Poly-Substance Use, Gateway and/or Companion Drug
Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. The aim of this paper is to develop a machine learning approach to predict adults at risk for OUD and to identify interactions between various characteristics that increase this risk.
In this approach, a data set was curated using the responses from the 2016 edition of the National Survey on Drug Use and Health (NSDUH). Using this data set, tree-based classifiers (decision tree and random forest) were trained, while employing downsampling to handle class imbalance. Predictions from the tree-based classifiers were also compared to the results from a logistic regression model. The results from the three classifiers were then interpreted synergistically to highlight individual characteristics and their interplay that pose a risk for OUD.
Random forest predicted adults at risk for OUD with remarkable accuracy, with the average area under the Receiver-Operating-Characteristics curve (AUC) over 0.89, even though the prevalence of OUD was only about 1 %. It showed a slight improvement over logistic regression. Logistic regression identified statistically significant characteristics, while random forest ranked the predictors in order of their contribution to OUD prediction. Early initiation of marijuana (before 18 years) emerged as the dominant predictor. Decision trees revealed that early marijuana initiation especially increased the risk if individuals: (i) were between 18–34 years of age, or (ii) had incomes less than $49,000, or (iii) were of Hispanic and White heritage, or (iv) were on probation, or (v) lived in neighborhoods with easy access to drugs.
Machine learning can accurately predict adults at risk for OUD, and identify interactions among the factors that pronounce this risk. Curbing early initiation of marijuana may be an effective prevention strategy against opioid addiction, especially in high risk groups.
Opioid Overdoses increase in medical marijuana legal states by 22%
Medical cannabis has been touted as a solution to the US opioid overdose crisis since Bachhuber et al. [M. A. Bachhuber, B. Saloner, C. O. Cunningham, C. L. Barry, JAMA Intern. Med. 174, 1668–1673] found that from 1999 to 2010 states with medical cannabis laws experienced slower increases in opioid analgesic overdose mortality. That research received substantial attention in the scientific literature and popular press and served as a talking point for the cannabis industry and its advocates, despite caveats from the authors and others to exercise caution when using ecological correlations to draw causal, individual-level conclusions. In this study, we used the same methods to extend Bachhuber et al.’s analysis through 2017. Not only did findings from the original analysis not hold over the longer period, but the association between state medical cannabis laws and opioid overdose mortality reversed direction from −21% to +23% and remained positive after accounting for recreational cannabis laws. We also uncovered no evidence that either broader (recreational) or more restrictive (low-tetrahydrocannabinol) cannabis laws were associated with changes in opioid overdose mortality. We find it unlikely that medical cannabis—used by about 2.5% of the US population—has exerted large conflicting effects on opioid overdose mortality. A more plausible interpretation is that this association is spurious. Moreover, if such relationships do exist, they cannot be rigorously discerned with aggregate data. Research into therapeutic potential of cannabis should continue, but the claim that enacting medical cannabis laws will reduce opioid overdose death should be met with skepticism.
Christine L. Miller, Ph.D.
This can happen because the pathways to addiction with different drugs share common features:
The common mechanism via dopamine is also the case for the effect of THC:
When marijuana can no longer excite the common pathway as the drug’s receptors become desensitized, the user switches to a new drug. The addiction to marijuana is no longer satisfying.
Research has confirmed that marijuana acts as a gateway drug for many users:
by Pamela McColl, SAM Canada
In addition to the findings on pot and illicit drug use, the study found that early cannabis use was associated with harmful drinking and smoking.
Follow Pamela McColl in Facebook at The Marijuana Victims Association
Australian researchers found that twins who use cannabis by age 17 are 2.1 to 5.2 times more likely to develop addiction issues. An Australian twins study determined this likelihood by comparing twins who used pot to the co-twins who hadn’t used marijuana.
December 8, 2020
Conclusions: This is the first study to longitudinally situate comorbid, past 30-day use of tobacco and marijuana and simultaneously examine bi-directional past 30-day use of these products for adolescents. Marijuana use more often and more strongly predicted subsequent tobacco use than the reverse, especially during middle adolescence (13-15 years old). Marijuana use should be considered when creating interventions that address adolescent e-cigarette use in the U.S.
69% adolescent drug users initiated with marijuana and 27% with inhalants.
About 40% of inhalant users transitioned to new drugs by year 8.
About 70% to 80% other drug users transitioned to new drugs by year 8.
There were racial/ethnic discrepancies in both drug use initiation and transition.
Two thirds of the adolescent drug users initiated their drug use trajectories with marijuana, one quarter with inhalants, and the remaining with hallucinogens, prescription drugs, and hard drugs. Adolescent drug users who initiated with different drugs showed unique trajectories to the use of a new drug. By year 8, the probability of using a new drug was about 40% and 70% to 80% for adolescents who initiated with inhalants and other drugs, respectively. The probability of using a new drug for adolescents who initiated with marijuana and inhalants accumulated stably over time, and its difference with that of other drug users diminished over time. The multivariate Cox regression models suggest the observed discrepancies generally held after controlling for covariates. There were also racial/ethnic differences in adolescent drug use initiation and progression, with Black/African American adolescents being the least likely to switch to the use of a new drug.
Adolescents’ initial use of marijuana and inhalants may lead to substantial risks of using other drugs over time. It is therefore important to screen adolescent drug use comprehensively and provide early interventions to prevent an escalation to more detrimental drugs. The findings provide new evidence to support aspects of both the gateway and generalized risk drug use theories.