Impact of national recommendations for routine pertussis vaccination during pregnancy on infant pertussis in Ontario, Canada: a population-based time-series study
Bibliographic record
Abstract
BACKGROUND: In February 2018, Canada's National Advisory Committee on Immunization (NACI) recommended antenatal tetanus-diphtheria-acellular pertussis (Tdap) immunization in every pregnancy regardless of previous Tdap immunization history. We examined the impact of the NACI recommendation on rates of infant pertussis in Ontario, Canada. METHODS: We conducted a population-based time-series study of all live births in Ontario between August 1, 2011 and February 28, 2020. We used interventional autoregressive integrated moving average models to examine the impact of the NACI recommendation on monthly rates of pertussis among infants ≤ 3 months of age. RESULTS: We observed 675 incident cases of pertussis among 1,368,024 infants 3 months of age or less between August 2011 and February 2020. The average monthly percent change in infant pertussis during the period up to and including publication of the NACI guidance and the period following publication were 0.0% (95% CI: -0.4-0.3%) and - 0.8% (95% CI -2.3% to -0.1%), respectively. Following interventional ARIMA modelling, publication of the NACI guidance was not associated with a statistically significant decrease in the monthly pertussis incidence trend (-0.67 cases per 100,000 infants; p = 0.73). CONCLUSION: Publication of national recommendations for antenatal Tdap immunization in every pregnancy did not significantly reduce infant pertussis rates. This may reflect the persistently low rate of antenatal vaccination following publication of the recommendations. Expanding the scope of practice of allied health care providers to include antenatal Tdap immunization and patient education regarding antenatal pertussis immunization should be considered to further optimize uptake of vaccination.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".