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

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 2020 s, only protective measures like naloxone expansion could significantly reduce overdose deaths.

Automated summary

This study used a system dynamics simulation model to analyze the opioid epidemic, finding that single interventions are unlikely to significantly reduce opioid use disorder and overdose deaths, but a combination of interventions can achieve good results. Uncertainties may affect the numerical results, but the policy conclusions remain unchanged.