Evidence mapPaperPMID 34847950Full record

ReviewBMC medicine2021

Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review.

Nuru Saadi, Y-Ling Chi, Srobana Ghosh, Rosalind M Eggo, Ciara V McCarthy, Matthew Quaife, Jeanette Dawa, Mark Jit, Anna Vassall

Abstract readReview
In one paragraph

Review in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Review
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  4. Comparative evaluation of behavioral epidemic models using COVID-19 data.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  6. Review
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  12. Review
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  20. Asymptotic Analysis of Optimal Vaccination Policies.Bulletin of mathematical biology · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nuru SaadiDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK. Nuru.Saadi@lshtm.ac.uk.ORCID 0000-0001-5299-5052
Y-Ling ChiInternational Decision Support Initiative, Center for Global Development, London, UK.
Srobana GhoshInternational Decision Support Initiative, Center for Global Development, London, UK.
Rosalind M EggoCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Ciara V McCarthyCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Matthew QuaifeCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Jeanette DawaWashington State University - Global Health Program, Nairobi, Kenya.
Mark Jit *Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Anna Vassall *Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.

Funding

Medical Research Council MC_PC 19065Medical Research Council MC_PC_19065Medical Research Council MR/S003975/1
6 · The paper itself

Abstract

backgroundHow best to prioritise COVID-19 vaccination within and between countries has been a public health and an ethical challenge for decision-makers globally. We reviewed epidemiological and economic modelling evidence on population priority groups to minimise COVID-19 mortality, transmission, and morbidity outcomes.

methodsWe searched the National Institute of Health iSearch COVID-19 Portfolio (a database of peer-reviewed and pre-print articles), Econlit, the Centre for Economic Policy Research, and the National Bureau of Economic Research for mathematical modelling studies evaluating the impact of prioritising COVID-19 vaccination to population target groups. The first search was conducted on March 3, 2021, and an updated search on the LMIC literature was conducted from March 3, 2021, to September 24, 2021. We narratively synthesised the main study conclusions on prioritisation and the conditions under which the conclusions changed.

resultsThe initial search identified 1820 studies and 36 studies met the inclusion criteria. The updated search on LMIC literature identified 7 more studies. 43 studies in total were narratively synthesised. 74% of studies described outcomes in high-income countries (single and multi-country). We found that for countries seeking to minimise deaths, prioritising vaccination of senior adults was the optimal strategy and for countries seeking to minimise cases the young were prioritised. There were several exceptions to the main conclusion, notably that reductions in deaths could be increased if groups at high risk of both transmission and death could be further identified. Findings were also sensitive to the level of vaccine coverage.

conclusionThe evidence supports WHO SAGE recommendations on COVID-19 vaccine prioritisation. There is, however, an evidence gap on optimal prioritisation for low- and middle-income countries, studies that included an economic evaluation, and studies that explore prioritisation strategies if the aim is to reduce overall health burden including morbidity.

Indexed as

COVID-19COVID-19 VaccinesAdultHumansPublic HealthSARS-CoV-2VaccinationCOVID-19 VaccinesCOVID-19, Vaccination, Mathematical modelling

Identifiers

PMID34847950
PMCPMC8632563

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.