scholarly journals China Stroke Statistics 2019: A Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations

2020 ◽  
Vol 5 (3) ◽  
pp. 211-239 ◽  
Author(s):  
Yong-Jun Wang ◽  
Zi-Xiao Li ◽  
Hong-Qiu Gu ◽  
Yi Zhai ◽  
Yong Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Disease Control ◽  
Neurological Diseases ◽  
Medical Insurance ◽  
Cerebrovascular Diseases ◽  
Hospital Death ◽  
Years Of Life Lost ◽  
Basic Medical Insurance ◽  
Control And Prevention ◽  
Basic Medical

China faces the greatest challenge from stroke in the world. The death rate for cerebrovascular diseases in China was 149.49 per 100 000, accounting for 1.57 million deaths in 2018. It ranked third among the leading causes of death behind malignant tumours and heart disease. The age-standardised prevalence and incidence of stroke in 2013 were 1114.8 per 100 000 population and 246.8 per 100 000 person-years, respectively. According to the Global Burden of Disease Study 2017, the years of life lost (YLLs) per 100 000 population for stroke increased by 14.6%; YLLs due to stroke rose from third highest among all causes in 1990 to the highest in 2017. The absolute numbers and rates per 100 000 population for all-age disability-adjusted life years (DALYs) for stroke increased substantially between 1990 and 2017, and stroke was the leading cause of all-age DALYs in 2017. The main contributors to cerebrovascular diseases include behavioural risk factors (smoking and alcohol use) and pre-existing conditions (hypertension, diabetes mellitus, dyslipidaemia and atrial fibrillation (AF)). The most prevalent risk factors among stroke survivors were hypertension (63.0%-84.2%) and smoking (31.7%-47.6%). The least prevalent was AF (2.7%-7.4%). The prevalences for major risk factors for stroke are high and most have increased over time. Based on the latest national epidemiological data, 26.6% of adults aged ≥15 years (307.6 million adults) smoked tobacco products. For those aged ≥18 years, age-adjusted prevalence of hypertension was 25.2%; adjusted prevalence of hypercholesterolaemia was 5.8%; and the standardised prevalence of diabetes was 10.9%. For those aged ≥40 years, the standardised prevalence of AF was 2.31%. Data from the Hospital Quality Monitoring System showed that 3 010 204 inpatients with stroke were admitted to 1853 tertiary care hospitals during 2018. Of those, 2 466 785 (81.9%) were ischaemic strokes (ISs); 447 609 (14.9%) were intracerebral haemorrhages (ICHs); and 95 810 (3.2%) were subarachnoid haemorrhages (SAHs). The average age of patients admitted was 66 years old, and nearly 60% were male. A total of 1555 (0.1%), 2774 (0.6%) and 1347 (1.4%) paediatric strokes (age <18 years) were identified among IS, ICH and SAH, respectively. Over one-third (1 063 892 (35.3%)) of the patients were covered by urban resident basic medical insurance, followed by urban employee basic medical insurance (699 513 (23.2%)) and new rural cooperative medical schema (489 361 (16.3%)). The leading risk factor was hypertension (67.4% for IS, 77.2% for ICH and 49.1% for SAH), and the leading comorbidity was pneumonia or pulmonary infection (10.1% for IS, 31.4% for ICH and 25.2% for SAH). In-hospital death/discharge against medical advice rate was 8.3% for stroke inpatients, ranging from 5.8% for IS to 19.5% for ICH. The median and IQR of length of stay was 10.0 (7.0–14.0) days, ranging from 10.0 (7.0–13.0) in IS to 14.0 (8.0–22.0) in SAH. Data from the Chinese Stroke Center Alliance demonstrated that the composite scores of guideline-recommended key performance indicators for patients with IS, ICH and SAH were 0.77±0.21, 0.72±0.28 and 0.59±0.32, respectively.

scholarly journals Comorbidity in lung cancer patients and its association with hospital readmission and fatality in China

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dawei Zhu ◽  
Ruoxi Ding ◽  
Yong Ma ◽  
Zhishui Chen ◽  
Xuefeng Shi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Hospital Readmission ◽  
Medical Insurance ◽  
Hospital Death ◽  
Comorbid Condition ◽  
Lung Cancer Patients ◽  
Basic Medical Insurance ◽  
Increased Risk ◽  
Basic Medical

Abstract Background Comorbidity has been established as one of the important predictors of poor prognosis in lung cancer. In this study, we analyzed the prevalence of main comorbidities and its association with hospital readmission and fatality for lung cancer patients in China. Methods The analyses are based on China Urban Employees’ Basic Medical insurance (UEBMI) and Urban Residents’ Basic Medical Insurance (URBMI) claims database and Hospital Information System (HIS) Database in the Beijing University Cancer Hospital in 2013–2016. We use Elixhauser Comorbidity Index to identify main types of comorbidities. Results Among 10,175 lung cancer patients, 32.2% had at least one comorbid condition, and the proportion of patients with one, two, and three or more comorbidities was 21.7, 8.3 and 2.2%, respectively. The most prevalent comorbidities identified were other malignancy (7.5%), hypertension (5.4%), pulmonary disease (3.7%), diabetes mellitus (2.5%), cardiovascular disease (2.4%) and liver disease (2.3%). The predicted probability of having comorbidity and the predicted number of comorbidities was higher for middle elderly age groups, and then decreased among patients older than 85 years. Comorbidity was positively associated with increased risk of 31-days readmission and in-hospital death. Conclusion Our study is the first to provide an overview of comorbidity among lung cancer patients in China, underlines the necessity of incorporating comorbidity in the design of screening, treatment and management of lung cancer patients in China.

scholarly journals Determinants of place of death for end-stage cancer patients: evidence from China

2019 ◽  
Vol 32 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Zhong Li ◽  
Shan Jiang ◽  
Chengzhong Xu ◽  
Fangfang Lu ◽  
Ruibo He ◽  
...  
Keyword(s):  
Health Insurance ◽  
Cancer Patients ◽  
Medical Insurance ◽  
Emergency Department Visits ◽  
Place Of Death ◽  
Hospital Death ◽  
Outpatient Services ◽  
Basic Medical Insurance ◽  
End Stage ◽  
Basic Medical

Abstract Objective To determine factors influence place of death (POD) for end-stage cancer patients and investigate how the healthcare utilization mediates on the effect of socioeconomic status (SES) on POD. Design A population-based, retrospective study from July 2015 to June 2017. Setting Yichang, China. Participants 894 end-stage cancer patients. Main outcome measure POD. Results Patients of hospital death experience more inpatient hospitalization services (IHS) and emergency department visits. Patients enrolled in the New Rural Cooperative Medical Scheme (OR = 7.60, P &lt; 0.001) and Urban Employee Basic Medical Insurance (OR = 28.0, P &lt; 0.001) have higher rates of hospital death than those in the Urban Resident-based Basic Medical Insurance. Living with spouse (OR = 1.72, P = 0.019) and receiving higher education (OR = 1.92, P = 0.004), increase the likelihood of hospital death by 72% and 92%, respectively. The probability of hospital death will increase by 14% and decrease by 4% per IHS and outpatient services occur, respectively. Outpatient services (Z = −2.28, P &lt; 0.001), and IHS (Z = 2.17, P &lt; 0.001) mediate 1.81% and 1.89%, respectively, of the effect of health insurance on POD. The overall effect of the mediators is non-statistically significant (Z = 0.09, P = 0.825). Conclusion POD is mainly driven by SES. The relationship between health insurance and POD is partly mediated by outpatient services and IHS, respectively. The results corroborated that hospital and home services should be coherently bridged. Furthermore, benefit packages for end-stage cancer patients could be redesigned.

scholarly journals The Effect of Health Shock and Basic Medical Insurance on Family Educational Investment for Children in China

2021 ◽  
Vol 18 (10) ◽  
pp. 5242
Author(s):  
Pu Liao ◽  
Zhihong Dou ◽  
Xingxing Guo
Keyword(s):  
Negative Impact ◽  
Medical Insurance ◽  
Generation Model ◽  
Two Phase ◽  
Parental Health ◽  
Health Shock ◽  
Basic Medical Insurance ◽  
Child Education ◽  
The Impact ◽  
Basic Medical

This paper explores the role of basic medical insurance in protecting family investment in child education. First, this paper establishes a two-phase overlapping generation model to theoretically analyse the impact of basic medical insurance on investment in child education under the influence of the impact of parental health. The results show that health shock reduces parental investment in child education, and medical insurance significantly alleviates the negative impact of parental health shock on investment in child education. Furthermore, this paper establishes a two-way fixed effect regression model based on the data of China Family Panel Studies (CFPS) in 2014 and 2016 to empirically test the above results. The results showed that parental health shocks negatively affect investment in child education, and paternal health shock has a more significant impact than maternal health shock. However, medical insurance significantly reduces this negative impact, provides security in investment in child education, and promotes the improvement of human capital.

scholarly journals The influence of medical insurance and social security cards on the floating population's settlement intention

2020 ◽  
Author(s):  
Yulin Li ◽  
Lingling Huang ◽  
Li Xiang ◽  
dongmei dou
Keyword(s):  
Social Security ◽  
Social Integration ◽  
Indirect Effect ◽  
Structural Equation ◽  
Medical Insurance ◽  
Floating Population ◽  
Dynamic Monitoring ◽  
Basic Medical Insurance ◽  
The Government ◽  
Basic Medical

Abstract Background There are many studies on the influencing factors of floating population's intention to settle down. Medical insurance and social security cards have an important guarantee for the floating population to live a stable life in the current residence, but there are limited studies focused on the influence of medical insurance and social security cards on their settlement intention. Therefore, the purpose of this paper is to study the influence of medical insurance and social security card on the settlement intention of floating population, so as to create better living and working conditions for floating population and improve their happiness in their current place of residence. Methods Based on the survey data of China's floating population dynamic monitoring in 2017, we explored the influence of medical insurance (urban employee basic medical insurance) and social security cards on the floating population's settlement intention with binary logistic regression and structural equation model. Additionally, this study was also to examine the comprehensive causal relationship, with social integration as the mediator variable. Results The floating population's settlement intention on participating in urban employee basic medical insurance is 23.2 percent higher than those who did not participate. Whether to apply for personal social security cards is related to the settlement intention. The standardized regression coefficients among social insurance and security, social integration, and settlement intention are positive values, and the Z values ​​of the overall effect, indirect effect, and direct effect are all greater than 1.96, and the confidence interval of the indirect effect does not include 0. The article found that this model is a partial intermediary, with an intermediary ratio of 10.66 percent. Conclusions The article highlights the important impact of urban workers' medical insurance and individual social security cards on the floating population. The results of this study may provide some reference for the government to formulate relevant policies.

scholarly journals A 15-Year Trend of Catastrophic Health Payment and its Inequality in China: Evidence from Longitudinal Data

2021 ◽  
Author(s):  
Yongjian Xu ◽  
Yiting Zhou ◽  
Andi Pramono
Keyword(s):  
Health Care ◽  
Decomposition Analysis ◽  
Medical Insurance ◽  
Catastrophic Health Expenditure ◽  
Chinese Government ◽  
Nutrition Survey ◽  
Basic Medical Insurance ◽  
Poor Households ◽  
Health Payment ◽  
Basic Medical

Abstract Background：The Chinese health care system has gone through two major cycles of reform since the 1980s. This study aims to comprehensively track the trends in the occurrence of catastrophic health payment and its inequality in the past 15 years, which may help better understand the influence of health system reforms on catastrophic health payment and its inequality. Methods：The study employed the subset of data from China Health and Nutrition Survey conducted from 1991 to 2015. Concentration index and decomposition analysis were used to measure the magnitude of income-related inequality in catastrophic health payment and decompose it into determinants respectively. Results: The incidence of catastrophic health expenditure in China increased from 3.10% in 1993 to 8.90% in 2004, and still maintained at a high level in the following years. The incidence gap of catastrophic health payment between the richest and poorest became increasingly wider over year. Moreover, the adjusted concentration indexes were all negative in each year, decreasing from -0.202 in 1991 to -0.613 in 2015. The basic medical insurance didn’t decrease the incidence of catastrophic health payment and showed the second largest contribution on the inequality in catastrophic health payment before 2004. However, this contribution began to decline after 2006. Conclusions: After the New Health Care Reform, although the Chinese government has taken many measures to protect poor households from catastrophic health payment, the incidence gap between the rich and poor has widened. China has nearly achieved universal coverage in recent years, however, the basic medical insurance in China was not enough to protect households from catastrophic health payment. Our study suggests that improving the generosity of existing basic medical insurance, and reforming the medical insurance payment system would be helpful to reduce the incidence of catastrophic health payment. The use of big data tools and techniques to effectively screen the poor households, and strengthening the social medical aid system would be helpful to decrease the pro-rich inequality in catastrophic health payment.

scholarly journals Inequity under Equality: Research on the Benefits Equity of Chinese Basic Medical Insurance

2020 ◽  
Author(s):  
Li Diao ◽  
Yiwei Liu
Keyword(s):  
Health Care ◽  
Low Income ◽  
Choice Model ◽  
Empirical Test ◽  
Medical Insurance ◽  
High Income ◽  
Mechanism Analysis ◽  
Basic Medical Insurance ◽  
Income Groups ◽  
Basic Medical

Abstract Background: The pursuit of equity is one of the basic principles behind the strengthening of health care reform. China's new rural cooperative medical insurance (NRCMI) and urban residents' basic medical insurance (URBMI) are both “equalized” in terms of fundraising and reimbursement. This paper studies the benefits equity under this "equalized" system.Methods: The data analysed in this paper are from the China Family Panel Studies (CFPS) from 2014 to 2016, implemented by the Institute of Social Science Survey at Peking University. A two-part model and a binary choice model are used in the empirical test.Results: The empirical test revealed that high-income people benefit more from basic medical insurance than low-income people. Mechanism analysis demonstrated that high-income people have higher medical insurance applicability and can utilize better health care. Since low-income people are unhealthier, inequity in benefits exacerbates health inequity. We also found that the benefits equity of URBMI is better than that of NRCMI.Conclusions: The government needs to pay more attention to the issue of medical insurance inequity. We should consider allowing different income groups to pay different premiums according to their medical expenses or applying different reimbursement policies for different income groups.

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