Geographical distribution disparities and prediction of health satisfaction among middle-aged and elderly adults in China: An analysis based on national data.

BACKGROUND

Health satisfaction among middle-aged and elderly adults has become a critical public health concern in China's aging society. Understanding geographical disparities in health satisfaction and developing prediction models are essential for targeted healthcare interventions and resource allocation.

METHODS

Our study conducted a cross-sectional analysis of 16,231 participants aged 45 and above from the China Health and Retirement Longitudinal Study 2015. We analysed the provincial spatial distribution of health satisfaction in China. We developed conventional logistic regression (LR), random forest (RF), gradient boosting machine (GBM), XGBoost, and a stacking ensemble model (SEM) to predict health satisfaction and investigate social and biological determinants. We used the SHapley Additive exPlanation (SHAP) method to interpret our machine learning predictive models.

RESULTS

Our analysis revealed significant geographical disparities in health satisfaction. The health satisfaction rate was 74.0 %, with regional variations: high in Xinjiang and Shanghai (>80 %), low in some provinces (60-70 %), and moderate in the remaining provinces and municipalities (70-80 %). The AUCs of LR, RF, GBM, XGBoost, and SEM were all around 0.8. SHAP analysis revealed demographics (e.g., age), behavioural factors (e.g., night sleep duration), health-related factors (e.g., troubling body pain, self-expectations of health status, depression, heart problems and stomach or other digestive system diseases) and biological factors (e.g., self-reported distance vision status, self-reported near vision status, self-reported hearing status and MCV) as important predictors of health satisfaction in middle-aged and elderly adults.

CONCLUSION

The findings highlight substantial geographical inequalities in health satisfaction among middle-aged and elderly Chinese adults. The predictive models developed in this study can help policymakers identify high-risk populations, enabling more targeted interventions to improve health satisfaction levels across different regions.