SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

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dc.contributor.authorCOVIDSurg Collaborative
dc.contributor.authorGlobalSurg Collaborative
dc.date.accessioned2021-12-31T15:29:08Z
dc.date.available2021-12-31T15:29:08Z
dc.date.issued2021-09-27
dc.descriptionScholarly articlesen_US
dc.description.abstractBackground: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.en_US
dc.identifier.citationGlobalSurg Collaborative: Adesoji Ademuyiwa, Bosede Afolabi, Peter Ajayi, Opeyemi Akinajo, Felix Alakaloko, Oluwole Atoyebi, Orimisan Belie, Chris Bode, Ihediwa Chibuike George, Olumide Elebute, Francis Ezenwankwo, Adedeji Fatuga, Oluwaseun Ladipo-Ajayi, Ayomide Makanjuola, Christian Makwe, Bolaji Mofikoya, Ephraim Ohazurike, Rufus Wale Ojewola, Kehinde Okunade, Adeyemi Okunowo, Thomas Olagboyega Olajide, Oluwafemi Oni, Justina Seyi-Olajide, Kehinde Tijani, Andrew Ugburo, Aloy Okechukwu Ugwu (Collaborators from Lagos University Teaching Hospital, Idi Araba). SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study. Brit J Surg 2021;1-8. znab101, https://doi.org/10.1093/bjs/znab101en_US
dc.identifier.otherhttps://doi.org/10.1093/bjs/znab101
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/9847
dc.language.isoenen_US
dc.publisherBritish Journal of Surgeryen_US
dc.subjectSurgical proceduresen_US
dc.subjectCommunityen_US
dc.subjectVacinesen_US
dc.subjectVaccinationen_US
dc.subjectMortalityen_US
dc.subjectResearch Subject Categories::MEDICINE::Surgeryen_US
dc.titleSARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort studyen_US
dc.typeArticleen_US
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