A cocoon immunisation strategy against pertussis for infants: does it make sense for Ontario?
Bibliographic record
Abstract
Pertussis deaths occur primarily among infants who have not been fully immunised. In Ontario, Canada, an adult booster dose was recently added to the publicly funded immunisation programme. We applied number-needed-to-treat analyses to estimate the number of adults that would need to be vaccinated (NNV) to prevent pertussis disease, hospitalisation and death among infants if a cocoon strategy were implemented. NNV=1/(P(M) X R) + 1/(P(F) X R), where P(M),P(F) (proportion of infants infected by mothers, fathers) were sourced from several studies. Rates of disease, hospitalisation or death (R) were derived from Ontario's reportable disease data and Discharge Abstract Database. After adjusting for under-reporting, the NNV to prevent one case, hospitalisation or death from pertussis was between 500-6,400, 12,000-63,000 and 1.1-12.8 million, respectively. Without adjustment, NNV increased to 5,000-60,000, 55,000-297,000 and 2.5-30.2 million, respectively. Rarer outcomes were associated with higher NNV. These analyses demonstrate the relative inefficiency of a cocoon strategy in Ontario, which has a well-established universal immunisation programme with relatively high coverage and low disease incidence. Other jurisdictions considering a cocoon programme should consider their local epidemiology.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".