Analysis of Regional Differences, Dynamic Evolution, and Influencing Factors of Medical Service Levels in Guangzhou Under the Health China Strategy

Abstract

Hanxiang Gong,1,2,* Tao Zhang,1,* Xi Wang,1 Baoxin Chen,3 Baoling Wu,1 Shufang Zhao1 1Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, 999078, People’s Republic of China; 2The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510260, People’s Republic of China; 3Pingshan Hospital, Southern Medical University, Shenzhen, Guangdong, 518118, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xi Wang, Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, 999078, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]: This study explores regional differences, dynamic evolution, and influencing factors of medical service levels in Guangzhou under the Health China Strategy to provide a basis for improving service quality and reducing disparities.Patients and Methods: An evaluation system was constructed using the entropy weight TOPSIS method. The Dagum Gini coefficient analyzed regional differences, Kernel density estimation assessed service levels’ distribution, and Tobit regression explored influencing factors. Data were collected from the “Guangzhou Statistical Yearbook”, Guangzhou Health Commission reports, and government work reports from 2017 to 2022.Results: The study shows that from 2017 to 2022, there were significant differences in medical service levels among different regions of Guangzhou, with higher service quality in central urban areas compared to remote and peripheral areas. The application of the entropy weight method revealed the importance of indicators such as medical business costs and the number of registered nurses per thousand population in evaluating service quality. According to the Dagum Gini coefficient decomposition method, regional differences in medical services in Guangzhou are the main factor causing uneven overall development quality. Kernel density estimation indicates a bimodal distribution of medical service quality, suggesting heterogeneity in service quality and an increasing trend in low-quality service areas. The Tobit model confirms that factors such as medical institution drug costs, bed occupancy rate, and medical human resources have a positive impact on improving service quality.Conclusion: This study uniquely integrates the entropy weight TOPSIS method, Dagum Gini coefficient decomposition, and Kernel density estimation to dissect regional disparities in Guangzhou’s medical services, offering a novel perspective on healthcare evolution under the Health China Strategy. The findings provide an innovative framework for optimizing resource allocation and enhancing service quality, guiding balanced development across regions.Keywords: Healthcare evaluation, Service quality disparities, Dynamic distribution, Resource optimization, Medical resource allocation

Keywords

• healthcare evaluation
• service quality disparities
• dynamic distribution
• resource optimization
• medical resource allocation