Longitudinal trajectories of suicidal ideation among child welfare-involved 7- to 12-year-old children

BACKGROUND

Young children involved in the child welfare system (CWS) are at high risk for suicidal ideation (SI) at a time when overall rates of suicide death in this age group are rising. Yet risk factors for and changes in SI over time are poorly understood in this population.

METHOD

We combined data from two large representative longitudinal studies of children involved in the United States CWS. We examined patterns of SI among children who were between ages 7 and 12 years at the initial survey wave (N = 2,186), assessed at three waves using a measure of SI in the past 2 weeks. We conducted a multinomial regression to understand the baseline demographic, child maltreatment, and mental health characteristics that distinguish the trajectories.

RESULTS

There were eight different subgroups (Non-Ideators, Late Ideators, Boomerang Ideators, Delayed Ideators, Desisters, Boomerang Non-Ideators, Late Desisters, and Persisters). Differences in race, type of maltreatment, sex, and mental health symptoms were identified when comparing Persisters (SI at all three waves) to other groups.

CONCLUSIONS

These findings can help researchers and practitioners to develop strategies for better identifying CWS-involved children who are in greatest need of suicide risk monitoring and intervention.

Trends and Disparities in Perinatal Opioid Use Disorder Treatment in Medicaid, 2007-2012

We described Medicaid-insured women by receipt of perinatal opioid use disorder (OUD) treatment; and trends and disparities in treatment. Using 2007 to 2012 Medicaid Analytic eXtract data from 45 states and D.C., we identified deliveries among women with OUD. Regressions modeled the association between patient characteristics and receipt of any OUD treatment, medication for OUD (MOUD), and counseling alone during the perinatal period. Rates of any OUD treatment and MOUD for women with perinatal OUD increased over the study period, but trends differed by subgroup. Compared with non-Hispanic White women, Black and American Indian/Alaskan Native (AI/AN) women were less likely to receive any OUD treatment, and Black women were less likely to receive MOUD. Over time, the disparity in receipt of MOUD between Black and White women increased. Overall gains in OUD treatment were driven by improvements in perinatal OUD care for White women and obscured disparities for Black and AI/AN women.

Estimating generalized propensity scores with survey and attrition weighted data

Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example.

Virtual Behavioral Health for Army Soldiers: Soldier Perspectives and Patterns of Treatment

Delivery of high-quality behavioral health (BH) care is essential to supporting the readiness of the U.S. armed forces and their families. The coronavirus disease 2019 (COVID-19) pandemic led to a dramatic expansion of virtual behavioral health (VBH) care: remote patient access to BH care using technology such as a computer or cellular phone. The U.S. Army asked RAND Arroyo Center to examine the use of VBH to inform recommendations on the role of VBH care in the future of BH care in the Military Health System. The authors analyzed administrative data on VBH and in-person BH care from prior to the pandemic through March 2022 and surveyed soldiers who received BH care to assess their perceptions of VBH care. Administrative data analyses showed that direct care providers were less likely to deliver VBH care than private-sector providers and relied heavily on audio rather than video VBH. In addition, soldiers who received VBH care typically received a mix of VBH and in-person visits. Survey respondents who used VBH care had similar perceptions of the quality of their care and more-positive views of VBH than respondents who did not use VBH care. Few respondents had declined VBH care in favor of in-person care. Using these findings, the authors make recommendations on the role of VBH care in overall BH delivered by the military.

A Tutorial for Propensity Score Weighting for Moderation Analysis With Categorical Variables: An Application Examining Smoking Disparities Among Sexual Minority Adults

OBJECTIVE

To provide step-by-step guidance and STATA and R code for using propensity score (PS) weighting to estimate moderation effects with categorical variables.

RESEARCH DESIGN

Tutorial illustrating the key steps for estimating and testing moderation using observational data. Steps include: (1) examining covariate overlap across treatment groups within levels of the moderator; (2) estimating the PS weights; (3) evaluating whether PS weights improved covariate balance; (4) estimating moderated treatment effects; and (5) assessing the sensitivity of findings to unobserved confounding. Our illustrative case study uses data from 41,832 adults from the 2019 National Survey on Drug Use and Health to examine if gender moderates the association between sexual minority status (eg, lesbian, gay, or bisexual [LGB] identity) and adult smoking prevalence.

RESULTS

For our case study, there were no noted concerns about covariate overlap, and we were able to successfully estimate the PS weights within each level of the moderator. Moreover, balance criteria indicated that PS weights successfully achieved covariate balance for both moderator groups. PS-weighted results indicated there was significant evidence of moderation for the case study, and sensitivity analyses demonstrated that results were highly robust for one level of the moderator but not the other.

CONCLUSIONS

When conducting moderation analyses, covariate imbalances across levels of the moderator can cause biased estimates. As demonstrated in this tutorial, PS weighting within each level of the moderator can improve the estimated moderation effects by minimizing bias from imbalance within the moderator subgroups.

Identifying Optimal Methods for Addressing Confounding Bias When Estimating the Effects of State-level Policies

BACKGROUND

Policy evaluation studies that assess how state-level policies affect health-related outcomes are foundational to health and social policy research. The relative ability of newer analytic methods to address confounding, a key source of bias in observational studies, has not been closely examined.

METHODS

We conducted a simulation study to examine how differing magnitudes of confounding affected the performance of 4 methods used for policy evaluations: (1) the two-way fixed effects difference-in-differences model; (2) a 1-period lagged autoregressive model; (3) augmented synthetic control method; and (4) the doubly robust difference-in-differences approach with multiple time periods from Callaway-Sant'Anna. We simulated our data to have staggered policy adoption and multiple confounding scenarios (i.e., varying the magnitude and nature of confounding relationships).

RESULTS

Bias increased for each method: (1) as confounding magnitude increases; (2) when confounding is generated with respect to prior outcome trends (rather than levels), and (3) when confounding associations are nonlinear (rather than linear). The autoregressive model and augmented synthetic control method had notably lower root mean squared error than the two-way fixed effects and Callaway-Sant'Anna approaches for all scenarios; the exception is nonlinear confounding by prior trends, where Callaway-Sant'Anna excels. Coverage rates were unreasonably high for the augmented synthetic control method (e.g., 100%), reflecting large model-based standard errors and wide confidence intervals in practice.

CONCLUSIONS

In our simulation study, no single method consistently outperformed the others, but a researcher's toolkit should include all methodologic options. Our simulations and associated R package can help researchers choose the most appropriate approach for their data.

Disparities in Perinatal and Emergency Care Receipt Among Women With Perinatal Opioid Use Disorder in Medicaid, 2007 to 2012

OBJECTIVES

This study aimed to better understand receipt of perinatal and emergency care among women with perinatal opioid use disorder (OUD) and explore variation by race/ethnicity.

METHODS

We used 2007-2012 Medicaid Analytic eXtract (MAX) data from all 50 states and the District of Columbia to examine 6,823,471 deliveries for women 18 to 44 years old. Logistic regressions modeled the association between (1) OUD status and receipt of perinatal and emergency care, and (2) receipt of perinatal and emergency care and race/ethnicity, conditional on OUD diagnosis and controlling for patient and county characteristics. We used robust SEs, clustered at the individual level, and included state and year fixed effects.

RESULTS

Women with perinatal OUD were less likely to receive adequate prenatal care (adjusted odds ratio [aOR], 0.45; 95% confidence interval [CI], 0.44-0.46) and attend the postpartum visit (aOR, 0.46; 95% CI, 0.45-0.47) and more likely to seek emergency care (aOR, 1.48; 95% CI, 1.45-1.51) than women without perinatal OUD. Among women with perinatal OUD, Black, Hispanic, and American Indian and Alaskan Native (AI/AN) women were less likely to receive adequate prenatal care (aOR, 0.68 [95% CI, 0.64-0.72]; aOR, 0.86 [95% CI, 0.80-0.92]; aOR, 0.71 [95% CI, 0.64-0.79]) and attend the postpartum visit (aOR, 0.85 [95% CI, 0.80-0.91]; aOR, 0.86 [95% CI, 0.80-0.93]; aOR, 0.83 [95% CI, 0.73-0.94]) relative to non-Hispanic White women. Black and AI/AN women were also more likely to receive emergency care (aOR, 1.13 [95% CI, 1.05-1.20]; aOR, 1.12 [95% CI, 1.00-1.26]).

CONCLUSIONS

Our findings suggest that women with perinatal OUD, in particular Black, Hispanic, and AI/AN women, may be missing opportunities for preventive care and comprehensive management of their physical and behavioral health during pregnancy.

Design of CLARO+ (Collaboration Leading to Addiction Treatment and Recovery from Other Stresses, Plus): A randomized trial of collaborative care to decrease overdose and suicide risk among patients with co-occurring disorders

BACKGROUND

The United States is mired in two intertwined epidemics of death from suicide and overdose. Opioid use disorder (OUD) and mental illness contribute to both, and individuals with co-occurring disorders (CODs) are a complex population at high risk. Although universal prevention makes sense from a public health perspective, medical and behavioral health providers often lack the time to proactively address these issues with all patients. In this study, we build upon a parent study called Collaboration Leading to Addiction Treatment and Recovery from Other Stresses (CLARO), a model of collaborative care in which care coordinators deliver preventative measures to high-risk patients and coordinate care with the patients' care team, with the goal of increasing MOUD retention and decreasing risk of suicide and overdose.

METHODS

CLARO+ adds intervention components on overdose prevention, recognition, and response training; lethal means safety counseling; and an effort to mail compassionate messages called Caring Contacts. Both CLARO and CLARO+ have been implemented at 17 clinics in New Mexico and California, and this study seeks to determine the difference in effectiveness between the two versions of the intervention. This paper describes the design protocol for CLARO+.

CONCLUSION

CLARO+ is an innovative approach that aims to supplement existing collaborative care with additional suicide and overdose prevention strategies.

CLINICALTRIALS

gov: NCT04559893.

Sexual violence victimization among American college students studying abroad

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.

Exploring the Association of State Policies and the Trajectories of Buprenorphine Prescriber Patient Caseloads

BACKGROUND

Increasing buprenorphine access is critical to facilitating effective opioid use disorder treatment. Buprenorphine prescriber numbers have increased substantially, but most clinicians who start prescribing buprenorphine stop within a year, and most active prescribers treat very few individuals. Little research has examined state policies' association with the evolution of buprenorphine prescribing clinicians' patient caseloads.

METHODS

Our retrospective cohort study design derived from 2006 to 2018 national pharmacy claims identifying buprenorphine prescribers and the number of patients treated monthly. We defined persistent prescribers based on results from a k-clustering approach and were characterized by clinicians who did not quickly stop prescribing and had average monthly caseloads greater than 5 patients for much of the first 6 years after their first dispensed prescription. We examined the association between persistent prescribers (dependent variable) and Medicaid coverage of buprenorphine, prior authorization requirements, and mandated counseling policies (key predictors) that were active within the first 2 years after a prescriber's first observed dispensed buprenorphine prescription. We used multivariable logistic regression analyses and entropy balancing weights to ensure better comparability of prescribers in states that did and did not implement policies.

RESULTS

Medicaid coverage of buprenorphine was associated with a smaller percentage of new prescribers becoming persistent prescribers (OR = 0.72; 95% CI = 0.53, 0.97). There was no evidence that either mandatory counseling or prior authorization was associated with the odds of a clinician being a persistent prescriber with estimated ORs equal to 0.85 (95% CI = 0.63, 1.16) and 1.13 (95% CI = 0.83, 1.55), respectively.

CONCLUSIONS

Compared to states without coverage, states with Medicaid coverage for buprenorphine had a smaller percentage of new prescribers become persistent prescribers; there was no evidence that the other state policies were associated with changes in the rate of clinicians becoming persistent prescribers. Because buprenorphine treatment is highly concentrated among a small group of clinicians, it is imperative to increase the pool of clinicians providing care to larger numbers of patients for longer periods. Greater efforts are needed to identify and support factors associated with successful persistent prescribing.

A randomized controlled trial of an online alcohol and sexual risk prevention program for college students studying abroad

INTRODUCTION

American college students who study abroad experience increases in their drinking behavior, concerning risky sexual behaviors, and high rates of sexual violence while abroad. Despite these concerns, institutions offer limited programming to students prior to departure to address these risks and no empirically supported interventions currently exist that are targeted toward preventing increased drinking, risky sexual behavior, and sexual violence abroad. To help address alcohol and sexual risk abroad, we designed a brief, single-session online predeparture intervention focused on risk and protective factors known to associate with alcohol and sexual risk abroad.

METHODS

Using a sample of 650 college students from 40 home institutions, we conducted a randomized controlled trial of the intervention and tested its effects on drinking (drinks per week, binge drinking frequency, alcohol related-consequences), risky sexual behaviors, and sexual violence victimization during the trip abroad (first month, last month abroad) and one-month and three-months after return home.

RESULTS

We observed small, but nonsignificant, intervention effects on drinks per week and binge drinking days during the first month abroad and three months after they had returned home to the United States, and small, significant effects on risky sexual behaviors during the first month abroad. The study found no observable effects at any time point for alcohol-related consequences or for sexual violence victimization abroad.

CONCLUSIONS

Though mostly nonsignificant, small initial intervention effects were promising in this first empirical test of an alcohol and sexual risk prevention program for study abroad students. However, students may need more intensive programming with booster sessions to experience lasting intervention effects during this particularly high-risk period.

CLINICALTRIALS

GOV IDENTIFIER

NCT03928067.

The Role of Pre-Treatment Traumatic Stress Symptoms in Adolescent Substance Use Treatment Outcomes

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.

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Physical activity and exercise outcomes in Huntington's disease (PACE-HD): results of a 12-month trial-within-cohort feasibility study of a physical activity intervention in people with Huntington's disease

INTRODUCTION

While physical activity (PA) is recognized as important in Huntington's disease (HD) disease management, there has been no long-term evaluation undertaken. We aimed to evaluate the feasibility of a nested (within cohort) randomized controlled trial (RCT) of a physical therapist-led PA intervention.

METHODS

Participants were recruited from six HD specialist centers participating in the Enroll-HD cohort study in Germany, Spain and U.S. Assessments were completed at baseline and 12 months and linked to Enroll-HD cohort data. Participants at three sites (cohort) received no contact between baseline and 12 month assessments. Participants at three additional sites (RCT) were randomized to PA intervention or control group. The intervention consisted of 18 sessions delivered over 12 months; control group participants received no intervention, however both groups completed monthly exercise/falls diaries and 6-month assessments.

RESULTS

274 participants were screened, 204 met inclusion criteria and 116 were enrolled (59 in cohort; 57 in RCT). Retention rates at 12-months were 84.7% (cohort) and 79.0% (RCT). Data completeness at baseline ranged from 42.3 to 100% and at 12-months 19.2-85.2%. In the RCT, there was 80.5% adherence, high intervention fidelity, and similar adverse events between groups. There were differences in fitness, walking endurance and self-reported PA at 12 months favoring the intervention group, with data completeness >60%. Participants in the cohort had motor and functional decline at rates comparable to previous studies.

CONCLUSION

Predefined progression criteria indicating feasibility were met. PACE-HD lays the groundwork for a future, fully-powered within cohort trial, but approaches to ensure data completeness must be considered.

CLINICALTRIALS

GOV: NCT03344601.

Methodological considerations for estimating policy effects in the context of co-occurring policies

Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18 years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models.

Hospice Care Experiences Among Decedents With Huntington's Disease

CONTEXT

Little is known about the hospice care experiences of those with Huntington's Disease (HD).

OBJECTIVES

Our objective is to provide the first national characterization of hospice care quality for patients with HD and their families.

METHODS

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.

RESULTS

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, P < 0.01). 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.

CONCLUSION

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.

Higher Moments for Optimal Balance Weighting in Causal Estimation

We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperform covariate-balancing propensity scores, and entropy balance when there are likely to be nonlinear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entropy balance. Our findings showcase that these two models should, by default, include higher-order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher moments.

A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents

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.

Comparative effectiveness of individual versus family-based substance use treatment on adolescent self-injurious thoughts and behaviors

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength

INTRODUCTION

Assessing 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.

METHODS

We 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 26 November 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.

RESULTS

After 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. 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-sectional), 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 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.

DISCUSSION

The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. 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.

Volunteer Recovery Support for Adolescents: Using propensity score based methods to understand dosage effects within a randomized controlled trial

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.

Moving beyond the classic difference-in-differences model: a simulation study comparing statistical methods for estimating effectiveness of state-level policies

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 (< 10%), raising concerns of spurious conclusions about policy effectiveness in the opioid literature. When considering performance across models, the linear AR models were optimal in terms of directional bias, root mean squared error, Type I error, and correct rejection rates.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01471-y.

Best practices in statistical computing

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.

Buprenorphine prescriber monthly patient caseloads: An examination of 6-year trajectories

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.

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods. This paper explores recent developments of entropy balancing methods to continuous exposure settings and the estimation of population dose-response curves using nonparametric estimation combined with entropy balancing weights, focusing on factors that would be important to applied researchers in medical or health services research. The methods developed here are applied to data from a study assessing the effect of non-randomized components of an evidence-based substance use treatment program on emotional and substance use clinical outcomes.

Comparing the Army's Suicide Rate to the General U.S. Population: Identifying Suitable Characteristics, Data Sources, and Analytic Approaches

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.

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation (PEACHPIE): A systematic review of study design and evidence strength

Introduction:

Assessing 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.

Methods:

We 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.

Results:

After 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.

Discussion:

The 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 policymakers. 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.

Methodological Challenges and Proposed Solutions for Evaluating Opioid Policy Effectiveness

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.

Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting

INTRODUCTION

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Expanding outcomes when considering the relative effectiveness of two evidence-based outpatient treatment programs for adolescents

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.

Alcohol and Sexual Risk among American College Students Studying Abroad

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 2630 students from 65 different US 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.

The state of the science in opioid policy research

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.

Quantifying the bias due to observed individual confounders in causal treatment effect estimates

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 article, 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, a rare neurological disease.

Identifying optimal level-of-care placement decisions for adolescent substance use treatment

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.

Robust estimation of the causal effect of time-varying neighborhood factors on health outcomes

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.

Practical factors determining adolescent substance use treatment settings: Results from four online stakeholder panels

Practical factors can significantly influence the setting, or level of care, where an adolescent receives substance use treatment. This study aimed to identify practical factors that stakeholders find most critical to consider when planning adolescent substance use treatment. We conducted online panels with four stakeholder groups: providers, policymakers, researchers, and parents. Stakeholders 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. We thematically analyzed stakeholders comments to explain how they differentiated relative importance. 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 rated higher the practical factors they perceived to (1) trump clinical appropriateness as the reason for placing an adolescent in a given setting, (2) steer adolescents toward specific settings, or (3) steer an adolescent away from specific settings. Conversely, participants rated lower the practical factors they perceived (1) applicable to clinical intervention rather than treatment setting, (2) unrelated to initial recommendations, (3) relevant to any setting, or (4) applicable only to certain contexts and sub-populations. These findings help elucidate why stakeholders view certain practical factors as critical to consider in actual 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.

Changes in Cigarette, E-Cigarette, and Cannabis Use among U.S. College Students Studying Abroad

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.

There and Back Again … Safely: Examining Students' Reports of Substance Use and Sexual Assault Prevention Program Receipt Prior to Departure Abroad

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 assault 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 heavier 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.

Physical Activity and Exercise Outcomes in Huntington Disease (PACE-HD): Protocol for a 12-Month Trial Within Cohort Evaluation of a Physical Activity Intervention in People With Huntington Disease

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.

An online alcohol and risky sex prevention program for college students studying abroad: study protocol for a randomized controlled trial

BACKGROUND

This study protocol describes a proposed randomized controlled trial that builds upon a successful pilot intervention study to address problematic and dangerous drinking among young adult college students studying abroad in foreign environments. Despite universities and colleges citing alcohol misuse as the most concerning issue for their students abroad, most institutions offer no empirically-based prevention efforts tailored to this at-risk population. The proposed intervention attempts to fill a major gap for the nearly 333,000 students completing study abroad programs each year by using empirically-based and theoretically-informed risk and protective factors to correct misperceived peer drinking norms and promote cultural engagement abroad. In addition to preventing heavy and problematic drinking, the intervention seeks to prevent risky sexual behaviors (e.g., sex without a condom) and experience of sexual violence victimization, which are strikingly common among study abroad students and have the potential for lasting physical and psychological effects upon return home.

METHODS/DESIGN

We will conduct a randomized controlled trial of an intervention with a sample of 1200 college students studying abroad from approximately 50 US universities and colleges. The brief, online intervention is text and video based and contains evidence-based components of personalized normative feedback to correct students' misperceived drinking norms, content to promote engagement with the cultural experience abroad and address difficulties adjusting to life in the foreign environment, and tips and strategies to prevent risky sexual behaviors and sexual violence victimization abroad. Participants will complete online surveys at five time points (predeparture, first month abroad, last month abroad, 1-month post-return, and 3-months post-return) to assess for intervention effects on drinking behavior, drinking consequences, risky sex, and sexual violence outcomes. We will examine whether the mechanisms targeted by the intervention (changes in perceived norms, engagement in the cultural experience abroad) serve as mediators of intervention efficacy.

DISCUSSION

The proposed study has the potential to fill an important gap in the research literature and provide empirical support for an online accessible, brief, and targeted approach that can easily be distributed to study abroad students to help prevent heavy alcohol use and sexual risk abroad. Trial registration ClinicalTrials.gov Identifier NCT03928067.

Prescription drug monitoring programs: Assessing the association between "best practices" and opioid use in Medicare

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 < score < median), or high strength (score ≥ median). Using generalized linear models, we quantified the association between the PDMP score category and opioid use measures-overall and stratified by disability/age. Sensitivity analyses assessed the general Medicare sample regardless of pain diagnoses, individual PDMP characteristics, and compared GEE model findings to models with state fixed effects.

PRINCIPAL FINDINGS

Compared to non-PDMP states, strong PDMP states had lower opioid cumulative doses (-296 mg; 95% CI: -512, -132), days supplied (-7.84; 95% CI: -10.6, -5.04), prescription fill rates (0.97; 95% CI: 0.95, 0.98), and mean daily doses (-2.31 mg; 95% CI: -3.14, -1.48) but greater prevalence of high opioid doses in disabled adults, whereas there was little or no change in older adults. Findings in states with weak PDMPs were substantively similar to those of strong PDMPs. Results from sensitivity analyses were mostly consistent with main findings except there was a null relationship with mean daily doses and high doses in models with state fixed effects.

CONCLUSIONS

Comprehensive or minimal adoption of PDMP best practices was associated with mostly comparable effects on Medicare beneficiaries' opioid use; however, these effects were concentrated among nonelderly disabled adults.

Eat, Pray, Move: A Pilot Cluster Randomized Controlled Trial of a Multilevel Church-Based Intervention to Address Obesity Among African Americans and Latinos

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.

Elevated high density lipoprotein cholesterol and low grade systemic inflammation is associated with increased gut permeability in normoglycemic men

BACKGROUND & AIMS

Serum lipids and lipoproteins are established biomarkers of cardiovascular disease risk that could be influenced by impaired gut barrier function via effects on the absorption of dietary and biliary cholesterol. The aim of this study was to examine the potential relationship between gut barrier function (gut permeability) and concentration of serum lipids and lipoproteins, in an ancillary analysis of serum samples taken from a previous study.

METHODS AND RESULTS

Serum lipids, lipoproteins and functional gut permeability, as assessed by the percentage of the urinary recovery of ⁵¹Cr-labelled EDTA absorbed within 24 h, were measured in a group of 30 healthy men. Serum lipopolysaccharide, high sensitivity C-reactive protein and interleukin-6 were also measured as markers of low-grade inflammation. The group expressed a 5-fold variation in total gut permeability (1.11-5.03%). Gut permeability was unrelated to the concentration of both serum total and low density lipoprotein (LDL)-cholesterol, but was positively associated with serum high density lipoprotein (HDL)-cholesterol (r = 0.434, P = 0.015). Serum HDL-cholesterol was also positively associated with serum endotoxaemia (r = 0.415, P = 0.023).

CONCLUSION

The significant association between increased gut permeability and elevated serum HDL-cholesterol is consistent with the role of HDL as an acute phase reactant, and in this situation, potentially dysfunctional lipoprotein. This finding may have negative implications for the putative role of HDL as a cardio-protective lipoprotein.

The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores

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.

Impact of prescription drug monitoring programs (PDMPs) on opioid utilization among Medicare beneficiaries in 10 US States

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.

Landmark estimation of survival and treatment effects in observational studies

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.

Acculturation and Drug Use Stigma Among Latinos and African Americans: An Examination of a Church-Based Sample

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 < .05; foreign-born Latinos with high English proficiency, β = 0.30, p < .05; and foreign-born Latinos with low English proficiency, β = 0.49, p < .001). Additionally, Latinos with low English proficiency had significantly higher mean levels of drug use stigma compared Latinos with high proficiency (both foreign-born and US-born). In this church-affiliated sample, Latinos' drug addiction stigma decreases with acculturation, but remains higher among the most acculturated Latinos compared to African-Americans. These attitudes may pose a barrier to treatment for Latino drug users.