Effectiveness of pertussis vaccination and duration of immunity
Bibliographic record
Abstract
BACKGROUND: A resurgence of pertussis cases among both vaccinated and unvaccinated people raises questions about vaccine effectiveness over time. Our objective was to study the effectiveness of the pertussis vaccine and characterize the effect of waning immunity and whole-cell vaccine priming. METHODS: We used the test-negative design, a nested case-control study with test-negative individuals as controls. We constructed multivariable logistic regression models to estimate odds ratios (ORs). Vaccine effectiveness was calculated as (1 - OR) × 100. We assessed waning immunity by calculating the odds of developing pertussis per year since last vaccination and evaluated the relative effectiveness of priming with acellular versus whole-cell vaccine. RESULTS: Between Dec. 7, 2009, and Mar. 31, 2013, data on 5867 individuals (486 test-positive cases and 5381 test-negative controls) were available for analysis. Adjusted vaccine effectiveness was 80% (95% confidence interval [CI] 71% to 86%) at 15-364 days, 84% (95% CI 77% to 89%) at 1-3 years, 62% (95% CI 42% to 75%) at 4-7 years and 41% (95% CI 0% to 66%) at 8 or more years since last vaccination. We observed waning immunity with the acellular vaccine, with an adjusted OR for pertussis infection of 1.27 (95% CI 1.20 to 1.34) per year since last vaccination. Acellular, versus whole-cell, vaccine priming was associated with an increased odds of pertussis (adjusted OR 2.15, 95% CI 1.30 to 3.57). INTERPRETATION: We observed high early effectiveness of the pertussis vaccine that rapidly declined as time since last vaccination surpassed 4 years, particularly with acellular vaccine priming. Considering whole-cell vaccine priming and/or boosters in pregnancy to optimize pertussis control may be prudent.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".