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Record W2280166798 · doi:10.1093/ofid/ofv133.1465

Preliminary End-of-Season Estimates of 2014/15 Influenza Vaccine Effectiveness in Preventing Laboratory-Confirmed Influenza-Related Hospitalization From the Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN)

2015· article· en· W2280166798 on OpenAlexaboutno aff
Shelly McNeil, Ardith Ambrose, Melissa K. Andrew, Guy Boivin, William Bowie, Gaël Dos Santos, May ElSherif, Karen Green, François Haguinet, Todd F. Hatchette, Kevin Katz, Jason J. LeBlanc, Mark Loeb, Donna MacKinnon‐Cameron, Anne McCarthy, Janet E. McElhaney, Allison McGeer, Jeff Powis, David Richardson, Makeda Semret, Rohita Sharma, Vivek Shinde, Daniel Smyth, Sylvie Trottier, Louis Valiquette, Duncan Webster, Lingyun Ye

Bibliographic record

VenueOpen Forum Infectious Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmunizationInfluenza seasonInfluenza vaccineVaccine-preventable diseasesVaccinationPediatricsVirologyMedical emergencyIntensive care medicineImmunology

Abstract

fetched live from OpenAlex

Background. Canada's 2014/15 influenza season was characterized by early, intense influenza A(H3N2) activity followed by a smaller influenza B peak. Characterization of circulating viruses demonstrated poor match with the A(H3N2) component of the 2014/15 vaccine suggesting vaccine effectiveness (VE) would be suboptimal. We provide end-of-season estimates of influenza VE for prevention of influenza-related hospitalization in adults. Methods. The SOS Network conducted active surveillance for influenza in hospitalized adults from 15 November 2014 to 30 April 2015 in 16 hospitals. A nasopharyngeal swab for influenza PCR was obtained from all adult patients admitted with acute respiratory illness. Cases were PCR-positive; those influenza-PCR negative within 7 days of symptom onset with known influenza vaccination status were controls. Crude VE estimates were adjusted using multivariable logistic regression with stepwise backward selection of covariates with a p-value of <0.1 in the univariate analysis. VE was estimated as (1 − OR) × 100. Results. 1693 cases and 1936 controls were enrolled; cases were older (mean age 76.1 versus 72.6y; p < .0001); 62.4% cases were >75y. Cases were more likely than controls to have received antivirals (AV) before admission (0.6% versus 0.1%; p = .009) and to be pregnant (1.3% versus 0.2%; p < .0005). 68.2% cases and 69.6% controls had received influenza vaccine ≥2 wks prior to symptom onset. Among all age groups, VE was −0.4% (95% CI −24.5 to 19.0%) against all strains, −32.7% (−91.2 to 7.9%) against A(H3N2) and 20.1% (−15.9 to 44.9%) against influenza B. Age, prior receipt of AV, BMI and ≥1 comorbidity were adjusted in the final regression model. In ≥ 65y, VE was −0.9% (−29.6 to 21.4%) against all strains, −36.2% (−105.9 to 9.9%) against A(H3N2) and 17.8% (−28.7 to 47.5%) against B. Conclusion. The 2014/15 season in Canada was dominated by circulation of a drifted influenza A(H3N2). Although VE cannot be predicted directly from virologic surveillance, mismatch between the circulating strain and vaccine strain this season did result in failure of the vaccine to provide protection against influenza-associated hospitalization. Influenza programs should ensure real-time VE assessment to inform adjunctive public health strategies to minimize influenza disease burden. Disclosures. S. Mcneil, GlaxoSmithKline: Grant Investigator and Investigator, Research grant; M. K. Andrew, GlaxoSmithKline: Grant Investigator, Research grant; G. Dos Santos, GSK Vaccines: Consultant, Salary; F. Haguinet, GSK Vaccines: Employee, Salary; T. Hatchette, GlaxoSmithKline: Grant Investigator, Research grant; J. Leblanc, GlaxoSmithKline: Grant Investigator, Research grant; J. E. Mcelhaney, Sanofi Pasteur: Independent Contractor, Speaker honorarium; R. Sharma, GSK Vaccines: Employee, Salary; V. Shinde, GSK Vaccines: Employee, Salary; L. Valiquette, Pfizer: Grant Investigator, Research grant. Merck: Grant Investigator, Research grant. GSK: Grant Investigator, Research grant

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.526
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.040
GPT teacher head0.373
Teacher spread0.333 · 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.

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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