Measuring the inequalities in healthcare resource in facility and workforce: A longitudinal study in China

Frontiers in Public Health

Public Health

Xiaoting Sun¹  Tao Wang²  Ting Xu³  Enhong Dong⁴  Weimin Gao⁵  Shixiang Zhang⁶  Lufa Zhang⁷ 

[1]College of Public Health and Family Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China

[2]Department of Emergency Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China

[3]Department of Health Management, School of Nursing and Health Management, Shanghai University of Medicine and Health Science, Shanghai, China

[4]Department of Health Management, School of Nursing and Health Management, Shanghai University of Medicine and Health Science, Shanghai, China

[5]Health and Medical Communication Research Center, School of Media and Communication, Shanghai Jiao Tong University, Shanghai, China

[6]Institute of Healthy Yangtze River Delta, Shanghai Jiao Tong University, Shanghai, China

[7]Department of Pharmacy, School of Pharmaceutical Sciences and Yunnan Key Laboratory of Pharmacology for Natural Products, Kunming Medical University, Kunming, China

[8]Emergency Medical Rescue Technology Research Institute, Shanghai University of Medicine and Health Science, Shanghai, China

[9]Institute of Healthy Yangtze River Delta, Shanghai Jiao Tong University, Shanghai, China

[10]Department of Public Economy and Social Policy, School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China

关键词: healthcare resource;    distribution;    inequality;    Theil index;    spatial autocorrelation analysis;   

DOI  :  10.3389/fpubh.2023.1074417

 received in 2022-10-19, accepted in 2023-02-15,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveThe study aimed to measure time trends of inequalities of the geographical distribution of health facilities and workforce in Shanghai from 2010 to 2016 and used a spatial autocorrelation analysis method to precisely detect the priority areas for optimizing health resource reallocation in metropolises like Shanghai in developing countries.MethodsThe study used secondary data from the Shanghai Health Statistical Yearbook and the Shanghai Statistical Yearbook from 2011 to 2017. Five indicators on health resources, namely, health institutions, beds, technicians, doctors, and nurses, were employed to quantitatively measure the healthcare resource in Shanghai. The Theil index and the Gini coefficient were applied to assess the global inequalities in the geographic distribution of these resources in Shanghai. Global and local spatial autocorrelation was performed using global Moran's index and local Moran's index to illustrate the spatial changing patterns and identify the priority areas for two types of healthcare resource allocation.ResultsShanghai's healthcare resources showed decreasing trends of inequalities at large from 2010 to 2016. However, there still existed an unchanged over-concentration distribution in healthcare facilities and workforce density among districts in Shanghai, especially for doctors at the municipal level and facility allocation at the rural level. Through spatial autocorrelation analysis, it was found that there exhibited a significant spatial autocorrelation in the density distribution of all resources, and some identified priority areas were detected for resource re-allocation policy planning.ConclusionThe study identified the existence of inequality in some healthcare resource allocations in Shanghai from 2010 to 2016. Hence, more detailed area-specific healthcare resource planning and distribution policies are required to balance the health workforce distribution at the municipal level and institution distribution at the rural level, and particular geographical areas (low–low and low–high cluster areas) should be focused on and fully considered across all the policies and regional cooperation to ensure health equality for municipal cities like Shanghai in developing countries.

【 授权许可】

Unknown   
Copyright © 2023 Dong, Sun, Xu, Zhang, Wang, Zhang and Gao.