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Background: Illegal opioids can create substantial harms, but the extent depends on multiple factors, including the amount consumed. Objectives: To examine how consumption varies across time and context, with implications for treatment and drug policy. Methods: We searched EBSCOhost and PubMed for literature on individuals: (1) not-in-treatment and purchasing from illegal markets, (2) reporting pre-treatment use at treatment intake, and (3) with opioid use disorder (OUD) receiving medically supplied opioids. A total of 135 articles were deemed relevant. Results: Average consumption intensities vary enormously, from below 100 morphine milligram equivalents (MME) per day for use outside of treatment where prices are high, to ~600 MME in typical illegal markets, and 1,100–1,800 MME per day when supply is free, as in heroin assisted treatment and injectable hydromorphone treatment. MME in methadone programs (190–460) is less than in the traditional British heroin prescribing system (600–1,300). Intensities tended to be higher in recent times, whereas the prices have been lower. Studies during the fentanyl era suggest MMEs per day may be much higher than in the past. Conclusion: The adaptability of consumption has several potential implications. Expansions in supply could have greater effects on quantity consumed than on prevalence. Treatment protocols and overdose prevention strategies may need to adjust for higher baseline consumption. Furthermore, assumptions about health harms from long-term use may need revisiting if they are predicated on lower, historical consumption intensities. These findings are caveated by limitations in reporting of data and variations in methodologies. Hence, greater investments in monitoring consumption intensities are warranted.
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Cues conditioned to withdrawal and negative reinforcement: Neglected but key motivational elements driving opioid addiction
Opioid use disorder (OUD) is a debilitating disorder that affects millions of people. Neutral cues can acquire motivational properties when paired with the positive emotional effects of drug intoxication to stimulate relapse. However, much less research has been devoted to cues that become conditioned to the aversive effects of opioid withdrawal. We argue that environmental stimuli promote motivation for opioids when cues are paired with withdrawal (conditioned withdrawal) and generate opioid consumption to terminate conditioned withdrawal (conditioned negative reinforcement). We review evidence that cues associated with pain drive opioid consumption, as patients with chronic pain may misuse opioids to escape physical and emotional pain. We highlight sex differences in withdrawal-induced stress reactivity and withdrawal cue processing and discuss neurocircuitry that may underlie withdrawal cue processing in dependent individuals. These studies highlight the importance of studying cues associated with withdrawal in dependent individuals and point to areas for exploration in OUD research.
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- Award ID(s):
- 1922598
- PAR ID:
- 10293696
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 7
- Issue:
- 15
- ISSN:
- 2375-2548
- Page Range / eLocation ID:
- eabf0364
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract AimsTo develop machine‐learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers of Excellence (COE) program and to evaluate the fairness of model performance across racial groups. MethodsWe studied 20 983 United States Medicaid enrollees aged 18 years or older who had COE visits between April 2019 and March 2021. We applied multivariate logistic regression, least absolute shrinkage and selection operator models, random forests, and eXtreme Gradient Boosting (XGB), to predict OUD acute events following the initial COE visit. Our models included predictors at the system, patient, and regional levels. We assessed model performance using multiple metrics by racial groups. Individuals were divided into a low, medium and high‐risk group based on predicted risk scores. ResultsThe training (n = 13 990) and testing (n = 6993) samples displayed similar characteristics (mean age 38.1 ± 9.3 years, 58% male, 80% White enrollees) with 4% experiencing OUD acute events at baseline. XGB demonstrated the best prediction performance (C‐statistic = 76.6% [95% confidence interval = 75.6%–77.7%] vs. 72.8%–74.7% for other methods). At the balanced cutoff, XGB achieved a sensitivity of 68.2%, specificity of 70.0%, and positive predictive value of 8.3%. The XGB model classified the testing sample into high‐risk (6%), medium‐risk (30%), and low‐risk (63%) groups. In the high‐risk group, 40.7% had OUD acute events vs. 16.5% and 5.0% in the medium‐ and low‐risk groups. The high‐ and medium‐risk groups captured 44% and 26% of individuals with OUD events. The XGB model exhibited lower false negative rates and higher false positive rates in racial/ethnic minority groups than White enrollees. ConclusionsNew machine‐learning algorithms perform well to predict risks of opioid use disorder (OUD) acute care use among United States Medicaid enrollees and improve fairness of prediction across racial and ethnic groups compared with previous OUD‐related models.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.abf0364
