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

Measuring Spatial Social Polarization in Public Health Research: A Scoping Review of Methods and Applications

Spatial social polarization (SSP) refers to the uneven spatial distribution and subsequent concentration of polarized social and/or economic groups in a specified geographic area. However, there is heterogeneity in how SSP is measured and operationalized in research. To this end, we conducted a scoping review to characterize the use of SSP measures in public health research, providing a foundation for those seeking to navigate this complex literature, select measurement options, and identify opportunities for methodological development. Using a structured search strategy, we searched PubMed for any primary research, published since 2007, that examined the relationship between SSP and health outcomes. Across 117 included studies, we found a body of evidence that was primarily set in the United States (n = 104), published between 2020 and 2022 (n = 52), and focused on non-communicable diseases (n = 40). We found that defining SSP in the context of privilege, deprivation, and segregation returns a variety of measures. Among measures, we categorized 18 of them as SSP measures, with the Index of Concentration at the Extremes (n = 43) being the most common, and 5 of them as composite indices based on numerous underlying variables spanning several domains like education and race/ethnicity. While most employed a single SSP measure (n = 64), some included up to 5 measures to examine the robustness of findings or to identify how a multidimensional approach to SSP affected associations. Our findings fill a critical literature gap by summarizing options for operationalizing SSP measures and documenting their respective methodologies. Future research should consider using multiple SSP measures to capture the multidimensionality of SSP, widen the scope of health outcomes, and clearly explain the choice of measure(s) and methods used to derive them. Our findings can inform future research questions and help guide researchers in the selection and utilization of the various SSP measures.

Adapting the NIMHD Research Framework for Type 2 Diabetes-Related Disparities

PURPOSE OF REVIEW

Type 2 diabetes (T2D) disproportionately impacts minority populations. The National Institute on Minority Health and Health Disparities (NIMHD) developed a research framework to encourage health disparities research that considers a multi-level, multi-domain perspective. The purpose of this review was to describe evidence on the levels and domains that influence T2D disparities among minority populations and use this information to adapt the NIMHD Research Framework for T2D.

RECENT FINDINGS

Screening identified 108 articles published between 2017 and 2023 covering 74,354,597 participants. Articles were classified under the following domains, Biological (18), Behavioral (22), Physical/Built Environment (19), Sociocultural Environment (42), and Health Care System (31). Article levels of influence included Individual (73), Interpersonal (18), Community (36), and Societal (10). Findings were used to adapt the NIMHD Research Framework with an eye towards advancing T2D-related health equity. The results of this review confirm the complex nature of T2D-related disparities and support the notion that drivers operate within and between multiple levels and multiple domains to influence T2D-related outcomes across the lifespan.

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.

Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review

BACKGROUND

Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

OBJECTIVE

This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes.

METHODS

We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains.

RESULTS

A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited.

CONCLUSIONS

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.

Piloting a Measure of Segregation at the Census Tract Level: Associations with Place and Racial/Ethnic Disparities in Life Expectancy

This study considers residential segregation as a critical driver of racial/ethnic health disparities and introduces a proxy measure of segregation that estimates the degree of segregation at the census tract level with a metric capturing the overrepresentation of a racialized/ethnic group in a census tract in relation to that group's representation at the city level. Using Dallas, Texas as a pilot city, the measure is used to investigate mean life expectancy at birth for relatively overrepresented Hispanic, non-Hispanic white, non-Hispanic Black, and Asian census tracts and examine for significant differences between mean life expectancy in relatively overrepresented census tracts and that group's mean life expectancy at the state level. Multivariable linear regression analysis was utilized to assess how segregation measured at the census tract level associates with life expectancy across different racialized/ethnic groups, controlling for socioeconomic disparities. This study aimed to expose the need to consider the possibility of neighborhood mechanisms beyond socioeconomic characteristics as an important determinant of health and draw attention to the importance of critically engaging the experience of place in examinations of racial and ethnic health disparities. Multivariable linear regression modeling resulted in significant findings for non-Hispanic Black, non-Hispanic white, and Asian groups, indicating increased census tract-level life expectancy for Black and white residents in highly segregated census tracts and decreased life expectancy for residents of tracts in which the Asian community is overrepresented when compared to state means. Unadjusted models demonstrated socioeconomic inequities between first and fourth quartile census tracts and pointed to the importance of mixed methods in health disparities research and the importance of including the voice of community members to account for places of daily lived experience and people's relationships with them.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Segregation and Childhood Blood Lead Levels in North Carolina

BACKGROUND AND OBJECTIVES

Using a local measure of racial residential segregation, estimate the association between racial residential segregation and childhood blood lead levels between the early 1990s and 2015 in North Carolina.

METHODS

This population-based observational study uses individual-level blood lead testing records obtained from the NC Department of Health and Human Services for 320 916 children aged <7 years who were tested between 1992 and 1996 or 2013 and 2015. NC childhood blood lead levels were georeferenced to the census tract. Neighborhood racial residential segregation, assessed using a local, spatial measure of the racial isolation of non-Hispanic Blacks (RINHB), was calculated at the census tract level.

RESULTS

From 1990 to 2015, RINHB increased in 50% of 2195 NC census tracts, although the degree of change varied by geographic region. In 1992 to 1996 blood lead testing data, a 1-standard-deviation increase in tract-level RINHB was associated with a 2.86% (95% confidence interval: 0.96%-4.81%) and 2.44% (1.34%-3.56%) increase in BLL among non-Hispanic Black and non-Hispanic White children, respectively. In 2013 to 2015 blood lead testing data, this association was attenuated but persisted with a 1-standard-deviation increase in tract-level RINHB associated with a 1.59% (0.50%-2.70%) and 0.76% (0.08%-1.45%) increase in BLL among non-Hispanic Black and non-Hispanic White children, respectively. In the supplemental information, we show the change in racial residential segregation across the entire United States, demonstrating that RINHB increased in 69% of 72 899 US census tracts.

CONCLUSIONS

Racially isolated neighborhoods are associated with higher childhood lead levels, demonstrating the disproportionate environmental burdens borne by segregated communities and warranting attention to providing whole child health care.

The Impact of Neighborhoods on Diabetes Risk and Outcomes: Centering Health Equity

Neighborhood environments significantly influence the development of diabetes risk factors, morbidity, and mortality throughout an individual's life. The social, economic, and physical environments of a neighborhood all affect the health risks of individuals and communities and also affect population health inequities. Factors such as access to healthy food, green spaces, safe housing, and transportation options can impact the health outcomes of residents. Social factors, including social cohesion and neighborhood safety, also play an important role in shaping neighborhood environments and can influence the development of diabetes. Therefore, understanding the complex relationships between neighborhood environments and diabetes is crucial for developing effective strategies to address health disparities and promote health equity. This review presents landmark findings from studies that examined associations between neighborhood socioeconomic, built and physical, and social environmental factors and diabetes-related risk and outcomes. Our framework emphasizes the historical context and structural and institutional racism as the key drivers of neighborhood environments that ultimately shape diabetes risk and outcomes. To address health inequities in diabetes, we propose future research areas that incorporate health equity principles and place-based interventions.

Racial Residential Segregation and the Health of Black Youth With Type 1 Diabetes

OBJECTIVES

Black youth with type 1 diabetes (T1D) are at heightened risk for suboptimal glycemic control. Studies of neighborhood effects on the health of youth with T1D are limited. The current study investigated the effects of racial residential segregation on the diabetes health of young Black adolescents with T1D.

METHODS

A total of 148 participants were recruited from 7 pediatric diabetes clinics in 2 US cities. Racial residential segregation (RRS) was calculated at the census block group level based on US Census data. Diabetes management was measured via self-report questionnaire. Hemoglobin A1c (HbA1c) information was gathered from participants during home-based data collection. Hierarchical linear regression was used to test the effects of RRS while controlling for family income, youth age, insulin delivery method (insulin pump versus syringe therapy), and neighborhood adversity.

RESULTS

HbA1c was significantly associated with RRS in bivariate analyses, whereas youth-reported diabetes management was not. In hierarchical regression analyses, whereas family income, age, and insulin delivery method were all significantly associated with HbA1c in model 1, only RRS, age, and insulin delivery method were significantly associated with HbA1c in model 2. Model 2 explained 25% of the variance in HbA1c (P = .001).

CONCLUSIONS

RRS was associated with glycemic control in a sample of Black youth with T1D and accounted for variance in HbA1c even after controlling for adverse neighborhood conditions. Policies to reduce residential segregation, along with improved screening for neighborhood-level risk, hold the potential to improve the health of a vulnerable population of youth.

Assessing community-level exposure to social vulnerability and isolation: spatial patterning and urban-rural differences

BACKGROUND

Environmental health disparity research involves the use of metrics to assess exposure to community-level vulnerabilities or inequities. While numerous vulnerability indices have been developed, there is no agreement on standardization or appropriate use, they have largely been applied in urban areas, and their interpretation and utility likely vary across different geographies.

OBJECTIVE

We evaluated the spatial distribution, variability, and relationships among different metrics of social vulnerability and isolation across urban and rural settings to inform interpretation and selection of metrics for environmental disparity research.

METHODS

For all census tracts in North Carolina, we conducted a principal components analysis using 23 socioeconomic/demographic variables from the 2010 United States Census and American Community Survey. We calculated or obtained the neighborhood deprivation index (NDI), residential racial isolation index (RI), educational isolation index (EI), Gini coefficient, and social vulnerability index (SVI). Statistical analyses included Moran's I for spatial clustering, t-tests for urban-rural differences, Pearson correlation coefficients, and changes in ranking of tracts across metrics.

RESULTS

Social vulnerability metrics exhibited clear spatial patterning (Moran's I ≥ 0.30, p < 0.01). Greater educational isolation and more intense neighborhood deprivation was observed in rural areas and greater racial isolation in urban areas. Single-domain metrics were not highly correlated with each other (rho ≤ 0.36), while composite metrics (i.e., NDI, SVI, principal components analysis) were highly correlated (rho > 0.80). Composite metrics were more highly correlated with the racial isolation metric in urban (rho: 0.54-0.64) versus rural tracts (rho: 0.36-0.48). Census tract rankings changed considerably based on which metric was being applied.

SIGNIFICANCE

High correlations between composite metrics within urban and rural tracts suggests they could be used interchangeably; single domain metrics cannot. Composite metrics capture different facets of vulnerabilities in urban and rural settings, and these complexities should be examined by researchers applying metrics to areas of diverse urban and rural forms.

Social Determinants of Cardiovascular Health: A Longitudinal Analysis of Cardiovascular Disease Mortality in US Counties From 2009 to 2018

Background Disparities in cardiovascular disease (CVD) outcomes persist across the United States. Social determinants of health play an important role in driving these disparities. The current study aims to identify the most important social determinants associated with CVD mortality over time in US counties. Methods and Results The authors used the Agency for Healthcare Research and Quality's database on social determinants of health and linked it with CVD mortality data at the county level from 2009 to 2018. The age-standardized CVD mortality rate was measured as the number of deaths per 100 000 people. Penalized generalized estimating equations were used to select social determinants associated with county-level CVD mortality. The analytic sample included 3142 counties. The penalized generalized estimating equation identified 17 key social determinants of health including rural-urban status, county's racial composition, income, food, and housing status. Over the 10-year period, CVD mortality declined at an annual rate of 1.08 (95% CI, 0.74-1.42) deaths per 100 000 people. Rural counties and counties with a higher percentage of Black residents had a consistently higher CVD mortality rate than urban counties and counties with a lower percentage of Black residents. The rural-urban CVD mortality gap did not change significantly over the past decade, whereas the association between the percentage of Black residents and CVD mortality showed a significant diminishing trend over time. Conclusions County-level CVD mortality declined from 2009 through 2018. However, rural counties and counties with a higher percentage of Black residents continued to experience higher CVD mortality. Median income, food, and housing status consistently predicted higher CVD mortality.

Association Between Social Determinants of Health and Glycemic Control Among African American People with Type 2 diabetes: The Jackson Heart Study

BACKGROUND

Social determinants of health have a significant impact on health outcomes. However, the complexity and interaction of multiple factors influencing glycemic control remain understudied.

PURPOSE

This study examined associations of socioeconomic position (income, education, and occupation), environmental (physical activity facilities, neighborhood social cohesion, neighborhood problem, and violence), behavioral (physical activity, nutrition, and smoking), and psychological factors (depressive symptoms, stress, and discrimination) with glycemic control (hemoglobin A1c [A1c]) using the World Health Organization Social Determinants of Health framework in African American adults with type 2 diabetes.

METHODS

A secondary data analysis was conducted using a longitudinal cohort of 1,240 African American adults with type 2 diabetes who participated in the community-based Jackson Heart Study. Socioeconomic position, environmental, behavioral, and psychological factors were measured using validated instruments in the Jackson Heart Study. Longitudinal structural equation modeling was used with glycemic control (A1c) collected over time (Exams 1-3) as the study outcome.

RESULTS

Our study presents the complex interplay of socioeconomic determinants of health and glycemic control over time. Higher socioeconomic position (higher income, higher level of education, and professional occupation) was directly associated with improvement in glycemic control over time. An association of socioeconomic position on glycemic control mediated through health behavior factors was also observed.

CONCLUSIONS

In this analysis, socioeconomic position components were determinants of glycemic control in African American adults with type 2 diabetes. Future studies aimed at reducing health disparities and achieving equality of outcomes in this population will benefit from embedding socioeconomic position components into their design.

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.

Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years

In the United States, concentrations of criteria air pollutants have declined in recent decades. Questions remain regarding whether improvements in air quality are equitably distributed across subpopulations. We assessed spatial variability and temporal trends in concentrations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and ozone (O3) across North Carolina from 2002-2016, and associations with community characteristics. Estimated daily PM2.5 and O3 concentrations at 2010 Census tracts were obtained from the Fused Air Quality Surface Using Downscaling archive and averaged to create tract-level annual PM2.5 and O3 estimates. We calculated tract-level measures of: racial isolation of non-Hispanic Black individuals, educational isolation of non-college educated individuals, the neighborhood deprivation index (NDI), and percentage of the population in urban areas. We fitted hierarchical Bayesian space-time models to estimate baseline concentrations of and time trends in PM2.5 and O3 for each tract, accounting for spatial between-tract correlation. Concentrations of PM2.5 and O3 declined by 6.4 μg/m3 and 13.5 ppb, respectively. Tracts with lower educational isolation and higher urbanicity had higher PM2.5 and more pronounced declines in PM2.5. Racial isolation was associated with higher PM2.5 but not with the rate of decline in PM2.5. Despite declines in pollutant concentrations, over time, disparities in exposure increased for racially and educationally isolated communities.

Relationship between Residential Segregation, Later-Life Cognition, and Incident Dementia across Race/Ethnicity

Systemic racism leads to racial/ethnic residential segregation, which can result in health inequities. We examined if the associations between residential segregation and later-life cognition and dementia differed based on segregation measure and by participant race/ethnicity. Tests of memory (n = 4616), language (n = 4333), visuospatial abilities (n = 4557), and incident dementia (n = 4556) were analyzed in older residents of Northern Manhattan, New York (mean age: 75.7 years). Segregation was measured at the block group-level using three indices: dissimilarity, isolation, and interaction. We fit multilevel linear or Cox proportional hazards models and included a race/ethnicity × segregation term to test for differential associations, adjusting for socioeconomic and health factors. Living in block groups with higher proportions of minoritized people was associated with -0.05 SD lower language scores. Living in block groups with higher potential contact between racial/ethnic groups was associated with 0.06-0.1 SD higher language scores. The findings were less pronounced for other cognitive domains and for incident dementia. Non-Hispanic Black adults were most likely to experience negative effects of neighborhood segregation on cognition (language and memory) and dementia. All indices partly capture downstream effects of structural racism (i.e., unequal distributions of wealth/resources) on cognition. Therefore, desegregation and equitable access to resources have the potential to improve later-life cognitive health.

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.

Characterizing the performance of emergency medical transport time metrics in a residentially segregated community

OBJECTIVE

To derive and characterize the performance of various metrics of emergency transport time in assessing for sociodemographic disparities in the setting of residential segregation. Secondarily to characterize racial disparities in emergency transport time of suspected stroke patients in Austin, Texas.

DATA SOURCES

We used a novel dataset of 2518 unique entries with detailed spatial and temporal information on all suspected stroke transports conducted by a public emergency medical service in Central Texas between 2010 and 2018.

STUDY DESIGN

We conducted one-way ANOVA tests with post-hoc pairwise t-tests to assess how mean hospital transport times varied by patient race. We also developed a spatially-independent metric of emergency transport urgency, the ratio of expected duration of self-transport to a hospital and the measured transport time by an ambulance.

DATA COLLECTION/EXTRACTION

We calculated ambulance arrival and destination times using sequential temporospatial coordinates. We excluded any entries in which patient race was not recorded. We also excluded entries in which ambulances' routes did not pass within 100 m of either the patient's location or the documented hospital destination.

PRINCIPAL FINDINGS

We found that mean transport time to a hospital was 2.5 min shorter for black patients compared to white patients. However, white patients' transport times to a hospital were found to be, on average, 4.1 min shorter than expected compared to 3.4 min shorter than expected for black patients. One-way ANOVA testing for the spatially-independent index of emergency transport urgency was not statistically significant, indicating that average transport time did not vary significantly across racial groups when accounting for variations in transport distance.

CONCLUSIONS

Using a novel transport urgency index, we demonstrate that these findings represent race-based variation in spatial distributions rather than racial bias in emergency medical transport. These results highlight the importance of closely examining spatial distributions when utilizing temporospatial data to investigate geographically-dependent research questions.

Social vulnerability associated with the self-reported diagnosis of type II diabetes: a multilevel analysis

OBJECTIVE

To analyze the contextual factors associated with type II diabetes mellitus in Belo Horizonte City.

METHODS

Cross-sectional study with 5,779 adults living in Belo Horizonte City, participating in the Risk and Protection Factors Surveillance System for Chronic Diseases through Telephone Survey (Vigitel), in 2008, 2009, and 2010. Multilevel regression models were used to test the association between contextual indicators of physical and social environments, and self-reported diagnosis of diabetes, adjusted for individual sociodemographic and lifestyle factors. Descriptive analyzes and multilevel logistic regression models were used, considering a 5% significance level.

RESULTS

The prevalence of diabetes was 6.2% (95%CI 5.54 - 6.92), and 3.1% of the variability of chance of presenting diabetes were explained by contextual characteristics. Living in areas with high density of private places for physical activity and high income was associated with a lower chance of having diabetes. The areas with high level of social vulnerability were strongly associated with the chance of presenting diabetes, adjusted for individual characteristics.

CONCLUSION

Characteristics of physical and social environments were associated with the chance of diabetes occurrence. Urban centers with opportunities to adopt healthy behaviors can help to reduce the occurrence of diabetes and its complications.

Self-efficacy, social distancing, and essential worker status dynamics among SGM people

As of October 2020, the COVID-19 pandemic has accounted for over 210,000 deaths in the U.S. Sexual and gender minority populations are more likely to work in essential industries while bearing a disproportionate burden of the virus. Constructs consistent with Protection Motivation Theory (perceived severity, vulnerability, self-efficacy, and response efficacy) were measured using an abridged version of Kleczkowski et al.'s 4-factor Protection Motivation Theory Psychological Measures to examine social distancing behaviors of these populations. 32.6% of the sample were essential workers. Greater self-efficacy predicted stricter social distancing behaviors. Non-essential and unemployed worker statuses were associated with increased odds of stricter social distancing behaviors relative to essential worker status. Essential worker status predicted lower self-efficacy. The indirect effect of essential worker status on social distancing through self-efficacy was significant. Findings suggest that interventions that encourage social distancing through enhanced self-efficacy may optimize health for sexual and gender minority essential workers.

Neighborhood social environments, healthy resources, and adult diabetes: Accounting for activity space exposures

This study broadens contextual environments to include adults' activity spaces-inside and outside the residential neighborhood-to examine how contextual exposures shape type 2 diabetes risk. We use novel longitudinal data from the Los Angeles Family and Neighborhood Survey, construct time-weighted exposure measures of adults' social-structural and healthy resource environments, and execute random effects logistic models predicting the probability of being diabetic. Results indicate that residential and activity space exposures are independently associated with adult diabetes, and that residential and activity space healthy resources combine to influence diabetes risk in synergistic ways. Living in more socioeconomically advantaged neighborhoods reduces diabetes risk, particularly when spending time in activity spaces with greater access to recreational facilities. Moreover, healthier activity space environments may compensate for living in neighborhoods devoid of healthy food options to lessen diabetes risk. Adopting an activity space framework can inform multilevel interventions aimed at alleviating type 2 diabetes and other chronic ailments.

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.

Inequalities and identity processes in crises: Recommendations for facilitating safe response to the COVID-19 pandemic

Structural inequalities and identity processes are pivotal to understanding public response to COVID-19. We discuss how identity processes can be used to promote community-level support, safe normative behaviour, and increase compliance with guidance. However, we caution how government failure to account for structural inequalities can alienate vulnerable groups, inhibit groups from being able to follow guidance, and lead to the creation of new groups in response to illegitimate treatment. Moreover, we look ahead to the longitudinal impacts of inequalities during pandemics and advise government bodies should address identity-based inequalities to mitigate negative relations with the public and subsequent collective protest.

Racial/ethnic segregation and health disparities: Future directions and opportunities

Health researchers have investigated the association between racial segregation and racial health disparities with multilevel approaches. This study systematically reviews these multilevel studies and identifies broad trends and potential directions for future research on racial segregation and health disparities in the US. After searching databases including CINAHL and MEDLINE, we identified and systematically reviewed 66 articles published between 2003 and 2019 and found four major gaps in racial/ethnic segregation and health disparities: (a) the concept of segregation was rarely operationalized at the neighborhood level, (b) except for the evenness and exposure dimension, other dimensions of segregation are overlooked, (c) little attention was paid to the segregation between whites and non-black minorities, particularly Hispanics and Asians, and (d) mental health outcomes were largely absent. Future directions and opportunities include: First, other segregation dimensions should be explored. Second, the spatial scales for segregation measures should be clarified. Third, the theoretical frameworks for black and non-black minorities should be tested. Fourth, mental health, substance use, and the use of mental health care should be examined. Fifth, the long-term health effect of segregation has to be investigated, and finally, other competing explanations for why segregation matters at the neighborhood level should be answered.

Racial residential segregation, racial discrimination, and diabetes: The Coronary Artery Risk Development in Young Adults study

Although racial residential segregation and interpersonal racial discrimination are associated with cardiovascular disease, few studies have examined their link with diabetes risk or management. We used longitudinal data from 2,175 black participants in the Coronary Artery Risk Development in Young Adults (CARDIA) Study to examine associations of racial residential segregation (Gi* statistic) and experiences of racial discrimination with diabetes incidence and management. Multivariable Cox models estimated associations for incident diabetes and GEE logistic regression estimated associations with diabetes management (meeting targets for HbA1c, systolic blood pressure, and LDL cholesterol). Neither segregation nor discrimination were associated with diabetes incidence or management.

A Bayesian spatio-temporal analysis on racial disparities in hypertensive disorders of pregnancy in Florida, 2005-2014

Disparities in hypertensive disorders of pregnancy (HDP) exist among racial and ethnic groups in the US. However, little is known about spatio-temporal variations in HDP disparities. We used a Bayesian hierarchical regression approach to investigate spatio-temporal variations in HDP disparities from 2005 to 2014. County-level variation was firstly examined, followed by census tract-level variation assessment in counties where high HDP disparities were observed. A significant disadvantage in HDP was revealed for African Americans in Florida overall (Odds Ratio: 1.27, 95% Confidence Interval: 1.25, 1.29), with significant spatial variations. The greatest HDP disparities between African Americans and non-African Americans occurred in North Central Florida counties (the Big Bend region of Florida), with consistent patterns from 2005 to 2014. Analyses at census tract-level further revealed significant neighborhood disparities within these counties. Findings from this study provide important information for public health agencies and policymakers to reduce HDP disparities at the population level.

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.