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The Interaction of Racial-Ethnic and Economic Concentration and its Association with Premature Mortality in U.S. Neighborhoods
Abstract
Recent research shows a significant link between race-ethnicity and income concentration and premature death rates in the U.S. However, most studies focus on Black-White residential concentration, overlooking racial-ethnic diversity. Our study examines the impact of racial-ethnic majority composition on mortality and how this relationship varies across different levels of economic concentration in neighborhoods, as defined by census tracts. Premature death rates (under 65 years of age) were retrieved from abridged period life tables from 67,140 U.S. census tracts derived from the U.S. Small-area Life Expectancy Project. Covariate factors were retrieved from the 2011–2015 American Community Survey (ACS) 5-year estimates. We measured racial-ethnic concentration by grouping neighborhoods using each tract’s majority racial-ethnic group, and approximated income concentration using the Index of Concentration of the Extremes. We used three-level random intercept models to examine the interaction of racial-ethnic and income concentration and its association with neighborhood mortality risk, accounting for covariates. Our study yielded three salient findings. First, mortality risk varied greatly in poor neighborhoods with different racial-ethnic compositions compared to affluent neighborhoods, with notable higher risk in Black-majority areas. Second, in diverse neighborhoods where no single ethnic group forms a majority—referred to as Minority-majority neighborhoods—the mortality risk is comparable to that in White-majority neighborhoods. Third, Hispanic/Latino- and Asian-majority neighborhoods had lower mortality risk than White-majority neighborhoods in areas with a high concentration of poverty, but similar mortality risk in affluent areas. The study suggests that racial-ethnic and socioeconomic area-based measures are important to consider together to address mortality inequities accurately.
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We aimed (i) to gain a better understanding of the demographic and socioeconomical distribution of ADHD risk in Sweden; and (ii) to contribute to the critical discussion on medicalization, i.e., the tendency to define and treat behavioural and social problems as medical entities. For this purpose, we analysed the risk of suffering from ADHD in the whole Swedish population aged between 5 and 60 years, across 96 different strata defined by combining categories of gender, age, income, and country of birth. The stratified analysis evidenced considerable risk heterogeneity, with prevalence values ranging from 0.03% in high income immigrant women aged 50–59, to 6.18% in middle income immigrant boys aged 10–14. Our study questions the established idea that behavioural difficulties conceptualized as ADHD should be primarily perceived as a neurological abnormality. Rather, our findings suggest that there is a strong sociological component behind how some individuals become impaired and subject to medicalization.
Purpose/Objective: Individuals with historically oppressed identities, such as disabled or racialized minorities, face inequities across all societal institutions, including education, criminal justice, and healthcare. Systems of oppression (e.g., ableism, racism) lead to inequities that have ultimately contributed to disproportionate rates of COVID-19 morbidity and mortality in the United States. In the midst of the COVID-19 pandemic, increased public attention regarding police brutality toward Black people and the reinvigoration of the national Black Lives Matter (BLM) movement further highlighted the detrimental effects of oppressive systems and the urgent need to promote equity in the United States. The disproportionate number of COVID-19-related deaths and police brutality are inextricably connected, as both are products of oppression toward minoritized communities. The co-occurrence of the pandemic and BLM movement protests also creates an opportunity for critical discourse on the intersection of ableism and anti-Black racism specifically within the field of rehabilitation psychology. Research Method/Design: The overarching goals of this review are to apply the Intersectional Ecological Model with the addition of the chronosystem to illustrate how systems of oppression lead to health disparity in COVID-19 survivorship and to provide recommendations to promote health equity. Conclusions/Implication: As the COVID-19 pandemic shifts to an endemic and efforts to eliminate oppressive systems continue, rehabilitation psychologists have an ongoing, evolving, and shared responsibility to employ socially-responsive solutions to promote optimal functioning for patients, families, and communities.
As shown by the recent increase in anti-Asian racism and violence worldwide, the label of “model minority” is flawed and fails to capture the breadth and depth of the experiences of Asian people. Our paper is the first to provide a scoping review of empirical research (ninety-seven studies) that examine the social, educational and health and wellbeing impacts of the model minority myth (MMM) for people of Asian descent in white-majority countries. Key findings indicate that the MMM is complex and contradictory and is present across multiple sectors. Therefore, the MMM needs to be understood holistically as a structural issue that has implications for reinforcing racial inequities. Future research should adopt multilevel frameworks and methods to further understand community and structural impacts of the MMM on people of Asian descent internationally and consider how the MMM contributes to a white racial grammar that normalizes racial stratification and racist structures.
Structural racism represents a key determinant of the racial health disparities that has characterized the U.S. population throughout its existence. While this reality has recently begun to gain increasing acknowledgment and acceptance within the health sciences, there are still considerable challenges related to defining the concept of structural racism and operationalizing it in empirical study. In this paper, building on the existing evidence base, we propose a comprehensive framework that centers structural racism in terms of its historical roots and continued manifestation in most domains of society, and offer solutions for the study of this phenomenon and the pathways that connect it to population-level health disparities. We showcase our framework by applying it to the known link between spatial and racialized clustering of incarceration – a previously cited representation of structural racism – and disparities in adverse birth outcomes. Through this process we hypothesize pathways that focus on social cohesion and community-level chronic stress, community crime and police victimization, as well as infrastructural community disinvestment. First, we contextualize these mechanisms within the relevant extant literature. Then, we make recommendations for future empirical pathway analyses. Finally, we identify key areas for policy, community, and individual-level interventions that target the impact of concentrated incarceration on birth outcomes among Black people in the U.S.
Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.
Asian Americans are the fastest growing U.S. immigrant group, projected to become the largest immigrant group by 2065, but the quantity of research on Asian Americans' health has not mirrored changing demographics. Asian Americans have been understudied for more than 25 years, with only 0.17% of National Institutes of Health (NIH) expenditures allocated to projects including Asian American, Native Hawaiian, and Pacific Islander populations (Ðoàn et al., 2019). This disproportionality may result in part from the model minority stereotype (MMS) being extended to health, perpetuating the ideas that Asian Americans are well-positioned with regard to health status and that associated research is not essential. Accordingly, the aims for this article are threefold: (a) bring attention to the inadequate representation of the Asian American population in health-related science, (b) question the MMS in health, and (c) outline potential pathways through which the MMS limits what is knowable on Asian American health issues and needs. We discuss the limited meaningfulness of nonrepresentative aggregated statistics purporting the model minority image and provide counterexamples. We also present a stereotype-constraints model with the MMS contributing to a bottleneck for Asian American health-related knowledge, accompanied by present-day circumstances (e.g., sparse data, few psychologists/behavioral medicine scientists focused on Asian American health). We conclude with initial recommendations for addressing MMS-associated constraints in psychology and more broadly. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Objectives
We examine associations between infant mortality rates (IMRs) and measures of structural racism and socio-economic marginalization in Chicago, Illinois. Our purpose was to determine whether the Index of Concentration at the Extremes (ICE) was significantly related to community-level IMRs.
Study design
We use a cross-sectional ecological public health design to examine community-level factors related to IMRs in Chicago neighborhoods.
Methods
We use data from the Chicago Department of Public Health and the American Community Survey to examine IMR inequities during the period 2012–2016. Calculations of the ICE for race and income were undertaken. In addition, we calculated racialized socio-economic status, which is the concentration of affluent Whites relative to poor Blacks in a community area. We present these ICE measures, as well as hardship, percent of births with inadequate prenatal care (PNC), and the percent of single-parent households as quintiles so that we can compare neighborhoods with the most disadvantage with neighborhoods with the least. Negative binomial regression was used to determine whether the ICE measures were independently related to community IMRs, net of hardship scores, PNC, and single-parent households.
Results
Spearman correlation results indicate significant associations in Chicago communities between measures of racial segregation and economic marginalization and IMRs. Community areas with the lowest ICERace scores (those with the largest concentrations of Black residents, compared with White) had IMRs that were 3.63 times higher than those communities with the largest concentrations of White residents. Most associations between community IMRS and measures of structural racism and socioeconomic marginalization are accounted for in fully adjusted negative binomial regression models. Only ICERace remained significantly related to IMRs.
Conclusions
We show that structural racism as represented by the ICE is independently related to IMRs in Chicago; community areas with the largest concentrations of Blacks residents compared with Whites are those with the highest IMRs. This relationship persists even after controlling for socio-economic marginalization, hardship, household composition/family support, and healthcare access. Interventions to improve birth outcomes must address structural determinants of health inequities.
Objective
To examine mediation and moderation of racial/ethnic all‐cause mortality disparities among Veteran Health Administration (VHA)‐users by neighborhood deprivation and residential segregation.
Data sources
Electronic medical records for 10/2008‐9/2009 VHA‐users linked to National Death Index, 2000 Area Deprivation Index, and 2006‐2009 US Census.
Study design
Racial/ethnic groups included American Indian/Alaskan Native (AI/AN), Asian, non‐Hispanic black, Hispanic, Native Hawaiian/Other Pacific Islander, and non‐Hispanic white (reference). We measured neighborhood deprivation by Area Deprivation Index, calculated segregation for non‐Hispanic black, Hispanic, and AI/AN using the Isolation Index, evaluated mediation using inverse odds‐weighted Cox regression models and moderation using Cox regression models testing for neighborhood*race/ethnicity interactions.
Principal findings
Mortality disparities existed for AI/ANs (HR = 1.07, 95%CI:1.01‐1.10) but no other groups after covariate adjustment. Neighborhood deprivation and Hispanic segregation neither mediated nor moderated AI/AN disparities. Non‐Hispanic black segregation both mediated and moderated AI/AN disparities. The AI/AN vs. non‐Hispanic white disparity was attenuated for AI/ANs living in neighborhoods with greater non‐Hispanic black segregation (P = .047). Black segregation's mediating effect was limited to VHA‐users living in counties with low black segregation. AI/AN segregation also mediated AI/AN mortality disparities in counties that included or were near AI/AN reservations.
Conclusions
Neighborhood characteristics, particularly black and AI/AN residential segregation, may contribute to AI/AN mortality disparities among VHA‐users, particularly in communities that were rural, had greater black segregation, or were located on or near AI/AN reservations. This suggests the importance of neighborhood social determinants of health on racial/ethnic mortality disparities. Living near reservations may allow AI/AN VHA‐users to maintain cultural and tribal ties, while also providing them with access to economic and other resources. Future research should explore the experiences of AI/ANs living in black communities and underlying mechanisms to identify targets for intervention.
Background
To the authors' knowledge, the etiology of survival disparities in patients with epithelial ovarian cancer (EOC) is not fully understood. Residential segregation, both economic and racial, remains a problem within the United States. The objective of the current study was to analyze the effect of residential segregation as measured by the Index of Concentration at the Extremes (ICE) on EOC survival in Florida by race and/or ethnicity.
Methods
All malignant EOC cases were identified from 2001 through 2015 using the Florida Cancer Data System (FCDS). Census‐defined places were used as proxies for neighborhoods. Using 5‐year estimates from the American Community Survey, 5 ICE variables were computed: economic (high vs low), race and/or ethnicity (non‐Hispanic white [NHW] vs non‐Hispanic black [NHB] and NHW vs Hispanic), and racialized economic segregation (low‐income NHB vs high‐income NHW and low‐income Hispanic vs high‐income NHW). Random effects frailty models were conducted.
Results
A total of 16,431 malignant EOC cases were diagnosed in Florida among women living in an assigned census‐defined place within the time period. The authors found that economic and racialized economic residential segregations influenced EOC survival more than race and/or ethnic segregation alone in both NHB and Hispanic women. NHB women continued to have an increased hazard of death compared with NHW women after controlling for multiple covariates, whereas Hispanic women were found to have either a similar or decreased hazard of death compared with NHW women in multivariable Cox models.
Conclusions
The results of the current study indicated that racial and economic residential segregation influences survival among patients with EOC. Research is needed to develop more robust segregation measures that capture the complexities of neighborhoods to fully understand the survival disparities in EOC.
Since 1987, the Mexican Migration Project (MMP) has collected and disseminated representative survey data on documented and undocumented migration to the United States. The MMP currently includes surveys of 161 communities, which together contain data on 27,113 households and 169,945 individuals, 26,446 of whom have U.S. migratory experience. These data are used here to trace the evolution of the Mexico-U.S. migration system from the late nineteenth to the early twenty-first century, revealing how shifts in U.S. immigration and border policies have been critical to the formation of different eras of migration characterized by distinctive patterns of migration, settlement, and return in different legal statuses. The current era is characterized by the repression of the large population of undocumented migrants and their U.S. citizen children by an ongoing regime of mass detention and deportation and the simultaneous recruitment of Mexican workers for exploitation on short-term temporary visas. As the dynamics of Mexican migration to the United States continue to change, they will be monitored and analyzed in subsequent waves of data collection by the MMP.
Background
Hispanics are the fastest growing ethnic group in the United States, and little is known about how Hispanic ethnic population density impacts cardiovascular disease (CVD) mortality.
Methods and Results
We examined county‐level deaths for Hispanics and non‐Hispanic whites from 2003 to 2012 using data from the National Center for Health Statistics’ Multiple Cause of Death mortality files. Counties with more than 20 Hispanic deaths (n=715) were included in the analyses. CVD deaths were identified using International Classification of Diseases, Tenth Revision (ICD‐10), I00 to I78, and population estimates were calculated using linear interpolation from 2000 and 2010 census data. Multivariate linear regression was used to examine the association of Hispanic ethnic density with Hispanic and non‐Hispanic white age‐adjusted CVD mortality rates. County‐level age‐adjusted CVD mortality rates were adjusted for county‐level demographic, socioeconomic, and healthcare factors. There were a total of 4 769 040 deaths among Hispanics (n=382 416) and non‐Hispanic whites (n=4 386 624). Overall, cardiovascular age‐adjusted mortality rates were higher among non‐Hispanic whites compared with Hispanics (244.8 versus 189.0 per 100 000). Hispanic density ranged from 1% to 96% in each county. Counties in the highest compared with lowest category of Hispanic density had 60% higher Hispanic mortality (215.3 versus 134.2 per 100 000 population). In linear regression models, after adjusting for county‐level demographic, socioeconomic, and healthcare factors, increasing Hispanic ethnic density remained strongly associated with mortality for Hispanics but not for non‐Hispanic whites.
Conclusions
CVD mortality is higher in counties with higher Hispanic ethnic density. County‐level characteristics do not fully explain the higher CVD mortality among Hispanics in ethnically concentrated counties.
Background:
Enhanced understanding of spatial social polarization as a determinant of infant mortality is critical to efforts aimed at advancing health equity. Our objective was to identify associations between spatial social polarization and risk of infant death.
Methods:
We conducted a cross-sectional analysis of all birth records issued to non-Hispanic (NH) Black and White women in Wayne County, MI, from 2010 to 2013 (n = 84 159), including linked death records for deaths occurring at less than 1 year of age. Spatial social polarization was measured in each Census tract of maternal residence (n = 599) using the Index of Concentration at the Extremes (ICE)-a joint measure of racial and economic segregation-estimated from American Community Survey 2009-2013 data. Log-Poisson regression models quantified relative risk (RR) of infant death (all-cause and cause-specific) associated with tertiles of the index, adjusting for maternal demographic characteristics and tract-level poverty.
Results:
The crude infant-mortality rate was more than 2-fold higher among NH Black infants compared with NH Whites (14.0 vs 5.9 deaths per 1000 live births). Half of the 845 infant deaths (72% NH Black, 28% NH White) occurred in tracts in the lowest tertile of the ICE distribution, representing areas of relative deprivation. After adjustments, risk of death among infants in the lowest tertile was 1.46 times greater than those in the highest tertile (adjusted infant-mortality rate = 3.7 deaths per 1000 live births in highest tertile vs 5.4 deaths per 1000 live births in lowest tertile, relative risk = 1.46, 95% confidence interval = 1.02, 2.09). Patterns of associations with the index differed by cause of death.
Conclusions:
These findings suggest efforts to support equitable community investments may reduce incidents of death and the disproportionate experience of loss among NH Black women.
The neighbourhoods in which people live reflects their social class and preferences, so studying socio-spatial mobility between neighbourhoods gives insight in the openness of spatial class structures of societies and in the ability of people to leave disadvantaged neighbourhoods. We study the extent to which people move between different types of neighbourhoods by socio-economic status in different inequality and segregation contexts in four European countries: Sweden, the Netherlands, the United Kingdom, and Estonia. The study is based on population registers and census data for the 2001-2011 period. For the UK, which has long had high levels of social inequalities and high levels of socio-economic segregation, we find that levels of mobility between neighbourhood types are low and opportunities to move to more socio-economically advantaged neighbourhoods are modest. In Estonia, which used to be one or the most equal and least segregated countries in Europe and now is one of the most liberal and market oriented countries, we find high levels of mobility, but these reproduce segregation patterns and it is difficult to move to better neighbourhoods for those in the most deprived neighbourhoods. In the Netherlands and Sweden, where social inequalities are the smallest, it is easiest to move from the most deprived to less deprived neighbourhoods. To conclusion, the combination of high levels of social inequalities and high levels of spatial segregation tend to lead to a vicious circle of segregation for low income groups, where it is difficult to undertake both upward social mobility and upward spatial mobility.
Although it is clear that racial segregation is linked to academic achievement gaps, the mechanisms underlying this link have been debated since James Coleman published his eponymous 1966 report. In this paper, I examine sixteen distinct measures of segregation to determine which is most strongly associated with academic achievement gaps. I find clear evidence that one aspect of segregation in particular—the disparity in average school poverty rates between white and black students' schools—is consistently the single most powerful correlate of achievement gaps, a pattern that holds in both bivariate and multivariate analyses. This implies that high-poverty schools are, on average, much less effective than lower-poverty schools and suggests that strategies that reduce the differential exposure of black, Hispanic, and white students to poor schoolmates may lead to meaningful reductions in academic achievement gaps.
Importance:
Hispanics are the largest minority group in the United States and face a disproportionate burden of risk factors for cardiovascular disease (CVD) and low socioeconomic position. However, Hispanics paradoxically experience lower all-cause mortality rates compared with their non-Hispanic white (NHW) counterparts. This phenomenon has been largely observed in Mexicans, and whether this holds true for other Hispanic subgroups or whether these favorable trends persist over time remains unknown.
Objective:
To disaggregate a decade of national CVD mortality data for the 3 largest US Hispanic subgroups.
Design, setting, and participants:
Deaths from CVD for the 3 largest US Hispanic subgroups-Mexicans, Puerto Ricans, and Cubans-compared with NHWs were extracted from the US National Center for Health Statistics mortality records using the underlying cause of death based on coding from the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (I00-II69). Mortality data were evaluated from January 1, 2003, to December 31, 2012. Population estimates were calculated using linear interpolation from the 2000 and 2010 US Census reports. Data were analyzed from November 2015 to July 2016.
Main outcomes and measures:
Mortality due to CVD.
Results:
Participants included 688 074 Mexican, 163 335 Puerto Rican, 130 397 Cuban, and 19 357 160 NHW individuals (49.0% men and 51.0% women; mean [SD] age, 75 [15] years). At the time of CVD death, Mexicans (age, 67 [18] years) and Puerto Ricans (age, 68 [17] years) were younger compared with NHWs (age, 76 [15] years). Mortality rates due to CVD decreased from a mean of 414.2 per 100 000 in 2003 to 303.3 per 100 000 in 2012. Estimated decreases in mortality rate for CVD from 2003 to 2012 ranged from 85 per 100 000 for all Hispanic women to 144 per 100 000 for Cuban men, but rate differences between groups vary substantially, with Puerto Ricans exhibiting similar mortality patterns to NHWs, and Mexicans experiencing lower mortality. Puerto Ricans experienced higher mortality rates for ischemic and hypertensive heart disease compared with other subgroups, whereas Mexicans experienced higher rates of cerebrovascular disease deaths.
Conclusions and relevance:
Significant differences in CVD mortality rates and changes over time were found among the 3 largest Hispanic subgroups in the United States. Findings suggest that the current aggregate classification of Hispanics masks heterogeneity in CVD mortality reporting, leading to an incomplete understanding of health risks and outcomes in this population.
Sex differences in mortality vary over time and place as a function of social, health, and medical circumstances. The magnitude of these variations, and their response to large socioeconomic changes, suggest that biological differences cannot fully account for sex differences in survival. Drawing on a wide swath of mortality data across countries and over time, we develop a set of empiric observations with which any theory about excess male mortality and its correlates will have to contend. We show that as societies develop, M/F survival first declines and then increases, a “sex difference in mortality transition” embedded within the demographic and epidemiologic transitions. After the onset of this transition, cross-sectional variation in excess male mortality exhibits a consistent pattern of greater female resilience to mortality under socio-economic adversity. The causal mechanisms underlying these associations merit further research.
The educational gradient in life expectancy is well documented in the United States and in other low-mortality countries. Highly educated Americans, on average, live longer than their low-educated counterparts, who have recently seen declines in adult life expectancy. However, limiting the discussion on lifespan inequality to mean differences alone overlooks other dimensions of inequality and particularly disparities in lifespan variation. The latter represents a unique form of inequality, with higher variation translating into greater uncertainty in the time of death from an individual standpoint, and higher group heterogeneity from a population perspective. Using data from the National Vital Statistics System from 1990 to 2010, this is the first study to document trends in both life expectancy and S25—the standard deviation of age at death above 25—by educational attainment. Among low-educated whites, adult life expectancy declined by 3.1 years for women and by 0.6 years for men. At the same time, S25 increased by about 1.5 years among high school–educated whites of both genders, becoming an increasingly important component of total lifespan inequality. By contrast, college-educated whites benefited from rising life expectancy and record low variation in age at death, consistent with the shifting mortality scenario. Among blacks, adult life expectancy increased, and S25 plateaued or declined in nearly all educational attainment groups, although blacks generally lagged behind whites of the same gender on both measures. Documenting trends in lifespan variation can therefore improve our understanding of lifespan inequality and point to diverging trajectories in adult mortality across socioeconomic strata.
Objectives
Residential segregation profoundly affects mental and physical health. However, impacts of residential segregation and other neighborhood characteristics on health among older Asian Americans are not fully understood. This study aimed to close this gap by examining effects of residential segregation, perceived neighborhood cohesion, and neighborhood disorder on all-cause mortality among older Chinese immigrants, as well as testing whether the association between residential segregation and mortality would be mediated by perceived neighborhood cohesion and neighborhood disorder.
Methods
Data were drawn from a subsample of 3,094 older Chinese Americans aged 60 and older (mean age = 72.8 years) from the Population Study of Chinese Elderly in Chicago. Residential segregation was derived using 2010–2014 American Community Survey data. Participants completed surveys on perceived neighborhood cohesion and neighborhood disorder between 2011 and 2013. All-cause mortality was tracked until December 2021.
Results
Residential segregation was associated with elevated all-cause mortality risk; this association, however, was no longer statistically significant after controlling for sociodemographic, behavioral, and health covariates. Perceived neighborhood cohesion, but not neighborhood disorder, was significantly associated with decreased mortality risks. There were no indirect effects of residential segregation on all-cause mortality through perceived neighborhood cohesion or neighborhood disorder. These effects were consistent across male and female participants.
Discussion
These results suggest the importance of neighborhood social environment, specifically perceptions of neighborhood cohesion, in influencing mortality risk among older Chinese immigrants. The findings also indicate the need to conduct further research to examine the health impact of residential segregation among this population.
Recent studies have shown that U.S. Census- and American Community Survey (ACS)-based estimates of income segregation are subject to upward finite sampling bias (Logan et al. 2018; Logan et al. 2020; Reardon et al. 2018). We identify two additional sources of bias that are larger and opposite in sign to finite sampling bias: measurement error-induced attenuation bias and temporal pooling bias. The combination of these three sources of bias make it unclear how income segregation has trended. We formalize the three types of bias, providing a method to correct them simultaneously using public data from the decennial census and ACS from 1990 to 2015-2019. We use these methods to produce bias-corrected estimates of income segregation in the United States from 1990 to 2019. We find that (1) segregation is on the order of 50% greater than previously believed; (2) the increase from 2000 to the 2005-2009 period was much greater than indicated by previous estimates; and (3) segregation has declined since 2005-2009. Correcting these biases requires good estimates of the reliability of self-reported income and of the year-to-year volatility in neighborhood mean incomes.
Background:
Large disparities in mortality exist across racial-ethnic groups and by location in the USA, but the extent to which racial-ethnic disparities vary by location, or how these patterns vary by cause of death, is not well understood. We aimed to estimate age-standardised mortality by racial-ethnic group, county, and cause of death and describe the intersection between racial-ethnic and place-based disparities in mortality in the USA, comparing patterns across health conditions.
Methods:
We applied small-area estimation models to death certificate data from the US National Vital Statistics system and population data from the US National Center for Health Statistics to estimate mortality by age, sex, county, and racial-ethnic group annually from 2000 to 2019 for 19 broad causes of death. Race and ethnicity were categorised as non-Latino and non-Hispanic American Indian or Alaska Native (AIAN), non-Latino and non-Hispanic Asian or Pacific Islander (Asian), non-Latino and non-Hispanic Black (Black), Latino or Hispanic (Latino), and non-Latino and non-Hispanic White (White). We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and generated age-standardised results using direct standardisation to the 2010 US census population.
Findings:
From 2000 to 2019, across 3110 US counties, racial-ethnic disparities in age-standardised mortality were noted for all causes of death considered. Mortality was substantially higher in the AIAN population (all-cause mortality 1028·2 [95% uncertainty interval 922·2-1142·3] per 100 000 population in 2019) and Black population (953·5 [947·5-958·8] per 100 000) than in the White population (802·5 [800·3-804·7] per 100 000), but substantially lower in the Asian population (442·3 [429·3-455·0] per 100 000) and Latino population (595·6 [583·7-606·8] per 100 000), and this pattern was found for most causes of death. However, there were exceptions to this pattern, and the exact order among racial-ethnic groups, magnitude of the disparity in both absolute and relative terms, and change over time in this magnitude varied considerably by cause of death. Similarly, substantial geographical variation in mortality was observed for all causes of death, both overall and within each racial-ethnic group. Racial-ethnic disparities observed at the national level reflect widespread disparities at the county level, although the magnitude of these disparities varied widely among counties. Certain patterns of disparity were nearly universal among counties; for example, in 2019, mortality was higher among the AIAN population than the White population in at least 95% of counties for skin and subcutaneous diseases (455 [97·8%] of 465 counties with unmasked estimates) and HIV/AIDS and sexually transmitted infections (458 [98·5%] counties), and mortality was higher among the Black population than the White population in nearly all counties for skin and subcutaneous diseases (1436 [96·6%] of 1486 counties), diabetes and kidney diseases (1473 [99·1%]), maternal and neonatal disorders (1486 [100·0%] counties), and HIV/AIDS and sexually transmitted infections (1486 [100·0%] counties).
Interpretation:
Disparities in mortality among racial-ethnic groups are ubiquitous, occurring across locations in the USA and for a wide range of health conditions. There is an urgent need to address the shared structural factors driving these widespread disparities.
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 Sciences Research, US National Institutes of Health.
BACKGROUND: Race and ethnicity, socioeconomic class, and geographic location are well-known social determinants of health in the US. Studies of population mortality often consider two, but not all three of these risk factors. OBJECTIVES: To disarticulate the associations of race (whiteness), class (socioeconomic status), and place (county) with risk of cause-specific death in the US. DESIGN: We conducted a retrospective analysis of death certificate data. Bayesian regression models, adjusted for age and race/ethnicity from the American Community Survey and the county Area Deprivation Index, were used for inference. MAIN MEASURES: County-level mortality for 11 leading causes of death (1999-2019) and COVID-19 (2020-2021). KEY RESULTS: County "whiteness" and socioeconomic status modified death rates; geospatial effects differed by cause of death. Other factors equal, a 20% increase in county whiteness was associated with 5-8% increase in death from three causes and 4-15% reduction in death from others, including COVID-19. Other factors equal, advantaged counties had significantly lower death rates, even when juxtaposed with disadvantaged ones. Patterns of residual risk, measured by spatial county effects, varied by cause of death; for example: cancer and heart disease death rates were better explained by age, socioeconomic status, and county whiteness than were COVID-19 and suicide deaths. CONCLUSIONS: There are important independent contributions from race, class, and geography to risk of death in the US.
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Free read only link: https://rdcu.be/c8xHT
Purpose
: Use a large nationally representative population to evaluate whether differences in mortality in relation to residential racial and ethnic segregation and diversity varied by gender, and race or Hispanic ethnicity in the United States.
Methods
: The Mortality Disparities in American Communities (MDAC) was used to evaluate mortality risk in relation to segregation. MDAC is a nationally representative record linkage of the 2008 American Community Survey data with mortality outcomes derived from the National Death Index through 2015. Gender-stratified mortality risk for White, Black, and Hispanic groups in relation to quartiles of residential segregation, composition, and diversity were modeled using parametric survival regression with an exponential distribution, adjusted for individual-level socioeconomic characteristics.
Results
: The study population included >3,950,000 individuals and >273,000 all-cause mortality outcomes. Statistically significant differences in associations were observed with Black segregation vs. Hispanic segregation across Black or Hispanic groups; some differences in stratification by gender for Hispanic and Non-Hispanic Black groups, but gender-stratified associations were more similar in non-Hispanic Whites.
Conclusion
: Future multidisciplinary and ethnographic research is needed to identify the specific structural mechanisms by which these associations differ to support means by which to more effectively target public health interventions.
We use panel data from the Panel Study of Income Dynamics (PSID) to document the changing volume and rate of intercounty migration among the poor. We evaluate whether the exchange of migrants between metro and nonmetro counties has exacerbated spatial disparities in poverty and whether some nonmetro counties have become “collecting grounds” for America's poor. The PSID highlights exceptionally high rates of intercounty migration among the nonmetro population, but especially among the poor. The results show that rural poor migrants circulate among nonmetro counties, often from one poor county to an even poorer one. This circulation of poor rural migrants has exacerbated the concentration of poverty in the most economically disadvantaged nonmetro counties. Concentrated poverty also is exacerbated by the migration of the “best and brightest” from nonmetro‐to‐metro counties, which also is associated with upward residential attainment (to counties with lower poverty rates). Significantly, metro‐to‐nonmetro migration is both selective of the poor and strongly associated with downward residential mobility to poorer nonmetro counties. Our findings are consistent with the hypothesis that poor nonmetro counties have become collecting grounds for America's poor.
Objective
To assess the influence of racial and economic residential segregation of home or hospital neighborhood on very preterm birth morbidity and mortality.
Study design
We constructed a retrospective cohort of n=6461 infants born <32 weeks using 2010-2014 New York City vital statistics-hospital data. We calculated racial and economic Index of Concentration at the Extremes (ICE) for home and hospital neighborhoods. Neonatal mortality and morbidity was death and/or severe neonatal morbidity. We estimated relative risks for ICE measures and NMM using log binomial regression, and the risk-adjusted contribution of delivery hospital using Fairlie decomposition.
Results
Infants whose mothers live in neighborhoods with the highest relative concentration of Black residents had a 1.6 times higher risk of NMM than those with the highest relative concentration of White residents (95% Confidence Interval=1.2, 2.1). Delivery hospital explained over half of neighborhood differences. Infants with both home and hospital in high-concentration Black neighborhoods had a 38% adjusted risk of NMM, compared with 25% of those with both home and hospital high-concentration White neighborhoods (P = .045).
Conclusions
Structural racism influences very preterm birth NMM through both the home and hospital neighborhood. Quality improvement interventions should incorporate a framework that includes neighborhood context.
Objective:
To overcome the absence of national, state, and local public health data on the unequal economic and social burden of COVID-19 in the United States.
Design:
We analyze US county COVID-19 deaths and confirmed COVID-19 cases and positive COVID-19 tests in Illinois and New York City zip codes by area percent poverty, percent crowding, percent population of color, and the Index of Concentration at the Extremes.
Setting:
US counties and zip codes in Illinois and New York City, as of May 5, 2020.
Main outcome measures:
Rates, rate differences, and rate ratios of COVID-19 mortality, confirmed cases, and positive tests by category of county and zip code-level area-based socioeconomic measures.
Results:
As of May 5, 2020, the COVID-19 death rate per 100 000 person-years equaled the following: 143.2 (95% confidence interval [CI]: 140.9, 145.5) vs 83.3 (95% CI: 78.3, 88.4) in high versus low poverty counties (≥20% vs <5% of persons below poverty); 124.4 (95% CI: 122.7, 126.0) versus 48.2 (95% CI: 47.2, 49.2) in counties in the top versus bottom quintile for household crowding; and 127.7 (95% CI: 126.0, 129.4) versus 25.9 (95% CI: 25.1, 26.6) for counties in the top versus bottom quintile for the percentage of persons who are people of color. Socioeconomic gradients in Illinois confirmed cases and New York City positive tests by zip code-level area-based socioeconomic measures were also observed.
Conclusions:
Stark social inequities exist in the United States for COVID-19 outcomes. We recommend that public health departments use these straightforward cost-effective methods to report on social inequities in COVID-19 outcomes to provide an evidence base for policy and resource allocation.
Reports of rising income segregation in the United States have been brought into question by the observation that post-2000 estimates are upwardly biased because of a reduction in the sample sizes on which they are based. Recent studies have offered estimates of this sample-count bias using public data. We show here that there are two substantial sources of systematic bias in estimating segregation levels: bias associated with sample size and bias associated with using weighted sample data. We rely on new correction methods using the original census sample data for individual households to provide more accurate estimates. Family income segregation rose markedly in the 1980s but only selectively after 1990. For some categories of families, segregation declined after 1990. There has been an upward trend for families with children but not specifically for families with children in the upper or lower 10% of the income distribution. Separate analyses by race/ethnicity show that income segregation was not generally higher among Blacks and Hispanics than among White families, and evidence of income segregation trends for these separate groups is mixed. Income segregation increased for all three racial groups for families with children, particularly for Hispanics (but not Whites or Blacks) in the upper 10% of the income distribution. Trends vary for specific combinations of race/ethnicity, presence of children, and location in the income distribution, offering new challenges for understanding the underlying processes of change.
High levels of black residential segregation emerged over the course of the twentieth century as the black population urbanized. Segregation was achieved by means of different mechanisms at different times and places, beginning with targeted violence directed at African Americans followed later by discrimination in real estate and banking using devices such as deed restrictions, restrictive covenants, and racial redlining, practices that were institutionalized in federal policies during the New Deal. By 1977, however, discrimination had been outlawed in most US markets and average black segregation began to decline. The declines, however, were inversely proportional to the size of the black community. As Latinos grew in number after the 1970s, levels of racial isolation within Hispanic neighborhoods also rose. At the same time, class segregation increased, especially among families with children, and inequalities of wealth and income grew to create a polarized urban geography. High concentrations of affluence now prevail for affluent whites and Asians living in wealthy post-industrial coastal areas, with high concentrations of poverty prevailing for poor blacks and Hispanics in older, declining industrial areas in the Midwest and the South, which also contain pockets of white and Asian poverty. The new political geography of race and class effectively denies blacks and Hispanics their access to quality education and undermines their lifetime earning prospects to create self-perpetuating system of social and economic stratification.
Several recent studies have concluded that residential segregation by income in the United States has increased in the decades since 1970, including a significant increase after 2000. Income segregation measures, however, are biased upward when based on sample data. This is a potential concern because the sampling rate of the American Community Survey (ACS)—from which post-2000 income segregation estimates are constructed—was lower than that of the earlier decennial censuses. Thus, the apparent increase in income segregation post-2000 may simply reflect larger upward bias in the estimates from the ACS, and the estimated trend may therefore be inaccurate. In this study, we first derive formulas describing the approximate sampling bias in two measures of segregation. Next, using Monte Carlo simulations, we show that the bias-corrected estimators eliminate virtually all of the bias in segregation estimates in most cases of practical interest, although the correction fails to eliminate bias in some cases when the population is unevenly distributed among geographic units and the average within-unit samples are very small. We then use the bias-corrected estimators to produce unbiased estimates of the trends in income segregation over the last four decades in large U.S. metropolitan areas. Using these corrected estimates, we replicate the central analyses in four prior studies on income segregation. We find that the primary conclusions from these studies remain unchanged, although the true increase in income segregation among families after 2000 was only half as large as that reported in earlier work. Despite this revision, our replications confirm that income segregation has increased sharply in recent decades among families with children and that income inequality is a strong and consistent predictor of income segregation.
Background
Substantial disparities in life expectancy exist between Chicago’s 77 defined community areas, ranging from approximately 69 to 85 years. Prior work in New York City and Boston has shown that community-level racial and economic segregation as measured by the Index of Concentration at the Extremes (ICE) is strongly related to premature mortality. This novel metric allows for the joint assessment of area-based income and racial polarisation. This study aimed to assess the relationships between racial and economic segregation and economic hardship with premature mortality in Chicago.
Methods
Annual age-adjusted premature mortality rates (deaths <65 years) from 2011 to 2015 were calculated for Chicago’s 77 community areas. ICE measures for household income (<US100 000), race (black vs non-Hispanic white), combined ICE measure incorporating income and race, and hardship index were calculated from 2015 American Community Survey 5-year estimates.
Results
Average annual premature mortality rates ranged from 94 (95% CI 61 to 133) deaths per 100 000 population age <65 to 699 (95% CI 394 to 1089). Compared with the highest ICE quintiles, communities in the lowest quintiles had significantly higher rates of premature mortality (ICEIncomerate ratio (RR)=3.06, 95% CI 2.51 to 3.73; ICERaceRR=3.07, 95% CI 2.62 to 3.58; ICEIncome+RaceRR=3.27, 95% CI 2.84 to 3.77). Similarly, compared with communities in the lowest hardship index quintile, communities in the highest quintile had significantly higher premature mortality rates (RR=2.79, 95% CI 2.18 to 3.57).
Conclusions
The strong relationships observed between ICE measures and premature mortality—particularly the combined ICE metric encompassing race and income—support the use of ICE in public health monitoring.
Disparities in adverse birth outcomes for Black women continue. Research suggests that societal factors such as structural racism explain more variation in adverse birth outcomes than individual-level factors and societal poverty alone. The Index of Concentration at the Extremes (ICE) measures spatial social polarization by quantifying extremes of deprived and privileged social groups using a single metric and has been shown to partially explain racial disparities in black carbon exposures, mortality, fatal and non-fatal assaults, and adverse birth outcomes such as preterm birth and infant mortality. The objective of this analysis was to assess if local measures of racial and economic segregation as proxies for structural racism are associated and preterm birth and infant mortality experienced by Black women residing in California. California birth cohort files were merged with the American Community Survey by zip code (2011–2012). The ICE was used to quantify privileged and deprived groups (i.e., Black vs. White; high income vs. low income; Black low income vs. White high income) by zip code. ICE scores range from − 1 (deprived) to 1 (privileged). ICE scores were categorized into five quintiles based on sample distributions of these measures: quintile 1 (least privileged)–quintile 5 (most privileged). Generalized linear mixed models were used to test the likelihood that ICE measures were associated with preterm birth or with infant mortality experienced by Black women residing in California. Black women were most likely to reside in zip codes with greater extreme income concentrations, and moderate extreme race and race + income concentrations. Bivariate analysis revealed that greater extreme income, race, and race + income concentrations increased the odds of preterm birth and infant mortality. For example, women residing in least privileged zip codes (quintile 1) were significantly more likely to experience preterm birth (race + income ICE OR = 1.31, 95% CI = 1.72–1.46) and infant mortality (race + income ICE OR = 1.70, 95% CI = 1.17–2.47) compared to women living in the most privileged zip codes (quintile 5). Adjusting for maternal characteristics, income, race, and race + income concentrations remained negatively associated with preterm birth. However, only race and race + income concentrations remained associated with infant mortality. Findings support that ICE is a promising measure of structural racism that can be used to address racial disparities in preterm birth and infant mortality experienced by Black women in California.
Background:
Metrics that quantify economic and social spatial polarization at multiple geographical levels are not routinely used by health agencies, despite rising inequalities.
Methods:
We employed the Index of Concentration at the Extremes (ICE), which quantifies how persons in a specified area are concentrated into the top vs bottom of a specified societal distribution, to examine associations with Massachusetts mortality data (2010-14). Our a priori hypotheses were that these associations would: be greater at the local [census tract (CT)] compared with city/town level; vary by race/ethnicity but not gender; and be greatest for our new ICE for racialized economic segregation. Mortality outcomes comprised: child (< 5 years); premature (< 65 years); and cause-specific (cancer; cardiovascular; diabetes; suicide; HIV/AIDS; accidental poisoning; smoking-attributable).
Results:
As illustrated by child mortality, in multilevel models jointly including CT and city/town metrics, the rate ratio comparing the worst to best-off ICE quintile for the total population ranged from 2.2 [95% confidence interval (CI) 1.6, 3.0] for the CT-level ICE for racialized economic segregation down to 1.1 (95% CI 0.8, 1.7) for the city/town-level ICE for racial segregation; similar patterns occurred by gender and for the non-Hispanic White population. Larger associations for the ICE for racialized economic segregation were at the CT-level for the Black non-Hispanic population (6.9; 95% CI 1.3, 36.9) and at the city/town level for the Hispanic population (6.4; 95% CI 1.2, 35.4).
Conclusions:
Results indicate that health agencies should employ measures of spatial social polarization at multiple levels to monitor health inequities.
Few studies have been able to measure the evolution of segregation on health disparities or assess whether those disparities existed in rural communities prior to the Great Migration of African Americans to urban centers. We use a newly developed measure of historical racial residential segregation based on individual-level data. The measure exploits complete census manuscript files to identify the races of next-door neighbors. This measure is the first and only measure of historical segregation for rural communities, allowing us to greatly extend the empirical analysis of the effects of racial segregation on health over space and time. Using this comprehensive measure of racial residential segregation, we estimate the historical relationship between racial segregation and mortality. We find that conditional on racial composition, racially segregated environments had higher mortality rates and it was not always the case that the outcomes for blacks were worse than those of whites. These effects of segregation on health differed between urban and rural locations. We conclude by noting how comprehensive measures of segregation can extend the analysis of structural factors in racial health disparities to rural residents and to the historical evolution of health disparities.
While black-white segregation has been consistently linked to detrimental health outcomes for blacks, whether segregation is necessarily a zero-sum arrangement in which some groups accrue health advantages at the expense of other groups and whether metropolitan segregation impacts the health of racial groups uniformly within the metropolitan area, remains unclear. Using nationally representative data from the 2008–2013 National Health Interview Survey linked to Census data, we investigate whether the association between metropolitan segregation and health is invariant within the metropolitan area or whether it is modified by neighborhood poverty for black and white Americans. In doing so, we assess the extent to which segregation involves direct health tradeoffs between blacks and whites. We conduct race-stratified multinomial and logistic regression models to assess the relationship between 1) segregation and level of neighborhood poverty and 2) segregation, neighborhood poverty, and poor health, respectively. We find that, for blacks, segregation was associated with a higher likelihood of residing in high poverty neighborhoods, net of individual-level socioeconomic characteristics. Segregation was positively associated with poor health for blacks in high poverty neighborhoods, but not for those in lower poverty neighborhoods. Hence, the self-rated health of blacks clearly suffers as a result of black-white segregation – both directly, and indirectly through exposure to high poverty neighborhoods. We do not find consistent evidence for a direct relationship between segregation and poor health for whites. However, we find some suggestive evidence that segregation may indirectly benefit whites through decreasing their exposure to high poverty environments. These findings underscore the critical role of concentrated disadvantage in the complex interconnection between metropolitan segregation and health. Weakening the link between racial segregation and concentrated poverty via local policy and planning has the potential for broad population-based health improvements and significant reductions in black-white health disparities.
Importance:
Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policy makers, clinicians, and researchers seeking to reduce disparities and increase longevity.
Objective:
To estimate annual life tables by county from 1980 to 2014; describe trends in geographic inequalities in life expectancy and age-specific risk of death; and assess the proportion of variation in life expectancy explained by variation in socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.
Design, setting, and participants:
Annual county-level life tables were constructed using small area estimation methods from deidentified death records from the National Center for Health Statistics (NCHS), and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Measures of geographic inequality in life expectancy and age-specific mortality risk were calculated. Principal component analysis and ordinary least squares regression were used to examine the county-level association between life expectancy and socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.
Exposures:
County of residence.
Main outcomes and measures:
Life expectancy at birth and age-specific mortality risk.
Results:
Counties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110. In 2014, life expectancy at birth for both sexes combined was 79.1 (95% uncertainty interval [UI], 79.0-79.1) years overall, but differed by 20.1 (95% UI, 19.1-21.3) years between the counties with the lowest and highest life expectancy. Absolute geographic inequality in life expectancy increased between 1980 and 2014. Over the same period, absolute geographic inequality in the risk of death decreased among children and adolescents, but increased among older adults. Socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors explained 60%, 74%, and 27% of county-level variation in life expectancy, respectively. Combined, these factors explained 74% of this variation. Most of the association between socioeconomic and race/ethnicity factors and life expectancy was mediated through behavioral and metabolic risk factors.
Conclusions and relevance:
Geographic disparities in life expectancy among US counties are large and increasing. Much of the variation in life expectancy among counties can be explained by a combination of socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Policy action targeting socioeconomic factors and behavioral and metabolic risk factors may help reverse the trend of increasing disparities in life expectancy in the United States.
Objectives:
To assess the use of local measures of segregation for monitoring health inequities by local health departments.
Methods:
We analyzed preterm birth and premature mortality (death before the age of 65 years) rates for Boston, Massachusetts, for 2010 to 2012, using the Index of Concentration at the Extremes (ICE) and the poverty rate at both the census tract and neighborhood level.
Results:
For premature mortality at the census tract level, the rate ratios comparing the worst-off and best-off terciles were 1.58 (95% confidence interval [CI] = 1.36, 1.83) for the ICE for income, 1.66 (95% CI = 1.43, 1.93) for the ICE for race/ethnicity, and 1.63 (95% CI = 1.40, 1.90) for the ICE combining income and race/ethnicity, as compared with 1.47 (95% CI = 1.27, 1.71) for the poverty measure. Results for the ICE and poverty measures were more similar for preterm births than for premature mortality.
Conclusions:
The ICE, a measure of social spatial polarization, may be useful for analyzing health inequities at the local level. Public Health Implications. Local health departments in US cities can meaningfully use the ICE to monitor health inequities associated with racialized economic segregation. (Am J Public Health. Published online ahead of print April 20, 2017: e1-e4. doi:10.2105/AJPH.2017.303713).
Despite growing interest in understanding how social factors drive poor health outcomes, many academics, policy makers, scientists, elected officials, journalists, and others responsible for defining and responding to the public discourse remain reluctant to identify racism as a root cause of racial health inequities. In this conceptual report, the third in a Series on equity and equality in health in the USA, we use a contemporary and historical perspective to discuss research and interventions that grapple with the implications of what is known as structural racism on population health and health inequities. Structural racism refers to the totality of ways in which societies foster racial discrimination through mutually reinforcing systems of housing, education, employment, earnings, benefits, credit, media, health care, and criminal justice. These patterns and practices in turn reinforce discriminatory beliefs, values, and distribution of resources. We argue that a focus on structural racism offers a concrete, feasible, and promising approach towards advancing health equity and improving population health.
Aims:
This study assessed the relationship between spatial social polarization measured by the index of the concentration of the extremes (ICE) and preterm birth (PTB) and infant mortality (IM) in New York City. A secondary aim was to examine the ICE measure in comparison to neighborhood poverty.
Methods:
The sample included singleton births to adult women in New York City, 2010-2014 ( n=532,806). Three ICE measures were employed at the census tract level: ICE - Income (persons in households in the bottom vs top 20th percentile of US annual household income), ICE -Race/Ethnicity (black non-Hispanic vs white non-Hispanic populations), and ICE - Income + Race/Ethnicity combined. Preterm birth was defined as birth before 37 weeks' gestation. Infant mortality was defined as a death before one year of age. A two-level generalized linear model with random intercept was utilized adjusting for individual-level covariates.
Results:
Preterm birth prevalence was 7.1% and infant mortality rate was 3.4 per 1000 live births. Women who lived in areas with the least privilege were more likely to have a preterm birth or infant mortality as compared to women living in areas with the most privilege. After adjusting for covariates, this association remained for preterm birth (ICE - Income: Adjusted Odds Ratio (AOR) 1.16 (1.10-1.21); ICE - Race/Ethnicity: AOR 1.41 (1.34-1.49); ICE - Income + Race/Ethnicity: AOR 1.36 (1.29-1.43)) and IM (ICE - Race/Ethnicity (AOR 1.80 (1.43-2.28) and ICE - Income + Race/Ethnicity (AOR 1.54 (1.23-1.94)). High neighborhood poverty was associated with PTB only (AOR 1.09 (1.04-1.14).
Conclusions:
These results provide preliminary evidence for the use of the ICE measure in examining structural barriers to healthy birth outcomes.
Research on residential segregation and health, primarily conducted in the USA, has chiefly employed city or regional measures of racial segregation. To test our hypothesis that stronger associations would be observed using local measures, especially for racialized economic segregation, we analyzed risk of fatal and non-fatal assault in Massachusetts (1995–2010), since this outcome is strongly associated with residential segregation. The segregation metrics comprised the Index of Concentration at the Extremes (ICE), the Index of Dissimilarity, and poverty rate, with measures computed at both the census tract and city/town level. Key results were that larger associations between fatal and non-fatal assaults and residential segregation occurred for models using the census tract vs. city/town measures, with the greatest associations observed for racialized economic segregation. For fatal assaults, comparing the bottom vs. top quintiles, the incidence rate ratio (and 95% confidence interval (CI)) in models using the census tract measures equaled 3.96 (95% CI 3.10, 5.06) for the ICE for racialized economic segregation, 3.26 (95% CI 2.58, 4.14) for the ICE for income, 3.14 (95% CI 2.47, 3.99) for poverty, 2.90 (95% CI 2.21, 3.81) for the ICE for race/ethnicity, and only 0.93 (95% CI 0.79, 1.11) for the Index of Dissimilarity; in models that included both census tract and city/town ICE measures, this risk ratio for the ICE for racialized economic segregation was higher at the census tract (3.29; 95% CI 2.43, 4.46) vs. city/town level (1.61; 95% CI 1.12, 2.32). These results suggest that, at least in the case of fatal and non-fatal assaults, research on residential segregation should employ local measures, including of racialized economic segregation, to avoid underestimating the adverse impact of segregation on health.
A systematic analysis of residential segregation and spatial interaction by income reveals that as income rises, minority access to integrated neighborhoods, higher levels of interaction with whites, and more affluent neighbors also increase. However, the income payoffs are much lower for African Americans than other groups, especially Asians. Although Hispanics and Asians have always displayed declining levels of minority-white dissimilarity and rising levels of minority-white interaction with rising income, income differentials on these outcomes for blacks did not appear until 1990 and since then have improved at a very slow pace. Given their higher overall levels of segregation and income's limited effect on residential attainment, African Americans experience less integration, more neighborhood poverty at all levels of income compared to other minority groups. The degree of black spatial disadvantage is especially acute in the nation's 21 hypersegregated metropolitan areas.
The past two decades have ushered in a period of widespread spatial diffusion of Hispanics well beyond traditional metropolitan gateways. This article examines emerging patterns of racial and ethnic residential segregation in new Hispanic destinations over the 1990-2010 period, linking county, place, and block data from the 1990, 2000, and 2010 decennial censuses. Our multiscalar analyses of segregation are framed by classical models of immigrant assimilation and alternative models of place stratification. We ask whether Hispanics are integrating spatially with the native population and whether recent demographic and economic processes have eroded or perpetuated racial boundaries in nonmetropolitan areas. We show that Hispanic residential segregation from whites is often exceptionally high and declining slowly in rural counties and communities. New Hispanic destinations, on average, have higher Hispanic segregation levels than established gateway communities. The results also highlight microscale segregation patterns within rural places and in the open countryside (i.e., outside places), a result that is consistent with emerging patterns of "white flight." Observed estimates of Hispanic-white segregation across fast-growing nonmetropolitan counties often hide substantial heterogeneity in residential segregation. Divergent patterns of rural segregation reflect local-area differences in population dynamics, economic inequality, and the county employment base (using Economic Research Service functional specialization codes). Illustrative maps of Hispanic boom counties highlight spatially uneven patterns of racial diversity. They also provide an empirical basis for our multivariate analyses, which show that divergent patterns of local-area segregation often reflect spatial variation in employment across different industrial sectors.
In this essay, we ask whether the distributions of life expectancy and mortality have become generally more unequal, as many seem to believe, and we report some good news. Focusing on groups of counties ranked by their poverty rates, we show that gains in life expectancy at birth have actually been relatively equally distributed between rich and poor areas. Analysts who have concluded that inequality in life expectancy is increasing have generally focused on life expectancy at age 40 to 50. This observation suggests that it is important to examine trends in mortality for younger and older ages separately. Turning to an analysis of age-specific mortality rates, we show that among adults age 50 and over, mortality has declined more quickly in richer areas than in poorer ones, resulting in increased inequality in mortality. This finding is consistent with previous research on the subject. However, among children, mortality has been falling more quickly in poorer areas with the result that inequality in mortality has fallen substantially over time. We also show that there have been stunning declines in mortality rates for African Americans between 1990 and 2010, especially for black men. Finally we offer some hypotheses about causes for the results we see, including a discussion of differential smoking patterns by age and socioeconomic status.
Background
Researchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, especially given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations.
Objectives
To estimate relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diameter of < 2.5 μ (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US.
Methods
Long-term (5 year average) census tract-level PM2.5 and O3 concentrations were calculated using output from a downscaler model (2002–2006). The downscaler uses a linear regression with additive and multiplicative bias coefficients to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calculated at the tract level, and tracts were classified by urbanicity, RI, and geographic region. We examined differences in estimated pollutant exposures by RI, urbanicity, and demographic subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to estimate associations between RI and air pollution levels in urban, suburban, and rural tracts.
Results
High RI tracts (≥ 80th percentile) had higher average PM2.5 levels in each category of urbanicity compared to low RI tracts (< 20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concentrations were significantly and positively associated with RI. The largest association between PM2.5 and RI was observed in the rural Midwest, where a one quintile increase in RI was associated with a 0.90 μg/m³ (95% confidence interval: 0.83, 0.99 μg/m³) increase in PM2.5 concentration. Associations between O3 and RI in the Northeast, Midwest and West were positive and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty.
Conclusion
RI is associated with higher 5 year estimated PM2.5 concentrations in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly associated with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health.
Narrowing of the life expectancy gap
In the United States, the rich can expect to enjoy better health and a longer life than the poor. Despite policies directed at improving the health of both the young and the poor, there is little evidence that this relationship has changed. Currie and Schwandt looked specifically at the life expectancy of present-day children and young adults, finding that mortality inequality has in fact declined over the past 25 years (see the Perspective by Bailey and Timpe).
Science , this issue p. 708 ; see also p. 661
Importance:
The relationship between income and life expectancy is well established but remains poorly understood.
Objectives:
To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation.
Design and setting:
Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.
Exposure:
Pretax household earnings as a measure of income.
Main outcomes and measures:
Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas.
Results:
The sample consisted of 1 408 287 218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61 175 per year). There were 4 114 380 deaths among men (mortality rate, 596.3 per 100 000) and 2 694 808 deaths among women (mortality rate, 375.1 per 100 000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = -0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001).
Conclusions and relevance:
In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.
The Color of Success tells of the astonishing transformation of Asians in the United States from the "yellow peril" to "model minorities"--peoples distinct from the white majority but lauded as well-assimilated, upwardly mobile, and exemplars of traditional family values--in the middle decades of the twentieth century. As Ellen Wu shows, liberals argued for the acceptance of these immigrant communities into the national fold, charging that the failure of America to live in accordance with its democratic ideals endangered the country's aspirations to world leadership. Weaving together myriad perspectives, Wu provides an unprecedented view of racial reform and the contradictions of national belonging in the civil rights era. She highlights the contests for power and authority within Japanese and Chinese America alongside the designs of those external to these populations, including government officials, social scientists, journalists, and others. And she demonstrates that the invention of the model minority took place in multiple arenas, such as battles over zoot suiters leaving wartime internment camps, the juvenile delinquency panic of the 1950s, Hawaii statehood, and the African American freedom movement. Together, these illuminate the impact of foreign relations on the domestic racial order and how the nation accepted Asians as legitimate citizens while continuing to perceive them as indelible outsiders. By charting the emergence of the model minority stereotype, The Color of Success reveals that this far-reaching, politically charged process continues to have profound implications for how Americans understand race, opportunity, and nationhood.
Residential segregation, by definition, leads to racial and socioeconomic disparities in neighborhood conditions. These disparities may in turn produce inequality in social and economic opportunities and outcomes. Because racial and socioeconomic segregation are not independent of each other, however, any analysis of their causes, patterns, and effects must rest on an understanding of the joint distribution of race/ethnicity and income among neighborhoods. In this article, we use a new technique to describe the average racial composition and income distributions in the neighborhoods of households with different income levels and race/ethnicity. Using data from the decennial censuses and the American Community Survey, we investigate how patterns of neighborhood context in the United States over the past two decades vary by household race/ethnicity, income, and metropolitan area. We find large and persistent racial differences in neighborhood context, even among households with the same annual income.
Scant data quantify associations between economic and racial/ethnic spatial polarization and individual's exposure to pollution.
We linked data on the socioeconomic position (SEP) of 1757 urban working class white, black, and Latino adults (age 25-64; Boston, MA: 2003-2004; 2008-2010) to: (1) spatiotemporal model-based estimates of cumulative black carbon exposure at their exact residential address, and (2) their census tract values for the Index of Concentration at the Extremes (ICE) for SEP and race/ethnicity.
ICE measures, but not individual- and household-SEP, remained independently associated with black carbon exposure.
The ICE may be useful for environmental health research.
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