估计俄亥俄州SARS-CoV-2血清阳性率：一种结合多种诊断测试的贝叶斯多层次事后分层方法
Estimating Seroprevalence of SARS-CoV-2 in Ohio: A Bayesian Multilevel Poststratification Approach with Multiple Diagnostic Tests
AI总结：
针对俄亥俄州SARS-CoV-2血清阳性率调查中低应答率、少阳性病例和多测试质量差的问题，提出一种无需样本权重的贝叶斯多层次事后分层方法，同时处理测试不完善与选择性应答的不确定性。
摘要：
全球范围内，SARS-CoV-2冠状病毒已感染超过5900万人，并导致超过139万人死亡。设计和监测减缓及阻止病毒传播的干预措施，需要了解有多少人曾经感染和目前正在感染、他们居住在哪里以及他们如何互动。第一步是准确评估人群中既往感染的流行率。目前针对SARS-CoV-2冠状病毒的具有人群代表性的流行率研究非常少，只有美国两个州——印第安纳州和康涅狄格州——报告了基于概率的样本调查，以刻画全州范围内SARS-CoV-2冠状病毒的流行率。困难之一在于，用于检测和表征SARS-CoV-2冠状病毒抗体的测试是新的，尚未得到充分表征，且总体上性能不佳。2020年7月，一项代表美国俄亥俄州所有成年人的调查收集了与SARS-CoV-2冠状病毒相关的生物标志物和防护行为信息。该调查的若干特征使得估计既往流行率变得困难：1）应答率低，2）阳性病例数量极少，3）使用了多种质量较差的血清学测试来检测SARS-CoV-2抗体。我们描述了一种新的贝叶斯方法，用于分析生物标志物数据，该方法同时应对这些挑战，并刻画选择性应答的潜在影响。该模型不需要调查样本权重，考虑了多种不完善的抗体测试结果，并表征了与样本调查及多种不完善、可能相关的测试相关的不确定性。
英文摘要：
Globally the SARS-CoV-2 coronavirus has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of the SARS-CoV-2 coronavirus, and only two American states -- Indiana and Connecticut -- have reported probability-based sample surveys that characterize state-wide prevalence of the SARS-CoV-2 coronavirus. One of the difficulties is the fact that the tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, not well characterized, and generally function poorly. During July, 2020, a survey representing all adults in the State of Ohio in the United States collected biomarkers and information on protective behavior related to the SARS-CoV-2 coronavirus. Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate, 2) very low number of positive cases, and 3) the fact that multiple, poor quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a new Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights, accounts for multiple, imperfect antibody test results, and characterizes uncertainty related to the sample survey and the multiple, imperfect, potentially correlated tests.
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