Evidence map›Paper›PMID 39702068›Full record

ArticleBMC public health2024

Factors associated with COVID-19 vaccination rates in countries with different income levels: a panel analysis.

Jeong-Yeon Cho, Sun-Hong Kwon, Jong-Seop Lee, Jinhyung Lee, Jong-Hwan Lee, Yuna Chae, Eui-Kyung Lee

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Jeong-Yeon Cho *School of Pharmacy, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea.
Sun-Hong Kwon *School of Pharmacy, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea.
Jong-Seop LeeSchool of Pharmacy, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea.
Jinhyung LeeDepartment of Economics, Sungkyunkwan University, Seoul, Republic of Korea.
Jong-Hwan LeeSchool of Pharmacy, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea.
Yuna ChaeSchool of Pharmacy, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea.
Eui-Kyung LeeSchool of Pharmacy, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea. ekyung@skku.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionVaccines against coronavirus disease (COVID-19) are being developed and supplied at an unprecedented rate. However, disparities in income levels among countries has influenced the supply and vaccination rate. This imbalance poses a potential risk factor, especially if vaccine-resistant variants emerge and the pandemic persists. To effectively combat a global pandemic such as COVID-19, understanding the key factors that influence vaccination rates worldwide is essential. This study utilizes cross-country panel regression to examine the factors associated with vaccination rates in countries at different income levels.

methodsWe analyzed weekly vaccination rates in relation to several COVID-related variables, including government suppression policies, vaccination coverage, and search trends from Google Trends. The data consistently spanned from March 2021 to February 2022. Random-effects panel regression models were employed to identify factors linked to weekly vaccination rates by income level. Independent variables included disease status, country characteristics, policy variables, and search trends.

resultsSignificant disparities in weekly vaccination rates were observed between income-level groups. High-income countries experienced considerable fluctuations during outbreaks, whereas, low- and lower-middle-income countries demonstrated steady increase over time. The random-effects model, stratified by income level, showed that the vaccination coverage and search trend for "COVID-19 vaccine" were commonly associated with higher vaccination rates across all income groups. However, other factors varied based on income level, and gross domestic product per capita was not significant in the regression based on income level.

conclusionVaccination rate and their associated factors differed across income levels. There is no universal strategy for boosting vaccination rates during a pandemic. Consequently, country specific approaches, including promotional programs to raise awareness and interest in vaccination, are essential for preparing for future pandemics.

Indexed as

COVID-19COVID-19 VaccinesIncomeDeveloping CountriesGlobal HealthHumansSARS-CoV-2VaccinationVaccination CoverageCOVID-19 VaccinesCommunicable disease controlCOVID-19Public health

Identifiers

PMID39702068
PMCPMC11660815

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LicenceCC BY-NC-ND
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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.