Understanding Healthcare Utilization In China Through The Andersen Behavioral Model: Review Of Evidence From The China Health And Nutrition Survey.

Introduction: Factors influencing healthcare utilization in China have been frequently analyzed and discussed from various angles, based upon different objectives. However, few studies have attempted to categorize and summarize key determinants of healthcare utilization in China.
Methods: To fill this gap, we reviewed all empirical studies that made use of data from the China Health and Nutrition Survey (CHNS), a longitudinal survey covering nine Chinese provinces for nearly three decades. The review was guided by Andersen's behavioral model, a conceptual framework widely used to analyze determinants of healthcare utilization.
Results: Our review discovered many strong and consistent predictors of healthcare utilization at the individual level, including predisposing factors (e.g., marriage status and education), enabling factors (e.g., income and wealth), and need factors (e.g., illness severity and health status); in contrast, contextual factors (e.g., employment rates and population health indices) have rarely been examined. Our review also revealed a few factors whose impacts differ from expectations in many studies (e.g., employment status and health insurance coverage). While several factors explored in the reviewed studies (e.g., urbanization and industrialization) are not part of Andersen's model, some factors specified in the model (e.g., values and knowledge about health and health services) remain unexplored in the context of China.
Conclusion: Individual-level factors received much more attention than contextual-level factors in the reviewed studies. It leads to an inadequate understanding of the roles played by contextual factors. Among the individual-level factors that have been extensively examined, enabling variables affect healthcare utilization more than predisposing and need factors.
Competing Interests: The authors report no conflicts of interest in this work.
(© 2019 Zhang et al.)