Influenza Vaccine Effectiveness Among Patients With Cancer: A Population-Based Study Using Health Administrative and Laboratory Testing Data From Ontario, Canada
Bibliographic record
Abstract
PURPOSE Seasonal influenza vaccination is recommended for patients with cancer despite concerns of disease or treatment-associated immunosuppression. The objective of this study was to evaluate vaccine effectiveness (VE) against laboratory-confirmed influenza for patients with cancer. PATIENTS AND METHODS We conducted an observational test-negative design study of previously diagnosed patients with cancer 18 years of age and older who underwent influenza testing during the 2010-2011 to 2015-2016 influenza seasons in Ontario, Canada. We linked individual-level cancer registry, respiratory virus testing, and health administrative data to identify the study population and outcomes. Vaccination status was determined from physician and pharmacist billing claims. We used multivariable logistic regression to estimate VE, adjusting for age, sex, rurality, income quintile, cancer characteristics, chemotherapy exposure, comorbidities, previous health care use, influenza season, and calendar time. RESULTS We identified 26,463 patients with cancer who underwent influenza testing, with 4,320 test-positive cases (16%) and 11,783 (45%) vaccinated. Mean age was 70 years, 52% were male, mean time since diagnosis was 6 years, 69% had solid tumor malignancies, and 23% received active chemotherapy. VE against laboratory-confirmed influenza was 21% (95% CI, 15% to 26%), and VE against laboratory-confirmed influenza hospitalization was 20% (95% CI, 13% to 26%). For patients with solid tumor malignancies, VE was 25% (95% CI, 18% to 31%), compared with 8% (95% CI, –5% to 19%) for patients with hematologic malignancies ( P = .015). Active chemotherapy usage did not significantly affect VE, especially among patients with solid tumor cancer. CONCLUSION Our results support recommendations for influenza vaccination for patients with cancer. VE was decreased for patients with hematologic malignancies, and there was no significant difference in VE among patients with solid tumor cancer receiving active chemotherapy. Strategies to optimize influenza prevention among patients with cancer are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".