Impact of a multicomponent intervention on maternal Tdap vaccination coverage during a major pertussis resurgence in the province of Quebec, Canada: A quasi-experimental study
Bibliographic record
Abstract
Amid an unprecedented pertussis resurgence in 2024, this study aimed to assess whether a multicomponent intervention implemented in the Chaudière-Appalaches (C-A) region in Quebec, Canada, increased tetanus-diphtheria-acellular pertussis (Tdap) vaccination coverage during pregnancy. We used a quasi-experimental design to compare trends in vaccination coverage from 2022–2024 in the intervention region (C-A) with both the rest of Quebec (RoQ) and seven more comparable regions (SMCR) in Quebec. To assess the intervention’s impact while accounting for seasonality, Difference-in-Differences estimates were computed using a quasi-binomial logistic regression model. Throughout the study period, coverage in C-A remained high (70–90%). After the intervention, pertussis vaccination coverage during pregnancy increased more in the C-A region than in the control regions (6.7%, 3.4%, and 2.0% for C-A, RoQ and SMCR, respectively, p < .001). The relative increase in vaccination coverage was also higher in the C-A region than in the control groups. The intervention led to a 4.2 additional percentage points increase in C-A compared to RoQ (95%CI: −0.4–8.8), and a statistically significant 5.8 additional percentage points increase compared to SMCR (95%CI: 1.5–10.1). These results suggest that the multicomponent intervention may have boosted maternal Tdap vaccination coverage during this pertussis outbreak, preventing severe outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".