Cost‐effectiveness analysis of antiviral treatment in the management of seasonal influenza A: point‐of‐care rapid test versus clinical judgment
Bibliographic record
Abstract
BACKGROUND: A point-of-care rapid test (POCRT) may help early and targeted use of antiviral drugs for the management of influenza A infection. OBJECTIVE: (i) To determine whether antiviral treatment based on a POCRT for influenza A is cost-effective and, (ii) to determine the thresholds of key test parameters (sensitivity, specificity and cost) at which a POCRT based-strategy appears to be cost effective. METHODS: An hybrid « susceptible, infected, recovered (SIR) » compartmental transmission and Markov decision analytic model was used to simulate the cost-effectiveness of antiviral treatment based on a POCRT for influenza A in the social perspective. Data input parameters used were retrieved from peer-review published studies and government databases. The outcome considered was the incremental cost per life-year saved for one seasonal influenza season. RESULTS: In the base-case analysis, the antiviral treatment based on POCRT saves 2 lives/100,000 person-years and costs $7600 less than the empirical antiviral treatment based on clinical judgment alone, which demonstrates that the POCRT-based strategy is dominant. In one and two way-sensitivity analyses, results were sensitive to the POCRT accuracy and cost, to the vaccination coverage as well as to the prevalence of influenza A. In probabilistic sensitivity analyses, the POCRT strategy is cost-effective in 66% of cases, for a commonly accepted threshold of $50,000 per life-year saved. CONCLUSION: The influenza antiviral treatment based on POCRT could be cost-effective in specific conditions of performance, price and disease prevalence.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".