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IntroductionLike its counterpart to the south, Canada ranks among the top five countries with the highest rates of opioid prescriptions. With many suffering from opioid use disorder first having encountered opioids via prescription routes, practitioners and health systems have an enduring need to identify and effectively respond to the problematic use of opioid prescription. There are strong challenges to successfully addressing this need: importantly, the patterns of prescription fulfillment that signal opioid abuse can be subtle and difficult to recognize, and overzealous enforcement can deprive those with legitimate pain management needs the appropriate care. Moreover, injudicious responses risk shifting those suffering from early-stage abuse of prescribed opioids to illicitly sourced street alternatives, whose varying dosage, availability, and the risk of adulteration can pose grave health risks.MethodsThis study employs a dynamic modeling and simulation to evaluate the effectiveness of prescription regimes employing machine learning monitoring programs to identify the patients who are at risk of opioid abuse while being treated with prescribed opioids. To this end, an agent-based model was developed and implemented to examine the effect of reduced prescribing and prescription drug monitoring programs on overdose and escalation to street opioids among patients, and on the legitimacy of fulfillments of opioid prescriptions over a 5-year time horizon. A study released by the Canadian Institute for Health Information was used to estimate the parameter values and assist in the validation of the existing agent-based model.Results and discussionThe model estimates that lowering the prescription doses exerted the most favorable impact on the outcomes of interest over 5 years with a minimum burden on patients with a legitimate need for pharmaceutical opioids. The accurate conclusion about the impact of public health interventions requires a comprehensive set of outcomes to test their multi-dimensional effects, as utilized in this research. Finally, combining machine learning and agent-based modeling can provide significant advantages, particularly when using the latter to gain insights into the long-term effects and dynamic circumstances of the former.

Substance use behavior among youth is a complex peer-group phenomenon shaped by many factors. Peer influence, easily accessible prescription opioids, and a youth’s socio-cultural environment play recognized roles in the initiation and persistence of youth nonmedical prescription opioid use. By altering the physical surroundings and social environment of youth, in-person school closures may change risk factors for youth drug use. Acknowledging past research on the importance of the presence of peers in youth substance use risk behavior, this paper reports the findings from the use of an agent-based simulation grounded in social impact theory to investigate possible impacts of in-person school closures due to COVID-19 on the prevalence of nonmedical prescription opioid use among youth. The presented model integrates data from the Ontario Student Drug Use and Health Survey and characterizes the accessibility of within-home prescription opioids. Under the status quo, the lifting of in-person school closures reliably entails an increase in the prevalence of youth with nonmedical prescription opioid use, but this effect is ameliorated if the prescription opioids are securely stored during the in-person school closures period.

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