The effect of racial residential segregation on black infant mortality

Association Between Changes in Racial Residential and School Segregation and Trends in Racial Health Disparities, 2000-2020: A Life Course Perspective

INTRODUCTION

Most studies of the relationship between racial segregation and racial health disparities have focused on residential segregation. School-based racial segregation is an additional form of segregation that may be associated with racial disparities in health. This study examines the relationship between both residential segregation and school segregation and racial health disparities among non-Hispanic Black compared to non-Hispanic White persons at the county level in the United States. It also examines the relationship between changes in residential and school segregation and subsequent trajectories in a variety of racial health disparities across the life course.

METHODS

Using the CDC WONDER Multiple Case of Death database, we derived an annual estimate of race-specific death rates and rate ratios for each county during the period 2000-2020. We then examined the relationship between baseline levels of residential and school segregation in 1991 as well as changes between 1991-2000 and the trajectories of the observed racial health disparities between 2000 and 2020. We used latent trajectory analysis to identify counties with similar patterns of residential and school segregation over time and to identify counties with similar trajectories in each racial health disparity. Outcomes included life expectancy, early mortality (prior to age 65), infant mortality, firearm homicide, total homicide, and teenage pregnancy rates.

RESULTS

During the period 1991-2020, racial residential segregation remained essentially unchanged among the 1051 counties in our sample; however, racial school segregation increased during this period. Increases in school segregation from 1991 to 2000 were associated with higher racial disparities in each of the health outcomes during the period 2000-2020 and with less progress in reducing these disparities.

CONCLUSION

This paper provides new evidence that school segregation is an independent predictor of racial health disparities and that reducing school segregation-even in the face of high residential segregation-could have a long-term impact on reducing racial health disparities. Furthermore, it suggests that the health consequences of residential segregation have not been eliminated from our society but are now being exacerbated by a new factor: school-based segregation. Throughout this paper, changes in school-based segregation not only show up as a consistent significant predictor of greater racial disparities throughout the life course, but at times, an even stronger predictor of health inequity than residential segregation.

Racial and Educational Isolation are Associated with Worse Outcomes in Congenital Heart Disease

Sociodemographic factors influence outcomes in children with congenital heart disease (CHD). We predict an association between measures of social isolation and outcomes in infants with complex CHD. These measures, racial (RI) and educational (EI) isolation range from 0 to 1, with 0 being no isolation and 1 being fully isolated within a specific population. We identified patients less than 1 year old undergoing CHD surgery in North Carolina from 2008 to 2013. We used negative binomial and logistic regression models to assess the case-mix adjusted associations between RI and EI and length of stay, complications, mortality, and resource utilization. We quantified the association of race with these indices and outcomes. We included 1217 infants undergoing CHD surgery. Black infants had increased LOS (p < 0.001), other complications (p = 0.03), and death (p = 0.02). RI up to 0.3 was associated with decreased outpatient encounters (p < 0.001). RI was associated with increased inpatient encounters RI up to 0.3 (p < 0.001) but decreased for RI beyond 0.3 (p = 0.01). There was an association with increased risk of one or more emergency department visits (p = 0.001) at higher levels of EI. Race and RI showed a cumulative effect with children with Black race and greater than median RI having increased LOS (p < 0.001) and fewer outpatient encounters (p = 0.02). RI, EI, and Black race are associated with poorer outcomes. Children with Black race and greater than median levels of RI are at the highest risk of poor outcomes. These differences may be caused by differential access to resources or community support.

Racial and Educational Isolation are Associated with Worse Outcomes in Congenital Heart Disease

Background

Sociodemographic factors influence outcomes in children with congenital heart disease (CHD). We predict an association between measures of social isolation and outcomes in infants with complex CHD. These measures, racial (RI) and educational (EI) isolation range from 0 to 1, with 0 being no isolation and 1 being fully isolated within a specific population.

Methods

We identified patients less than 1-year-old undergoing CHD surgery in North Carolina from 2008-2013. We used negative binomial and logistic regression models to assess the case-mix adjusted associations between RI and EI and length of stay, complications, mortality, and resource utilization. We quantified the association of race with these indices and outcomes.

Results

We included 1,217 infants undergoing CHD surgery. Black infants had increased LOS (p < 0.001), other complications (p = 0.03), and death (p = 0.02). RI up to 0.3 was associated with decreased outpatient encounters (p < 0.001). RI was associated with increased inpatient encounters RI up to 0.3 (p < 0.001) but decreased for RI beyond 0.3 (p = 0.01). There was an association with increased risk of one or more emergency department visits (p = 0.001) at higher levels EI. Race and RI showed a cumulative effect with children with Black race and greater than median RI having increased LOS (p < 0.001) and fewer outpatient encounters (p = 0.02).

Conclusions

RI, EI, and Black race are associated with poorer outcomes. Children with Black race and greater than median levels of RI are at the highest risk of poor outcomes. These differences may be caused by differential access to resources or community support.

Using Social Vulnerability Indices to Predict Priority Areas for Prevention of Sudden Unexpected Infant Death in Cook County, IL: Cross-Sectional Study

Background

The incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly the same level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racial and socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods. Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities.

Objective

We sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred in Cook County, IL, from 2015 to 2019 and predicting where it would occur in 2021-2025 using a semiautomated, reproducible workflow based on open-source software and data.

Methods

This cross-sectional retrospective study queried geocoded medical examiner data from 2015-2019 to identify SUID cases in Cook County, IL, and aggregated them to "communities" as the unit of analysis. We compared demographic factors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We used social vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each given community for 2015-2019. We applied indicators from 2020 to the trained model to make predictions for 2021-2025.

Results

Validation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95% (95% CI 93%-97%) and a specificity of 98% (95% CI 94%-100%). Case counts at the community level ranged from a minimum of 0 to a maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. All communities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibited lower median proportions of non-Hispanic White residents at 17% versus 60% (P<.001) and higher median proportions of non-Hispanic Black residents at 32% versus 3% (P<.001). Our predictive model showed moderate accuracy when assessed on the training data (Nagelkerke R2=70.2% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), Auburn Gresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021 and 2025.

Conclusions

Sharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to 2019. Our predictive model and maps identify precise regions within the county for local health departments to target for intervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communities will be most affected by SUID.

Association Between Patient Race and Ethnicity and Outcomes With COVID-19: A Retrospective Analysis From a Large Mid-Atlantic Health System

BACKGROUND

Members of racial and ethnic minority groups have been disproportionately impacted by coronavirus-2019 (COVID-19). The objective of the study is to describe associations between race and ethnicity on clinical outcomes such as need for mechanical ventilation and mortality.

METHODS

Retrospective cohort study of patients with severe COVID-19 infection admitted within a large, not-for-profit healthcare system in the mid-Atlantic region between March and July, 2020. Patient demographic data and clinical outcomes were abstracted from the electronic health record. Logistic regressions were performed to estimate associations between race and ethnicity and the clinical outcomes.

RESULTS

The study population (N  =  2931) was stratified into 1 of 3 subgroups: non-Hispanic White (n  =  466), non-Hispanic Black (n  =  1611), and Hispanic (n  =  654). The average age of White, Black, and Hispanic patients was 69  ±  17.06, 64  ±  15.9, and 50  ±  15.53 years old, respectively (P < .001). Compared to White patients, Black and Hispanic patients were at increased odds of needing mechanical ventilation due to COVID-19 pneumonia (odds ratio [OR] Black  =  1.35, 95% confidence interval [CI]  =  1.04 to 1.75, P < .05; OR Hispanic  =  1.43, 95% CI  =  1.06 to 1.93, P < .05). When compared to White patients, Hispanic patients were at decreased odds of death (OR  =  0.45, 95% CI  =  0.32 to 0.63, P < .001). However, when adjusting for age, there were no statistically significant differences in the odds of death between these groups (adjusted OR [aOR] Black  =  1.05, 95% CI  =  0.80 to 1.38, P  =  .71; aOR Hispanic  =  1.10, 95% CI  =  0.76 to 1.60, P  =  .62).

CONCLUSION

Our analysis demonstrated that Hispanic patients were more likely require mechanical ventilation but had lower mortality when compared to White patients, with lower average age likely mediating this association. These findings emphasize the importance of outreach efforts to communities of color to increase prevention measures and vaccination uptake to reduce infection with COVID-19.

Study designs, measures and indexes used in studying the structural racism as a social determinant of health in high income countries from 2000-2022: evidence from a scoping review

BACKGROUND

Globally, structural racism has been well documented as an important social determinant of health (SODH) resulting in racial inequality related to health. Although studies on structural racism have increased over the years, the selection of appropriate designs, measures, and indexes of measurement that respond to SODH has not been comprehensively documented. Therefore, the lack of evidence seems to exist. This scoping review was conducted to map and summarize global evidence on the use of various designs, measures, and indexes of measurement when studying structural racism as a social determinant of health.

METHODS

We performed a scoping review of global evidence from 2000 to 2022 published in 5 databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo, Web of Science, ProQuest, and relevant grey literature on structural racism. We conducted a systematic search using keywords and subject headings around 3 concepts. We included peer reviewed original research/review articles which conceived the framework of social determinants of health (SODH) and studied structural racism.

RESULTS

Our review identified 1793 bibliographic citations for screening and 54 articles for final review. Articles reported 19 types of study design, 87 measures of exposure and 58 measures of health outcomes related to structural racism. 73 indexes or scales of measurement were used to assess health impacts of structural racism. Majority of articles were primary research (n = 43/54 articles; 79.6%), used quantitative research method (n = 32/54 articles; 59.3%) and predominantly conducted in the United States (n = 46/54 articles; 85.2.6%). Cross-sectional study design was the most used design (n = 17/54 articles; 31.5%) followed by systematic review (n = 7/54 articles; 13.0%) and narrative review (n = 6/54 articles; 11.1%). Housing and residential segregation was the largest cluster of exposure with the highest impact in infant health outcome.

CONCLUSIONS

Our review found several key gaps and research priorities on structural racism such as lack of longitudinal studies and availability of structural or ecological data, lack of consensus on the use of consolidated appropriate measures, indexes of measurement and appropriate study designs that can capture complex interactions of exposure and outcomes related to structural racism holistically.

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA

Persons of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores

Racial residential segregation (RRS) – defined here as the geographic separation of Black individuals and communities from other racial/ethnic groups into separate, unequal neighborhoods – fosters environments inimical to health through disinvestment of resources and concentration of disadvantages. Neighborhood environments influence children’s health and development, but relationships between RRS and cognitive development are poorly understood. We find that: (1) non-Hispanic Black children were more likely to experience multiple adverse exposures in early childhood, and (2) among non-Hispanic Black children, high levels of RRS augmented the detrimental effect of elevated blood levels on reading test scores. Non-linear models were used to model exposure to lead and RRS, and their interaction.

Racial/ethnic disparities in academic performance may result from a confluence of adverse exposures that arise from structural racism and accrue to specific subpopulations. This study investigates childhood lead exposure, racial residential segregation, and early educational outcomes. Geocoded North Carolina birth data is linked to blood lead surveillance data and fourth-grade standardized test scores (n = 25,699). We constructed a census tract-level measure of racial isolation (RI) of the non-Hispanic Black (NHB) population. We fit generalized additive models of reading and mathematics test scores regressed on individual-level blood lead level (BLL) and neighborhood RI of NHB (RINHB). Models included an interaction term between BLL and RINHB. BLL and RINHB were associated with lower reading scores; among NHB children, an interaction was observed between BLL and RINHB. Reading scores for NHB children with BLLs of 1 to 3 µg/dL were similar across the range of RINHB values. For NHB children with BLLs of 4 µg/dL, reading scores were similar to those of NHB children with BLLs of 1 to 3 µg/dL at lower RINHB values (less racial isolation/segregation). At higher RINHB levels (greater racial isolation/segregation), children with BLLs of 4 µg/dL had lower reading scores than children with BLLs of 1 to 3 µg/dL. This pattern becomes more marked at higher BLLs. Higher BLL was associated with lower mathematics test scores among NHB and non-Hispanic White (NHW) children, but there was no evidence of an interaction. In conclusion, NHB children with high BLLs residing in high RINHB neighborhoods had worse reading scores.

Racial/Ethnic Residential Segregation and the First Wave of SARS-CoV-2 Infection Rates: A Spatial Analysis of Four U.S. Cities

Previous research has linked racial/ethnic residential segregation to a number of poor health conditions, including infectious disease. Here, we examine how racial/ethnic residential segregation is related to the novel coronavirus, SARS-CoV-2. We examine infection rates by zip code level segregation in four major cities across the U.S.: New York City, Chicago, Houston, and San Diego. We also include a number of area-level Census variables in order to analyze how other factors may help account for the infection rate. We find that both Black and Latino residential clustering are significantly and positively related to a higher SARS-CoV-2 infection rate across all four cities, and that this effect is strong even when accounting for a number of other social conditions and factors that are salient to the transmission of infectious disease. As a result, we argue that neighborhood-level racial/ethnic patterning may serve as an important structural mechanism for disparities in SARS-CoV-2 infection.

Assessing Disparity Using Measures of Racial and Educational Isolation

We develop a local, spatial measure of educational isolation (EI) and characterize the relationship between EI and our previously developed measure of racial isolation (RI). EI measures the extent to which non-college educated individuals are exposed primarily to other non-college educated individuals. To characterize how the RI-EI relationship varies across space, we propose a novel measure of local correlation. Using birth records from the State of Michigan (2005–2012), we estimate associations between RI, EI, and birth outcomes. EI was lower in urban communities and higher in rural communities, while RI was highest in urban areas and parts of the southeastern United States (US). We observed greater heterogeneity in EI in low RI tracts, especially in non-urban tracts; residents of high RI tracts are likely to be both educationally and racially isolated. Associations were also observed between RI, EI, and gestational length (weeks) and preterm birth (PTB). For example, moving from the lowest to the highest quintile of RI was associated with a 1.11 (1.07, 1.15) and 1.16 (1.10, 1.22) increase in odds of PTB among NHB and NHW women, respectively. Moving from the lowest to the highest quintile of EI was associated with a 1.07 (1.02, 1.12) and 1.03 (1.00, 1.05) increase in odds of PTB among NHB and NHW women, respectively. This work provides three tools (RI, EI, and the local correlation measure) to researchers and policymakers interested in how residential isolation shapes disparate outcomes.

The geography of ethnoracial low birth weight inequalities in the United States

In this article, we describe, decompose, and examine correlates of the geography of ethnoracial inequalities in low birth weight (LBW) in the United States. Drawing on the population of singleton births to U.S.-born White, Black, Latinx, and Native American parents in the first decade of the twenty-first century (N = 28.2 million births), we calculate county-level LBW rates and rate ratios. Results demonstrate a stark racial hierarchy in which Black infants experience the most significant disadvantage, but we also document substantial local-level variation organized in what we call a regionalized patchwork of inequality, with high-disparity counties bordering low-disparity counties coupled with regional clustering. Examining the component parts of local disparities - the LBW rates for Whites and groups of color - we find strong evidence that spatial variation in ethnoracial LBW inequalities is driven by greater variation in infants of color's health across counties relative to Whites. Further, LBW rates for groups of color are only weakly to moderately correlated with Whites' LBW rates, indicating that the same contexts can produce racially divergent health outcomes. Examining contextual factors that predict LBW disparities, we find that more segregated, socioeconomically unequal, and urban counties have larger LBW disparities. We conclude by positing an approach to health disparities that conceptualizes ethnoracial differences in health as fundamentally relational and spatial phenomena produced by systems of White advantage.

Racial Segregation and Respiratory Outcomes among Urban Black Residents with and at Risk of Chronic Obstructive Pulmonary Disease

Rationale: Racial residential segregation has been associated with worse health outcomes, but the link with chronic obstructive pulmonary disease (COPD) morbidity has not been established.Objectives: To investigate whether racial residential segregation is associated with COPD morbidity among urban Black adults with or at risk of COPD.Methods: Racial residential segregation was assessed using isolation index, based on 2010 decennial census and baseline address, for Black former and current smokers in the multicenter SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), a study of adults with or at risk for COPD. We tested the association between isolation index and respiratory symptoms, physiologic outcomes, imaging parameters, and exacerbation risk among urban Black residents, adjusting for established COPD risk factors, including smoking. Additional mediation analyses were conducted for factors that could lie on the pathway between segregation and COPD outcomes, including individual and neighborhood socioeconomic status, comorbidity burden, depression/anxiety, and ambient pollution.Measurements and Main Results: Among 515 Black participants, those residing in segregated neighborhoods (i.e., isolation index ⩾0.6) had worse COPD Assessment Test score (β = 2.4; 95% confidence interval [CI], 0.7 to 4.0), dyspnea (modified Medical Research Council scale; β = 0.29; 95% CI, 0.10 to 0.47), quality of life (St. George's Respiratory Questionnaire; β = 6.1; 95% CI, 2.3 to 9.9), and cough and sputum (β = 0.8; 95% CI, 0.1 to 1.5); lower FEV1% predicted (β = -7.3; 95% CI, -10.9 to -3.6); higher rate of any and severe exacerbations; and higher percentage emphysema (β = 2.3; 95% CI, 0.7 to 3.9) and air trapping (β = 3.8; 95% CI, 0.6 to 7.1). Adverse associations attenuated with adjustment for potential mediators but remained robust for several outcomes, including dyspnea, FEV1% predicted, percentage emphysema, and air trapping.Conclusions: Racial residential segregation was adversely associated with COPD morbidity among urban Black participants and supports the hypothesis that racial segregation plays a role in explaining health inequities affecting Black communities.

Effects of accumulated environmental, social and host exposures on early childhood educational outcomes

BACKGROUND

Persistent disparities in academic performance may result from a confluence of adverse exposures accruing disproportionately to specific subpopulations.

OBJECTIVE

Our overarching objective was to investigate how multiple exposures experienced over time affect early childhood educational outcomes. We were specifically interested in whether there were: racial/ethnic disparities in prevalence of adverse exposures; racial/ethnic disparities in associations observed between adverse exposures and early childhood educational outcomes; and interactions between exposures, suggesting that one exposure augments susceptibility to adverse effects of another exposure.

METHODS

We link geocoded North Carolina birth data for non-Hispanic white (NHW) and non-Hispanic black (NHB) children to blood lead surveillance data and 4th grade end-of-grade (EOG) standardized test scores (n = 65,151). We construct a local, spatial index of racial isolation (RI) of NHB at the block group level. We fit race-stratified multi-level models of reading and mathematics EOG scores regressed on birthweight percentile for gestational age, blood lead level, maternal smoking, economic disadvantage, and RI, adjusting for maternal- and child-level covariates and median household income.

RESULTS

There were marked racial/ethnic disparities in prevalence of adverse exposures. Specifically, NHB children were more likely than NHW children to be economically disadvantaged (80% vs. 40%), live in block groups with the highest quintile of RI (46% vs. 5%), have higher blood lead levels (4.6 vs. 3.7 μg/dL), and lower birthweight percentile for gestational age (mean: 39th percentile vs. 51st percentile). NHB children were less likely to have mothers who reported smoking during pregnancy (11% and 22%). We observed associations between key adverse exposures and reading and math EOG scores in 4th grade. Higher birthweight percentile for gestational age was associated with higher EOG scores, while economic disadvantage, maternal smoking, and elevated blood lead levels were associated with lower EOG scores. Associations observed for NHB and NHW children were generally not statistically different from one another, with the exception of neighborhood RI. NHB children residing in block groups in the highest RI quintile had reading and math scores 1.54 (0.74, 2.34) and 1.12 (0.38, 1.87) points lower, respectively, compared to those in the lowest RI quintile; statistically significant decrements in EOG scores associated with RI were not observed for NHW children. We did not find evidence of multiplicative interactions between exposures for NHB or NHW children.

DISCUSSION

Key adverse host, environmental, and social exposures accrue disproportionately to NHB children. Decrements in test scores associated with key adverse exposures were often but not always larger for NHB children, but were not significantly different from those estimated for NHW children. While we did not observe interactive effects, NHB children on average experience more deleterious combined exposures, resulting in larger decrements to test scores compared to NHW children.

Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States

This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30, 2020. The analysis focuses on the effects of racial residential segregation on mortality and infection rates for the overall population and on racial and ethnic mortality gaps. To account for potential confounding, I assemble a dataset that includes 50 county-level factors that are potentially related to residential segregation and COVID-19 infection and mortality rates. These factors are grouped into eight categories: demographics, density and potential for public interaction, social capital, health risk factors, capacity of the health care system, air pollution, employment in essential businesses, and political views. I use double-lasso regression, a machine learning method for model selection and inference, to select the most important controls in a statistically principled manner. Counties that are 1 SD above the racial segregation mean have experienced mortality and infection rates that are 8% and 5% higher than the mean. These differences represent an average of four additional deaths and 105 additional infections for each 100,000 residents in the county. The analysis of mortality gaps shows that, in counties that are 1 SD above the Black-White segregation mean, the Black mortality rate is 8% higher than the White mortality rate. Sensitivity analyses show that an unmeasured confounder that would overturn these findings is outside the range of plausible covariates.

Assessing the effect of socio-economic features of low-income communities and COVID-19 related cases: An empirical study of New York City

This study examined the effect of socio-economic features of low-income communities and COVID-19 related cases in New York City. The study developed hypotheses and conceptual framework of low-income communities and COVID-19 associated cases based on literature and theoretical review. The proposed framework was then tested using Structural Equation Model (SEM) with secondary data collected from New York Health and Mental Hygiene Department, US Census Bureau, and the Centers for Disease Control and Prevention. The findings revealed that unfavourable working conditions, underlying health conditions, and poor living conditions significantly and positively affects the number of COVID-19 confirmed cases. The study further revealed a positive and significant relationship between confirmed COVID-19 cases and COVID-19 related deaths. Theoretically, this study provides empirical results and a conceptual framework that could be used by other researchers to investigate low-income communities and COVID-19 related topics. Practically, this study called on the federal and state governments to effectively apply the health justice approach to eliminate healthcare discrimination for people living in low-income and marginalised communities as well as providing accessible, safe housing for the more vulnerable who need a place to self-quarantine due to COVID-19 exposure. Further practical and theoretical implications policies are discussed.

SARS-CoV-2 transmission: a sociological review

The current pandemic is defined by the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that can lead to coronavirus disease 2019 (COVID-19). How is SARS-CoV-2 transmitted? In this review, we use a global lens to examine the sociological contexts that are potentially and systematically involved in high rates of SARS-CoV-2 transmission, including lack of personal protective equipment, population density and confinement. Altogether, this review provides an in-depth conspectus of the current literature regarding how SARS-CoV-2 disproportionately impacts many minority communities. By contextualising and disambiguating transmission risks that are particularly prominent for disadvantaged populations, this review can assist public health efforts throughout and beyond the COVID-19 pandemic.

Regional variation in Black infant mortality: The contribution of contextual factors

BACKGROUND

Compared to other racial/ethnic groups, infant mortality rates (IMR) are persistently highestamong Black infants in the United States, yet there is considerable regional variation. We examined state and county-level contextual factors that may explain regional differences in Black IMR and identified potential strategies for improvement.

METHODS AND FINDINGS

Black infant mortality data are from the Linked Birth/Infant Death files for 2009-2011. State and county contextual factors within social, economic, environmental, and health domains were compiled from various Census databases, the Food Environment Atlas, and the Area Health Resource File. Region was defined by the nine Census Divisions. We examined contextual associations with Black IMR using aggregated county-level Poisson regression with standard errors adjusted for clustering by state. Overall, Black IMR varied 1.5-fold across regions, ranging from 8.78 per 1,000 in New England to 13.77 per 1,000 in the Midwest. In adjusted models, the following factors were protective for Black IMR: higher state-level Black-White marriage rate (rate ratio (RR) per standard deviation (SD) increase = 0.81, 95% confidence interval (CI):0.70-0.95), higher state maternal and child health budget per capita (RR per SD = 0.96, 95% CI:0.92-0.99), and higher county-level Black index of concentration at the extremes (RR per SD = 0.85, 95% CI:0.81-0.90). Modeled variables accounted for 35% of the regional variation in Black IMR.

CONCLUSIONS

These findings are broadly supportive of ongoing public policy efforts to enhance social integration across races, support health and social welfare program spending, and improve economic prosperity. Although contextual factors accounted for about a third of regional variation, further research is needed to more fully understand regional variation in Black IMR disparities.

Racial Segregation and Intraventricular Hemorrhage in Preterm Infants

BACKGROUND

Intraventricular hemorrhage (IVH) disproportionately affects black neonates. Other conditions that are more common in black neonates, including low birth weight and preterm delivery, have been linked with residential racial segregation (RRS). In this study, we investigated the association between RRS and IVH.

METHODS

A retrospective cohort of neonates born between 24 and 32 weeks' gestation was constructed by using birth certificates linked to medical records from California, Missouri, and Pennsylvania between 1995 and 2009. Dissimilarity, a measure of RRS indicating the proportion of minorities in the census tract of the mother in comparison to the larger metropolitan area, was linked to patient data, yielding a cohort of 70 775 infants. Propensity score analysis matched infants born to mothers living in high segregation to those living in less segregated areas on the basis of race, sociodemographic factors, and medical comorbidities to compare the risk of developing IVH.

RESULTS

Infants born to mothers in the most segregated quartile had a greater risk of developing IVH compared with those in the lowest quartile (12.9% vs 10.4%; P < .001). In 17 918 pairs matched on propensity scores, the risk of developing IVH was greater in the group exposed to a segregated environment (risk ratio = 1.08, 95% confidence interval: 1.01-1.15). This effect was stronger for black infants alone (risk ratio = 1.16; 95% confidence interval: 1.03-1.30).

CONCLUSIONS

RRS is associated with an increased risk of IVH in preterm neonates, but the effect size varies by race. This association persists after balancing for community factors and birth weight, representing a novel risk factor for IVH.

Racial and ethnic disparities in adverse birth outcomes: Differences by racial residential segregation

Racial and ethnic disparities in adverse birth outcomes have persistently been wide and may be explained by individual and area-level factors. Our primary objective was to determine if county-level black-white segregation modified the association between maternal race/ethnicity and adverse birth outcomes using birth records from the National Center for Health Statistics (2012). Based on maternal residence at birth, county-level black-white racial residential segregation was calculated along five dimensions of segregation: evenness, exposure, concentration, centralization, and clustering. We conducted a two-stage analysis: (1) county-specific logistic regression to determine whether maternal race and ethnicity were associated with preterm birth and term low birth weight; and (2) Bayesian meta-analyses to determine if segregation moderated these associations. We found greater black-white and Hispanic-white disparities in preterm birth in racially isolated counties (exposure) relative to non-isolated counties. We found reduced Hispanic-white disparities in term low birth weight in racially concentrated and centralized counties relative to non-segregated counties. Area-level poverty explained most of the moderating effect of segregation on disparities in adverse birth outcomes, suggesting that area-level poverty is a mediator of these associations. Segregation appears to modify racial/ethnic disparities in adverse birth outcomes. Therefore, policy interventions that reduce black-white racial isolation, or buffer the poor social and economic correlates of segregation, may help to reduce disparities in preterm birth and term low birth weight.

Residential Racial Isolation and Spatial Patterning of Hypertension in Durham, North Carolina

INTRODUCTION

Neighborhood characteristics such as racial segregation may be associated with hypertension, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. The objectives of our study were to 1) evaluate the geographic heterogeneity of hypertension; 2) describe whether and how patient-level risk factors and racial isolation relate to geographic heterogeneity in hypertension; and 3) examine cross-sectional associations of hypertension with racial isolation.

METHODS

We obtained electronic health records from the Duke Medicine Enterprise Data Warehouse for 2007-2011. We linked patient data with data on racial isolation determined by census block of residence. We constructed a local spatial index of racial isolation for non-Hispanic black patients; the index is scaled from 0 to 1, with 1 indicating complete isolation. We used aspatial and spatial Bayesian models to assess spatial variation in hypertension and estimate associations with racial isolation.

RESULTS

Racial isolation ranged from 0 (no isolation) to 1 (completely isolated). A 0.20-unit increase in racial isolation was associated with 1.06 (95% credible interval, 1.03-1.10) and 1.11 (95% credible interval, 1.07-1.16) increased odds of hypertension among non-Hispanic black and non-Hispanic white patients, respectively. Across Durham, census block-level odds of hypertension ranged from 0.62 to 1.88 among non-Hispanic black patients and from 0.32 to 2.41 among non-Hispanic white patients. Compared with spatial models that included patient age and sex, residual heterogeneity in spatial models that included age, sex, and block-level racial isolation was 33% lower for non-Hispanic black patients and 20% lower for non-Hispanic white patients.

CONCLUSION

Racial isolation of non-Hispanic black patients was associated with increased odds of hypertension among both non-Hispanic black and non-Hispanic white patients. Further research is needed to identify latent spatially patterned factors contributing to hypertension.

Residential Racial Isolation and Spatial Patterning of Type 2 Diabetes Mellitus in Durham, North Carolina

Neighborhood characteristics such as racial segregation may be associated with type 2 diabetes mellitus, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. We constructed a local, spatial index of racial isolation (RI) for black residents in a defined area, measuring the extent to which they are exposed only to one another, to estimate associations of diabetes with RI and examine how RI relates to spatial patterning in diabetes. We obtained electronic health records from 2007-2011 from the Duke Medicine Enterprise Data Warehouse. Patient data were linked to RI based on census block of residence. We used aspatial and spatial Bayesian models to assess spatial variation in diabetes and relationships with RI. Compared with spatial models with patient age and sex, residual geographic heterogeneity in diabetes in spatial models that also included RI was 29% and 24% lower for non-Hispanic white and black residents, respectively. A 0.20-unit increase in RI was associated with an increased risk of diabetes for white (risk ratio = 1.24, 95% credible interval: 1.17, 1.31) and black (risk ratio = 1.07, 95% credible interval: 1.05, 1.10) residents. Improved understanding of neighborhood characteristics associated with diabetes can inform development of policy interventions.

Racial residential segregation and adverse birth outcomes: A systematic review and meta-analysis

RATIONALE

Persistent racial disparities in adverse birth outcomes are not fully explained by individual-level risk factors. Racial residential segregation-degree to which two or more groups live apart from one another-may contribute to the etiology of these birth outcome disparities. Our aim was to assess associations between segregation and adverse birth outcomes by race. This review focused on formal measures of segregation, using Massey and Denton's framework (1998) that identifies five distinct operationalizations of segregation, in addition to proxy measures of segregation such as racial composition, in order to gain a deeper understanding of the operationalizations of segregation most salient for birth outcomes.

METHOD

Review and meta-analyses were conducted using PubMed, PsycINFO and Web of Science and included articles from inception through April 30, 2017.

RESULTS

Forty-two articles examined associations between segregation and adverse birth outcomes among Black and White mothers separately. Meta-analyses showed that among Black mothers, exposure was associated with increased risk of preterm birth (OR = 1.17, 95% CI = 1.10, 1.26), and low birth weight (OR = 1.13, 95% CI=1.06, 1.21), and Black racial composition was associated with increased risk of preterm birth (OR = 1.20, 95% CI=1.05, 1.37), among those living in most- compared to least-segregated neighborhoods. Few studies were conducted among White mothers and only exposure was associated with increased risk of preterm birth and low birth weight. Qualitative analyses indicated that among Black mothers, exposure and hypersegregation were associated with multiple adverse birth outcomes; findings were mixed for evenness and clustering.

CONCLUSIONS AND FUTURE DIRECTIONS

Associations between segregation and adverse birth outcomes differ by race. Methodological heterogeneity between studies may obscure true associations. Research can be advanced through use of multilevel frameworks and by examining mechanistic pathways between segregation and adverse birth outcomes. Elucidation of pathways may provide opportunities to intervene to reduce seemingly intractable racial disparities in adverse birth outcomes.

Racial/Ethnic Residential Segregation, Obesity, and Diabetes Mellitus

Persistent racial/ethnic disparities in obesity and type 2 diabetes mellitus seen in the US are likely due to a combination of social, biological, and environmental factors. A growing number of studies have examined the role of racial/ethnic residential segregation with respect to these outcomes because this macro-level process is believed to be a fundamental cause of many of the factors that contribute to these disparities. This review provides an overview of findings from studies of racial/ethnic residential segregation with obesity and diabetes published between 2013 and 2015. Findings for obesity varied by geographic scale of the segregation measure, gender, ethnicity, and racial identity (among Hispanics/Latinos). Recent studies found no association between racial/ethnic residential segregation and diabetes prevalence, but higher segregation of Blacks was related to higher diabetes mortality. Implications of these recent studies are discussed as well as promising areas of future research.

Optimizing matching and analysis combinations for estimating causal effects

Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with the best post-matching covariate balance, but this work finds that such an approach does not always minimize mean squared error (MSE). These findings have important implications for future research utilizing matching. To minimize MSE, investigators should consider additional diagnostics, and use of simulations tailored to the study of interest to identify the optimal matching and analysis combination.

Twenty Years of Neighborhood Effect Research: An Assessment

This paper reviews the magnitude and empirical findings of social epidemiological neighborhood effects research. An electronic keyword literature search identified 1369 empirical and methodological neighborhood effects papers published in 112 relevant journals between 1990 and 2014. Analyses of temporal trends were conducted by focus, journal type (e.g., epidemiology, public health, or social science), and specific epidemiologic journal. Select papers were then critically reviewed. Results show an ever-increasing number of papers published, notably since the year 2000, with the majority published in public health journals. The variety of health outcomes analyzed is extensive, ranging from infectious disease to obesity to criminal behavior. Papers relying on data from experimental designs are thought to yield the most credible results, but such studies are few and findings are inconsistent. Papers relying on data from observational designs and multilevel models typically show small statistically significant effects, but most fail to appreciate fundamental identification problems. Ultimately, of the 1170 empirically focused neighborhood effects papers published in the last 24 years, only a handful have clearly advanced our understanding of the phenomena. The independent impact of neighborhood contexts on health remains unclear. It is time to expand the social epidemiological imagination.

Influence of residential segregation on survival after AIDS diagnosis among non-Hispanic blacks

PURPOSE

Non-Hispanic blacks (NHBs) are disproportionately affected by the AIDS epidemic. With the advent of highly active antiretroviral therapy (HAART), survival after AIDS diagnosis has increased dramatically, yet survival among NHBs is shorter compared with non-Hispanic whites. Racial residential segregation may be an important factor influencing observed racial disparities in survival.

METHODS

We linked data on 30,813 NHBs from the Florida Department of Health HIV/AIDS Reporting system (1993-2004) with death records and applied segregation indices and poverty levels to the data. Weighted Cox models were used to examine the association between segregation measured on five dimensions and survival, controlling for demographic factors, clinical factors, and area-level poverty. Analyses were stratified by pre-HAART (1993-1995), early HAART (1996-1998), and late-HAART (1999-2004) eras.

RESULTS

In the late-HAART era, adjusting for area-level poverty, segregation remained a significant predictor of survival on two dimensions: Concentration (hazard ratio, 1.32; 95% confidence interval, 1.13-1.56) and centralization (hazard ratio, 1.44; 95% confidence interval, 1.12-1.84). Area-level poverty was an independent predictor of survival.

CONCLUSIONS

These findings suggest that certain dimensions of segregation and poverty are associated with survival after AIDS diagnosis.

Neighborhood deprivation and preterm birth: an application of propensity score matching

PURPOSE

On the basis of a neighborhood deprivation index (NDI), this study aims to examine the association between neighborhood deprivation and preterm birth (PTB) by applying propensity score matching (PSM) methods.

METHODS

NDI was calculated for all census tracts in South Carolina based on the US Census data. Live births in South Carolina during 2008 to 2009 (n = 98,456) were assigned to an NDI quartile group based on residential addresses. PSM was used to create matched pairs by NDI quartiles to avoid any potential inference on imbalanced data. The differences of prevalence of PTB were calculated for exposed and reference deprivation groups.

RESULTS

Neighborhood deprivation was higher among blacks than whites. The overall prevalence of PTB was 8.5% for whites and 12.6% for blacks. Living in neighborhoods with higher deprivation was associated with increased risk of PTB among blacks compared with living in neighborhoods with lower deprivation among blacks. However, random-effect regression models showed that the most deprived whites experienced 1.13 times the odds of having PTB than the least deprived whites.

CONCLUSIONS

The racial disparities in adverse birth outcomes might be partially explained by neighborhood deprivation in South Carolina. PSM may be an appropriate approach to avoid imbalanced data inferences.

Integrating space with place in health research: a multilevel spatial investigation using child mortality in 1880 Newark, New Jersey

Research on neighborhoods and health increasingly acknowledges the need to conceptualize, measure, and model spatial features of social and physical environments. When ignoring underlying spatial dynamics, we run the risk of biased statistical inference and misleading results. In this article, we propose an integrated multilevel spatial approach for Poisson models of discrete responses. In an empirical example of child mortality in 1880 Newark, New Jersey, we compare this multilevel spatial approach with the more typical aspatial multilevel approach. Results indicate that spatially defined egocentric neighborhoods, or distance-based measures, outperform administrative areal units, such as census units. In addition, although results do not vary by specific definitions of egocentric neighborhoods, they are sensitive to geographic scale and modeling strategy. Overall, our findings confirm that adopting a spatial multilevel approach enhances our ability to disentangle the effect of space from that of place, pointing to the need for more careful spatial thinking in population research on neighborhoods and health.

Use of segregation indices, Townsend Index, and air toxics data to assess lifetime cancer risk disparities in metropolitan Charleston, South Carolina, USA

BACKGROUND

Studies have demonstrated a relationship between segregation and level of education, occupational opportunities, and risk behaviors, yet a paucity of research has elucidated the association between racial residential segregation, socioeconomic deprivation, and lifetime cancer risk.

OBJECTIVES

We examined estimated lifetime cancer risk from air toxics by racial composition, segregation, and deprivation in census tracts in Metropolitan Charleston.

METHODS

Segregation indices were used to measure the distribution of groups of people from different races within neighborhoods. The Townsend Index was used to measure economic deprivation in the study area. Poisson multivariate regressions were applied to assess the association of lifetime cancer risk with segregation indices and Townsend Index along with several sociodemographic measures.

RESULTS

Lifetime cancer risk from all pollution sources was 28 persons/million for half of the census tracts in Metropolitan Charleston. Isolation Index and Townsend Index both showed significant correlation with lifetime cancer risk from different sources. This significance still holds after adjusting for other sociodemographic measures in a Poisson regression, and these two indices have stronger effect on lifetime cancer risk compared to the effects of sociodemographic measures.

CONCLUSIONS

We found that material deprivation, measured by the Townsend Index and segregation measured by the Isolation index, introduced high impact on lifetime cancer risk by air toxics at the census tract level.

The social determinants of infant mortality and birth outcomes in Western developed nations: a cross-country systematic review

Infant mortality (IM) and birth outcomes, key population health indicators, have lifelong implications for individuals, and are unequally distributed globally. Even among western industrialized nations, striking cross-country and within-country patterns are evident. We sought to better understand these variations across and within the United States of America (USA) and Western Europe (WE), by conceptualizing a social determinants of IM/birth outcomes framework, and systematically reviewing the empirical literature on hypothesized social determinants (e.g., social policies, neighbourhood deprivation, individual socioeconomic status (SES)) and intermediary determinants (e.g., health behaviours). To date, the evidence suggests that income inequality and social policies (e.g., maternal leave policies) may help to explain cross-country variations in IM/birth outcomes. Within countries, the evidence also supports neighbourhood SES (USA, WE) and income inequality (USA) as social determinants. By contrast, within-country social cohesion/social capital has been underexplored. At the individual level, mixed associations have been found between individual SES, race/ethnicity, and selected intermediary factors (e.g., psychosocial factors) with IM/birth outcomes. Meanwhile, this review identifies several methodological gaps, including the underuse of prospective designs and the presence of residual confounding in a number of studies. Ultimately, addressing such gaps including through novel approaches to strengthen causal inference and implementing both health and non-health policies may reduce inequities in IM/birth outcomes across the western developed world.

Segregation and cardiovascular illness: the role of individual and metropolitan socioeconomic status

Demographic and epidemiologic research suggest that cardiovascular illness is negatively linked to socioeconomic status and positively related to racial residential segregation. Relying on 2005 data from the Behavior Risk Factor Surveillance Survey and the American Community Survey, this study examines how segregation and SES (individual and metropolitan) impact hypertension for a sample of 200,102 individuals. Multilevel analyses indicate that both segregation and hypersegregation are associated with hypertension, net of individual and spatial SES. While individual and metropolitan SES have independent effects on hypertension, these effects also differ across segregation type. In segregated and hypersegregated environments, highly educated and high-earning individuals seem to be protected against hypertension. In extremely hypersegregated areas, areas where there is very little interaction with non-black residents, SES does not have any protective benefit. These findings reveal that SES has differential effects across segregation types and that hypertension in disadvantaged (extremely hypersegregated) areas may be a function of structural constraints rather than socioeconomic position.

Invited commentary: integrating a life-course perspective and social theory to advance research on residential segregation and health

Research on racial residential segregation and health typically uses multilevel, population-based, slice-in-time data. Although research using this approach, including that by Kershaw et al. (Am J Epidemiol. 2013;177(4):299-309), has been valuable, I argue that to advance our understanding of how residential segregation influences health and health disparities, it is critical to incorporate a life-course perspective and integrate social theory. Applying a life-course perspective would entail modeling transitions, cumulative risk, and developmental and dynamic processes and mechanisms, as well as recognizing the contingency of contextual effects on different social groups. I discuss the need for analytic methods appropriate for modeling health effects of distal causes experienced across the life course, such as segregation, that operate through multiple levels and sequences of mediators, potentially across decades. Sociological theories of neighborhood attainment (e.g., segmented assimilation, ethnic resurgence, and place stratification theories) can guide effect-modification tests to help illuminate health effects resulting from intersections of residential processes, race/ethnicity, immigration, and other social determinants of health. For example, nativity and immigration history may crucially shape residential processes and exposures, but these have received limited attention in prior segregation-health literature.

Future directions in research on institutional and interpersonal discrimination and children's health

Research evidence indicates that 2 forms of racial discrimination-perceived interpersonal discrimination and racial/ethnic residential segregation (a form of institutional discrimination)-may influence children's health and disparities. Although research on these 2 forms of discrimination and health has primarily focused on adults, smaller bodies of work have documented that perceived interpersonal discrimination and segregation have a negative effect on infants' health, and that perceived interpersonal discrimination may negatively affect children's mental health. Three directions for research are (1) incorporating a life-course perspective into studies of discrimination and children's health, (2) linking residential segregation with geography-of-opportunity conceptual frameworks and measures, and (3) considering residential segregation along with segregation in other contexts that influence children's health (e.g., schools).

Navigating non-positivity in neighbourhood studies: an analysis of collective efficacy and violence

BACKGROUND

In multilevel studies, strong correlations of neighbourhood exposures with individual and neighbourhood confounders may generate problems with non-positivity (ie, inferences that are 'off-support'). The authors used propensity restriction and matching to (1) assess the utility of propensity restriction to ensure analyses are 'on-support' and (2) examine the relation between collective efficacy and violence in a previously unstudied city.

METHODS

Associations between neighbourhood collective efficacy and violent victimisation were estimated in data from New York City in 2005 (n=4000) using marginal models and propensity matching.

RESULTS

In marginal models adjusted for individual confounders and limited to observations 'on-support', under conditions of high collective efficacy, the estimated prevalence of violent victimisation was 3.5/100, while under conditions of low collective efficacy, it was 7.5/100, resulting in a difference of 4.0/100 (95% CI 2.6 to 5.8). In propensity-matched analysis, the comparable difference was 4.0/100 (95% CI 2.1 to 5.9). In analyses adjusted for individual and neighbourhood confounders and limited to observations 'on-support', the difference in violent victimisation associated with collective efficacy was 3.1/100 (95% CI 1.2 to 5.2) in marginal models and 2.4/100 (95% CI 0.2 to 4.5) in propensity-matched analysis. Analyses without support restrictions produced surprisingly similar results.

CONCLUSIONS

Under conditions of high collective efficacy, there was about half the prevalence of violence compared with low collective efficacy. The results contribute to a growing body of evidence that suggests collective efficacy may shape violence, and illustrate how careful techniques can be used to disentangle exposures from highly correlated confounders without relying on model extrapolation.

Racial residential segregation and rates of gonorrhea in the United States, 2003-2007

OBJECTIVES

In the United States, Black persons are disproportionately affected by sexually transmitted infections (STIs), including gonorrhea. Individual behaviors do not fully explain these racial disparities. We explored the association of racial residential segregation with gonorrhea rates among Black persons and hypothesized that specific dimensions of segregation would be associated with gonorrhea rates.

METHODS

We used 2003 to 2007 national STI surveillance data and 2000 US Census Bureau data to examine associations of 5 dimensions of racial residential segregation and a composite measure of hypersegregation with gonorrhea rates among Black persons in 257 metropolitan statistical areas, overall and by sex and age. We calculated adjusted rate ratios with generalized estimating equations.

RESULTS

Isolation and unevenness were significantly associated with gonorrhea rates. Centralization was marginally associated with gonorrhea. Isolation was more strongly associated with gonorrhea among the younger age groups. Concentration, clustering, and hypersegregation were not associated with gonorrhea.

CONCLUSIONS

Certain dimensions of segregation are important in understanding STI risk among US Black persons. Interventions to reduce sexual risk may need to account for racial residential segregation to maximize effectiveness and reduce existent racial disparities.

Racial residential segregation and low birth weight in Michigan's metropolitan areas

OBJECTIVES

We examined the influence of racial residential segregation, independent of neighborhood economic factors, on the overall and specific etiological risks of low birth weight.

METHODS

We geocoded all singleton births in Michigan metropolitan areas during 2000 to census tracts. We used hierarchical generalized linear models to investigate the association between low birth weight (< 2500 g) and neighborhood-level economic and racial segregation, controlling for individual and neighborhood characteristics. We analyzed competing risks of the 2 etiologies of low birth weight: intrauterine growth restriction and preterm birth.

RESULTS

Living in a Black segregated area was associated with increased odds (odds ratio [OR] = 1.15; 95% confidence interval [CI] = 1.03, 1.29; P < .05) of low birth weight after adjusting for individual- and tract-level measures. The analysis suggested that the association between low birth weight and racial segregation was attributable primarily to increased risk of intrauterine growth restriction (OR = 1.19; 95% CI = 1.03, 1.37; P < .05).

CONCLUSIONS

Odds of low birth weight are higher in racially segregated Black neighborhoods in Michigan's metropolitan areas, independent of economic factors. The association appears to operate through intrauterine growth restriction rather than preterm birth.

A spatial measure of neighborhood level racial isolation applied to low birthweight, preterm birth, and birthweight in North Carolina

Research on racial residential segregation (RRS) and birth outcomes has focused on RRS at a broad geographic scale, in an aspatial framework, and in northern US cities. We developed a spatial measure of neighborhood level racial isolation of blacks. We examined the association between this new measure and low birthweight, preterm birth, and birthweight in the southern state of North Carolina. Natality data were obtained from the North Carolina Detailed Birth Record 1998-2002 files. Using multiple regression with cluster corrected standard errors, infants born to black and white mothers living in black isolated neighborhoods had, on average, decreased birthweight, and increased odds of low birthweight and preterm birth compared to their counterparts in less isolated areas. White mothers in predominantly black neighborhoods experienced greater increases in odds of each poor birth outcome than did black mothers. Black isolation may be proxying concentrated socioeconomic disadvantage, including disamenities in the built environment.

The co-occurrence of obesity, elevated blood pressure, and acanthosis nigricans among American Indian school children: identifying individual heritage and environment-level correlates

OBJECTIVE

To estimate the prevalence and explore the social and cultural etiologic roots of weight status, blood pressure, and acanthosis nigricans among American Indian children on a reservation in South Dakota.

METHODS

This observational study was conducted in 26 schools from 1998 to 2002 and included 5,422 observations representing 3,841 children, ages 3 to 19. Trained staff measured height, weight, blood pressure, and assessed the presence of acanthosis nigricans (AN). Percent Indian heritage (PIH) was abstracted from tribal records. Sociodemographic environment (SDE) was calculated using the 2000 Census at the city/town level. Descriptive analyses were conducted using one measurement time point, including tests for trend and co-occurrence of risk factors using the [kappa] statistic. Hierarchical, multivariate logistic regression estimated associations with overweight/obesity status, accounting for multiple measures on individuals and SDE.

RESULTS

The overall prevalence of overweight/obesity was 46%, of hypertension 9%, and of AN 14%. The co-occurrence of risk factors was moderate to high. PIH and AN were positively associated in unadjusted analysis. Controlling for sex, age, and SDE, higher PIH was a significant correlate of overweight/obesity, although when hypertension (OR = 5.92, CI = 3.27-10.72), prehypertension (OR = 3.80, CI = 1.99-7.26), and AN (OR = 16.20, CI = 8.08-32.48) were included in the model PIH was no longer significant. SDE was not significantly associated with overweight/obesity.

CONCLUSION

PIH appeared to be an important correlate of overweight and obesity, except when adjusted for the co-occurrence of high blood pressure and AN. Overall, the prevalence and co-occurrence of various risk factors in this population was high. Obesity prevention initiatives targeting families and communities are needed, as well as access to screening and treatment services.

Spatial distribution and self-correlation of mother and child health indicators in the state of Parana, Brazil

Aiming to analyze the spatial distribution and self-correlation of data of mother-child health in Parana, Brazil, variables were selected from the Information System on Live Births, grouped into socioeconomic indicators: teenage mother, low education, high parity, race/color black of newborn; healthcare indicators: the prenatal coverage, prematurity and cesarean delivery and result indicators: low birth weight. The indicators were distributed in thematic maps and spatial self-correlation was measured using Moran's index that quantifies the degree of self-correlation. There was significant spatial self-correlation of teenage mother, low education and high parity of the "high-high" type in the macro-regions East, Campos Gerais and South; of low coverage of antenatal care in Campos Gerais, Central-south and North and of cesarean delivery in the Northwest. Elevated proportions of indicators of risk to the health of mother and child were found in the regions East, Campos Gerais and South. These results support the evaluation and planning of health services.

Racial/ethnic residential segregation: framing the context of health risk and health disparities

An increasing body of public health literature links patterns of racial/ethnic residential segregation to health status and health disparities. Despite substantial new empirical work, meaningful understanding of the pathways through which segregation operates to influence health remains elusive. The literature on segregation and health was appraised with an emphasis on select conceptual, methodological, and analytical issues. Recommendations for advancing the next generation of racial/ethnic residential segregation and health research will require closer attention to sharpening the methodology of measuring segregation, testing mediating pathways and effect modification, incorporating stronger test of causality, exploring factors of resilience in segregated areas, applying a life-course perspective, broadening the scope of the investigation of segregation to include nativity status in blacks and other racial/ethnic groups, and linking segregation measures with biological data.

Do measures matter? Comparing surface-density-derived and census-tract-derived measures of racial residential segregation

BACKGROUND

Racial residential segregation is hypothesized to affect population health by systematically patterning health-relevant exposures and opportunities according to individuals' race or income. Growing interest into the association between residential segregation and health disparities demands more rigorous appraisal of commonly used measures of segregation. Most current studies rely on census tracts as approximations of the local residential environment when calculating segregation indices of either neighborhoods or metropolitan areas. Because census tracts are arbitrary in size and shape, reliance on this geographic scale limits understanding of place-health associations. More flexible, explicitly spatial derivations of traditional segregation indices have been proposed but have not been compared with tract-derived measures in the context of health disparities studies common to social epidemiology, health demography, or medical geography. We compared segregation measured with tract-derived as well as GIS surface-density-derived indices. Measures were compared by region and population size, and segregation measures were linked to birth record to estimate the difference in association between segregation and very preterm birth. Separate analyses focus on metropolitan segregation and on neighborhood segregation.

RESULTS

Across 231 metropolitan areas, tract-derived and surface-density-derived segregation measures are highly correlated. However overall correlation obscures important differences by region and metropolitan size. In general the discrepancy between measure types is greatest for small metropolitan areas, declining with increasing population size. Discrepancies in measures are greatest in the South, and smallest in Western metropolitan areas. Choice of segregation index changed the magnitude of the measured association between segregation and very preterm birth. For example among black women, the risk ratio for very preterm birth in metropolitan areas changed from 2.12 to 1.68 for the effect of high versus low segregation when using surface-density-derived versus tract-derived segregation indices. Variation in effect size was smaller but still present in analyses of neighborhood racial composition and very preterm birth in Atlanta neighborhoods.

CONCLUSION

Census tract-derived measures of segregation are highly correlated with recently introduced spatial segregation measures, but the residual differences among measures are not uniform for all areas. Use of surface-density-derived measures provides researchers with tools to further explore the spatial relationships between segregation and health disparities.

Racial/ethnic disparities in infant mortality

AIMS

This study examines predictors of neonatal and postneonatal mortality among infants born to black, white, and Hispanic women.

METHODS

Linked birth/infant death records from North Carolina for the period 1999-2007 were the source of data. Logistic regression models were constructed to estimate the effect of maternal and infant characteristics on neonatal (<28 days) and postneonatal (28-364 days) mortality.

RESULTS

Analyses revealed no racial/ethnic differences in neonatal mortality, but increased risk among black infants for postneonatal death. Inadequate prenatal care was associated with an increased risk for neonatal mortality across all three racial/ethnic groups. Younger maternal age and lower educational levels were associated with postneonatal deaths for black and white women, but not Hispanic. A previous child loss, being unmarried and labor/delivery complications increased the risk for postneonatal mortality only among white women. Black infants had higher rates of death attributed to sudden infant death syndrome (SIDS), infections, low birth weight (LBW)/prematurity, respiratory conditions, and injuries.

CONCLUSIONS

Early initiation of prenatal care, access to risk-appropriate obstetric and neonatal services, and participation in intervention programs that support parenting of LBW/preterm infants throughout the first year of life are likely to yield the greatest impact in reducing infant mortality.

Effects of socioeconomic and racial residential segregation on preterm birth: a cautionary tale of structural confounding

Confounding associated with social stratification or other selection processes has been called structural confounding. In the presence of structural confounding, certain covariate strata will contain only subjects who could never be exposed, a violation of the positivity or experimental treatment effect assumption. Thus, structural confounding can prohibit the exchangeability necessary for meaningful causal contrasts across levels of exposure. The authors explored the presence and magnitude of structural confounding by estimating the independent effects of neighborhood deprivation and neighborhood racial composition (segregation) on rates of preterm birth in Wake and Durham counties, North Carolina (1999-2001). Tabular analyses and random-intercept fixed-slope multilevel logistic models portrayed different structural realities in these counties. The multilevel modeling results suggested some nonsignificant effect of residence in tracts with high levels of socioeconomic deprivation or racial residential segregation on adjusted odds of preterm birth for white and black women living in these counties, and the confidence limit ratios indicated fairly consistent levels of precision around the estimates. The results of the tabular analysis, however, suggested that many of these regression modeling findings were off-support and based on no actual data. The implications for statistical and public health inference, in the presence of no data, are considered.

Is segregation bad for your health?

For decades, racial residential segregation has been observed to vary with health outcomes for African Americans, although only recently has interest increased in the public health literature. Utilizing a systematic review of the health and social science literature, the authors consider the segregation-health association through the lens of 4 questions of interest to epidemiologists: How is segregation best measured? Is the segregation-health association socially or biologically plausible? What evidence is there of segregation-health associations? Is segregation a modifiable risk factor? Thirty-nine identified studies test an association between segregation and health outcomes. The health effects of segregation are relatively consistent, but complex. Isolation segregation is associated with poor pregnancy outcomes and increased mortality for blacks, but several studies report health-protective effects of living in clustered black neighborhoods net of social and economic isolation. The majority of reviewed studies are cross-sectional and use coarse measures of segregation. Future work should extend recent developments in measuring and conceptualizing segregation in a multilevel framework, build upon the findings and challenges in the neighborhood-effects literature, and utilize longitudinal data sources to illuminate opportunities for public health action to reduce racial disparities in disease.

Invited commentary: residential segregation and health--the complexity of modeling separate social contexts

When researching racial disparities in health, residential segregation cannot be ignored. Because of segregation, contextual differences by race are so pronounced that ignoring them may lead to mis-estimating the effect of individual-level factors. However, given the stark racial separation of social contexts, researching how residential segregation and neighborhood inequality contribute to racial health disparities remains methodologically challenging. Estimating the contribution of neighborhood effects to health disparities would require overlap in the racial distributions of neighborhood environment, for example, in the distributions of neighborhood poverty. Because of segregation, though, the extent of such overlap is extremely restricted. Previous analyses of the 2000 US Census found, on average, only a 24% overlap between the distribution of neighborhood poverty for black children and that for white children in metropolitan areas. Propensity score methods may be 1 useful tool for addressing limited overlap or exchangeability. However, as shown by their application to the segregation and health relation, their use should be informed by a sound conceptualization of the scale of the social exposure of interest, the hypothesized pathways between the exposure and the health outcome, and possible unmeasured confounders.