Kara E. Rudolph

Background: Environmental noise has been linked to negative health outcomes, like poor sleep, poor mental health, and cardiovascular disease, and likely accounts for more than 1 million disability-adjusted life years annually in Western Europe. Adolescence may be a particularly sensitive period for noise exposure due to an increased need for sleep, failure to meet sleep guidelines, and increased risk for first onset of some mental health disorders. However, the potential health effects of living in high-noise environments have not been studied in US adolescents, rarely in European adolescents, and mental health outcomes studied have not corresponded to diagnoses from the Diagnostic and Statistical Manual of Mental Disorders (DSM).Methods: Using a US-based nationally representative survey of urban adolescents (N = 4,508), we estimated associations of day-night average sound levels exceeding the US Environmental Protection Agency’s 55 decibel limit with sleep outcomes and lifetime mental health DSM diagnoses. We implemented doubly robust targeted minimum loss-based estimation coupled with propensity score matching to account for numerous potential adolescent, household, and environmental confounders.Results: Living in a high- versus low-noise Census block group was associated with later bedtimes on weeknights (0.48 hours, 95% confidence interval [CI] = –0.15, 1.12) and weekend nights (0.65 hours, 95% CI = 0.37, 0.93), but not with total hours slept. Associations between living in a high- versus low-noise Census block group and mental disorders were mixed, with wide CIs, and not robust to sensitivity analyses.Conclusions: We find evidence for an association between residence in a high-noise area and later bedtimes among urban adolescents but no consistent evidence of such an association with mental health disorders.

Transported mediation effects may contribute to understanding how and why interventions may work differently when applied to new populations. However, we are not aware of any estimators for such effects. Thus, we propose several different estimators of transported stochastic direct and indirect effects: an inverse-probability of treatment stabilized weighted estimator, a doubly robust estimator that solves the estimating equation, and a doubly robust substitution estimator in the targeted minimum loss-based framework. We demonstrate their finite sample properties in a simulation study.

Understanding the mediation mechanisms by which an exposure or intervention affects an outcome can provide a look into what has been called a “black box” of many epidemiologic associations, thereby providing further evidence of a relationship and possible points of intervention. Rapid methodologic developments in mediation analyses mean that there are a growing number of approaches for researchers to consider, each with its own set of assumptions, advantages, and disadvantages. This has understandably resulted in some confusion among applied researchers. Here, we provide a brief overview of the mediation methods available and discuss points for consideration when choosing a method. We provide an in-depth explication of 2 of the many potential estimators for illustrative purposes: the Baron and Kenny mediation approach, because it is the most commonly used, and a recently developed approach for estimating stochastic direct and indirect effects, because it relies on far fewer assumptions. We illustrate the decision process and analytical procedure by estimating potential school- and peer-based mechanisms linking neighborhood poverty to adolescent substance use in the National Comorbidity Survey Adolescent Supplement.