Reimbursement Lag of New Drugs Under Taiwan's National Health Insurance System Compared With United Kingdom, Canada, Australia, Japan, and South Korea
Bibliographic record
Abstract
Drug lag-delayed approval or reimbursement-is a major barrier to accessing cutting-edge drugs. Unlike approval lag, reimbursement lag is under-researched. We investigated the key determinants of reimbursement lag under Taiwan National Health Insurance (NHI), and compared this lag with those in the United Kingdom, Canada, Australia, Japan, and South Korea. Using retrospective data on 190 new NHI-reimbursed drugs from 2007 to 2014, we studied reimbursement lag in Taiwan vs. other countries, and investigated associated factors using generalized linear models (GLMs). The median reimbursement lags during before ("first-generation") and after ("second-generation") NHI drug reimbursement scheme in Taiwan were 378 and 458 days, respectively. The "first-generation" lag was shorter only than that in South Korea, whereas the "second-generation" lag only exceeded those of the United Kingdom and Japan. In GLM models, higher drug expenditure and the introduction of the "second-generation" NHI were two statistically significant parameters associated with reimbursement lag among antineoplastic and immunomodulating agents. For other drug classes, the reimbursement price proposed by pharmaceutical companies and use of price-volume agreements were two statistically significant parameters associated with longer reimbursement lags. The current reimbursement lag in Taiwan is longer than 1 year, but only longer than those of the United Kingdom and Japan. The determinants differ between drug categories. A specific review process for antineoplastic and immunomodulating drugs may expedite reimbursement. There is a clear need for systematic data collection and analysis to ascertain factors associated with reimbursement lag and thereby inform future policy making.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".