Incidence of influenza virus infection among pregnant women: a systematic review
Bibliographic record
Abstract
The World Health Organization (WHO) considers pregnant women to be a risk group for severe influenza disease. We conducted a systematic review to evaluate influenza disease incidence in pregnant women in order to inform estimates of influenza vaccine impact for low-resource countries. We performed electronic literature searches, targeting studies on the following outcomes in pregnant women: attack rate, hospitalization rate, intensive care unit admission rate, mortality rate, and disability-adjusted life years lost. Only original studies published in peer-reviewed journals that had laboratory confirmation for influenza virus infection and included population-based incidence rates with denominator data were included. We summarized study characteristics in descriptive tables and outcome-specific Forest plots. We generated summary incidence rates using random effects models and assessed statistical heterogeneity by visual examination of Forest plots, and by χ 2 and I2 tests. We identified 1543 articles, of which nine articles met the study inclusion criteria. Five were case series, three were cohort studies, and one was a randomized controlled trial. Eight studies were from high-income countries, and one was from an upper middle-income country. Six studies reported results for pandemic influenza, and three reported seasonal influenza. Statistical heterogeneity was high for all outcomes, and methodologies and duration of surveillance varied considerably among studies; therefore, we did not perform meta-analyses. Study quality was very low according to GRADE criteria. More data on influenza disease incidence in pregnant women, particularly in low- and middle-income countries and for seasonal influenza disease, are needed to inform public health decision-making.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".