Number needed to vaccinate to prevent hospitalizations of pregnant women due to inter-pandemic influenza in Sweden, 2003–2009
Bibliographic record
Abstract
BACKGROUND: The evidence of increased risk of severe disease for healthy pregnant women due to inter-pandemic influenza consists mainly of observational studies of health service utilization in USA and Canada. However, these results can be context dependent and estimates in a European setting are sparse. For policy purposes we therefore decided to elucidate the potential value of vaccination in Sweden. MATERIALS AND METHODS: We conducted a retrospective, register-based study of hospitalizations due to inter-pandemic influenza or respiratory infection attributable to influenza in pregnant women in Sweden. With aggregated data from 2003 to 2009 we assessed the number needed to vaccinate (NNV) to prevent one such hospitalization. RESULTS: We included on average 96,000 pregnant women/year and identified 9-48 hospitalizations/season fulfilling the case definition. Assuming 80% vaccine effectiveness the NNV was >1,900 pregnant women. The estimate is higher than those found in the USA, Canada, and UK. The difference may be explained by differing methods to estimate NNV, but also differences in propensity to hospitalize and the basic health status of the pregnant women. CONCLUSIONS: Because of the increased risk associated with influenza A(H1N1)pdm09, vaccination is presently offered to all pregnant women in Sweden, but vaccination against other inter-pandemic influenza types seems disputable. The study illustrates the context dependence of preventive health measures and points to the need for national NNV estimates and international harmonization of study methods for comparisons between countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".