Objective Middle-aged and older adults are more likely to suffer from chronic diseases because of their particular health characteristics, which lead to a high incidence of catastrophic health expenditure (CHE). This study plans to analyse the different factors affecting CHE in middle-aged and older adults with chronic diseases, target the vulnerable characteristics, and suggest groups that medical insurance policies should pay more attention to. Methods The data used in this study came from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database. The method of calculating the CHE was adopted from the World Health Organization (WHO). The logistic regression was used to determine the family characteristics of chronic disease in middle-aged and older adults with a high probability of incurring CHE. Results The incidence of CHE in middle-aged and older adults with chronic disease was highest in sub-poverty level families (26.20%) was lowest in wealthier level families (20.07%). Households with malignant tumours had the highest CHE incidence under any circumstances, especially if the householder had been using inpatient service in the past year. Among the comparison of CHE incidence in different types of medical insurance, the Urban and Rural Residents’ Basic Medical Insurance (URRBMI) was the highest (27.46%). The incidence of CHE was 2.73 times (95% CI 2.30–3.24) and 2.16 times (95% CI 1.81–2.57) higher among people who had used inpatient services in the past year or outpatient services in the past month than those who had not used them. Conclusions Relatively wealthy economic conditions cannot significantly reduce the financial burden of chronic diseases in middle-aged and older adults. For this particular group with multiple vulnerabilities, such as physical and social vulnerability, the high demand and utilization of health services are the main reasons for the high incidence of CHE. After achieving the goal of lowering the threshold of universal access to health services, the medical insurance system in the next stage should focus on multiple vulnerable groups and strengthen the financial protection for middle-aged and older adults with chronic diseases, especially for patients with malignant tumours.

BackgroundThere are huge differences in female breast cancer mortality between urban and rural China. In order to better prevent breast cancer equally in urban and rural areas, it is critical to trace the root causes of past inequities and predict how future differences will change. Moreover, carcinogenic factors from micro-individual to macro-environment also need to be analyzed in detail. However, there is no systematic research covering these two aspects in the current literature.MethodsBreast cancer mortality data in urban and rural China from 1994 to 2019 are collected, which from China Health Statistical Yearbook. The Age-Period-Cohort model is used to examine the effects of different age groups, periods, and birth cohorts on breast cancer mortality. Nordpred project is used to predict breast cancer mortality from 2020 to 2039.ResultsThe age effect gradually increases and changes from negative to positive at the age of 40–44. The period effect fluctuates very little and shows the largest difference between urban and rural areas in 2019. The birth cohort effect gradually decreases with urban-rural effects alternating between strong and weak. In the predicted results, the urban-rural mortality gap becomes first narrow and then wide and shows a trend of younger death.ConclusionsFrom the perspective of a temporal system, the changing trend of breast cancer mortality is highly consistent with the history of social and economic structural changes in China. From the perspective of the theory of social determinants of health, individuals, families, institutions and governments need to participate in the prevention of breast cancer.

Background China’s medical insurance schemes and poverty alleviation policy at this stage have achieved population-wide coverage and the system's universal function. At the late stage of the elimination of absolute poverty task, how to further exert the poverty alleviation function of the medical insurance schemes has become an important agenda for targeted poverty alleviation. To analyse the risk of catastrophic health expenditure (CHE) occurrence in middle-aged and older adults with vulnerability characteristics from the perspectives of social, regional, disease, health service utilization and medical insurance schemes. Methods We used data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database and came up with 9190 samples. The method for calculating the CHE was adopted from WHO. Logistic regression was used to determine the different characteristics of middle-aged and older adults with a high probability of incurring CHE. Results The overall regional poverty rate and incidence of CHE were similar in the east, central and west, but with significant differences among provinces. The population insured by the urban and rural integrated medical insurance (URRMI) had the highest incidence of CHE (21.17%) and health expenditure burden (22.77%) among the insured population. Integration of Medicare as a medical insurance scheme with broader benefit coverage did not have a significant effect on the incidence of CHE in middle-aged and older people with vulnerability characteristics. Conclusions Based on the perspective of Medicare improvement, we conducted an in-depth exploration of the synergistic effect of medical insurance and the poverty alleviation system in reducing poverty, and we hope that through comprehensive strategic adjustments and multidimensional system cooperation, we can lift the vulnerable middle-aged and older adults out of poverty.

Background As the fifth-largest global mortality risk factor, air pollution has caused nearly one-tenth of the world’s deaths, with a death toll of 5 million. 21% of China’s disease burden was related to environmental pollution, which is 8% higher than the US. Air pollution will increase the demand and utilisation of Chinese residents’ health services, thereby placing a greater economic burden on the government. This study reveals the spatial impact of socioeconomic, health, policy and population factors combined with environmental factors on government health expenditure. Methods Spearman’s correlation coefficient and GeoDetector were used to identify the determinants of government health expenditure. The GeoDetector consist of four detectors: factor detection, interaction detection, risk detection, and ecological detection. One hundred sixty-nine prefecture-level cities in China are studied. The data sources are the 2017 data from China’s Economic and Social Big Data Research Platform and WorldPOP gridded population datasets. Results It is found that industrial sulfur dioxide attributed to government health expenditure, whose q value (explanatory power of X to Y) is 0.5283. The interaction between air pollution factors and other factors will increase the impact on government health expenditure, the interaction value (explanatory power of × 1∩× 2 to Y) of GDP and industrial sulfur dioxide the largest, whose values is 0.9593. There are 96 simple high-risk areas in these 169 areas, but there are still high-risk areas affected by multiple factors. Conclusion First, multiple factors influence the spatial heterogeneity of government health expenditure. Second, health and socio-economic factors are still the dominant factors leading to increased government health expenditure. Third, air pollution does have an important impact on government health expenditure. As a catalytic factor, combining with other factors, it will strengthen their impact on government health expenditure. Finally, an integrated approach should be adopted to synergisticly governance the high-risk areas with multi-risk factors.

Background The high incidence of catastrophic health expenditure (ICHE) among middle-aged and elderly population is a major deterrent for reducing the financial risk of disease. Current research is predominantly based on the assumption of spatial homogeneity of nationwide population characteristics, ignoring the differences in regional characteristics. Thus, our study aimed to explore the impact of various influencing factors on the ICHE from a spatiotemporal perspective. Methods We used data from the China Health and Retirement Longitudinal Study (waves 1 to 4), to conduct a retrospective cohort study across 28 provinces, from 2011 to 2018. We measured regional incidences of catastrophic health expenditure using methods recommended by the World Health Organization. Ordinary least squares (OLS) and geographical and temporal weighted regression (GTWR) were used as the global and local estimation models, respectively. The Fortheringham method was used to test the spatiotemporal non-stationarity. Results National ICHE showed a gradual increase from 2011 to 2015, but suddenly decreased from 2015 to 2018, also showing the spatial heterogeneity. And the southwest area and Hebei showed persistently high ICHE (Qinghai even reached the highest value of 27.5% in 2015). Out-of-pocket payment, gross domestic product, PM2.5, ageing, incidence of non-communicable diseases and disabilities, number of nurses, and health insurance coverage in the global estimation passed the significance test, and the GTWR model showed a better model fit (0.769) than the OLS model (0.388). Furthermore, except for health insurance coverage, all seven variables had spatiotemporal non-stationarity among their impacts on ICHE. Conclusion In this longitudinal study, we found spatiotemporal non-stationarity among the variable relationships, supporting regional governments’ adoption of regional-target policies. First, after the completion of universal health insurance coverage, the spatiotemporal non-stationarity of the prevalence of non-communicable diseases and disability and ageing should be the focus of the next phase of health insurance design, where improvements to compensation coverage and benefit packages are possible policy instruments. Second, the governance and causes of catastrophic health expenditure need to be laid out from a macro perspective rather than only from the individual/household perspective, especially for the potential impact of economic development, air pollution and nursing resources.