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 使用两种模式（即隶属关系）社交网络分析（SNA）。滥用处方阿片类药物和 美国苯二氮卓类药物的使用量迅速增加，造成了公共卫生危机。药物过量导致的死亡率为 高于机动车事故，是全国可预防的主要死亡原因之一。医生 购物被定义为同时从多个医疗保健从业者处获取受管制物质， 超过推荐剂量。医生购物是获得控制的主要方法 滥用物质，以及药物滥用升级的指标，与两倍的致命风险相关 过量。由于对医生购物的分类标准缺乏共识，导致了不同的选择 可疑处方活动的估计比率。这种模糊性给理解因素带来了障碍 潜在的医生购物，并阻碍处方药政策的评估。目前，数值 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，导致误报和漏报。拟议研究的目标是确定 是否将有关参与者在患者的两种模式社交网络中的结构位置的信息纳入 处方者可以制定更有效的非法行为指标。两种模式网络分析侧重于联系 两个不同类别或实体集之间的关系。在拟议的项目中，数据形成了两种模式网络 处方者属于一类，患者属于另一类，并且仅在处方者和患者之间存在联系。 双模式 SNA 非常适合检查两组参与者之间社会互动的结构模式，并且 用于确定网络中最活跃和最核心的参与者。始终以关键的处方者为目标 处方药网络中的参与者可能表明战略性医生购物行为或信息 患者之间共享。 SNA 的见解将用于微调医生购物指标，从而改善我们的 有能力发现虐待的早期迹象，以及间歇性、模糊性或不太强烈的行为，但 还是有问题。拟议研究的具体目标是： 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； 2) 识别特征 医生购物患者、目标处方者和服务点。据我们所知，这是第一项研究 处方药滥用的研究使用两种模式 SNA 作为其方法论。这项研究的意义在于 将两种模式 SNA（一种复杂的系统科学方法）应用于药物滥用研究，并具有 促进从现有数据集中经济有效地提取信息以转化为政策和 实践。这项研究的长期目标是利用 SNA 的见解来改进检测和 预防医生购物及相关欺诈活动，最终减少患病率和公众 处方药滥用对健康的影响。