The impacts of the National Medication Price-Negotiated Policy on the financial burden of cancer patients in Shandong province, China: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: In order to further regulate the price of anticancer medication and alleviate the financial burden of cancer patients, the Chinese government implemented the National Medication Price-Negotiated Policy (NMPNP) in 2017. This study aims to assess the impacts of implementation of the NMPNP on the access of anticancer medication and the financial burden for cancer patients in Shandong province, and to provide evidence to inform the design of similar policies in other developing countries. METHODS: A quasi-experiment design of an interrupt time series analysis was conducted. The month of September 2017 was taken as the intervention point when the Shandong Provincial Reimbursement Drug Lists was updated based on the result of the NMPNP in 2017. The data used were the aggregated monthly claim data of cancer patients from 2016 to 2021, which were obtained from four cities in Shandong province. The outpatient and inpatient care visits per capita, proportion of OOP expenditure and medication costs in outpatient and inpatient medical costs were used as outcome variables. A segmented regression model was used to analyze the change of the access of anticancer medication and the financial burden for cancer patients. RESULTS: The outpatient care visits per capita significantly decreased after the intervention. Compared to preintervention trend, the proportion of OOP expenditure in outpatient medical costs decreased by average 0.25 percentage point per month (p < 0.0001) after the intervention, however the proportion of OOP expenditure in inpatient medical costs increased by 0.02 percentage point per month (p = 0.76). Since the intervention, the proportion of medication costs in outpatient medical costs averagely rose by 0.28 percentage point (p < 0.0001), and its implementation caused the proportion of medication costs in inpatient medical costs averagely decreased 0.2 percentage point (p < 0.0001). CONCLUSIONS: The NMPNP improved the access of anticancer medication, and relieved the financial burden of outpatient care. However, it did not effectively alleviate the financial burden of inpatient care. Additionally, the NMPNP impacted the behavior of the healthcare providers. The policymakers should closely monitor the change of providers behaviors, and dynamically adjust financial incentives policies of healthcare providers during the implementation of similar medication price negotiated policies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".