Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
BMJ Open. 2023 Jun 8;13(6):e072650. doi: 10.1136/bmjopen-2023-072650.
The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample.
To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study.
A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms.
At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence.
Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.
SARS-CoV-2 感染波的持续监测和新病原体的出现对基于诊断的有效公共卫生监测策略构成了挑战。关于 SARS-CoV-2 感染事件和症状的纵向人群代表性研究很少。我们旨在通过对阿尔卑斯社区样本中自我报告症状的定期监测，描述 2020 年和 2021 年期间 COVID-19 大流行的演变。
为此，我们设计了一项纵向人群代表性研究，即南蒂罗尔合作卫生研究 COVID-19 研究。
2020 年 8 月之前，通过拭子和血液检测对 845 名参与者的活跃和过去感染进行回顾性调查，允许进行调整后的累积发病率估计。其中，700 名无既往感染或接种史的参与者每月随访至 2021 年 7 月，报告首次感染和症状：通过数字问卷远程评估 COVID-19 病史、社会接触、生活方式和社会人口统计学数据。通过纵向聚类和动态相关分析对时间症状轨迹和感染率进行建模。负二项式回归和随机森林分析评估了症状的相对重要性。
在基线时，SARS-CoV-2 感染的累积发病率为 1.10%（95%CI 0.51%，2.10%）。症状轨迹模拟了自我报告和确诊的感染病例。聚类分析确定了两组高频和低频症状。像发热和嗅觉丧失等症状属于低频症状。对检测阳性最具鉴别力的症状（嗅觉丧失、疲劳和关节肌肉疼痛）证实了先前的证据。
定期从人群代表性样本中进行症状跟踪是一种有效的筛选工具，可辅助实验室诊断新型病原体，本研究显示了 COVID-19 模式。综合监测系统可能受益于公民更直接地参与主动症状跟踪。
