Changes in use of antidiabetic medications following price regulations in China (1999–2009)
Bibliographic record
Abstract
Abstract Background and Objectives Pharmaceutical expenditures are a major burden in China, partly because of pharmaceutical sales covering hospital operating costs, so a series of reductions in maximum retail prices of specific products, including antidiabetic medications, have been implemented. This is the first large-scale, longitudinal study evaluating effects of two rounds of price regulations on use of antidiabetic medications. Methods We used quarterly purchasing data collected by IMS Health from more than 1000 hospitals in China (1999–2009). We used interrupted time-series analysis to assess changes in the population-adjusted market volume of insulin and oral hypoglycaemic products and in the percentage market share of price-regulated products following price regulations implemented in December 2001 and December 2006. Results After the 2001 price regulation, there was a significant increase in market volume trend of insulin products (0.06 standard units/1000 population/quarter; 95% confidence interval: 0.04–0.08); utilisation of oral hypoglycaemic products remained stable. After the 2006 price regulation, there were significant increases in the market volume trend of both insulin products (0.18 standard units/1000 population/quarter; 0.12–0.23) and oral hypoglycaemic products (10.31 standard units/1000 population/quarter; 5.65–14.98). Market share of price-regulated insulin and oral hypoglycaemic products did not change significantly after either price regulation. Conclusion Our results indicate that lowering the retail prices of specific products was associated with increases in total per capita utilisation of antidiabetic medications without shifts in use between non- and price-regulated products. Broad volume increases suggest access to antidiabetic medications by additional people, increased use by those with prior access, or both.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".