Trends in Canadian infant pertussis hospitalizations in the pre- and post-acellular vaccine era, 1981–2016
Bibliographic record
Abstract
OBJECTIVE: The acellular pertussis vaccine was introduced into the routine childhood immunization schedule across Canada in 1997-98 and adolescent booster doses were added between 1999 and 2005. We sought to assess the impact of these changes on infant pertussis hospitalizations and admissions to intensive care units (ICU) in Canada. METHODS: Hospitalizations with a primary diagnosis of pertussis were extracted from the Canadian Discharge Abstract Database (DAD) for cases with hospital discharge dates between 1981 and 2016 using relevant ICD-9 and ICD-10 codes. Only cases with age less than one year at time of admission were included. Disease severity was assessed by admission to ICU. Cases were categorized into two periods: pre-program implementation period (1981-1995) and the post-program implementation period (2006-2016). Incidence rates, risk ratios, and rate differences were calculated for each period and comparisons for the two periods were done using chi-squared and t-tests. Quasi Poisson analysis was used to investigate trends. RESULTS: When comparing the pre- and post-implementation periods, the average annual hospitalization rates for infants less than 1 year declined from 165.1 (95% CI 161.3, 168.9) to 33.6 (95% CI 31.6, 35.6) pertussis-related admissions per 100,000 population, with a corresponding reduction in the risk ratio of 4.9 (95% CI 4.6, 5.2). The risk of admission into an ICU was 1.58 times higher in the pre- versus post-implementation period while the highest reduction in average annual hospitalizations was 263.3 admissions per 100,000 population in infants 2 months of age. In the post-implementation period, infants less than 1 month of age had the highest average annual hospitalization rate at 126.6 (95% CI 113.1, 140.1) hospitalizations per 100,000 infants. CONCLUSION: Infant pertussis hospitalizations have reduced greatly over time. Infants under 2 months of age remain the most at-risk age group for hospitalization and admission to ICU.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".