Drug classification scheme review and the economic impact analysis of abandoning non-prescription drug reimbursement in Thailand
Bibliographic record
Abstract
In Thailand, controversies regarding the drug classification system persist; it was believed that the number of schemes should be changed, and most drugs classified into improper schemes. The system is not also fully used to allocate healthcare budgets, while several other countries cease non-prescription (OTC) drugs reimbursement to allocate this expenditure to catastrophic diseases instead. This study thus aims to (i) review drug classification system in Thailand by comparing to other countries, (ii) evaluate the economic impact of delisting OTC drugs from drug reimbursement list using non-sedating antihistamines (AH) in patients suffering from intermittent allergic rhinitis as a case study, and (iii) formulate an updated Thailand's drug classification system. For the first part; The US, the UK, Japan, Singapore, Malaysia, the Philippines and Canada were selected to compare. The schemes and written criteria were targeted review from each respective country's drug regulatory agency website, available published research, and expert interviews. The actual drug schemes of 53 selected drugs were then compared across different countries. For the second part, a decision tree model was used to conduct budget impact analysis using the healthcare system perspective to compare continuing (policy 1) vs abandoning reimbursement of non-sedating AH (policy 2). The primary outcome was cost-saving. Sensitivity analyses were performed. It was found that all eight�countries classify drugs into two major categories: prescription and non-prescription drugs. Some countries further subclassify non-prescription drugs. Most selected drugs in Thailand are behind-the-counter drugs, varied from antihistamines, antihypertensives to vaccines. Thai people easier access to drugs that need prescriptions in other countries since no prescriptions required. For the economic impact, when assuming non-sedating AH were no longer reimbursed, and doctor (MD) visits were decreased from 70% to 30%, Thailand would save 2.24 billion baht (72.39 million USD). The most impact parameter is MD visit probability. Cost-saving can be achieved when decreasing MD visit probability in policy 2 to a particular point, depending on the MD visit probability in policy 1. In conclusion, the number of schemes in the drug classification system is not an issue, but putting drugs in schemes is. The new reimbursement policy of OTC drug is also worth considering policy. The updated system provided in this study should be further developed by expert consultations and public hearing. More campaigns to support self-care culture in Thai populations are required.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".