Resurgence of Pertussis in Europe
Bibliographic record
Abstract
BACKGROUND: A resurgence of pertussis has been observed in Canada, the United States and Australia since the 1980s, but inconsistent data are currently available for Europe. The objective of this paper is to describe the epidemiology of pertussis in Western European countries to discuss future vaccination strategies. METHODS: The European Community funded a network for the epidemiologic surveillance of measles and pertussis in 1998. Sixteen European countries provided national surveillance data for pertussis for the period 1998-2002 in a standard format. Data were pooled and analyzed to describe incidence rates by age group, seasonality, proportion of hospitalized patients and deaths among notified cases. RESULTS: Children younger than 1 year had the highest incidence during the entire period. Rates in the older than 14 years age group increased by 115% during the study period. Northern countries showed the highest incidence figures in all age groups. Among children younger than 1 year, 70% were admitted into hospital. Children younger than 6 months of age and those not vaccinated were most likely to be hospitalized. Thirty-two deaths were reported, 87% of which were in children younger than 6 months of age. CONCLUSIONS: Pertussis is far from being controlled in Europe. Whereas the incidence in children younger than 1 year was high but remained stable, rates in adults doubled in 5 years.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".