Data-driven Targeting of COVID-19 Vaccination Programs: An Analysis of the Evidence on Impact, Implementation, Ethics and Equity

dc.contributor.authorMcLaren, Zoe
dc.date.accessioned2023-02-10T19:22:42Z
dc.date.available2023-02-10T19:22:42Z
dc.date.issued2023-01-13
dc.description.abstractThe data-driven targeting of COVID-19 vaccination programs is a major determinant of the ongoing toll of COVID-19. Targeting of access to, outreach about and incentives for vaccination can reduce total deaths by 20-50 percent relative to a first-come-first-served allocation. This piece performs a systematic review of the modeling literature on the relative benefits of targeting different groups for vaccination and evaluates the broader scholarly evidence – including analyses of real-world challenges around implementation, equity, and other ethical considerations – to guide vaccination targeting strategies. Three-quarters of the modeling studies reviewed concluded that the most effective way to save lives, reduce hospitalizations and mitigate the ongoing toll of COVID-19 is to target vaccination program resources to high-risk people directly rather than reducing transmission by targeting low-risk people. There is compelling evidence that defining vulnerability based on a combination of age, occupation, underlying medical conditions and geographic location is more effective than targeting based on age alone. Incorporating measures of economic vulnerability into the prioritization scheme not only reduces mortality but also improves equity. The data-driven targeting of COVID-19 vaccination program resources benefits everyone by efficiently mitigating the worst effects of the pandemic until the threat of COVID-19 has passed.en_US
dc.description.sponsorshipMelody Pinamang and Yetunde Oshagbemi provided excellent research assistance. The author has no competing interests or funding sources to report.en_US
dc.description.urihttps://www.medrxiv.org/content/10.1101/2023.01.12.23284481v1en_US
dc.format.extent23 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2vocj-qrnm
dc.identifier.urihttps://doi.org/10.1101/2023.01.12.23284481
dc.identifier.urihttp://hdl.handle.net/11603/26800
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC School of Public Policy Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.titleData-driven Targeting of COVID-19 Vaccination Programs: An Analysis of the Evidence on Impact, Implementation, Ethics and Equityen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-2515-9731en_US

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