Neighborhood Characteristics

ImportanceResidence in a disadvantaged neighborhood is a key driver of racial and ethnic disparities in the diagnosis and management of chronic diseases; however, its impact on disparities in access to waitlisting and kidney transplantation (KT) is unclear.ObjectiveTo examine the association between neighborhood disadvantage and access to waitlisting and KT.Design, setting, and participantsThis retrospective cohort study (January 1, 2015, to December 31, 2021) used a US national registry to assess adults (aged ≥18 years) with end-stage kidney disease (ESKD) and adult KT candidates. Statistical analysis was performed in March 2025.ExposureResidential neighborhood disadvantage score (built environment disadvantage, criminal injustice, education disadvantage, unemployment, housing instability, poverty, social fragmentation, transportation barrier, and wealth inequality) ascertained by American Community Survey and other public data sources.Main outcomes and measuresThe adjusted hazard ratios (AHRs) of waitlisting and KT (any KT, live-donor KT [LDKT], and preemptive KT) were assessed across tertiles of the neighborhood disadvantage score using cause-specific hazard models. Interaction terms were used to quantify these aforementioned associations by race and ethnicity.ResultsThe study included 501 444 adults with ESKD initiating dialysis (mean [SD] age, 63.9 [14.6] years; 293 937 [58.6%] male; 25 790 [5.1%] Asian [Asian American, Native Hawaiian, and Pacific Islander], 133 923 [26.7%] Black, 66 323 [13.2%] Hispanic, and 275 408 [54.9%] White) and 95 068 KT candidates on the waitlist (mean [SD] age, 53.7 [13.0] years; 60 328 [63.5%] male; 6956 [7.3%] Asian, 25 215 [26.5%] Black, 15 685 [16.5%] Hispanic, and 47 212 [49.7%] White). A total of 173 880 adults with ESKD (34.7%) and 26 718 KT candidates (28.1%) resided in high-disadvantage neighborhoods. After adjustment, adults residing in high-disadvantage neighborhoods were less likely to be waitlisted (AHR, 0.71; 95% CI, 0.69-0.72) compared with those in low-disadvantage neighborhoods. Specifically, Asian (AHR, 0.87; 95% CI, 0.80-0.95), Black (AHR, 0.68; 95% CI, 0.66-0.70), Hispanic (AHR, 0.89; 95% CI, 0.86-0.92), and White (AHR, 0.68; 95% CI, 0.66-0.71) adults in high-disadvantage neighborhoods were less likely to be waitlisted compared with White adults in low-disadvantage neighborhoods. Overall, candidates residing in high-disadvantage neighborhoods were less likely to receive any KT (AHR, 0.89; 95% CI, 0.87-0.92), LDKT (AHR, 0.65; 95% CI, 0.62-0.69), and preemptive KT (AHR, 0.62; 95% CI, 0.58-0.67). Notably, Black candidates residing in high-disadvantage neighborhoods were less likely to receive KT (AHR, 0.60; 95% CI, 0.58-0.62), LDKT (AHR, 0.23; 95% CI, 0.21-0.25), and preemptive KT (AHR, 0.22; 95% CI, 0.20-0.25) compared with White candidates in low-disadvantage neighborhoods.Conclusions and relevanceIn this cohort study of adults with ESKD and KT candidates, residence in high-disadvantage neighborhoods was associated with reduced access to waitlisting and KT; it also was associated with persistent racial and ethnic disparities in LDKT and preemptive KT. These results suggest that to support equitable access, clinicians and transplant programs should work with social workers and community advocates to implement initiatives (eg, outreach and financial support) that address structural barriers and direct resources to affected neighborhoods.

ImportanceBlack, Indigenous, and Latino communities are disproportionately affected by the US overdose epidemic. Structural inequalities, encompassing social, economic, and infrastructural dimensions, have been increasingly theorized as fundamental drivers of these disparities.ObjectiveTo investigate whether there is an association between neighborhood-level structural racism and opioid-involved overdose deaths in an urban area.Design, setting, and participantsThis ecological serial cross-sectional study of 796 census tracts (2017-2019) and 792 census tracts (2020-2022) in Chicago, Illinois, used a geospatial and intersectional analytic approach. A quasi-Poisson spatial regression was conducted to examine associations between neighborhood-level structural racism and census tract-level opioid-involved overdose deaths before the COVID-19 pandemic (2017-2019) and during the COVID-19 pandemic (2020-2022). Eigenvector spatial filtering was used to control for residual spatial autocorrelation. Population density was also accounted for in the regression model. Two structural racism indicators (historical redlining and contemporary racialized economic segregation) were combined to develop an index that captures 4 distinct neighborhood intersectional groups of racism over an 80-year period. Average marginal effect calculations were also performed to support the interpretability of the findings. Data were analyzed from February 19, 2024, to July 3, 2025.ExposureA combined measure of 2 structural racism indicators (historical redlining and contemporary racialized economic segregation).Main outcomes and measuresOverdose deaths were aggregated to census tracts; the main outcome measure was the number of overdose deaths at the census tract-level.ResultsThe total sample sizes were 796 census tracts before the COVID-19 pandemic (2017-2019) and 792 census tracts during the COVID-19 pandemic (2020-2022). As defined by the study's combined measure of structural racism, census tracts with high levels of racism in the past and/or present showed statistically significantly higher number of fatal overdoses compared with tracts with low levels of racism both in the past and present. Just before the COVID-19 pandemic (ie, 2017-2019), tracts with high sustained levels of structural racism past and present had, on average, over 2 more fatal overdoses per tract compared with sustained advantaged tracts (average marginal effect, 2.60; 95% CI, 2.02-3.19; P Conclusions and relevanceThese findings provide preliminary evidence that structural racism could be a root cause of opioid-involved overdose deaths. Future research is needed to identify mechanisms linking structural racism to overdose deaths and to develop effective policies and programs to reduce fatal overdose rates.

IntroductionSleep, depression, stress, and neighborhood support are independently linked to cognition, but how these factors interact when sleep quality is poor remains understudied.MethodsWe analyzed cross-sectional baseline data from 233 adults aged ≥ 65 years in the Aging Adult Brain Connectome study. Sleep quality, depressive symptoms, stress, and neighborhood support were assessed with validated scales, and cognition was measured using the Preclinical Alzheimer's Cognitive Composite (PACC). Models tested two- and three-way interactions, adjusting for sociodemographics.ResultsPoor sleep quality was associated with lower PACC scores (β = -0.57, p = 0.002). This association was even more pronounced in older adults who also had depressive symptoms (β = -0.09, p DiscussionPoor sleep quality was associated with lower cognition, compounded by psychosocial burden and buffered by neighborhood support.HighlightsPoor sleep quality worsened late-life cognitive performance in older adults. Depressive symptoms and stress further worsened the effect of poor sleep on cognitive performance. Neighborhood support buffered negative sleep-psychosocial impacts on cognitive performance.

ImportanceSeveral integrative indices at the neighborhood level have been developed in the US, but direct comparisons across these indices for the prevalence of cardiovascular-kidney-metabolic (CKM) conditions are limited. Moreover, it is not known whether certain indices better capture place-based variability in CKM conditions or provide additional information when compared with a single measure of income.ObjectiveTo determine how much variability is explained by neighborhood indices in the prevalence of CKM conditions at the census tract level and the incremental value of each index when added to median household income.Design, setting, and participantsThis cross-sectional study of US national surveillance data (Behavioral Risk Factor Surveillance System and American Community Survey) included all census tracts with complete data for exposures, covariates, and outcomes from 2010 to 2022. Analyses were completed between October 2024 and July 2025.ExposuresSeven neighborhood indices available at the census tract level (Area Deprivation Index, Child Opportunity Index, Environmental Justice Index, Neighborhood Deprivation Index, Social Deprivation Index, Social Vulnerability Index, Structural Racism Effect Index) and median household income at the census tract level from the American Community Survey.Main outcomes and measuresThe primary outcome was prevalence of CKM conditions (coronary heart disease [CHD], stroke, and chronic kidney disease [CKD]) at the census tract level. Differences in the exposures and outcomes were visualized by mapping index scores, median household income, and prevalences of CKM conditions for all census tracts. To assess index agreement, pairwise correlations were computed with Spearman rank correlation coefficients, and to assess the incremental variability explained by each index when added to median household income, change in r2 was calculated.ResultsOf the 65 476 US census tracts included, median (IQR) prevalence was 5.9% (4.7%-7.4%) for CHD, 3.3% (2.6%-4.1%) for stroke, and 2.9% (2.5%-3.4%) for CKD. The indices and income were modestly to highly correlated (range, 0.46-0.94), with some discordance in how each measure classified census tracts by quartiles of index scores. All indices and income were significantly associated with CKM condition prevalence at the census tract level in multivariable linear regression models, adjusted for median population size and age. The r2 values of the indices with CHD, stroke, and CKD ranged from 0.379 (SE = 0.033) for the correlation between the Environmental Justice Index and stroke to 0.688 (SE = 0.002) for the correlation between the Structural Racism Effect Index and stroke. Additionally, the improvement in variability for CHD, stroke, and CKD (change in r2) explained by the addition of each index to income ranged from 0.014 (SE = 0.001) for the correlation between the Environmental Justice Index and CHD to 0.195 (SE = 0.002) for the correlation between Structural Racism Effect Index and stroke.Conclusions and relevanceIn this cross-sectional study, there were similar associations across neighborhood indices and income with the prevalence of CKM conditions. These findings inform how different place-based measures can be applied in public health research and policy.