Background: In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease’s rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. Objective: The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. Methods: We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China’s new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China’s response to the epidemic and to provide lessons for other countries’ prevention and control of COVID-19. Results: In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus’s sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. Conclusions: China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

Based on data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), this paper calculates the health distribution of the elderly using the Quality of Well-Being Scale (QWB) score, and then estimates health inequality among the elderly in rural China using the Wagstaff index (WI) and Erreygers index (EI). Following this, it compares health inequalities among the elderly in different age groups, and finally, uses the Shapley and recentered influence function-index-ordinary least squares (RIF-I-OLS) model to decompose the effect of four factors on health inequality among the elderly in rural China. The QWB score distribution shows that the health of the elderly in rural China improved with social economic development and medical reform from 2002 to 2014. However, at the same time, we were surprised to find that the health level of the 65–74 years old group has been declining steadily since 2008. This phenomenon implies that the incidence of chronic diseases is moving towards the younger elderly. The WI and EI show that there is indeed pro-rich health inequality among the rural elderly, the health inequality of the younger age groups is more serious than that of the older age groups, and the former incidence of health inequality is higher. Health inequality in the age group of 65–74 years old is higher than that in other groups, and the trend of change fluctuated downward from 2002 to 2014. Health inequality in the age group of 75–84 years old is lower than that in the group of 65–74 years old, but higher than that in the other age groups. The results of Shapley decomposition show that demographic characteristics, socioeconomic status (SES), health care access, and quality of later life contributed 0.0054, 0.0130, 0.0442, and 0.0218 to the health inequality index of the elderly, which accounted for 6.40%, 15.39%, 52.41%, and 25.80% of health inequality index. From the results of RIF-I-OLS decomposition, this paper has analyzed detailed factors’ marginal effects on health inequality from four dimensions, which indicates that the health inequality among the elderly in rural China was mainly caused by the disparity of income, medical expenses, and living arrangement.

Background: Total healthcare expenditures are concentrated among a small number of patients. To date, studies on the concentration of health care expenditures in developing countries are limited, mainly focusing on concentration measures and the demographic, clinical and socioeconomic characteristics of high-cost users (HCU). The drivers of the skewed overall distribution of health care expenditures are opaque. Using inpatient administrative claims data, this study provides new evidence on the concentration of healthcare expenditures in China; the demographic and clinical characteristics of high-cost users; and the drivers of the overall distribution of healthcare expenditures. Methods: Utilizing administrative claims data for hospitalization in a prefecture-level city in China, we investigated the concentration of healthcare expenditure. We used recentered influence function (RIF) regression to examine the drivers of healthcare expenditure concentration, decomposing and estimating the effects of demographic and disease characteristics on the overall distribution of health care expenditures. Results: Using a sample of 87,841 adults, we found extreme skewness in the distribution of inpatient medical expenditures in China, with approximately 49% of annual medical expenditures generated by the top 10% of inpatient groups. HCUs tend to be elderly and male, with high-frequency hospitalizations and long lengths of stay. In addition, healthcare expenditure concentration was related to diseases of the circulatory system, malignant neoplasms, diseases of the musculoskeletal system and connective tissue, diseases of the digestive system, injury and poisoning, and diseases of the respiratory system. Malignant and major diseases reinforced the concentration of healthcare spending, and a 10% increase in the prevalence of malignancy would result in a predicted Gini coefficient increase of 7.2%, heart disease of 0.92% and cerebrovascular disease of 1.5%. The above significant positive effects were not observed for hypertension and diabetes mellitus. Conclusions: Our study provides new insights into the concentration of inpatient medical expenditures in China, including the precise picture of HCU expenditure concentration, the drivers of HCU expenditure concentration and the magnitude of their impact. With the aging of China's population and the profound shift in the disease spectrum, policymakers need to strengthen the early detection and intervention management of specific chronic diseases and high-risk populations, especially the early diagnosis and treatment of key cancers.

Health inequality poses a challenge to improving the quality of life of older adults as well as the service system. The literature rarely explores the moderating role of medical services accessibility in the association between socioeconomic deprivation and health inequality. This study examines the socioeconomic deprivation and medical services accessibility associated with health inequality among older Chinese adults, which will contribute to the medical policy reform. Using data from the 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we analyse 14,232 older adults. This paper uses a concentration index (CI) to measure the income-related health inequality among the target population and employs a recentered influence function–concentration index–ordinary least squares (RIF-CI-OLS) model to empirically analyse the correlation between socioeconomic deprivation and health inequality among older Chinese adults. Based on the correlation analysis, we discuss the moderating effect of medical services accessibility. We find that health inequality exists among older Chinese adults and that the relative deprivation in socioeconomic status (SES) is significantly associated with health inequality (⁠ $β∈[0.1109,0.1909]$⁠, P < 0.01). The correlation between socioeconomic deprivation and health inequality is moderated by medical services accessibility, which means that an increase in medical services accessibility can weaken the correlation between socioeconomic deprivation and health inequality. China needs an in-depth reform of its medical services accessibility system to promote the equitable distribution of medical services resources, strengthen medical costs and quality management, and ultimately mitigate the SES reason for health inequality among older Chinese adults.