A dynamic model of the opioid drug epidemic with implications for policy

• Published in: The American journal of drug and alcohol abuse Vol. 47; no. 1; pp. 5 - 15
• Main Authors: Homer, Jack, Wakeland, Wayne
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 02.01.2021; Taylor & Francis Ltd
• Subjects: Addictions; Data; Dosage; Drug overdose; Epidemics; fentanyl; historical time series; illicit market; Intervention; Literature reviews; Naloxone; Narcotics; Opioid epidemic; Opioids; Overdoses; policy intervention; Policy making; Research subjects; Simulation; simulation model; Substance abuse treatment; Substance use disorder; system dynamics; Time series; historical time series; policy intervention; system dynamics; fentanyl; simulation model; naloxone; illicit market; Opioid epidemic
• ISSN: 0095-2990; 1097-9891; 1097-9891
• DOI: 10.1080/00952990.2020.1755677

Background: The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to address the complexities of opioid policymaking. Objectives: Develop a system dynamics simulation model that reflects the complexities and can anticipate intended and unintended intervention effects. Methods: The model was developed from literature review and data gathering. Its outputs, starting in 1990, were compared against 12 historical time series. Four illustrative interventions were simulated for 2020-2030: reducing prescription dosage by 20%, cutting diversion by 30%, increasing addiction treatment from 45% to 65%, and increasing lay naloxone use from 4% to 20%. Sensitivity testing was performed to determine effects of uncertainties. No human subjects were studied. Results: The model fits historical data well with error percentage averaging 9% across 201 data points. Interventions to reduce dosage and diversion reduce the number of persons with opioid use disorder (PWOUD) by 11% and 16%, respectively, but each of these interventions reduces overdoses by only 1%. Boosting treatment reduces overdoses by 3% but increases PWOUD by 1%. Expanding naloxone reduces overdose deaths by 12% but increases PWOUD by 2% and overdoses by 3%. Combining all four interventions reduces PWOUD by 24%, overdoses by 4%, and deaths by 18%. Uncertainties may affect these numerical results, but policy findings are unchanged. Conclusion: No single intervention significantly reduces both PWOUD and overdose deaths, but a combination strategy can do so. Entering the 2020s, only protective measures like naloxone expansion could significantly reduce overdose deaths.