Spatial accessibility and equity of primary healthcare in Zhejiang, China

Enhancing the accessibility and equity of primary healthcare (PHC) is a crucial objective of China's healthcare reform. However, spatial barriers remain a significant factor contributing to the inequitable access to PHC services among residents.

This study aims to quantify the spatial accessibility (SA) and evaluate the equity of PHC resources in a pilot province for healthcare reform, and its municipalities, thereby providing insights that can be generalized to the broader context of China.

The study used the navigation function provided by Gaode Map to estimate the time it takes for residents to visit PHC institutions. The two-step floating catchment area (2SFCA) method, weighted by a Gaussian function, was employed to measure the SA of PHC institutions, general practitioners (GPs), and beds across various residential areas. The Gini coefficient was utilized to assess disparities in SA among different regions. Additionally, Getis-Ord Gi* analysis was conducted to visualize these spatial disparities.

The study analyzed 25,601 residential and 1,451 healthcare points in Zhejiang Province, revealing significant disparities in SA of PHC. Urban residents reached PHC institutions faster than rural ones (7.05 ± 4.7 min vs. 9.17 ± 7.78 min, P < 0.001). Within 30 min, 98.4% of residential points and 99.1% of the population accessed PHC institutions. Disparities in PHC resources were notable, with Lishui having the highest SA for institutions and GPs, and Shaoxing for beds. Equity assessment showed high inequity for institutions (Gini 0.553), moderate for beds (Gini 0.497), and reasonable for GPs (Gini 0.332)​. Getis-Ord Gi* analysis demonstrated that areas further from urban centers were more likely to exhibit clusters of hotspots or cold spots.

The study highlights substantial disparities in SA of PHC and equity across Zhejiang, underscoring the need for strategic resource distribution. Future research should include diverse transportation modes and more precise demand point data to enhance understanding of accessibility dynamics.