Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials

BACKGROUND: The COVID-19 vaccination program, which uses various types of vaccines, has been applied since the beginning of 2021. However, the efficacy in the context of seroconversion rate remains unclear. OBJECTIVE: To assess the seroconversion rates among different COVID-19 vaccines using a netw...

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Main Author: Hindom, Maria
Format: Article PeerReviewed
Language:English
Published: F1000 2022
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Online Access:https://repository.ugm.ac.id/283779/1/37b7414f-937f-423d-bf44-122cb83a3ff0_110281_-_jonny_fajar.pdf
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spelling id-ugm-repo.2837792023-11-22T06:50:58Z https://repository.ugm.ac.id/283779/ Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials Hindom, Maria Humoural Immunology and Immunochemistry Immunology not elsewhere classified BACKGROUND: The COVID-19 vaccination program, which uses various types of vaccines, has been applied since the beginning of 2021. However, the efficacy in the context of seroconversion rate remains unclear. OBJECTIVE: To assess the seroconversion rates among different COVID-19 vaccines using a network meta-analysis approach. METHODS: A network meta-analysis of randomized controlled trials (RCTs) was conducted during the study period. Data of interest, such as seroconversion rate and the type of COVID-19 vaccine, were extracted from each study. The analysis was performed using single-arm analysis by calculating the cumulative seroconversion rate. A network meta-analysis was conducted using the Bayesian method. RESULTS: A total of 31 RCTs were included in our analysis. Our pooled calculation revealed that the seroconversion rates of inactivated messenger ribonucleic acid (mRNA), protein subunit, and vector COVID-19 vaccines during the follow-up periods were 93.2%, 93.9%, 65.3%, and 54.7%, respectively, at ≤ 15 days; 96.0%, 94.8%, 91.2%, and 89.7%, respectively, between days 16–30; and 98.5%, 98.6%, 98.5%, and 96.2%, respectively, between days 31–60.The indirect comparison revealed that in the follow-up periods of ≤ 15 and 16–30 days, the inactivated and mRNA COVID-19 vaccines had superior seroconversion rates compared with those of the protein subunit and vector vaccines. In the follow-up period of 31–60 days, the highest seroconversion rates were found in the inactivated, mRNA, and protein subunit COVID-19 vaccines. CONCLUSION: This study provides valuable information regarding the comparison of seroconversion rates of COVID-19 vaccines. F1000 2022 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/283779/1/37b7414f-937f-423d-bf44-122cb83a3ff0_110281_-_jonny_fajar.pdf Hindom, Maria (2022) Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials. F1000Research, 11 (299). pp. 1-19. https://f1000research.com/ 10.12688/f1000research.110281.1
institution Universitas Gadjah Mada
building UGM Library
continent Asia
country Indonesia
Indonesia
content_provider UGM Library
collection Repository Civitas UGM
language English
topic Humoural Immunology and Immunochemistry
Immunology not elsewhere classified
spellingShingle Humoural Immunology and Immunochemistry
Immunology not elsewhere classified
Hindom, Maria
Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials
description BACKGROUND: The COVID-19 vaccination program, which uses various types of vaccines, has been applied since the beginning of 2021. However, the efficacy in the context of seroconversion rate remains unclear. OBJECTIVE: To assess the seroconversion rates among different COVID-19 vaccines using a network meta-analysis approach. METHODS: A network meta-analysis of randomized controlled trials (RCTs) was conducted during the study period. Data of interest, such as seroconversion rate and the type of COVID-19 vaccine, were extracted from each study. The analysis was performed using single-arm analysis by calculating the cumulative seroconversion rate. A network meta-analysis was conducted using the Bayesian method. RESULTS: A total of 31 RCTs were included in our analysis. Our pooled calculation revealed that the seroconversion rates of inactivated messenger ribonucleic acid (mRNA), protein subunit, and vector COVID-19 vaccines during the follow-up periods were 93.2%, 93.9%, 65.3%, and 54.7%, respectively, at ≤ 15 days; 96.0%, 94.8%, 91.2%, and 89.7%, respectively, between days 16–30; and 98.5%, 98.6%, 98.5%, and 96.2%, respectively, between days 31–60.The indirect comparison revealed that in the follow-up periods of ≤ 15 and 16–30 days, the inactivated and mRNA COVID-19 vaccines had superior seroconversion rates compared with those of the protein subunit and vector vaccines. In the follow-up period of 31–60 days, the highest seroconversion rates were found in the inactivated, mRNA, and protein subunit COVID-19 vaccines. CONCLUSION: This study provides valuable information regarding the comparison of seroconversion rates of COVID-19 vaccines.
format Article
PeerReviewed
author Hindom, Maria
author_facet Hindom, Maria
author_sort Hindom, Maria
title Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials
title_short Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials
title_full Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials
title_fullStr Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials
title_full_unstemmed Seroconversion rates among different designs of COVID-19 vaccines: a network meta-analysis of randomized controlled trials
title_sort seroconversion rates among different designs of covid-19 vaccines: a network meta-analysis of randomized controlled trials
publisher F1000
publishDate 2022
url https://repository.ugm.ac.id/283779/1/37b7414f-937f-423d-bf44-122cb83a3ff0_110281_-_jonny_fajar.pdf
https://repository.ugm.ac.id/283779/
https://f1000research.com/
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