COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.

Autor:	Lang AL; d4l Data4Life gGmbH, Potsdam, Germany., Hohmuth N; d4l Data4Life gGmbH, Potsdam, Germany.; Institute of Medical Informatics, Charité University Medicine Berlin, Berlin, Germany., Višković V; d4l Data4Life gGmbH, Potsdam, Germany., Konigorski S; Digital Health Center, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany.; Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.; Department of Statistics, Harvard University, Cambridge, MA, United States., Scholz S; Health Services Research and Health Economics, Martin Luther University Halle-Wittenberg, Halle Saale, Germany., Balzer F; Institute of Medical Informatics, Charité University Medicine Berlin, Berlin, Germany., Remschmidt C; d4l Data4Life gGmbH, Potsdam, Germany., Leistner R; Department of Gastroenterology, Infectiology and Rheumatology, Charité University Medicine Berlin, Berlin, Germany.
Jazyk:	angličtina
Zdroj:	Journal of medical Internet research [J Med Internet Res] 2024 Jun 04; Vol. 26, pp. e47070. Date of Electronic Publication: 2024 Jun 04.
DOI:	10.2196/47070
Abstrakt:	Background: The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. Objective: This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. Methods: Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. Results: We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. Conclusions: Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools. (©Anna-Lena Lang, Nils Hohmuth, Vukašin Višković, Stefan Konigorski, Stefan Scholz, Felix Balzer, Cornelius Remschmidt, Rasmus Leistner. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.06.2024.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu