Social Networks, Physician Characteristics, and Inappropriate Prescribing of Commonly Misused Prescription Drugs

项目摘要 近年来，类阿片和苯二氮卓类药物的误用和滥用急剧增加。尽管 药物过量和滥用的急剧增加，医生在其中所扮演的角色， 不适当的处方仍然研究不足。然而，34%的滥用者 处方药直接从一个医生那里获得。基于网络的模型， 分析了社会关系结构如何塑造规范和行为， 被用来了解医生的行为和改变医生的做法。应用网络 不适当处方的方法将使监测超越基于患者的算法， 医生处方模式和规范。这种重新定位有可能帮助识别 医生谁是在不适当的处方风险，了解这些倾向如何 与他们的网络位置相关联，并设计基于网络的干预措施，以减少 处方药滥用的蔓延。在本研究中，我们的目的是：（1）检查之间的关联 社会网络位置，医生特征，以及不同形式的不适当 处方和共同处方;（2）使用网络和行为协同进化模型来分析 社会影响力在不适当的处方做法的扩散中的作用;（3）比较 保险网络优化的相对有效性，以消除不适当的处方， 处方药监测计划和保险处方集限制。我们将借鉴 2015年1月至2016年1月，IMS Health的LRx数据库中的苯二氮卓类药物和阿片类药物处方 1，2005年和2016年12月31日。2009年的数据涵盖了224，140，604名独特的患者，916，338名 处方者，包括1.35亿阿片类处方和4600万苯二氮卓类处方 处方我们将增加LRx数据与医疗补助管理医疗索赔从三个 states.利用IMS和Medicaid数据，我们将构建纵向医生转诊网络。 使用这些数据的拟议分析将更深入地了解社交网络如何 可以利用其地位和社会影响力来打击不适当的受控药物处方。 物质.医生在帮助结束处方药流行方面具有独特的地位。过去 研究表明，改变处方行为需要针对以下方面进行干预： 医生群体，而不是个人，因为处方规范得到加强， 实践社区。我们的研究将加强努力，更好地了解处方药 滥用并导致公共和私人利益攸关方以及 支付者可以通过改进检测来降低不适当处方的发生率， 算法，更有效的政策和教育工作的目标，以及新的政策。

PROJECT SUMMARY Opioid and benzodiazepine misuse and abuse has increased dramatically in recent years. Despite precipitous increases in overdoses and rising misuse, the role that physicians play in inappropriate prescribing remains understudied. However, 34% of individuals who misused prescription medications obtained them directly from a single doctor. Network-based models, which analyze how the structure of social relations shape norms and behaviors, have successfully been used to understand physician behavior and change physician practices. Applying network methods to inappropriate prescribing will move surveillance beyond patient-based algorithms to physician prescribing patterns and norms. This reorientation has the potential to help identify physicians who are at risk of inappropriate prescribing, understand how these tendencies correlate with their network positions, and design network-based interventions to reduce the spread of prescription drug abuse. In this study, we aim to: (1) Examine the association between social network position, physician characteristics, and different forms of inappropriate prescribing and co-prescribing; (2) Use network and behavior co-evolution models to analyze the role of social influence in the diffusion of inappropriate prescribing practices; (3) Compare the relative effectiveness of insurance network optimization to eliminate inappropriate prescribers, Prescription Drug Monitoring Programs, and insurance formulary restrictions. We will draw on prescriptions of benzodiazepines and opioids from IMS Health's LRx database between January 1, 2005 and December 31, 2016. The 2009 data covered 224,140,604 unique patients, 916,338 prescribers, encompassing 135 million opioid prescriptions and 46 million benzodiazepine prescriptions. We will augment the LRx data with Medicaid Managed Care claims from three states. Using IMS and Medicaid data, we will construct longitudinal physician referral networks. The proposed analyses using this data will provide greater insight into how social network position and social influence can be leveraged to combat inappropriate prescribing of controlled substances. Physicians are uniquely positioned to help end the prescription drug epidemic. Past research indicates that changing prescribing behavior will require interventions targeted at groups of physicians, rather than individuals, since prescribing norms are reinforced in communities of practice. Our study will strengthen efforts to better understand prescription drug abuse and lead to actionable recommendations that public and private stakeholders, as well as payers can take to reduce the rate of inappropriate prescribing through improved detection algorithms, more efficient targeting of policies and educational efforts, and new policies.