Evaluation of Influenza Antiviral Prophylaxis for Long-term Care Residents: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Influenza is a pervasive respiratory infection that disproportionately burdens long-term care residents. To limit outbreaks, guidelines recommend antiviral prophylaxis, particularly oseltamivir or zanamivir, despite acknowledging the inadequate supporting evidence. Therefore, we aimed to review the literature on the efficacy of oseltamivir, zanamivir, and baloxavir prophylaxis for influenza in long-term care. METHODS: Medline, Embase, PubMed, and several other databases were searched from inception to 16 August 2023. For inclusion, observational studies or randomized controlled trials had to report influenza-like illness (ILI) or infection rates among adult long-term care populations receiving prophylaxis. Outcome values were meta-analyzed as intervention-specific pooled proportions (PPs) and risk ratios when applicable. Risk of bias was assessed via the Cochrane risk of bias tool 2.0 and Joanna Briggs Institute checklist. RESULTS: In total, 14 studies were included, comprising 12 672 residents. Individuals given oseltamivir or zanamivir experienced the fewest symptomatic, test-confirmed infections (oseltamivir PP: 0.7%; 95% confidence interval [CI]: 0.1-4.7; zanamivir PP: 3.0%; 95% CI: 0.9-9.4) and ILIs (oseltamivir PP: 2.8%; 95% CI: 1.8-4.3; zanamivir PP: 3.4%; 95% CI: 1.3-7.2). However, no significant statistical differences were detected versus most other interventions (ILI PP range: 4.5%-6.4%, infection PP range: 4.6%-7.9%). Similarly, in studies directly comparing either antiviral to placebo, there were no associated benefits despite every risk ratio being below 1 (0.51-0.75) because of expansive 95% CIs. CONCLUSIONS: Oseltamivir or zanamivir could provide some benefit but low statistical power behind most estimates precluded definitive conclusions. Therefore, additional studies (randomized controlled trials) are needed to expand the evidence base and validate whether prophylaxis is beneficial in this setting.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".