Treatment of Moderate to Severe Respiratory COVID-19—A Cost-Utility Analysis
Bibliographic record
Abstract
Abstract Background Due to COVID-19’s significant morbidity and mortality, identifying the most cost-effective pharmacologic treatment strategy is critical. As such, we determined the most cost-effective strategy for moderate to severe COVID-19 respiratory infections using the United States health care system as a representative model. Methods A decision analytic model modelled a base case scenario of a 60-year-old patient admitted to hospital with COVID-19. Patients requiring oxygen were considered moderate severity, and patients with severe COVID-19 required intubation with intensive care. Strategies modelled included giving remdesivir to all patients, remdesivir in severe infections, remdesivir in moderate infections, dexamethasone to all patients, dexamethasone in severe infections, remdesivir in moderate/dexamethasone in severe infections, and best supportive care. Data for the model came from the published literature. The time horizon was 1 year; no discounting was performed due to the short duration. The perspective was of the payer in the United States health care system. Results Supportive care for moderate/severe COVID-19 cost $11,112.98/0.8256 QALY. Remdesivir in moderate/dexamethasone in severe infections was the most cost-effective with an incremental cost-effectiveness ratio of $19,764.56/QALY gained compared to supportive care. Probabilistic sensitivity analyses showed remdesivir for moderate/dexamethasone for severe COVID-19 infection was most cost-effective in 88.6% of scenarios and dexamethasone in moderate-severe infections in 11.4% of scenarios. With lower willingness to pay thresholds ($250-$37,500), dexamethasone for severe infections was favoured. Conclusions Remdesivir for moderate/dexamethasone for severe COVID-19 infections was the0020most cost-effective strategy. Further data is required for remdesivir to better assess its cost effectiveness in treatment of COVID-19.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".