MétaCan
Menu
← Back to cohort
Record W2130888209 · doi:10.1186/1471-2458-12-458

The potential effect of temporary immunity as a result of bias associated with healthy users and social determinants on observations of influenza vaccine effectiveness; could unmeasured confounding explain observed links between seasonal influenza vaccine and pandemic H1N1 infection?

2012· article· en· W2130888209 on OpenAlexaffabout
Natasha S. Crowcroft, Laura C. Rosella

Bibliographic record

VenueBMC Public Health · 2012
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsConfoundingMedicineVaccinationObservational studyPandemicBiostatisticsInfluenza vaccineImmunologyEnvironmental healthTrivalent influenza vaccineInfluenza A virusEpidemiologyPublic healthOrthomyxoviridaeDemographyVirusDiseaseInternal medicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Five observational studies from Canada found an association between seasonal influenza vaccine receipt and increased risk of pandemic influenza H1N1 2009 infection. This association remains unexplained. Although uncontrolled confounding has been suggested as a possible explanation, the nature of such confounding has not been identified. Observational studies of influenza vaccination can be affected by confounding due to healthy users and the influence of social determinants on health. The purpose of this study was to investigate the influence that these two potential confounders may have in combination with temporary immunity, using stratified tables. The hypothesis is that respiratory virus infections may activate a temporary immunity that provides short-term non-specific protection against influenza and that the relationship with being a healthy user or having a social determinant may result in confounding. METHODS: We simulated the effect of confounding on vaccine effectiveness assuming that this could result from both social determinants and healthy user effects as they both influence the risk of seasonal influenza and non-influenza respiratory virus infections as well as the likelihood of being vaccinated. We then examined what impact this may have had on measurement of seasonal influenza vaccine effectiveness against pandemic influenza. RESULTS: In this simulation, failure to adjust for healthy users and social determinants would result in an erroneously increased risk of pandemic influenza infection associated with seasonal influenza vaccination. The effect sizes were not however large. CONCLUSIONS: We found that unmeasured healthy user effects and social determinants could result in an apparent association between seasonal influenza vaccine and pandemic influenza infection by virtue of being related to temporary immunity. Adjustment for social determinants of health and the healthy user effects are required in order to improve the quality of observational studies of influenza vaccine effectiveness.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.093
metaresearch head score (Gemma)0.195
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.195
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.006
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.279
GPT teacher head0.434
Teacher spread0.155 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations10
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueBMC Public Health→Same topicInfluenza Virus Research Studies→French-language works237,207→