Strategies to increase the coverage of influenza and pneumonia vaccination in older adults: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is urgent to implement interventions to increase vaccination rates of influenza/pneumonia vaccines in older adults, yet the effectiveness of different intervention strategies has not been thoroughly evaluated. OBJECTIVE: We aimed to assess the effectiveness of intervention strategies for increasing the coverage of influenza/pneumonia vaccination in older adults. METHODS: PubMed, Web of Science, Cochrane Library, Embase, China Biology Medicine disc, China National Knowledge Infrastructure and Wanfang were searched from 1 January 2000 to 1 October 2022. RCTs that assessed any intervention strategies for increasing influenza/pneumonia vaccination coverage or willingness in older adults were included. A series of random-effects network meta-analysis was conducted by using frequentist frameworks. RESULTS: Twenty-two RCTs involving 385,182 older participants were eligible for further analysis. Eight types of intervention strategies were evaluated. Compared with routine notification, health education (odds ratio [OR], 1.85 [95%CI, 1.19 to 2.88]), centralised reminder (OR, 1.63 [95%CI, 1.07 to 2.47]), health education + onsite vaccination (OR, 2.89 [95%CI, 1.30 to 6.39]), and health education + centralised reminder + onsite vaccination (OR, 20.76 [95%CI, 7.33 to 58.74]) could effectively improve the vaccination rate. The evidence grade was low or very low due to the substantial heterogeneity among studies. CONCLUSIONS: Our findings suggest that health education + centralised reminder + onsite vaccination may potentially be an effective strategy regardless of cost, but the evidence level was low. More rigorous trials are needed to identify the association between strategies and vaccination rates among older adults and to integrate such evidence into clinical care to improve vaccination rates.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".