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Tailoring COVID-19 vaccination strategies in high-seroprevalence settings: Insights from Ethiopia.
Vaccines 12:745 (2024)
This study aimed to retrospectively assess the cost-effectiveness of various COVID-19 vaccination strategies in Ethiopia. It involved healthcare workers (HCWs) and community participants; and was conducted through interviews and serological tests. Local SARS-CoV-2 variants and seroprevalence rates, as well as national COVID-19 reports and vaccination status were also analyzed. A cost-effectiveness analysis was performed to determine the most economical vaccination strategies in settings with limited vaccine access and high SARS-CoV-2 seroprevalence. Before the arrival of the vaccines, 65% of HCWs had antibodies against SARS-CoV-2, indicating prior exposure to the virus. Individuals with prior infection exhibited a greater antibody response to COVID-19 vaccines and experienced fewer new infections compared to those without prior infection, regardless of vaccination status (5% vs. 24%, p < 0.001 for vaccinated; 3% vs. 48%, p < 0.001 for unvaccinated). The cost-effectiveness analysis indicated that a single-dose vaccination strategy is optimal in settings with high underlying seroprevalence and limited vaccine availability. This study underscores the need for pragmatic vaccination strategies tailored to local contexts, particularly in high-seroprevalence regions, to maximize vaccine impact and minimize the spread of COVID-19. Implementing a targeted approach based on local seroprevalence information could have helped Ethiopia achieve higher vaccination rates and prevent subsequent outbreaks.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Covid-19 Vaccine ; Ethiopia ; Sars-cov-2 Infection ; Cost-effectiveness ; Hybrid Immunity ; Low-income Setting; Sars-cov-2; Workers; Cohort
e-ISSN
2076-393X
Zeitschrift
Vaccines
Quellenangaben
Band: 12,
Heft: 7,
Artikelnummer: 745
Verlag
MDPI
Verlagsort
Basel
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
Förderungen
Jimma University
University of Bonn
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
EU
German Federal Ministry of Education and Research
Bavarian State Ministry of Sciences, Research, and the Arts
University of Bonn
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
EU
German Federal Ministry of Education and Research
Bavarian State Ministry of Sciences, Research, and the Arts