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Record W2152052309 · doi:10.1586/14760584.2.4.477

Pharmacoeconomics of elderly vaccination against invasive pneumococcal infections: costeffectiveness analyses and implications for The Netherlands

2003· article· en· W2152052309 on OpenAlexaboutno aff
Maarten J. Postma, Marie‐Louise A. Heijnen, Philippe Beutels, J. C. Jager

Bibliographic record

VenueExpert Review of Vaccines · 2003
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationMedicinePharmacoeconomicsPneumococcal vaccinationCost effectivenessVaccine efficacyCost-effectiveness analysisPneumococcal infectionsIntensive care medicineEnvironmental healthImmunologyStreptococcus pneumoniaeBiologyRisk analysis (engineering)

Abstract

fetched live from OpenAlex

We performed a review of cost-effectiveness of elderly pneumococcal vaccination to prevent invasive disease. It concerns studies in the USA, Canada, Netherlands and Spain and a multinational study of five European countries. Cost-effectiveness of elderly vaccination against invasive pneumococcal infections varies from cost-saving to EUR 33,000 per life-year gained. The Dutch study estimates cost-effectiveness at EUR 10,100 per life-year gained (price level: 1995). This is below the level that has recently been defined for treatment of high cholesterol (EUR 20,000 per life-year gained) and may therefore be considered as favorable. Almost all studies base their estimate of vaccine efficacy on the same case-control study from the USA. We identify a need for a systematic review on the efficacy of the pneumococcal vaccine. Also, we suggest further analysis with respect to potential effects on cost-effectiveness of extended influenza vaccination for the Dutch elderly in recent years and inclusion of pneumococcal re-vaccination. Pending this additional information, we conclude that cost-effectiveness of vaccination against invasive pneumococcal infections for Dutch elderly is favorable (as in several other countries) and justifies implementation from a pharmacoeconomic point of view.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.419
Teacher spread0.373 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations12
Published2003
Admission routes1
Has abstractyes

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