Background: With the acceleration of aging and urbanization, how to make cities more age-friendly has become a hot topic. During the long-term demographic transition, the health of the elderly has become an important consideration in urban planning and management. The health of the elderly is a complex issue. However, previous studies have mainly focused on the health defects related to disease incidence, loss of function, mortality, etc., yet a comprehensive evaluation of health status is lacking. The Cumulative Health Deficit Index (CHDI) is a composite index that combines psychological and physiological indicators. Health deficits can reduce the quality of life of the elderly and increase the burden on families, cities and even society, so it is indispensable to understand the individual factors and regional factors that affect CHDI. The research on the spatial differentiation of CHDI and its driving factors can provide scientific geographic information basis for the construction of age-friendly cities and healthy city planning. It also has great significance in narrowing the differences in the health status among regions and reducing the overall burden of the country. Methods: This research analyzed a nationwide dataset, the China Longitudinal Aging Social Survey in 2018 conducted by the Renmin University of China, which contained 11,418 elderly aged 60 years and older from 28 provinces/municipalities/autonomous regions that represent 95% of the population in mainland China. The Cumulative Health Deficit Index (CHDI) was the first time constructed using the entropy-TOPSIS method to evaluate the health status of the elderly. Entropy-TOPSIS is to quantify the importance of each indicator by calculating the entropy value to improve the reliability and accuracy of the results and avoid the influence of previous researchers’ subjective assignments and model assumptions on the results. The selected variables include physical health 27 indicators (self-rated health, basic mobility assessment, daily activity ability, disease and treatment) and mental health 36 indicators (cognitive ability, depression and loneliness, social adjustment, and filial piety concept). The research used the Geodetector methods (factor detection and interaction detection) that combined individual and regional indicators to analyze the spatial variation characters and reveal the driving factors of CHDI. Results: The weight of mental health indicators (75.73) is three times that of physical health indicators (24.27), and its composition formula is: CHDI value = (14.77% disease and treatment + 5.54% daily activity ability + 2.14% health self-assessment + 1.81% basic mobility assessment) + (33.37% depression and loneliness + 25.21% cognitive ability + 12.46% social adjustment + 4.7% filial piety). Individual CHDI was more associated with age and was more evident in females than males. CHDI average values show the distribution trend of Hu Line (HL) in the geographic information graph that the CHDI in West HL regions are lower than in the East HL regions. The highest CHDI cities are in Shanxi, Jiangsu, and Hubei, whereas the lowest CHDI cities are Inner Mongolia, Hunan and Anhui. The geographical distribution maps of the 5-levels of CHDI levels show very different CHDI classification levels among the elderly in the same region. Further, the top three influential factors are personal income, empty nest, aged 80+, and regional factors also obviously affect CHDI values, such as the proportion participating in insurance, population density, and GDP. The two different factors in individual and regional factors all show a two-factor interaction effect, and enhancement or nonlinear enhancement. The top three ranks are personal income ∩ quality of air (0.94), personal income ∩ GDP (0.94), and personal income ∩ urbanization rate (0.87). Conclusions: CHDI is a subjective and objective comprehensive index, and mental indicators are primary factors. Thus attaching importance to the psychological care of the elderly is the key to building a healthy aging society. The large individual heterogeneity and spatial differentiation of CHDI in the elderly were demonstrated by map visualization. The analysis of the influencing factors of CHDI by the Geodetector method proves that spatial differentiation is mainly affected by individual economic and social security factors, but also by the interaction with regional factors such as quality of air, GDP, and urbanization rate. This research fills a gap in the elderly health status in the field of spatial geography. The results can provide empirical evidence for policymakers to take measures according to local conditions to improve the health status of the elderly according to regional differences in physical and mental conditions. It also plays a guiding role for the country in balancing regional economic development, promoting healthy and sustainable urban development, and creating age-friendly cities.

Since the 1970s, neoliberalism has transformed the global economy, replacing full-time, long-term employment with flexible work arrangements, contributing to precarious employment (PE). PE has been linked to negative health outcomes, but research on the multiple dimensions of PE remains limited. This study employed a longitudinal cohort design using data from the Japanese Panel Study of Employment Dynamics (JPSED) from 2017 to 2023, focusing on individuals aged 16-65 who were employed but not on full-time permanent contracts. We developed a modified Employment Precariousness Scale (EPRES-JP) for evaluation. The entropy-based TOPSIS method with adjustable weight coefficients (Ea-TOPSIS) method was applied to calculate the Precarious Employment Index (PEI), and logistic regression analyzed its association with health outcomes, while Structural Equation Modeling (SEM) explored potential mediating pathways. The study found that the "Rights" dimension (social insurance and leave access) had the highest average weight at 40.2 %, with the impact of COVID-19 (introduced in 2021) significantly contributing to the PEI. Higher PEI was associated with greater risks of headaches (OR_0.1 = 1.11, 95 % CI [1.06, 1.16]), fatigue (OR_0.1 = 1.09, 95 % CI [1.04, 1.14]), anxiety (OR_0.1 = 1.12, 95 % CI [1.07, 1.17]), depression (OR_0.1 = 1.18, 95 % CI [1.13,1.24]), loss of appetite (OR_0.1 = 1.22, 95 % CI [1.16, 1.28]), and sleep disturbances (OR_0.1 = 1.20, 95 % CI [1.15, 1.25]). SEM identified mediating pathways between PE, job and life satisfaction, and health outcomes. These findings deepen our understanding of how PE affects worker health. Enhancing job characteristics and life satisfaction may mitigate the health risks of PE. Broader labor policies are essential to improve job security and worker well-being.

Individuals migrating with chronic diseases often face substantial health risks, and their patterns of healthcare-seeking behavior are commonly influenced by mobility. However, to our knowledge, no research has used spatial statistics to verify this phenomenon. Utilizing data from the China Migrant Dynamic Survey of 2017, we conducted a geostatistical analysis to identify clusters of chronic disease patients among China’s internal migrants. Geographically weighted regressions were utilized to examine the driving factors behind the reasons why treatment was not sought by 711 individuals among a population sample of 9272 migrant people with chronic diseases. The results indicate that there is a spatial correlation in the clustering of internal migrants with chronic diseases in China. The prevalence is highly clustered in Zhejiang and Xinjiang in north-eastern China. Hotspots were found in the northeast (Jilin and Liaoning), the north (Hebei, Beijing, and Tianjin), and the east (Shandong) and also spread into surrounding provinces. The factors that affect the migrants with no treatment were found to be the number of hospital beds per thousand population, the per capita disposable income of medical care, and the number of participants receiving health education per 1000 Chinese population. To rectify this situation, the local government should “adapt measures to local conditions.” Popularizing health education and coordinating the deployment of high-quality medical facilities and medical workers are effective measures to encourage migrants to seek reasonable medical treatment.

Under the long-term household registration system and social segmentation, the migrant population cannot enjoy equal medical services with the local population in the cities. The number of migrant populations with chronic diseases (MPCDs) is increasing, and emerging problems such as unreasonable medical treatment behaviour. Previous studies mainly explored the influencing factors of the prevalence of chronic diseases and the health-seeking behaviour of the migrant population at the individual level. However, the factors that influence health outcomes and care-seeking behaviour need to be considered at both the macro and micro levels. This study is of great significance to formulating strategies and measures for the prevention and control of chronic diseases, rationally allocating health resources, and weakening health inequity. Methods The dataset in this research is China Migrants Dynamic Survey conducted by the China National The dataset in this research is China Migrants Dynamic Survey conducted by the China National Health Commission in 2017, which contained 169,989 samples from 31 provinces/municipalities/autonomous regions. Spatial auto-correlation (global spatial auto-correlation and local spatial auto-correlation) is used to analyze the spatial aggregation of a migrant population with chronic diseases. Geographically Weighted Regression was used to analyze the influencing factors of the health-seeking behaviour of the migrant population with chronic diseases after illness or injury. The selected variables from individual and regional levels include 23 indicators in seven dimensions(Population variables, migrant feature variables, Social variables, Economic variables, Health knowledge variables, Family variables, and Medical environment variables). Results MPCDs with hypertension, diabetes, and comorbidity are accounting for 77.82%, 12.93%, and 9.25% respectively. The regions with the largest number of each disease and comorbidity are Inner Mongolia, Xinjiang, Jiangsu, and the least regions are Hainan. There is a spatial correlation in the spatial distribution of migrant population with chronic diseases, Guangxi, and Jiangxi .Heilongjiang, Inner Mongolia and Jilin show high-high clustering. There are spatial variations in the influencing factors of irrational healthcare-seeking behaviours (no treatment, local pharmacies treatment, and community medical institutions). The main factors can be summarized as population variables (Age, school), family variables (Living together, I-E ratio, Difficult), Migrant feature variables (Unemployed, Stay ), Personal income, Health education, and Medical accessibility. Conclusion MPCDs have a high prevalence within two weeks and poor initiative to seek medical care. Effective measures to control MPCDs strengthen health education and health promotion. Family factors also significantly affect the MPCDs' health-seeking behaviour. Regional medical and security departments should ensure MPCDs and their migrating families can enjoy equal rights and interests of medical services as the local registered population. In addition, according to the population structure and the distribution of the migrant population, the rational allocation of corresponding health education resources and medical resources in different areas is also an effective measure to guide MPCDs to seek medical treatment reasonably. This study provides a reference for managing chronic diseases in the spatial dimension of the migrant population in China.