医学
可信区间
2019年冠状病毒病(COVID-19)
置信区间
人口学
大流行
星团(航天器)
人口
限制
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
整群抽样
预测区间
统计
作者
Abigail Norris Turner,David Kline,Alison Norris,W. Gene Phillips,Elisabeth Root,Jon Wakefield,Zhenfeng Li,Stanley Lemeshow,Morgan Spahnie,Amanda Luff,Yue Chu,Mary Kate Francis,Maria F. Gallo,Payal Chakraborty,Megan Lindstrom,Gerard Lozanski,William C. Miller,Samuel J. Clark
标识
DOI:10.1016/j.annepidem.2021.11.009
摘要
Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non–ignorable non–response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%–2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300–177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%–2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000–240,000). Estimates did not change meaningfully due to non–response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.
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