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Vaccines for preventing influenza in the elderly

2006· review· en· W4211105107 on OpenAlexaff
Daniela Rivetti, Tom Jefferson, Roger E. Thomas, Melanie Rudin, Alessandro Rivetti, Carlo Di Pietrantonj, Vittorio Demicheli

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2006
Typereview
Languageen
Field
Topic
Canadian institutionsUniversity of CalgaryCochrane
Fundersnot available
KeywordsMedicineVaccinationInfluenza vaccineCochrane LibraryMEDLINEVaccine efficacyMeta-analysisPediatricsInternal medicineImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Influenza vaccination of elderly individuals is recommended worldwide and has been targeted toward the elderly and those at serious risk of complications. OBJECTIVES: Our aim was to review the evidence of efficacy, effectiveness and safety of influenza vaccines in individuals aged 65 years or older. SEARCH STRATEGY: We searched the following databases on The Cochrane Library, the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Database of Systematic Reviews, and the Database of Abstracts of Reviews of Effectiveness (Issue 1, 2006); MEDLINE (January 1966 to March Week 3 2006); EMBASE (Dialog 1974 to 1979; SilverPlatter 1980 to December 2005); Biological Abstracts (SilverPlatter 1969 to December 2004); and Science Citation Index (Web of Science 1974 to December 2004). SELECTION CRITERIA: We considered randomised, quasi-randomised, cohort and case-control studies assessing efficacy against influenza (laboratory-confirmed cases) or effectiveness against influenza-like illness (ILI) or safety. Any influenza vaccine given independently, in any dose, preparation or time schedule, compared with placebo or with no intervention was considered. DATA COLLECTION AND ANALYSIS: We grouped reports first according to the setting of the study (community or long-term care facilities) and then by level of viral circulation and vaccine matching. We further stratified by co-administration of pneumococcal polysaccharide vaccine (PPV) and by different types of influenza vaccines. We analysed the following outcomes: influenza, influenza-like illness, hospital admissions, complications and deaths. MAIN RESULTS: Sixty-four studies were included in the efficacy / effectiveness assessment, resulting in 96 data sets. In homes for elderly individuals (with good vaccine match and high viral circulation) the effectiveness of vaccines against ILI was 23% (6% to 36%) and non-significant against influenza (RR 1.04: 95% CI 0.43 to 2.51). We found no correlation between vaccine coverage and ILI attack rate. Well matched vaccines prevented pneumonia (VE 46%; 30% to 58%), hospital admission (VE 45%; 16% to 64%) and deaths from influenza or pneumonia (VE 42%, 17% to 59%). In elderly individuals living in the community, vaccines were not significantly effective against influenza (RR 0.19; 95% CI 0.02 to 2.01), ILI (RR 1.05: 95% CI 0.58 to 1.89), or pneumonia (RR 0.88; 95% CI 0.64 to 1.20). Well matched vaccines prevented hospital admission for influenza and pneumonia (VE 26%; 12% to 38%) and all-cause mortality (VE 42%; 24% to 55%). After adjustment for confounders, vaccine performance was improved for admissions to hospital for influenza or pneumonia (VE* 27%; 21% to 33%), respiratory diseases (VE* 22%; 15% to 28%) and cardiac disease (VE* 24%; 18% to 30%); and for all-cause mortality (VE* 47%; 39% to 54%). The public health safety profiles of the vaccines appear to be acceptable. AUTHORS' CONCLUSIONS: In long-term care facilities, where vaccination is most effective against complications, the aims of the vaccination campaign are fulfilled, at least in part. However, according to reliable evidence the usefulness of vaccines in the community is modest. The apparent high effectiveness of the vaccines in preventing death from all causes may reflect a baseline imbalance in health status and other systematic differences in the two groups of participants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0160.003
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.180
GPT teacher head0.429
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations193
Published2006
Admission routes1
Has abstractyes

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