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解决纵向社会接触研究中的调查疲劳偏差以改善大流行防范能力。

Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness.

作者信息

Dan Shozen, Ling Zhi, Chen Yu, Tegegne Joshua, Jaeger Veronika K, Karch André, Mishra Swapnil, Ratmann Oliver

机构信息

Department of Mathematics, Imperial College London, London, UK.

National University of Singapore, Saw Swee Hock School of Public Health, Singapore, Singapore.

出版信息

Sci Rep. 2025 May 23;15(1):17935. doi: 10.1038/s41598-025-02235-0.

Abstract

Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by survey fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants' engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that survey fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for survey fatigue were associated with substantially improved estimation accuracy relative to models with no survey fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach addressing survey fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.

摘要

社交接触调查是评估人群感染风险以及非药物干预措施在疾病爆发、流行和大流行期间对社会行为影响的重要工具。在新冠疫情期间进行了大量纵向社交接触调查,然而数据分析受到调查疲劳的困扰,调查疲劳是指报告的社交接触平均数量随着重复参与次数的增加以及参与者参与度随时间下降而减少的现象。利用2020年4月至2021年12月德国COVIMOD研究的数据,我们证明调查疲劳因社会人口学因素而异,并且在报告与孩子接触的父母(父母代理报告)、学生、中年人、全职工作者和个体经营者中始终最为严重。我们进一步发现,以首次参与者的数据作为金标准时,相对于未进行调查疲劳调整的模型,纳入简单逻辑函数以控制调查疲劳的统计模型的估计准确性有显著提高,并且不需要对重复参与次数设限。这些结果表明,现有的纵向接触调查数据可以通过一种易于实施的统计方法来有意义地解释,该方法可解决调查疲劳带来的混杂问题,并且包括重复参与者的纵向设计对于未来的社交接触调查设计是一个可行的选择。

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