Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study

BackgroundCardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs.MethodsData were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests—a machine learning approach—to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment.ResultsOur sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke.ConclusionMachine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs

Additional file 1 of Measuring and improving performance of clinicians: an application of patient-based records

Additional file 1: Table A1. Definitions of clinician level variables. Table A2. Part of fact table of procedure level. Table A3. Fact table of proceduregrading. Table A4. Fact table of procedure role. Figure A1. Dendrogram ofClustering. Figure A2. Distribution of continuous demographic characteristic by clusters (n=244). Fig. A3. Distribution of classified demographic characteristic by clusters (n=244). Fig. A4. Distribution of SPS by clusters (n =244)

Rating hospital performance in China:review of publicly available measures and development of a ranking system

BACKGROUND: In China, significant emphasis and investment in health care reform since 2009 has brought with it increasing scrutiny of its public hospitals. Calls for greater accountability in the quality of hospital care have led to increasing attention toward performance measurement and the development of hospital ratings. Despite such interest, there has yet to be a comprehensive analysis of what performance information is publicly available to understand the performance of hospitals in China. OBJECTIVE: This study aims to review the publicly available performance information about hospitals in China to assess options for ranking hospital performance. METHODS: A review was undertaken to identify performance measures based on publicly available data. Following several rounds of expert consultation regarding the utility of these measures, we clustered the available options into three key areas: research and development, academic reputation, and quality and safety. Following the identification and clustering of the available performance measures, we set out to translate these into a practical performance ranking system to assess variation in hospital performance. RESULTS: A new hospital ranking system termed the China Hospital Development Index (CHDI) is thus presented. Furthermore, we used CHDI for ranking well-known tertiary hospitals in China. CONCLUSIONS: Despite notable limitations, our assessment of available measures and the development of a new ranking system break new ground in understanding hospital performance in China. In doing so, CHDI has the potential to contribute to wider discussions and debates about assessing hospital performance across global health care systems

Effect of alkaline hydrogen peroxide assisted with two modification methods on the physicochemical, structural and functional properties of bagasse insoluble dietary fiber

Bagasse is one of major by-product of sugar mills, but its utilization is limited by the high concentration of lignin. In this study, the optimal alkaline hydrogen peroxide (AHP) treatment conditions were determined by the response surface optimization method. The results showed that the lignin removal rate was 62.23% and the solid recovery rate was 53.76% when bagasse was prepared under optimal conditions (1.2% H2O2, 0.9% NaOH, and 46°C for 12.3 h), while higher purity of bagasse insoluble dietary fiber (BIDF) was obtained. To further investigate the modification effect, AHP assisted with high-temperature-pressure cooking (A–H) and enzymatic hydrolysis (A–E) were used to modify bagasse, respectively. The results showed that the water holding capacity (WHC), oil holding capacity (OHC), bile salt adsorption capacity (BSAC), and nitrite ion adsorption capacity (NIAC) were significantly improved after A-H treatment. With the A–E treatment, cation exchange capacity (CEC) and BSAC were significantly increased, while WHC, OHC, and glucose adsorption capacity (GAC) were decreased. Especially, the highest WHC, OHC, BSAC and NIAC were gained by A–H treatment compared to the A–E treatment. These changes in the physicochemical and functional properties of bagasse fiber were in agreement with the microscopic surface wrinkles and pore structure, crystallinity and functional groups. In summary, the A–H modification can effectively improve the functional properties of bagasse fiber, which potentially can be applied further in the food industry.</p

The Application of Graph Theoretical Analysis to Complex Networks in Medical Malpractice in China: Qualitative Study

BackgroundStudies have shown that hospitals or physicians with multiple malpractice claims are more likely to be involved in new claims. This finding indicates that medical malpractice may be clustered by institutions. ObjectiveWe aimed to identify the underlying mechanisms of medical malpractice that, in the long term, may contribute to developing interventions to reduce future claims and patient harm. MethodsThis study extracted the semantic network in 6610 medical litigation records (unstructured data) obtained from a public judicial database in China. They represented the most serious cases of malpractice in the country. The medical malpractice network of China was presented as a knowledge graph based on the complex network theory; it uses the International Classification of Patient Safety from the World Health Organization as a reference. ResultsWe found that the medical malpractice network of China was a scale-free network—the occurrence of medical malpractice in litigation cases was not random, but traceable. The results of the hub nodes revealed that orthopedics, obstetrics and gynecology, and the emergency department were the 3 most frequent specialties that incurred malpractice; inadequate informed consent work constituted the most errors. Nontechnical errors (eg, inadequate informed consent) showed a higher centrality than technical errors. ConclusionsHospitals and medical boards could apply our approach to detect hub nodes that are likely to benefit from interventions; doing so could effectively control medical risks

Impact of the COVID-19 Pandemic on Chronic Disease Care in India, China, Hong Kong, Korea, and Vietnam

This study aims to provide evidence on how the COVID-19 pandemic has impacted chronic disease care in diverse settings across Asia. Cross-sectional surveys were conducted to assess the health, social, and economic consequences of the pandemic in India, China, Hong Kong, Korea, and Vietnam using standardized questionnaires. Overall, 5672 participants with chronic conditions were recruited from five countries. The mean age of the participants ranged from 55.9 to 69.3 years. A worsened economic status during the COVID-19 pandemic was reported by 19% to 59% of the study participants. Increased difficulty in accessing care was reported by 8% to 24% of participants, except Vietnam: 1.6%. The worsening of diabetes symptoms was reported by 5.6% to 14.6% of participants, except Vietnam: 3%. In multivariable regression analyses, increasing age, female participants, and worsened economic status were suggestive of increased difficulty in access to care, but these associations mostly did not reach statistical significance. In India and China, rural residence, worsened economic status and self-reported hypertension were statistically significantly associated with increased difficulty in access to care or worsening of diabetes symptoms. These findings suggest that the pandemic disproportionately affected marginalized and rural populations in Asia, negatively affecting population health beyond those directly suffering from COVID-19