Systematic review of the impact of the National Medication Price Negotiated Policy on the accessibility of drugs in China, 2016–2024
Bibliographic record
Abstract
OBJECTIVE: To alleviate the economic burden of innovative drugs on patients in China, the government has been negotiating drug prices since 2016 to enhance their accessibility. This systematic review aimed to discuss the impact of the National Medication Price Negotiation Policy (NMPNP) on the accessibility of drugs in China in the years 2016-2024. DESIGN: Systematically reviewed the studies' findings and evaluated their quality using the Newcastle-Ottawa Scale (NOS) collaborative tool. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 were used to facilitate transparent and complete reporting of our systematic review. DATA SOURCES: PubMed, Web of Science, CNKI and Wanfang were searched from 1 January 2016 to 1 October 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: The inclusion criteria of this study is a quantitative study to evaluate the accessibility of negotiated drugs after the implementation of the NMPNP. DATA EXTRACTION AND SYNTHESIS: Two researchers independently searched the literature, extracted the data and cross-checked them. Any disagreements were resolved by discussion or consultation with a third party. The quality of systematic reviews was assessed using the NOS. RESULTS: A total of 32 studies were included in this review, 8 of the studies were assessed to be high quality based on the NOS, 17 as moderate quality and the remaining 7 as low quality. Most of them showed that after the implementation of the NMPNP, the availability, affordability, defined daily doses, hospital purchase volume and expenditure of negotiated drugs increased, and the price and defined daily dose cost of negotiated drugs decreased. However, a few studies found that some drugs are difficult to be admitted to hospitals and consumption dropped after the implementation of the NMPNP due to low clinical demand and weak competitiveness. CONCLUSIONS: The implementation of NMPNP improved drug accessibility for patients and most regions had good implementation effects which can provide some insights for other countries. However, the high utilisation of successfully negotiated drugs has increased health insurance expenditures, potentially affecting the fund's stability. This necessitates government regulation of both the use of these drugs and health insurance funds. Moreover, different regions and medical institutions had different development levels and resource allocations, resulting in uneven effects of the NMPNP which need to be improved in the future.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".