Reprint of Trends and key correlates of prescription opioid injection misuse in the United States

Opioid and benzodiazepine misuse in the United States: The impact of socio-demographic characteristics

BACKGROUND AND OBJECTIVES

In 2021, drug overdose deaths in the United States reached a new record of 107,622. Misuse of opioids and benzodiazepines accounts for a large portion of drug overdose deaths. However, the effects of socio-demographic characteristics on misuse of opioids and benzodiazepines are not evident. Thus, this study examines the socio-demographic characteristics associated with misuse of opioids and benzodiazepines among adults in the United States.

METHODS

Data from 2015-2019 National Survey on Drug Use and Health was utilized in the multinomial logistic regression analysis and included 202,935 adults ages ≥18 years.

RESULTS

During 2015-2019, 3.3% of the adults misused opioids, 1.2% misused benzodiazepines, and 0.9% misused both drugs in the preceding year of the survey. Those who were younger, bisexual, non-Hispanic White, had a history of delinquency in the past year, had alcohol dependence/abuse, marijuana dependence/abuse, nicotine dependence and use, and experienced major depressive episodes were more likely to misuse opioids, benzodiazepines, or both.

CONCLUSION AND SCIENTIFIC SIGNIFICANCE

A large portion of US adults are misusing opioids, benzodiazepines, and both drugs. Specifically, bisexual individuals experience higher odds of opioid misuse, benzodiazepine misuse and misuse of both drugs compared with heterosexuals, while males are experiencing lower odds of benzodiazepine misuse compared with females. Individuals aged 26-49 experience the highest odds of opioid misuse, though misuse of both drugs was higher among the 18-25 age group. Findings underscore the use of targeted preventive measures to reduce misuse of these drugs among at-risk populations identified in this study.

Trends and distinct profiles of persons who inject drugs in the United States, 2015-2019

Drug injection represents a major health problem in the US, with severe health consequences including the transmission of blood-borne infections. An examination of the most recent trends in drug injection is warranted by the fast-evolving drug epidemic and recent policy changes such as the federal funding ban on needle exchange programs. This research examines current drug injection trends, patterns, and socioeconomic and behavioral profiles of people who inject drugs (PWID). Data were derived from the 2002 to 2019 National Survey on Drug Use and Health (NSDUH). After examining the annual prevalence of drug injection since 2002, a latent class analysis was conducted to identify drug injection and other substance misuse patterns among PWID using the latest (2015-2019) NSDUH datasets. Associations between class membership and behavioral health comorbidities and treatment receipt were also assessed. The drug injection prevalence among US adults aged 18-64 increased from 0.21% in 2002/2004 to 0.36% in 2017/2019. Three distinctive groups were identified: the heroin injection group (45.2%), the methamphetamine injection group (28.0%), and the multi-drug injection group (26.8%). The methamphetamine injection group reported greater risks of experiencing serious psychological distress, suicidality, and limited substance use treatment. Special attention is needed for those who primarily injected methamphetamine. Programs to promote harm reduction and increase access to addiction treatment need to be expanded in at-risk communities while accounting for their distinct socioeconomic and drug use/misuse profiles.

Using machine learning to predict opioid misuse among U.S. adolescents

This study evaluated prediction performance of three different machine learning (ML) techniques in predicting opioid misuse among U.S. adolescents. Data were drawn from the 2015-2017 National Survey on Drug Use and Health (N = 41,579 adolescents, ages 12-17 years) and analyzed in 2019. Prediction models were developed using three ML algorithms, including artificial neural networks, distributed random forest, and gradient boosting machine. The performance of the ML prediction models was compared with performance of the penalized logistic regression. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used as metrics of prediction performance. We used the AUPRC as the primary measure of prediction performance given that it is considered more informative for assessing binary classifiers on imbalanced outcome variable than AUROC. The overall rate of opioid misuse among U.S. adolescents was 3.7% (n = 1521). Prediction performance was similar across the four models (AUROC values range from 0.809 to 0.815). In terms of the AUPRC, the distributed random forest showed the best performance in prediction (0.172) followed by penalized logistic regression (0.162), gradient boosting machine (0.160), and artificial neural networks (0.157). Findings suggest that machine learning techniques can be a promising technique especially in the prediction of outcomes with rare cases (i.e., when the binary outcome variable is heavily lopsided) such as adolescent opioid misuse.