Morales Ivonne, Nguyen Van Kính, Abd El Aziz Mirna, Sultani Ayten, Bärnighausen Till, Becher Heiko, Ciesek Sandra, Kampmann Beate, Lange Berit, Rupp Jan, Scheithauer Simone, Ward Helen, Karch André, Denkinger Claudia M
Heidelberg Institute for Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany.
Department of Infectious Disease and Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany.
Euro Surveill. 2025 Jun;30(25). doi: 10.2807/1560-7917.ES.2025.30.25.2400255.
Establishing population-based cohorts is indispensable for effective epidemic prevention, preparedness and response. Existing passive surveillance systems face limitations in their capacity to promptly provide representative data for estimating disease burden and modelling disease transmission. This perspective paper introduces a framework for establishing a dynamic and responsive nationally representative population-based cohort, with Germany as an example country. We emphasise the need for comprehensive demographic representation, innovative strategies to address participant attrition, efficient data collection and testing using digital tools, as well as novel data integration and analysis methods. Financial considerations and cost estimates for cohort establishment are discussed, highlighting potential cost savings through integration with existing research infrastructures and digital approaches. The framework outlined for creating, operating and integrating the cohort within the broader epidemiological landscape illustrates the potential of a population-based cohort to offer timely, evidence-based insights for robust public health interventions during both epidemics and pandemics, as well as during inter-epidemic periods.
建立基于人群的队列对于有效的疫情预防、防范和应对至关重要。现有的被动监测系统在迅速提供具有代表性的数据以估计疾病负担和模拟疾病传播方面存在局限性。本观点论文以德国为例，介绍了一个建立动态且响应迅速的全国代表性基于人群队列的框架。我们强调需要全面的人口统计学代表性、解决参与者流失问题的创新策略、使用数字工具进行高效的数据收集和检测，以及新颖的数据整合和分析方法。讨论了队列建立的财务考量和成本估算，强调了通过与现有研究基础设施和数字方法整合实现潜在成本节约的可能性。在更广泛的流行病学背景下创建、运营和整合队列所概述的框架，展示了基于人群的队列在疫情和大流行期间以及疫情间期为强有力的公共卫生干预措施及时提供循证见解的潜力。
