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 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".