Overcoming Repeated Testing Schedule Bias in Estimates of Disease Prevalence
纠正疾病患病率估计中的重复检测安排偏差AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究疾病监测中的检测安排偏差与患病率识别，纵向观测本身不足以归入所列面板或因果方法方向。
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纠正疾病患病率估计中的重复检测安排偏差
- 研究问题
- 当机构反复检测成员并隔离阳性者时，检测阳性率为何可能偏离真实患病率，什么条件下仍能识别并估计患病率？
- 方法与思路
- 作者分析纵向检测与隔离安排产生偏差的机制，区分阳性率无偏和患病率可识别的条件，并提出新的估计方法。
- 材料陈述的贡献
- 摘要称，论文找到较弱的患病率识别条件，并用模拟及俄亥俄州立大学匿名检测数据展示所提估计程序的使用。
- 阅读关注点
- 应核对目标总体与患病率定义、隔离如何改变检测样本、无偏条件与识别条件的差别，以及估计所需的检测记录。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明；摘要未列出具体识别条件或误差处理方式，不能推断实际偏差大小及刊本变化。
原文摘要
During the COVID-19 pandemic, many institutions such as universities and workplaces implemented testing regimens with every member of some population tested longitudinally, and those testing positive isolated for some time. Although the primary purpose of such regimens was to suppress disease spread by identifying and isolating infectious individuals, testing results were often also used to obtain prevalence and incidence estimates. Such estimates are helpful in risk assessment and institutional planning and various estimation procedures have been implemented, ranging from simple test-positive rates to complex dynamical modeling. Unfortunately, the popular test-positive rate is a biased estimator of prevalence under many seemingly innocuous longitudinal testing regimens with isolation. We illustrate how such bias arises and identify conditions under which the test-positive rate is unbiased. Further, we identify weaker conditions under which prevalence is identifiable and propose a new estimator of prevalence under longitudinal testing. We evaluate the proposed estimation procedure via simulation study and illustrate its use on a dataset derived by anonymizing testing data from The Ohio State University. Supplementary materials for this article are available online.