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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.

The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever-increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well.

Observational studies are often used to understand relationships between exposures and outcomes. When analyzing such data, propensity score (PS) and balance weighting are commonly used techniques that aim to reduce the imbalances between exposure groups by weighting the groups to look alike on the observed confounders. There is now a plethora of available methods to estimate PS and balancing weights as well as rich guidance on how to properly employ these in an analysis. In such studies, unbiased and robust estimation of the causal treatment effect is not guaranteed unless several conditions hold. The literature has shown that accurate inference requires these key criteria: 1) the treatment allocation mechanism is known (e.g., no unobserved confounders), 2) the relationship between the baseline covariates and the outcome is known (if one is to rely on the outcome model itself), 3) adequate balance (comparability) of baseline covariates is achieved post-weighting, 4) a proper set of covariates to control for confounding bias is known, and 5) a large enough sample size is available. In this article, we use simulated data of various sizes to investigate the influence of these five criteria on statistical inference. We have two notable new findings to help improve practice. First, our findings provide important new evidence that the maximum Kolmogorov-Smirnov statistic is the proper statistical measure to assess balance on the baseline covariates, in contrast to the mean standardised mean difference used in many applications, and 0.1 is a suitable threshold to consider as acceptable balance. Second, we also find a clear recommendation that 60 − 80 observations, per confounder per treatment group, are required to obtain a reliable and unbiased estimation of the causal treatment effect.

In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate the utility of the mean and maximum Absolute Standardized Mean Difference (ASMD) as a test of balance between a synthetic control unit and treated unit, and provide guidance on what constitutes a poor fit when using a synthetic control. We explore and compare other potential metrics using a simulation study. We provide an application of our proposed balance metric to the 2013 Los Angeles (LA) Firearm Study [9]. Using Uniform Crime Report data, we apply the SCM to obtain a counterfactual for the LA firearm-related crime rate based on a weighted combination of control units in a donor pool of cities. We use this counterfactual to estimate the effect of the LA Firearm Study intervention and explore the impact of changing the donor pool and pre-intervention duration period on resulting matches and estimated effects. We demonstrate how decision-making about the quality of a synthetic control can be improved by using ASMD. The mean and max ASMD clearly differentiate between poor matches and good matches. Researchers need better guidance on what is a meaningful imbalance between synthetic control and treated groups. In addition to the use of gap plots, the proposed balance metric can provide an objective way of determining fit.

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when evaluating the effects of opioid policies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms.

Significance Many US states have tried to regulate firearm storage and use to reduce the 39,000 firearms-related deaths that occur each year. Looking at three classes of laws that regulate children’s access to firearms, the carrying of a concealed firearm, and the use of a firearm in self-defense, we found that state laws restricting firearm storage and use are associated with a subsequent 11% decrease in the firearms-related death rate. In a hypothetical situation in which there are 39,000 firearms deaths nationally under the permissive combination of these three laws, we expect 4,475 (80% CI, 1,761 to 6,949) more deaths nationally than under the restrictive combination of these laws., Although 39,000 individuals die annually from gunshots in the US, research examining the effects of laws designed to reduce these deaths has sometimes produced inconclusive or contradictory findings. We evaluated the effects on total firearm-related deaths of three classes of gun laws: child access prevention (CAP), right-to-carry (RTC), and stand your ground (SYG) laws. The analyses exploit changes in these state-level policies from 1970 to 2016, using Bayesian methods and a modeling approach that addresses several methodological limitations of prior gun policy evaluations. CAP laws showed the strongest evidence of an association with firearm-related death rate, with a probability of 0.97 that the death rate declined at 6 y after implementation. In contrast, the probability of being associated with an increase in firearm-related deaths was 0.87 for RTC laws and 0.77 for SYG laws. The joint effects of these laws indicate that the restrictive gun policy regime (having a CAP law without an RTC or SYG law) has a 0.98 probability of being associated with a reduction in firearm-related deaths relative to the permissive policy regime. This estimated effect corresponds to an 11% reduction in firearm-related deaths relative to the permissive legal regime. Our findings suggest that a small but meaningful decrease in firearm-related deaths may be associated with the implementation of more restrictive gun policies.

It is often of interest to use observational data to estimate the causal effect of a target exposure or treatment on an outcome. When estimating the treatment effect, it is essential to appropriately adjust for selection bias due to observed confounders using, for example, propensity score weighting. Selection bias due to confounders occurs when individuals who are treated are substantially different from those who are untreated with respect to covariates that are also associated with the outcome. A comparison of the unadjusted, naive treatment effect estimate with the propensity score adjusted treatment effect estimate provides an estimate of the selection bias due to these observed confounders. In this paper, we propose methods to identify the observed covariate that explains the largest proportion of the estimated selection bias. Identification of the most influential observed covariate or covariates is important in resource-sensitive settings where the number of covariates obtained from individuals needs to be minimized due to cost and/or patient burden and in settings where this covariate can provide actionable information to healthcare agencies, providers, and stakeholders. We propose straightforward parametric and nonparametric procedures to examine the role of observed covariates and quantify the proportion of the observed selection bias explained by each covariate. We demonstrate good finite sample performance of our proposed estimates using a simulation study and use our procedures to identify the most influential covariates that explain the observed selection bias in estimating the causal effect of alcohol use on progression of Huntington’s disease (HD), a rare neurological disease.

Parkinsonism & related disorders 101, 75-89 (2022). doi:10.1016/j.parkreldis.2022.06.013, Published by Elsevier Science, Amsterdam [u.a.]

IntroductionAssessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.MethodsWe included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26, 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating.ResultsAfter 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes.DiscussionThe reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policy-makers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Objectives: Key stakeholders can have differing views about which information is essential to inform placement decisions for all patients. This study examined consensus across stakeholder groups on the most important individual needs and treatment outcomes for informing decisions specifically about the level of care for an adolescent in substance use treatment. Methods: We conducted an online modified-Delphi process with treatment providers, policymakers, researchers, and parents of adolescents who have received substance use treatment. Participants rated 48 individual needs from the Global Appraisal of Individual Needs–Initial that were mapped onto the 6 dimensions of the American Society of Addiction Medicine Criteria. In addition, participants rated 10 treatment outcomes from the Substance Abuse and Mental Health Services Administration's National Outcome Measures. We assessed consensus within stakeholder groups using the RAND/UCLA Appropriateness Method. We considered the items reaching consensus with the highest ratings across stakeholder groups as the most important individual needs and treatment outcomes. Results: We recruited 194 participants (81 providers, 54 policymakers, 32 researchers, 27 parents). Participants identified suicidality and severity of substance use disorder symptoms as the most important individual needs, and reduction in substance use as the most important treatment outcome. Conclusions: Standardized procedures for matching adolescents to levels of care for substance use treatment should at a minimum be based on assessments of suicidality and severity of substance use disorder symptoms, and consider reduction in substance use as a primary treatment outcome. These findings can progress the development of “level-of-care” decision rules specifically for adolescents.

Many universities have pre departure programs that aim to prevent alcohol misuse and sexual assault among college students abroad, yet little is known about students’ uptake of this preventative information. In this study, 2,245 study abroad students from 63 different institutions were asked about receiving pre departure alcohol/drug and sexual assa ult prevention information. Only 38% of students reported receiving pre departure information specific to alcohol/drug misuse abroad and only 22% reported receiving sexual assault prevention information prior to departure. Notably, students who were heavie r drinkers prior to departure and those who intended to drink the most while abroad reported lower rates of information receipt. Overall, these findings suggest that either students are not receiving the preventative information that universities intend them to receive or the programming is not impactful enough to be recalled. Thus, there may be missed opportunities to better prepare students for risks specific to study abroad programs.

Over the past 15 years, the suicide rate among members of the U.S. armed forces has doubled, with the greatest increase observed among soldiers in the Army. This increasing rate is paralleled by a smaller increase in the general U.S. population, observed across both genders, in virtually every age group and in nearly every state. An empirical question exists: What is the extent or degree to which the suicide trend in the Army is unique to that service, relative to what is observed in the general U.S. population? The Army has typically attempted to address this question by standardizing the general population to look like the Army on demographic characteristics. However, given the rise in suicide rates over the past decade, the Army wanted to better understand whether standardization based solely on age and gender is enough. Expanding the characteristics on which the general population is standardized to match the Army could be useful to gain a better understanding of the suicide trends in the Army. However, such a change also brings with it some challenges, including the lack of readily available data in the general U.S. population. In addition, even an expanded set of characteristics still results in having a large number of unmeasured factors that cannot be included in this type of analysis. In this study, the authors explore how accounting for age, gender, race/ethnicity, time, marital status, and educational attainment affects suicide rate differences between soldiers and a comparable subset of the general U.S. population.

OBJECTIVE: Research has just begun to identify American college students who study abroad as a group at-risk for sexual violence victimization. The purpose of the current study was to examine the context of these incidents. PARTICIPANTS: We utilized a large longitudinal sample of 2,428 United States college students studying abroad for between 4 and 21 weeks in 12 different foreign countries. METHODS: We estimated descriptive statistics and logistic regression models to assess prevalence and correlates of sexual victimization abroad, as well as effect size comparisons to quantify changes in drinking abroad. RESULTS: Over one-fifth of students experienced sexual violence while abroad, including sexual assault and verbal coercion. Women, younger students, those who experienced sexual violence prior, and heavier drinkers had the greatest odds of sexual violence victimization abroad. CONCLUSIONS: Findings point to the need for evidence-based programming to reduce sexual violence risk among American college students studying in foreign countries.

OBJECTIVE: To estimate the impact of implementing prescription drug monitoring program (PDMP) best practices on prescription opioid use. DATA SOURCES: 2007–2012 Medicare claims for noncancer pain patients, and PDMP attributes from the Prescription Drug Abuse Policy System. STUDY DESIGN: We derived PDMP composite scores using the number of best practices adopted by states (range: 0‐14), classifying states as either no PDMP, low strength (0

Purpose: To implement a multilevel, church-based intervention with diverse disparity populations using community-based participatory research and evaluate feasibility, acceptability, and preliminary effectiveness in improving obesity-related outcomes. Design: Cluster randomized controlled trial (pilot). Setting: Two midsized (∼200 adults) African American baptist and 2 very large (∼2000) Latino Catholic churches in South Los Angeles, California. Participants: Adult (18+ years) congregants (n = 268 enrolled at baseline, ranging from 45 to 99 per church). Intervention: Various components were implemented over 5 months and included 2 sermons by pastor, educational handouts, church vegetable and fruit gardens, cooking and nutrition classes, daily mobile messaging, community mapping of food and physical activity environments, and identification of congregational policy changes to increase healthy meals. Measures: Outcomes included objectively measured body weight, body mass index (BMI), and systolic and diastolic blood pressure (BP), plus self-reported overall healthiness of diet and usual minutes spent in physical activity each week; control variables include sex, age, race–ethnicity, English proficiency, education, household income, and (for physical activity outcome) self-reported health status. Analysis: Multivariate linear regression models estimated the average effect size of the intervention, controlling for pair fixed effects, a main effect of the intervention, and baseline values of the outcomes. Results: Among those completing follow-up (68%), the intervention resulted in statistically significantly less weight gain and greater weight loss (−0.05 effect sizes; 95% confidence interval [CI] = −0.06 to −0.04), lower BMI (−0.08; 95% CI = −0.11 to −0.05), and healthier diet (−0.09; 95% CI = −0.17 to −0.00). There was no evidence of an intervention impact on BP or physical activity minutes per week. Conclusion: Implementing a multilevel intervention across diverse congregations resulted in small improvements in obesity outcomes. A longer time line is needed to fully implement and assess effects of community and congregation environmental strategies and to allow for potential larger impacts of the intervention.

Introduction\ud Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD).\ud \ud Methods\ud We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models.\ud \ud Results\ud Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users.\ud \ud Conclusions\ud Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors – including light and moderate alcohol use – are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.

Randomized controlled trials are the gold standard for measuring causal effects. However, they are often not always feasible, and causal treatment effects must be estimated from observational data. Observational studies do not allow robust conclusions about causal relationships unless statistical techniques account for the imbalance of pretreatment confounders across groups and key assumptions hold. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the observed imbalances between treatment groups by weighting the groups to look alike on the observed confounders. Notably, there are many methods available to estimate PSBW. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size for a given application. Moreover, it is critical to assess the validity of key assumptions required for robust estimation of the needed treatment effects, including the overlap and no unmeasured confounding assumptions. We present a step-by-step guide to the use of PSBW for estimation of causal treatment effects that includes steps on how to evaluate overlap before the analysis, obtain estimates of PSBW using multiple methods and select the optimal one, check for covariate balance on multiple metrics, and assess sensitivity of findings (both the estimated treatment effect and statistical significance) to unobserved confounding. We illustrate the key steps using a case study examining the relative effectiveness of substance use treatment programs and provide a user-friendly Shiny application that can implement the proposed steps for any application with binary treatments.

Recent studies show that during study abroad experiences, college students greatly increase their drinking behavior, experience multiple alcohol-related consequences, engage in sexual risk behaviors, and are at-risk for sexual violence victimization. These studies, however, have been limited by small sample sizes of students from single institutions who are often studying in a particular country or region. To better understand the extent of the risks experienced by college students studying abroad, we conducted a longitudinal survey study of 2,630 students from 65 different U.S. colleges and universities studying in 12 diverse locations abroad. Total drinks per week and heavy drinking days more than doubled while students were abroad, with heavier predeparture drinkers, men, and students under age 21 experiencing the greatest increases. There were few observed overall changes in alcohol-related consequences and risky sexual behaviors; however, specific groups, such as those with heavier predeparture consequences and sexual risk behaviors, men, and those on longer abroad programs reported greater consequences and risky sex abroad. One in five students (21%) experienced sexual violence abroad, with non-consensual physical contact the most prevalent form. Women, those under age 21, and those with a history of sexual violence were most likely to experience sexual violence abroad. Findings indicate that study abroad students may warrant targeted predeparture programming to help prevent heavy drinking and sexual violence experiences abroad.

Objective Characterize the state of the science in opioid policy research based on a literature review of opioid policy studies. Methods We conducted a scoping review of studies evaluating the impact of U.S. state-level and federal-level policies on opioid-related outcomes published in 2005−2018. We characterized: 1) state and federal policies evaluated, 2) opioid-related outcomes examined, and 3) study design and analytic methods (summarized overall and by policy category). Results In total, 145 studies were reviewed (79 % state-level policies, 21 % federal-level policies) and classified with respect to 8 distinct policy categories and 7 outcome categories. The majority of studies evaluated policies related to prescription opioids (prescription drug monitoring programs (PDMPs), opioid prescribing policies, federal regulation of prescription opioids, pain clinic laws) and considered policy impacts with respect to proximal outcomes (e.g., opioid prescribing behaviors). In total, only 29 (20 % of studies) met each of three key criteria for rigorous design: analysis of longitudinal data with a comparison group design, adjustment for difference between policy-enacting and comparison states, and adjustment for potentially confounding co-occurring policies. These more rigorous studies were predominately published in 2017–2018 and primarily evaluated PDMPs, marijuana laws, treatment-related policies, and overdose prevention policies. Conclusions Our results indicated that study design rigor varied notably across policy categories, highlighting the need for broader adoption of rigorous methods in the opioid policy field. More evaluation studies are needed regarding overdose prevention policies and policies related to treatment access. Greater examination of distal outcomes and potential unintended consequences are also warranted.

Over the last two decades, there has been a surge of opioid-related overdose deaths resulting in a myriad of state policy responses. Researchers have evaluated the effectiveness of such policies using a wide-range of statistical models, each of which requires multiple design choices that can influence the accuracy and precision of the estimated policy effects. This simulation study used real-world data to compare model performance across a range of important statistical constructs to better understand which methods are appropriate for measuring the impacts of state-level opioid policies on opioid-related mortality. Our findings show that many commonly-used methods have very low statistical power to detect a significant policy effect (

Background: Study abroad experiences among U.S. college students are risky environments for alcohol use; however little work has examined how studying abroad may affect other substance use. Purpose: This study assesses changes in frequency of cigarette, e-cigarette, and cannabis use among students studying abroad. Methods: We recruited study abroad students from 63 institutions who studied in the 12 most popular destinations; 2,086 students completed surveys before leaving the U.S. and during their last month abroad. Descriptive and multivariate mixed-effects regression analysis compared substance use by location between students and country natives using publicly available data, identified behavioral and demographic predictors of frequency of substance use abroad, and estimated average adjusted prevalence of substance use by location. Results: Students’ cigarette smoking increased three-fold while abroad; patterns of use varied significantly by location and mirrored smoking behaviors of native young adults. Rates of e-cigarette use declined while abroad and rates of cannabis use were similar abroad to pre-departure rates. However, cannabis use varied substantially by location and were higher than native peer rates. For all substances, most frequent substance use abroad was reported among males and those with prior histories of single drug and polydrug use. Conclusion: Findings highlight the importance of continued research on drug use abroad among U.S. college students and pave the way for initial prevention and intervention efforts to target drug use abroad, particularly for cigarettes due to the negative health consequences of use and for cannabis given the potential legal ramification of use in non-legalized countries.

Background Reliable evaluations of state-level policies are essential for identifying effective policies and informing policymakers’ decisions. State-level policy evaluations commonly use a difference-in-differences (DID) study design; yet within this framework, statistical model specification varies notably across studies. More guidance is needed about which set of statistical models perform best when estimating how state-level policies affect outcomes. Methods Motivated by applied state-level opioid policy evaluations, we implemented an extensive simulation study to compare the statistical performance of multiple variations of the two-way fixed effect models traditionally used for DID under a range of simulation conditions. We also explored the performance of autoregressive (AR) and GEE models. We simulated policy effects on annual state-level opioid mortality rates and assessed statistical performance using various metrics, including directional bias, magnitude bias, and root mean squared error. We also reported Type I error rates and the rate of correctly rejecting the null hypothesis (e.g., power), given the prevalence of frequentist null hypothesis significance testing in the applied literature. Results Most linear models resulted in minimal bias. However, non-linear models and population-weighted versions of classic linear two-way fixed effect and linear GEE models yielded considerable bias (60 to 160%). Further, root mean square error was minimized by linear AR models when we examined crude mortality rates and by negative binomial models when we examined raw death counts. In the context of frequentist hypothesis testing, many models yielded high Type I error rates and very low rates of correctly rejecting the null hypothesis ( Conclusions The findings highlight notable limitations of commonly used statistical models for DID designs, which are widely used in opioid policy studies and in state policy evaluations more broadly. In contrast, the optimal model we identified--the AR model--is rarely used in state policy evaluation. We urge applied researchers to move beyond the classic DID paradigm and adopt use of AR models.

Background In a recently published randomized controlled trial (RCT) of Volunteer Recovery Support for Adolescents (VRSA), a secondary finding indicated that better adherence to planned VRSA telephone session frequency resulted in significantly higher remission rates relative to lower session adherence. However, interpretation of this dose-response relationship may have been confounded by participant characteristics such as baseline levels of substance use and mental health problems. Methods The present study used statistical methods designed to approximate RCTs when comparing more than two nonequivalent groups that include an assessment of the potential impact of omitted variables. Classification and Regression Tree (CRT) analysis was used to establish the cut-point between high (H) and low (L) VRSA dosage groups. Because we were interested in generalizing to youth with poor attendance, the L-VRSA group served as the reference group. Balancing weights for H-VRSA and a services as usual (SAU) control group were calculated to ensure similarity of baseline pretreatment characteristics to the reference group, and sensitivity of findings to unobserved confounding variables was assessed. Results Findings suggested that superior remission rates at the end of the intervention phase were the result of high adherence to planned VRSA session frequency. Recommendations to achieve high VRSA participation among a larger segment of youth and to test whether longer VRSA duration improves the stability of recovery outcomes are provided. Conclusion Few published dose-response studies have adequately controlled for selection confounds from both observed and unobserved confounding. As such, the present study aims to both assess the impact of different dosage levels of VRSA and provide a template for how to apply state-of-the-art statistical methods designed to approximate randomized controlled trials to such studies.

Religious congregations play an important role in HIV prevention and care. However, most research on congregation-based HIV activities has focused on prevention. Using data from a nationally repres...

Background Many active buprenorphine prescribers treat few patients monthly, but little information is available regarding how prescribers’ buprenorphine caseload fluctuates over time or how long it takes new prescribers to reach higher patient caseloads. We examine buprenorphine-prescribing clinicians’ patient caseloads over time and explore prescriber characteristics associated with different caseload trajectories. Methods Using 2006–2018 national buprenorphine pharmacy claims, we calculate monthly patient caseloads for buprenorphine prescribers for 6 years following a clinician’s first filled buprenorphine prescription. We use K-means clustering to identify clusters of clinician caseload trajectories and bivariate analyses to examine prescriber and county characteristics associated with different trajectory classes. Results We identified 42,067 buprenorphine prescribers with 3 trajectory classes. High-volume (1.4%;n = 571) whose mean monthly patient caseload increased to approximately 40 patients through the initial 20 months and stabilized at 40 or more patients; moderate-volume (9.2%;n = 3891) whose mean patient caseload increased during the initial 20 months, stabilizing at 15–20 patients; and low-volume (89.4%;n = 37,605), who typically had fewer than 5 patients monthly. Most low-volume prescribers (n = 31,470; 83.7% of all prescribers) initially treated 1–2 patients for several months, followed by no subsequent prescribing. Conclusion Almost three-quarters of buprenorphine prescribers treated no more than a few patients for several months before ceasing buprenorphine prescribing; only 10% of prescribers averaged more than 10 patients per month over the next 6 years. Efforts are needed to identify factors contributing to prescribers being willing to continue prescribing buprenorphine over time and to prescribe to more patients in order to increase access to buprenorphine treatment.

Background Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). While there are promising treatments for SUDs, adolescents respond to these treatments differentially in part based on the setting in which treatments are delivered. One way to address such individualized response to treatment is through the development of adaptive interventions (AIs): sequences of decision rules for altering treatment based on an individual’s needs. This protocol describes a project with the overarching goal of beginning the development of AIs that provide recommendations for altering the setting of an adolescent’s substance use treatment. This project has three discrete aims: (1) explore the views of various stakeholders (parents, providers, policymakers, and researchers) on deciding the setting of substance use treatment for an adolescent based on individualized need, (2) generate hypotheses concerning candidate AIs, and (3) compare the relative effectiveness among candidate AIs and non-adaptive interventions commonly used in everyday practice. Methods This project uses a mixed-methods approach. First, we will conduct an iterative stakeholder engagement process, using RAND’s ExpertLens online system, to assess the importance of considering specific individual needs and clinical outcomes when deciding the setting for an adolescent’s substance use treatment. Second, we will use results from the stakeholder engagement process to analyze an observational longitudinal data set of 15,656 adolescents in substance use treatment, supported by the Substance Abuse and Mental Health Services Administration, using the Global Appraisal of Individual Needs questionnaire. We will utilize methods based on Q-learning regression to generate hypotheses about candidate AIs. Third, we will use robust statistical methods that aim to appropriately handle casemix adjustment on a large number of covariates (marginal structural modeling and inverse probability of treatment weights) to compare the relative effectiveness among candidate AIs and non-adaptive decision rules that are commonly used in everyday practice. Discussion This project begins filling a major gap in clinical and research efforts for adolescents in substance use treatment. Findings could be used to inform the further development and revision of influential multi-dimensional assessment and treatment planning tools, or lay the foundation for subsequent experiments to further develop or test AIs for treatment planning. Electronic supplementary material The online version of this article (10.1186/s13722-017-0099-4) contains supplementary material, which is available to authorized users.

Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.

Background Adolescents respond differentially to substance use treatment based on their individual needs and goals. Providers may benefit from guidance (via decision rules) for personalizing aspects of treatment, such as level-of-care (LOC) placements, like choosing between outpatient or inpatient care. The field lacks an empirically-supported foundation to inform the development of an adaptive LOC-placement protocol. This work begins to build the evidence base for adaptive protocols by estimating them from a large observational dataset. Methods We estimated two-stage LOC-placement protocols adapted to individual adolescent characteristics collected from the Global Appraisal of Individual Needs assessment tool (n = 10,131 adolescents). We used a modified version of Q-learning, a regression-based method for estimating personalized treatment rules over time, to estimate four protocols, each targeting a potentially distinct treatment goal: one primary outcome (a composite of ten positive treatment outcomes) and three secondary (substance frequency, substance problems, and emotional problems). We compared the adaptive protocols to non-adaptive protocols using an independent dataset. Results Intensive outpatient was recommended for all adolescents at intake for the primary outcome, while low-risk adolescents were recommended for no further treatment at followup while higher-risk patients were recommended to inpatient. Our adaptive protocols outperformed static protocols by an average of 0.4 standard deviations (95 % confidence interval 0.2−0.6) of the primary outcome. Conclusions Adaptive protocols provide a simple one-to-one guide between adolescents’ needs and recommended treatment which can be used as decision support for clinicians making LOC-placement decisions.

Background Exercise is emerging as an important aspect in the management of disease-related symptoms and functional decline in people with Huntington disease (HD). Long-term evaluation of physical activity and exercise participation in HD has yet to be undertaken. Objective The objective is to investigate the feasibility of a nested randomized controlled trial (RCT) alongside a longitudinal observational study of physical activity and exercise outcomes in people with HD. Design This will be a 12-month longitudinal observational study (n = 120) with a nested evaluation of a physical activity intervention (n = 30) compared with usual activity (n = 30) using a “trial within a cohort” design. Setting The study will take place in HD specialist clinics in Germany, Spain, and the United States, with intervention delivery in community settings. Participants The participants will have early-mid–stage HD and be participating in the Enroll-HD study. Intervention This will be a 12-month physical activity behavioral change intervention, delivered by physical therapists in 18 sessions, targeting uptake of aerobic exercise and increased physical activity. Measurements All participants (n = 120) will complete Enroll-HD assessments (motor, cognitive, behavioral, and quality of life) at baseline and at 12 months. Additional Physical ACtivity and Exercise Outcomes in Huntington Disease (PACE-HD) assessments include fitness (predicted maximal oxygen uptake [V o2max]), self-reported and quantitative measures of physical activity, disease-specific symptoms, and walking endurance. RCT participants (n = 60) will complete an additional battery of quantitative motor assessments and a 6-month interim assessment. Enroll-HD data will be linked to PACE-HD physical activity and fitness data. Limitations The limitations include that the embedded RCT is open, and assessors at RCT sites are not blinded to participant allocation. Conclusion PACE-HD will enable determination of the feasibility of long-term physical activity interventions in people with HD. The novel “trial within a cohort” design and incorporation of data linkage have potential to reduce participant burden. This design could be applied to other neurological diseases and movement disorders where recruitment and retention are challenging.

Practical factors can significantly influence the setting, or level of care, in which an adolescent receives substance use treatment. This study aimed to identify the practical factors that stakeholders find most critical to consider when planning substance use treatment for adolescents. We conducted online panels with four stakeholder groups: providers, policymakers, researchers, and parents. Participants nominated, rated, and commented on the importance of 10 practical factors that could influence treatment setting decisions. We assessed consensus on the rated importance of practical factors using the RAND/UCLA Appropriateness Method, and we thematically analyzed comments made by stakeholders to explain how they differentiated relative importance. Overall, 153 stakeholders (66 providers, 38 policymakers, 27 researchers, and 22 parents) identified continuity of care, coordination of care across service sectors, and quality of care as the practical factors of highest importance. Participants considered practical factors to be of higher importance when they perceived a practical factor to: trump clinical appropriateness as the reason for placing an adolescent in a given setting, steer an adolescent toward a specific setting, or steer an adolescent away from specific settings. Conversely, participants rated lower the practical factors they perceived applicable to clinical intervention rather than treatment setting, unrelated to initial recommendations, relevant to any setting, or applicable only to certain contexts and sub-populations. These findings help elucidate why stakeholders view certain practical factors as critical to consider in real-world decisions about substance use treatment settings for adolescents. Future research should investigate how to incorporate these practical factors alongside clinical needs and treatment goals in placement criteria and treatment matching.

The fundamental difficulty of establishing causal relationships between an exposure and an outcome in observational data involves disentangling causality from confounding factors. This problem underlies much of neighborhoods research, which abounds with studies that consider associations between neighborhood characteristics and health outcomes in longitudinal data. Such analyses are confounded by selection issues; individuals with above average health outcomes (or associated characteristics) may self-select into advantaged neighborhoods. Techniques commonly used to assess causal inferences in observational longitudinal data, such as inverse probability of treatment weighting (IPTW), may be inappropriate in neighborhoods data due to unique characteristics of such data. We advance the IPTW toolkit by introducing a procedure based on a multivariate kernel density function which is more appropriate for neighborhoods data. The proposed weighting method is applied in conjunction with a marginal structural model. Our empirical analyses use longitudinal data from the Health and Retirement Study; our exposure of interest is an index of neighborhood socioeconomic status (NSES), and we examine its influence on cognitive function. Our findings illustrate the importance of the choice of method for IPTW-the comparison weighting methods provide poor balance across the set of covariates (which is not the case for our preferred procedure) and yield misleading results when applied in the outcomes models. The utility of the multivariate kernel is also validated via simulation. In addition, our findings emphasize the importance of IPTW-controlling for covariates within a regression without IPTW indicates that NSES affects cognition, whereas IPTW-weighted models fail to show a statistically significant effect.

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: Long-term fine particulate matter (PM2.5) exposure is linked with cardiovascular disease, and disadvantaged status may increase susceptibility to air pollution-related health effects. In addition, there are concerns that this association may be partially explained by confounding by socioeconomic status (SES). Objectives: We examined the roles that individual- and neighborhood-level SES (NSES) play in the association between PM2.5 exposure and cardiovascular disease. Methods: The study population comprised 51,754 postmenopausal women from the Women’s Health Initiative Observational Study. PM2.5 concentrations were predicted at participant residences using fine-scale regionalized universal kriging models. We assessed individual-level SES and NSES (Census-tract level) across several SES domains including education, occupation, and income/wealth, as well as through an NSES score, which captures several important dimensions of SES. Cox proportional-hazards regression adjusted for SES factors and other covariates to determine the risk of a first cardiovascular event. Results: A 5 μg/m3 higher exposure to PM2.5 was associated with a 13% increased risk of cardiovascular event [hazard ratio (HR) 1.13; 95% confidence interval (CI): 1.02, 1.26]. Adjustment for SES factors did not meaningfully affect the risk estimate. Higher risk estimates were observed among participants living in low-SES neighborhoods. The most and least disadvantaged quartiles of the NSES score had HRs of 1.39 (95% CI: 1.21, 1.61) and 0.90 (95% CI: 0.72, 1.07), respectively. Conclusions: Women with lower NSES may be more susceptible to air pollution-related health effects. The association between air pollution and cardiovascular disease was not explained by confounding from individual-level SES or NSES. Citation: Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. 2016. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environ Health Perspect 124:1840–1847; http://dx.doi.org/10.1289/EHP199

HIV-related stigma and mistrust contribute to HIV disparities. Addressing stigma with faith partners may be effective, but few church-based stigma reduction interventions have been tested. We implemented a pilot intervention with 3 Latino and 2 African American churches (4 in matched pairs) in high HIV prevalence areas of Los Angeles County to reduce HIV stigma and mistrust and increase HIV testing. The intervention included HIV education and peer leader workshops, pastor-delivered sermons on HIV with imagined contact scenarios, and HIV testing events. We surveyed congregants at baseline and 6 month follow-up (n = 1235) and found statistically significant (p

Clinical studies aimed at identifying effective treatments to reduce the risk of disease or death often require long term follow-up of participants in order to observe a sufficient number of events to precisely estimate the treatment effect. In such studies, observing the outcome of interest during follow-up may be difficult and high rates of censoring may be observed which often leads to reduced power when applying straightforward statistical methods developed for time-to-event data. Alternative methods have been proposed to take advantage of auxiliary information that may potentially improve efficiency when estimating marginal survival and improve power when testing for a treatment effect. Recently, Parast et al. (J Am Stat Assoc 109(505):384-394, 2014) proposed a landmark estimation procedure for the estimation of survival and treatment effects in a randomized clinical trial setting and demonstrated that significant gains in efficiency and power could be obtained by incorporating intermediate event information as well as baseline covariates. However, the procedure requires the assumption that the potential outcomes for each individual under treatment and control are independent of treatment group assignment which is unlikely to hold in an observational study setting. In this paper we develop the landmark estimation procedure for use in an observational setting. In particular, we incorporate inverse probability of treatment weights (IPTW) in the landmark estimation procedure to account for selection bias on observed baseline (pretreatment) covariates. We demonstrate that consistent estimates of survival and treatment effects can be obtained by using IPTW and that there is improved efficiency by using auxiliary intermediate event and baseline information. We compare our proposed estimates to those obtained using the Kaplan-Meier estimator, the original landmark estimation procedure, and the IPTW Kaplan-Meier estimator. We illustrate our resulting reduction in bias and gains in efficiency through a simulation study and apply our procedure to an AIDS dataset to examine the effect of previous antiretroviral therapy on survival.

More than 30 years into the HIV epidemic, HIV-related stigma remains a barrier to prevention and treatment efforts (Earnshaw, Bogart, Dovidio, & Williams, 2013). The U.S. National HIV/AIDS Strategy holds that stigma reduction is essential to reducing HIV-related disparities (White House Office of National AIDS Policy, 2010). African Americans and Latinos continue to be disproportionately affected by HIV, accounting for 65% of new infections in 2010 while representing only 28% of the population (Prevention, 2012). Faith-based organizations (FBOs) have been suggested as key community partners in addressing HIV disparities (Nunn, et al., 2013; Sachs, 2008; Sutton & Parks, 2013; UNAIDS, 2009; Woldehanna, Ringheim, & Murphy, 2005), but their roles in addressing HIV stigma are unclear. Prior literature on congregation-based HIV prevention interventions has focused almost exclusively on African American churches (Agate, et al., 2005; Baldwin, et al., 2008; Berkley-Patton, et al., 2010; Berkley-Patton, Moore, Hawes, Thompson, & Bohn, 2012; Griffith, Pichon, Campbell, & Allen, 2010; MacMaster, et al., 2007; Marcus, et al., 2004; Tyrell, et al., 2008; Wingood, Simpson-Robinson, Braxton, & Raiford, 2011). Although these and many community initiatives such as The Balm in Gilead (http://www.balmingilead.org/index.php/hiv.html) aim to work with congregations to reduce stigma as part of HIV education and testing programs, there have been few published evaluations of the extent to which such efforts actually reduce stigma. Further, few studies have measured HIV stigma in church-affiliated populations (Berkley-Patton, et al., 2013; Berkley-Patton, Thompson, et al., 2012; Bluthenthal, et al., 2012; Lindley, Coleman, Gaddist, & White, 2010; Muturi & An, 2010), despite being noted consistently as a barrier to congregation-based HIV programming (Williams, Palar, & Derose, 2011). HIV stigma in congregational settings is often attributed to religious taboos on homosexual contact between men, multiple sex partners, and drug use, which are likely to be viewed through a moral lens, facilitating stigmatization and the casting of blame (and shame). However, no previous church-based studies to our knowledge have measured these related stigmatizing attitudes (regarding homosexuality and drug addiction) and how they might contribute to HIV stigma. To develop effective HIV stigma reduction interventions in collaboration with FBOs, it is important to explore dimensions and predictors of HIV stigma among church-affiliated populations. We therefore explored HIV stigma and associated stigmas regarding same-sex sexual relations and drug addiction, using baseline data from an intervention study that aimed to reduce HIV stigma among congregants from three Latino and two African American churches in high HIV-prevalence communities. Background on HIV Stigma Previous research has identified two types of stigma relevant to HIV: instrumental stigma refers to concern about the potential consequences of interacting with a stigmatized person, such as becoming infected with HIV, while symbolic stigma refers to concern about what the stigmatized condition, such as HIV, symbolizes (Bos, Schaalma, & Pryor, 2008; Herek & Capitanio, 1998). Instrumental stigma can play out in feelings of discomfort about interacting with HIV-positive individuals, while symbolic stigma encompasses both an HIV-positive individual’s feelings of shame for having HIV as well as non-infected individuals’ rejection and blame towards those with HIV. To understand the predictors of stigma in our study, we draw on the seminal work of Goffman (1963), who described stigmatization as a social process involving the discrediting of members of an entire group based on one or more attributes. We also use the work of Herek (1999), who identified four characteristics of HIV that are likely to evoke stigma: 1) the cause is perceived to be the bearer’s responsibility; 2) the condition is unalterable or degenerative; 3) the condition is perceived to be contagious or to place others in harm’s way; and 4) the condition is readily apparent to others. We also draw upon previous work on the predictors of HIV stigma in general (i.e., not specifically HIV-positive) U.S. populations, as well as the few studies of HIV stigma among religious congregants. The most consistent individual-level, independent factors associated with lower instrumental and symbolic HIV stigma among general U.S. populations have been younger age, higher education, personal contact with people with HIV or AIDS, greater knowledge about HIV transmission, and more favorable attitudes towards gays (Herek, 1999). The two previous U.S. church-based studies of which we are aware that quantitatively measured HIV stigma focused on African Americans and were somewhat limited in terms of how much they explored the predictors of stigma. Berkley-Patton et al. (2013) found that an HIV education and testing intervention did not reduce HIV stigma among congregants and community members served by outreach programs at four African American churches, and found that only greater HIV knowledge and income (but not age, gender, or religiosity) were predictive of lower HIV stigma score at baseline. Lindley et al. (2010), in a study of congregants, pastors, and pastoral care lay leaders from 20 African American churches, found that male gender, older age, and lower HIV knowledge were associated with higher HIV stigma. Focus of this Study In order to inform African American and Latino church-based interventions to reduce HIV stigma, we focused this study on factors that might be associated with stigma. First, we examined whether personally knowing someone with HIV was associated with lower HIV stigma (Herek & Capitanio, 1997; Mall, Middelkoop, Mark, Wood, & Bekker, 2013; Nambiar & Rimal, 2012). Such an association would support the contact hypothesis (Pettigrew & Tropp, 2006), which suggests that intergroup contact can reduce prejudice. We also examined whether stigmas related to drug addiction and homosexuality were associated with higher HIV stigma (Capitanio & Herek, 1999; Herek & Capitanio, 1999; Price & Hsu, 1992; St. Lawrence, Husfeldt, Kelly, Hood, & Smith, 1990). Finally, since research has found that individuals who have never discussed HIV with anyone have more negative attitudes toward people with HIV (Genberg, et al., 2009), we also explore whether specific types of communication about HIV within and outside church are associated with HIV stigma. In this paper, we: 1) describe HIV stigma scales that we adapted from previous work to characterize four HIV stigma dimensions (discomfort, shame, rejection, and blame); 2) assess the extent to which these HIV stigma scales tap into different components of HIV stigma and examine their relationships to stigmas regarding drug addiction and homosexuality; and 3) test the following hypotheses: a) African American and Latino religious congregants who know someone with HIV will express lower HIV stigma, controlling for other factors; and b) Congregants with higher drug addiction and homosexuality stigmas will have higher HIV stigma after controlling for other factors. No previous studies have examined HIV-related attitudes among Latino congregants, the ways in which multiple dimensions of HIV stigma compare across congregants of different races and ethnicities, the extent to which attitudes regarding homosexuality and drug addiction are associated with HIV-related attitudes in congregational settings, or whether knowing someone with HIV influences HIV-related attitudes in congregational settings.

This study sought to examine whether: (1) the health composition of the social networks of children living in subsidized housing within market rate developments (among higher-income neighbors) differs from the social network composition of children living in public housing developments (among lower-income neighbors); and (2) children’s social network composition is associated with children’s own health. We found no significant differences in the health characteristics of the social networks of children living in these different types of public housing. However, social network composition was significantly associated with several aspects of children’s own health, suggesting the potential importance of social networks for the health of vulnerable populations.

Objectives. In a survey of families living in public housing, we investigated whether caretakers’ social networks are linked with children’s health status. Methods. In 2011, 209 children and their caretakers living in public housing in suburban Montgomery County, Maryland, were surveyed regarding their health and social networks. We used logistic regression models to examine the associations between the perceived health composition of caretaker social networks and corresponding child health characteristics (e.g., exercise, diet). Results. With each 10% increase in the proportion of the caretaker’s social network that exercised regularly, the child’s odds of exercising increased by 34% (adjusted odds ratio = 1.34; 95% confidence interval = 1.07, 1.69) after the caretaker’s own exercise behavior and the composition of the child’s peer network had been taken into account. Although children’s overweight or obese status was associated with caretakers’ social networks, the results were no longer significant after adjustment for caretakers’ own weight status. Conclusions. We found that caretaker social networks are independently associated with certain aspects of child health, suggesting the importance of the broader social environment for low-income children’s health.

The RAND Corporation's Gun Policy in America initiative is a unique attempt to systematically and transparently assess available scientific evidence on the real effects of firearm laws and policies. Good gun policies require consideration of many factors, including the law and constitutional rights, the interests of various stakeholder groups, and information about the likely effects of different laws or policies on a range of outcomes. This study seeks to provide the third-objective information about what the scientific literature examining gun policy can tell us about the likely effects of laws. The study synthesizes the available scientific data on the effects of various firearm policies on firearm deaths, violent crime, the gun industry, participation in hunting and sport shooting, and other outcomes. By highlighting where scientific evidence is accumulating, the authors hope to build consensus around a shared set of facts that have been established through a transparent, nonpartisan, and impartial review process. In so doing, they also illuminate areas where more and better information could make important contributions to establishing fair and effective gun policies.

Drawing upon data from the Deployment Life Study, this article examines whether female military spouses (SPs) are disadvantaged relative to matched civilian peers in terms of hours worked and earnings, paying particular attention to gaps among the highest educated women. Female SPs do earn less than comparable civilian peers in terms of raw dollars and percentage earnings. Moreover, military wives who are part of the labor force work as many hours as their civilian counterparts, but still earn significantly less for that work. Contrary to predictions, the most educated SPs are not disproportionately affected compared to spouses with less education. These results suggest that SPs at all education levels could benefit from employment assistance; in particular, women already participating in the labor force may benefit from support in finding higher paying jobs.

Propensity score analysis (PSA) is a common method for estimating treatment effects, but researchers dealing with data from survey designs are generally not properly accounting for the sampling weights in their analyses. Moreover, recommendations given in the few existing methodological articles on this subject are susceptible to bias. We show in this article through derivation, simulation, and a real data example that using sampling weights in the propensity score estimation stage and the outcome model stage results in an estimator that is robust to a variety of conditions that lead to bias for estimators currently recommended in the statistical literature. We highly recommend researchers use the more robust approach described here. This article provides much needed rigorous statistical guidance for researchers working with survey designs involving sampling weights and using PSAs.

Substance use patterns among Latinos likely reflect changes in attitudes resulting from acculturation, but little is known about Latinos’ attitudes regarding drug addiction. We surveyed a church-based sample of Latinos and African Americans (N = 1,235) about attitudes toward drug addiction and socio-demographics. Linear regression models compared Latino subgroups with African-Americans. In adjusted models, Latinos had significantly higher drug addiction stigma scores compared to African Americans across all subgroups (US-born Latinos, β = 0.22, p