Pertussis Disease and Antenatal Vaccine Effectiveness in Australian Children
Bibliographic record
Abstract
BACKGROUND: Population-level studies of severe pertussis extending beyond infancy are sparse, and none in the context of antenatal vaccination. We compared hospitalized pertussis cases from birth to 15 years of age before and after introduction of antenatal immunization. METHODS: Active surveillance of laboratory-confirmed pertussis hospitalizations in a national network of pediatric hospitals in Australia January 2012 to June 2019. Impact of maternal vaccination was assessed by vaccine effectiveness (VE) in cases and test-negative controls with <2 months of age and by before-after comparison of age distribution of cases. Among cases eligible for one or more vaccine doses, we examined proportions age-appropriately immunized and with comorbidities by age group. RESULTS: Among 419 eligible cases, the proportion <2 months of age significantly decreased from 33.1% in 2012 to 2014 compared with 19.6% in 2016 to 2019 when mothers of only 4 of 17 (23.5%) cases <2 months of age had received antenatal vaccination. VE was estimated to be 84.3% (95% CI, 26.1-96.7). Across all years (2012-2019), of 55 cases 4-11 months of age, 21 (38%) had ≥2 vaccine doses, whereas among 155 cases ≥12 months of age, 122 (85.2%) had ≥3 vaccine doses. Prevalence of comorbidities (primarily cardiorespiratory) increased from 5 (2.1%) <6 months of age to 36 (24.2%) ≥12 months of age (P < 0.001), with 6/16 (38%) cases ≥12 months of age who required intensive care having comorbidities. CONCLUSIONS: Below the age of 12 months, prevention of severe pertussis will be maximized by high maternal antenatal vaccine uptake and timeliness of infant vaccine doses. Despite full immunization, we found children ≥12 months of age accounted for 27% of hospitalizations <15 years, with 24% having comorbities, suggesting new vaccine strategies, such as additional doses or more immunogenic vaccines, require evaluation.
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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 | 0.013 |
| 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 | 0.000 | 0.001 |
| 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".