Shojaati Narjes, Osgood Nathaniel D
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Front Digit Health. 2023 Jun 20;5:1174845. doi: 10.3389/fdgth.2023.1174845. eCollection 2023.
Like 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 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.
This 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.
The 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.
与南部的邻国一样，加拿大是阿片类药物处方率最高的五个国家之一。许多患有阿片类药物使用障碍的人最初都是通过阿片类药物处方途径接触到这类药物的，因此从业者和医疗系统一直需要识别并有效应对阿片类药物处方的问题性使用。成功满足这一需求面临诸多严峻挑战：重要的是，表明阿片类药物滥用的处方执行模式可能很微妙且难以识别，而过度严格的执法可能会使那些有合理疼痛管理需求的人得不到适当的治疗。此外，不明智的应对措施可能会使那些处于阿片类药物处方早期滥用阶段的人转向非法来源的街头替代品，这些替代品剂量不一、供应情况各异且存在掺假风险，会带来严重的健康风险。
本研究采用动态建模与模拟方法，以评估采用机器学习监测程序的处方制度在识别接受阿片类药物处方治疗时存在阿片类药物滥用风险患者方面的有效性。为此，开发并实施了一个基于主体的模型，以检验减少处方开具和处方药监测计划对患者过量用药及转向使用街头阿片类药物的影响，以及在5年时间范围内阿片类药物处方执行的合理性。利用加拿大卫生信息研究所发布的一项研究来估计参数值，并协助验证现有的基于主体的模型。
该模型估计，在5年内降低处方剂量对所关注的结果产生了最有利的影响，同时对有合理需求使用药用阿片类药物的患者造成的负担最小。正如本研究所采用的那样，关于公共卫生干预措施影响的准确结论需要一整套结果来检验其多维度效应。最后，将机器学习与基于主体的建模相结合可带来显著优势，尤其是在利用后者深入了解前者的长期影响和动态情况时。
