Pertussis Surveillance in Canada: Trends to 2012
Bibliographic record
Abstract
OBJECTIVE: The purpose of this report is to provide a summary of the pertussis activity in Canada. METHODS: A descriptive analysis of pertussis incidence by year, age group, gender and province/territory was conducted using national surveillance data, clinical administrative data and vital statistics data. RESULTS: Pertussis is an endemic cyclical disease in Canada with peaks in activity occurring every 2 to 5 years. Canada has experienced a decline in pertussis activity following the introduction of routine pertussis immunization programs. The incidence of pertussis is highest in infants and children. Hospitalization and mortality are more common among infants, particularly those less than three months of age. Trends in pertussis vary by province and territory. Canada experienced a notable increase in incidence in 2012. Reasons for this increase are unknown. CONCLUSION: Our understanding of the epidemiology of pertussis in Canada could be enhanced by improved approaches for monitoring the disease. Although the peak in activity observed in 2012 could be an isolated event, further work to support outbreak response in provinces and territories, including rapid research tools and resources, should be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".