Economic Evaluation of Evusheld for Preexposure Prevention of COVID-19 in High-Risk Populations: Early Evidence from Thailand
Bibliographic record
Abstract
BACKGROUND AND AIMS: The introduction of Coronavirus disease 2019 (COVID-19) vaccines urged all Thais to seek prevention of serious illness and death from COVID-19. However, immunocompromised individuals might not be able to achieve an efficient immune response from these vaccines. This study aimed to evaluate the cost-effectiveness and budget impact of introducing Evusheld (tixagevimab plus cilgavimab) for three patient groups-organ transplant, autoimmune disease, and dialysis patients, from the Thai government perspective. METHODS: A Markov decision model was developed to compare the use of Evusheld plus COVID-19 vaccines versus COVID-19 vaccines alone. The methodology followed the National HTA Guidelines of Thailand. Model input parameters were collected locally from retrospective data and from a literature review. RESULTS: Evusheld helped prevent COVID-19 infection, severe infection, and death in all three patient groups. Using the Thai threshold of 160,000 Thai Baht (THB) per quality-adjusted life year (QALY) gained, the only scenario found to be cost-effective was that of dialysis patients with inadequate immune response, with an incremental cost-effectiveness ratio (ICER) of 54,700 THB per QALY gained. To make a policy of Evusheld provision cost-effective in other groups, the price of Evusheld had to be lower (a reduction of 44-88% of its current price). The results of one-way sensitivity analysis indicated that the cost-effectiveness of Evusheld was sensitive to changes in the rate of infection, cost and efficacy of Evusheld, proportion of inadequate immune responses, and the probability of moving from a 'recovered' to 'susceptible' status. CONCLUSION: Among three COVID-19-vaccinated immunocompromised patient populations, this study concluded that Evusheld was cost-effective for dialysis patients with inadequate immune response to the COVID-19 vaccine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".