Faculty Bibliography

The AIDS and COVID-19 pandemics demonstrated that nations at similar economic development levels varied widely in their capacity to protect the health of their residents. For AIDS, Britain and Australia brought gay representatives into official counsels and adopted harm reduction far more rapidly than the United States or Spain, and East African countries responded more effectively than South Africa or the Democratic Republic of the Congo. National responses to COVID-19 varied widely, with New Zealand, China, and Vietnam more effective than Italy, Brazil, or the United States. Further, as phylogenetic research has demonstrated, these pandemics spread from one country to another, with those that responded poorly acting as sources for mutations and potentially sources of transmission to countries with more effective responses. Many observers expressed surprise at the poor responses of the United States to COVID-19, but in retrospect the cutbacks in public health funding at state and national levels made it clear that this was a predictable weakness even in addition to the political vacillations that crippled the US and Brazilian responses. In a time of global sociopolitical and climate instability, it is important to measure and conduct research into spatial and time variations in 1. public health and medical funding, 2. social influence networks, social cohesion and trust, and stigmatization, 3. income inequality, 4. social conflict, and 5. other factors that affect responsiveness to pandemics.

INTRODUCTION/BACKGROUND:Neighborhood walkability has been established as a potentially important determinant of various health outcomes that are distributed inequitably by race/ethnicity and sociodemographic status. The objective of this study is to assess the differences in walkability across major urban centers in the U.S. METHODS:City- and census tract-level differences in walkability were assessed in 2020 using the 2019 Walk Score across 500 large cities in the U.S. RESULTS:At both geographic levels, high-income and majority White geographic units had the lowest walkability overall. Walkability was lower with increasing tertile of median income among majority White, Latinx, and Asian American and Native Hawaiian and Pacific Islander neighborhoods. However, this association was reversed within majority Black neighborhoods, where tracts in lower-income tertiles had the lowest walkability. Associations varied substantially by region, with the strongest differences observed for cities located in the South. CONCLUSIONS:Differences in neighborhood walkability across 500 U.S. cities provide evidence that both geographic unit and region meaningfully influence associations between sociodemographic factors and walkability. Structural interventions to the built environment may improve equity in urban environments, particularly in lower-income majority Black neighborhoods.

Diabetes and hypertension are socially patterned by individual race/ethnicity and by neighborhood economic context, but distributions among Asian subgroups are undercharacterized. We examined variation in prevalence for both conditions, comparing between US Asian subgroups, including within South Asian nationalities, and comparing within subgroups by neighborhood economic context. We obtained data on a non-probability sample of 633,664 patients ages 18-64 in New York City, NY, USA (2014-2017); 30,138 belonged to one of seven Asian subgroups (Asian Indian, Bangladeshi, Pakistani, Chinese, Korean, Japanese, and Filipino). We used electronic health records to classify disease status. We characterized census tract economic context using the Index of Concentration at the Extremes and estimated prevalence differences using multilevel models. Among Asian men, hypertension prevalence was highest for Filipinos. Among Asian women, hypertension prevalence was highest for Filipinas and Bangladeshis. Diabetes prevalence was highest among Pakistanis and Bangladeshis of both genders, exceeding all other Asian and non-Asian groups. There was consistent evidence of an economic gradient for both conditions, whereby persons residing in the most privileged neighborhood tertile had the lowest disease prevalence. The economic gradient was particularly strong for diabetes among Pakistanis, whose prevalence in the most deprived tertile exceeded that of the most privileged by 9 percentage points (95% CI 3, 14). Only Koreans departed from the trend, experiencing the highest diabetes prevalence in the most privileged tertile. US Asian subgroups largely demonstrate similar neighborhood economic gradients as other groups. Disaggregating Asian subgroups, including within South Asian nationalities, reveals important heterogeneity in prevalence.

We evaluated whether using county-level data to characterize public health measures in cities biases the characterization of city populations. We compared 4 public health and sociodemographic measures in 447 US cities (percent of children living in poverty, percent of non-Hispanic Black population, age-adjusted cardiovascular disease mortality, life expectancy at birth) to the same measures calculated for counties that contain those cities. We found substantial and highly variable city-county differences within and across metrics, which suggests that use of county data to proxy city measures could hamper accurate allocation of public health resources and appreciation of the urgency of public health needs in specific locales.

BACKGROUND AND AIMS/OBJECTIVE:While substance use can lead to incarceration, the disruptive effects of incarceration may lead to, or increase psychosocial vulnerability and substance use. Using causal inference methods, we measured longitudinal associations between incarceration and post-release substance use among Black men who have sex with men (BMSM), populations facing disproportionate risk of incarceration and substance use. METHODS:Using data from the HIV Prevention Trials Network (HPTN 061) study (N = 1553) we estimated associations between past 6-month incarceration and binge drinking, marijuana use, and stimulant use post release (at 12-month follow-up visit). Adjusted models used inverse probability weighting (IPW) to control for baseline (pre-incarceration) substance use and additional risk factors. RESULTS:There were 1133 participants present at the twelve-month follow-up visit. Participants were predominately non-Hispanic Blacks and unemployed. At baseline, 60.1 % reported a lifetime history of incarceration, 22.9 % were HIV positive and 13.7 % had a history of an STI infection. A total of 43 % reported a history of depression. In adjusted analyses with IPW, recent incarceration was associated with crack-cocaine (adjusted odds ratio (AOR): 1.53, 95 % confidence interval (CI): 1.03, 2.23) and methamphetamine use (AOR: 1.52, 95 % CI: 0.94-2.45). Controlling for pre-incarceration binge drinking, incarceration was associated with post-release binge drinking (AOR: 1.47, 95 % CI: 1.05, 2.04); in fully adjusted models the AOR was 1.14 (95 % CI: 0.81, 1.62). Incarceration was not associated with marijuana use. CONCLUSION/CONCLUSIONS:Findings underscore the need to provide substance use treatment in custody and post-release, and to consider alternatives to incarceration for substance using populations.

Using data from the United States Behavioral Risk Factor Surveillance System (2003-2012; N = 3,397,124 adults), we estimated associations between prevalent diabetes and four county-level exposures (fast food restaurant density, convenience store density, unemployment, active commuting). All associations confirmed our a priori hypotheses in conventional multilevel analyses that pooled across years. In contrast, using a random-effects within-between model, we found weak, ambiguous evidence that within-county changes in exposures were associated with within-county change in odds of diabetes. Decomposition revealed that the pooled associations were largely driven by time-invariant, between-county factors that may be more susceptible to confounding versus within-county associations.

OBJECTIVE:Researchers have linked geographic disparities in obesity to community-level characteristics, yet many prior observational studies have ignored temporality and potential for bias. METHODS:Repeated cross-sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003-2012) to examine the influence of county-level characteristics (active commuting, unemployment, percentage of limited-service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5-year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county-mean-centered values for each year. Cross-sectional (between-county) and longitudinal (within-county) associations were estimated using a random-effects within-between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county-years within counties within states. RESULTS:(95% CI: -0.72 to -0.31) decrease in BMI over time. CONCLUSIONS:These results suggest that community-level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.

Purpose: Non-Hispanic black communities in the US experience below-average life expectancy (LE). However, little is known about how the magnitude of these inequities vary between major US cities. We sought to understand variability in the relationship between percent of census tract residents who were non-Hispanic black and tract-level LE.
Method(s): We obtained census tract-level estimates of LE in 492 large US cities from the US Small Area Life Expectancy Estimates Project and combined them with socio-demographic data from the American Community Survey. We fit a multilevel linear null model to partition the variance in LE between the tract, city, and state levels. We estimated a random slope model to quantify the degree to which the association between percent non-Hispanic black and LE in census tracts varied between cities.
Result(s): In a null model, 10% of LE variation was at the state level, 21% at the city level, and 69% was within cities at the tract level. Detroit and Flint, Michigan, both majority-black cities, had the lowest city-level average LE estimates (>5 years below average), and Chicago had the widest range for tract LEs (30.1 years). Nationally, a 10-point increase in tract percent non-Hispanic black was associated with 1.1 years shorter LE (95% CI: 1.0, 1.1). However, there was considerable variation in this association (standard deviation for random slope = 0.29).
Conclusion(s): The magnitude of inequalities in LE by tract racial composition varied considerably between cities. Further research to understand this variability can inform efforts to address urban health inequities.
Copyright