通过时空回归和分层后,提高英国国家COVID-19感染调查的代表性
Koen B Pouwels1,2, David W Eyre3,4,5,6, Thomas House7,8
1Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK. koen.pouwels@ndph.ox.ac.uk.
Nature communications
|June 24, 2024
概括
准确的COVID-19感染和抗体估计需要考虑疫苗接种状态. 如果不适应疫苗接种,可能会导致在人口调查中对抗体患病率的过高估计.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 传染病建模 传染病建模
背景情况:
- 人口代表性估计SARS-CoV-2感染和抗体水平对于有效的政策制定至关重要.
- 在全民调查中,参与偏见可能来自关键人口群体之间的差异率.
- 了解这些偏见对于准确的公共卫生监测至关重要.
研究的目的:
- 为了获得英国PCR阳性和抗体患病率的代表性估计.
- 评估不考虑疫苗接种状态对调查估计的影响.
- 在不同COVID-19浪潮期间,识别不同地区和年龄组调查贡献的变化.
主要方法:
- 在英国国家COVID-19感染调查 (CIS) 数据上利用了时空回归和分层后模型.
- 从2020年12月到2022年5月,分析了PCR阳性 (超过640万次测试) 和抗体流行率 (超过190万次测试).
- 针对疫苗接种状况的估计值与分层后和不分层后的估计值进行比较.
主要成果:
- 由于没有考虑疫苗接种情况,导致PCR阳性的轻微低估.
- 在没有进行疫苗接种调整的情况下,抗体水平的大幅高估 (高达21个百分点) 发生了,特别是在疫苗接种量较低的组中.
- 在对每个COVID-19浪潮的贡献中观察到显著的区域和年龄相关变化.
结论:
- 疫苗接种状态是影响感染病调查参与和结果估计的关键因素.
- 根据疫苗接种状态进行后分层是必要的,以准确地估计人口水平的SARS-CoV-2流行率.
- 未来的传染病监测应纳入关键参与驱动因素,如疫苗接种,以减轻偏见.
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