Jensen Elizabeth T, Cook Suzanne F, Allen Jeffery K, Logie John, Brookhart Maurice Alan, Kappelman Michael D, Dellon Evan S
Center for Esophageal Diseases and Swallowing, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill; Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill.
World Wide Epidemiology, GlaxoSmithKline, Research Triangle Park.
Ann Epidemiol. 2015 Jul;25(7):519-525.e2. doi: 10.1016/j.annepidem.2015.03.008. Epub 2015 Mar 21.
Considerations for using administrative claims data in research have not been well-described. To increase awareness of how enrollment factors and insurance benefit use may contribute to prevalence estimates, we evaluated how differences in operational definitions of the cohort impact observed estimates.
We conducted a cross-sectional study estimating the prevalence of five gastrointestinal conditions using MarketScan claims data for 73.1 million enrollees. We extracted data obtained from 2009 to 2012 to identify cohorts meeting various enrollment, prescription drug benefit, or health care utilization characteristics. Next, we identified patients meeting the case definition for each of the diseases of interest. We compared the estimates obtained to evaluate the influence of enrollment period, drug benefit, and insurance usage.
As the criteria for inclusion in the cohort became increasingly restrictive the estimated prevalence increased, as much as 45% to 77% depending on the disease condition and the definition for inclusion. Requiring use of the insurance benefit and a longer period of enrollment had the greatest influence on the estimates observed.
Individuals meeting case definition were more likely to meet the more stringent definition for inclusion in the study cohort. This may be considered a form of selection bias, where overly restrictive inclusion criteria definitions may result in selection of a source population that may no longer represent the population from which cases arose.
关于在研究中使用行政索赔数据的考量尚未得到充分描述。为了提高对入组因素和保险福利使用如何可能影响患病率估计的认识，我们评估了队列操作定义的差异如何影响观察到的估计值。
我们进行了一项横断面研究，使用市场扫描（MarketScan）索赔数据对7310万参保人估计五种胃肠道疾病的患病率。我们提取了2009年至2012年的数据，以确定符合各种入组、处方药福利或医疗保健利用特征的队列。接下来，我们确定符合每种感兴趣疾病病例定义的患者。我们比较获得的估计值，以评估入组期、药物福利和保险使用的影响。
随着纳入队列的标准变得越来越严格，估计患病率增加，根据疾病状况和纳入定义，增加幅度高达45%至77%。要求使用保险福利和更长的入组期对观察到的估计值影响最大。
符合病例定义的个体更有可能符合纳入研究队列的更严格定义。这可能被视为一种选择偏倚形式，其中过于严格的纳入标准定义可能导致选择一个可能不再代表病例来源人群的源人群。
