Test-adjusted estimation for pertussis incidence in greater Toronto, Canada, 1993–2006
Bibliographic record
Abstract
BACKGROUND: Pertussis remains a major public health concern, particularly affecting young children. While most identified cases occur in this group, the burden among older children and adults who undergo less frequent testing is not well characterized. METHODS: We analyzed pertussis testing and case data in the Greater Toronto Area from 1993 to 2006. We applied a meta-regression-based method for test adjustment by age and sex, estimating case counts in each demographic group as if they were tested at the same rate as the most tested group (< 1-year males). RESULTS: Before adjustment, incidence was highest in the < 1-year group and declined with age, with the ≥ 80-year group having an incidence rate ratio (IRR) of 0.011 (95% CI: 0.006-0.020) relative to male children aged < 1 year. After adjustment, the 2-4-year group showed the highest relative incidence (IRR: 5.503, 95% CI: 2.121-14.281). The highest estimated underdiagnosed case rates were in the 2-4-year group at 13.69 per diagnosed case in males (95% CI: 5.913-21.467) and the 10-19-age group in females at 6.80 per diagnosed case (95% CI: 4.684-8.917). CONCLUSION: Our use of a novel test-adjustment method for estimating incidence suggests that while pertussis is most diagnosed in infants, it is substantially underdiagnosed in older age groups generally, but particularly so in preschool-aged children and the elderly. As undiagnosed infection in these populations may play a key role in sustaining transmission, this finding has implications for vaccine booster policy. CLINICAL TRIAL NUMBER: Not applicable (not a clinical trial).
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".