Brenner H
Department of Epidemiology, University of Ulm, Germany.
J Clin Epidemiol. 1996 Nov;49(11):1303-7. doi: 10.1016/0895-4356(95)00026-7.
Capture--recapture methods are increasingly employed to correct for underascertainment of prevalent or incident cases in epidemiological surveillance. Routine systems of disease surveillance, such as morbidity registries or mortality statistics, are, however, often prone to errors in disease diagnosis. This article provides a quantitative assessment of the performance of the two-source capture--recapture method for disease monitoring in the presence of false-positive and false-negative diagnoses in one of the two sources. Expected capture--recapture case counts and traditional case counts are algebraically derived as functions of the individual case ascertainment probabilities of both sources and of the probabilities of diagnostic misclassification. It is shown that misdiagnoses can lead to underestimation or overestimation of case numbers by the capture--recapture approach, depending on the specific circumstances of disease monitoring. Nevertheless, the net bias is typically less severe than with traditional case counts. The findings are illustrated with examples from the field of cancer registration. Strategies are discussed that might minimize the problem of misdiagnoses in the design of capture--recapture studies or that might be used to correct for it in the analysis.
捕获-再捕获方法在流行病学监测中越来越多地用于校正流行病例或新发病例的漏报情况。然而，常规疾病监测系统，如发病率登记或死亡率统计，往往在疾病诊断方面容易出错。本文对两源捕获-再捕获方法在其中一个来源存在假阳性和假阴性诊断情况下进行疾病监测的性能进行了定量评估。预期捕获-再捕获病例数和传统病例数通过代数推导得出，是两个来源的个体病例确定概率以及诊断错误分类概率的函数。结果表明，根据疾病监测的具体情况，误诊可能导致捕获-再捕获方法低估或高估病例数。然而，净偏差通常比传统病例数的情况要轻。通过癌症登记领域的实例对研究结果进行了说明。讨论了在捕获-再捕获研究设计中可能将误诊问题降至最低或在分析中用于校正该问题的策略。
