Effectiveness of neuraminidase inhibitors in preventing hospitalization during the H1N1 influenza pandemic in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: In British Columbia (BC), Canada, neuraminidase inhibitors (NIs) were publicly funded during the 2009 A(H1N1)pdm09 pandemic for treatment of high-risk patients and/or anyone with moderate-to-severe illness. We assessed antiviral effectiveness (AVE) against hospitalization in that context. METHODS: A population-based cohort study was conducted using linked administrative data. The cohort included all individuals living in BC during the study period (1 September to 31 December 2009) with a diagnostic code consistent with influenza or pandemic H1N1. The main study period pertained to the second-wave A(H1N1)pdm09 circulation (1 October to 31 December 2009), with sensitivity analyses around the more specific pandemic peak (18 October to 7 November). Exposure was defined by same-day NI prescription. The main outcome was all-cause hospitalization within 14 days of the outpatient influenza diagnosis. Cox proportional hazards models assessed AVE with 1 : 1 propensity-score matching and covariate adjustment. RESULTS: After matching, there were 304/58,061 NI-exposed and 345/58,061 unexposed patients hospitalized during the main study period. The very young [<6 months (35.0; 95% CI 16.7-73.4)], the old [65-79 years (13.7; 95% CI 10.1-18.6)] and the very old [≥80 years (38.7; 95% CI 26.6-56.5)] had the highest hospitalization rate per 1000 patients overall. Fully adjusted AVE against all-cause hospitalization during the main study period was 16% (95% CI 2%-28%), similar to the pandemic peak (15%; 95% CI -4%-30%). CONCLUSIONS: The use of NIs was associated with modest protection against hospitalization during the 2009 pandemic, but appeared underutilized in affected age groups with the highest hospitalization risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".