1713Interventions to Increase Healthcare Worker Influenza Vaccination: a meta-analysis
Bibliographic record
Abstract
Background. Rates of healthcare worker (HCW) influenza vaccination remain suboptimal, however the most effective way to increase uptake is controversial. We conducted a systematic review of the literature of interventions to increase influenza vaccine coverage in HCWs. Methods. An expert librarian searched the following databases to July 9, 2013: MEDLINE, EMBASE, CENTRAL, Web of Science, Scopus, and CINAHL. References and conference abstracts were also searched. Interventions were classified into 9 categories (see Results). Two reviewers independently extracted data and classified risk of bias. Primary outcomes were 1) reduction in number of unvaccinated HCWs, and 2) interventions that achieved 95% vaccination rates; each at 1 year (early) and 3 ±1 years (late) after intervention implementation. Results. 9193 titles/abstracts were reviewed, and 121 were included (Figure 1). Of 174 comparisons, 78 were low risk of bias (RoB), 30 were moderate RoB, and 66 were high RoB. There were 132 before/after studies, 23 randomized trials, 12 surveys, 7 cohort studies, and 1 case-control study. All interventions were significantly associated with a reduction in unvaccinated HCWs (listed from largest to smallest effect size): condition of service [12 studies; 357,560 HCWs; 93% reduction in unvaccinated HCWs, (95%CI 91-95%); I2 = 99%], vaccine-or-mask [12 studies; 581,926 HCWs; 74% (61-88%); 100%], declination forms [14 studies; 209,290 HCWs; 41% (35–46%); 98%], audit-and-feedback [15 studies; 545,403 HCWs; 35% (29–40%); 99%], increased vaccine access [46 studies; 764,570 HCWs; 32% (27–36%); 100%], role models [18 studies; 204,514 HCWs; 30% (24–36%); 99%], peer-vaccination [7 studies; 120,670 HCWs; 29% (10–45%); 100%], incentives [17 studies; 188,933 HCWs; 28% (21–33%); 99%], and education/promotion only [16 studies; 554,706 HCWs; 11% (7–16%); 99%]. The interventions that achieved 95% HCW vaccination rates were: condition of service (13/13 early; 4/4 late), vaccine-or-mask (3/15 early; 0/2 late), and role models (1/19 early; 1/10 late). Conclusion. All interventions examined increased HCW influenza vaccine uptake to various degrees. However, only condition of service policies appear to result in sustained HCW vaccination rates of >95%. Disclosures. B. Coleman, Sanofi Pasteur: Investigator, Research grant; GSK: Grant Investigator, Research grant; Novartis: Grant Investigator, Research grant A. Mcgeer, Sanofi Pasteur: Grant Investigator and Scientific Advisor, Research grant; GSK: Grant Investigator, Research grant; Novartis: Grant Investigator, Research grant
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.051 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".