Monitoring the impact of regulatory measures on medicine pricing in Thailand: an observation over a 16-year span
Bibliographic record
Abstract
Background: Following a 2007 report by the Thai Food and Drug Administration highlighting disparities in drug pricing across different sectors, there has been a concerted effort to establish and enforce a cohesive medicine pricing policy in Thailand. This study aims to explore the government interventions on medicine pricing in Thailand. Methods: Employing a mixed-methods approach, this research included a literature review and a cross-sectional survey of medicine prices using the World Health Organization/Health Action International (WHO/HAI) methodology. Data were collected from both public and private sectors across six provinces in Thailand during April-May 2023. Additionally, international price comparisons were conducted with countries including Australia, Canada, Denmark, Malaysia, and New Zealand. Results: The research identified a significant reduction in the median price ratios (MPRs) of medicines, closer alignment of prices with international benchmarks, and decreased variability in pricing between regions and sectors. These changes illustrated the positive effects of Thailand's pricing policies implemented over the past 16 years. Conclusions: The strategic interventions implemented by the Thai government have markedly enhanced the regulation and affordability of medicine prices. However, to sustain these achievements and ensure the viability of the local pharmaceutical industry, ongoing efforts and policy adaptations are essential. This study emphasises the critical need for continuous evaluation of these policies to respond effectively to evolving healthcare and economic conditions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".