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Record W4210405795 · doi:10.9778/cmajo.20210011

Coverage for pertussis vaccination during pregnancy with 4 models of vaccine delivery: a quasiexperimental, multicentre observational study

2022· article· en· W4210405795 on OpenAlexaffvenueabout
Yinan Li, Nicholas Brousseau, Maryse Guay, Ève Dubé, Zineb Laghdir, Isabelle Boucoiran, Bruce Tapiéro, Caroline Quach

Bibliographic record

VenueCMAJ Open · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsUniversité de MontréalUniversité LavalMcGill UniversityHôpital Charles-Le MoyneCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineVaccinationObservational studyLogistic regressionDiphtheriaTetanusPertussis vaccinePregnancyPediatricsConfidence intervalImmunizationInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Vaccination of pregnant people with a vaccine containing acellular pertussis (tetanus-diphtheria-acellular pertussis [Tdap]) has been recommended in Canada since 2018, and the evaluation of delivery models for efficient maternal Tdap administration is a priority for the Quebec Ministry of Health. We implemented 3 vaccine delivery models, in addition to the existing standard of practice model, and compared the vaccine coverage achieved by the 4 models in Quebec. METHODS: In this quasiexperimental, multicentre observational study, we recruited pregnant people at less than 21 weeks' gestation in 4 Quebec regions from April to October 2019. We compared 4 vaccine delivery models: local community service centres (centre local de services communautaires [CLSCs], baseline), family medicine groups (FMGs), obstetrics clinic and the oral glucose challenge test (OGCT). In addition to the CLSCs, 3 FMGs, 1 obstetric clinic and a hospital-based OGCT screening program participated. We determined vaccination status from a self-reported questionnaire, the Quebec Immunization Registry or medical charts. We compared model-specific (for participants recruited to a model and subsequently vaccinated within that model) and overall vaccine coverage (considering all vaccine delivery pathways) and used logistic regression to adjust for sociodemographic variables. RESULTS: Overall, 946 of 1000 recruited pregnant people were eligible for analyses. Vaccination via the FMGs achieved the highest model-specific vaccine coverage (67.8%, 95% confidence interval [CI] 60.5%-74.4%), but coverage was not significantly different from the CLSCs (63.8%, 95% CI 57.6%-69.6%). For overall vaccine coverage, the FMG (86.5%, 95% CI 80.6%-90.9%) and obstetrics models (85.9%, 95% CI 80.9%-89.7%) achieved significantly higher vaccine coverage than the CLSCs (66.3%, 95% CI 60.1%-71.9%). The OGCT model did not improve overall vaccine coverage (61.8%, 95% CI 56.1%-67.2%). INTERPRETATION: Compared with CLSCs, overall vaccine coverage was higher when Tdap was offered in FMGs or an obstetrics clinic providing prenatal care. Health professionals involved in pregnancy follow-up recommending and offering the vaccine may be a key factor in optimizing vaccine coverage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.307
Teacher spread0.245 · 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 teacher head, not a consensus.

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

Citations11
Published2022
Admission routes3
Has abstractyes

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