The 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. Three waves of PHRESH Zzz (n = 2699), a longitudinal study conducted in two low-income, predominately Black neighborhoods, were utilized for this analysis. The 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 the effects of neighborhood social environments on sleep as well as direct/indirect effects through psychological distress. 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. 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.

In the present study, we examined the associations between physical characteristics of neighborhoods and sleep health outcomes and assessed the mediating role of physical activity in these associations. A longitudinal study (the Pittsburgh Hill/Homewood Research on Eating, Shopping, and Health (PHRESH) Zzz Study; n = 1,051) was conducted in 2 low-income, predominately African-American neighborhoods in Pittsburgh, Pennsylvania, with repeated measures of neighborhood characteristics and sleep health outcomes from 2013 to 2018. Built environment measures of walkability, urban design, and neighborhood disorder were captured from systematic field observations. Sleep health outcomes included insufficient sleep, sleep duration, wakefulness after sleep onset, 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 operating through physical activity. Urban design features were associated with decreased wakefulness after sleep onset (risk difference (RD) = −1.26, 95% confidence interval (CI): −4.31, −0.33). Neighborhood disorder (RD = −0.46, 95% CI: −0.86, −0.07) and crime rate (RD = −0.54, 95% 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.

The geographic availability of pre-exposure prophylaxis (PrEP) providers is one important factor that significantly affects PrEP uptake. While most previous studies have employed spatial accessibility in static residential neighborhood definitions or self-reported healthcare accessibility, we examined the associations of the objectively measured geographic density of PrEP services with current PrEP use, using global positioning system (GPS) among sexual minority men (SMM) in New York City. 250 HIV-negative SMM participated in a 2-week GPS monitoring (January 2017–January 2018). Geographic PrEP density was measured as total numbers of PrEP providers in (1) individual activity space defined as daily path area of GPS points, (2) residential street network buffers and (3) census tract and ZIP code of residential locations. Geographic PrEP density within GPS-based activity space was positively associated with current PrEP use (prevalence ratio for 50-m activity space = 1.10, 95% confidence interval: [1.02, 1.18]). PrEP provider counts in residential buffer areas and administrative neighborhoods were not associated with PrEP use. Although it is not generalizable beyond New York City, our finding suggests the importance of daily mobility pattern in HIV prevention and PrEP implementation strategies.