Neighborhood Features and Cognitive Function: Moderating Roles of Individual Socioeconomic Status.

INTRODUCTION: There is an interest in exploring the associations between neighborhood characteristics and individual cognitive function; however, little is known about whether these relationships can be modified by individual socioeconomic status, such as educational attainment and income. METHODS: Drawing from the 2010-2018 Health and Retirement Study, this study analyzed 10,621 older respondents (aged 65+) with a total of 33,931 person-waves. These respondents did not have dementia in 2010 and stayed in the same neighborhood throughout the study period. Cognitive function was measured with a 27-point indicator biennially, and neighborhood characteristics (i.e., walkability, concentrated disadvantage, and social isolation) were assessed in 2010. All analyses were performed in 2023. RESULTS: Cognitive function is positively associated with neighborhood walkability and negatively related to concentrated disadvantage, suggesting that exposures to these neighborhood characteristics have long-lasting impacts on cognitive function. Furthermore, individual socioeconomic status modifies the relationship between neighborhood characteristics and cognitive function. Compared with those graduating from college, respondents without a bachelor's degree consistently have lower cognitive function but the educational gap in cognitive function narrows with increases in walkability (b= -0.152, SE=0.092), and widens when neighborhood concentrated disadvantage (b=0.212, SE=0.070) or social isolation (b=0.315, SE=0.125) rises. The income gap in cognitive function shrinks with increases in walkability (b= -0.063, SE=0.027). CONCLUSIONS: The moderating role of socioeconomic status indicates that low-socioeconomic status older adults who also live in disadvantaged neighborhoods face a higher risk of poor cognitive function. Low-education and low-income aging adults may have the most to gain from investments to improve neighborhood characteristics.

Reviewing Racial Disparities in Living Donor Kidney Transplantation: a Socioecological Approach.

Despite kidney transplantation having superior outcomes to dialytic therapies, disparities continue to exist among rates of kidney transplantation between Black and non-Hispanic White patients, which cannot be explained by differences in individual characteristics. To better evaluate the persistent Black/White disparities in living kidney transplantation, we review the extant literature and include the critical factors and recent development in living kidney transplantation in the socioecological approach. We also emphasize the potential vertical and hierarchical associations among factors in the socioecological model. Specifically, this review explores the possibility that the relatively low living kidney transplantation among Blacks may be a consequence of individual, interpersonal, and structural inequalities in various social and cultural dimensions. At the individual level, the Black/White differences in socioeconomic conditions and transplant knowledge may account for the low transplantation rates among Blacks. Interpersonally, the relatively weak social support and poor communication between Black patients and their providers may contribute to disparities. At the structural level, the race-based glomerular filtration rate (GFR) calculation that is widely used to screen Black donors is a barrier to receiving living kidney transplantation. This factor is directly related to structural racism in the health care system but its potential impact on living donor transplantation is underexplored. Finally, this literature review emphasizes the current perspective that a race-free GFR should be considered and a multidisciplinary and interprofessional perspective is necessary to devise strategies and interventions to reduce the Black/White disparities in living donor kidney transplantation in the U.S.

Social Vulnerability and the Prevalence of Opioid Use Disorder Among Older Medicare Beneficiaries in U.S. Counties.

OBJECTIVES: Recent research has investigated the factors associated with the prevalence of opioid use disorder (OUD) among older adults (65+), which has rapidly increased in the past decade. However, little is known about the relationship between social vulnerability and the prevalence of OUD, and even less is about whether the correlates of the prevalence of OUD vary across the social vulnerability spectrum. This study aims to fill these gaps. METHODS: We assemble a county-level data set in the contiguous United States (U.S.) by merging 2021 Medicare claims with the CDC's social vulnerability index and other covariates. Using the total number of older beneficiaries with OUD as the dependent variable and the total number of older beneficiaries as the offset, we implement a series of nested negative binomial regression models and then analyze by social vulnerability quartiles. RESULTS: Higher social vulnerability is associated with higher prevalence of OUD in U.S. counties. This association cannot be fully explained by the differences in the characteristics of older Medicare beneficiaries (e.g., average age) and/or other social conditions (e.g., social capital) across counties. Moreover, the group comparison tests indicate correlates of the prevalence of OUD vary across social vulnerability quartiles in that the average number of mental disorders is positively related to OUD prevalence in the least and the most vulnerable counties and social capital benefits the less vulnerable counties. DISCUSSION: A perspective drawing upon contextual factors, especially social vulnerability, may be more effective in reducing OUD among older adults in U.S. counties than a one-size-fits-all approach.

Cross-sectional study of food insecurity and medical expenditures by race and ethnicity.

OBJECTIVE: Food insecurity is a risk factor for morbidity and mortality leading to high medical expenditures, but race/ethnicity was used as adjustments in the literature. The study sought to use race/ethnicity as a key predictor to compare racial differences in associations between food insecurity and expenditures of seven health services among non-institutionalized adults. DESIGN: This cross-sectional study used Medical Expenditure Panel Survey that collects information on food insecurity in 2016 (n=24,179) and 2017 (n=22,539). We examined the association between race/ethnicity and food insecurity status and documented the extent to which impacts of food insecurity on medical expenditures varied by race/ethnicity. We fit multivariable models for each racial group, adjusting for states, age, gender, insurance, and education. Adults older than 18 years were included. RESULTS: The results show that blacks experienced an inter-racial disparity in food insecurity whereas Hispanics experienced intra-racial disparity. A higher percentage of blacks (28.7%) reported at least one type of food insecurity (11.2% of whites). Around 20% of blacks reported being worried about running out of food and the corresponding number is 8.4% among whites. Hispanics reported more food insecurity issues than whites. Moreover, food insecurity is positively associated with expenditures on emergency room utilization (99% increase for other races vs. 51% increase for whites) but is negatively associated with dental care utilization (43% decrease for blacks and 44% for whites). Except for Hispanics, prescription expenditure has the most positive association with food insecurity, and food insecure blacks are the only group that did not significantly use home health. CONCLUSION: The study expanded our understanding of food insecurity by investigating how it affected seven types of medical expenditures for each of four racial populations. An interdisciplinary effort is needed to enhance the food supply for minorities. Policy interventions to address intra-racial disparities among Hispanics and inter-racial disparities among African Americans are imperative to close the gap.

Neighborhood characteristics and opioid use disorder among older Medicare beneficiaries: An examination of the role of the COVID-19 pandemic.

This study investigates how the associations between residential characteristics and the risk of opioid user disorder (OUD) among older Medicare beneficiaries (age≥65) are altered by the COVID-19 pandemic. Applying matching techniques and multilevel modeling to the Medicare fee-for-service claims data, this study finds that county-level social isolation, concentrated disadvantage, and residential stability are significantly associated with OUD among older adults (N = 1,080,350) and that those living in counties with low levels of social isolation and residential stability experienced a heightened risk of OUD during the pandemic. The results suggest that the COVID-19 pandemic has aggravated the impacts of residential features on OUD.

Multiscale dimensions of county-level disparities in opioid use disorder rates among older Medicare beneficiaries.

Background: Opioid use disorder (OUD) among older adults (age ≥ 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates. Methods: Data are from a variety of national sources including the Centers for Medicare & Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016-2020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely "level of influence" (the percentage of older Medicare beneficiaries affected by a certain determinant), "scalability" (the spatial process of a determinant as global, regional, or local), and "specificity" (the determinant that has the strongest association with the OUD rate). Results: The results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others. Conclusions: Our findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process.

County social isolation and opioid use disorder among older adults: A longitudinal analysis of Medicare data, 2013-2018.

This study aims to fill three knowledge gaps: (1) unclear role of ecological factors in shaping older adults' risk of opioid use disorder (OUD), (2) a lack of longitudinal perspective in OUD research among older adults, and (3) underexplored racial/ethnic differences in the determinants of OUD in older populations. This study estimates the effects of county-level social isolation, concentrated disadvantage, and income inequality on older adults' risk of OUD using longitudinal data analysis. We merged the 2013-2018 Medicare population (aged 65+) data to the American Community Survey 5-year county-level estimates to create a person-year dataset (N = 47,291,217 person-years) and used conditional logit fixed-effects modeling to test whether changes in individual- and county-level covariates alter older adults' risk of OUD. Moreover, we conducted race/ethnicity-specific models to compare how these associations vary across racial/ethnic groups. At the county-level, a one-unit increase in social isolation (mean = -0.197, SD = 0.511) increased the risk of OUD by 5.5 percent (OR = 1.055; 95% CI = [1.018, 1.094]) and a one-percentage-point increase in the working population employed in primary industry decreases the risk of OUD by 1 percent (OR = 0.990; 95% CI = [0.985, 0.996]). At the individual-level, increases in the Medicare Hierarchical Condition Categories risk score, physical comorbidity, and mental comorbidity all elevate the risk of OUD. The relationship between county-level social isolation and OUD is driven by non-Hispanic whites, while Hispanic beneficiaries are less sensitive to the changes in county-level factors than any other racial ethnic groups. Between 2013 and 2018, US older adults' risk of OUD was associated with both ecological and individual factors, which carries implications for intervention. Further research is needed to understand why associations of individual factors with OUD are comparable across racial/ethnic groups, but county-level social isolation is only associated with OUD among non-Hispanic white beneficiaries.

Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties.

PURPOSE: Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks. METHODS: Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups-High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk-and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors. RESULTS: The key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana. CONCLUSIONS: The COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Social isolation, residential stability, and opioid use disorder among older Medicare beneficiaries: Metropolitan and non-metropolitan county comparison.

Research has shown that the prevalence of opioid use disorder (OUD) may rise substantially as society ages, but this issue receives the least attention in the literature. To address this gap, this study utilizes county-level data from multiple data sources (1) to investigate whether social isolation is associated with OUD prevalence among older Medicare beneficiaries, (2) to examine whether and how residential stability moderates the association between social isolation and OUD prevalence in US counties, and (3) to determine if there are any differences in these associations between metropolitan and non-metropolitan counties. The results show that social isolation is a significant factor for county-level OUD prevalence, regardless of metropolitan status. In addition, counties with high residential stability have low prevalence of OUD among older adults and this association is stronger in metropolitan than in non-metropolitan counties. Nonetheless, high levels of residential stability reinforce the positive relationship between social isolation and OUD prevalence. As a result, when developing policies and interventions aimed at reducing OUD among older adults, place of residence must be taken into account.

Structural Inequalities Established the Architecture for COVID-19 Pandemic Among Native Americans in Arizona: a Geographically Weighted Regression Perspective.

Native Americans are disproportionately affected by COVID-19. The present study explores whether areas with high percentages of Native American residents are experiencing the equal risks of contracting COVID-19 by examining how the relationships between structural inequalities and confirmed COVID-19 cases spatially vary across Arizona using a geographically weighted regression (GWR). GWR helps with the identification of areas with high confirmed COVID-19 cases in Arizona and with understanding of which predictors of social inequalities are associated with confirmed COVID-19 cases at specific locations. We find that structural inequality indicators and presence of Native Americans are significantly associated with higher confirmed COVID-19 cases; and the relationships between structural inequalities and confirmed COVID-19 cases are significantly stronger in areas with high concentration of Native Americans, particular on Tribal lands. The findings highlight the negative effects that lack of infrastructure (i.e., housing with plumbing, transportation, and accessible health communication) may have on individual and population health, and, in this case, associated with the increase of confirmed COVID-19 cases.

County Social Vulnerability and Influenza Vaccine Rates: National and Local Estimates for Medicare Recipients.

INTRODUCTION: Seasonal influenza vaccination among older adults is well below the recommendation of Healthy People 2020. Although geographic disparities in influenza vaccination are well documented, it remains unclear how community attributes correlate with influenza vaccination rates. Social vulnerability measures play an important role in interventions addressing vaccine equity; however, social vulnerability dimensions as corollaries of vaccination are poorly understood. To inform vaccine equity interventions, this analysis investigates spatially varying associations between county social vulnerability and influenza vaccination rate among Medicare recipients. METHODS: County-level 2018 data (N=3,105) from the Centers for Disease Control and Prevention's Social Vulnerability Index were merged with the percentage of Medicare recipients vaccinated against influenza. Multilevel linear regression and geographically weighted regression generated global and local estimates, adjusted for potential confounders. Analyses were conducted in November 2020-April 2021. RESULTS: A 10-percentile point increase in the overall Social Vulnerability Index was associated with an 0.87-point decrease in percentage vaccinated (p<0.001) with substantial variation by Social Vulnerability Index theme and geography. A 10-percentile point increase in socioeconomic vulnerability was associated with a 1.6-point decrease in vaccination (p<0.001) with stronger associations in higher Social Vulnerability Index quartiles and in parts of the Midwest, South, and coastal Northeast. Other Social Vulnerability Index themes had smaller associations with mixed directions: household composition and disability estimates were negative, whereas estimates for minority status and language and housing and transportation were positive. CONCLUSIONS: Medicare recipients in socioeconomically vulnerable counties have low influenza vaccination rates, particularly in select regions of the country. Best practices to improve vaccine access and uptake should be targeted and should explicitly consider local socioeconomic vulnerability.

Income inequality and opioid prescribing rates: Exploring rural/urban differences in pathways via residential stability and social isolation.

While opioid prescribing rates have drawn researchers' attention, little is known about the mechanisms through which income inequality affects opioid prescribing rates and even less focuses on whether there is a rural/urban difference in mediating pathways. Applying mediation analysis techniques to a unique ZIP code level dataset from several sources maintained by the Centers for Medicare and Medicaid Services, we explicitly examine two mechanisms through residential stability and social isolation by rural/urban status and find that (1) income inequality is not directly related to opioid prescribing rates, but it exerts its influence on opioid prescribing via poor residential stability and elevated social isolation; (2) social isolation accounts for two-thirds of the mediating effect of income inequality on opioid prescribing rates among urban ZIP codes, but the proportion halves among rural ZIP codes; (3) residential stability plays a larger role in understanding how income inequality matters in rural than in urban ZIP codes; and (4) beneficiary characteristics only matter in urban ZIP codes. These findings offer nuanced insight into how income inequality affects opioid prescribing rates and suggests that the determinants of opioid prescribing rates vary by rural/urban status. Future research may benefit from identifying place-specific factors for opioid prescribing rates.

Trends in Opioid Use Disorder Among Older Adults: Analyzing Medicare Data, 2013-2018.

INTRODUCTION: Opioid use disorder has grown rapidly over the years and is a public health crisis in the U.S. Although opioid use disorder is widely studied, relatively little is known about it among older adults. The goal of this study is to gain a better understanding of opioid use disorder among older Medicare beneficiaries over time and across several sociodemographic dimensions. METHODS: Data from the 2013-2018 Centers for Medicare & Medicaid Services Master Beneficiary Summary Files were analyzed in 2020 to examine the trends in opioid use disorder prevalence among Fee-for-Service Medicare beneficiaries aged ≥65 years. Utilizing the overarching opioid use disorder flag, trends in opioid use disorder prevalence were examined for the following sociodemographic dimensions: age, sex, race/ethnicity, and dual eligibility status (i.e., enrolled in both Medicare and Medicaid owing to low income). Chi-square tests were used to compare opioid use disorder prevalence across groups. RESULTS: Since 2013, estimated rates of opioid use disorder among older adults have increased by >3-fold overall in the U.S. Estimated opioid use disorder is more prevalent among the young-old (i.e., ages 65-69 years) beneficiaries than among other older adults, and dually eligible beneficiaries have consistently shared a heavier burden of opioid use disorder than Medicare-only beneficiaries. Regarding race/ethnicity, Blacks and American Indians/Alaskan Natives are more vulnerable to opioid use disorder than other groups. CONCLUSIONS: The descriptive trends between 2013 and 2018 indicate that estimated opioid use disorder prevalence has increased greatly over the study period in all sociodemographic subgroups of older adults, highlighting an urgent challenge for public health professionals and gerontologists.

Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes.

We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups are associated with COVID-19 positivity rates; (2) the percentages of remote workers are negatively associated with positivity rates, whereas older population and household size show a positive association; and (3) while ZIP codes in the Bronx and Queens have higher COVID-19 positivity rates, the strongest spatial effects are clustered in Brooklyn and Manhattan.

COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation.

OBJECTIVE: To investigate how racial/ethnic density and residential segregation shape the uneven burden of COVID-19 in US counties and whether (if yes, how) residential segregation moderates the association between racial/ethnic density and infections. DESIGN: We first merge various risk factors from federal agencies (e.g. Census Bureau and Centers for Disease Control and Prevention) with COVID-19 cases as of June 13th in contiguous US counties (N = 3,042). We then apply negative binomial regression to the county-level dataset to test three interrelated research hypotheses and the moderating role of residential segregation is presented with a figure. RESULTS: Several key results are obtained. (1) Counties with high racial/ethnic density of minority groups experience more confirmed cases than those with low levels of density. (2) High levels of residential segregation between whites and non-whites increase the number of COVID-19 infections in a county, net of other risk factors. (3) The relationship between racial/ethnic density and COVID-19 infections is enhanced with the increase in residential segregation between whites and non-whites in a county. CONCLUSIONS: The pre-existing social structure like residential segregation may facilitate the spread of COVID-19 and aggravate racial/ethnic health disparities in infections. Minorities are disproportionately affected by the novel coronavirus and focusing on pre-existing social structures and discrimination in housing market may narrow the uneven burden across racial/ethnic groups.

Rural/Urban Differences in the Predictors of Opioid Prescribing Rates Among Medicare Part D Beneficiaries 65 Years of Age and Older.

PURPOSE: While research has been done comparing rural/urban differences in opioid prescribing to the disabled Medicare Part D population, research on opioid prescribing among the aged Medicare Part D population is lacking. This study aims to fill this gap by exploring the predictors of opioid prescribing to aged Medicare Part D beneficiaries and investigating whether these predictors vary across rural and urban areas. METHODS: This is an analysis of ZIP Codes in the continental United States (18,126 ZIP Codes) utilizing 2017 data from Centers for Medicare & Medicaid Services. The analytic approach includes aspatial descriptive analysis, exploratory spatial analysis with geographically weighted regression, and explanatory analysis with spatial error regime modeling. FINDINGS: Both beneficiary and prescriber characteristics play an important role in determining opioid prescribing rates in urban ZIP Codes, but most of them fail to explain the opioid prescribing rates in rural ZIP Codes. CONCLUSION: We identify potential spatial nonstationarity in opioid prescribing rates, indicating the complex nature of opioid-related issues. This means that the same stimulus may not lead to the same change in opioid prescribing rates, because the change may be place specific. By understanding the rural/urban differences in the predictors of opioid prescribing, place-specific policies can be developed that can guide more informed opioid prescribing practices and necessary interventions.

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

PURPOSE: To 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. METHODS: Using 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. RESULTS: Percentage 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). CONCLUSIONS: Counties 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.

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.

Evaluating Residential Segregation's Relation to the Clustering of Poor Health across American Cities.

Residential segregation by race/ethnicity is widely recognized as a leading source of health disparities. Not clear from past research, however, is the overall health burden cities face due to clustering brought about by segregation. This study builds on previous research by directly measuring how spatially unequal health outcomes are within segregated cities. Utilizing Census-tract data from the Center for Disease Control and Prevention's 500 Cities project, we examine how different dimensions of spatial segregation are associated with the clustering of poor self-rated health in cities. We make novel usage of the Global Moran's I statistic to measure the spatial clustering of poor health within cities. We find spatial segregation is associated with poor health clustering, however the race/ethnicity and dimension of segregation matter. Our study contributes to existing research on segregation and health by unpacking the localized associations of residential segregation with poor health clustering in U.S. cities.

Racial/ethnic disparities in depression: Investigating how sources of support and types of integration matter.

Although social support and social integration are key predictors of depression and exhibit racial/ethnic patterns in the US, previous research has not examined how they shape racial/ethnic disparities in depression. Applying hybrid models to data from the Americans' Changing Lives study from 1986 to 2002, this study analyzes how sources of social support (spouse and friend/relative) and types of social integration (informal/formal) explain black-white and Hispanic-white disparities in depression. We find that strong social support and high social integration are negatively associated with depression and that the patterns of social support and integration vary by race/ethnicity. The results of hybrid models show that social support from one's spouse and friend/relative account for over 25 percent of the black-white disparity, whereas formal social integration including religious groups widens the black-white differential by roughly 10 percent. However, Hispanic-white disparities in depression are mostly a result of the difference in socioeconomic status. The change in spousal support is the most powerful predictor for the change in depression across race/ethnicity groups. Our findings suggest that the racial/ethnic differences in sources of social support and types of social integration play important roles in shaping racial/ethnic disparities in depression.