Exploring the inequality-mortality relationship in the US with Bayesian spatial modeling

A systematic review and meta-analysis of the relationship between economic inequality and prosocial behaviour

How does economic inequality relate to prosocial behaviour? Existing theories and empirical studies from multiple disciplines have produced mixed results. Here we conduct a systematic review and meta-analysis to systematically synthesize empirical studies. Results from 192 effect sizes and over 2.5 million observations in 100 studies show that the relationship varies from being negative to positive depending upon the study (95% prediction interval -0.450 to 0.343). However, on average, there is a small, negative relationship between economic inequality and prosocial behaviour (r = -0.064, P = 0.004, 95% confidence interval -0.106 to -0.021). There is generally no evidence that results depend upon characteristics of the studies, participants, the way prosocial behaviour and inequality were assessed, and the publication discipline. Given the prevalence of economic inequality and the importance of prosocial behaviour, this systematic review and meta-analysis provides a timely study on the relationship between economic inequality and prosocial behaviour.

Neighbourhood Social Determinants of Health and Geographical Inequalities in Premature Mortality in Taiwan: A Spatiotemporal Approach

Geographical inequalities in premature mortality and the role of neighbourhood social determinants of health (SDOH) have been less explored. This study aims to assess the geographical inequalities in premature mortality in Taiwan and how neighbourhood SDOH contribute to them and to examine the place-specific associations between neighbourhood SDOH and premature mortality. We used township-level nationwide data for the years 2015 to 2019, including age-standardized premature mortality rates and three upstream SDOH (ethnicity, education, and income). Space-time scan statistics were used to assess the geographical inequality in premature mortality. A geographical and temporal weighted regression was applied to assess spatial heterogeneity and how neighbourhood SDOH contribute to geographic variation in premature mortality. We found geographical inequality in premature mortality to be clearly clustered around mountainous rural and indigenous areas. The association between neighbourhood SDOH and premature mortality was shown to be area-specific. Ethnicity and education could explain nearly 84% variation in premature mortality. After adjusting for neighbourhood SDOH, only a handful of hotspots for premature mortality remained, mainly consisting of rural and indigenous areas in the central-south region of Taiwan. These findings provide empirical evidence for developing locally tailored public health programs for geographical priority areas.

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.

Income Inequality across the Rural-Urban Continuum in the United States, 1970-2016

The growth in macro‐level income inequality in the United States is well established, but less is known about patterns of inequality at subnational scales and how they vary between and within rural and urban localities. Using data from the Decennial Census and American Community Survey, we produce estimates of within‐county income inequality from 1970 to 2016 and analyze differences in inequality levels, the persistence of high (low) inequality, and populations' exposure to high (low) inequality across the rural‐urban continuum. We find that income inequality has historically been higher in non‐metropolitan than metropolitan counties, but inequality levels converged by 2016 due to growing inequality in metropolitan counties. Additionally, levels of inequality were generally persistent within counties over time, except that counties characterized by low inequality in 1970 were unlikely to remain as such in 2016. Third, non‐trivial shares of the metropolitan population resided in low‐inequality contexts in 1970, but virtually none of the U.S. population resided in such places by 2016. Residence in high‐inequality counties is normative in rural and urban America. This statistical analysis provides an updated portrait of income inequality across the rural‐urban continuum, and should spur additional research on stratification in rural America during an era of growing inequality.

Using Census Data to Understand County-Level Differences in Overall Drug Mortality and Opioid-Related Mortality by Opioid Type

Objectives. To examine associations of county-level demographic, socioeconomic, and labor market characteristics on overall drug mortality rates and specific classes of opioid mortality. Methods. We used National Vital Statistics System mortality data (2002-2004 and 2014-2016) and county-level US Census data. We examined associations between several census variables and drug deaths for 2014 to 2016. We then identified specific classes of counties characterized by different levels and rates of growth in mortality from specific opioid types between 2002 to 2004 and 2014 to 2016. We ran multivariate and multivariable regression models to predict probabilities of membership in each "opioid mortality class" on the basis of county-level census measures. Results. Drug mortality rates overall are higher in counties characterized by more economic disadvantage, more blue-collar and service employment, and higher opioid-prescribing rates. High rates of prescription opioid overdoses and overdoses involving both prescription and synthetic opioids cluster in more economically disadvantaged counties with larger concentrations of service industry workers. High heroin and "syndemic" opioid mortality counties (high rates across all major opioid types) are more urban, have larger concentrations of professional workers, and are less economically disadvantaged. Syndemic opioid counties also have greater concentrations of blue-collar workers. Conclusions. Census data are essential tools for understanding the importance of place-level characteristics on opioid mortality. Public Health Implications. National opioid policy strategies cannot be assumed universally applicable. In addition to national policies to combat the opioid and larger drug crises, emphasis should be on developing locally and regionally tailored interventions, with attention to place-based structural economic and social characteristics.

Lost in the Black Belt South: health outcomes and transportation infrastructure

The importance of transportation infrastructure to health outcomes has been increasingly recognized. However, the relationship between transportation and health is underexplored in rural areas. This study fills the gap by investigating rural health outcomes in association with two transportation infrastructures-highways and airports-in the Black Belt counties of the USA, a region characterized as predominantly rural and black and as having high poverty and unemployment. Spatial regression models are applied to analyze the 2010 data. The results suggest Black Belt counties have poorer health outcomes than their non-Black Belt counterparts, and the difference increases as the percentage of blacks increases. The results also show that the higher accessibility to an airport a county has, the better its health outcomes. Highways, however, do not have a statistically significant association with health outcomes. The poor health outcomes in the Black Belt counties are also influenced by poverty, rurality, unemployment, and low educational attainment. This research was the first to study transportation, especially airports, in the rural US South with relation to health outcomes. Our findings shed new light on removing the health disadvantages accumulated in the Black Belt.

Factors Associated With County-Level Differences in U.S. Drug-Related Mortality Rates

INTRODUCTION

Over the past 2 decades, drug-related deaths have grown to be a major U.S. public health problem. County-level differences in drug-related mortality rates are large. The relative contributions of social determinants of health to this variation, including the economic, social, and healthcare environments, are unknown.

METHODS

Using data from the U.S. Centers for Disease Control and Prevention Multiple-Cause of Death Files (2006-2015, analyzed in 2017); U.S. Census Bureau; U.S. Department of Agriculture Economic Research Service; Agency for Healthcare Research and Quality; and Northeast Regional Center for Rural Development, this paper modeled associations between county-level drug-related mortality rates and economic, social, and healthcare environments. Spatial autoregressive models controlled for state fixed effects and county demographic characteristics.

RESULTS

The average county-level age-adjusted drug-related mortality rate was 16.6 deaths per 100,000 population (2006-2015), but there were substantial geographic disparities in rates. Controlling for county demographic characteristics, average mortality rates were significantly higher in counties with greater economic and family distress and in counties economically dependent on mining. Average mortality rates were significantly lower in counties with a larger presence of religious establishments, a greater percentage of recent in-migrants, and counties with economies reliant on public (government) sector employment. Healthcare supply factors did not contribute to between-county disparities in mortality rates.

CONCLUSIONS

Drug-related mortality rates are not randomly distributed across the U.S. Future research should consider the specific pathways through which economic, social, and healthcare environments are associated with drug-related mortality.

Looking through a different lens: Examining the inequality-mortality association in U.S. counties using spatial panel models

Two areas still need further examination in the ecological study of inequality and mortality. First, the evidence for the relationship between income inequality and mortality remains inconclusive, particularly when the analytic unit is small (e.g., county in the U.S.). Second, most previous studies are cross-sectional and are unable to address the recent diverging patterns whereby mortality has decreased and income inequality increased. This study aims to contribute to both topic areas by studying the relationship between inequality and mortality via a spatiotemporal approach that simultaneously considers the spatial structure and the temporal trends of inequality and mortality using county panel data between 1990 and 2010 for the conterminous U.S. Using both spatial panel random effect model and spatial panel fixed effect models, we found that (a) income inequality was not a significant factor for mortality after taking into account the spatiotemporal structure and the most salient factors for mortality (e.g., socioeconomic status); (b) the spatial panel fixed effect model indicated that income inequality was negatively associated with mortality over the time, a relationship mirroring the diverging patterns; and (c) the significant spatial and temporal fixed effects suggested that both dimensions are critical factors in understanding the inequality-mortality relationship in the U.S. Our findings extend support to the argument that income inequality does not affect mortality and suggest that the cross-sectional findings may be a consequence of ignoring the temporal trends.

Climatic conditions and human mortality: spatial and regional variation in the United States

Previous research on climatic conditions and human mortality in the United States has three gaps: largely ignoring social conditions, lack of nationwide focus, and overlooking potential spatial variations. Our goal is to understand whether climatic conditions contribute to mortality after considering social conditions and to investigate whether spatial non-stationarity exists in these factors. Applying geographically weighted regression to a unique nationwide county-level dataset, we found that (1) net of other factors, average July temperatures are positively (detrimentally) associated with mortality while January temperatures mainly have a curvilinear relationship, (2) the mortality-climatic condition associations are spatially non-stationary, (3) the relationships between social conditions (e.g., social capital) and mortality are stable geographically, and (4) without a spatial approach to understanding the environment-mortality relationship, important spatial variations are overlooked. Our findings suggest that a universal approach to coping with the relationships between rapid climate changes and health may not be appropriate and effective.