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Background: Prominent theories suggest that individuals with co-occurring traumatic stress symptoms (TSS) and substance use (SU) may be less responsive to SU treatment compared to those with SU only. However, empirical findings in adult samples are mixed, and there has been limited work among adolescents. This study assesses the association between TSS and SU treatment outcomes among trauma-exposed adolescents, using statistical methods to reduce potential confounding from important factors such as baseline SU severity. Method: 2,963 adolescents with lifetime history of victimization received evidence-based SU treatment in outpatient community settings. At baseline, 3- and 6-months, youth were assessed using the Global Appraisal of Individual Needs Traumatic Stress Scale and the Substance Frequency Scale. Propensity score weighting was used to mitigate potential confounding due to baseline differences in sociodemographic characteristics and SU across youth with varying levels of TSS. Results: Propensity score weighting successfully balanced baseline differences in sociodemographic factors and baseline SU across youth. Among all youth, mean SU was lower at both 3- and 6- month follow-up relative to baseline, indicating declining use. After adjusting for potential confounders, we observed no statistically significant relationship between TSS and SU at either 3- or 6-month follow-up. Conclusions: Based on this investigation, conducted among a large sample of trauma-exposed youth receiving evidence-based outpatient SU treatment, baseline TSS do not appear to be negatively associated with SU treatment outcomes. However, future research should examine whether youth with TSS achieve better outcomes through integrative treatment for both SU and TSS. The results of this study provide keenly needed evidence that, among youth with prior victimization, presence and level of traumatic stress symptoms at substance use treatment initiation does not lead to significantly worse treatment outcomes for youth in outpatient treatment. This suggests that evidence-based outpatient substance use treatment modalities may be effective at improving substance use outcomes even when co-existing traumatic stress symptoms are present.

Snacking (ie, eating between meals) is common among US preschool-aged children, but associations with weight status are unclear.This research evaluated associations of snack frequency, size, and energy density as well as the percent of daily energy from snacking with weight status and sociodemographic characteristics among US children aged 2 to 5 years.Cross-sectional analysis of 2007-2018 National Health and Nutrition Examination Survey data using two, caregiver proxy, 24-hour dietary recalls.US children aged 2 to 5 years (n = 3,313) with at least one snack occasion over 2 days of intake.Snacking parameters included frequency (number of occasions per day), size (kilocalories per occasion), and energy density (kilocalories per gram per occasion) as well as percent of daily energy from snacking.Generalized linear regression models evaluated associations of snacking with child weight status (ie, normal weight and overweight/obesity), adjusting for survey weights, energy misreporting, mean meal size, and sociodemographic covariates.Children with overweight/obesity consumed more frequent snacks (2.8 [0.06] vs 2.5 [0.03] snacks/day, respectively; P0.001), larger snacks (188 [4] vs 162 [23] kcal/occasion, respectively; P0.001), and a greater percent of daily energy from snacking (29.80% [1.00%] vs 26.09% [0.40%], respectively; P0.001) than children with normal weight. Mean snack frequency and size as well as percentage of daily energy from snacking varied with child age, gender, and head of household education. Associations of snacking with child race and ethnicity were less consistent.These nationally representative findings provide evidence that the consumption of larger, more frequent snacks is associated with overweight/obesity among US children aged 2 to 5 years and snacking varies by sociodemographic characteristics.

OBJECTIVE: This study aimed to evaluate snack food-group composition by weight status among United States adolescents. METHODS: Cross-sectional analysis of adolescent food-group-component intake from snacking occasions using two 24-hour dietary recalls from the 2007 through 2018 National Health and Nutrition Examination Survey (NHANES; n = 5264; 12–19 years) was conducted. ANCOVA models evaluated food intakes by BMI percentile (BMI%; normal weight [NW]

To determine the associations between trauma variables, acute phase-related variables, and patient-level characteristics with functional recovery during inpatient rehabilitation for individuals with spinal cord injury (SCI). The associations were evaluated by linking individuals' records between the Pennsylvania Trauma Systems Outcomes Study and the National SCI Model Systems databases.Retrospective cohort analysis.Two SCI Model Centers in Pennsylvania, United States.We used a record linkage toolkit in Python to link 735 individuals with traumatic SCI between the databases. The percentage for true-match and error were 92.0% and 0.1%, respectively. The functional recovery during inpatient rehabilitation was determined in 604 individuals with SCI by ordinary least squares regression (OLS) and gradient boosting regression (GBR) analyses.The OLS and GBR analyses indicated older age, greater impairment (SCI level combined with American Spinal Injury Association impairment scale), presence of diabetes mellitus, pulmonary complications during acute care, and longer length of stay at an inpatient rehabilitation facility were associated with lower functional recovery (OLSTrauma and acute care variables in addition to patient characteristics were associated with functional recovery during inpatient rehabilitation in individuals with SCI. Further investigation is needed to understand the role of diabetes mellitus and pulmonary complications, which have not been previously associated with functional recovery in individuals with SCI.

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research., 50 pgs., 4 figures

This article is concerned with causal mediation analysis with varying indirect and direct effects. We propose a varying coefficient mediation model, which can also be viewed as an extension of moderation analysis on a causal diagram. We develop a new estimation procedure for the direct and indirect effects based on B-splines. Under mild conditions, rates of convergence and asymptotic distributions of the resulting estimates are established. We further propose a F-type test for the direct effect. We conduct simulation study to examine the finite sample performance of the proposed methodology, and apply the new procedures for empirical analysis of behavioral economics data.

Weight loss interventions are successful to the extent that participants adhere to calorie-reduced dietary prescriptions and may be undermined by dietary lapses triggered by external or internal factors, such as stress. The purpose of this study was to examine whether an acceptance-based treatment (ABT) versus a standard behavioral treatment (SBT) moderated the effect of stress on dietary lapses. Ecological momentary assessment data were collected from 189 participants who were randomly assigned to either ABT or SBT. Between-subject and within-subject stress were used to predict lapse occurrence along with intervention condition, and the interaction between intervention condition and stress using a generalized linear mixed-effects model. Gender, time of day, and baseline mean lapses/day were included as control variables. Higher mean baseline lapses/day, female gender, and later time of day were significantly associated with increased odds of lapse. Between-subject stress was positively and significantly related to the odds of lapse. Compared to SBT, ABT showed a significant decrease in the odds of lapse at an individual’s average level of within-subject stress, but the interaction of intervention condition with within-subject stress was not statistically significant. Participants with high overall stress levels lapsed more frequently than those with low overall stress levels. There was a significant decrease in the odds of lapse for the ABT group at a participant’s own average stress level, suggesting that ABT may be beneficial at participants’ usual stress levels but when they deviate from their usual stress level, there are no treatment differences.

Introduction Smoking history is a known risk factor for significant chronic diseases as well as pulmonary infections; however, the impact of smoking status on coronavirus disease 2019 (COVID-19) outcomes has not been conclusively characterized. This study aims to evaluate the association of smoking status on COVID-19 outcomes, and to explore the mechanism by which smoking and smoking-related comorbidities relate to COVID-19 outcomes. Aims and Methods Patients admitted with SARS-CoV-2 infection from November 2020 through January 2021 were included in this study. Causal mediation models investigating the associations between smoking status and the outcomes of mortality, intensive care unit (ICU) admission, advanced respiratory support, mechanical ventilation, ICU length of stay, and hospital length of stay, through mediation via smoking-related comorbidities, were examined. Results Active smokers did not experience worse COVID-19 outcomes once hospitalized. Former smokers had a higher odds of mortality (total effect OR 1.59, 95% CI 1.07 to 2.38, p = .01; indirect effect OR 1.45, 95% CI 1.09 to 1.93, p < .001), and advanced respiratory support (total effect OR 1.31, 95% CI 1.04 to 1.67, p = .02; indirect effect OR 1.26, 95% CI 1.03 to 1.54, p = .02), which were mediated by smoking-related comorbidities. While there was a nonsignificant increase in the total effect for mechanical ventilation, smoking-related comorbidities were significant mediators for their increased need (total effect OR 1.40, 95% CI 0.92 to 2.14, p = .13; indirect effect OR 1.47, 95% CI 1.10 to 1.87, p < .001). Conclusions Although active smokers did not experience worse COVID-19 outcomes compared to never smokers, these results should be interpreted with caution. Compared to never smokers, former smokers had greater odds of mortality, advanced respiratory support, and mechanical ventilation which was significantly mediated through smoking-related comorbidities. Implications Previous studies have linked smoking status with worse COVID-19 outcomes, and have inferred that smoking-related comorbidities may play a role in these findings. This causal mediation analysis provides statistical evidence supporting this hypothesis, clarifying the risk that smoking-related comorbidities impart on COVID-19 outcomes in those with a smoking history.

OBJECTIVE: This study aimed to examine the relationship between electronic cigarette use and subsequent combustible cigarette use, controlling for confounding by using a propensity score method approach. METHODS: Data from the first three annual waves of the Population Assessment of Tobacco and Health study were analyzed (n = 6309). Participants were tobacco naïve at Wave 1, had used e-cigarettes exclusively (n = 414), used combustible cigarettes exclusively (n = 46), or not used any tobacco products (n = 5849) at Wave 2. We conducted entropy balancing propensity score analysis to examine the association between exclusive e-cigarette or cigarette initiation and subsequent cigarette use at Wave 3, adjusting for nonresponse bias, sampling bias, and confounding. RESULTS: Among tobacco naïve youth, exclusive e-cigarette use was associated with greater risk for subsequent combustible cigarette smoking initiation (OR = 3.42, 95% CI = (1.99, 5.93)) and past 30-day combustible cigarette use (OR = 2.88, 95% CI = (1.22, 6.86)) in the following year. However, the latter risk was comparatively lower than the risk if youth started with a combustible cigarette (OR = 25.79, 95% CI = (9.68, 68.72)). Results of sensitivity analyses indicated that estimated effects were robust to unmeasured confounding. CONCLUSIONS: Use of e-cigarettes in tobacco-naïve youth is associated with increased risk of subsequent past 30-day combustible cigarette use but the risk is an order of magnitude higher if they start with a combustible cigarette.

This study leverages data from a pilot randomized controlled trial to investigate whether the effectiveness of a text-delivered mHealth intervention targeting adolescent depression and anxiety differs according to residential- and activity space-based measures of exposure to community-level socioeconomic disadvantage. For depression, we find that intervention efficacy is significantly stronger for youth residing in more disadvantaged neighborhoods, even after controlling for individual level socioeconomic status, as well as marginal moderating effects of activity space-based neighborhood disadvantage on treatment efficacy. We do not find evidence of treatment efficacy moderation by neighborhood disadvantage regarding anxiety. While the generalizability of our findings is restricted to this sample and for this intervention, this research serves as a motivating example and initial evidence for how mHealth intervention efficacy can vary by characteristics of the environment, in particular community-level disadvantage. Future clinical research should investigate whether the effectiveness of mHealth interventions may be enhanced by personalization based on an individual's contextual environmental exposures.

Background Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e., the indirect effect through the mediator). This indirect effect is frequently assumed to be time-invariant. With improvements in data collection technology, it is possible to obtain repeated assessments over time resulting in intensive longitudinal data. This calls for an extension of traditional mediation analysis to incorporate time-varying variables as well as time-varying effects. Methods We focus on estimation and inference for the time-varying mediation model, which allows mediation effects to vary as a function of time. We propose a two-step approach to estimate the time-varying mediation effect. Moreover, we use a simulation-based approach to derive the corresponding point-wise confidence band for the time-varying mediation effect. Results Simulation studies show that the proposed procedures perform well when comparing the confidence band and the true underlying model. We further apply the proposed model and the statistical inference procedure to data collected from a smoking cessation study. Conclusions We present a model for estimating time-varying mediation effects that allows both time-varying outcomes and mediators. Simulation-based inference is also proposed and implemented in a user-friendly R package.

Introduction The addictive nature of nicotine makes smoking cessation an extremely challenging process. With prolonged exposure, tobacco smoking transforms from being a positive reinforcer to a negative one, as smoking is used to mitigate aversive withdrawal symptoms. Studying the variations in withdrawal symptoms, especially during their peak in the first week of a quit attempt, could help improve cessation treatment for the future. The time-varying mediation model effectively studies whether altering withdrawal symptoms act as mediators in the pathway between treatment and cessation. Aims and Methods This secondary data analysis of a randomized clinical smoking cessation trial of three pharmacotherapy regimens (nicotine patch, varenicline, and nicotine patch + mini-lozenge) analyzes ecological momentary assessment (EMA) data from the first 4 weeks post-target quit day (TQD). We assess whether withdrawal symptoms (eg, negative mood, cessation fatigue, and craving) mediate the pathway between pharmacotherapy and daily smoking status and whether this effect varies over time. Results We found a statistically significant time-varying mediation effect of varenicline on smoking status through craving, which shows decreasing risk of lapse via reduction in craving. We did not find significant time-varying mediation effects through negative mood and cessation fatigue. Conclusions This study supports the importance of craving suppression in the smoking cessation process. It also helped identify specific timepoints when withdrawal symptoms increased that would likely benefit from targeted cessation intervention strategies. Implications This study aimed to understand the underlying dynamic mechanisms of the smoking cessation process using a new analytical approach that capitalizes on the intensive longitudinal data collected via EMAs. The findings from this study further elucidate the smoking cessation process and provide insight into behavioral intervention targets and the timing of such interventions through the estimation of time-varying mediation effects.

Summary Objective Young men are less likely to engage in weight loss behaviours than their female counterparts. This may be because of an increased likelihood for young men, particularly young black men, with overweight/obesity to misperceive their weight status. This study examined racial differences in weight status perception accuracy and associations between this perception and weight loss behaviours among young men. Associations between weight loss behaviours and contextual factors were also explored. Methods Data from 1417 young adult (YA) men with overweight/obesity from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) were analysed. Associations between weight status perception accuracy, contextual factors, and weight loss attempts and behaviours were examined with logistic regression. Results YA men with overweight/obesity were more likely to report weight loss attempts and behaviours if they perceived themselves as being overweight (OR = 3.10; 95% CI, 2.18‐4.41; P < .01; OR = 3.20, 95% CI, 2.16‐4.72, P < .01, respectively). Greater education and income were associated with a greater likelihood of reporting weight loss attempts and healthy weight loss behaviours. Greater reported depressive symptoms were associated with reported weight loss attempts but not healthy weight loss behaviours. There were no differences by race for reported weight loss attempts or behaviours. Conclusion Among YA men with overweight/obesity, perceiving oneself as overweight was associated with reporting weight loss attempts and healthy weight loss behaviours. Future research should consider how weight status perception accuracy affects weight loss attempts among YA men and what additional factors may account for racial differences.

Information criteria (ICs) based on penalized likelihood, such as Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

OBJECTIVES: US adolescents with overweight (OW) and obesity (OB) consume larger and more frequent snacks (i.e., foods consumed between meals) than adolescents with normal weight (NW); but, the food group sources of these snacks by body weight status remains largely unknown. This research aimed to evaluate snack food group composition by weight status among US adolescents. METHODS: Cross-sectional analysis evaluated 10 USDA Food Patterns Equivalents Database (FPED) components from snacking occasions using two, 24-hr dietary recalls among U.S. adolescents (n = 6,591;12–19 y) participating in the 2005–2016 National Health and Nutrition Examination Survey. Analysis of covariance was used to evaluate food intakes by weight status, adjusted for energy misreporting and other covariates. RESULTS: Consumption of added sugars, solid fats, and refined grains from snacks alone represented 64%, 38%, 32%, respectively, of recommended daily limits. Adolescents with OB consumed greater total daily energy from snacks (NW = 441(11)kcal; OW = 540(17)kcal; OB = 645(23)kcal, P

Behavioral weight loss treatment (BT) for individuals with overweight and obesity is effective but leaves room for improvement. Mindfulness and acceptance-based treatments may bolster weight loss outcomes; yet, little is known about the efficacy of the individual components or the combinations of components that are most effective in producing weight loss above and beyond standard BT strategies for weight loss. This protocol manuscript describes the use of a multiphase optimization strategy to evaluate the independent and combinatory efficacy of three mindfulness and acceptance-based components (mindful awareness, willingness, values). Using a 2 × 2 × 2 factorial design, participants (N = 288) will be randomized to one of eight conditions, each representing a combination of core MABT strategies in addition to foundational BT strategies. Assessments occur at baseline, mid-treatment (week 24 through 26), post-treatment, and at 6, 12, and 24-month follow-up. The primary aim is to elucidate the independent efficacy of each MABT component on weight loss above gold-standard BT. The secondary aims are to evaluate the independent effect of these components on calorie intake, physical activity, and overall quality of life; evaluate target engagement (i.e., the degree to which each treatment component affects proposed mechanisms of action); and evaluate the potential moderating effect of susceptibility to internal and external food cues on outcomes. The exploratory aim is to quantify any component interaction effects (which may be synergistic, fully additive, or partially additive).

Background Research has evaluated the effect of surgical timing on patient functional recovery in individuals with spinal cord injury (SCI); however, there is a critical need to assess how demographics, clinical characteristics, and process of care affect functional outcomes. Objective We examined the association between demographic, clinical, and process of care factors with post-acute functional status (locomotion and transfer mobility scores) and discharge disposition (home vs. institution) in individuals with SCI. Methods This study was a retrospective cohort analysis of the Pennsylvania Trauma Systems Outcomes Study (PTOS) database for individuals with traumatic SCI (N = 2223). We conducted multinomial and binomial logistic regression analyses to examine post-acute functional status and discharge disposition, respectively. Results The results indicated that older age, longer length of stay, lower Glasgow Coma Scale (GCS), higher Injury Severity Score (ISS), and individuals with tetraplegia had significantly lower motor functional score at discharge from an acute hospital. In addition, older age, individuals with public-sponsored insurance, longer length of stay, lower GCS, and higher ISS had significantly higher odds of being discharged to an institution, as compared to home. Individuals of Hispanic ethnicity, as compared to White, had lower odds of being discharged to an institution. Conclusions The regression models developed in this study were able to better classify discharge destinations compared to the functional outcomes at discharge from the acute hospital. Further research is necessary to determine how these factors and their associations vary nationally across the US, which have the potential to inform trauma and acute care post-SCI.

Background and Aims EMR and endoscopic submucosal dissection (ESD) are treatment modalities for Barrett’s esophagus involving high-grade dysplasia or early cancer. Injectional corticosteroid therapy decreases the risk of procedure-related esophageal stricture (ES) formation. Our aim was to assess the efficacy of topical budesonide on the rate of ES formation after EMR or ESD. Methods Patients included prospectively from 3 tertiary endoscopy centers received 3 mg budesonide orally twice a day for 8 weeks after esophageal EMR or ESD of 50% or more of the esophageal circumference between January 1, 2014 and June 30, 2018. These patients were matched (1:3 ratio) retrospectively with a consecutive patient cohort who underwent EMR or ESD of 50% or more of the esophageal circumference without concomitant corticosteroid therapy. The primary endpoint was the presence of ES at the 12-week follow-up. Results Twenty-five patients (budesonide) were matched with 75 patients (no budesonide). Most underwent EMR for Barrett’s esophagus with biopsy-proven high-grade dysplasia or suspected T1a cancer. Although most baseline characteristics did not differ significantly, patients in the budesonide cohort tended to have a higher proportion of circumferential EMR. The proportion of patients with ES was not significantly lower in the budesonide cohort (16% vs 28%). On logistic regression analysis, budesonide remained associated with a lower incidence of ES (P = .023); however, when controlling for baseline characteristics with a propensity score weighted logistic regression model, there was no significant effect on ES formation (P = .176). Conclusions Topical budesonide might be associated with a reduction of ES after EMR or ESD; however, further studies are needed to verify our results.

Propensity score models are frequently used to estimate causal effects in observational studies. One unresolved issue in fitting these models is handling missing values in the propensity score model covariates. As these models usually contain a large set of covariates, using only individuals with complete data significantly decreases the sample size and statistical power. Several missing data imputation approaches have been proposed, including multiple imputation (MI), MI with missingness pattern (MIMP), and treatment mean imputation. Generalized boosted modeling (GBM), which is a nonparametric approach to estimate propensity scores, can automatically handle missingness in the covariates. Although the performance of MI, MIMP, and treatment mean imputation have previously been compared for binary treatments, they have not been compared for continuous exposures or with single imputation and GBM. We compared these approaches in estimating the generalized propensity score (GPS) for a continuous exposure in both a simulation study and in empirical data. Using GBM with the incomplete data to estimate the GPS did not perform well in the simulation. Missing values should be imputed before estimating propensity scores using GBM or any other approach for estimating the GPS.

BACKGROUND Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. OBJECTIVE This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. METHODS We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. RESULTS We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. CONCLUSIONS The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.

Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.

A high prevalence of dieting has been reported among preadolescent females. It is important to understand factors influencing the emergence of dieting because dieting is associated with increased likelihood of overeating, greater weight gain over time, and other chronic health problems. Previous studies suggest that mothers' own dieting behavior influences their daughters' dieting (i.e., modeling). Because it is not possible to randomly assign girls to a mother who is dieting versus not dieting, causal inference regarding the effects of mothers' modeling behaviors on daughters' dieting is not straightforward.In an observational study, data were collected on four occasions of measurement across a 6-year period, with 2-year intervals between assessments on 181 girls and their parents. Propensity score methods were used to estimate the causal effects of mothers' dieting on the emergence of daughters' dieting between ages 7 and 11, examining the moderating effect of weight status.Girls whose mothers were currently dieting were significantly more likely to diet before age 11 than those whose mothers were not currently dieting, and this effect did not vary by girls' or mothers' weight status.We conclude by discussing the implications of the effects of mothers' dieting on daughters' early dieting as well as the potential of propensity score methods in the field of obesity compared with traditional methodology such as regression analysis.

Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. These methods were developed in the context of data with no hierarchical structure or clustering. Yet in many applications the data have a clustered structure that is of substantive importance, such as when individuals are nested within healthcare providers or within schools. Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures. In this paper, we focus on propensity score weighting for a continuous, rather than binary, exposure in a multilevel setting. Using simulations, we compare several specifications of the propensity score: a random effects model, a fixed effects model, and a single-level model. Additionally, our simulations compare the performance of marginal versus cluster-mean stabilized propensity score weights. In our results, regression specifications that accounted for the multilevel structure reduced bias, particularly when cluster-level confounders were omitted. Furthermore, cluster mean weights outperformed marginal weights.

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.

This study uses propensity scores to statistically approximate the causal effect of having aggressive friends on aggressive behavior in childhood. Participants were 1,355 children (53% girls; 31% minority) in 97 third and fifth grade classrooms enrolled in the Classroom Peer Ecologies Project. Propensity scores were calculated to control for the impact of 21 relevant confounder variables related to having aggressive friendships and aggressive behavior. The 21 variables included demographic, social, and behavioral characteristics measured at the beginning of the school year. Presence/absence of aggressive friends was measured in the middle of the school year, and aggressive behavior was measured at the end of the school year. Results indicate a significant effect of having one or more aggressive friends on children's aggressive behavior above and beyond the effects of the 21 demographic, social, and behavioral variables. The propensity score model is compared to two other models of peer influence. The strengths and practical challenges of using propensity score analysis to study peer influence are discussed.

The current study seeks to advance understanding about how to address substance use and co-occurring mental health problems in adolescents. Specifically, we compared the effectiveness of two evidence-based treatment programs (Motivational Enhancement Treatment/Cognitive Behavior Therapy, 5 Sessions [MET/CBT5] and Adolescent Community Reinforcement Approach [A-CRA]) for both substance use and mental health outcomes (i.e., crossover effects). We used statistical methods designed to approximate randomized controlled trials when comparing nonequivalent groups using observational study data. Our methods also included an assessment of the potential impact of omitted variables. We found that after applying balancing weighting to ensure similarity of the baseline samples (given the nonrandomized study design), both groups significantly improved on the two substance use outcomes (days abstinent and percent of youth in recovery) and on the two mental health outcomes (post-traumatic stress disorder (PTSD) symptoms and general emotional problems). Youth in A-CRA were significantly more likely to be in recovery at the 3-month follow-up compared to youth in MET/CBT5, but the size of this effect was very small. Youth receiving MET/CBT5 appeared to show significantly more improvement in the two mental health measures compared to youth in A-CRA, though these effect sizes were also very small. The findings indicate that adolescents with co-occurring substance use and mental health problems improve on both substance use and mental health outcomes with both treatments even though they are not specifically targeting mental health problems.

Background Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. Objective This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. Methods We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. Results We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. Conclusions The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.

Objective To determine the influence of individual and neighborhood factors that combined are associated with asthma and diabetes in a sample of urban Philadelphians using data mining, a novel technique in public health research. Methods We obtained secondary data collected between May 2011 and November 2014 on individual's health and perception of neighborhood characteristics (N = 450) and Philadelphia LandCare Program data that provided relevant environmental data for the analysis (N = 676). RapidMiner open access data mining software was used to perform decision tree analyses. Results Individual- and neighborhood-level environmental factors were intricately related in the decision tree models, having varying influence on the outcomes of asthma and diabetes. The decision trees had high specificity (95–100) and classified factors that were associated with an absence of disease (diabetes/asthma). Conclusion Improved neighborhood-level conditions related to social and physical disorder were consistently found to be associated with an absence of both asthma and diabetes in this urban population. Policy implications This study illustrates the potential utility of applying data mining techniques for understanding complex public health issues.

To determine associations among objectively-measured nocturnal sleep time, bedtime and obesogenic behaviors, including dietary intake, timing of intake, and physical activity, in a diverse sample of school-aged children who presented for behavioral treatment to enhance sleep duration.Eighty-seven children (8-11 y, 66.7% female, zBMI: 0.86 ± 1.0) who self-reported sleeping9.5 h/night were studied for one week using wrist actigraphy to estimate sleep; hip-worn accelerometers to measure physical activity; and 24 h dietary recalls to capture dietary intake and meal timing. Pearson and Spearman's rho correlations and linear regressions controlling for age, gender and race were used for statistical analyses.Mean bedtime was 10:31 PM (±58.2 min) and mean nocturnal sleep time was 7.7 h (±37.5 min). Although later bedtime was associated with shorter sleep time (r = -0.61, p 0.001), the two variables were differentially related to obesity risk factors. Later bedtime, but not sleep time, correlated with greater daily fat intake, later first meal of the day, and greater after-dinner intake (all p 0.05). Nocturnal sleep time, but not bedtime, correlated with zBMI (p = 0.04). Both sleep time and later bedtime were associated with a later last meal of the day (all p 0.05). Findings remained consistent after controlling for demographic factors. In short-sleeping school-aged children, bedtime may be more predictive of dietary obesity risk factors whereas sleep duration may be more predictive of zBMI. Results suggest that health providers should consider both bedtime and sleep duration for reducing obesity risk in children.Enhancing Sleep Duration: Effects on Children's Eating and Activity Behaviors, NCT03186508, https://clinicaltrials.gov/ct2/show/NCT03186508.

Earlier intention-to-treat (ITT) findings from a community-randomized trial demonstrated effects of the Communities That Care (CTC) prevention system on reducing problem behaviors among youth. In ITT analyses, youth were analyzed according to their original study community’s randomized condition even if they moved away from the community over the course of follow-up and received little to no exposure to intervention activities. Using inverse probability weights (IPWs), this study estimated effects of CTC in the same randomized trial among youth who remained in their original study communities throughout follow-up. Data were from the Community Youth Development Study, a community-randomized trial of 24 small towns in the United States. A cohort of 4,407 youth was followed from fifth grade (prior to CTC implementation) to eighth grade. IPWs for one’s own moving status were calculated using fifth- and sixth-grade covariates. Results from inverse probability weighted multilevel models indicated larger effects for youth who remained in their study community for the first 2 years of CTC intervention implementation compared to ITT estimates. These effects included reduced likelihood of alcohol use, binge drinking, smokeless tobacco use, and delinquent behavior. These findings strengthen support for CTC as an efficacious system for preventing youth problem behaviors.

Behavioral, biobehavioral, and biomedical interventions presume a causal story. This causal story is used to create a conceptual model of why the intervention caused the observed outcome. Methods are needed to investigate the extent to which changes in the outcome are due to exposure to an intervention. This chapter describes the use of a factorial experiment in which an intervention’s components are varied, the mediating mechanisms and outcome are measured, and then an analysis is performed to investigate an intervention’s causal story. To illustrate the procedures, a case study examines an intervention guided by the model of stigma communication (MSC; Smith, Commun. Theory 17:462–485, 2007; Stigma communication and health. In T. L. Thompson, R. Parrott, & J. Nussbaum (Eds.), Handbook of health communication (2nd ed., pp. 455–468). London, UK: Taylor & Francis, 2011; Commun. Monogr. 79:522–538, 2012). In the case study, we show how to conduct a mediation analysis when four components of an intervention have been manipulated in a 24 factorial experiment (N = 299), and, in addition, four hypothesized mediators have been measured. We reflect on how the results provide more precise conclusions about the theory guiding the intervention and opportunities for refinement.

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. Language: en

Nearly 20% of South African youth experience forced or coerced sexual intercourse. Understanding the factors associated with forced sex is important for informing prevention programs aimed at reducing sexual violence and HIV and AIDS. Multilevel regression models test the association between sexual situation self-efficacy and forced sex among 2,893 South African adolescents. Findings suggest that youth are more likely to experience forced sex after periods of time when their levels of self-efficacy are lower than their average levels of self-efficacy. Furthermore, youth who are lower on their self-efficacy compared to their peers are more likely to experience forced sex. Implications for prevention research are discussed.

Summary We examine the causal effect of parental restrictive feeding practices on children's weight status. An important mediator is children's self-regulation status. Recent approaches interpret mediation effects on the basis of the potential outcomes framework. Inverse probability weighting based on propensity scores are used to adjust for confounding and to reduce the dimensionality of confounders simultaneously. We show that combining machine learning algorithms and logistic regression to estimate the propensity scores can be more accurate and efficient in estimating the controlled direct effects than using logistic regression alone. A data application shows that the causal effect of mother's restrictive feeding differs according to whether the daughter eats in the absence of hunger.

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.

The robust correlation between peer and adolescent alcohol use (AU) has been taken as evidence for both socialization and selection processes in the etiology of adolescent AU. Accumulating evidence from studies using a diverse range of methodological and statistical approaches suggests that both processes are involved. A major challenge in testing whether peer AU predicts an adolescent’s drinking (socialization) or whether an adolescent’s drinking predicts peer AU (selection) is the myriad of potentially confounding factors that might lead to an overestimation of socialization and selection effects. After creating AU transition groups based on peer and adolescent AU across two waves (N = 765; age = 10–15; 53 % female), we test whether transitions into AU by adolescents and peers predict later peer and adolescent AU respectively, using (1) propensity score analysis to balance transition groups on 26 potential confounds, (2) a longitudinal design with three waves to establish temporal precedence, and (3) both adolescent (target) and peer self-report of peer AU to disentangle effects attributable to shared reporter bias. Both selection and socialization were supported using both peer self-report of AU and adolescent-report of peer AU. Although cross-sectional analyses suggested peer self-reported models were associated with smaller effects than perceived peer AU, longitudinal analyses suggest a similar sized effect across reporter of peer AU for both selection and socialization. The implications of these findings for the etiology and treatment of adolescent AU are discussed.