Aroke Hilary, Katenka Natallia, Kogut Stephen, Buchanan Ashley
University of Rhode Island, Department of Pharmacy Practice, 7 Greenhouse Road, Kingston, RI 02881.
Department of Computer Science & Statistics, 9 Greenhouse Road, Suite 247, Kingston, RI 02881.
Complex Netw Their Appl X (2021). 2022;1016:716-730. doi: 10.1007/978-3-030-93413-2_59.
The United States has been experiencing an unprecedented level of opioid overdose-related mortality due in part to excessive use of prescription opioids. Peer-driven network interventions may be beneficial. A key assumption of social network interventions is that of some members of the network act as key players and can influence the behavior of others in the network. We used opioid prescription records to create a social network of patients who use prescription opioid in the state of Rhode Island. The study population was restricted to patients on stable opioid regimens who used one source of payment and received the same opioid medication from ≥ 3 prescribers and pharmacies. An exponential random graph model (ERGM) was employed to examine the relationship between patient attributes and the likelihood of tie formation and modularity was used to assess for homophily (the tendency of individuals to associate with similar people). We used multivariable logistic regression to assess predictors of high betweenness centrality, a measure of influence within the network. 372 patients were included in the analysis; average age was 51 years; 53% were female; 57% were prescribed oxycodone, 34% were prescribed hydrocodone and 9% were prescribed buprenorphine/naloxone. After controlling for the main effects in the ERGM model, homophily was associated with age group, method of payment, number and type of opioid prescriptions filled, mean daily dose, and number of providers seen. Type of opioid and number of prescribers were identified as significant predictors of high betweenness centrality. We conclude that patients who use multiple prescribers or have a diagnosis of opioid use disorder may help promote positive health behaviors or disrupt harmful behaviors in an opioid prescription network.
美国一直经历着前所未有的与阿片类药物过量相关的死亡率，部分原因是处方阿片类药物的过度使用。同伴驱动的网络干预可能会有所帮助。社会网络干预的一个关键假设是，网络中的一些成员充当关键角色，能够影响网络中其他人的行为。我们使用阿片类药物处方记录创建了罗德岛州使用处方阿片类药物的患者社交网络。研究人群仅限于使用单一支付来源且从≥3名开处方者和药房获得相同阿片类药物的稳定阿片类药物治疗方案的患者。采用指数随机图模型（ERGM）来检验患者属性与联系形成可能性之间的关系，并使用模块度来评估同质性（个体与相似的人交往的倾向）。我们使用多变量逻辑回归来评估中介中心性高的预测因素，中介中心性是网络内影响力的一种度量。372名患者纳入分析；平均年龄为51岁；53%为女性；57%的患者被开具羟考酮，34%被开具氢可酮，9%被开具丁丙诺啡/纳洛酮。在控制了ERGM模型中的主要效应后，同质性与年龄组、支付方式、所开具阿片类药物处方的数量和类型、日均剂量以及就诊提供者数量相关。阿片类药物类型和开处方者数量被确定为中介中心性高的显著预测因素。我们得出结论，使用多名开处方者或被诊断为阿片类药物使用障碍的患者可能有助于在阿片类药物处方网络中促进积极的健康行为或扰乱有害行为。
