Health economics study of paliperidone palmitate in the treatment of schizophrenia: a 12-month cohort study.

BACKGROUND: To analyze the economic benefits of paliperidone palmitate in the treatment of schizophrenia. METHODS: We collected 546 patients who met the diagnostic criteria for schizophrenia according to the 《International Statistical Classification of Diseases and Related Health Problems,10th》(ICD-10). We gathered general population data such as gender, age, marital status, and education level, then initiated treatment with paliperidone palmitate. Then Follow-up evaluations were conducted at 1, 3, 6, 9, and 12 months after the start of treatment to assess clinical efficacy, adverse reactions, and injection doses. We also collected information on the economic burden before and after 12 months of treatment, as well as the number of outpatient visits and hospitalizations in the past year to analyze economic benefits. RESULTS: The baseline patients totaled 546, with 239 still receiving treatment with paliperidone palmitate 12 months later. After 12 months of treatment, the number of outpatient visits per year increased compared to before (4 (2,10) vs. 12 (4,12), Z=-5.949, P < 0.001), while the number of hospitalizations decreased (1 (1,3) vs. 1 (1,2), Z = 5.625, P < 0.001). The inpatient costs in the direct medical expenses of patients after 12 months of treatment decreased compared to before (5000(2000,12000) vs. 3000 (1000,8050), P < 0.05), while there was no significant change in outpatient expenses and direct non-medical expenses (transportation, accommodation, meal, and family accompanying expenses, etc.) (P > 0.05); the indirect costs of patients after 12 months of treatment (lost productivity costs for patients and families, economic costs due to destructive behavior, costs of seeking non-medical assistance) decreased compared to before (300(150,600) vs. 150(100,200), P < 0.05). CONCLUSION: Palmatine palmitate reduces the number of hospitalizations for patients, as well as their direct and indirect economic burdens, and has good economic benefits.

A gender specific risk assessment of coronary heart disease based on physical examination data.

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .

Does green finance improve the industrial eco-efficiency in China?

Improving industrial eco-efficiency is of great significance for building a beautiful China and achieving its carbon peak and neutrality targets. Based on the panel data of 30 provinces in China from 2007 to 2018, this paper uses the super-efficiency SBM model to measure industrial eco-efficiency and empirically tests the influence of green finance on Chinese industrial eco-efficiency from the national and regional levels. The results show that the average level of industrial eco-efficiency in China is relatively stable during the study period with a large space for advancement. Second, there is spatial heterogeneity in Chinese industrial eco-efficiency, showing a gradually decreasing "southeast-northwest" ladder-like distribution. Third, the national-level regression results show that there is a significant "U-shaped" relationship between green financing and industrial eco-efficiency. In addition, the regression results at the regional level indicate that there is regional heterogeneity in the impact of green finance on industrial eco-efficiency. Finally, based on the research conclusions, specific suggestions on how green finance can improve industrial eco-efficiency in China are put forward, including vigorously developing green finance at the macro and micro levels, and exerting the positive effects of green finance in improving industrial eco-efficiency according to the area and the development level of green finance.

Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach.

OBJECTIVES: To identify patterns of clinical conditions among high-cost older adults health care users and explore the associations between characteristics of high-cost older adults and patterns of clinical conditions. METHODS: We analyzed data from the Shanghai Basic Social Medical Insurance Database, China. A total of 2927 older adults aged 60 years and over were included as the analysis sample. We used latent class analysis to identify patterns of clinical conditions among high-cost older adults health care users. Multinomial logistic regression models were also used to determine the associations between demographic characteristics, insurance types, and patterns of clinical conditions. RESULTS: Five clinically distinctive subgroups of high-cost older adults emerged. Classes included "cerebrovascular diseases" (10.6% of high-cost older adults), "malignant tumor" (9.1%), "arthrosis" (8.8%), "ischemic heart disease" (7.4%), and "other sporadic diseases" (64.1%). Age, sex, and type of medical insurance were predictors of high-cost older adult subgroups. CONCLUSIONS: Profiling patterns of clinical conditions among high-cost older adults is potentially useful as a first step to inform the development of tailored management and intervention strategies.