The impact of pertussis vaccine programme changes on pertussis disease burden in Manitoba, 1992–2017—an age-period-cohort analysis
Bibliographic record
Abstract
BACKGROUND: Changes to pertussis vaccination programmes can have impacts on disease burden that should be estimated independently from factors such as age- and period-related trends. We used age-period-cohort (APC) models to explore pertussis incidence in Manitoba over a 25-year period (1992-2017). METHODS: We identified all laboratory-confirmed cases of pertussis from Manitoba's Communicable Diseases Database and calculated age-standardized incidence rates. We used APC models to investigate trends in pertussis incidence. RESULTS: During the study period, 2479 cases were reported. Age-standardized rates were highest during a large outbreak in 1994 (55 cases/100 000 person-years), with much lower peaks in 1998, 2012 and 2016. We saw strong age and cohort effects in the APC models, with a steady decrease in incidence with increasing age and increased risk in the cohort born between 1980 and 1995. CONCLUSIONS: The highest risk for pertussis was consistently in young children, regardless of birth cohort or time period. The 1981 programme change to an adsorbed whole-cell pertussis vaccine with low effectiveness resulted in reduced protection in the 1981-95 birth cohort and contributed to the largest outbreak of disease during the 25-year study period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".