An empirical study looking at the potential impact of increasing cost-effectiveness threshold on reimbursement decisions in Thailand
Bibliographic record
Abstract
• Economic evidence (cost-effectiveness information) can be used to inform policy-making process in supporting the movement towards universal health coverage. Published literature has focused on methods to set a cost-effectiveness threshold (CET) which can be used to guide the cost-effectiveness of a health technology. Higher CET could increase the opportunity that health technologies will be reimbursed by a healthcare payer. Other published literature focused on the discussion of whether CET should be increased. • Although there has been a debate around an optimal CET and a significant development of methodologies for estimating CET, to our knowledge, no other country has changed their existing explicit CET. Thailand is in a unique position to help answer the question of what happened when CET was increased. The objectives were to explore the impact of increasing CET on the submitted medicine price and the reimbursement decision to the National List of Essential Medicine. • The current findings showed that a change in CET did not significantly influence the likelihood of a positive benefit package listing recommendation, or the medicine prices set by manufacturers for public payers. The findings shed light to the potential impact of increasing a CET and highlighted a need for further research into the role of CET in informing policy decisions (with a qualitative approach), to better guide CET policy in Thailand and globally. There has been lots of debate regarding an appropriate value of cost-effectiveness threshold (CET). To our knowledge, Thailand is the only country which has explicit CET and has increased the CET. Therefore, Thailand is in a unique position to help answer the question of what happened when CET was increased. The study objectives were to explore the impact of increasing CET on the submitted medicine price by industry and the decision to be included in the National List of Essential Medicine in Thailand. Retrospective secondary data analyses were conducted using data from economic evaluation reports being reviewed by the National Drug Subcommittee. In total, 55 reports were included in the analysis, which represented 295 observations as each report could have more than one medicine for different indication and/or target population. The intervention of interest was the change in CET policy from 100,000 THB/QALY in 2008 to 120,000 THB/QALY in 2010 to 160,000 THB/QALY in 2013. There is no evidence suggesting the increase in CET affected the submitted medicine prices (price change=19%, p-value=0.457) or increased the likelihood of a positive reimbursement decision (OR=1.596, p-value=0.532). There were other factors which may influence medicine prices and reimbursement decision. The change in the CET did not significantly affect health resource allocation. The findings do not support whether the current CET value in Thailand should be increased. Future research should continue to monitor the submission and re-analyse the current work as more data become available using both quantitative and qualitative approaches.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".