Impact of influenza vaccination on healthcare utilization – A systematic review
Bibliographic record
Abstract
INTRODUCTION: Although a vaccine-preventable disease, influenza causes approximately 3-5 million cases of severe illness and about 290,000-650,000 deaths worldwide, which occur primarily among people 65 years and older. Nonetheless, prevention of influenza and its complications rely mainly on vaccination. We aimed to systematically evaluate influenza vaccine effectiveness at reducing healthcare utilization in older adults, defined as the reduction of outpatient visits, ILI and influenza hospitalizations, utilization of antibiotics and cardiovascular events by vaccination status during the influenza season. METHODS: We searched MEDLINE, EMBASE, CINAHL, Cochrane Library and considered any seasonal influenza vaccine, excluding the pandemic (2009-10 season) vaccine. Reviewers independently assessed data extraction and quality assessment. RESULTS: Of the 8308 citations retrieved, 22 studies were included in the systematic review. Overall, two studies (9%) were deemed at moderate risk of bias, thirteen (59%) at serious risk of bias and seven (32%) at critical risk of bias. For outpatient visits, we found modest evidence of protection by the influenza vaccine. For all-cause hospitalization outcomes, we found a wide range of results, mostly deemed at serious risk of bias. The included studies suggested that the vaccine may protect older adults against influenza hospitalizations and cardiovascular events. No article meeting our inclusion criteria explored the use of antibiotics and ILI hospitalizations. The high heterogeneity between studies hindered the aggregation of data into a meta-analysis. CONCLUSION: The variability between studies prevented us from drawing a clear conclusion on the effectiveness of the influenza vaccine on healthcare utilization in older adults. Overall, the data suggests that the vaccine may result in a reduction of healthcare utilization in the older population. Further studies of higher quality are necessary.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".