Race, disadvantage, and violence: A spatial exploration of macrolevel covariates of police-involved homicides within and between US counties.

Efforts to explore the macrolevel determinants of police-involved homicides have expanded in recent years due in part to increased scrutiny and media attention to such events, and increased data availability of these events through crowdsourced databases. However, little empirical research has examined the spatial determinants of such events. The present study extends the extant macrolevel research on police-involved homicides by employing an underutilized spatial econometric model, the spatial Durbin model (SDM), to assess the direct and indirect county effects of racial threat, economic threat, social disorganization, and community violence on police killings within and between US counties from 2013 through 2020. Results indicate a direct inverse relationship between racial threat and police-involved homicides, no support for economic threat, and a direct positive association with two measures of social disorganization. Additionally, we find firearm availability exhibits significant direct and indirect spatial dependence on focal county police-involved homicides, reflecting spatial spillover processes. In essence, as firearm availability in neighboring counties increases, police-involved homicides within a focal county increase. The implications of these findings for racial threat, economic threat, social disorganization, and community violence are discussed.

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.

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.

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.

Living Alone During Old Age and the Risk of Dementia: Assessing the Cumulative Risk of Living Alone.

OBJECTIVES: This study examines the association between living alone during old age and dementia. Whereas most previous studies on this topic utilize measures of living alone status that were obtained at a single point in time, we compare this typical approach to one that measures long-term exposure to living alone among older adults and assesses whether dementia is more likely to occur within individuals with more accumulated time living alone. METHODS: Data come from the Health and Retirement Study, with a follow-up period of 2000-2018. A total of 18,171 older adults were followed during this period, resulting in 78,490 person-waves analyzed in a series of multi-level logistic models. Contemporaneous living alone was recorded when a respondent's household size was equal to 1 in a given wave. Cumulative living alone was calculated by adding the number of living alone statuses up to a given wave. RESULTS: Contemporaneous living alone was either not associated (male-only subsample), or inversely associated (female-only subsample) with dementia. By contrast, a one-unit (i.e., one wave) increase in cumulative living alone was associated with about a 10% increase in the odds of dementia for both men (odds ratio [OR] = 1.111) and women (OR = 1.088), net of several covariates, including marital status, age, social activities, and social support. DISCUSSION: Living alone during late life is an important risk factor for dementia, but the cognitive effects of solitary living probably do not take hold immediately for most older adults and potentially demonstrate a dose-response relationship.

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.

Examining the Reciprocity Between Perceived Discrimination and Health: A Longitudinal Perspective.

This study aims to fill two interrelated knowledge gaps in the extant literature on the association between perceived discrimination and health. First, potential selection bias associated with pre-existing health conditions has rarely been rigorously tested in empirical studies. Second, whether there is a reciprocal relationship between perceived discrimination and health has been underexplored. Using longitudinal data from the Americans' Changing Lives data, waves 3 to 5 (N=1,058), we test the reciprocity between perceived discrimination and health with a formal mediation analysis technique. We also use the Heckman correction to adjust for the potential selection bias associated with attrition. Our analysis indicates that perceived discrimination is associated with poor self-rated health and depressive symptoms even when previous health conditions are considered. Furthermore, net of other confounders, there is a reciprocal relationship between perceived discrimination and depressive symptoms. However, this reciprocity does not hold for self-rated health. These findings indicate that there is a vicious circle between perceived discrimination and mental health. That is, poor mental health may lead to perceived discrimination, and heightened perceived discrimination may subsequently increase depressive symptoms. Sensitivity tests suggest that this reciprocity may vary by gender and race.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties.

INTRODUCTION: This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate. METHODS: This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022. RESULTS: The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local. CONCLUSIONS: The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

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.

Community Boosts Immunity? Exploring the Relationship Between Social Capital and COVID-19 Social Distancing.

The early stages of the COVID-19 pandemic required a dramatic change in social practices, including distancing from social settings, to limit its spread. While social capital has considerable potential in facilitating the adoption of these norms, it also comes with considerable limitations that potentially undermine its effectiveness. We draw upon recently released mobility data from Google, network data from Facebook, and demographic data from the 2018 American Community Survey to determine how both organizational and networked measures of social capital relate to different forms of distancing. In addition, we employ geographically weighted regression to identify how these relationships vary across the nation. Findings indicate that while both forms of social capital can positively relate to distancing, the impacts are spatially inconsistent and, in some locations, social capital can discourage distancing. In sum, more policy efforts are needed to address not only low-social capital, but also unhelpful social capital.

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.

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.