Economic evaluation of remdesivir for the treatment of severe COVID‐19 patients in China under different scenarios
Bibliographic record
Abstract
AIMS: The present study aimed to evaluate the cost-effectiveness of the 5-day remdesivir regimen compared with standard of care among severe COVID-19 patients in China, the evidence on which is essential to inform the necessity of securing access to remdesivir. METHODS: A dynamic transmission model that extended the susceptible-exposed-infected-recovered framework by incorporating asymptomatic, presymptomatic and waiting-to-be-diagnosed patients was constructed to conduct the cost-effectiveness analysis from the healthcare system perspective. To estimate epidemic parameters, the model was first calibrated to the observed epidemic curve in Wuhan from 23 January to 19 March 2020. Following the calibration, the infected compartment was replaced by 3 severity-defined health states to reflect differential costs and quality of life associated with disease gravity. Costs and quality-adjusted life year (QALY) outcomes of 9 million simulated people were accrued across time to evaluate the incremental cost-effectiveness ratio of remdesivir. As robustness checks, an alternative modelling technique using decision tree, additional epidemic scenarios representing different epidemic intensities, and 1-way parameter variations were also analysed. RESULTS: Remdesivir treatment cost CN¥97.93 million more than standard of care. Also, the net QALY gain from 5-day remdesivir treatment was 6947 QALYs. As such, the incremental cost-effectiveness ratio was CN¥14 098/QALY, substantially lower than the gross domestic product per capita threshold. The peak daily number of severe cases was 19% lower in the remdesivir treatment strategy. Overall, results were robust in alternative scenarios and sensitivity analyses. CONCLUSION: Given the cost-effectiveness profile, access to remdesivir for severe COVID-19 patients in China should be considered.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".