Influenza Vaccine Effectiveness in Mainland China: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Influenza endangers human health but can be prevented in part by vaccination. Assessing influenza vaccine effectiveness (VE) provides scientific evidence for developing influenza vaccination policy. We conducted a systematic review and meta-analysis of studies that evaluated influenza VE in mainland China. We searched six relevant databases as of 30 August 2019 to identify studies and used Review Manager 5.3 software to analyze the included studies. The Newcastle-Ottawa scale was used to assess the risk of publication bias. We identified 1408 publications, and after removing duplicates and screening full texts, we included 21 studies in the analyses. Studies were conducted in Beijing, Guangzhou, Suzhou, and Zhejiang province from the 2010/11 influenza season through the 2017/18 influenza season. Overall influenza VE for laboratory confirmed influenza was 36% (95% CI: 25-46%). In the subgroup analysis, VE was 45% (95% CI: 18-64%) for children 6-35 months who received one dose of influenza vaccine, and 57% (95% CI: 50-64%) who received two doses. VE was 47% (95% CI: 39-54%) for children 6 months to 8 years, and 18% (95% CI: 0-33%) for adults ≥60 years. For inpatients, VE was 21% (95% CI: -11-44%). We conclude that influenza vaccines that were used in mainland China had a moderate effectiveness, with VE being higher among children than the elderly. Influenza VE should be continuously monitored in mainland China to provide evidence for policy making and improving uptake of the influenza vaccine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.027 | 0.003 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".