Neighborhood Context, Homeownership and Home Value: An Ecological Analysis of Implications for Health

Increasing access, equitability, and rigor in the assessment of Neighborhood Mortgage Discrimination

Mortgage discrimination alters the distribution of investment, opportunity, and economic advantage-key contributors of health disparities. Leveraging Home Mortgage Disclosure Act data, we assessed mortgage denial risk in 380 U.S. urban areas. We estimated the risks by census tract-relative to the urban-specific average-using a Bayesian spatial model with conditionally autoregressive distributions fitted with integrated nested Laplace approximation. This approach borrows information through spatial and non-spatial smoothing, resulting in stable estimates in the presence of sparse data. The method, publicly accessible, allows researchers to apply our approach, fostering deeper insights into mortgage lending discrimination and systematic neighborhood disinvestment.

Can federal policy help overcome systemically reinforced racial inequities in social determinants of health? An observational study of Georgia and neighboring U.S. states

BACKGROUND

Despite increasing attention to racial inequities in social determinants of health and health outcomes, less attention has been focused on how structural barriers - embedded in programs and codified in laws - shape opportunities to achieve health.

METHODS

To better understand how U.S. federal policies targets structural barriers to opportunity and health at the population level, we conducted a legal review to identify landmark pieces of federal policy that held potential to impact key social determinants of health. Then, using publicly available data for Georgia and five neighboring U.S. states (Alabama, Florida, North Carolina, South Carolina, and Tennessee), we conducted an observational case study to examine recent trends for access to health care, housing, and education because they were each associated with comprehensive federal legislation meant to alleviate inequities resulting from long-standing structural barriers and were each identified by Healthy People 2030 as key social determinants of health.

RESULTS

From 2010 to 2021, population-level improvements were seen in health insurance rates, mortgage and rental burden, and educational attainment, with improvements seen for both Black and White populations in Georgia, regionally in the Southeast region, and nationally in the United States. However, seemingly meaningful gaps between the Black and White populations across social determinants of health have not been eliminated at any geographical level.

CONCLUSIONS

This analysis adds to a growing body of evidence that historically racialized social structures hamper Black populations' opportunities to build wealth, gain a quality education, own a home in a neighborhood of opportunity, and access health care, compared to their White peers. Given that the root causes of health disparities and inequities lie at the intersection of health, health care, economics, education, and other social systems, a multisectoral approach to policy is needed to address these systemic issues. While federal laws do provide momentum for proximal benefits for social change, in modern federalism they alone are insufficient to address needed local system change and nonlegal policy interventions, implemented at the local programmatic level, may serve as complementary mechanism to address the lingering effects of barriers to equal opportunity.

Identifying and Assisting Burn Patients and Their Families With Needs Through Administration of an Assessment Tool on Admission

Using a modified Safe Environment for Every Kid Questionnaire (Needs Survey), we previously showed a significant correlation between adverse childhood experiences (ACEs) and family needs. Herein, we retrospectively assessed whether patients' and their families' needs identified using the Needs Survey were addressed prior to discharge. We hypothesized that, without the knowledge gained by administering this tool, many basic needs may not have been fully addressed. Seventy-nine burn patients and families previously enrolled in our ACE studies were included. Answers to the Needs Surveys were reviewed to identify families with needs. Medical records were reviewed to determine if a social worker assessment (SWA) was completed per standard of care and if their needs were addressed prior to discharge. Of the 79 burn patients who received inpatient care and completed the Needs Survey, family needs were identified in 67 (84.8%); 42 (62.7%) received an SWA, and 25 (37.3%) did not. Those who did not receive a SWA had a shorter hospitalization and suffered less severe burns. Demographics, socioeconomics, ACEs, and identified needs were similar between the groups. Our study showed that SWA was performed on many patients with basic needs. However, with the focus of SWAs being on discharge arrangements, not all needs were addressed, and individualized resources were often not provided. Administering the Needs Survey on admission may help our social workers streamline and expedite this process to help support successful recovery for our burn patients and their families.

Intersectional Stigma and Prevention Among Gay, Bisexual, and Same Gender-Loving Men in New York City, 2020: System Dynamics Models

Objectives. To create causal loop diagrams that characterize intersectional stigma experiences among Black, gay, bisexual, same gender-loving, and other men who have sex with men and to identify intervention targets to reduce stigma and increase testing and prevention access. Methods. Between January and July 2020, we conducted focus groups and in-depth interviews with 80 expert informants in New York City, which were transcribed, coded, and analyzed. These qualitative insights were developed iteratively, visualized, and validated in a causal loop diagram (CLD) using Vensim software. Results. The CLD revealed 3 key feedback loops-medical mistrust and HIV transmission, serosorting and marginalization of Black and gay individuals, and family support and internalized homophobia-that contribute to intersectional HIV and related stigmas, homophobia, and systemic racism. On the basis of these results, we designed 2 novel intervention components to integrate into an existing community-level anti-HIV stigma and homophobia intervention. Conclusions. HIV stigma, systemic racism, and homophobia work via feedback loops to reduce access to and uptake of HIV testing, prevention, and treatment. Public Health Implications. The CLD method yielded unique insights into reciprocal feedback structures that, if broken, could interrupt stigmatization and discrimination cycles that impede testing and prevention uptake. (Am J Public Health. 2022;112(S4):S444-S451. https://doi.org/10.2105/AJPH.2022.306725).

Spatial distribution of neighborhood-level housing prices and its association with all-cause mortality in Seoul, Korea (2013-2018): A spatial panel data analysis

Housing prices are known to be a relevant indicator of the socioeconomic position of the neighborhood. In a society where the market system mainly drives housing prices, residents' spatial patterning is formulated according to their socioeconomic position. Dividing the 2013-2018 entire study period into three periods, we explored the spatial distribution of housing prices and all-cause mortality and their association in Seoul, the country's capital city. The government authorities' data and 2015 census data were used for the study. We mapped the spatial distribution of housing prices and all-cause mortality and investigated the changes in distribution. We conducted a pooled ordinary least square (OLS) and spatial panel regression analysis to estimate housing prices elasticity of all-cause mortality. We also explored the possible mediating role of housing prices on the educational composition's effect on all-cause mortality. We found the common trends of increasing spatial patterning of housing prices and all-cause mortality. The magnitude of spatial patterning was far greater in housing prices than all-cause mortality. A pooled OLS regression analysis found that a 1% increase in housing price was associated with a 0.11% reduction in all-cause mortality after controlling the explanatory variables. Attenuation in the regression coefficient's magnitude was found after adding the neighborhood's educational composition to the model. As a result of spatial panel analysis, we found a direction and scale similar to the housing price elasticity of all-cause mortality in the final pooled OLS model. The results suggested that spatial health inequality in Korea's urban space mainly stems from socioeconomic inequality.

Community Characteristics and COVID-19 Outcomes: A Study of 159 Counties in Georgia, United States

BACKGROUND

The COVID-19 pandemic affects population groups differently, worsening existing social, economic, and health inequities.

PURPOSE

This study examined 159 counties within Georgia to identify community characteristics associated with county-level COVID-19 case, hospitalization, and death rates.

METHODS

Data from the 2020 County Health Rankings, the 2010 US Census, and the Georgia Department of Public Health COVID-19 Daily Status Report were linked using county Federal Information Processing Standard codes and evaluated through multivariable linear regression models.

RESULTS

The percentages of children in poverty, severe housing problems, and people not proficient in the English language were significant predictors associated with increases in case, hospitalization, and death rates. Diabetic prevalence was significantly associated with increases in the hospitalization and death rates; in contrast, the percentages of people with excessive drinking and female were inversely associated with hospitalization and death rates. Other independent variables showing an association with death rate included the percentages of people reporting fair or poor health and American Indian/Alaska Native.

IMPLICATION

Local authorities' proper allocation of resources and plans to address community social determinants of health are essential to mitigate disease transmission and reduce hospitalizations and deaths associated with COVID-19, especially among vulnerable groups.

Urban HEART Detroit: the Application of a Health Equity Assessment Tool

The Urban Health Assessment Response Tool (Urban HEART) was developed by the World Health Organization. In 2016, the Urban HEART was adapted and used by the Healthy Environments Partnership, a long-standing community-based participatory research partnership focused on addressing social determinants of health in Detroit, Michigan, to identify health equity gaps in the city. This paper uses the tool to: (1) examine the geographic distributions of key determinants of health in Detroit, across the five Urban HEART specified domains: physical environment and infrastructure, social and human development, economics, governance, and population health, and (2) determine whether these indicators are associated with the population health indicators at the neighborhood level. In addition to the Urban HEART matrix, we developed various tools including graphs and maps to further examine Detroit's health equity gaps. Although not required by Urban HEART, we statistically analyzed the associations between each indicator with the health outcomes. Our results showed that all the domains contained one or more indicators associated with one or more health outcomes, making this an effective tool to study health equity in Detroit. The Urban HEART Detroit project comes at a critical time where the nation is focusing on health equity and understanding underlying determinants of health inequities in urban areas. A tool like Urban HEART can help identify these areas for rapid intervention to prevent unnecessary burden from disease. We recommend the application of the Urban HEART, in active dialog with community groups, organizations, and leaders, to promote health equity.

Residential Racial and Socioeconomic Segregation as Predictors of Housing Discrimination in Detroit Metropolitan Area

This study examined neighborhood racial and socioeconomic characteristics associated with housing discrimination (HD) in the Detroit Metropolitan Area, Michigan. Using novel neighborhood level data from the Fair Housing Center of Metropolitan Detroit in combination with the American Community Survey, incidence rate ratios (IRRs) were derived to examine associations between HD cases and percentage of homeowners, non-Hispanic White (NHW) residents, and median income. Models were stratified to examine these associations for race-, disability- and rent-related HD outcomes. Between 2008–2017, 988 HD incidents were reported. Independently, neighborhood proportion NHW, income, and homeownership were inversely associated with all-types of HD. Jointly, the neighborhood predictors remained significant indicators. Similar patterns were observed in race-, disability- and rent-related HD when neighborhood predictors were examined independently. In the joint models, household income no longer predicted race-related HD, while proportion NHW no longer predicted disability- and rent-related HD. Results suggest HD may be more frequent in neighborhoods with greater proportions of NHB or Hispanic residents, those with lower incomes, and greater proportion of rental households. These findings have great social and health implications and warrant further exploration of how HD contributes to social and health inequities in lower income, predominantly NHB and Hispanic neighborhoods and those with more renters.

Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States

IMPORTANCE

An association between social and neighborhood characteristics and health outcomes has been reported but remains poorly understood owing to complex multidimensional factors that vary across geographic space.

OBJECTIVES

To quantify social determinants of health (SDOH) as multiple dimensions across the continental United States (the 48 contiguous states and the District of Columbia) at a small-area resolution and to examine the association of SDOH with premature mortality within Chicago, Illinois.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, census tracts from the US Census Bureau from 2014 were used to develop multidimensional SDOH indices and a regional typology of the continental United States at a small-area level (n = 71 901 census tracts with approximately 312 million persons) using dimension reduction and clustering machine learning techniques (unsupervised algorithms used to reduce dimensions of multivariate data). The SDOH indices were used to estimate age-adjusted mortality rates in Chicago (n = 789 census tracts with approximately 7.5 million persons) with a spatial regression for the same period, while controlling for violent crime.

MAIN OUTCOMES AND MEASURES

Fifteen variables, measured as a 5-year mean, were selected to characterize SDOH as small-area variations for demographic characteristics of vulnerable groups, economic status, social and neighborhood characteristics, and housing and transportation availability at the census-tract level. This SDOH data matrix was reduced to 4 indices reflecting advantage, isolation, opportunity, and mixed immigrant cohesion and accessibility, which were then clustered into 7 distinct multidimensional neighborhood typologies. The association between SDOH indices and premature mortality (defined as death before age 75 years) in Chicago was measured by years of potential life lost and aggregated to a 5-year mean. Data analyses were conducted between July 1, 2018, and August 30, 2019.

RESULTS

Among the 71 901 census tracts examined across the continental United States, a median (interquartile range) of 27.2% (47.1%) of residents had minority status, 12.1% (7.5%) had disabilities, 22.9% (7.6%) were 18 years and younger, and 13.6% (8.1%) were 65 years and older. Among the 789 census tracts examined in Chicago, a median (interquartile range) of 80.4% (56.3%) of residents had minority status, 10.2% (8.2%) had disabilities, 23.2% (10.9%) were 18 years and younger, and 9.5% (7.1%) were 65 years and older. Four SDOH indices accounted for 71% of the variance across all census tracts in the continental United States in 2014. The SDOH neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all census tracts across the continental United States but characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago (R2 = 0.63; P < .001), even after accounting for violent crime and spatial structures.

CONCLUSIONS AND RELEVANCE

The modeling of SDOH as multivariate indices rather than as a singular deprivation index may better capture the complexity and spatial heterogeneity underlying SDOH. During a time of increased attention to SDOH, this analysis may provide actionable information for key stakeholders with respect to the focus of interventions.

Determining the health benefits of green space: Does gentrification matter?

Urban green space is demonstrated to benefit human health. We evaluated whether neighborhood gentrification status matters when considering the health benefits of green space, and whether the benefits are received equitably across racial and socioeconomic groups. Greater exposure to active green space was significantly associated with lower odds of reporting fair or poor health, but only for those living in gentrifying neighborhoods. In gentrifying neighborhoods, only those with high education or high incomes benefited from neighborhood active green space. Structural interventions, such as new green space, should be planned and evaluated within the context of urban social inequity and change.

Housing Discrimination and Health: Understanding Potential Linking Pathways Using a Mixed-Methods Approach

Few studies have examined the impact of housing discrimination on health. This study explores potential pathways linking housing discrimination and health using concept mapping, a mixed-method approach. Participants included employees from twenty Fair Housing Organizations nationwide who participated in two online sessions, brainstorming, and structuring. Responses were generated representing biological, social, economic, and physical connections between housing discrimination and health. Using hierarchical cluster analysis, five clusters were identified: (1) Access and barriers; (2) Opportunities for growth; (3) Neighborhood and communities; (4) Physical effects of housing discrimination; and (5) Mental health. Clusters 1 (4.09) and 2 (4.08) were rated as most important for health, while clusters 2 (3.93) and 3 (3.90) were rated as most frequently occurring. These findings add to the limited evidence connecting housing discrimination to health and highlight the need for studies focusing on the long-term health effects of housing discrimination on individuals and neighborhoods.