Seasonal influenza vaccination rate and vaccine effectiveness among older adults in mainland China: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Both vaccine coverage and its effectiveness determine the influence of seasonal influenza vaccination on influenza control within a population. We conducted a systematic review and meta-analysis to estimate the influenza vaccination rate (IVR) and vaccine effectiveness (VE) among older adults in mainland China. METHODS: We searched five databases for the last 12 years, selecting studies that included people aged 60 years or older in mainland China. Random or fixed effects models were used to generate summary IVR and VE. The heterogeneity was assessed by subgroup analyses and meta-regression. Potential biases of the included studies were examined using the Agency for Healthcare Research and Quality Inventory and the Newcastle-Ottawa Scale. RESULTS: For IVR, we included 65 studies, involving 149 458 672 participants. The overall pooled IVR was found to be 17% (95% CI: 14%-21%), with lower IVRs observed in areas lacking free vaccination policies (7%, 95% CI: 5%-9%) and among individuals with chronic diseases (13%, 95% CI: 8%-19%). To assess VE against laboratory-confirmed influenza, we included 14 studies, involving 13 950 participants. The overall pooled VE was 33% (95% CI: 10%-51%), with a higher VE estimate observed for influenza A(H1N1) pdm09 (51%, 95% CI: 13%-73%) and when the vaccine matched the circulating virus strain (37%, 95% CI: 9%-56%). CONCLUSIONS: IVRs among older adults in the included areas are low, especially among those lacking access to free policies and those with chronic diseases. Furthermore, the current vaccine provides low protection. It is crucial to increase influenza vaccination uptake and develop more effective vaccines for older adults.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".