Adjusting Coronavirus Prevalence Estimates for Laboratory Test Kit Error
Adjusting Coronavirus Prevalence Estimates for Laboratory Test Kit Error
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DOI：
10.1093/aje/kwaa174
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发表时间：
2021-01-01
影响因子：
5
通讯作者：
Tian, Lu
中科院分区：
文献类型：
--
作者：
Sempos, Christopher T.;Tian, Lu
建议对代表性人群进行检测，以确定活动性严重急性呼吸综合征冠状病毒2型感染和/或感染抗体的流行率或人口百分比，这对于制定公共政策决定、放松限制或继续执行国家、州和地方政府规定以实施隔离措施至关重要。然而，所有的实验室测试都是不完美的，其灵敏度和特异性估计低于100%——在某些情况下，远远低于100%。这种误差将导致有偏差的患病率估计。如果真实的患病率很低，可能在1%-5%的范围内，那么测试误差将导致一个恒定的背景偏差，很可能会比真实的患病率本身更大，甚至可能大得多。因此，需要的是一种根据测试误差调整患病率估计的方法。本文概述了针对测试误差调整患病率估计值的方法，包括计划中的前瞻性研究和已经进行的回顾性研究。如果使用这些方法，还将有助于协调各国和世界范围内的研究结果。调整可导致更准确的患病率估计和更好的政策决定。然而，调整不会提高单个测试的准确性。
Testing representative populations to determine the prevalence or the percentage of the population with active severe acute respiratory syndrome coronavirus 2 infection and/or antibodies to infection is being recommended as essential for making public policy decisions to ease restrictions or to continue enforcing national, state, and local government rules to shelter in place. However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100%-in some cases, considerably less than 100%. That error will lead to biased prevalence estimates. If the true prevalence is low, possibly in the range of 1%-5%, then testing error will lead to a constant background of bias that most likely will be larger, and possibly much larger, than the true prevalence itself. As a result, what is needed is a method for adjusting prevalence estimates for testing error. Methods are outlined in this article for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted. If used, these methods also would help harmonize study results within countries and worldwide. Adjustment can lead to more accurate prevalence estimates and to better policy decisions. However, adjustment will not improve the accuracy of an individual test.