A Pragmatic Clinical Approach to Substance Abuse Prevention.

Research has led to the development of hundreds of evidence-based prevention interventions, most of which are school-based prevention programs. Most primary care clinicians and child/adolescent behavioral health clinicians are unfamiliar with and/or lack training in evidence-based prevention interventions. However, most clinicians in these settings routinely screen children for developmental delays and skills deficits that increase the risk of developing substance abuse and a broad range of mental health and behavior problems by adolescence. It is hoped that the broader use of these practical evidence-based prevention tools may expand the prevention workforce to address the current youth mental health crisis.

Maternal Childhood Maltreatment, Internal Working Models, and Perinatal Substance Use: Is There a Role for Hyperkatifeia? A Systematic Review.

The parent-infant relationship is critical for socioemotional development and is adversely impacted by perinatal substance use. This systematic review posits that the mechanisms underlying these risks to mother-infant relationships center on 3 primary processes: (1) mothers' childhood maltreatment experiences; (2) attachment styles and consequent internal working models of interpersonal relationships; and (3) perinatal substance use. Further, the review considers the role of hyperkatifeia, or hypersensitivity to negative affect which occurs when people with substance use disorders are not using substances, and which drives the negative reinforcement in addiction. The authors performed a systematic review of articles (published 2000-2022) related to these constructs and their impact on mother-infant relationships and offspring outcomes, including original clinical research articles addressing relationships between these constructs, and excluding case studies, reviews, non-human animal studies, intervention studies, studies with fewer than 30% female-sex participants, clinical guidelines, studies limited to obstetric outcomes, mechanistic/biological studies, and studies with methodological issues precluding interpretation. Overall 1844 articles were screened, 377 were selected for full text review, and data were extracted from 157 articles. Results revealed strong relationships between mothers' childhood maltreatment experiences, less optimal internal working models, and increased risk for perinatal substance use, and importantly, all of these predictors interacted with hyperkatifeia and exerted a marked impact on mother-infant relationships with less data available on offspring outcomes. These data strongly support the need for future studies addressing the additive impact of maternal childhood maltreatment experiences, suboptimal internal working models, and perinatal substance use, with hyperkatifeia as a potential moderator, and their interacting effects on mother-infant socioemotional outcomes.

A Pragmatic Clinical Approach to Substance Abuse Prevention.

Research has led to the development of hundreds of evidence-based prevention interventions, most of which are school-based prevention programs. Most primary care clinicians and child/adolescent behavioral health clinicians are unfamiliar with and/or lack training in evidence-based prevention interventions. However, most clinicians in these settings routinely screen children for developmental delays and skills deficits that increase the risk of developing substance abuse and a broad range of mental health and behavior problems by adolescence. It is hoped that the broader use of these practical evidence-based prevention tools may expand the prevention workforce to address the current youth mental health crisis.

The short-term impact of 3 smoked cannabis preparations versus placebo on PTSD symptoms: A randomized cross-over clinical trial.

IMPORTANCE: There is a pressing need for development of novel pharmacology for the treatment of Posttraumatic Stress Disorder (PTSD). Given increasing use of medical cannabis among US military veterans to self-treat PTSD, there is strong public interest in whether cannabis may be a safe and effective treatment for PTSD. OBJECTIVE: The aim of the present study was to collect preliminary data on the safety and potential efficacy of three active concentrations of smoked cannabis (i.e., High THC = approximately 12% THC and < 0.05% CBD; High CBD = 11% CBD and 0.50% THC; THC+CBD = approximately 7.9% THC and 8.1% CBD, and placebo = < 0.03% THC and < 0.01% CBD) compared to placebo in the treatment of PTSD among military veterans. METHODS: The study used a double-blind, cross-over design, where participants were randomly assigned to receive three weeks of either active treatment or placebo in Stage 1 (N = 80), and then were re-randomized after a 2-week washout period to receive one of the other three active treatments in Stage 2 (N = 74). The primary outcome measure was change in PTSD symptom severity from baseline to end of treatment in Stage 1. RESULTS: The study did not find a significant difference in change in PTSD symptom severity between the active cannabis concentrations and placebo by the end of Stage 1. All three active concentrations of smoked cannabis were generally well tolerated. CONCLUSIONS AND RELEVANCE: The present study is the first randomized placebo-controlled trial of smoked cannabis for PTSD. All treatment groups, including placebo, showed good tolerability and significant improvements in PTSD symptoms during three weeks of treatment, but no active treatment statistically outperformed placebo in this brief, preliminary trial. Additional well-controlled and adequately powered studies with cannabis suitable for FDA drug development are needed to determine whether smoked cannabis improves symptoms of PTSD. TRIAL REGISTRATION: Identifier: NCT02759185; ClinicalTrials.gov.

The Neurocognitive Effects of Cannabis Across the Lifespan.

Purpose of review: This review examines the neurocognitive effects of cannabis and relevant developmental factors across adolescence (age 13-21), adulthood (21-65), and older adulthood (65+). Recent findings: Cannabis use is robustly associated with poorer neurocognitive functioning; however, studies that carefully control for confounds have often not found any evidence for impairment. Notably, the endocannabinoid system may underly how cannabis use affects neurocognitive functions, including heightened vulnerability during adolescence. In contrast, the endocannabinoid system may underlie protective neurocognitive effects of cannabis in older adults. Notably, older adults have reported sharp increases in recent cannabis use. Summary: As legalization increases the accessibility, variety, and potency of cannabis, strong empirical evidence is needed to understand its neurocognitive effects across the lifespan. In particular, rigorous study designs are needed to investigate the neurocognitive effects of cannabis, including among vulnerable populations (adolescents, older adults) and mediating (e.g., endocannabinoid system) and moderating factors (e.g., alcohol use).

Integrated Treatment of Adolescents with Co-occurring Depression and Substance Use Disorder.

Substance use disorders (SUDs) are commonly co-occurring among adolescents with depression. Integrated treatment is important given treatment implications and increased rates of suicidality. All adolescents should be screened for SUD using Screening, Brief Intervention, and Referral to Treatment. Review of randomized controlled trials in adolescents reveals motivational enhancement therapy/cognitive behavioral therapy is an evidence-based intervention and should be considered first-line treatment. If depression does not improve, fluoxetine should be considered, as it is well-tolerated in substance-involved adolescents with depression. Adolescents who do not show improvement in SUD or who have severe SUD should be referred to evidence-based SUD treatment.

Editorial: Prescription for Addiction.

In the most recent 2016 National Survey on Drug Use and Health (NSDUH),1 more than 1.3 million adolescents aged 12 to 17 years reported past-year nonmedical use of prescription drugs. About 70% started using in the past year, and about half report nonmedical use of prescription opioid medications. Misuse of prescription opioid medication is associated with 19 times greater risk of transitioning to heroin compared to no misuse.2 Although the national opioid crisis affects all age groups, the steepest rise in unintentional opioid overdose fatalities has been in young adults aged 18 to 25 years.2,3 More than 80% of young adult injection heroin users report that they started using prescription opioids, most during adolescence, before transitioning to heroin.3 While it is clear that the seeds of opioid addiction often begin during adolescence, there are many gaps in our knowledge and understanding of sources and predictive risk factors to guide development of more effective and targeted youth interventions.

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes predictors from a MGLMM are always preferable to scatterplots of empirical Bayes predictors generated by separate models, unless the true association between outcomes is zero.

Prevalence of Nonmedical Use of Prescription Opioids and Association With Co-occurring Substance Use Disorders Among Adolescents in Substance Use Treatment.

PURPOSE: We sought to describe the prevalence of the nonmedical use of prescription opioids (NMUPO) and its association with co-occurring substance use disorders in a sample of adolescents in substance treatment. METHODS: Adolescents in two substance treatment programs were recruited for participation between 2009 and 2013. The Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM) was administered to assess substance use patterns and lifetime abuse or dependence. RESULTS: A total of 378 adolescents completed the CIDI-SAM (mean age 16.1 [standard deviation = 1.1], 78% male, 50% white, non-Hispanic). Of the 378 adolescents, 62 (16.4%) reported NMUPO and 59 (15.6%) were diagnosed with opioid/heroin abuse or dependence. The mean age at first NMUPO was 14.3 (standard deviation = 1.4). NMUPO was associated with a 3.31-fold (95% confidence interval: 2.83-3.79) increase in having three or more co-occurring substance use diagnoses. CONCLUSIONS: NMUPO is quite prevalent among adolescents in substance use treatment. Intervention to interrupt NMUPO from progressing to heroin use or developing into a disorder is critical.

Prescription opioids prior to injection drug use: Comparisons and public health implications.

BACKGROUND: The intertwining prescription opioid and heroin epidemic is a major public health problem in the United States, with increasing morbidity and mortality among persons who use these substances. We examined differences between persons who reported being hooked on prescription opioids prior to injecting for the first time and those who did not by demographics, injection and non-injection characteristics, and overdose. METHODS: Between June and December 2015, persons who inject drugs were recruited using respondent-driven sampling as part of the National HIV Behavioral Surveillance system in Denver, Colorado. RESULTS: Of 599 participants (median age, 40: IQR, 19-69; 71% male; 58% white, non-Hispanic), 192 (32%) reported being hooked on prescription opioids before they injected for the very first time. Compared to participants who were not hooked before they injected, participants who reported being hooked were significantly more likely to be younger, more recent injectors, report a slightly older age at first injection, and report heroin as the first drug injected as well as the drug most frequently injected. Those who reported being hooked were also more likely to be more frequent users of benzodiazepines, non-injection prescription opioids, and non-injection heroin as well as report injecting on a daily or more than daily basis. Being hooked on prescription opioids prior to injection drug use was associated with a 1.55 (95% CI: 1.14, 2.10) fold increase in the risk of at least one overdose in the past 12months. CONCLUSIONS: Being hooked on prescription opioids prior to injection might result in a higher risk profile for persons who inject drugs.

On estimating and testing associations between random coefficients from multivariate generalized linear mixed models of longitudinal outcomes.

Different types of outcomes (e.g. binary, count, continuous) can be simultaneously modeled with multivariate generalized linear mixed models by assuming: (1) same or different link functions, (2) same or different conditional distributions, and (3) conditional independence given random subject effects. Others have used this approach for determining simple associations between subject-specific parameters (e.g. correlations between slopes). We demonstrate how more complex associations (e.g. partial regression coefficients between slopes adjusting for intercepts, time lags of maximum correlation) can be estimated. Reparameterizing the model to directly estimate coefficients allows us to compare standard errors based on the inverse of the Hessian matrix with more usual standard errors approximated by the delta method; a mathematical proof demonstrates their equivalence when the gradient vector approaches zero. Reparameterization also allows us to evaluate significance of coefficients with likelihood ratio tests and to compare this approach with more usual Wald-type t-tests and Fisher's z transformations. Simulations indicate that the delta method and inverse Hessian standard errors are nearly equivalent and consistently overestimate the true standard error. Only the likelihood ratio test based on the reparameterized model has an acceptable type I error rate and is therefore recommended for testing associations between stochastic parameters. Online supplementary materials include our medical data example, annotated code, and simulation details.

Cooccurring Psychiatric and Substance Use Disorders.

Research shows that the majority of adolescents with substance use disorders also have other cooccurring psychiatric disorders, which has been associated with poorer treatment outcomes. Despite considerable consensus that treatment of cooccurring disorders should be integrated or concurrent, most such youth do not receive it. In addition to systemic and economic barriers, few studies have been conducted that inform evidence-based integrated treatment approaches. This article provides a review of current research from which empirically derived principles of integrated treatment can originate and which have informed the development of at least one evidence-based model of integrated mental health and substance treatment.

Substance Abuse Prevention.

Substance use disorders account for approximately 6% of deaths worldwide and cost about $700 billion in the United States. Approximately 80% of drug users begin using during adolescence, underscoring the public health importance of effective substance prevention programs for youth and families. Prevention science designates 3 intervention categories: (1) universal prevention, targeting all individuals in the population, (2) selective interventions, targeting high-risk groups, and (3) indicated prevention interventions for youth with risk-taking behaviors. School-based and non-school-based interventions are reviewed, as well as the limitations of existing research, gaps in access and availability, and directions for future research and development.

The importance of distribution-choice in modeling substance use data: a comparison of negative binomial, beta binomial, and zero-inflated distributions.

BACKGROUND: It is important to correctly understand the associations among addiction to multiple drugs and between co-occurring substance use and psychiatric disorders. Substance-specific outcomes (e.g. number of days used cannabis) have distributional characteristics which range widely depending on the substance and the sample being evaluated. OBJECTIVES: We recommend a four-part strategy for determining the appropriate distribution for modeling substance use data. We demonstrate this strategy by comparing the model fit and resulting inferences from applying four different distributions to model use of substances that range greatly in the prevalence and frequency of their use. METHODS: Using Timeline Followback (TLFB) data from a previously-published study, we used negative binomial, beta-binomial and their zero-inflated counterparts to model proportion of days during treatment of cannabis, cigarettes, alcohol, and opioid use. The fit for each distribution was evaluated with statistical model selection criteria, visual plots and a comparison of the resulting inferences. RESULTS: We demonstrate the feasibility and utility of modeling each substance individually and show that no single distribution provides the best fit for all substances. Inferences regarding use of each substance and associations with important clinical variables were not consistent across models and differed by substance. CONCLUSION: Thus, the distribution chosen for modeling substance use must be carefully selected and evaluated because it may impact the resulting conclusions. Furthermore, the common procedure of aggregating use across different substances may not be ideal.