Healthcare professionals’ knowledge, attitude and practice towards National Centralized Drug Procurement policy in central China: A cross-sectional study

Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm

PURPOSE

The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast cancer to identify patients with a high risk of sentinel lymph node metastasis.

METHODS

In this machine learning study, we retrospectively collected 225 female breast cancer patients who underwent sentinel lymph node biopsy (SLNB). Feature screening was performed using the logistic regression analysis. Subsequently, five ML algorithms, namely LOGIT, LASSO, XGBOOST, RANDOM FOREST model and GBM model were employed to train and develop an ML model. In addition, model interpretation was performed by the Shapley Additive Explanations (SHAP) analysis to clarify the importance of each feature of the model and its decision basis.

RESULTS

Combined univariate and multivariate logistic regression analysis, identified Multifocal, LVI, Maximum Diameter, Shape US, Maximum Cortical Thickness as significant predictors. We than successfully leveraged machine learning algorithms, particularly the RANDOM FOREST model, to develop a predictive model for sentinel lymph node metastasis in breast cancer. Finally, the SHAP method identified Maximum Diameter and Maximum Cortical Thickness as the primary decision factors influencing the ML model's predictions.

CONCLUSION

With the integration of pathological and imaging characteristics, ML algorithm can accurately predict sentinel lymph node metastasis in breast cancer patients. The RANDOM FOREST model showed ideal performance. With the incorporation of these models in the clinic, can helpful for clinicians to identify patients at risk of sentinel lymph node metastasis of breast cancer and make more reasonable treatment decisions.

Drug supply and assurance: a cross-sectional study of drug shortage monitoring varieties in China

BACKGROUND

Drug shortage is a worldwide problem that seriously threatens public health. China released the most comprehensive list of key drug shortage monitoring varieties ever in 2022. We aim to analyze the attributes and characteristics of the medicines within the list to provide a reference for improving China's supply security of shortage drugs.

METHODS

We used public data to extract information on drug types, dosage forms, indications, classification of clinical uses, whether they were included in medical catalogs such as the National Essential Drugs, and the number of drug and active pharmaceutical ingredient (API) manufacturers. A descriptive statistical analysis was used.

RESULTS

Of the 980 drugs on the list, 99.59% were chemicals and 92.65% were injectables. Drugs for blood and hematopoietic organs, the cardiovascular system, and the digestive tract and metabolism ranked among the top three shortages. Verification of the medical catalogs showed that 90.41% of the drugs belonged to the national essential drugs, 95.10% were medicare drugs, 2.55% were volume-based procurement drugs, and 14.70% were for rare diseases, and 42.04% were for children. In terms of drug supply capacity, 21.33% of drug approvals are less than 10, and there were even 26 drugs for exclusive production, close to 90% of manufacturers need to purchase APIs from outside. Among the 256 APIs included in the list, 152 APIs had less than 10 manufacturers, and there were even 5 APIs produced by only one enterprise nationwide.

CONCLUSIONS

The situation of drug shortages in China was severe and complex, with serious shortages of medicines adapted to basic medical and healthcare needs and clinically necessary medicines, and a need to improve the production capacity of drugs and the ability to supply APIs. We recommend strengthening drug monitoring and stockpiling and accelerating the approval of shortage drugs to improve drug supply security.

The Public's Self-Avoidance and Other-Reliance in the Reporting of Medical Insurance Fraud: A Cross-Sectional Survey in China

Purpose

To understand the public's self-willingness to report medical insurance fraud and their expectations on others, to provide a reference for the government to do a good job in medical insurance anti-fraud.

Methods

Data were obtained from a questionnaire survey of 846 respondents in China. Descriptive statistical analyses and multinomial logistic regression were used to analyze the different subjective attitudes of the public toward different subjects when faced with medical insurance fraud and the influencing factors.

Results

511 (60.40%) respondents were willing to report medical insurance fraud, while 739 (87.35%) respondents expected others to report it. 485 (57.33%) respondents were willing and expected others to report medical insurance fraud, followed by those who were not willing but expected others to report it (254, 30.02%). Compared to those who were unwilling to report themselves and did not want others to report, those who believe their reporting is useless (OR=3.13, 95% CI=1.15-8.33) and those who fear for their safety after reporting (OR=2.96, 95% CI=1.66-5.26) were more likely to expect others to report. Self-reporting willingness was stronger among the public who were satisfied with the government's protective measures for the safety of whistleblowers (OR=4.43, 95% CI=1.38-14.17). The public who believe that both themselves and others have responsibilities to report medical insurance fraud were willing to report and expect others to do the same.

Conclusion

The public had a "self-avoidance" and "other-reliance" mentality in medical insurance anti-fraud. The free-rider mentality, lack of empathy, concerns about own risk after reporting, and the interference of decentralized responsibility were important factors contributing to this public mentality. At this stage, the government should prevent the public's "collective indifference" in medical insurance anti-fraud efforts. Improving the safety and protection of whistleblowers and making everyone feel more responsible and valued may be effective incentives to enhance the public's willingness to report.

Impact of China's National Centralized Drug Procurement Policy on pharmaceutical enterprises' financial performance: a quasi-natural experimental study

Introduction

In China, the interest relationship between pharmaceutical enterprises and medical institutions has harmed the healthy development of pharmaceutical enterprises. In November 2018, the National Centralized Drug Procurement (NCDP) policy was published. The NCDP policy severs the interest relationship and significantly impacts on pharmaceutical enterprises's financial performance.

Methods

Using the implementation of China's National Centralized Drug Procurement (NCDP) policy as a quasi-natural experiment, this study evaluated the impact of participation in the NCDP policy on pharmaceutical enterprises' financial performance. We developed a difference-in-difference model to estimate the change in financial performance after NCDP implementation, based on financial data on Chinese listed pharmaceutical enterprises.

Results

We found that the bid-winning enterprises' financial performance significantly improved after participating in NCDP. This may be related to lower costs, market share expansion, and increased research and development investment by the bid-winning enterprises.

Discussion

To further promote the high-quality development of pharmaceutical enterprises in China, the government should expand the variety of drugs on the NCDP list (NCDP drugs), while improving the drug patent protection system and the policies to support the bid-winning enterprises.