Taragin M I, Wildman D, Trout R
Department of Medicine, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, New Brunswick 08903-0019.
Med Decis Making. 1994 Oct-Dec;14(4):369-73. doi: 10.1177/0272989X9401400407.
Estimates of disease prevalence are needed for the interpretation of test results as well as for public health decisions. Assessing prevalence may be difficult if a definitive test is unavailable, impractical, or expensive. A formula derived from Bayes' theorem can calculate the prevalence of disease in a population by incorporating test results with a knowledge of the sensitivity and specificity of a test. This paper reviews this formula and provides examples evaluating the prevalence of HIV disease, the usefulness of ventilation-perfusion scans in diagnosing pulmonary embolism, and settings where screening tests should not be applied. These examples demonstrate that precise yet inexpensive estimates of disease prevalence are possible by enhancing the usefulness of an inaccurate test.
疾病患病率的估计对于解释检测结果以及做出公共卫生决策而言是必要的。如果没有确定性的检测方法，或者该方法不切实际或成本过高，那么评估患病率可能会很困难。根据贝叶斯定理推导出来的一个公式，可以通过将检测结果与检测的灵敏度和特异性知识相结合，来计算人群中的疾病患病率。本文回顾了这个公式，并提供了一些例子，用于评估艾滋病毒疾病的患病率、通气灌注扫描在诊断肺栓塞中的有用性，以及不应该应用筛查检测的情况。这些例子表明，通过提高不准确检测的有用性，可以获得精确且成本低廉的疾病患病率估计。
