ArticlePloS one2022
A method for estimating the impact of new vaccine technologies on vaccination coverage rates.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vaccines are one of the most cost-effective tools for improving human health and well-being. The impact of a vaccine on population health is partly determined by its coverage rate, the proportion of eligible individuals vaccinated. Coverage rate is a function of the vaccine presentation and the population in which that presentation is deployed. This population includes not only the individuals vaccinated, but also the logistics and healthcare systems responsible for vaccine delivery. Because vaccine coverage rates remain below targets in many settings, vaccine manufacturers and purchasers have a shared interest in better understanding the relationship between vaccine presentation, population characteristics, and coverage rate. While there have been some efforts to describe this relationship, existing research and tools are limited in their ability to quantify coverage rate changes across a broad set of antigens, vaccine presentations, and geographies. In this article, we present a method for estimating the impact of improved vaccine technologies on vaccination coverage rates. It is designed for use with low- and middle-income country vaccination programs. This method uses publicly available data and simple calculations based on probability theory to generate coverage rate values. We first present the conceptual framework and mathematical approach. Using a Microsoft Excel-based implementation, we then apply the method to a vaccine technology in early-stage development: micro-array patch for a measles-rubella vaccine (MR-MAP). Example outputs indicate that a complete switch from the current subcutaneous presentation to MR-MAP in the 73 countries ever eligible for Gavi support would increase overall vaccination coverage by 3.0-4.9 percentage points depending on the final characteristics of the MR-MAP. This change equates to an additional 2.6-4.2 million children vaccinated per year. Our method can be readily extended to other antigens and vaccine technologies to provide quick, low-cost estimates of coverage impact. As vaccine manufacturers and purchasers face increasingly complex decisions, such estimates could facilitate objective comparisons between options and help these decision makers obtain the most value for money.
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Registered trials
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