Risky‐prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other “risky” drugs is a national public health concern. We use a novel data framework—a directed network connecting physicians who encounter the same patients in a sequence of visits—to investigate if risky‐prescribing diffuses across physicians through a process of peer‐influence. Using a shared‐patient network of 10 661 Ohio‐based physicians constructed from Medicare claims data over 2014‐2015, we extract information on the order in which patients encountered physicians to derive a directed patient‐sharing network. This enables the novel decomposition of peer‐effects of a medical practice such as risky‐prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer‐effects for contagion in risky‐prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky‐prescribing in patients of peer‐physicians. Estimated peer‐effects were strongest when the patient‐sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer‐effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis‐specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared‐patient networks. These findings suggest network‐based interventions for reducing risky‐prescribing.

中文翻译：

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利用关系方向性来增强社交网络中同伴影响力的统计建模


风险处方是指过度或不适当地开出药物，单独或组合使用会带来不良健康后果的重大风险。在美国，阿片类药物和其他“危险”药物的处方是一个全国性的公共卫生问题。我们使用一种新颖的数据框架——一个连接在一系列就诊中遇到相同患者的医生的定向网络——来调查风险处方是否通过同行影响过程在医生之间扩散。我们使用根据 2014 年至 2015 年医疗保险索赔数据构建的由 10 661 名俄亥俄州医生组成的共享患者网络，提取有关患者遇到医生的顺序的信息，从而得出定向患者共享网络。这使得医疗实践的同伴效应（例如风险处方）能够以新颖的方式分解为定向（出站和入站）和双向（相互）关系组件。使用这个框架，我们开发了风险处方行为传染的同伴效应以及溢出效应模型。后者是根据疑似与同行医生患者的危险处方相关的不良健康事件来衡量的。当患者共享关系是相互的而不是定向的时，估计的同伴效应最强。通过模拟，我们确认我们的建模和估计策略可以准确且精确地同时估计每种类型的同伴效应（相互的和定向的）。我们还表明，未能解释这些不同的机制（模型错误指定的一种形式）会产生误导性结果，这证明了在构建医生共享患者网络时保留方向信息的重要性。 这些发现表明基于网络的干预措施可以减少风险处方。