Acellular pertussis vaccine effectiveness and waning immunity in Alberta, Canada: 2010–2015, a Canadian Immunization Research Network (CIRN) study
Bibliographic record
Abstract
BACKGROUND: Pertussis is still frequently reported in Canada. In Alberta, pertussis incidence ranged from 1.8 to 20.5 cases per 100,000 persons for 2004-2015. Most cases occurred in those aged <15 years. In Alberta, acellular formulations replaced whole-cell in 1997. We investigated pertussis vaccine effectiveness (VE) using a test-negative design (TND) study. METHODS: We included all persons who had a real-time PCR laboratory test for Bordetella pertussis between January 1, 2010 and August 31, 2015, in the province of Alberta, Canada. Vaccination history was obtained from Alberta's immunization repository. Vaccination status was classified as complete, incomplete, or unvaccinated, based on the province's vaccination schedule. Persons who had received ≥one dose of whole cell vaccine were excluded from analysis. Multivariable logistic regression models were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) for pertussis infection by time since last vaccination. We adjusted for vaccination status, age, sex, neighbourhood income, urban/rural status, and the presence of a co-morbid condition. VE was calculated as [(1 - aOR) * 100]. RESULTS: Of the 12,149 tests available, 936 (7.7%) were positive for Bordetella pertussis. Among the full cohort, VE was 90% (95% CI 87-92%) at 1 year, 81% (95% CI 77-85%) at 1-3 years, 76% (95% CI 68-82%) at 4-7 years, and 37% (95% CI 11-56%) at 8 or more years since a last dose of acellular pertussis vaccine. CONCLUSIONS: Pertussis VE was highest in the first year after vaccination, then declined noticeably as years since a last vaccination increased. Our results suggest that a large number of adolescents and adults are susceptible to infection with Bordetella pertussis. Regular boosters throughout childhood, adolescence, and during pregnancy may be needed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".