Mediating role of psychological distress in the associations between neighborhood social environments and sleep health

Abstract

Study objectives: 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.

Methods: 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.

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.

Keywords: actigraphy; crime; mediation analysis; neighborhood; neighborhood disorder; psychological distress; sleep; social cohesion.

Figure 1.

Conceptual diagram.

Figure 2.

Directed acyclic graph (DAG) for mediation analysis. A_i,j represents neighborhood characteristics for ith subject at jth visit; M_i,j represents psychological distress for ith subject at jth visit; Y_i,j represents sleep health outcomes for ith subject at jth visit. C_1,2,3i,j represents exposure–outcome confounders (C₁), exposure–mediator confounders (C₂), and mediator–outcome confounders (C₃). Arrows from j to j + 1 were omitted for readability.

Figure 3.

Exposure–mediator and mediator–outcome associations. Each mixed-effects regression model was adjusted for age, sex, income, education, employment, marital status, family structure, and the number of years in current neighborhood.

Figure 4.

Mediation analysis results. 4-a is for insufficient sleep, 4-b for sleep duration, 4-c for WASO, and 4-d for sleep efficiency outcomes. Each plot represents the four built environment characteristics. Total, total effect, NDE, natural direct effect, NIE, natural indirect effect.

Figure 4.

Mediation analysis results. 4-a is for insufficient sleep, 4-b for sleep duration, 4-c for WASO, and 4-d for sleep efficiency outcomes. Each plot represents the four built environment characteristics. Total, total effect, NDE, natural direct effect, NIE, natural indirect effect.