Spatial-temporal Characteristics and Driving Factors of the Coupling Coordination between Primary Health Care and Socioeconomic Development: A Case Study of Beijing, China

Abstract

Abstract Background Primary Health Care (PHC) plays a central role in achieving universal health coverage and is a key focus of China's healthcare reforms. It is important to balance PHC development across different regions and ensure its harmonious growth with economic and social factors. This balance is essential for equitable health access and overall societal well-being. This study aims to examine the spatial imbalance in PHC development and its coordination with socioeconomic factors, trying to explore the driving factors behind the imbalance. Methods This study focuses on the 16 districts of Beijing from 2012 to 2021, constructing a social, economic, and PHC evaluation indicator system. First, this study used the entropy weight method and coupling coordination model to analyze the Coupling Coordination Degree (CCD) of PHC, economic, and social development. Then, Moran's I was used to test the spatial association characteristics of PHC development and the CCD. Lastly, the Geodetector was used to explore the driving factors behind the dual imbalance in PHC. Results Between 2012 and 2021, Beijing's PHC resources significantly increased. The evaluation values for PHC in the central city, near suburbs, and far suburbs were 0.46, 0.23, and 0.06, respectively, with a Moran’s I of 0.22 (P = 0.02), signifying a spatial imbalance in PHC development. The CCD of these regions were 0.72, 0.57, and 0.35, respectively, with a Moran’s I of 0.48 (P = 0.002), signifying the CCD imbalance. Factors such as population (q = 0.90), physician (q = 0.87), and government financial spending (q = 0.82) emerged as principal drivers of this dual imbalance. Conclusions Over the last decade, Beijing's PHC resources have significantly increased, but spatial imbalance persists. Several districts exhibit a lag in primary health care relative to socio-economic advancements, with pronounced systemic imbalances in the far suburbs. Factors driving this dual imbalance include geographical features, resident economic status, demographics, medical resources, and government regulation ability. Enhanced governmental investment and policy support for primary health care in the far suburbs are recommended.