Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis
Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis
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DOI：
10.2196/12110
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发表时间：
2019-04-01
影响因子：
8.5
通讯作者：
Vullikanti, Anil
中科院分区：
文献类型：
--
作者：
Basak, Arinjoy;Cadena, Jose;Vullikanti, Anil
背景：处方阿片类药物的过度使用和滥用已成为美国重大的公共卫生负担。据估计，2016年约有1150万人滥用处方阿片类药物用于非医疗目的。这导致了美国大量的药物过量死亡。以往的研究已经研究了阿片类药物滥用的时空集群，但他们一直局限于圆形regions.Objective：本研究的目的是确定阿片类药物使用者和阿片类药物处方索赔使用医疗保险数据的时空热点。我们使用医疗保险支付数据研究了受益人数量和阿片类药物处方率显著较高的时空集群，这些数据来自医疗保险和医疗补助服务。我们使用网络扫描统计来检测具有任意形状的重要集群，Kulldorff扫描统计来检查每年（2013年，2014年和2015年）的重要集群，并使用基于预期的版本来检查相对于过去几年的重要集群。回归分析被用来描述县的人口是任何显着集群的一部分，和数据挖掘技术被用来发现异常providers.Results的专业：我们检查了异常空间集群与阿片类药物处方索赔和受益人计数，并发现了一些共同的模式跨国家：最异常集群中的县在2014年和2015年相当稳定，但从2013年开始缩小。在弗吉尼亚州，一个县的非洲裔美国人比例越高，该县在阿片类药物受益人计数方面异常的几率就越低，2015年约为0.96。对于阿片类药物处方索赔计数，赔率为0.92。这种模式在3个州和3年内是一致的。在弗吉尼亚州，该县获得医疗补助的人数增加，该县在这两种类型的计数中处于异常集群的几率增加到1.16。更多的人可以直接购买保险计划，将一个县处于异常集群的几率降低到0.85。基于预期的扫描统计，捕捉随时间的变化，揭示了不同的集群比Kulldorff统计。提供者的阿片类药物受益人和阿片类药物索赔的数量异常高，包括专业，如医生的助理，执业护士，和家庭practice.Conclusions：我们的医疗保险索赔数据的分析提供了县和提供者的专业，有较高的几率是异常的特点。实证分析确定了高度精确的空间热点，可能会遇到处方阿片类药物滥用和过量。该方法是通用的，可以应用于监测供应商和他们的处方行为在滥用的高风险地区。
Background: Overuse and misuse of prescription opioids have become significant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for nonmedical purposes in 2016. This has led to a significant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions.Objective: The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data.Methods: We examined spatiotemporal clusters with significantly higher number of beneficiaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare & Medicaid Services. We used network scan statistics to detect significant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the significant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the significant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any significant cluster, and data mining techniques were used to discover the specialties of the anomalous providers.Results: We examined anomalous spatial clusters with respect to opioid prescription claims and beneficiary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician's assistant, nurse practitioner, and family practice.Conclusions: Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse.