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Although early sexual initiation has been linked to negative outcomes, it is unknown whether these effects are causal. In this study, we use propensity score methods to estimate the causal effect of early sexual initiation on young adult sexual risk behaviors and health outcomes using data from the National Longitudinal Study of Adolescent to Adult Health. We found that early sexual initiation predicted having two or more partners (for both males and females) and having a sexually transmitted infection in the past year (females only) but did not predict depressive symptoms in the past week (for either gender). These results underscore the importance of continued programmatic efforts to delay age of sexual initiation, particularly for females.

Mediation analysis has been widely applied to explain why and assess the extent to which an exposure or treatment has an impact on the outcome in health psychology studies. Identifying a mediator or assessing the impact of a mediator has been the focus of many scientific investigations. This tutorial aims to introduce causal mediation analysis with binary exposure, mediator, and outcome variables, with a focus on the resampling and weighting methods, under the potential outcomes framework for estimating natural direct and indirect effects. We emphasize the importance of the temporal order of the study variables and the elimination of confounding. We define the causal effects in a hypothesized causal mediation chain in the context of one exposure, one mediator, and one outcome variable, all of which are binary variables. Two commonly used and actively maintained R packages, mediation and medflex, were used to analyze a motivating example. R code examples for implementing these methods are provided. [ABSTRACT FROM AUTHOR]

Background: Students with early-starting conduct problems often do poorly in school; they are disproportionately placed in restrictive educational placements outside of mainstream classrooms. Although intended to benefit students, research suggests that restrictive placements may exacerbate the maladjustment of youth with conduct problems. Mixed findings, small samples, and flawed designs limit the utility of existing research. Methods: This study examined the impact of restrictive educational placements on three adolescent outcomes (high school noncompletion, conduct disorder, depressive symptoms) in a sample of 861 students with early-starting conduct problems followed longitudinally from kindergarten (age 5-6). Causal modeling with propensity scores was used to adjust for confounding factors associated with restrictive placements. Analyses explored the timing of placement (elementary vs. secondary school) and moderation of impact by initial problem severity. Results: Restrictive educational placement in secondary school (but not in elementary school) was iatrogenic, increasing the risk of high school noncompletion and the severity of adolescent conduct disorder. Negative effects were amplified for students with conduct problem behavior with less cognitive impairment. Conclusions: To avoid harm to students and to society, schools must find alternatives to restrictive placements for students with conduct problems in secondary school, particularly when these students do not have cognitive impairments that might warrant specialized educational supports.

In this article, we study the causal inference problem with a continuous treatment variable using propensity score-based methods. For a continuous treatment, the generalized propensity score is defined as the conditional density of the treatment-level given covariates (confounders). The dose–response function is then estimated by inverse probability weighting, where the weights are calculated from the estimated propensity scores. When the dimension of the covariates is large, the traditional nonparametric density estimation suffers from the curse of dimensionality. Some researchers have suggested a two-step estimation procedure by first modeling the mean function. In this study, we suggest a boosting algorithm to estimate the mean function of the treatment given covariates. In boosting, an important tuning parameter is the number of trees to be generated, which essentially determines the trade-off between bias and variance of the causal estimator. We propose a criterion called average absolute correlation coefficient (AACC) to determine the optimal number of trees. Simulation results show that the proposed approach performs better than a simple linear approximation or L2 boosting. The proposed methodology is also illustrated through the Early Dieting in Girls study, which examines the influence of mothers’ overall weight concern on daughters’ dieting behavior.

This article presents marginal structural models with inverse propensity weighting (IPW) for assessing mediation. Generally, individuals are not randomly assigned to levels of the mediator. Therefore, confounders of the mediator and outcome may exist that limit causal inferences, a goal of mediation analysis. Either regression adjustment or IPW can be used to take confounding into account, but IPW has several advantages. Regression adjustment of even one confounder of the mediator and outcome that has been influenced by treatment results in biased estimates of the direct effect (i.e., the effect of treatment on the outcome that does not go through the mediator). One advantage of IPW is that it can properly adjust for this type of confounding, assuming there are no unmeasured confounders. Further, we illustrate that IPW estimation provides unbiased estimates of all effects when there is a baseline moderator variable that interacts with the treatment, when there is a baseline moderator variable that interacts with the mediator, and when the treatment interacts with the mediator. IPW estimation also provides unbiased estimates of all effects in the presence of nonrandomized treatments. In addition, for testing mediation we propose a test of the null hypothesis of no mediation. Finally, we illustrate this approach with an empirical data set in which the mediator is continuous, as is often the case in psychological research. (Contains 5 tables and 8 figures.)

BackgroundAn increasing number of mobile app interventions have been developed for problem drinking among college students; however, few studies have examined the integration of a mobile app with continuous physiological monitoring and alerting of affective states related to drinking behaviors. ObjectiveThe aim of this paper was to evaluate the acceptability and feasibility of Mind the Moment (MtM), a theoretically based intervention for female college students with problem drinking that combines brief, in-person counseling with ecological momentary intervention (EMI) on a mobile app integrated with a wearable sensorband. MethodsWe recruited 10 non-treatment seeking, female undergraduates from a university health clinic who scored a 3 or higher on the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) to participate in this pilot study. Study activities involved an in-person baseline intake and 1 follow-up assessment, 2 in-person alcohol brief intervention counseling sessions, and use of MtM technology components (sensorband and EMI on a mobile app) for approximately 3-4 weeks. The intervention used motivational interviewing (MI) and cognitive behavioral therapy (CBT) strategies for reducing risks associated with drinking. We used both qualitative and quantitative assessments to measure acceptability of the intervention and feasibility of delivery. Use patterns of the sensorband and mobile app were also collected. ResultsQuantitative and qualitative data indicated high levels of acceptability for the MtM intervention. Altogether, participants made reports on the app on 26.7% (78/292) the days the technology was available to them and completed a total of 325 reports with wide variation between participants. Qualitative findings indicated that sensorband-elicited alerts promoted an increase in awareness of thoughts, feelings, and behaviors related to current environmental stressors and drinking behaviors in theoretically meaningful ways. Specific challenges related to functionality and form of the sensorband were identified. ConclusionsDelivering intervention material “just-in-time,” at the moment participants need to use behavioral strategies has great potential to individualize behavioral interventions for reducing problem drinking and other health behaviors. These findings provide initial evidence for the promise of wearable sensors for increasing potency of theoretically grounded mobile health interventions and point to directions for future research and uptake of these technologies.

Using seven waves of data, collected twice a year from the 8th through the 11th grades in a low-resource community in Cape Town, South Africa, we aimed to describe the developmental trends in three specific leisure experiences (leisure boredom, new leisure interests, and healthy leisure) and substance use (cigarettes, alcohol, and marijuana) behaviors and to investigate the ways in which changes in leisure experiences predict changes in substance use behaviors over time. Results indicated that adolescents' substance use increased significantly across adolescence, but that leisure experiences remained fairly stable over time. We also found that adolescent leisure experiences predicted baseline substance use and that changes in leisure experiences predicted changes in substance use behaviors over time, with leisure boredom emerging as the most consistent and strongest predictor of alcohol, cigarette, and marijuana use. Implications for interventions that target time use and leisure experiences are discussed. (Contains 3 notes and 5 tables.)