Impact of vaccination on antibiotic usage: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Vaccines may reduce antibiotic use and the development of resistance.Objectives: To provide a comprehensive, up-to-date assessment of the evidence base relating to the effect of vaccines on antibiotic use.Methods: Ovid MEDLINE, Embase, the Cochrane Library, ClinicalTrials.gov and WHO Trials Registry.Study eligibility criteria: Randomised-controlled trials (RCTs) and observational studies published January 1998 to March 2018.Participants: Any population.Interventions: Vaccines versus placebo, no vaccine or another vaccine.Methods: Titles, abstracts, and full-texts were screened independently by two reviewers. Certainty of RCT evidence was assessed using GRADE.Results: 4980 records identified; 895 full-text reports assessed; 96 studies included (24 RCTs, 72 observational). There was high certainty evidence that influenza vaccine reduces days of antibiotic use among healthy adults (1 RCT; n = 4253; rate reduction 28·1% [95% CI 16·0, 38·4]); moderate certainty evidence that influenza vaccines probably reduce antibiotic use in children aged 6 months to 14 years (3 RCTs; n = 610; ratio of means 0·62 [95% CI 0·54, 0·70) and probably reduces community antibiotic use in children aged 3-15 years (1 RCT; n = 10,985 person-seasons; risk ratio 0·68 [95% CI 0·58, 0·83]); moderate certainty evidence that pneumococcal vaccination probably reduces antibiotic use in children aged 6 weeks to 6 years (2 RCTs; n = 47,945; rate ratio 0·93 [95% CI 0·87, 0·99]) and reduces illness episodes requiring antibiotics in children aged 12-35 months (1 RCT; n = 264; rate ratio 0·85 [95% CI 0·75, 0·97]). Other RCT evidence was of low or very low certainty, and observational evidence was affected by confounding. Conclusions: The evidence base is poor. Although some vaccines may reduce antibiotic use, collection of high-quality data in future vaccine trials is needed to improve the evidence base. Funding: Wellcome TrustPROSPERO registration: CRD42018103881
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.052 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".