Background Regional differences in economic development, natural environment, health care level, and social structure may lead to differences in the provincial distribution of the health status of the elderly population.
Objective To explore the provincial distribution characteristics, regional differences, and influencing factors of the self-assessed health of the elderly population, with the aim of providing a policy basis for improving the health of the elderly population and promoting healthy aging according to local conditions.
Methods Using 31 provinces (municipalities and autonomous regions) in China as the basicstudy unit and based on the method of Wagstaff, the self-rated health data of the elderly population (aged 60 years and above) in each province from the 2010 and 2020 national censuses and the 2015 1% National Population Sample Survey were converted into ill-health scores as a measure of self-assessed health, and higher scores represented worse health status perception. Global Moran's I was used to evaluate spatial autocorrelation, range −1, 1, with a value of 1 as a perfect clustered pattern. Local Moran's I was used to evaluate the tendency of local autocorrelation, and high-high aggregation/low-low aggregation indicated that both target province and its neighboring provinces showed higher/lower ill-health scores. Spatial econometric models were selected by Lagrange multiplier test and Hausman test to explore influencing factors of the self-assessed health of the elderly population.
Results In 2010, 2015, and 2020, the national ill-health scores of the elderly population were 1.831, 1.873, and 1.547, respectively, and the corresponding Global Moran's I statistics were 0.347, 0.482, and 0.511, respectively (P<0.01), indicating that the ill-health scores of the elderly population showed a significant spatial positive autocorrelation, and the degree of spatial aggregation was increasing gradually. From 2010 to 2020, the high-high aggregation of ill-health scores among the elderly population was concentrated in the inland northwest, while the low-low aggregation was concentrated in the southeast coast, gradually showing a "southeast-central-northwest" stepped incremental pattern of differentiation. The Lagrange multiplier test and Hausman test suggested that the fixed-effects spatial lagged model was a better choice, and the regression model showed a spatial autocorrelation in the ill-health scores of the elderly population, with an autocorrelation coefficient of 0.3969 (P<0.001); the ill-health scores of the elderly population were negatively correlated with the natural logarithms of gross regional product per capita, and the number of beds in health care facilities per 1000 population, with regression coefficients of −0.8297 and −0.0454 (P<0.05) respectively, and positively correlated with the annual average concentration of PM2.5, illiteracy rate, and the number of health technicians per 1000 population, with regression coefficients of 0.0033, 0.0297, and 0.0765 (P<0.05), respectively.
Conclusion From 2010 to 2020, the overall self-assessed health level of China's elderly population showed an upward trend and a spatial positive autocorrelation, with better self-assessed health in the southeast coast and poorer ratings in the northwestern inland. Additionally, there was a gradual decline in self-assessed health of the elderly population from the southeast to the central regions and further to the northwest in terms of spatial distribution. Economic development level, environmental pollution, health resource allocation, and education level are important factors influencing the self-assessed health of the elderly population.
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