A Survey of Pertussis Surveillance and Immunization Practices in Canada, 2012
Bibliographic record
Abstract
BACKGROUND: In 2012 there was an increase in the incidence of pertussis in the Americas. The Pan American Health Organization (PAHO) made a number of recommendations to strengthen surveillance, investigate outbreaks, to measure adequacy and timeliness of immunizations within the population and monitor policies related to high risk individuals such as health care workers and pregnant women. OBJECTIVE: To review measures taken in Canada by provinces and territories to control and prevent pertussis spread. METHODS: A survey was developed based on PAHO recommendations and distributed through the Council of Chief Medical Officers of Health to all provinces and territories. RESULTS: All provinces participated in the survey. Strong surveillance is aided by consistent use of case definitions; most provinces use the national case definition. Outbreaks are investigated at the local/provincial level. Immunization coverage is not well captured but efforts are underway to improve monitoring through surveys and immunization registries. Policies have been implemented related to high risk individuals but evaluations of these policies have not been undertaken as of yet. CONCLUSION: Based on the PAHO recommendations, Canada is well poised to provide surveillance data on pertussis. There are gaps in surveillance, in standardization among jurisdictions and in immunization coverage data which may need to be addressed to gain a better understanding of the impact of pertussis in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 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".