A simulation study of the performance of statistical models for count outcomes with excessive zeros.

BACKGROUND: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data. METHODS: The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three prevailing count distribution-based models (ie, Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero-inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of the statistical models and approaches across data conditions that varied in sample size ( N = 100 $$ N=100 $$ to 500), zero rate (0.2 to 0.8), and intervention effect sizes. RESULTS: Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of the linear model with a log-transformed outcome variable was unsatisfactory. CONCLUSIONS: The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros.

Changes in high-risk sexual behavior, HIV and other STI testing, and PrEP use during the COVID-19 pandemic in a longitudinal cohort of adolescent men who have sex with men 13 to 18 years old in the United States.

The COVID-19 pandemic has disrupted sexual health services among those most vulnerable to HIV acquisition, such as adolescent men who have sex with men (AMSM). We sought to characterize the changes in sexual-risk behaviors, HIV and other STI testing, and pre-exposure prophylaxis (PrEP) use among a longitudinal cohort of AMSM aged 13 to 18 years before and during the COVID-19 pandemic. We observed a significant decline in HIV testing and a marginal decrease in other STI testing since the pandemic began in March 2020. Outreach efforts and innovative remote delivery of sexual health services are needed to support access to healthcare services among AMSM as the pandemic persists.

RESUMEN: La pandemia de COVID-19 ha afectado la prestación de servicios de salud sexual para los más vulnerables, tales como los hombres adolescentes que tienen relaciones sexuales con hombres (AMSM; por sus siglas en ingles). En una cohorte longitudinal de AMSM de 13 a 18 años, examinamos los cambios en comportamientos sexuales de alto riesgo, la prueba de VIH, las pruebas de otras enfermedades de transmisión sexual, y el uso de Profilaxis Preexposición (PrEP) para el VIH antes y durante la pandemia. Desde el inicio de la pandemia en marzo de 2020, observamos una disminución significativa en la frecuencia de pruebas de VIH y una disminución marginal en la frecuencia de pruebas de otras enfermedades de transmisión sexual. Mientras persista la pandemia, serán necesarios más esfuerzos de divulgación e innovaciones en la prestación remota de servicios de salud sexual para apoyar el acceso a dichos servicios por parte de AMSM.

A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses.

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.

Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis.

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1-3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9-12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses.

Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study.

Meta-analysis using individual participant data (IPD) is an important methodology in intervention research because it (a) increases accuracy and precision of estimates, (b) allows researchers to investigate mediators and moderators of treatment effects, and (c) makes use of extant data. IPD meta-analysis can be conducted either via a one-step approach that uses data from all studies simultaneously, or a two-step approach, which aggregates data for each study and then combines them in a traditional meta-analysis model. Unfortunately, there are no evidence-based guidelines for how best to approach IPD meta-analysis for count outcomes with many zeroes, such as alcohol use. We used simulation to compare the performance of four hurdle models (3 one-step and 1 two-step models) for zero-inflated count IPD, under realistic data conditions. Overall, all models yielded adequate coverage and bias for the treatment effect in the count portion of the model, across all data conditions. However, in the zero portion, the treatment effect was underestimated in most models and data conditions, especially when there were fewer studies. The performance of both one- and two-step approaches depended on the formulation of the treatment effects, suggesting a need to carefully consider model assumptions and specifications when using IPD.

Do Brief Alcohol Interventions Reduce Driving After Drinking Among College Students? A Two-step Meta-analysis of Individual Participant Data.

AIMS: College students who drink are at an increased risk of driving after drinking and alcohol-involved traffic accidents and deaths. Furthermore, the persistence of driving after drinking over time underscores a need for effective interventions to prevent future drunk driving in adulthood. The present study examined whether brief alcohol interventions (BAIs) for college students reduce driving after drinking. METHODS: A two-step meta-analysis of individual participant data (IPD) was conducted using a combined sample of 6801 college students from 15 randomized controlled trials (38% male, 72% White and 58% first-year students). BAIs included individually delivered Motivational Interviewing with Personalized Feedback (MI + PF), Group Motivational Interviewing (GMI), and stand-alone Personalized Feedback (PF) interventions. Two outcome variables, driving after two+/three+ drinks and driving after four+/five+ drinks, were checked, harmonized and analyzed separately for each study and then combined for meta-analysis and meta-regression analysis. RESULTS: BAIs lowered the risk of driving after four+/five+ drinks (19% difference in the odds of driving after drinking favoring BAIs vs. control), but not the risk of driving after two+/three+ drinks (9% difference). Subsequent subgroup analysis indicated that the MI + PF intervention was comparatively better than PF or GMI. CONCLUSIONS: BAIs provide a harm reduction approach to college drinking. Hence, it is encouraging that BAIs reduce the risk of driving after heavy drinking among college students. However, there may be opportunities to enhance the intervention content and timing to be more relevant for driving after drinking and improve the outcome assessment and reporting to demonstrate its effect.

A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data: Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems.

This paper introduces a meta-analytic mediation analysis approach for individual participant data (IPD) from multiple studies. Mediation analysis evaluates whether the effectiveness of an intervention on health outcomes occurs because of change in a key behavior targeted by the intervention. However, individual trials are often statistically underpowered to test mediation hypotheses. Existing approaches for evaluating mediation in the meta-analytic context are limited by their reliance on aggregate data; thus, findings may be confounded with study-level differences unrelated to the pathway of interest. To overcome the limitations of existing meta-analytic mediation approaches, we used a one-stage estimation approach using structural equation modeling (SEM) to combine IPD from multiple studies for mediation analysis. This approach (1) accounts for the clustering of participants within studies, (2) accommodates missing data via multiple imputation, and (3) allows valid inferences about the indirect (i.e., mediated) effects via bootstrapped confidence intervals. We used data (N = 3691 from 10 studies) from Project INTEGRATE (Mun et al. Psychology of Addictive Behaviors, 29, 34-48, 2015) to illustrate the SEM approach to meta-analytic mediation analysis by testing whether improvements in the use of protective behavioral strategies mediate the effectiveness of brief motivational interventions for alcohol-related problems among college students. To facilitate the application of the methodology, we provide annotated computer code in R and data for replication. At a substantive level, stand-alone personalized feedback interventions reduced alcohol-related problems via greater use of protective behavioral strategies; however, the net-mediated effect across strategies was small in size, on average.

Adapting narrative exposure therapy with a tribal community: A community-based approach.

OBJECTIVE: American Indian (AI) individuals are at increased risk for present-day trauma exposure and associated negative outcomes, as well as ongoing effects of intergenerational trauma exposure and adversity. However, few empirically supported treatments exist that are specifically tailored and/or tested with AI communities. This study describes the process of selecting, adapting, and implementing narrative exposure therapy (NET) with an AI community. METHODS: A community and academic partnership was formed and worked together to make culturally mindful changes to NET to best fit the needs of the community. The partnership incorporated community leaders/Elders (n = 7), providers (n = 11), and participants seeking treatment (n = 50) to implement an iterative process of adapting and implementing the adapted form of NET. RESULTS: Key adaptions included addressing historical and intergenerational trauma, greater protections for confidentiality in a small community, and incorporation of cultural customs and traditions. Overall, the adapted form of NET was favorably received by the participants, and the implementation appeared to be feasible, with improved retention over past trials of adapted trauma-focused treatments with this community and with highly positive satisfaction ratings and feedback. CONCLUSIONS: NET was shown to be an appropriate approach for this AI community and should be considered as a treatment option for other AI communities. Future work should consider strategies outlined in this adaption as well as following a similar process for working with AI communities to implement culturally appropriate interventions for trauma-related symptoms.

A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes.

Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis. The current study proposes a novel statistical approach, the Zero-inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero-inflated Poisson regression model, thus it is attractive for meta-analysis when individual participant-level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero-inflation bias in most situations.

Randomized Control Trial of Culturally Adapted Cognitive Processing Therapy for PTSD Substance Misuse and HIV Sexual Risk Behavior for Native American Women.

An overlooked sequela of HIV risk is trauma exposure, yet few HIV interventions address trauma exposure, mental health, and substance misuse. In a two-arm randomized controlled trial 73 Native American women were randomized to a culturally-adapted Cognitive Processing Therapy (CPT) or 6-weeks waitlist. Outcomes assessed: PTSD symptom severity, alcohol use frequency, substance abuse or dependence diagnosis, and high-risk sexual behavior defined as vaginal/anal intercourse (a) under the influence of alcohol and/or illicit substances, (b) with a partner who was concurrently sexually active with someone else, and/or (c) with more than one partner in the past 6 weeks. Among immediate intervention participants, compared to waitlist participants, there were large reductions in PTSD symptom severity, high-risk sexual behavior, and a medium-to-large reduction in the frequency of alcohol use. CPT appears to improve mental health and risk behaviors, suggesting that addressing PTSD may be one way of improving HIV-risk related outcomes.

Moderating Factors in an Anti-stigma Intervention for African American Women with HIV in the United States: A Secondary Analysis of the UNITY Trial.

African American women experience higher rates of HIV than other women in the United States, and stigma has been identified as an important determinant of engagement in HIV care. Our study examined whether key variables moderated the effect of an anti-stigma intervention on outcomes among African American women receiving treatment for HIV. Twelve potential moderators included: age, years lived with HIV, marital status, employment status, education level, PTSD diagnosis, alcohol use, social support, baseline CD4 count, baseline viral load, and number of children. Outcomes included changes in: HIV-related stigma, social support, depressive symptoms, PTSD symptoms, alcohol use, viral load, and engagement in HIV care. Results suggest that the intervention is associated with greater improvement in engagement in care among participants with PTSD or depression at baseline, and may help maintain engagement in care among participants experiencing certain mental health conditions. This provides opportunities to address discriminatory structural barriers that lead to stigma and drop-offs in HIV care.

HIV-Related Stigma and Viral Suppression Among African-American Women: Exploring the Mediating Roles of Depression and ART Nonadherence.

We used baseline data from a sample of African-American women living with HIV who were recruited to participate in a stigma-reduction intervention in Chicago and Birmingham (2013-2015) to (1) evaluate the relationship between HIV-related stigma and viral suppression, and (2) assess the role of depression and nonadherence to antiretroviral therapy (ART) as mediators. Data from women were included in this secondary analysis if they were on ART, had viral load data collected within 8-weeks of study entry and had complete covariate data. We used logistic regression to estimate the total effect of HIV-related stigma (14-item Stigma Scale for Chronic Illness) on viral suppression (< 200 copies/mL), and serial mediation analysis to estimate indirect effects mediated by depressive symptoms (8-item Patient Health Questionnaire) and ART nonadherence (number of days with missed doses). Among 100 women who met study inclusion criteria, 95% reported some level of HIV-related stigma. In adjusted models, higher levels of HIV-related stigma were associated with lower odds of being virally suppressed (AOR = 0.93, 95% CI = 0.89-0.98). In mediation analysis, indirect effects through depression and ART nonadherence were not significant. Findings suggest that HIV-related stigma is common among African-American women living with HIV, and those who experience higher levels of stigma are less likely to be virally suppressed. However, the mechanisms remain unclear.

Development and Evaluation of a Web-Based Resource for Suicidal Thoughts: NowMattersNow.org.

BACKGROUND: Nearly half of people who die by suicide see a health care provider in the month before their death. With the release of new care guidelines, detection of suicidal patients will likely increase. Providers need access to suicide-specific resources that can be used as part of immediate, brief interventions with a suicidal patient. Web-based suicide prevention resources have the potential to address this need. OBJECTIVE: This study aimed to describe the development of the NowMattersNow.org website as a resource for individuals with suicidal thoughts and to evaluate the utility of the site via user experience surveys. METHODS: NowMattersNow.org is an online video-based free public resource that provides evidence-based teachings, examples, and resources for managing suicidal thoughts and intense emotions focused largely around skills from dialectical behavior therapy. Developed with assistance from mental health consumers, it is intended to address gaps in access to services for suicidal patients in health care systems. Visitors stay an average of a minute and a half on the website. From March 2015 to December 2017, a user experience survey measured self-reported changes on a 1 (not at all) to 5 (completely overwhelming) scale regarding intensity of suicidal thoughts and negative emotions while on the website. Longitudinal regression analyses using generalized estimating equations evaluated the magnitude and statistical significance of user-reported changes in suicidal ideation and negative emotion. In secondary analyses, user-reported changes specific to subgroups, including men aged 36 to 64 years, mental health care providers, and other health care providers were evaluated. RESULTS: During the period of analysis, there were 138,386 unique website visitors. We analyzed surveys (N=3670) collected during that time. Subsamples included men aged 36 to 64 years (n=512), mental health providers (n=460), and other health care providers (n=308). A total of 28% (1028/3670) of survey completers rated their suicidal thoughts as a 5 or "completely overwhelming" when they entered the website. We observed significant reductions in self-reported intensity of suicidal thoughts (-0.21, P<.001) and negative emotions (-0.32, P<.001), including decreases for users with the most severe suicidal thoughts (-6.4%, P<.001), most severe negative emotions (-10.9%, P<.001), and for middle-aged men (-0.13, P<001). Results remained significant after controlling for length of visit to website (before the survey) and technology type (mobile, desktop, and tablet). CONCLUSIONS: Survey respondents reported measurable reductions in intensity of suicidal thoughts and emotions, including those rating their suicidal thoughts as completely or almost completely overwhelming and among middle-aged men. Although results from this user-experience survey administered at one point in time to a convenience sample of users must be interpreted with caution, results provide preliminary support for the potential effectiveness of the NowMattersNow.org website as a tool for short-term management of suicidal thoughts and negative emotions.

Exploring the Relationship Between Stimulant Use and Gambling in College Students.

Both gambling and stimulant use are common and can lead to problems on college campuses with consequences that impact the financial, emotional, academic and physical well-being of students. Yet few studies have been conducted to understand the co-occurrence of these conditions and the increased risk factors if any that may exist for gambling and related problems. The present study is among the first to document the co-occurrence of these behaviors in both a random sample of students (N = 4640), and then to explore to what extent stimulant use impacts subsequent gambling and related problems 12 months later in an at-risk sample (N = 199). Results revealed a three-fold higher rate of recent problem gambling for those who used stimulants versus those who had not (11 vs. 4 %). For those already gambling, stimulant use predicted an increased frequency in gambling 12 months later. Implications for prevention and screening are discussed.

Brief motivational interventions for college student drinking may not be as powerful as we think: an individual participant-level data meta-analysis.

BACKGROUND: For over 2 decades, brief motivational interventions (BMIs) have been implemented on college campuses to reduce heavy drinking and related negative consequences. Such interventions include in-person motivational interviews (MIs), often incorporating personalized feedback (PF), and stand-alone PF interventions delivered via mail, computer, or the Web. Both narrative and meta-analytic reviews using aggregate data from published studies suggest at least short-term efficacy of BMIs, although overall effect sizes have been small. METHODS: This study was an individual participant-level data (IPD) meta-analysis of 17 randomized clinical trials evaluating BMIs. Unlike typical meta-analysis based on summary data, IPD meta-analysis allows for an analysis that correctly accommodates the sampling, sample characteristics, and distributions of the pooled data. In particular, highly skewed distributions with many zeroes are typical for drinking outcomes, but have not been adequately accounted for in existing studies. Data are from Project INTEGRATE, one of the largest IPD meta-analysis projects to date in alcohol intervention research, representing 6,713 individuals each with 2 to 5 repeated measures up to 12 months postbaseline. RESULTS: We used Bayesian multilevel over dispersed Poisson hurdle models to estimate intervention effects on drinks per week and peak drinking, and Gaussian models for alcohol problems. Estimates of overall intervention effects were very small and not statistically significant for any of the outcomes. We further conducted post hoc comparisons of 3 intervention types (individual MI with PF, PF only, and group MI) versus control. There was a small, statistically significant reduction in alcohol problems among participants who received an individual MI with PF. Short-term and long-term results were similar. CONCLUSIONS: This study questions the efficacy and magnitude of effects of BMIs for college drinking prevention and intervention and suggests a need for the development of more effective intervention strategies.

Modeling Cyclical Patterns in Daily College Drinking Data with Many Zeroes.

Daily college drinking data often have highly skewed distributions with many zeroes and a rising and falling pattern of use across the week. Alcohol researchers have typically relied on statistical models with dummy variables for either the weekend or all days of the week to handle weekly patterns of use. However, weekend versus weekday categorizations may be too simplistic and saturated dummy variable models too unwieldy, particularly when covariates of weekly patterns are included. In the present study we evaluate the feasibility of cyclical (sine and cosine) covariates in a multilevel hurdle count model for evaluating daily college alcohol use data. Results showed that the cyclical parameterization provided a more parsimonious approach than multiple dummy variables. The number of drinks when drinking had a smoothly rising and falling pattern that was reasonably approximated by cyclical terms, but a saturated set of dummy variables was a better model for the probability of any drinking. Combining cyclical terms and multilevel hurdle models is a useful addition to the data analyst toolkit when modeling longitudinal drinking with high zero counts. However, drinking patterns were not perfectly sinusoidal in the current application, highlighting the need to consider multiple models and carefully evaluate model fit.

Prospective predictors of unprotected anal intercourse among HIV-seropositive men who have sex with men initiating antiretroviral therapy.

Contemporary HIV prevention efforts are increasingly focused on those already living with HIV/AIDS (i.e., "prevention with positives"). Key to these initiatives is research identifying the most risky behavioral targets. Using a longitudinal design, we examined socio-demographic and psychosocial factors that prospectively predicted unprotected anal intercourse (UAI) in a sample of 134 HIV-seropositive men who have sex with men (MSM) initiating, changing, or re-starting an antiretroviral therapy regimen as part of a behavioral intervention study. Computer-based questionnaires were given at baseline and 6 months. In a sequential logistic regression, baseline measures of UAI (step 1), socio-demographic factors such as Latino ethnicity (step 2), and psychosocial factors such as crystal methamphetamine use, greater life stress, and lower trait anxiety (step 3) were predictors of UAI at 6 months. Problem drinking was not a significant predictor. Prevention efforts among MSM living with HIV/AIDS might focus on multiple psychosocial targets, like decreasing their crystal methamphetamine use and teaching coping skills to deal with life stress.

The validity of self-reported medication adherence as an outcome in clinical trials of adherence-promotion interventions: Findings from the MACH14 study.

In medication adherence-promotion trials, participants in the intervention arm are often cognizant of the researcher's aim to improve adherence; this may lead to their inflating reports of their own adherence compared to control arm participants. Using data from 1,247 HIV-positive participants across eight U.S. Studies in the Multi-site Adherence Collaboration on HIV (MACH14) collaboration, we evaluated the validity of self-reported adherence by examining whether its association with two more objective outcomes [1], electronically monitored adherence and [2] viral load, varied by study arm. After adjusting for potential confounders, there was no evidence of greater overestimation of self-reported adherence among intervention arm participants, supporting its potential as a trial outcome indicator.

Experiential suicide prevention training for substance use treatment intensive outpatient program counselors: Stepped wedge cluster-randomized training study.

INTRODUCTION: The goal of this study was to evaluate changes in knowledge, attitudes, and other key suicide prevention issues for substance use disorder (SUD) counselors trained to administer the Preventing Addiction Related Suicide (PARS) module as part of a large-scale clinical trial. PARS is a 3-hour, PowerPoint based intervention designed with and for community SUD agencies for their Intensive Outpatient (IOP) group therapy programs. A previous randomized study of 906 patients from 15 community SUD sites showed positive changes in patients' suicide prevention knowledge, attitudes, and help seeking. METHODS: Counselor participants completed measures of knowledge and attitudes about suicide and their confidence treating suicidal patients at each step of a large, stepped wedge cluster randomized trial of PARS, including after the final step. Data analysis compared scores in steps prior to counselors' training in PARS with scores in the steps following counselors' PARS training. RESULTS: A total of 126 counselors participated in the study (89 % of those approached; 89-92 % retention across follow-up). Evaluation of both PARS efficacy and the PARS training by SUD counselors was highly rated. Counselor scores after receiving PARS training (vs. scores prior to PARS training) showed greater suicide knowledge, less maladaptive attitudes about suicide, and greater confidence in working with suicidal patients. CONCLUSIONS: Based on this rigorous test of PARS training for SUD counselors working in community SUD intensive outpatient programs, PARS training, as well as doing the PARS intervention, was rated as highly effective and acceptable. PARS offers an integrated, brief, engaging, and effective training method to improve suicide care for both SUD counselors and SUD clients.

The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations.

Understanding the efficacy and relative effectiveness of a brief alcohol intervention (BAI) relies on obtaining a credible intervention effect estimate. Outcomes in BAI trials are often count variables, such as the number of drinks consumed, which may be overdispersed (i.e., greater variability than expected based on a given model) and zero-inflated (i.e., greater probability of zeros than expected based on a given model). Ignoring such distribution characteristics can lead to biased estimates and invalid statistical conclusions. In this critical review, we identified and reviewed 64 articles that reported count outcomes from a systematic review of BAI trials for adolescents and young adults from 2013 to 2018. Given many statistical models to choose from when analyzing count outcomes, we reviewed the models used and reporting practices in the BAI trial literature. A majority (61.3%) of analyses with count outcomes used linear models despite violations of normality assumptions; 75.6% of outcome variables demonstrated clear overdispersion. We provide an overview of available count models (Poisson, negative binomial, zero-inflated or hurdle, and marginalized zero-inflated Poisson regression) and formulate practical guidelines for reporting outcomes of BAIs. We provide a visual step-by-step decision guide for selecting appropriate statistical models and reporting results for count outcomes. We list accessible resources to help researchers select an appropriate model with which to analyze their data. Recent advances in count distribution-based models hold promise for evaluating count outcomes to gauge the efficacy and effectiveness of BAIs and identify critical covariates in alcohol epidemiologic research. We recommend that researchers report the distributional properties of count outcomes, such as the proportion of zero counts, and select an appropriate statistical analysis for count outcomes using the provided decision tree. By following these recommendations, future research may yield more accurate, transparent, and reproducible results.