A Bayesian learning model to predict the risk for cannabis use disorder

The Relationship Between Self-Reported Childhood Maltreatment and Mental Health in Substance Use Disorders: A 6-Year Retrospective Analysis

OBJECTIVE

This longitudinal cohort study aims to investigate the relationship between self-reported childhood maltreatment (CM) and the retrospective trajectory of substance use, mental health, and satisfaction with life in individuals with substance use disorders.

METHODS

One hundred eleven treatment-seeking individuals with substance use disorder were recruited from clinical settings and monitored prospectively for 6 years. The participants' substance use, mental health, and satisfaction with life were assessed using standardized measures. Cluster analysis divided the cohort into two groups-low CM and high CM-based on their scores on the Childhood Trauma Questionnaire Short Form at year 6. Mixed-effects linear models were fitted to assess the association between longitudinal scores on drug use, mental health, and satisfaction with life and CM group.

RESULTS

Most participants (92%) reported at least 1 CM. Out of all participants, 36% were categorized into the high-CM group, while 59% were categorized into the low-CM group. CM group was not associated with the amount of substance or alcohol use. CM group was significantly associated with the longitudinal course of mental health and life satisfaction.

CONCLUSIONS

This study underscores the association between self-reported CM and mental health and life satisfaction in patients with substance use disorder. Our results may imply an increased risk of adverse outcomes in patients with high levels of CM, while bearing in mind that both current and retrospective mental health and substance use problems can influence the accuracy of recalling CM.

Validation of a Bayesian learning model to predict the risk for cannabis use disorder

BACKGROUND

Cannabis use disorder (CUD) is a growing public health problem. Early identification of adolescents and young adults at risk of developing CUD in the future may help stem this trend. A logistic regression model fitted using a Bayesian learning approach was developed recently to predict the risk of future CUD based on seven risk factors in adolescence and youth. A nationally representative longitudinal dataset, Add Health was used to train the model (henceforth referred as Add Health model).

METHODS

We validated the Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and Christchurch Health and Development Study (CHDS) using longitudinal data from participants until they were approximately 30 years old (to be consistent with the training data from Add Health). If a participant was diagnosed with CUD at any age during this period, they were considered a case. We calculated the area under the curve (AUC) and the ratio of expected and observed number of cases (E/O). We also explored recalibrating the model to account for differences in population prevalence.

RESULTS

The cohort sizes used for validation were 424 (53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were 0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after recalibration) were 0.995 and 0.999.

CONCLUSION

The external validation of the Add Health model on two different cohorts lends confidence to the model's ability to identify adolescent or young adult cannabis users at high risk of developing CUD in later life.

Impacts of recreational cannabis legalization on use and harms: A narrative review of sex/gender differences

Legalization of cannabis use for non-medical (recreational) purposes is changing the global cannabis landscape. As attitudes toward cannabis use become more positive and prevalence of use increases in complex ways, concerns emerge about the potential for increased cannabis-attributable harms. Understanding the who, why, and when of this likely increase in cannabis-attributable harms is thus an important public health priority. Both sex and gender contribute to variability in the use, effects, and harms of cannabis and thus sex/gender considerations are important when evaluating the impacts of cannabis legalization. The goal of this narrative review is to broadly discuss sex/gender differences in attitudes toward and prevalence of cannabis use, whether there are sex/gender differences in the impacts of cannabis legalization, and why these sex/gender differences might exist. One of our strongest conclusions is that men have always been more likely to use cannabis than women, yet the sex/gender gap in prevalence of cannabis use has narrowed over time, and this might be partly due to cannabis legalization. The existing evidence suggests that there have also been sex/gender differences in the impacts of legalization on cannabis-attributable harms such as cannabis-involved motor vehicle collisions and hospitalizations, though these results are more variable. The body of literature reviewed has focused almost exclusively on samples of cisgender research participants, and thus future research should encourage inclusion of transgender and gender-diverse participants. More consideration of sex- and gender-based analysis in research evaluating long-term impacts of cannabis legalization is a clear research priority.

Parent-child Relations, Religiousness, and Adolescent Substance Use Disorders

Research has shown that parent-child relationships and religiousness are negatively associated with substance use among adolescents, but few studies have addressed their relationship with substance use disorders (SUDs). This study explored whether high quality parent-child relations are negatively associated with the risk of an SUD among adolescents, especially when religiousness is high. The data used to assess this inquiry were from 4 years (2016–2019) of the U.S. National Survey on Drug Use and Health (NSDUH). SUDs in the past year were based on a set of questions consistent with criteria enumerated in the DSM-IV. Latent measures of parent-child relations and religiousness were also constructed. The results of an augmented inverse probability weighting (AIPW) model furnished empirical evidence in support of the notion that the lowest risk of an SUD occurred among those reporting high quality parent-child relations and high religiousness, even after adjusting for a substantial number of selection factors.