Impacts of the zero mark-up policy on hospitalization expenses of T2DM and cholecystolithiasis inpatients in SC province, western China: an interrupted time series analysis.

Background: Since 2009, a series of ambitious health system reforms have been launched in China, including the zero mark-up drug policy (ZMDP); the policy was intended to reduce substantial medicine expenses for patients by abolishing the 15% mark-up on drugs. This study aims to evaluate the impacts of ZMDP on medical expenditures from the perspective of disease burden disparities in western China. Method: Two typical diseases including Type 2 diabetes mellitus (T2DM) in internal medicine and cholecystolithiasis (CS) in surgery were selected from medical records in a large tertiary level-A hospital in SC Province. The monthly average medical expenses of patients from May 2015 to August 2018 were extracted to construct an interrupted time series (ITS) model to evaluate the impact of policy implementation on the economic burden. Results: A total of 5,764 cases were enrolled in our study. The medicine expenses for T2DM patients maintained a negative trend both before and after the intervention of ZMDP. It had declined by 74.3 CNY (P < 0.001) per month on average in the pre-policy period and subsequently dropped to 704.4 CNY (P = 0.028) immediately after the policy. The level change of hospitalization expenses was insignificant (P = 0.197), with a reduction of 677.7 CNY after the policy, while the post-policy long-term trend was significantly increased by 97.7 CNY (P = 0.035) per month contrasted with the pre-policy period. In addition, the anesthesia expenses of T2DM patients had a significant increase in the level under the impact of the policy. In comparison, the medicine expenses of CS patients significantly decreased by 1,014.2 CNY (P < 0.001) after the policy, while the total hospitalization expenses had no significant change in level and slope under the influence of ZMDP. Furthermore, the expenses of surgery and anesthesia for CS patients significantly increased by 320.9 CNY and 331.4 CNY immediately after the policy intervention. Conclusion: Our study indicated that the ZMDP has been an effective intervention to reduce the excessive medicine expenses for both researched medical and surgical diseases, but failed to show any long-term advantage. Moreover, the policy has no significant impact on relieving the overall hospitalization burden for either condition.

Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics.

Background: Medication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management. Methods: This cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey. Demographic and clinical characteristics were extracted from the questionnaire and electronic medical records. The sample was randomly divided into a training dataset and a test dataset with a radio of 8:2 after data preprocessing. Four imputing methods, five sampling methods, three screening methods, and 18 machine learning algorithms were used to groom data and develop and validate models. Bootstrapping was performed to generate the validation set for external validation and univariate analysis. Models were compared on the basis of predictive performance metrics. Finally, we validated the sample size on the best model. Results: This study included 980 patients with T2D, of whom 184 (18.8%) were defined as medication non-adherence. The results indicated that the model used modified random forest as the imputation method, random under sampler as the sampling method, Boruta as the feature screening method and the ensemble algorithms and had the best performance. The area under the receiver operating characteristic curve (AUC), F1 score, and area under the precision-recall curve (AUPRC) of the best model, among a total of 1,080 trained models, were 0.8369, 0.7912, and 0.9574, respectively. Age, present fasting blood glucose (FBG) values, present HbA1c values, present random blood glucose (RBG) values, and body mass index (BMI) were the most significant contributors associated with risks of medication adherence. Conclusion: We found that machine learning methods could be used to predict the risk of non-adherence in patients with T2D. The proposed model was well performed to identify patients with T2D with non-adherence and could help improve individualized T2D management.

Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms.

OBJECTIVE: Medication adherence plays a key role in type 2 diabetes (T2D) care. Identifying patients with high risks of non-compliance helps individualized management, especially for China, where medical resources are relatively insufficient. However, models with good predictive capabilities have not been studied. This study aims to assess multiple machine learning algorithms and screen out a model that can be used to predict patients' non-adherence risks. METHODS: A real-world registration study was conducted at Sichuan Provincial People's Hospital from 1 April 2018 to 30 March 2019. Data of patients with T2D on demographics, disease and treatment, diet and exercise, mental status, and treatment adherence were obtained by face-to-face questionnaires. The medication possession ratio was used to evaluate patients' medication adherence status. Fourteen machine learning algorithms were applied for modeling, including Bayesian network, Neural Net, support vector machine, and so on, and balanced sampling, data imputation, binning, and methods of feature selection were evaluated by the area under the receiver operating characteristic curve (AUC). We use two-way cross-validation to ensure the accuracy of model evaluation, and we performed a posteriori test on the sample size based on the trend of AUC as the sample size increase. RESULTS: A total of 401 patients out of 630 candidates were investigated, of which 85 were evaluated as poor adherence (21.20%). A total of 16 variables were selected as potential variables for modeling, and 300 models were built based on 30 machine learning algorithms. Among these algorithms, the AUC of the best capable one was 0.866±0.082. Imputing, oversampling and larger sample size will help improve predictive ability. CONCLUSIONS: An accurate and sensitive adherence prediction model based on real-world registration data was established after evaluating data filling, balanced sampling, and so on, which may provide a technical tool for individualized diabetes care.