with diameters 2.5 μm or smaller (PM2.5) and probability of death have not applied multilevel analysis disaggregating data for US census tract, states, and counties, nor tested its interaction by socioeconomic status (SES). Such an approach could provide a more refined identification and targeting of populations exposed to increased risk from PM2.5.

OBJECTIVE

To assess the association between PM2.5 and age-specific mortality risk (ASMR) using disaggregated data at the census tract level and evaluate such association according to census tract SES.

DESIGN, SETTING, AND PARTICIPANTS

This nationwide cross-sectional study used a linkage of 3 different data sets. ASMR for the period of 2010 to 2015 was obtained from the National Center for Health Statistic, SES data covering a period from 2006 to 2016 came from the American Community Survey, and mean PM2.5 exposure levels from 2010 to 2015 were derived from well-validated atmospheric chemistry and machine learning models. Data were analyzed in April 2021.

EXPOSURES

The main exploratory variable was mean census tract-level long-term exposure to PM2.5 from 2010 to 2015.

MAIN OUTCOMES AND MEASURES

The primary outcome was census tract-level ASMR. Multilevel models were used to quantify the geographic variation in ASMR at levels of census tract, county, and state. Additional analysis explored the interaction of SES in the association of ASMR with PM2.5 exposure.

RESULTS

Data from 67 148 census tracts nested in 3087 counties and 50 states were analyzed. The association between exposure to PM2.5 and ASMR varied substantially across census tracts. The magnitude of such association also varied across age groups, being higher among adults and older adults. Census tracts accounted for most of the total geographic variation in mortality risk (range, 77.0%-94.2%). ASMR was higher in deciles with greater PM2.5 concentration. For example, ASMR for age 75 to 84 years was 54.6 per 1000 population higher in the decile with the second-highest PM2.5 concentration than in the decile with the lowest PM2.5 concentration. The ASMR, PM2.5 concentrations, and magnitude of the association between both were higher in the census tracts with the lowest SES.

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that census tracts with lower SES presented higher PM2.5 concentrations. ASMR and air pollution varied substantially across census tracts. There was an association between air pollution and ASMR across all age groups in the United States. These findings suggest that equitable public policies aimed at improving air quality are needed and important to increase life expectancy.

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis

OBJECTIVE

Countries in sub-Saharan Africa suffer the highest rates of child mortality worldwide. Urban areas tend to have lower mortality than rural areas, but these comparisons likely mask large within-city inequalities. We aimed to estimate rates of under-five mortality (U5M) at the neighbourhood level for Ghana's Greater Accra Metropolitan Area (GAMA) and measure the extent of intraurban inequalities.

METHODS

We accessed data on >700 000 women aged 25-49 years living in GAMA using the most recent Ghana census (2010). We summarised counts of child births and deaths by five-year age group of women and neighbourhood (n=406) and applied indirect demographic methods to convert the summaries to yearly probabilities of death before age five years. We fitted a Bayesian spatiotemporal model to the neighbourhood U5M probabilities to obtain estimates for the year 2010 and examined their correlations with indicators of neighbourhood living and socioeconomic conditions.

RESULTS

U5M varied almost five-fold across neighbourhoods in GAMA in 2010, ranging from 28 (95% credible interval (CrI) 8 to 63) to 138 (95% CrI 111 to 167) deaths per 1000 live births. U5M was highest in neighbourhoods of the central urban core and industrial areas, with an average of 95 deaths per 1000 live births across these neighbourhoods. Peri-urban neighbourhoods performed better, on average, but rates varied more across neighbourhoods compared with neighbourhoods in the central urban areas. U5M was negatively correlated with multiple indicators of improved living and socioeconomic conditions among peri-urban neighbourhoods. Among urban neighbourhoods, correlations with these factors were weaker or, in some cases, reversed, including with median household consumption and women's schooling.

CONCLUSION

Reducing child mortality in high-burden urban neighbourhoods in GAMA, where a substantial portion of the urban population resides, should be prioritised as part of continued efforts to meet the Sustainable Development Goal national target of less than 25 deaths per 1000 live births.

Geographic Associations Between Social Factors and SARS-CoV-2 Testing Early in the COVID-19 Pandemic, February-June 2020, Massachusetts

OBJECTIVES

Widespread SARS-CoV-2 testing is critical to identify infected people and implement public health action to interrupt transmission. With SARS-CoV-2 testing supplies and laboratory capacity now widely available in the United States, understanding the spatial heterogeneity of associations between social determinants and the use of SARS-CoV-2 testing is essential to improve testing availability in populations disproportionately affected by SARS-CoV-2.

METHODS

We assessed positive and negative results of SARS-CoV-2 molecular tests conducted from February 1 through June 17, 2020, from the Massachusetts Virtual Epidemiologic Network, an integrated web-based surveillance and case management system in Massachusetts. Using geographically weighted regression and Moran's I spatial autocorrelation tests, we quantified the associations between SARS-CoV-2 testing rates and 11 metrics of the Social Vulnerability Index in all 351 towns in Massachusetts.

RESULTS

Median SARS-CoV-2 testing rates decreased with increasing percentages of residents with limited English proficiency (median relative risk [interquartile range] = 0.96 [0.95-0.99]), residents aged ≥65 (0.97 [0.87-0.98]), residents without health insurance (0.96 [0.95-1.04], and people residing in crowded housing conditions (0.89 [0.80-0.94]). These associations differed spatially across Massachusetts, and localized models improved the explainable variation in SARS-CoV-2 testing rates by 8% to 12%.

CONCLUSION

Indicators of social vulnerability are associated with variations in SARS-CoV-2 testing rates. Accounting for the spatial heterogeneity in these associations may improve the ability to explain and address the SARS-CoV-2 pandemic at substate levels.

Social Determinants Matter For Hospital Readmission Policy: Insights From New York City

This study assessed the impact of individual social risk factor variables and social determinants of health (SDOH) measures on hospital readmission rates and penalties used in the Centers for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP). Using 2012-16 hospital discharge data from New York City, we projected HRRP penalties by augmenting CMS's readmission model for heart attack, heart failure, and pneumonia with SDOH scores constructed at each of four geographic levels and a measure of individual-level social risk. Including additional SDOH scores in the model, especially those constructed with the most granular geographic data, along with social risk factor variables substantially affects projected penalties for hospitals treating the highest proportion of patients with high SDOH scores. Improved performance occurred even after we included peer-group stratification in the HRRP model pursuant to the 21st Century Cures Act. Small improvements in model accuracy were associated with substantial shifts in projected performance. Our results suggest that CMS's continued omission of relevant patient and geographic data from the HRRP readmission model misallocates penalties attributable to SDOH and social risk factor effects to hospitals with the largest share of high-risk patients.

The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong

Human mortality is in part a function of multiple socioeconomic factors that differ both spatially and temporally. Adjusting for other covariates, the human lifespan is positively associated with household wealth. However, the extent to which mortality in a geographical region is a function of socioeconomic factors in both that region and its neighbors is unclear. There is also little information on the temporal components of this relationship. Using the districts of Hong Kong over multiple census years as a case study, we demonstrate that there are differences in how wealth indicator variables are associated with longevity in (a) areas that are affluent but neighbored by socially deprived districts versus (b) wealthy areas surrounded by similarly wealthy districts. We also show that the inclusion of spatially-distributed variables reduces uncertainty in mortality rate predictions in each census year when compared with a baseline model. Our results suggest that geographic mortality models should incorporate nonlocal information (e.g., spatial neighbors) to lower the variance of their mortality estimates, and point to a more in-depth analysis of sociospatial spillover effects on mortality rates.

White Privilege, White Poverty: Reckoning with Class and Race in America

This essay argues that a failure to think and talk critically and candidly about White privilege and White poverty is a key threat to the United States of America's precarious democracy. Whiteness frames one of America's most pressing collective challenges-the poor state of the nation's health, which lags behind other wealthy nations and is marred by deep and entrenched class- and race-based inequities. The broadscale remedies experts recommend demand what is in short supply: trust in evidence, experts, government, and one another. The authors' prescription is threefold, beginning with a call for intersectional health studies and reports that avoid one-dimensional misrepresentations of widespread health problems as simply Black or White problems. Second, there is the need for a "critical consciousness" about race and class. Lastly, the essay calls for widescale opportunities for Americans to engage in cross-racial and cross-class democratic conversations about their struggles and aspirations in search of common ground.

How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 2000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most 'important' for April 2020 with respect to April 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for 'virus' in January 2020 followed by a decline through February and then a surge through March and April. The world's collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations.