Duration of Pertussis Immunity After DTaP Immunization: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Pertussis incidence is increasing, possibly due to the introduction of acellular vaccines, which may have decreased the durability of immune response. We sought to evaluate and compare the duration of protective immunity conferred by a childhood immunization series with 3 or 5 doses of diphtheria-tetanus-acellular pertussis (DTaP). METHODS: We searched Medline and Embase for articles published before October 10, 2013. Included studies contained a measure of long-term immunity to pertussis after 3 or 5 doses of DTaP. Twelve articles were eligible for inclusion; 11 of these were included in the meta-analysis. We assessed study quality and used meta-regression models to evaluate the relationship between the odds of pertussis and time since last dose of DTaP and to estimate the probability of vaccine failure through time. RESULTS: We found no significant difference between the annual odds of pertussis for the 3- versus 5-dose DTaP regimens. For every additional year after the last dose of DTaP, the odds of pertussis increased by 1.33 times (95% confidence interval: 1.23-1.43). Assuming 85% vaccine efficacy, we estimated that 10% of children vaccinated with DTaP would be immune to pertussis 8.5 years after the last dose. Limitations included the statistical model extrapolated from data and the different study designs included, most of which were observational study designs. CONCLUSIONS: Although acellular pertussis vaccines are considered safer, the adoption of these vaccines may necessitate earlier booster vaccination and repeated boosting strategies to achieve necessary "herd effects" to control the spread of pertussis.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.079 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".