Improving access to medicines and beyond: the national volume-based procurement policy in China
Bibliographic record
Abstract
Since 2019, the Chinese central government has taken significant steps to centralize national purchasing power and has implemented a pooled procurement system. In this paper, we provide an in-depth analysis of China's National Volume-Based Procurement (NVBP) policy, which represents a unique approach to pooled procurement within the pharmaceutical sector. The primary objectives of the NVBP are to reduce drug prices, enhance access to affordable medications, and improve the overall functioning of the pharmaceutical industry in China. Our analysis delves into the key features of the NVBP, including its centralized procurement system, volume-based procurement approach, and the guaranteed procurement volumes allocated to winning bidders. We also address the challenges and implications associated with the NVBP, such as its impact on the pharmaceutical industry, the sustainability of price reductions, and the importance of striking a balance between price reduction and industry sustainability. Through a comparative analysis, we shed light on the distinct characteristics of China's approach to pooled procurement and its potential ramifications for healthcare policies and practices. By examining the NVBP within the broader context of China's evolving healthcare landscape, we aim to contribute to a deeper understanding of the implications and effectiveness of this unique policy initiative.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".