Evidence map›Paper›PMID 36415459›Full record

ArticlemedRxiv : the preprint server for health sciences2022

Estimating the impact of COVID-19 vaccine allocation inequities: a modeling study.

Nicolò Gozzi, Matteo Chinazzi, Natalie E Dean, Ira M Longini, M Elizabeth Halloran, Nicola Perra, Alessandro Vespignani

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Nicolò GozziNetworks and Urban Systems Centre, University of Greenwich, UK.
Matteo ChinazziLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.
Natalie E DeanDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, USA.
Ira M LonginiDepartment of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
M Elizabeth HalloranFred Hutchinson Cancer Center, Seattle, WA, USA.
Nicola PerraSchool of Mathematical Sciences, Queen Mary University, London, UK.
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Access to COVID-19 vaccines on the global scale has been drastically impacted by structural socio-economic inequities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) sampled from all WHO regions. We focus on the first critical months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that, in this high vaccine availability scenario, more than 50% of deaths (min-max range: [56% - 99%]) that occurred in the analyzed countries could have been averted. We further consider a scenario where LMIC had similarly early access to vaccine doses as high income countries; even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [7% - 73%]) could have been averted. In the absence of equitable allocation, the model suggests that considerable additional non-pharmaceutical interventions would have been required to offset the lack of vaccines (min-max range: [15% - 75%]). Overall, our results quantify the negative impacts of vaccines inequities and call for amplified global efforts to provide better access to vaccine programs in low and lower middle income countries.

Identifiers

PMID36415459
PMCPMC9681050

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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 OpenQuestion graph.