Percentage Of Black Residents Research Articles

Examining the relationship between social determinants of health, measures of structural racism and county-level overdose deaths from 2017-2020.

Despite being an important determinant of health outcomes, measures of structural racism are lacking in studies examining the relationship between the social determinants of health (SDOH) and overdose deaths. The aim of this study is to examine the association between per capita revenue generated from fines and forfeitures, a novel measure of structural racism, and other SDOH with county-level overdose deaths from 2017-2020. This longitudinal analysis of 2,846 counties from 2017-2020 used bivariate and multivariate Generalized Estimating Equations models to estimate associations between county overdose mortality rates and SDOH characteristics, including the fines and forfeitures measure. In our multivariate model, higher per capita fine and forfeiture revenue (5.76; CI: 4.76, 6.78), households receiving food stamps (1.15; CI: 0.77, 1.53), residents that are veterans (1.07; CI: 0.52, 1.63), substance use treatment availability (4.69; CI: 3.03, 6.33) and lower population density (-0.002; CI: -0.004, -0.001) and percent of Black residents (-0.7`; CI: -1.01, -0.42) were significantly associated with higher overdose death rates. There was a significant additive interaction between the fines and forfeitures measure (0.10; CI: 0.03, 0.17) and the percent of Black residents. Our findings suggest that structural racism, along with other SDOH, is associated with overdose deaths. Future research should focus on connecting individual-level data on fines and forfeitures to overdose deaths and other health outcomes, include measures of justice-related fines, such as court fees, and assess whether interventions aimed at increasing economic vitality in disadvantaged communities impact overdose deaths in a meaningful way.

Intersections of oppression: Examining the interactive effect of racial discrimination and neighborhood poverty on PTSD symptoms in Black women.

Black Americans living in urban environments are disproportionately impacted by posttraumatic stress disorder (PTSD). Both racial discrimination and neighborhood poverty are factors that contribute to this health disparity. However, studies focused on the intersection of these two oppressive systems on PTSD symptoms are lacking. To address this gap in the literature, we assessed the interactive effects of racial discrimination and neighborhood poverty on PTSD symptoms in an urban sample of trauma-exposed Black women (N = 300). Simple moderation analysis was used to assess the main and interactive effects of racial discrimination and neighborhood poverty on PTSD symptoms. The overall model significantly predicted PTSD symptoms, with a main effect of racial discrimination (B = 1.87, p = .009) and neighborhood poverty rate (B = 0.29, p = .008), independent of prior trauma exposure and percentage of Black residents in the zip code. More frequent experiences of racial discrimination and higher rates of neighborhood poverty both predicted higher PTSD symptoms. There was also a trending interaction of racial discrimination and neighborhood poverty (B = -0.05, p = .054), where the effect of neighborhood poverty on PTSD symptoms was only present for those who reported fewer experiences of racial discrimination. Our results suggest that people who have experienced more instances of racial discrimination show high levels of PTSD symptoms regardless of neighborhood poverty rates and highlight the importance of considering multiple levels of oppression that Black individuals face while diagnosing and treating stress-related psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding Food Access in Flint: An Analysis of Racial and Socioeconomic Disparities

The primary objective of this study was to describe the food landscape of Flint, Michigan, and the surrounding townships. We investigated the relationship between the location of food outlets and the racial composition of census tracts. We collected data from multiple sources; however, Data Axle, a repository of information on the U.S. and Canadian businesses, was our primary data source. Data were collected and verified between September 2020 and December 2021. The final fact-checking was completed in June 2022. We used ArcGIS 10.8.1 and SPSS Version 28 to map and analyze the data. We conducted negative binomial regression analyses to identify the difference in the likelihood of finding food retailers in census tracts where the percentage of Black residents was low and those where the percentage of Blacks was high. The article examines 1,137 food retailers in the study area: 407 were in Flint, and the remainder in the surrounding townships. Restaurants—especially fast food and take-out establishments—dominated the food environment. In addition, small groceries and convenience stores proliferated in the grocery store category. The racial composition of the census tracts mattered. Census tracts in which more than 40% of the residents are Black have a mean of 7.6 food outlets. In comparison, census tracts in which 40% or less of the residents are Black have a mean of 11.3 food outlets; the difference is significant. Census tracts with a high percentage of Blacks also had significantly fewer restaurants. The results of this study show Flint’s food landscape to be more complex and robust than described in earlier studies. It underscores the point that researchers should not rely solely on documenting the presence of supermarkets or traditional grocery stores when addressing food insecurity and food access. In the case of Flint, such food outlets comprise only 2.2% of the food landscape. Focusing exclusively on these food retailers misses several important types of food venues that residents rely on to secure food. This siloed approach also ignores the resilience and ingenuity of residents to respond to limited access to traditional food retailers.

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021.

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Race, poverty and unemployment as quantitative predictors of voter turnout in St. Louis amidst COVID-19 and a racial justice movement

PurposeThe 2020 election season brought with it a global public health pandemic and a reenergized racial justice movement. Given the social context of the intertwined pandemics of COVID-19 and racialized violence, do the traditional predictors of voter turnout – race, poverty rates and unemployment rates – remain significant?Design/methodology/approachUsing county-level, publicly available data from twelve Midwest states with similar demographic and cultural characteristics, voter turnout in St. Louis City and St. Louis County were predicted using race, poverty rates and unemployment rates.FindingsFindings demonstrate that despite high concentration of poverty rates and above average percentages of Black residents, voter turnout was significantly higher than predicted. Additionally, findings contradict previous studies that found higher unemployment rates resulted in higher voter participation rates.Originality/valueThis study suggests that the threat of COVID-19 and fear of an increase in police violence may have introduced physical risk as a new theoretical component to rational choice theory for the general election in 2020.

Are Neighborhood Characteristics Associated With Outcomes After THA and TKA? Findings From a Large Healthcare System Database.

Are Neighborhood Characteristics Associated With Outcomes After THA and TKA? Findings From a Large Healthcare System Database.

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study.

Higher-than-expected heart failure (HF) readmissions affect half of US hospitals every year. The Hospital Reduction Readmission Program has reduced risk-adjusted readmissions, but it has also produced unintended consequences. Shared care models have been advocated for HF care, but the association of shared care networks with HF readmissions has never been investigated. This study aims to evaluate the association of shared care networks with 30-day HF excessive readmission rates using a longitudinal observational study. We curated publicly available data on hospital discharges and HF excessive readmission ratios from hospitals in California between 2012 and 2017. Shared care areas were delineated as data-driven units of care coordination emerging from discharge networks. The localization index, the proportion of patients who reside in the same shared care area in which they are admitted, was calculated by year. Generalized estimating equations were used to evaluate the association between the localization index and the excessive readmission ratio of hospitals controlling for race/ethnicity and socioeconomic factors. A total of 300 hospitals in California in a 6-year period were included. The HF excessive readmission ratio was negatively associated with the adjusted localization index (β=-.0474, 95% CI -0.082 to -0.013). The percentage of Black residents within the shared care areas was the only statistically significant covariate (β=.4128, 95% CI 0.302 to 0.524). Higher-than-expected HF readmissions were associated with shared care networks. Control mechanisms such as the Hospital Reduction Readmission Program may need to characterize and reward shared care to guide hospitals toward a more organized HF care system.

Racial Disparities in Use of Memory Care and Prevalence of Duals in Assisted Living

Assisted living (AL) communities with memory care licenses are disproportionately located in affluent and predominantly White communities and Black older adults are underrepresented in AL. But little is known about characteristics of AL that care for Black residents. We estimated the association of facility-level characteristics as proxy measures for AL resources, such as memory care designations and percentage of dual-eligible residents, across low (0-5%), medium (5-10%) and high (>10%) percentages of Black residents. We found broad differences among communities in the three levels of Black-resident prevalence. High percentage of Black residents was associated with large differences in the percentage of Medicaid-enrolled residents (high 54% duals [s.d.=34], med 28% [31], low=13% [22], p<0.001). ALs with high Black populations were less likely to have a memory-care designation than ALs with medium and low percentages of Black residents (high 4.7% memory care, med 11%, low 17%).

Policing the pandemic: estimating spatial and racialized inequities in New York City police enforcement of COVID-19 mandates

ABSTRACT The use of policing to enforce public health guidelines has historically produced harmful consequences, and early evidence from the police enforcement of COVID-19 mandates suggested Black New Yorkers were disproportionately represented in arrests. The over-policing of Black and low-income neighborhoods during a pandemic risks increased transmission, potentially exacerbating existing health inequities. To assess racialized and class-based inequities in the enforcement of COVID-19 mandates at the ZIP-code-level, we conducted a retrospective spatial analysis of demographic factors and public health policing in New York City from 12 March–24 May 2020. Policing outcomes (COVID-19 criminal court summonses and public health and nuisance arrests) were measured using publicly available police administrative data. After controlling for two measures of social distancing compliance, a standard deviation increase in percentage of Black residents was associated with a 73% increase (95% CI: 35%, 123%) in the COVID-19-specific summons rate and a 34% increase (95% CI: 17%, 53%) in the public health and nuisance arrest rate. Percentage of Black residents and historical stop-and-frisk rates had stronger associations with COVID-19 summons rates than multiple measures of social distancing compliance. Findings demonstrate pronounced spatial and racialized inequities in pandemic policing of public health that mimic historical policing practices deemed racially discriminatory. If the field of public health supports criminalization and punishment as public health strategies, it risks reinscribing racialized health inequities.

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts

BackgroundAssociations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020.MethodsUsing publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time.ResultsAssociations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12–1.13]) in early spring, IRR = 1.01 [95%CI: 1.00–1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26–1.31] in spring, IRR = 1.07 [95%CI: 1.05–1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27–1.33] in spring, IRR = 1.20 [95%CI: 1.17–1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18–1.21] in spring, IRR = 1.14 [95%CI: 1.13–1.15] in fall).ConclusionsTown-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

Promoting Diversity, Equity, and Inclusion in the Selection of Obstetrician-Gynecologists.

In the setting of long-standing structural racism in health care, it is imperative to highlight inequities in the medical school-to-residency transition. In obstetrics and gynecology, the percentage of Black residents has decreased in the past decade. The etiology for this troubling decrease is unknown, but racial and ethnic biases inherent in key residency application metrics are finally being recognized, while the use of these metrics to filter applicants is increasing. Now is the time for action and for transformational change to rectify the factors that are detrimentally affecting the racial diversity of our residents. This will benefit our patients and learners with equitable health care and better outcomes.

Transit-Induced Gentrification or Vice Versa?

Problem, research strategy, and findings In the 1990s, some cities across the United States invested significantly in expanding light rail transit (LRT) systems. As a result, transit-oriented development (TOD) near new stations became a desirable planning goal for local governments and lucrative to developers. In this study we examined evidence of gentrification in all LRT station areas before and after the opening of these new stations. We also tested the prevalence of gentrification in station areas specifically characterized as TODs. Longitudinal data across seven regions, from 1970 to 2010, demonstrated signs of gentrification in proximity to new stations compared with control areas. By 2000, the share of the White population grew near LRT stations, whereas the percentage of Black residents remained flat. From 1990 to 2010, we found signs of gentrification based on both demographic and economic indicators, including low-income populations. We also tested the relationship among built environment variables and a gentrification index, which revealed a positive correlation with walkability, density, and diversity variables. Our study did not include data to compare the differences in effectiveness between market-rate versus affordable housing, which should be part of future research. Takeaway for practice The results of this study should aid planners and policymakers in understanding the patterns of gentrification before and after the opening of LRT stations. Planners should be concerned with negative effects on Black and low-income residents well before LRT stations open. Our study provides evidence for the need to expand housing and job opportunities in a mixed-use environment as a critical strategy to reduce gentrification near LRT stations and in TODs.

Associations of Race/Ethnicity and Food Insecurity With COVID-19 Infection Rates Across US Counties

Food insecurity is prevalent among racial/ethnic minority populations in the US. To date, few studies have examined the association between pre-COVID-19 experiences of food insecurity and COVID-19 infection rates through a race/ethnicity lens. To examine the associations of race/ethnicity and past experiences of food insecurity with COVID-19 infection rates and the interactions of race/ethnicity and food insecurity, while controlling for demographic, socioeconomic, risk exposure, and geographic confounders. This cross-sectional study examined the associations of race/ethnicity and food insecurity with cumulative COVID-19 infection rates in 3133 US counties, as of July 21 and December 14, 2020. Data were analyzed from November 2020 through March 2021. Racial/ethnic minority groups who experienced food insecurity. The dependent variable was COVID-19 infections per 1000 residents. The independent variables of interest were race/ethnicity, food insecurity, and their interactions. Among 3133 US counties, the mean (SD) racial/ethnic composition was 9.0% (14.3%) Black residents, 9.6% (13.8%) Hispanic residents, 2.3% (7.3%) American Indian or Alaska Native residents, 1.7% (3.2%) Asian American or Pacific Islander residents, and 76.1% (20.1%) White residents. The mean (SD) proportion of women was 49.9% (2.3%), and the mean (SD) proportion of individuals aged 65 years or older was 19.3% (4.7%). In these counties, large Black and Hispanic populations were associated with increased COVID-19 infection rates in July 2020. An increase of 1 SD in the percentage of Black and Hispanic residents in a county was associated with an increase in infection rates per 1000 residents of 2.99 (95% CI, 2.04 to 3.94; P < .001) and 2.91 (95% CI, 0.39 to 5.43; P = .02), respectively. By December, a large Black population was no longer associated with increased COVID-19 infection rates. However, a 1-SD increase in the percentage of Black residents in counties with high prevalence of food insecurity was associated with an increase in infections per 1000 residents of 0.90 (95% CI, 0.33 to 1.47; P = .003). Similarly, a 1-SD increase in the percentage of American Indian or Alaska Native residents in counties with high levels of food insecurity was associated with an increase in COVID-19 infections per 1000 residents of 0.57 (95% CI, 0.06 to 1.08; P = .03). By contrast, a 1-SD increase in Hispanic populations in a county remained independently associated with a 5.64 (95% CI, 3.54 to 7.75; P < .001) increase in infection rates per 1000 residents in December 2020 vs 2.91 in July 2020. Furthermore, while a 1-SD increase in the proportion of Asian American or Pacific Islander residents was associated with a decrease in infection rates per 1000 residents of -1.39 (95% CI, -2.29 to 0.49; P = .003), the interaction with food insecurity revealed a similar association (interaction coefficient, -1.48; 95% CI, -2.26 to -0.70; P < .001). This study sheds light on the association of race/ethnicity and past experiences of food insecurity with COVID-19 infection rates in the United States. These findings suggest that the channels through which various racial/ethnic minority population concentrations were associated with COVID-19 infection rates were markedly different during the pandemic.

Race, explicit racial attitudes, implicit racial attitudes, and COVID-19 cases and deaths: An analysis of counties in the United States.

ObjectivesTo examine the potential moderating effects of explicit racial attitudes and implicit racial attitudes on the relationship between percent of Black county residents and COVID-19 cases and deaths.MethodsWe collected data from a variety of publicly available sources for 817 counties in the US. (26% of all counties). Cumulative COVID-19 deaths and cases from January 22 to August 31, 2020 were the dependent variables; explicit racial attitudes and implicit racial attitudes served as the moderators; subjective poor or fair health, food insecurity, percent uninsured, percent unemployed, median family income, percent women, percent of Asian county resident, percent of Hispanic county residents, and percent of people 65 or older were controls.ResultsThe percent of Black county residents was positively associated with COVID-19 cases and deaths at the county level. The relationship between percent of Black residents and COVID-19 cases was moderated by explicit racial attitudes and implicit racial attitudes.ConclusionsImplicit racial attitudes can take on a shared property at the community level and effectively explain racial disparities. COVID-19 cases are highest when both the percent of Black county residents and implicit racial attitudes are high.

Regional variation in COVID-19 disparities: connections with immigrant and Latinx communities in U.S. counties

PurposeTo evaluate associations between counties’ COVID-19 cases and racial-ethnic and nativity composition, considering heterogeneity across Latin American-origin subgroups and regions of the United States. MethodsUsing county-level data and multilevel negative binomial models, we evaluate associations between COVID-19 cases and percentages of residents that are foreign-born, Latinx, Black, or Asian, presenting estimates for all counties combined and stratifying across regions. Given varying risk factors among Latinx, we also evaluate associations for percentages of residents from specific Latin American-origin groups. ResultsPercentage of foreign-born residents is positively associated with COVID-19 case rate (IRR = 1.106; 95% CI: 1.074–1.139). Adjusted associations for percentage Latinx are nonsignificant for all counties combined, but this obscures heterogeneity. Counties with more Central Americans have higher case rates (IRR = 1.130; 95% CI: 1.067–1.197). And, in the Northeast and Midwest, counties with more Puerto Ricans have higher case rates. Associations with percentage Asians are nonsignificant after adjusting for percentage foreign-born. With the confirmation of prior evidence, the percentage of Black residents is positively and robustly associated with COVID-19 case rate (IRR = 1.031; 95% CI: 1.025–1.036). ConclusionsCounties with more immigrants, as well as more Central American or Black residents, have more COVID-19 cases. In the Northeast and Midwest, counties with more Puerto Rican residents also have more COVID-19 cases.

The Sexual and Reproductive Health Burden Index: Development, Validity, and Community-Level Analyses of a Composite Spatial Measure.

The Sexual and Reproductive Health Burden Index (SRHBI) was developed to provide a composite spatial measure of sexual and reproductive health (SRH) indicators that can be widely adopted by urban public health departments for the planning of SRH services. The index was constructed using eight indicators: teen births, low birthweight, infant mortality, new HIV diagnoses, people living with HIV, and incidences of gonorrhea, chlamydia, and syphilis. Chicago Department of Public Health data (2014-2017) were used to calculate index scores for Chicago community areas; scores were mapped to provide geovisualization and global Moran's I was calculated to assess spatial autocorrelation. Cronbach's alpha was calculated to assess internal reliability of the SRHBI. Pearson correlations were conducted to assess concurrent validity and correlation with community-level factors. Linear regression was conducted to assess community-level predictors of the SRHBI. Application of the SRHBI in Chicago demonstrates substantial variation in SRH burden across Chicago's urban landscape with significant spatial autocorrelation of scores (I = .580, p = .001). Internal reliability of the measure was excellent with α = .937. The SRHBI was significantly correlated with other indicators of SRH including rate of prenatal care initiation in the first trimester, rate of preterm births, reported sexual assault incidence, cervical cancer incidence, prostate cancer incidence, and rate of smoking during pregnancy. This suggests good concurrent validity of the measure. Linear regression revealed that the percent of Black residents, percent of household couples that are same-sex, community violence, economic hardship, and population density were significant predictors of the SRHBI. The SRHBI provides a valid, useful, and replicable measure to assess and communicate community-level SRH burden in urban environments. The SRHBI may be scaled through a multi-city public data dashboard and utilized by urban public health departments to optimally target and tailor SRH interventions to communities.

Abstract C063: Impact of neighborhood socioeconomic status on breast cancer subtypes among Black women

Abstract Background: Compared to White women, African American/Black women are more likely to develop triple-negative breast cancer (TNBC), an aggressive breast cancer subtype. While many studies have examined individual-level socioeconomic status (SES) as a major social determinant of more aggressive breast tumor phenotypes, the impact of neighborhood-level SES (nSES) on breast cancer is not well understood, particularly among Blacks. Objective: To evaluate the impact of nSES on breast cancer subtypes among Black women with breast cancer. Methods: We evaluated the association of interest among 1,220 Black women with invasive breast cancer from 2006 to 2018 enrolled from 10 counties of New Jersey in the Women’s Circle of Health Study (WCHS). Residential address at diagnosis was geocoded to the census tract-level. Neighborhood SES was measured using the NCI’s census tract-level SES index, a time-dependent score constructed by a factor analysis of seven variables measuring different aspects of census tract SES (education index, percent unemployed, percent working class, median household income, percent below 150% of poverty line, median house value, and median rent). In a case-only analysis, we used multilevel multinomial logistic regressions to estimate nSES in relation to breast cancer subtype (TNBC, HER2-enriched, Luminal B vs. Luminal A). Models were adjusted for individual-level SES variables, body mass index and reproductive factors, and census tract-level percentage of Black residents. We tested whether the associations were modified by percentage of Black residents and participant’s education. Results: Higher nSES was found to predict a lower risk of TNBC. Compared to tertile 1 (lowest nSES score), the odds ratio (OR) was 0.91 (95% CI: 0.65, 1.29) for tertile 2 and 0.52 (95% CI: 0.34, 0.79) for tertile 3 (highest nSES score; p-trend: 0.001). Higher nSES was borderline significantly associated with a lower risk of Luminal B (OR: 0.61 comparing highest vs. lowest nSES tertile; 95% CI: 0.36, 1.03; p-trend: 0.055). The inverse association between nSES and TNBC was observed only among Black women living in census tracts with lower proportion of Black residents (p-for-interaction=0.08). Compared to the lowest nSES tertile, OR was 0.38 (95% CI: 0.21, 0.68) for the highest tertile (p-trend: &amp;lt;0.001). There was a suggestion that the inverse relationship of nSES with TNBC was also more pronounced among women who had relatively lower levels of education. Conclusion: Neighborhood socioeconomic environment may influence the development of TNBC among Black women, which might be buffered by some contextual factors in areas with more Black residents. Further investigation to elucidate these factors is needed. Citation Format: Bo Qin, Adana A.M. Llanos, Riddhi Babel, Jesse J. Plascak, Karen Pawlish, Christine B. Ambrosone, Kitaw Demissie, Chi-Chen Hong, Elisa V. Bandera. Impact of neighborhood socioeconomic status on breast cancer subtypes among Black women [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr C063.

Child protective services decision-making: The role of children's race and county factors.

The current study investigates the role of race and county characteristics in substantiation and out-of-home placement decisions in the United States. Using multilevel models, we analyzed data from counties in the United States available through the National Child Abuse and Neglect Data Systems and Adoption and Foster Care Analysis and Reporting System to investigate the interactions between children's race and the context in which they live. Our sample consisted exclusively of children whose cases had been investigated; therefore, we were able to focus on the role played by race and county characteristics in substantiation and out-of-home placement decisions made by Child Protective Services, net of the heightened risk factors (or potential biases) that lead to disparate rates of reporting. Adjusting for state and county of investigation, Black, American Indian/Alaskan Native, and multiracial children were more likely than White (non-Hispanic) children to be substantiated or placed out of home, whereas Asian children were less likely to be substantiated or placed out of home. Notably, differences across groups are far smaller in magnitude when demographic and geographic differences are taken into account. Higher county-level poverty, percentages of Black residents, and juvenile arrest rates were associated with lower odds of substantiation and out-of-home placement among investigated children, whereas an elevated percentage of single-headed households was associated with higher odds of both outcomes. We also found that living in a rural county was associated with greater odds of substantiation but lower odds of out-of-home placement. Important differences by race were found for these associations. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Racial disparities of liver cancer mortality in Wisconsin.

To calculate tract-level estimates of liver cancer mortality in Wisconsin and identify relationships with racial and socioeconomic variables. County-level standardized mortality ratios (SMRs) of liver cancer in Wisconsin were calculated using traditional indirect adjustment methods for cases from 2003 to 2012. Tract-level SMRs were calculated using adaptive spatial filtering (ASF). The tract-level SMRs were checked for correlations to a socioeconomic advantage index (SEA) and percent racial composition. Non-spatial and spatial regression analyses with tract-level SMR as the outcome were conducted. County-level SMR estimates were shown to mask much of the variance within counties across their tracts. Liver cancer mortality was strongly correlated with the percent of Black residents in a census tract and moderately associated with SEA. In the multivariate spatially-adjusted regression analysis, only Percent Black composition remained significantly associated with an increased liver cancer SMR. Using ASF, we developed a high-resolution map of liver cancer mortality in Wisconsin. This map provided details on the distribution of liver cancer that were inaccessible in the county-level map. These tract-level estimates were associated with several racial and socioeconomic variables.

The association of neighborhood racial mix and ED visit count in a cohort of patients on hemodialysis

BackgroundNeighborhood racial mix is associated with dialysis facility performance metrics and mortality outcomes in patients on hemodialysis. We explored the association of neighborhood racial mix with emergency department (ED) visits in patients receiving hemodialysis.MethodsUsing Looking Glass (Montefiore’s clinical database) we identified a cohort of patients on hemodialysis with an index ED visit at any of 4 Montefiore Hospital locations, between January 2013 and December 2017 and followed it for number of ED visits through December of 2017 or dropout due to death. The racial mix data for the Bronx block group of each subject’s residence was derived from the Census Bureau. We then used negative binomial regression to test the association of quintile of percent of Black residents per residential block group with ED visits in unadjusted and adjusted models. To adjust further for quality offered by local dialysis facilities, with the facility zip code as the locus, we used data from the “Dialysis Compare” website.ResultsThree thousand nine-hundred and eighteen subjects were identified and the median number of ED visits was 3 (interquartile range (IQR) 1–7) during the study period. Subjects living in the highest quintile of percent Black residents were older, more commonly female and had lower poverty rates and higher rates of high school diplomas. Unadjusted models showed a significant association between the highest quintiles of Black neighborhood residence and count of ED visits. Fully adjusted, stratified models revealed that among males, and Hispanic and White subjects, living in neighborhoods with the highest quintiles of Black residents was associated with significantly more ED visits (p-trend =0.001, 0.02, 0.01 respectively). No association was found between dialysis facility locations’ quintile of Black residents and quality metrics.ConclusionsLiving in a neighborhood with a higher percentage of Black residents is associated with a higher number of ED visits in males and non-Black patients on hemodialysis.