A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study.

BACKGROUND: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression. OBJECTIVE: In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD. METHODS: We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing'an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model. RESULTS: The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm-based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%. CONCLUSIONS: Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD.

Research on the design of serious illness insurance scheme in Shanghai based on micro-simulation.

BACKGROUND: Urban and rural residents' basic medical insurance (URRBMI) is an institutional arrangement for rural residents and unemployed urban residents in China. The serious illness medical insurance system (SIMIS) was established to provide additional medical cover. At present, the SIMIS payment method in China is based on large expenses, and only a few areas, such as Shanghai, pay according to the treatment of serious diseases. This study aims to simulate and analyse the effect of the two payment methods on SIMIS in Shanghai. METHODS: We developed a micro-simulation model to predict the number and characteristics of SIMIS participants among urban and rural residents in Shanghai and to simulate the process of medical treatment, medical consumption, and medical insurance payments for each insured person from 2020 to 2025. We then summarised and analysed the payment compensation effect, and compared it with Shanghai's current policies. RESULTS: The payment of SIMIS according to high expenses, the total medical expenses of seriously ill patients show an increasing trend, with an average annual growth rate of 3.56%. The URRBMI fund payment covers 56%-58% of total medical expenses, and the SIMIS fund covers 5%-7% of the total medical expenses. Both cover 62%-63% of total medical expenses. Self-payment under SIMIS covers 22%-23% of the total medical expenses, total self-payment covers 14%-15% of the total medical expenses, and the medical expenses borne by individuals cover 36%-38% of the total medical expenses.The fund expenditure is 213 million yuan and average annual cost borne by individual patients ranges from 40 000 to 60 000 yuan. CONCLUSIONS: The policy of designing SIMIS according to national guidelines does not meet the development needs of Shanghai. Shanghai should take the current policy of paying compensation according to the treatment of serious illness as the policy basis, consider the security needs of patients with large medical expenses outside the scope of protection, and adjust policies appropriately to prevent poverty caused by illness.

Analysis of the effect of serious illness medical insurance on relieving the economic burden of rural residents in China: a case study in Jinzhai County.

BACKGROUND: In 2003, China established a New Rural Cooperative Medical System (NRCMS) for rural residents to alleviate the burden of medical expenses among rural residents. However, its reimbursement for high medical costs was insufficient. Therefore, China gradually established the Serious Illness Insurance System (SIMIS) based on NRCMS. After receiving payment through NRCMS, patients in rural areas who met the requirements of SIMIS policy would receive a second payment for their high medical expenses. This study aimed to analyze the effect of the implementation of SIMIS on alleviating the economic burden of rural residents in Jinzhai County. METHODS: The study used the inpatient reimbursement data of NRCMS in Jinzhai County, Anhui Province, from 2013 to 2016. We adopted descriptive and regression discontinuity (RD) methods to analyze the payment effect of SIMIS. The RD analysis targeted patients (n = 7353) whose annual serious illness expenses were between CNY 10,000 (1414 USD) and CNY 30,000 (4242 USD), whereas the descriptive analysis was used for data of the patients compensated by SIMIS (n = 2720). RESULTS: The results of RD showed that the actual medical insurance payment proportion increased by about 2.5% (lwald = 0.025, P < 0.01), inside medical insurance self-payment proportion increased by about 2% (lwald = 0.020, P < 0.10), and outside medical insurance self-payment proportion decreased by about 1.6% (lwald = - 0.016, P < 0.05). The descriptive results showed that patients with serious illnesses mostly chose to go to a hospital outside the county. The annual average number of hospitalizations was 3.64. The reimbursement mainly came from the NRCMS. The payment amount of SIMIS was relatively small, and the out-of-pocket medical expenses were still high. CONCLUSION: The medical technology level of Jinzhai County could not meet the needs of patients with seriously illnesses, the number of beneficiaries of SIMIS was small, and the ability to relieve the burden of medical expenses of the rural residents was insufficient. The high out-of-pocket expenses increased the possibility that only people with good economic conditions could benefit from the reimbursement of SIMIS, resulting in inequity.

Characteristics of COVID-19 near China's epidemic center.

BACKGROUND: This study described and analyzed the age, gender, infection sources, and timing characteristics of the 416 confirmed cases in two cities near the center of China's COVID-19 outbreak. METHODS: This study used publicly available data to examine gender, age, source of infection, date returned from Hubei, date of disease onset, date of first medical visit, date of final diagnosis, and date of recovery of COVID-19 cases. RESULTS: Public-use data revealed similar risks of infection by age and that the numbers of new and final diagnoses of confirmed cases first increased, peaked at about 2 weeks, and then gradually decreased. The main sources of infection were firsthand or secondhand exposure in Hubei Province and contact with confirmed cases, which mostly involved contact with infected household members. The mean periods from disease onset to first medical visit, first visit to final diagnosis, and final diagnosis to recovery were 4.44, 3.18, and 13.42 days, respectively. CONCLUSIONS: The results suggest that the measures taken to control the rate of infection were effective. Prevention and control efforts should respond as quickly as possible, isolate and control activities of individuals leaving infected areas, and restrict household contact transmission.

The integration of urban and rural medical insurance to reduce the rural medical burden in China: a case study of a county in Baoji City.

BACKGROUND: In 2003, the Chinese government launched the New Rural Cooperative medical System(NRCMS) for its rural population. In 2007, the Urban Resident Basic Medical Insurance Scheme(URBMS) was inaugurated, which aimed to cover all urban residents who are out of the labor market. However, the accessibility and fairness of the healthcare service have hindered the progress of universal healthcare. At the beginning of 2010, the Integration of Urban and Rural Medical Insurance Scheme(IURMIS) was implemented to bridge the gap in medical care between urban and rural areas. The main objective of this study is to determine the extent to which the IURMIS has been successful. METHODS: The statistical software packages SPSS 19.0 and STATA 12.0 were used for all analyses, and P < 0.05 was set as the required level of significance. Data were collected from outpatients from 2009 (July to December, n = 20,459) through 2011 (n = 65,258 in 2010, n = 59,036 in 2011) and from inpatients in 2011 (n = 3662). Due to the enormous amount of data and the short time span, most of our analysis was descriptive. However, regression discontinuity (RD) and the chi-squared (χ2) test were used to compare the ratios of medical expenses before and after the IURMIS. RESULTS: In the RD analysis, there was a downward trend in the mean medical expense (Coef. = - 0.66, P < 0.001), and rural outpatients flowed to township hospitals and village clinics after the implementation of the IURMIS (Coef. = - 0.45, P < 0.001). However, compensation expenses showed an upward trend (Coef. = 11.59, P < 0.001). In the analysis of inpatient expenses, the average expenses (CNY 2067) and hospitalization days (10.0) for all the hospitals were less than those in the Chinese Fourth National Health Services Survey (CNY 3412 and 10.3, respectively). CONCLUSIONS: Rural residents' healthcare options and quality were improved and medical expenses were significantly reduced after implementation of the scheme. These results provide an evidence-based reference for improving the integration of the urban and rural medical security systems throughout China.

Charting a path forward: policy analysis of China's evolved DRG-based hospital payment system.

Background: At present, the diagnosis-related groups-based prospective payment system (DRG-PPS) that has been implemented in China is merely a prototype called the simplified DRG-PPS, which is known as the 'ceiling price for a single disease'. Given that studies on the effects of a simplified DRG-PPS in China have usually been controversial, we aim to synthesize evidence examining whether DRGs can reduce medical costs and length of stay (LOS) in China. Methods: Data were searched from both Chinese [Wan Fang and China National Knowledge Infrastructure Database (CNKI)] and international databases (Web of Science and PubMed), as well as the official websites of Chinese health departments in the 2004-2016 period. Only studies with a design that included both experimental (with DRG-PPS implementation) and control groups (without DRG-PPS implementation) were included in the review. Results: The studies were based on inpatient samples from public hospitals distributed in 12 provinces of mainland China. Among them, 80.95% (17/21) revealed that hospitalization costs could be reduced significantly, and 50.00% (8/16) indicated that length of stay could be decreased significantly. In addition, the government reports showed the enormous differences in pricing standards and LOS in various provinces, even for the same disease. Conclusions: We conclude that the simplified DRGs are useful in controlling hospitalization costs, but they fail to reduce LOS. Much work remains to be done in China to improve the simplified DRG-PPS.

Health status and burden of health care costs among urban elderly in China.

This study evaluates health status among elderly residents in urban China and medical insurance schemes for them. A total of 931 urban elderly individuals aged 60 years or above in 3 cities were interviewed. The survey gathered data on individual sociodemographic characteristics, self-reported health status, chronic disease diagnoses, use of and expenditure related to health care services, satisfaction with medical insurance, and so on. The prevalence of chronic disease was 79.1% among the respondents, and medical expenditure on chronic disease per patient was 4656 Yuan in half a year. Over the 2 earlier weeks, 27.4% of respondents had felt sick, and 63.5% of them had had outpatient visits; 29.5% of respondents had been hospitalized in the past year. Among different groups, the inpatient reimbursement rate ranged from 45.5% to 81.2%, and the outpatient reimbursement rate was between 4.1% and 100%. The health care demand had not been met for many elderly individuals because of the lack of general outpatient coverage in the basic medical insurance scheme.

Modelling medical care usage under medical insurance scheme for urban non-working residents.

This research investigates and evaluates China's urban medical care usage for non-working residents using microsimulation techniques. It focuses on modelling medical services usage and simulating medical expenses on hospitalization treatments as well as clinic services for serious illness in an urban area for the period of 2008-2010. A static microsimulation model was created to project the impact of the medical insurance scheme. Four kinds of achievements have been made. For three different scenarios, the model predicted the hospitalization services costs and payments, as well as the balance of the social pool fund and the medical burden on families.

Constructing an urban population model for medical insurance scheme using microsimulation techniques.

China launched a pilot project of medical insurance reform in 79 cities in 2007 to cover urban nonworking residents. An urban population model was created in this paper for China's medical insurance scheme using microsimulation model techniques. The model made it clear for the policy makers the population distributions of different groups of people, the potential urban residents entering the medical insurance scheme. The income trends of units of individuals and families were also obtained. These factors are essential in making the challenging policy decisions when considering to balance the long-term financial sustainability of the medical insurance scheme.