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Record W4378472349 · doi:10.2196/42143

The Impact of Priority Settings at the Start of COVID-19 Mass Vaccination on Subsequent Vaccine Uptake in Japan: One-Year Prospective Cohort Study

2023· article· en· W4378472349 on OpenAlexvenueno aff
Daisuke Hori, Tsukasa Takahashi, Akihiko Ozaki, Takahiro Tabuchi

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceUniversity of TsukubaKanagawa UniversityJapan Agency for Medical Research and Development
KeywordsCoronavirus disease 2019 (COVID-19)Mass vaccinationEnvironmental healthVaccination2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineVirologyProspective cohort studyOutbreakDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Distributing COVID-19 vaccines to the public was an important task for the governments of each country. Because of various limitations, priority settings for vaccination were determined at the time of mass vaccination. However, trends between vaccine intention and uptake, as well as reasons for getting vaccinated or not getting vaccinated, among these groups were understudied, undermining verification of the legitimacy of priority selection. OBJECTIVE: This study aims to illustrate a trend from prior COVID-19 vaccine intention, when the vaccine was not available, to the actual uptake within 1 year when all residents had access to the vaccine, to illustrate a change of reason for getting vaccinated or not getting vaccinated and to examine whether priority settings predicted subsequent vaccination uptake. METHODS: Prospective cohort, web-based, self-administered surveys were conducted in Japan at 3 time points: February 2021, September to October 2021, and February 2022. In total, 13,555 participants (age: mean 53.1, SD 15.9 years) provided valid responses, with a 52.1% follow-up rate. On the basis of the information obtained in February 2021, we identified 3 types of priority groups: health care workers (n=831), people aged ≥65 years (n=4048), and those aged 18 to 64 years with underlying medical conditions (n=1659). The remaining patients were treated as nonpriority (n=7017). Modified Poisson regression analysis with a robust error estimated the risk ratio for COVID-19 vaccine uptake after adjusting for socioeconomic background, health-seeking behavior, attitude toward vaccines, and COVID-19 infection history. RESULTS: In February 2021, a total of 5182 out of 13,555 (38.23%) respondents expressed their intention to get vaccinated. In February 2022, a total of 1570 out of 13,555 (11.6%) respondents completed the third dose and 10,589 (78.1%) respondents completed the second dose. Prior vaccine intention and subsequent vaccine coverage rates were higher in the priority groups. Protection of themselves and their families from potential infection was the most frequent reason for getting vaccinated, whereas concern about side effects was the most frequent reason for hesitation across the groups. Risk ratios for received, reserved, or intended for vaccination in February 2022 were 1.05 (95% CI 1.03-1.07) for the health care worker group, 1.02 (95% CI 1.005-1.03) for the older adult group, and 1.01 (95% CI 0.999-1.03) for the preexisting conditions group compared with the nonpriority group. Prior vaccine intention and confidence in vaccines were strong predictors of vaccine uptake. CONCLUSIONS: The priority settings at the start of the COVID-19 vaccination program had a significant impact on vaccine coverage after 1 year. The priority group for vaccination achieved higher vaccination coverage in February 2022. There was room for improvement among the nonpriority group. The findings of this study are essential for policy makers in Japan and other countries to develop effective vaccination strategies for future pandemics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.369
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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