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Record W2952408556 · doi:10.1016/j.vaccine.2019.06.011

Can routinely collected laboratory and health administrative data be used to assess influenza vaccine effectiveness? Assessing the validity of the Flu and Other Respiratory Viruses Research (FOREVER) Cohort

2019· article· en· W2952408556 on OpenAlexafffund
Jeffrey C. Kwong, Sarah A. Buchan, Hannah Chung, Michael A. Campitelli, Kevin L. Schwartz, Natasha S. Crowcroft, Michael L. Jackson, Timothy Karnauchow, Kevin Katz, Allison McGeer, James Dayre McNally, David Richardson, Susan E. Richardson, Laura C. Rosella, Andrew E. Simor, Marek Smieja, George Zahariadis, Aaron Campigotto, Jonathan B. Gubbay

Bibliographic record

VenueVaccine · 2019
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsNewfoundland and Labrador Centre for Applied Health ResearchLondon Health Sciences CentreMcMaster UniversityUniversity Health NetworkHealth Sciences CentrePublic Health OntarioWilliam Osler Health SystemSunnybrook Health Science CentreSinai Health SystemNorth York General HospitalUniversity of OttawaHospital for Sick ChildrenSt. John’s Health Sciences CentreUniversity of TorontoChildren's Hospital of Eastern Ontario
FundersInstitut canadien d'information sur la santéCanadian Institutes of Health ResearchPublic Health OntarioH2020 HealthOntario Ministry of Health and Long-Term CareCancer Care Ontario
KeywordsMedicineInfluenza vaccineInfluenza-like illnessConfidence intervalOdds ratioCohort studyCohortSelection biasOddsInfluenza A virusOrthomyxoviridaeVaccinationDemographyInternal medicineImmunologyLogistic regressionVirusPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Linking data on laboratory specimens collected during clinical practice with health administrative data permits highly powered vaccine effectiveness (VE) studies to be conducted at relatively low cost, but bias from using convenience samples is a concern. We evaluated the validity of using such data for estimating VE. METHODS: We created the Flu and Other Respiratory Viruses Research (FOREVER) Cohort by linking individual-level data on respiratory virus laboratory tests, hospitalizations, emergency department visits, and physician services. For community-dwelling adults aged > 65 years, we assessed the presence and magnitude of information and selection biases, generated VE estimates under various conditions, and compared our VE estimates with those from other studies. RESULTS: We included 65,648 unique testing episodes obtained from 54,434 individuals during the 2010-11 to 2015-16 influenza seasons. To examine information bias, we found the proportion testing positive for influenza for patients with unknown interval from illness onset to specimen collection was more similar to patients for whom illness onset date was ≤ 7 days before specimen collection than to patients for whom illness onset was > 7 days before specimen collection. To assess the presence of selection bias, we found the likelihood of influenza testing was comparable between vaccinated and unvaccinated individuals, although the adjusted odds ratios were significantly greater than 1 for some healthcare settings and during some influenza seasons. Over 6 seasons, VE estimates ranged between 36% (95%CI, 27-44%) in 2010-11 and 5% (95%CI, -2, 11%) in 2014-15. VE estimates were similar under a range of conditions, but were consistently higher when accounting for misclassification of vaccination status through a quantitative sensitivity analysis. VE estimates from the FOREVER Cohort were comparable to those from other studies. CONCLUSIONS: Routinely collected laboratory and health administrative data contained in the FOREVER Cohort can be used to estimate influenza VE in community-dwelling older adults.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.548
GPT teacher head0.545
Teacher spread0.004 · 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 designObservational
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

Citations41
Published2019
Admission routes2
Has abstractyes

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