The capacity and influencing factors of primary health care services in China

Abstract

Objective To analyze the capacity and influencing factors of primary health care services and to provide a scientific basis for promoting the development of primary health care services in China. Methods The entropy weight technique for order preference by similarity to ideal solution (TOPSIS) and rank‒sum ratio (RSR) methods, which are based on the health resource density index (HRDI), were used to analyze the capacity of primary health care services, and multiple stepwise regression analysis was used to analyze the influencing factors. Results Taking the HRDI of primary health care service capacity in 2021 as a reference, the six evaluation indicators of 14 regions, including Hebei, Liaoning, and Shanghai, were higher than the Chinese average. According to the entropy weight TOPSIS method, the average C-values of primary health care service capacity in China from 2017–2021 were 0.303, 0.313, 0.324, 0.331, and 0.326, respectively, with the C-values of regions such as Shanghai, Beijing, and Henan ranking in the top ten, whereas those of regions such as Xinjiang, Qinghai, and Tibet ranked in the bottom five. According to grade divisions by the RSR method, Tianjin, Shandong, Jiangsu, Shanghai, and Beijing were ranked at the good grade level; Xinjiang, Qinghai, Inner Mongolia, and Tibet at the poor grade level; and the remaining 22 regions at the medium grade level. According to the multivariate stepwise regression, population density, health technicians, and the number of beds per 1,000 people were the main factors affecting the capacity of primary health care services in China.