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Record W4391850176 · doi:10.1016/j.vaccine.2024.01.079

A population-based, province-wide, record-linkage interrupted time series analysis of impact of the universal seasonal influenza vaccination policy on seasonal influenza vaccine uptake among 5–64-year-olds in the province of Manitoba, Canada

2024· article· en· W4391850176 on OpenAlexafffundabout
George N. Okoli, Christiaan H. Righolt, Geng Zhang, Paul Van Caeseele, I fan Kuo, Silvia Alessi‐Severini, Salaheddin M. Mahmud

Bibliographic record

VenueVaccine · 2024
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsGovernment of ManitobaGovernment of British ColumbiaMinistry of HealthUniversity of ManitobaManitoba HealthGeorge & Fay Yee Centre for Healthcare Innovation
FundersManitoba Centre for Health Policy, University of ManitobaCanada Research Chairs
KeywordsDemographyVaccinationPopulationSeasonal influenzaResidenceMedicineTrend analysisGeographyEnvironmental healthImmunologyDiseaseInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Internal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Universal seasonal influenza vaccination policy (USIVP) was introduced in Manitoba, Canada in 2010. Its impact on seasonal influenza vaccine (SIV) uptake remains underexplored. METHODS: We used population-wide data from Manitoba to assess the impact of the USIVP on SIV uptake. The study covered twenty influenza seasons (2000/01-2019/20). We summarized SIV uptake for influenza seasons before and after the USIVP. Utilizing a single-group interrupted time series analysis and appropriately accounting for autocorrelation, we estimated absolute change and annual trend in SIV uptake percentages among 5-17-, 18-44-, and 45-64-year-olds across strata of certain population socioeconomic and health-related characteristics following the USIVP. RESULTS: Average SIV uptake percentage in all age groups was significantly higher after compared with before the USIVP. Following the USIVP, there was no significant absolute change in SIV uptake percentage among 18-44- and 45-64-year-olds overall; however, a significant decrease was observed among 18-44-year-old males in the higher income quintiles, across healthcare utilization, and in some regions of residence. A significant increase was observed among 5-17-year-olds in the lowest income quintiles, in Northern Manitoba, and among those with less healthcare utilization, and no chronic disease. Overall, there was mostly no significant annual trend in SIV uptake percentage among 18-44-year-olds, and while a significant upward and downward trend was observed among 5-17-year-olds and 45-64-year-olds, respectively, a significant downward trend was observed across all strata of population characteristics within all age groups in Northern Manitoba. CONCLUSIONS: The USIVP in Manitoba was followed by an absolute increase in SIV uptake percentage only in some socioeconomically disadvantaged subpopulations among 5-17-year-olds. While there was mostly an upward annual trend in SIV uptake percentage among 5-17-year-olds, a downward trend was observed among 45-64-year-olds and across all age groups and subpopulations in socioeconomically disadvantaged Northern Manitoba. These findings are novel for Manitoba and require investigation and public health attention.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.020
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.007
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.024
GPT teacher head0.316
Teacher spread0.293 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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