Kurz Megan, Tatangelo Mark, Morin Kristen A, Zanette Michelle, Krebs Emanuel, Marsh David C, Nosyk Bohdan
Centre for Advancing Health Outcomes, Vancouver, BC, Canada.
Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
PLoS One. 2025 May 16;20(5):e0322064. doi: 10.1371/journal.pone.0322064. eCollection 2025.
Given the growth of collaborative care strategies for people with opioid use disorder and the changing composition of the illicit drug supply, there is a need to identify and analyze clinic-level outcomes for centers prescribing opioid agonist treatment (OAT). We aimed to determine and validate whether prescriber networks, constructed with administrative data, can successfully identify distinct clinical practice facilities in Ontario, Canada.
We executed a retrospective population-based cohort study using OAT prescription records from the Canadian Addiction Treatment Centres in Ontario, Canada between 01/01/2013 and 12/31/2020. Social network analysis was utilized to create networks with connections between physicians based on their shared OAT clients. We defined connections two different ways, by including the number of clients shared or a relative threshold on the percentage of shared OAT clients per physician. Clinics were identified using modularity maximization, with sensitivity analyses applying Louvain, Walktrap, and Label Propagation algorithms. Concordance between network-identified facilities and the (gold standard) de-identified facility-level IDs was assessed using overall, positive and negative agreement, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
From 144 physicians at 105 clinics with 32,842 OAT clients, we assessed 250 different versions of the created networks. The three different detection algorithms had wide variation in concordance, with ranges on sensitivity from 0.02 to 0.88 and PPV from 0.06 to 0.97. The optimal result, derived from the modularity maximization method, achieved high specificity (0.98, 95% CI: 0.98, 0.98) and NPV (0.98, 95% CI: 0.97, 0.98) and moderate PPV (0.54, 95% CI: 0.52, 0.57) and sensitivity (0.45, 95% CI: 0.43, 0.47). This scenario had an overall agreement of 0.96, negative agreement of 0.98, and positive agreement of 0.49.
Social network analysis can be used to identify clinics prescribing OAT in the absence of clinic-level identifiers, thus facilitating construction and comparison of clinic-level caseloads and treatment outcomes.
鉴于阿片类药物使用障碍患者的协作护理策略不断发展以及非法药物供应构成的变化，有必要识别和分析开具阿片类激动剂治疗（OAT）的中心在临床层面的结果。我们旨在确定并验证利用行政数据构建的处方医生网络能否成功识别加拿大安大略省不同的临床实践机构。
我们进行了一项基于人群的回顾性队列研究，使用了2013年1月1日至2020年12月31日期间加拿大安大略省成瘾治疗中心的OAT处方记录。利用社会网络分析，根据医生共享的OAT患者创建医生之间有联系的网络。我们通过两种不同方式定义联系，一种是纳入共享患者数量，另一种是设定每位医生共享OAT患者百分比的相对阈值。使用模块度最大化方法识别诊所，并运用Louvain、Walktrap和标签传播算法进行敏感性分析。使用总体一致性、阳性一致性和阴性一致性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）评估网络识别的机构与（黄金标准）去识别化的机构层面ID之间的一致性。
在105家诊所的144名医生及32,842名OAT患者中，我们评估了创建网络的250个不同版本。三种不同的检测算法在一致性方面差异很大，敏感性范围为0.02至0.88，PPV范围为0.06至0.97。通过模块度最大化方法得出的最佳结果具有高特异性（0.98，95%CI：0.98，0.98）和NPV（0.98，95%CI：0.97，0.98）以及中等的PPV（0.54，95%CI：0.52，0.57）和敏感性（0.45，95%CI：0.43，0.47）。这种情况下总体一致性为0.96，阴性一致性为0.98，阳性一致性为0.49。
在没有诊所层面标识符的情况下，社会网络分析可用于识别开具OAT的诊所，从而便于构建和比较诊所层面的病例负荷及治疗结果。
