Availability of Medications for Opioid Use Disorder in Community Mental Health Facilities.

Importance: Medications for opioid use disorder (MOUD) are an effective but underutilized treatment. Opioid use disorder prevalence is high among people receiving treatment in community outpatient mental health treatment facilities (MHTFs), but MHTFs are understudied as an MOUD access point. Objective: To quantify availability of MOUD at community outpatient MHTFs in high-burden states as well as characteristics associated with offering MOUD. Design, Setting, and Participants: This cross-sectional study performed a phone survey between April and July 2023 among a representative sample of community outpatient MHTFs within 20 states most affected by the opioid crisis, including all Certified Community Behavioral Health Centers (CCBHCs). Participants were staff at 450 surveyed community outpatient MHTFs in 20 states in the US. Main Outcomes and Measures: MOUD availability. A multivariable logistic regression was fit to assess associations of facility, county, and state-level characteristics with offering MOUD. Results: Surveys with staff from 450 community outpatient MHTFs (152 CCBHCs and 298 non-CCBHCs) in 20 states were analyzed. Weighted estimates found that 34% (95% CI, 29%-39%) of MHTFs offered MOUD in these states. Facility-level factors associated with increased odds of offering MOUD were: self-reporting being a CCBHC (odds ratio [OR], 2.11 [95% CI, 1.08-4.11]), providing integrated mental and substance use disorder treatment (OR, 5.21 [95% CI, 2.44-11.14), having a specialized treatment program for clients with co-occurring mental and substance use disorders (OR, 2.25 [95% CI, 1.14-4.43), offering housing services (OR, 2.54 [95% CI, 1.43-4.51]), and laboratory testing (OR, 2.15 [95% CI, 1.12-4.12]). Facilities that accepted state-financed health insurance plans other than Medicaid as a form of payment had increased odds of offering MOUD (OR, 1.95 [95% CI, 1.01-3.76]) and facilities that accepted state mental health agency funds had reduced odds (OR, 0.43 [95% CI, 0.19-0.99]). Conclusions and Relevance: In this study of 450 community outpatient MHTFs in 20 high-burden states, approximately one-third offered MOUD. These results suggest that further study is needed to report MOUD uptake, either through increased prescribing at all clinics or through effective referral models.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 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.

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.

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.

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.

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.

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.

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.