在北卡罗来纳州中部地区估计SARS-CoV-2血清流行:在复杂的样本设计中考虑结果错误分类
Nishma P Vias1, Caitlin A Cassidy2, Jessie K Edwards2
1From the Adams School of Dentistry, University of North Carolina at Chapel Hill, NC.
Epidemiology (Cambridge, Mass.)
|August 1, 2023
概括
准确的COVID-19血清流行率估计需要考虑不完美的诊断测试,特别是复杂的调查数据. 新的方法为人口研究提供了有效的估计和置信区间,这对于理解传播动态至关重要.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 基于人口的血清流行研究对于跟踪COVID-19社区传播和告知大流行应对至关重要.
- 血清发病率的诊断测试在灵敏度和特异性方面具有固有的局限性,可能会对估计产生偏见.
- 现有的调整方法在流行病学研究中常见的复杂样本设计中经常失败.
研究的目的:
- 利用复杂的样本数据开发和展示准确的血清流行率估计和置信区间构建的方法.
- 为了考虑到在血清流行研究中诊断试验的不完美表现.
- 解决当前方法的局限性,当应用于非方便采样策略时.
主要方法:
- 提出了用于血清流行点估计和置信区间构建的新型统计方法.
- 将这些方法应用于查瑟姆县COVID-19队列 (C4) 研究的数据,这是一个纵向,复杂的样本设计.
- 进行模拟以评估拟议估计器对标准方法的偏差和置信区间覆盖率.
主要成果:
- 北卡罗来纳州查瑟姆县的SARS-CoV-2血清流行率从1月的10.4%升至2021年7月的95.6%.
- 拟议的估计方法在模拟中证明了最小偏差和理想的置信区间覆盖率.
- 对于C4研究数据,成功获得了有效的估计值和置信区间.
结论:
- 本文介绍了一种简单的方法,用于对复杂的样本设计进行有效的血清流行率估计,并考虑到测试准确性.
- 这种方法适用于其他传染病流行研究,在这些研究中已知测试性能指标.
- 这些发现强调了在流行病监测中考虑测量错误的重要性.
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