Measles seroprevalence by birth cohort across the lifespan: a population-based, cross-sectional serosurvey in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: In 2025, the province of British Columbia (BC), Canada, experienced heightened measles activity, mostly involving unvaccinated communities. Publicly funded measles vaccination for children aged 12 months has been routine since 1969, with a second dose at age 18 months added in 1996, and rescheduled to 4 to 6 years (school entry) in 2012. We aimed to assess population-based measles seroprevalence in relation to historic measles endemicity and vaccination considerations. METHODS: In August 2024, we undertook a cross-sectional serosurvey, testing more than 1000 anonymized residual sera for measles antibody. We collected sera from outpatients attending a laboratory network in the Lower Mainland, BC, with equal numbers from 10 age groups, from 1 year to older than 80 years. RESULTS: Measles seropositivity was 89% (95% confidence interval [CI] 87% to 91%) overall and 93% (95% CI 91% to 94%) if equivocal results were also considered positive. Seropositivity was 90% or higher in all age groups except 10- to 19-year-olds (82% [95% CI 74% to 89%]), 20- to 29-year-olds (69% [95% CI 59% to 78%]), and 30- to 39-year-olds (73% [95% CI 63% to 81%]). Results remained below 80% for 20- to 39-year-olds, and significantly below all other age groups, even considering equivocal results as positive. In birth cohort analysis, seropositivity appeared lower among those due for their second vaccine dose during the COVID-19 pandemic or born during the postvaccination era to mothers with a higher likelihood of previous infection when measles was endemic. INTERPRETATION: Our measles serosurvey findings inform vaccine coverage and complement other case-based surveillance indicating robust population-level immunity outside of unvaccinated clusters or communities. In addition to showing age-related antibody decline, serosurveillance provides insights into potential cohort effects that may have implications for vaccination programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| 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 | 0.001 |
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".