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考虑抽样偏差对比不同年龄组的新冠病毒(SARS-CoV-2)发病率——使用比利时2021年秋季的检测数据

Comparing the incidence of SARS-CoV-2 across age groups considering sampling biases - use of testing data of autumn 2021 in Belgium.

作者信息

Lajot Adrien, Cornelissen Laura, Van Cauteren Dieter, Meurisse Marjan, Brondeel Ruben, Dupont-Gillain Christine

机构信息

Department of Epidemiology and public health, Sciensano, Brussels, Belgium.

Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.

出版信息

Arch Public Health. 2023 Apr 23;81(1):66. doi: 10.1186/s13690-023-01072-9.

Abstract

BACKGROUND

To design efficient mitigation measures against COVID-19, understanding the transmission dynamics between different age groups was crucial. The role of children in the pandemic has been intensely debated and involves both scientific and ethical questions. To design efficient age-targeted non-pharmaceutical interventions (NPI), a good view of the incidence of the different age groups was needed. However, using Belgian testing data to infer real incidence (RI) from observed incidence (OI) or positivity ratio (PR) was not trivial.

METHODS

Based on Belgian testing data collected during the Delta wave of Autumn 2021, we compared the use of different estimators of RI and analyzed their effect on comparisons between age groups.

RESULTS

We found that the RI estimator's choice strongly influences the comparison between age groups.

CONCLUSION

The widespread implementation of testing campaigns using representative population samples could help to avoid pitfalls related to the current testing strategy in Belgium and worldwide. This approach would also allow a better comparison of the data from different countries while reducing biases arising from the specificities of each surveillance system.

摘要

背景

为设计针对新冠疫情的有效缓解措施,了解不同年龄组之间的传播动态至关重要。儿童在疫情中的作用一直备受激烈争论,涉及科学和伦理问题。为设计有效的针对特定年龄的非药物干预措施(NPI),需要清楚了解不同年龄组的发病率。然而,利用比利时的检测数据从观察到的发病率(OI)或阳性率(PR)推断实际发病率(RI)并非易事。

方法

基于2021年秋季德尔塔浪潮期间收集的比利时检测数据,我们比较了不同RI估计方法的使用情况,并分析了它们对年龄组间比较的影响。

结果

我们发现RI估计方法的选择对年龄组间的比较有很大影响。

结论

使用具有代表性的人群样本广泛开展检测活动,有助于避免比利时乃至全球当前检测策略相关的陷阱。这种方法还能更好地比较不同国家的数据,同时减少每个监测系统特殊性所产生的偏差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/876d/10122804/4066a9291a71/13690_2023_1072_Fig1_HTML.jpg

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