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Measuring waning protection from seasonal influenza vaccination during nine influenza seasons, Ontario, Canada, 2010/11 to 2018/19

2024· article· en· W4392104998 on OpenAlex
Hannah Chung, Michael A. Campitelli, Sarah A. Buchan, Aaron Campigotto, Natasha S. Crowcroft, Jonathan B. Gubbay, James K. H. Jung, Timothy Karnauchow, Kevin Katz, Allison McGeer, James Dayre McNally, David Richardson, Susan E. Richardson, Laura C. Rosella, Margaret L. Russell, Kevin L. Schwartz, Andrew E. Simor, Marek Smieja, Maria E. Sundaram, Bryna Warshawsky, George Zahariadis, Jeffrey C. Kwong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEurosurveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster UniversityUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science CentrePublic Health OntarioWilliam Osler Health SystemSinai Health SystemNorth York General HospitalInstitute for Clinical Evaluative SciencesUniversity of OttawaHospital for Sick ChildrenLondon Health Sciences CentreWestern UniversityUniversity of TorontoUniversity of CalgaryChildren's Hospital of Eastern Ontario
FundersDepartment of Family and Community Medicine, University of TorontoCanadian Institutes of Health ResearchCanadian Immunization Research NetworkUniversity of TorontoPublic Health AgencyPublic Health Agency of Canada
KeywordsVaccinationMedicineOdds ratioInfluenza vaccineSeasonal influenzaOddsConfidence intervalInfluenza A virusImmunityDemographyImmunologyInternal medicineVirusLogistic regressionCoronavirus disease 2019 (COVID-19)Immune systemDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background Waning immunity from seasonal influenza vaccination can cause suboptimal protection during peak influenza activity. However, vaccine effectiveness studies assessing waning immunity using vaccinated and unvaccinated individuals are subject to biases. Aim We examined the association between time since vaccination and laboratory-confirmed influenza to assess the change in influenza vaccine protection over time. Methods Using linked laboratory and health administrative databases in Ontario, Canada, we identified community-dwelling individuals aged ≥ 6 months who received an influenza vaccine before being tested for influenza by RT-PCR during the 2010/11 to 2018/19 influenza seasons. We estimated the adjusted odds ratio (aOR) for laboratory-confirmed influenza by time since vaccination (categorised into intervals) and for every 28 days. Results There were 53,065 individuals who were vaccinated before testing for influenza, with 10,264 (19%) influenza-positive cases. The odds of influenza increased from 1.05 (95% CI: 0.91–1.22) at 42–69 days after vaccination and peaked at 1.27 (95% CI: 1.04–1.55) at 126–153 days when compared with the reference interval (14–41 days). This corresponded to 1.09-times increased odds of influenza every 28 days (aOR = 1.09; 95% CI: 1.04–1.15). Individuals aged 18–64 years showed the greatest decline in protection against influenza A(H1N1) (aOR per 28 days = 1.26; 95% CI: 0.97–1.64), whereas for individuals aged ≥ 65 years, it was against influenza A(H3N2) (aOR per 28 days = 1.20; 95% CI: 1.08–1.33). We did not observe evidence of waning vaccine protection for individuals aged < 18 years. Conclusions Influenza vaccine protection wanes during an influenza season. Understanding the optimal timing of vaccination could ensure robust protection during seasonal influenza activity.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.313
Teacher spread0.224 · 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