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代表 COVID-19 的慕尼黑队列(KoCo19):从大流行开始到德尔塔病毒变异。

The representative COVID-19 cohort Munich (KoCo19): from the beginning of the pandemic to the Delta virus variant.

机构信息

Institute of Computational Biology, Helmholtz Munich, German Research Centre for Environmental Health, 85764, Neuherberg, Germany.

Core Facility Statistical Consulting, Helmholtz Munich, German Research Centre for Environmental Health, 85764, Neuherberg, Germany.

出版信息

BMC Infect Dis. 2023 Jul 13;23(1):466. doi: 10.1186/s12879-023-08435-1.

Abstract

BACKGROUND

Population-based serological studies allow to estimate prevalence of SARS-CoV-2 infections despite a substantial number of mild or asymptomatic disease courses. This became even more relevant for decision making after vaccination started. The KoCo19 cohort tracks the pandemic progress in the Munich general population for over two years, setting it apart in Europe.

METHODS

Recruitment occurred during the initial pandemic wave, including 5313 participants above 13 years from private households in Munich. Four follow-ups were held at crucial times of the pandemic, with response rates of at least 70%. Participants filled questionnaires on socio-demographics and potential risk factors of infection. From Follow-up 2, information on SARS-CoV-2 vaccination was added. SARS-CoV-2 antibody status was measured using the Roche Elecsys® Anti-SARS-CoV-2 anti-N assay (indicating previous infection) and the Roche Elecsys® Anti-SARS-CoV-2 anti-S assay (indicating previous infection and/or vaccination). This allowed us to distinguish between sources of acquired antibodies.

RESULTS

The SARS-CoV-2 estimated cumulative sero-prevalence increased from 1.6% (1.1-2.1%) in May 2020 to 14.5% (12.7-16.2%) in November 2021. Underreporting with respect to official numbers fluctuated with testing policies and capacities, becoming a factor of more than two during the second half of 2021. Simultaneously, the vaccination campaign against the SARS-CoV-2 virus increased the percentage of the Munich population having antibodies, with 86.8% (85.5-87.9%) having developed anti-S and/or anti-N in November 2021. Incidence rates for infections after (BTI) and without previous vaccination (INS) differed (ratio INS/BTI of 2.1, 0.7-3.6). However, the prevalence of infections was higher in the non-vaccinated population than in the vaccinated one. Considering the whole follow-up time, being born outside Germany, working in a high-risk job and living area per inhabitant were identified as risk factors for infection, while other socio-demographic and health-related variables were not. Although we obtained significant within-household clustering of SARS-CoV-2 cases, no further geospatial clustering was found.

CONCLUSIONS

Vaccination increased the coverage of the Munich population presenting SARS-CoV-2 antibodies, but breakthrough infections contribute to community spread. As underreporting stays relevant over time, infections can go undetected, so non-pharmaceutical measures are crucial, particularly for highly contagious strains like Omicron.

摘要

背景

基于人群的血清学研究可以估算 SARS-CoV-2 感染的流行率,尽管有相当数量的轻症或无症状疾病。在接种疫苗后,这对于决策制定变得更加重要。KoCo19 队列在慕尼黑普通人群中跟踪大流行情况已超过两年,在欧洲独树一帜。

方法

招募发生在大流行初期,包括来自慕尼黑私人家庭的 5313 名 13 岁以上的参与者。在大流行的关键时期进行了四次随访,响应率至少为 70%。参与者填写了关于社会人口统计学和感染潜在危险因素的问卷。从第二次随访开始,增加了关于 SARS-CoV-2 疫苗接种的信息。使用罗氏 Elecsys® Anti-SARS-CoV-2 抗-N 检测(表明既往感染)和罗氏 Elecsys® Anti-SARS-CoV-2 抗-S 检测(表明既往感染和/或接种疫苗)来测量 SARS-CoV-2 抗体状态。这使我们能够区分获得抗体的来源。

结果

SARS-CoV-2 估计的累积血清阳性率从 2020 年 5 月的 1.6%(1.1-2.1%)上升到 2021 年 11 月的 14.5%(12.7-16.2%)。相对于官方数字的漏报情况随着检测政策和能力的波动而波动,在 2021 年下半年成为两倍以上的因素。与此同时,针对 SARS-CoV-2 病毒的疫苗接种运动增加了具有抗体的慕尼黑人群的比例,2021 年 11 月,86.8%(85.5-87.9%)的人产生了抗-S 和/或抗-N。接种疫苗后(BTI)和未接种疫苗(INS)的感染发生率不同(INS/BTI 比值为 2.1,0.7-3.6)。然而,未接种疫苗人群的感染率高于接种疫苗人群。考虑到整个随访时间,出生在德国以外、从事高风险工作和每个居民的居住面积被确定为感染的危险因素,而其他社会人口统计学和健康相关变量则不是。尽管我们发现 SARS-CoV-2 病例在家庭内存在显著的聚集性,但没有发现进一步的地理空间聚集性。

结论

疫苗接种增加了具有 SARS-CoV-2 抗体的慕尼黑人群的覆盖率,但突破性感染会导致社区传播。随着时间的推移,漏报情况仍然存在,感染可能无法被发现,因此非药物措施至关重要,特别是对于像奥密克戎这样具有高度传染性的菌株。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b806/10339498/98d8ef423470/12879_2023_8435_Fig1_HTML.jpg

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