Faculty Bibliography

IMPORTANCE/UNASSIGNED:Identifying the mechanisms of structural racism, such as racial and ethnic segregation, is a crucial first step in addressing the persistent disparities in access to live donor kidney transplantation (LDKT). OBJECTIVE/UNASSIGNED:To assess whether segregation at the candidate's residential neighborhood and transplant center neighborhood is associated with access to LDKT. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:In this cohort study spanning January 1995 to December 2021, participants included non-Hispanic Black or White adult candidates for first-time LDKT reported in the US national transplant registry. The median (IQR) follow-up time for each participant was 1.9 (0.6-3.0) years. MAIN OUTCOME AND MEASURES/UNASSIGNED:Segregation, measured using the Theil H method to calculate segregation tertiles in zip code tabulation areas based on the American Community Survey 5-year estimates, reflects the heterogeneity in neighborhood racial and ethnic composition. To quantify the likelihood of LDKT by neighborhood segregation, cause-specific hazard models were adjusted for individual-level and neighborhood-level factors and included an interaction between segregation tertiles and race. RESULTS/UNASSIGNED:Among 162 587 candidates for kidney transplant, the mean (SD) age was 51.6 (13.2) years, 65 141 (40.1%) were female, 80 023 (49.2%) were Black, and 82 564 (50.8%) were White. Among Black candidates, living in a high-segregation neighborhood was associated with 10% (adjusted hazard ratio [AHR], 0.90 [95% CI, 0.84-0.97]) lower access to LDKT relative to residence in low-segregation neighborhoods; no such association was observed among White candidates (P for interaction = .01). Both Black candidates (AHR, 0.94 [95% CI, 0.89-1.00]) and White candidates (AHR, 0.92 [95% CI, 0.88-0.97]) listed at transplant centers in high-segregation neighborhoods had lower access to LDKT relative to their counterparts listed at centers in low-segregation neighborhoods (P for interaction = .64). Within high-segregation transplant center neighborhoods, candidates listed at predominantly minority neighborhoods had 17% lower access to LDKT relative to candidates listed at predominantly White neighborhoods (AHR, 0.83 [95% CI, 0.75-0.92]). Black candidates residing in or listed at transplant centers in predominantly minority neighborhoods had significantly lower likelihood of LDKT relative to White candidates residing in or listed at transplant centers located in predominantly White neighborhoods (65% and 64%, respectively). CONCLUSIONS/UNASSIGNED:Segregated residential and transplant center neighborhoods likely serve as a mechanism of structural racism, contributing to persistent racial disparities in access to LDKT. To promote equitable access, studies should assess targeted interventions (eg, community outreach clinics) to improve support for potential candidates and donors and ultimately mitigate the effects of segregation.

Despite well-studied associations of state firearm laws with lower state- and county-level firearm homicide, there is a shortage of studies investigating differences in the effects of distinct state firearm law categories on various cities within the same state using identical methods. We examined associations of 5 categories of state firearm laws-pertaining to buyers, dealers, domestic violence, gun type/trafficking, and possession-with city-level firearm homicide, and then tested differential associations by city characteristics. City-level panel data on firearm homicide cases of 78 major cities from 2010 to 2020 was assessed from the Centers for Disease Control and Prevention's National Vital Statistics System. We modeled log-transformed firearm homicide rates as a function of firearm law scores, city, state, and year fixed effects, along with time-varying city-level confounders. We considered effect measure modification by poverty, unemployment, vacant housing, and income inequality. A one z-score increase in state gun type/trafficking, possession, and dealer law scores was associated with 25% (95% confidence interval [CI]:-0.37,-0.1), 19% (95% CI:-0.29,-0.07), and 17% (95% CI:-0.28, -0.4) lower firearm homicide rates, respectively. Protective associations were less pronounced in cities with high unemployment and high housing vacancy, but more pronounced in cities with high income inequality. In large US cities, state-level gun type/trafficking, possession, and dealer laws were associated with lower firearm homicide rates, but buyers and domestic violence laws were not. State firearm laws may have differential effects on firearm homicides based on city characteristics, and city-wide policies to enhance socioeconomic drivers may add benefits of firearm laws.

Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.

The present study examined the associations between physical characteristics of neighborhoods and sleep health outcomes and assessed the mediating role of physical activity on these associations. A longitudinal study (PHRESH Zzz, n=1,051) was conducted in two low-income, predominately African-American neighborhoods with repeated measures of neighborhood characteristics and sleep health outcomes from 2013 to 2018. Built environment measures of walkability, urban design, and physical neighborhood disorder were captured from systematic field observations. Sleep health outcomes included insufficient sleep, sleep duration, wakefulness after sleep onset (WASO), and sleep efficiency measured from 7-day actigraphy data. G-computations based on structural nested mean models were used to examine the total effects of each built environment feature and causal mediation analyses were used to evaluate direct and indirect effects through physical activity. Urban design features were associated with decreased WASO (β: -1.26, 95% confidence interval [-4.31, -0.33]). Neighborhood disorder (β: -0.46, CI [-0.86, -0.07]) and crime rate (β: -0.54, CI [-0.93, -0.08]) were negatively associated with sleep efficiency. Neighborhood walkability was not associated with sleep outcomes. We did not find a strong and consistent mediating role of physical activity. Interventions to improve sleep should target modifiable factors, including urban design and neighborhood disorder.

INTRODUCTION:The purpose of this study is to examine the associations between built environments and life expectancy across a gradient of urbanicity in the U.S. METHODS:Census tract‒level estimates of life expectancy between 2010 and 2015, except for Maine and Wisconsin, from the U.S. Small-Area Life Expectancy Estimates Project were analyzed in 2022. Tract-level measures of the built environment included: food, alcohol, and tobacco outlets; walkability; park and green space; housing characteristics; and air pollution. Multilevel linear models for each of the 4 urbanicity types were fitted to evaluate the associations, adjusting for population and social characteristics. RESULTS:Old housing (built before 1979) and air pollution were important built environment predictors of life expectancy disparities across all gradients of urbanicity. Convenience stores were negatively associated with life expectancy in all urbanicity types. Healthy food options were a positive predictor of life expectancy only in high-density urban areas. Park accessibility was associated with increased life expectancy in all areas, except rural areas. Green space in neighborhoods was positively associated with life expectancy in urban areas but showed an opposite association in rural areas. CONCLUSIONS:After adjusting for key social characteristics, several built environment characteristics were salient risk factors for decreased life expectancy in the U.S., with some measures showing differential effects by urbanicity. Planning and policy efforts should be tailored to local contexts.

Sleep disparities in sexual minority male (SMM) populations have received relatively little attention but they may be critical to explaining other health disparities seen among SMM, via neural or hormonal pathways. Recent research suggests that crime may be a psychosocial stressor that contributes to sleep disparities but that finding has been based on subjective measures of crime. We conducted the P18 Neighborhood Study of 250 SMM in New York City, including 211 with adequate GPS tracking data. We used the GPS tracking data to define daily path area activity spaces and tested the associations of violent crime in those activity spaces and in the subject's residential neighborhood, perceived neighborhood safety, and witnessing crime with a subjective measure of sleep. Using quasi-Poisson regression, adjusted for individual and neighborhood socio-demographics, we found that SMM who witnessed more types of crime experienced significantly more nights of poor sleep over the course of a month (RR: 1.16, 95%CI: 1.05-1.27, p-value: < 0.01). We did not find any associations between violent crime rates in either the activity area or residential area and sleep. Our findings support the conclusion that personal exposure to crime is associated with sleep problems and provide further evidence for the pathway between stress and sleep. The lack of association between neighborhood crime levels and sleep suggests that there must be personal experience with crime and ambient presence is insufficient to produce an effect.

PURPOSE/OBJECTIVE:To examine associations between COVID-19-related stressors and symptoms of depression and anxiety in Black cisgender sexual minority men (SMM) and transgender women during the initial peak of the COVID-19 pandemic. METHODS:Participants from the N2 Cohort Study comprised Black cisgender SMM and Black transgender women in Chicago, IL, completed a face-to-face video or phone interview between April 20 and July 31, 2020. The survey included 18 measures of individual, network, and structural COVID-19 stressors such as income loss, network COVID-19 diagnoses, and housing loss, as well as 5 outcome measures: anxiety, depression, loneliness, worry and hope. RESULTS:Of 226 participants, 56.6% experienced anxiety on at least 1 of the last 14 days, 48.7% experienced depression, 48.7% experienced loneliness, 42.0% experienced worry, and 51.8% did not experience hope. Completing the study during a later phase of reopening was associated with hopefulness, RR = 1.37 95% CI [1.02, 1.85]. Fifteen of the 18 multi-level COVID-19 stressors were associated with 1 or more symptoms of depression and anxiety, for example, physical stress reactions, income loss, food loss, medication loss, network COVID-19 diagnoses, partner violence, housing loss, and neighborhood pandemic concerns (aRRs = 0.61-2.78, ps < 0.05). CONCLUSION/CONCLUSIONS:COVID-19-related stressors were associated with depression and anxiety symptoms in Black cisgender SMM and transgender women. Mitigation strategies to reduce virus transmission should be supplemented with measures to prevent depression and anxiety among marginalized populations, such as targeted economic relief and eHealth/mHealth interventions.

OBJECTIVES:To determine the association between individual, network, and structural COVID-19-related stressors and changes in sleep duration and quality among Black cisgender sexual minority men (SMM) and Black transgender women during the COVID-19 peak infectivity rate in Chicago. METHODS:From April 20, 2020 to July 31, 2020, we conducted the N2 COVID Study in Chicago (n = 226). The survey included questions regarding multi-level COVID-19-related stressors (eg, food unavailability, partner violence, housing instability, concern about neighborhood COVID-19), sleep duration, and sleep quality. RESULTS:About 19.5% of our sample reported a shorter duration of sleep during the initial peak COVID-19 infectivity, while 41.2% reported more sleep and 38.9% reported about the same. Compared to the prepandemic period, 16.8% reported that their sleep quality worsened in the COVID-19 pandemic, while 27.9% reported their sleep quality had improved and 55.3% reported it was about the same. In multivariable models, we found that ≥1 day of physical stress reaction, worrying about being infected with COVID-19, traveling during COVID-19 being a financial burden, not having enough medication, knowing someone who was diagnosed with COVID-19, partner violence and housing instability were associated with poor sleep health in the COVID-19 pandemic (adjusted risk ratio: 1.82-3.90, p &lt; .05). CONCLUSIONS:These data suggest that COVID-19-related stressors impacted poor sleep duration and quality during the pandemic among this cohort. Multi-level interventions to reduce COVID-19-related stressors (eg, meditation, intimate partner violence prevention and housing programs) may be useful for improving sleep health among Black cisgender sexual minority men and Black transgender women.

STUDY OBJECTIVES/OBJECTIVE:Characteristics of neighborhood social environments, such as safety and social cohesion, have been examined as determinants of poor sleep. The current study investigates associations between neighborhood social characteristics and sleep health, as well as the mediating role of psychological distress on these possible associations. METHODS:Three waves of PHRESH Zzz (n=2,699), a longitudinal study conducted in two low-income, predominately Black neighborhoods, were utilized for this analysis. Characteristics of neighborhood social environments were measured using crime rates, a neighborhood social disorder index, and self-reported social cohesion. Sleep health was measured via 7 days of wrist-worn actigraphy as insufficient sleep, sleep duration, wake after sleep onset (WASO), and sleep efficiency. G-estimations based on structural nested mean models and mediation analyses were performed to estimate effects of neighborhood social environments on sleep as well as direct/indirect effects through psychological distress. RESULTS:Crime rate around residential addresses was associated with increased risk of insufficient sleep (risk ratio: 1.05 [1.02, 1.12]), increased WASO (β: 3.73 [0.26, 6.04]), and decreased sleep efficiency (β: -0.54 [-0.91, -0.09]). Perceived social cohesion was associated with decreased risk of insufficient sleep (OR: 0.93 [0.88, 0.97]). Psychological distress mediated part of the associations of crime and social cohesion with insufficient sleep. CONCLUSIONS:Neighborhood social environments may contribute to poor sleep health in low-income, predominantly Black neighborhoods, and psychological distress can be a salient pathway linking these neighborhood characteristics and sleep health.