Doctor Shopping for Controlled Substances: Insights from Two-Mode Social Network Analysis

医生购买管制药物：两种模式社交网络分析的见解

• 批准号: 9321366
• 负责人: Brea Louise Perry
• 金额: $ 28.79万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9321366
• 关键词: Algorithms; Behavior; Benzodiazepines; Big Data; Caring; Cause of Death; Centers for Disease Control and Prevention (U.S.); Characteristics; Communities; Complex; Consensus; Data; Data Set; Databases; Dependence; Detection; Drug Monitoring; Drug Prescriptions; Drug abuse; Early Diagnosis; Epidemic; Evaluation; Future; Goals; Health; Healthcare; Imagery; Insurance; Intervention; Learning; Machine Learning; Measures; Methodology; Methods; Modeling; Network-based; Outcome; Overdose; Pathway Analysis; Patients; Pattern; Pharmaceutical Preparations; Policies; Positioning Attribute; Predisposition; Prevalence; Prevention; Provider; Public Health; Research; Research Personnel; Risk; Role; Sampling; Science; Services; Social Interaction; Social Network; Solo Practices; Structure; Symptoms; System; Translations; Travel; United States National Institutes of Health; Variant; Visit; Withdrawal; Work; cost effective; dosage; drug seeking behavior; improved; insight; mortality; novel; operation; prescription drug abuse; prescription opioid; prescription opioid abuse; programs; response; standard measure; substance misuse; trend; vehicular accident

The proposed project will analyze patterns of doctor shopping for prescription opioids and benzodiazepines using two-mode (i.e. affiliation) social network analysis (SNA). Abuse of prescription opioids and benzodiazepines in the U.S. has risen rapidly, creating a public health crisis. Mortality from drug overdose is higher than motor vehicle accidents and is among the nation's leading preventable causes of death. Doctor shopping is defined as obtaining controlled substances from multiple health care practitioners simultaneously, exceeding the recommended dosage. Doctor shopping is a principle method of obtaining controlled substances for misuse, and an indicator of escalating drug abuse that is associated with a two-fold risk for fatal overdose. Lack of consensus about criteria for classifying doctor shopping has led to wide variation in estimated rates of questionable prescribing activity. This ambiguity poses barriers to understanding factors underlying doctor shopping, and impedes the evaluation of prescription drug policies. Currently, numerical thresholds or multiple provider episodes (MPEs; i.e. overlapping prescriptions) are most often used to identify doctor shoppers, resulting in false positives and negatives. A goal of the proposed study is to determine whether incorporating information about actors' structural position in a two-mode social network of patients and prescribers can produce more valid indictors of illicit behavior. Two-mode network analysis focuses on ties between two different classes or sets of entities. In the proposed project, data form a two-mode network with prescribers in one class and patients in the other, and ties are present only between prescribers and patients. Two-mode SNA is ideal for examining structural patterns in social interaction between two sets of actors, and for determining the most active and central actors in a network. Consistently targeting prescribers that are key players in prescription drug networks may be indicative of strategic doctor shopping behavior or information sharing among patients. Insights from SNA will be used to fine-tune doctor-shopping indicators, improving our ability to detect early signs of abuse, as well as behavior that is intermittent, ambiguous, or less intense, but still problematic. The specific aims of the proposed study are to: 1) Utilize SNA to develop and assess network- based metrics of doctor shopping in comparison to traditional indicators; and 2) Identify characteristics of doctor shopping patients, targeted prescribers, and the point of service. To our knowledge, this is the first study of prescription drug abuse to use two-mode SNA as its methodological approach. This study is significant in applying two-mode SNA – a complex, systems science methodology – to drug abuse research and has the potential to facilitate cost-effective information extraction from extant datasets for translation to policy and practice. The long-term goal of this research is to leverage insights from SNA to improve detection and prevention of doctor shopping and related fraudulent activities, and ultimately reduce the prevalence and public health impact of prescription drug abuse.

拟议的项目将分析医生购买处方阿片类药物和苯二氮卓类药物的模式 使用双模式（即隶属关系）社会网络分析（SNA）。滥用处方类阿片和 苯二氮卓类药物在美国迅速增加，造成了公共卫生危机。药物过量的死亡率 高于机动车事故，是美国主要的可预防死亡原因之一。医生 购物被定义为同时从多个卫生保健从业者处获得受控物质， 超过推荐剂量。医生购物是一个原则的方法获得控制 药物滥用加剧的指标，与致命性药物滥用的双重风险有关。 服药过量对医生购物的分类标准缺乏共识，导致了 可疑处方活动的估计发生率。这种模糊性对理解因素构成了障碍 潜在的医生购物，并阻碍了处方药政策的评估。目前，数字 阈值或多个提供者事件（MPE;即重叠处方）最常用于识别 医生购物者，导致假阳性和阴性。拟议研究的一个目标是确定 是否在患者的双模式社会网络中纳入有关参与者结构位置的信息， 开处方者可以产生更有效的非法行为指标。双模式网络分析侧重于联系 在两个不同的类或实体集合之间。在拟议的项目中，数据形成一个双模式网络， 处方者在一个类别中，患者在另一个类别中，并且仅在处方者和患者之间存在关系。 双模式国民账户体系是研究两组行为者之间社会互动的结构模式的理想方法， 用于确定网络中最活跃和最核心的参与者。一致针对处方者， 处方药网络中的参与者可以指示策略性的医生购物行为或信息 患者之间的分享。SNA的见解将用于微调医生购物指标，改善我们的 能够发现虐待的早期迹象，以及间歇性，模糊或不太强烈的行为，但 仍然有问题。本研究的具体目标是：1）利用SNA开发和评估网络， 与传统指标相比，基于医生购物的指标;以及2）识别 医生购物病人，有针对性的处方，和服务点。据我们所知，这是第一次研究 使用双模式国民账户体系作为其方法学途径。这项研究在 将双模式SNA（一种复杂的系统科学方法）应用于药物滥用研究， 有可能促进从现有数据集中以成本效益高的方式提取信息，用于政策和 实践这项研究的长期目标是利用SNA的见解来提高检测和 防止医生购物及相关欺诈活动，最终降低患病率和公众 滥用处方药对健康的影响。