Race, disadvantage, and violence: A spatial exploration of macrolevel covariates of police-involved homicides within and between US counties.

Efforts to explore the macrolevel determinants of police-involved homicides have expanded in recent years due in part to increased scrutiny and media attention to such events, and increased data availability of these events through crowdsourced databases. However, little empirical research has examined the spatial determinants of such events. The present study extends the extant macrolevel research on police-involved homicides by employing an underutilized spatial econometric model, the spatial Durbin model (SDM), to assess the direct and indirect county effects of racial threat, economic threat, social disorganization, and community violence on police killings within and between US counties from 2013 through 2020. Results indicate a direct inverse relationship between racial threat and police-involved homicides, no support for economic threat, and a direct positive association with two measures of social disorganization. Additionally, we find firearm availability exhibits significant direct and indirect spatial dependence on focal county police-involved homicides, reflecting spatial spillover processes. In essence, as firearm availability in neighboring counties increases, police-involved homicides within a focal county increase. The implications of these findings for racial threat, economic threat, social disorganization, and community violence are discussed.

Cross-sectional study of food insecurity and medical expenditures by race and ethnicity.

OBJECTIVE: Food insecurity is a risk factor for morbidity and mortality leading to high medical expenditures, but race/ethnicity was used as adjustments in the literature. The study sought to use race/ethnicity as a key predictor to compare racial differences in associations between food insecurity and expenditures of seven health services among non-institutionalized adults. DESIGN: This cross-sectional study used Medical Expenditure Panel Survey that collects information on food insecurity in 2016 (n=24,179) and 2017 (n=22,539). We examined the association between race/ethnicity and food insecurity status and documented the extent to which impacts of food insecurity on medical expenditures varied by race/ethnicity. We fit multivariable models for each racial group, adjusting for states, age, gender, insurance, and education. Adults older than 18 years were included. RESULTS: The results show that blacks experienced an inter-racial disparity in food insecurity whereas Hispanics experienced intra-racial disparity. A higher percentage of blacks (28.7%) reported at least one type of food insecurity (11.2% of whites). Around 20% of blacks reported being worried about running out of food and the corresponding number is 8.4% among whites. Hispanics reported more food insecurity issues than whites. Moreover, food insecurity is positively associated with expenditures on emergency room utilization (99% increase for other races vs. 51% increase for whites) but is negatively associated with dental care utilization (43% decrease for blacks and 44% for whites). Except for Hispanics, prescription expenditure has the most positive association with food insecurity, and food insecure blacks are the only group that did not significantly use home health. CONCLUSION: The study expanded our understanding of food insecurity by investigating how it affected seven types of medical expenditures for each of four racial populations. An interdisciplinary effort is needed to enhance the food supply for minorities. Policy interventions to address intra-racial disparities among Hispanics and inter-racial disparities among African Americans are imperative to close the gap.

COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation.

OBJECTIVE: To investigate how racial/ethnic density and residential segregation shape the uneven burden of COVID-19 in US counties and whether (if yes, how) residential segregation moderates the association between racial/ethnic density and infections. DESIGN: We first merge various risk factors from federal agencies (e.g. Census Bureau and Centers for Disease Control and Prevention) with COVID-19 cases as of June 13th in contiguous US counties (N = 3,042). We then apply negative binomial regression to the county-level dataset to test three interrelated research hypotheses and the moderating role of residential segregation is presented with a figure. RESULTS: Several key results are obtained. (1) Counties with high racial/ethnic density of minority groups experience more confirmed cases than those with low levels of density. (2) High levels of residential segregation between whites and non-whites increase the number of COVID-19 infections in a county, net of other risk factors. (3) The relationship between racial/ethnic density and COVID-19 infections is enhanced with the increase in residential segregation between whites and non-whites in a county. CONCLUSIONS: The pre-existing social structure like residential segregation may facilitate the spread of COVID-19 and aggravate racial/ethnic health disparities in infections. Minorities are disproportionately affected by the novel coronavirus and focusing on pre-existing social structures and discrimination in housing market may narrow the uneven burden across racial/ethnic groups.

Neighborhood Poverty and Physical Health at Midlife: The Role of Life-Course Exposure.

Studies of the effect of neighborhood poverty on health are dominated by research designs that measure neighborhood poverty at a single point in time, ignoring the potential influence of exposure to neighborhood poverty over the life course. Applying latent class analysis to restricted residential history data from the National Longitudinal Survey of Youth, 1979 Cohort, we identify four trajectories of life-course exposure to high-poverty neighborhoods between adolescence and midlife and then examine how these groups differ in their physical health conditions (SF-12 score) and self-rated health at around age 40. Linear and logistic regression analyses show that life-course exposure to high-poverty neighborhoods is a stronger predictor of midlife physical health than are point-in-time measures of neighborhood poverty observed during either adolescence or midlife. Our findings suggest that a life-course approach can enhance our understanding of how neighborhood poverty affects physical health.

Modeling the Importance of Within- and Between-County Effects in an Ecological Study of the Association Between Social Capital and Mental Distress.

INTRODUCTION: Levels of mental distress in the United States are a health policy concern. The association between social capital and mental distress is well documented, but evidence comes primarily from individual-level studies. Our objective was to examine this association at the county level with advanced spatial econometric methods and to explore the importance of between-county effects. METHODS: We used County Health Rankings and Roadmaps data for 3,106 counties of the contiguous United States. We used spatial Durbin modeling to assess the direct (within a county) and indirect (between neighboring counties) effects of social capital on mental distress. We also examined the spatial spillover effects from neighboring counties based on higher-order spatial weights matrices. RESULTS: Counties with the highest prevalence of mental distress were found in regional clusters where levels of social capital were low, including the Black Belt, central/southern Appalachia, on the Mississippi River, and around some Indian Reservations. Most of the association between social capital and mental distress was indirect, from the neighboring counties, although significant direct effects showed the within-county association. Models also confirmed the importance of county-level socioeconomic status. CONCLUSION: We found that county social capital is negatively related to mental distress. Counties are not isolated places and are often part of wider labor and housing markets, so understanding spatial dependencies is important in addressing population-level mental distress.

Racial/ethnic disparities in depression: Investigating how sources of support and types of integration matter.

Although social support and social integration are key predictors of depression and exhibit racial/ethnic patterns in the US, previous research has not examined how they shape racial/ethnic disparities in depression. Applying hybrid models to data from the Americans' Changing Lives study from 1986 to 2002, this study analyzes how sources of social support (spouse and friend/relative) and types of social integration (informal/formal) explain black-white and Hispanic-white disparities in depression. We find that strong social support and high social integration are negatively associated with depression and that the patterns of social support and integration vary by race/ethnicity. The results of hybrid models show that social support from one's spouse and friend/relative account for over 25 percent of the black-white disparity, whereas formal social integration including religious groups widens the black-white differential by roughly 10 percent. However, Hispanic-white disparities in depression are mostly a result of the difference in socioeconomic status. The change in spousal support is the most powerful predictor for the change in depression across race/ethnicity groups. Our findings suggest that the racial/ethnic differences in sources of social support and types of social integration play important roles in shaping racial/ethnic disparities in depression.

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.

The Black-White Disparity in Sexually Transmitted Diseases During Pregnancy: How Do Racial Segregation and Income Inequality Matter?

BACKGROUND: We investigate the roles of residential racial segregation and income inequality for the black-white disparity in acquiring sexually transmitted diseases (STD) during pregnancy in a multilevel framework. METHODS: The analytic sample consisted of non-Hispanic white (n = 79,271) and non-Hispanic black (n = 17,669) mothers from 2012 population birth data from Pennsylvania. We used the 2009 to 2013 American Community Survey for neighborhood characteristics of mothers; we used multilevel models. RESULTS: First, neighborhood-level factors are important for understanding this disparity because racial segregation and income inequality are significantly associated with acquiring STD during pregnancy, regardless of race. Second, racial segregation moderates the relationships between race/ethnicity and the acquisition of STD during pregnancy. White mothers are more vulnerable to neighborhood segregation than black mothers, and black mothers are less likely to acquire STD during pregnancy than white mothers if they reside with co-ethnics. Third, mothers residing in the most socioeconomically disadvantaged neighborhoods-as indicated by both absolute and relative measures of income inequality-have the highest odds of acquiring STD during pregnancy, regardless of race. CONCLUSIONS: Neighborhood-level segregation and income inequality are important for understanding the acquisition of STDs during pregnancy. Our findings have important implications for future research and for place-specific prevention and intervention to reduce the racial disparity in STD during pregnancy.

A multi-group path analysis of the relationship between perceived racial discrimination and self-rated stress: how does it vary across racial/ethnic groups?

OBJECTIVE: The objective of this study was to answer three questions: (1) Is perceived discrimination adversely related to self-rated stress via the social capital and health care system distrust pathways? (2) Does the relationship between perceived discrimination and self-rated stress vary across race/ethnicity groups? and (3) Do the two pathways differ by one's race/ethnicity background? DESIGN: Using the Philadelphia Health Management Corporation's Southeastern Pennsylvania Household Survey, we classified 9831 respondents into 4 race/ethnicity groups: non-Hispanic White (n = 6621), non-Hispanic Black (n = 2359), Hispanic (n = 505), and non-Hispanic other races (n = 346). Structural equation modeling was employed to simultaneously estimate five sets of equations, including the confirmatory factor analysis for both social capital and health care distrust and both direct and indirect effects from perceived discrimination to self-rated stress. RESULTS: The key findings drawn from the analysis include the following: (1) in general, people who experienced racial discrimination have higher distrust and weaker social capital than those without perceived discrimination and both distrust and social capital are ultimately related to self-rated stress. (2) The direct relationship between perceived discrimination and self-rated stress is found for all race/ethnicity groups (except non-Hispanic other races) and it does not vary across groups. (3) The two pathways can be applied to non-Hispanic White and Black, but for Hispanic and non-Hispanic other races, we found little evidence for the social capital pathway. CONCLUSIONS: For non-Hispanic White, non-Hispanic Black, and Hispanic, perceived discrimination is negatively related to self-rated stress. This finding highlights the importance of reducing interpersonal discriminatory behavior even for non-Hispanic White. The health care system distrust pathway can be used to address the racial health disparity in stress as it holds true for all four race/ethnicity groups. On the other hand, the social capital pathway seems to better help non-Hispanic White and Black to mediate the adverse effect of perceived discrimination on stress.

Differential investments and opportunities: How do neighborhood conditions moderate the relationship between perceived housing discrimination and social capital?

Though the adverse consequences of perceived housing discrimination have been documented, little is known about whether such experience undermines one's social capital in a neighborhood and even less is about whether and how this relationship is altered by neighborhood features. We proposed a framework that simultaneously considers within-individual and between-neighborhood processes. We applied multilevel structural equation models to data from Philadelphia (n = 9987) and found that (a) perceived housing discrimination was negatively associated with one's social capital even after other confounders were considered, (b) this negative association could be partly explained by the proliferated daily stress and anxiety mechanisms, (c) differential exposures to neighborhood social disadvantage accounted for the variation in social capital across neighborhoods, and (d) the adverse association between perceived housing discrimination and social capital could be attenuated by neighborhood stability. The findings suggested that appropriate interventions should buffer the negative association of perceived housing discrimination with social capital.

Residential segregation and racial disparities in self-rated health: How do dimensions of residential segregation matter?

Previous research on segregation and health has been criticized for overlooking the fact that segregation is a multi-dimensional concept (i.e., evenness, exposure, concentration, centralization, and clustering) and recent evidence drawn from non-black minorities challenges the conventional belief that residential segregation widens racial health disparities. Combining a survey data (n = 18,752) from Philadelphia with the 2010 Census tract (n = 925) data, we examine two theoretical frameworks to understand why the association of segregation with health may differ by race/ethnicity. Specifically, we investigate how each dimension of segregation contributed to racial disparities in self-rated health. We found (1) high levels of white/black concentration could exacerbate the white/black health disparities up to 25 percent, (2) the white/Hispanic health disparities was narrowed by increasing the level of white/Hispanic centralization, and (3) no single dimension of segregation statistically outperforms others. Our findings supported that segregation is bad for blacks but may be beneficial for Hispanics.

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.

The Long-term Effects of Self-Esteem on Depression: The Roles of Alcohol and Substance Uses during Young Adulthood.

Using the National Longitudinal Surveys of Youth 1979, this study examines the roles of alcohol and substance use as mediators in the mechanism between self-esteem and depression, and investigates whether the mechanism works for both men and women. Results demonstrate that alcohol and substance use during young adulthood mediates the effect of self-esteem on depression among men. Furthermore, self-esteem during young adulthood remains a determinant of high depression in middle adulthood. However, we did not find evidence to support that same mechanism among women. Our findings provide insight into how self-esteem affects depression over the transition from young to middle adulthood, and elucidate potential gendered responsivity to low self-esteem.

Perceived Housing Discrimination and Self-Reported Health: How Do Neighborhood Features Matter?

BACKGROUND: While the association between perceived discrimination and health has been investigated, little is known about whether and how neighborhood characteristics moderate this association. PURPOSE: We situate discrimination in the housing context and use relative deprivation and social capital perspectives to fill the knowledge gap. METHODS: We applied multilevel logistic modeling to 9,842 adults in 830 neighborhoods in Philadelphia to examine three hypotheses. RESULTS: First, the detrimental effect of discrimination on self-reported health was underestimated without considering neighborhood features as moderators. The estimated coefficient (ß) increased from approximately 0.02 to 1.84 or higher. Second, the negative association between discrimination and self-reported health was enhanced when individuals with discrimination experience lived in neighborhoods with higher housing values (ß = 0.42). Third, the adverse association of discrimination with self-reported health was attenuated when people reporting discrimination resided in neighborhoods marked by higher income inequality (ß = -4.34) and higher concentrations of single-parent households with children (ß = -0.03) and minorities (ß = -0.01). CONCLUSIONS: We not only confirmed the moderating roles of neighborhood characteristics, but also suggested that the relative deprivation and social capital perspectives could be used to understand how perceived housing discrimination affects self-reported health via neighborhood factors.

To What Extent do Sleep Quality and Duration Mediate the Effect of Perceived Discrimination on Health? Evidence from Philadelphia.

Little research investigates whether sleep mediates the adverse effect of perceived discrimination on health and even less is known about whether sleep quality and sleep duration mediate the relationships in the same fashion. We applied a recently developed mediation analysis approach to a survey administered in 2008 in Philadelphia, PA, that includes 9042 adults. Health was measured with self-rated health, stress, and mental illness. Perceived discrimination was operationalized with self-reported discriminatory experience in two social contexts, namely health care system and housing market. Sleep quality and duration were measured with a five-point Likert scale and the self-reported sleep time at night, respectively. After controlling for one's demographic, socioeconomic, and health-related characteristics, the mediation analysis quantified how much sleep quality and duration can account for the effect of perceived discrimination on these health outcomes. The key findings are: (a) sleep quality and duration accounted for approximately 15 to 25% of the adverse effect of perceived discrimination. (b) Sleep quality is more important than sleep duration in mediating the relationship between perceived discrimination and health. (c) The proportion of the effect mediated by sleep differs by the social context where perceived discrimination occurred. It was confirmed that sleep mediates the relationship between perceived discrimination and health and the interventions to improve sleep, particularly sleep quality, should help to attenuate the effect of perceived discrimination on health.

Exploring geographic variation in US mortality rates using a spatial Durbin approach.

Previous studies focused on identifying the determinants of mortality in US counties have examined the relationships between mortality and explanatory covariates within a county only, and have ignored the well-documented spatial dependence of mortality. We challenge earlier literature by arguing that the mortality rate of a certain county may also be associated with the features of its neighboring counties beyond its own features. Drawing from both the spillover (i.e., same direction effect) and social relativity (i.e., opposite direction effect) perspectives, our spatial Durbin modeling results indicate that both theoretical perspectives provide valuable frameworks to guide the modeling of mortality variation in US counties. Our empirical findings support that mortality rate of a certain county is associated with the features of its neighbors beyond its own features. Specifically, we found support for the spillover perspective in which the percentage of the Hispanic population, concentrated disadvantage, and the social capital of a specific county are negatively associated with the mortality rate in the specific county and also in neighboring counties. On the other hand, the following covariates fit the social relativity process: health insurance coverage, percentage of non-Hispanic other races, and income inequality. Their direction of the associations with mortality in the specific county is opposite to that of the relationships with mortality in neighboring counties. Methodologically, spatial Durbin modeling addresses the shortcomings of traditional analytic approaches used in ecological mortality research such as ordinary least squares, spatial error, and spatial lag regression. Our results produce new insights drawn from unbiased estimates.

Self-rated health and residential segregation: how does race/ethnicity matter?

Despite recent declines, racial segregation remains a detriment to minority neighborhoods. However, existing research is inconclusive as to the effects racial segregation has on health. Some argue that racial segregation is related to poor health outcomes, whereas others suspect that racial segregation may actually lead to improved health for some minority communities. Even less is known about whether minority access to white neighborhoods improves health. We address these gaps with individual data from the 2010 Public Health Management Corporation's Southeastern Pennsylvania Household Health Survey and census tract data from the 2010 Decennial Census and the 2006-2010 American Community Survey. We implement logistic multilevel models to determine whether and how a resident's self-rated health is affected by the racial/ethnic segregation of their neighborhoods. Our key finding suggests that the effects of segregation on self-rated health depend on an individual's race/ethnicity, with blacks and Latino residents most likely to experience adverse effects. Particularly, minorities living in predominantly white communities have a significantly higher likelihood to report poor/fair health than they would in segregated minority neighborhoods. These findings make clear that access to white neighborhoods is not sufficient to improve minority health; fuller neighborhood integration is necessary to ensure all have health equity.

Stochastic variability in stress, sleep duration, and sleep quality across the distribution of body mass index: insights from quantile regression.

BACKGROUND: Obesity has become a problem in the USA and identifying modifiable factors at the individual level may help to address this public health concern. A burgeoning literature has suggested that sleep and stress may be associated with obesity; however, little is know about whether these two factors moderate each other and even less is known about whether their impacts on obesity differ by gender. PURPOSE: This study investigates whether sleep and stress are associated with body mass index (BMI) respectively, explores whether the combination of stress and sleep is also related to BMI, and demonstrates how these associations vary across the distribution of BMI values. METHODS: We analyze the data from 3,318 men and 6,689 women in the Philadelphia area using quantile regression (QR) to evaluate the relationships between sleep, stress, and obesity by gender. RESULTS: Our substantive findings include: (1) high and/or extreme stress were related to roughly an increase of 1.2 in BMI after accounting for other covariates; (2) the pathways linking sleep and BMI differed by gender, with BMI for men increasing by 0.77-1 units with reduced sleep duration and BMI for women declining by 0.12 unit with 1 unit increase in sleep quality; (3) stress- and sleep-related variables were confounded, but there was little evidence for moderation between these two; (4) the QR results demonstrate that the association between high and/or extreme stress to BMI varied stochastically across the distribution of BMI values, with an upward trend, suggesting that stress played a more important role among adults with higher BMI (i.e., BMI > 26 for both genders); and (5) the QR plots of sleep-related variables show similar patterns, with stronger effects on BMI at the upper end of BMI distribution. CONCLUSIONS: Our findings suggested that sleep and stress were two seemingly independent predictors for BMI and their relationships with BMI were not constant across the BMI distribution.