Population immunity to measles in Canada using Canadian Health Measures survey data – A Canadian Immunization Research Network (CIRN) study
Bibliographic record
Abstract
We aimed to determine population immunity to measles in Canada, and to assess the risk of future outbreaks. We tested 11,176 sera from Cycles 2 (2009-2011) and 3 (2011-2013) cohorts from the biobank of Statistics Canada's Canadian Health Measures Survey (CHMS) using the BioPlex 2220 MMRV IgG assay. We then tested all BioPlex negative and equivocal samples using a more sensitive Plaque Reduction Neutralization Test (PRNT). We determined the weighted proportion of positive, equivocal, and negative samples by age, sex, region and whether individuals were born in Canada. We found that 90.0% (95% confidence interval (CI): 88.2, 91.9) of samples were positive, 4.5% (95% CI: 3.4, 5.5) were equivocal and 5.5% (95% CI: 4.3, 6.7) were negative. Individuals in the 12-19 year age band had the lowest proportion positive at 78.7% (95% CI: 74.2, 83.2) and the highest proportion of positive samples was found in those 60-79 years (99.6%, 95% CI: 99.3, 99.9). Seropositivity was consistently <90% across a broad range of pediatric and adult age bands (6-39 years). We found that a slightly higher proportion of females were positive (91.9%, 95% CI: 90.1, 93.6) compared to males (88.3%, 95% CI: 85.8, 90.7). When taking into account interaction between age and born in Canada status, we found individuals born in Canada aged 19 and under were less susceptible (OR = 0.6 (95% CI: 0.4, 0.95)) compared to those born outside Canada whereas, those aged 20 and over were more susceptible (OR = 1.7 (95% CI: 1.1, 2.8)). Our findings indicate that measles immunity in Canada is below the 95% immunity threshold required to sustain measles elimination, underscoring the importance of maintaining high vaccine coverage to prevent future measles outbreaks and sustain Canada's elimination status.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".