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

社交网络、医生特征以及常见误用处方药的不当处方

基本信息

批准号：
10090907
负责人：
Marissa D King
金额：
$ 12.01万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2020-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10090907
关键词：
Adult Algorithms Behavior Benzodiazepines Characteristics Community of Practice Data Databases Detection Diffusion Drops Drug Prescriptions Epidemic Evolution Formularies Geography Health Hospitalization Individual Insurance Intervention Lead Louisiana Managed Care Medicaid Methods Modeling Network-based Opioid Overdose Pathway Analysis Patient-Focused Outcomes Patients Pattern Physician&apos s Role Physicians Play Policies Positioning Attribute Privatization Provider Recommendation Research Research Personnel Risk Role Shapes Social Network Tennessee United States Washington analytical method base behavior change benzodiazepine abuse benzodiazepine misuse combat design improved insight misuse of prescription only drugs opioid misuse opioid use disorder prescription drug abuse prescription monitoring program prescription opioid relative effectiveness role model social social relationships social structure tool

项目摘要

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.

项目概要近年来，阿片类药物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未得到充分研究。然而，34% 的人滥用处方药直接从一位医生那里获得。基于网络的模型，分析社会关系结构如何塑造规范和行为，成功地被用来了解医生的行为并改变医生的做法。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开药的模式和规范。这种重新定位有可能帮助识别面临不适当处方风险的医生，了解这些倾向如何与其网络位置相关联，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，我们的目标是：（1）检查之间的关联社交网络位置、医生特征以及不同形式的不当行为处方和共同处方； (2) 使用网络和行为协同进化模型来分析社会影响力在不当处方做法传播中的作用； (3)比较保险网络优化的相对有效性，以消除不适当的处方者，处方药监测计划和保险处方限制。我们将借鉴 1 月份 IMS Health LRx 数据库中苯二氮卓类药物和阿片类药物的处方 2005年1月1日和2016年12月31日。2009年的数据覆盖了224,140,604名独立患者，916,338名处方者，包括 1.35 亿张阿片类药物处方和 4600 万张苯二氮卓类药物处方处方。我们将通过来自三个机构的 Medicaid Managed Care 索赔来扩充 LRx 数据州。使用 IMS 和医疗补助数据，我们将构建纵向医生转诊网络。使用这些数据进行的拟议分析将更深入地了解社交网络如何可以利用地位和社会影响来打击受控药物的不当处方物质。医生在帮助结束处方药流行方面具有独特的优势。过去的研究表明，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，因为处方规范在实践社区。我们的研究将加强努力更好地了解处方药滥用并提出可行的建议，公共和私人利益相关者以及付款人可以通过改进检测来降低不当处方率算法、更有效地瞄准政策和教育工作以及新政策。