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Record W4386542066 · doi:10.3389/fpubh.2023.1232507

COVID-19 vaccine equity: a retrospective population-based cohort study examining primary series and first booster coverage among persons with a history of immigration and other residents of Ontario, Canada

2023· article· en· W4386542066 on OpenAlexafffundabout
Susitha Wanigaratne, Hong Lu, Sima Gandhi, Janavi Shetty, Thérèse A. Stukel, Pierre‐Philippe Piché‐Renaud, Julia Brandenberger, Samiya Abdi, Astrid Guttmann

Bibliographic record

VenueFrontiers in Public Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchMinistry of Health, British ColumbiaInstitute for Clinical Evaluative Sciences
KeywordsBooster (rocketry)ImmigrationEquity (law)MedicineCohortPopulationCohort studyRetrospective cohort studyDemographyPandemicCoronavirus disease 2019 (COVID-19)Family medicineGeographyGerontologyEnvironmental healthPolitical scienceDiseaseInfectious disease (medical specialty)Sociology

Abstract

fetched live from OpenAlex

Introduction: Immigrants were disproportionately impacted by COVID-19 and experience unique vaccination barriers. In Canada (37 million people), 23% of the population is foreign-born. Immigrants constitute 60% of the country's racialized (non-white) population and over half of immigrants reside in Ontario, the country's most populous province. Ontario had several strategies aimed at improving vaccine equity including geographic targeting of vaccine supply and clinics, as well as numerous community-led efforts. Our objectives were to (1) compare primary series vaccine coverage after it was widely available, and first booster coverage 6 months after its availability, between immigrants and other Ontario residents and (2) identify subgroups experiencing low coverage. Materials and methods: Using linked immigration and health administrative data, we conducted a retrospective population-based cohort study including all community-dwelling adults in Ontario, Canada as of January 1, 2021. We compared primary series (two-dose) vaccine coverage by September 2021, and first booster (three-dose) coverage by March 2022 among immigrants and other Ontarians, and across sociodemographic and immigration characteristics. We used multivariable log-binomial regression to estimate adjusted risk ratios (aRR). Results: Of 11,844,221 adults, 22% were immigrants. By September 2021, 72.6% of immigrants received two doses (vs. 76.4%, other Ontarians) and by March 2022 46.1% received three doses (vs. 58.2%). Across characteristics, two-dose coverage was similar or slightly lower, while three-dose coverage was much lower, among immigrants compared to other Ontarians. Across neighborhood SARS-CoV-2 risk deciles, differences in two-dose coverage were smaller in higher risk deciles and larger in the lower risk deciles; with larger differences across all deciles for three-dose coverage. Compared to other Ontarians, immigrants from Central Africa had the lowest two-dose (aRR = 0.60 [95% CI 0.58-0.61]) and three-dose coverage (aRR = 0.36 [95% CI 0.34-0.37]) followed by Eastern Europeans and Caribbeans, while Southeast Asians were more likely to receive both doses. Compared to economic immigrants, resettled refugees and successful asylum-claimants had the lowest three-dose coverage (aRR = 0.68 [95% CI 0.68-0.68] and aRR = 0.78 [95% CI 0.77-0.78], respectively). Conclusion: Two dose coverage was more equitable than 3. Differences by immigrant region of birth were substantial. Community-engaged approaches should be re-invigorated to close gaps and promote the bivalent booster.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.076
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
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 head0.030
GPT teacher head0.279
Teacher spread0.249 · 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

Citations9
Published2023
Admission routes3
Has abstractyes

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