Revisiting the epidemiology of pertussis in Canada, 1924–2015: a literature review, evidence synthesis, and modeling study
Bibliographic record
Abstract
Abstract Background Disease surveillance is central to the public health understanding of pertussis epidemiology. In Canada, public reporting practices have significantly changed over time, creating challenges in accurately characterizing pertussis epidemiology. Debate has emerged over whether pertussis resurged after the introduction of adsorbed pertussis vaccines (1981–1985), and if the incidence fell to its pre-1985 after the introduction of acellular pertussis vaccines (1997–1998). Here, we aim to assemble a unified picture of pertussis disease incidence in Canada. Methods Using publicly available pertussis surveillance reports, we collected, analyzed and presented Canadian pertussis data for the period (1924–2015), encompassing the pre-vaccine era, introduction of vaccine, changes to vaccine technology, and the introduction of booster doses. Information on age began to be reported since 1952, but age reporting practices (full, partial or no ages) have evolved over time, and varied across provinces/territories. For those cases reported without age each year, we impute an age distribution by assuming it follows that of the age-reported cases. Results Below the age of 20 years, the adjusted age-specific incidence from 1969 to 1988 is substantially higher than existing estimates. In children
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.035 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".