Enrollment factors and bias of disease prevalence estimates in administrative claims data.
Enrollment factors and bias of disease prevalence estimates in administrative claims data.
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DOI：
10.1016/j.annepidem.2015.03.008
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发表时间：
2015-07
影响因子：
5.6
通讯作者：
Dellon ES
中科院分区：
文献类型：
--
作者：
Jensen ET;Cook SF;Allen JK;Logie J;Brookhart MA;Kappelman MD;Dellon ES
在研究中使用行政索赔数据的考虑因素尚未得到很好的描述。为了提高人们对登记因素和保险福利使用如何影响观察到的估计的认识，我们评估了队列操作定义的差异如何影响疾病患病率的估计。我们进行了一项横断面研究，利用 MarketScan 的 7,310 万名参与者的索赔数据估计了五种胃肠道疾病的患病率。我们提取了 2009 年至 2012 年获得的数据，以确定满足各种入组、处方药福利或医疗保健利用特征的队列。接下来，我们确定了符合每种感兴趣疾病的病例定义的患者。我们比较了获得的估计值，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，估计患病率增加了 45% 至 77%，具体取决于疾病状况和纳入队列的定义。要求使用福利和更长的注册期对观察到的估计影响最大。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的定义。这可能被认为是一种选择偏差，过度限制性的队列定义可能会导致选择的研究人群可能不再代表源人群。
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 observed estimates, we evaluated how differences in operational definitions of the cohort impacted estimates of disease prevalence. 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 2009–2012 to identify cohorts meeting various enrollment, prescription drug benefit, or healthcare 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 in the cohort. Requiring use of the 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of selection bias, where overly restrictive cohort definitions may result in selection of a study population that may no longer represent the source population.