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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.

Little is known about the hospice care experiences of those with Huntington's Disease (HD).Our objective is to provide the first national characterization of hospice care quality for patients with HD and their families.We used national Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey data to examine caregiver-reported experiences of hospice care. We analyzed data from 550 caregivers of patients with HD and 1,098,819 caregivers of patients without HD who died January 2016-June 2019 while receiving hospice care from 3,845 hospices nationwide. Outcomes (on a 0-100 scale) were eight publicly-reported quality of care measures, and four individual survey items about receiving help for specific symptoms. Analyses were propensity-score weighted and adjusted for patient and caregiver characteristics.Experiences of care among patients with HD were similar to or better than for patients without HD. Across all hospice and care settings, the only significant difference was for Providing Emotional, and Spiritual Support (90.9 [HD] vs. 88.2 [non-HD], a medium effect size, P0.01). However, patients with HD more often received care in settings with worse experiences for all patients; within the same hospice and same setting of care, measure scores were significantly higher for patients with HD compared to those without HD (2.3-4.6 points higher on a 0-100 scale) for all measures except Getting Hospice Care Training.Our findings highlight the benefits of hospice care for those with HD and their families and may be useful for patients with HD when making decisions regarding options for end-of-life care.

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.

Huntington’s Disease (HD) is a rare inherited neurodegenerative disorder with a typical onset between the ages of 30 - 50. Juvenile onset Huntington’s Disease (JoHD), defined by onset of symptoms before the age of 21, manifests differently from adult-onset HD. JoHD, is thought to be present in approximately 5% of HD cases, although the exact prevalence is unknown. It has not been studied extensively. JOIN-HD is a prospective, observational, multi-national patient registry of individuals (both patients and caregivers) affected by JoHD. The primary objective of the registry is to identify individuals affected by JoHD and to map their locations globally. Secondary objectives include supporting focused research for this population and identifying unmet needs of JoHD families to improve advocacy, care and support. It is anticipated that JOIN-HD will serve as a tool to facilitate recruitment to future research and clinical trials through the identification of potentially eligible participants. Pre-registration for JOIN-HD opened in Q1 2021, and Stage I is due to launch in Q3 2021. Participants will be invited to self-enrol and participate remotely via an electronic data capture portal. Stage I will capture participant demographics and information about the links participants have with the HD community. Two further stages of the registry are planned, with Stage II collecting data on medical history/experience of JoHD and Stage III incorporating a Clinician led interview.

Background The course of Huntington’s disease (HD) is believed to be modulated by lifestyle and genetic factors. However, we do not understand how the interplay of these affects disease progression. An efficient method of measuring lifestyle factors involves the use of digital monitoring devices, but their long-term use in clinical HD populations has not yet been explored. Aim Investigate the use of digital technologies in a longitudinal observational study to inform our understanding of the contribution of multi-domain lifestyle and genetic factors in the progression of HD. Methods We plan to recruit 300-450 people with early to mid-stage HD to a 12-month observational study measuring aspects of physical activity, nutrition and sleep. Participants with existing genome wide association study (GWAS) data will be preferentially recruited. Assessment of dietary, sleep and physical activity habits will be performed at baseline and 12-month follow-up Clinical measures will be obtained from the corresponding annual Enroll-HD assessment (within 8 weeks of the DOMINO-HD assessment). Each participant will wear a Fitbit for the duration of the study. Lifestyle, genetic and clinical data will be linked and propensity score weighting methodology will be applied to examine the causal effect of the multi-domain lifestyle and genetic measures on HD progression. Results The start of recruitment was delayed by 10 months due to Covid-19. As of 1st July 2021, we have recruited 36 participants across 5 clinical sites, with recruitment planned to continue until March 2022. Conclusion Successful collection of longitudinal lifestyle data, combined with functional clinical measures and genetic factors will allow, for the first time, the investigation of causal relationships between environmental and genetic modifiers with HD progression. We can then use the information generated to design lifestyle interventions aimed at improving quality of life and prognosis in HD.

Background Evidence of best practice for long term physical activity Huntington’s disease (HD) is lacking, due in part, to inherent challenges in the delivery and evaluation of such life-style interventions. Aim To investigate the feasibility of a nested randomized controlled trial (RCT) of physical activity into Enroll-HD, an established HD cohort study. Methods We conducted a ‘Trial within a Cohort’ (TWiC) evaluation of an exercise intervention compared to usual activity in individuals with early-mid stage HD. All participants completed their usual Enroll-HD assessments and PACE-HD assessments, which included measures of fitness (predicted maximal oxygen uptake) and self-reported and quantitative measures of physical activity. We explored the use of propensity score weighting to compare the individuals in the intervention arm of the RCT to those in the cohort. Results Of the 274 participants screened, 204 met the inclusion criteria and of those, 54 (26.5%) declined to participate and 34 (16.7%) were not contactable. Recruitment targets were only narrowly missed; 59/60 (98.3%) for the cohort and 57/60 (93.5%) for the RCT. Retention rates at 12 months were ~ 85 in both groups. Percentage (%) data completeness for outcomes measures at baseline ranged from 42.3-100% and at 12 month follow up from 19.2–85.2 %. Imbalances in pretreatment confounders for the cohort and the RCT could not to be addressed via propensity score weighting likely due to small sample sizes. Conclusion A targeted recruitment strategy was instrumental in achieving target recruitment; retention at 12 months was excellent. Pre-specified criteria for minimum dataset data completion (both PACE-HD and linked Enroll-HD datasets) were met. The TWiC design, with linkage to Enroll-HD, is feasible for long-term physical activity evaluation in HD provided sample size requirements can be achieved. *PACE-HD & Enroll-HD site principal investigators Teresa Montojo. Neurology Department, Fundacion Jimenez Diaz, Madrid, Spain. Jesus Miguel Ruiz Idiago. Neuropsychiatry Unit, Hospital Mare de Deu de la Merce, Barcelona, Spain. Department of Psychiatry and Forensic Medicine, Universitat Autonoma de Barcelona, Spain. Julie Hershberg. University of Southern California, Division of Biokinesiology and Physical Therapy. Re+active physical therapy & wellness, Los Angeles, CA, USA. Yvette Bordelon. Department of Neurology, University of California, Los Angeles, CA, USA. Karen Marder, Columbia University Irving Medical Center, New York, NY, USA. Lori Quinn, Teachers College, Columbia University, New York, NY, USA. Ralf Reilmann. George-Huntington-Institute and Institute for Clinical Radiology, University of Munster, Munster, Germany. Kathrin Reetz. University Hospital Aachen, Germany. Bernhard Landwehrmeyer. University Hospital Ulm, Germany.

Randomized controlled trials are the gold standard for measuring the causal effects of treatments on clinical outcomes. However, randomized trials are not always feasible, and causal treatment effects must, therefore, often be inferred from observational data. Observational study designs do not allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of confounders across groups while key assumptions hold. Propensity score (PS) and balance weighting are two useful techniques that aim to reduce the imbalances between treatment groups by weighting the groups to look alike on the observed confounders. There are many methods available to estimate PS and balancing weights. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size. Weighted analyses are further complicated by small studies with limited sample sizes, which is common when studying rare diseases. To address these issues, we present a step-by- step guide to covariate balancing strategies, including how to evaluate overlap, obtain estimates of PS and balancing weights, check for covariate balance, and assess sensitivity to unobserved confounding. We compare the performance of a number of commonly used estimation methods on a synthetic data set based on the Physical Activity and Exercise Outcomes in Huntington Disease (PACE-HD) study, which explored whether enhanced physical activity affects the progression and severity of the disease. We provide general guidelines for the choice of method for estimation of PS and balancing weights, interpretation, and sensitivity analysis of results.

Self-injurious thoughts and behaviors (SITB) are of increasing concern among adolescents, especially those who use substances. Some evidence suggests that existing evidence-based substance use treatments (EBTs) could impact not only their intended substance use targets but also SITB. However, which types of substance use treatments may have the greatest impact on youth SITB is not yet clear. Based on prior literature showing that family support and connection may buffer youth from SITB, we initially hypothesized that family-based EBTs would show greater improvement in SITB compared to those receiving individually focused EBTs and that the size of the effects would be small given the comparison between two active, evidence-based interventions, and base rates of SITB.In a sample of 2893 youth in substance use treatment, we compared the effectiveness of individually and family-based EBTs in reducing SITBs. The study used entropy balancing and regression modeling to balance the groups on pre-treatment characteristics and examine change in outcomes over a one-year follow-up period.Both groups improved in self-injury and suicide attempts over the one-year study period, but only youth in individual treatment improved in suicidal ideation. However, the study found no significant difference between the changes over time in the two groups for any outcome. As expected, effect sizes were small and power was constrained in this study given the rarity of the outcomes, but effect sizes are similar to those observed with substance use outcomes.The results provide important exploratory evidence on the potential relative effectiveness of these two treatments for SITBs. This study supports prior findings that EBTs for youth substance use may help to improve SITB and suggests that different treatment formats (individual or family-based) could result in different benefits for SITB outcomes.

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 (

This tutorial describes how to use a menu-driven Shiny application (app) based on the Toolkit for Weighting and Analysis of Nonequivalent Groups (TWANG) R package. This tutorial provides an introduction about how to use the TWANG Shiny app to estimate propensity score weights and related treatment effects for comparing three or more treatment groups when using observational data.

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.

There is a widely held belief that children’s general and psychological health benefits from owning and/or interacting with pets. In our study, we aimed to determine whether children who live with ...

Background and aims Prescription Drug Monitoring Programs (PDMPs) are a principal strategy used in the United States to address prescription drug abuse. We (1) compared opioid use pre- and post-PDMP implementation and (2) estimated differences of PDMP impact by reason for Medicare eligibility and plan type. Design Analysis of opioid prescription claims in US states that implemented PDMPs relative to non-PDMP states during 2007-12. Setting Florida, Louisiana, Nebraska, New Jersey, Vermont, Georgia, Wisconsin, Maryland, New Hampshire and Arkansas, USA. Participants A total of 310 105 disabled and older adult Medicare enrolees. Measurements Primary outcomes were monthly total opioid volume, mean daily morphine milligram equivalent (MME) dose per prescription and number of opioid prescriptions dispensed. The key predictors were PDMP status and time. Tests for moderation examined PDMP impact by Medicare eligibility (disability versus age) and drug plan [privately provided Medicare Advantage (MAPD) versus fee-for-service (PDP)]. Findings Overall, PDMP implementation was associated with reduced opioid volume [-2.36 kg/month, 95% confidence interval (CI) = -3.44, -1.28] and no changes in mean MMEs or opioid prescriptions 12 months after implementation compared with non-PDMP states. We found evidence of strong moderation effects. In PDMP states, estimated monthly opioid volumes decreased 1.67 kg (95% CI = -2.38, -0.96) and 0.75 kg (95% CI = -1.32, -0.18) among disabled and older adults, respectively, and 1.2 kg, regardless of plan type. MME reductions were 3.73 mg/prescription (95% CI = -6.22, -1.24) in disabled and 3.02 mg/prescription (95% CI = -3.86, -2.18) in MAPD beneficiaries, but there were no changes in older adults and PDP beneficiaries. Dispensed prescriptions increased 259/month (95% CI = 39, 479) among the disabled and decreased 610/month (95% CI = -953, -257) among MAPD beneficiaries. Conclusions Prescription drug monitoring programs (PDMPs) are associated with reductions in opioid use, measured by volume, among disabled and older adult Medicare beneficiaries in the United States compared with states that do not have PDMPs. PDMP impact on daily doses and daily prescriptions varied by reason for eligibility and plan type. These findings cannot be generalized beyond the 10 US states studied.

Background There is increasing international interest in alternatives to the use of arrest for minor drug offences. While Australia has been at the forefront in the provision of diversionary programs for minor drug offences there remain key gaps in knowledge about the cost-effectiveness of different approaches. Here we set out to assess the cost-effectiveness of cannabis cautioning schemes whereby police refer minor cannabis use and possession offenders to education and/or treatment instead of arresting and charging them. Methods This study used a purpose built nation-wide online survey to evaluate cost-effectiveness of cannabis cautioning versus a traditional response for minor cannabis offences (arrest). The survey was completed by a self-selected group of detected cannabis offenders. The outcome measure was self-reported cannabis use days in the previous month post-intervention. Cost data included costs of policing, court, penalties, assessment, treatment and educational sessions. Propensity score weighting and doubly robust regression analyses were utilised to address differences between the groups. Results There were 195 respondents who reported being arrested for a cannabis possession/use offence and 355 who reported receiving a formal cannabis caution. After matching on a range of characteristics (age, prior criminal conviction, cannabis consumption, employment status, self-reported criminal activity prior to detection, severity of dependence) there was no statistically significant difference in cannabis use pre- and post-police intervention between the two groups(N=544). After matching and bootstrapping the costs there was a significant difference in costs; the mean cost for the charge group (net of fines) was $733 (SD 151) and $388 (SD 111) for the caution group. Conclusion These results indicate that after matching on a range of relevant characteristics there were no differences across groups in the change in self-reported cannabis use days, but cannabis cautioning was less costly than charge/arrest. These results add to the evidence about the efficacy and desirability of alternatives to arrest both within Australia and abroad.

Studies on blended education pay little attention to implementation, thus limiting the understanding of how such programs contribute to student math learning. This article examines the implementation of a widely used blended algebra curriculum and the relationship between implementation and student outcomes. The study was conducted in 74 middle schools and 73 high schools in 51 school districts located in seven states. The study included both treatment and control schools. The blended-curriculum combines inquiry-based teaching and learning with technology (math software). The study found that teachers implemented the blended curriculum with low fidelity. Teachers had most difficulty allocating the recommended amount of time for the math lab and content. The study also found that the blended-curriculum teachers in the second year reverted to more traditional approach to instruction and spent less time on inquiry based instruction than in the first year, although they continued to use this approach at a higher level than teachers in the control schools. The study findings suggest that teacher adjustment of instruction in the second year, specifically balancing the amount of traditional instruction with inquiry instruction, in combination to the use of the math software contributed to the performance of the program.

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.