公共卫生
环境卫生
索引(排版)
医学
中国
生物统计学
流行病学
卫生政策
公共卫生信息学
人口
国际卫生
地理
护理部
计算机科学
万维网
内科学
考古
作者
Zhengqi Wei,Keke Wei,Li Y,Lijie Nie,Yizhuang Zhou
标识
DOI:10.1186/s12889-024-18212-7
摘要
Abstract Background With the development of the economy, public health has become increasingly important. Therefore, it is important to establish a comprehensive and scientific the public health level index (PHL) system to measure public health level as a research priority. The current research has limitations in exploring the PHL system; therefore, the field still lacks a comprehensive indicator system to measure the level of public health. Therefore, this paper aims to develop a multi-level public health index system and utilizes China as a case study to evaluate its public health status. The objective is to offer insights and recommendations for the improvement of public health initiatives in China and other regions. Methods Utilizing data from 2011 to 2020, a comprehensive PHL was developed to encompass three vital indices: the Public Health Service Index (PHS), the Public Health Resource Index (PHR), and the Population Health Level Index (PHL). Subsequently, the PHL, PHS, PHR, and PH were meticulously calculated using a comprehensive evaluation method. Amid the current disparity between public health and economic progress, both the spatial Durbin model and the spatial lag model were finally employed to examine the influence of economic level (EL) on PHL, thus affirming the consistent reliability and accuracy of PHS. Results Our findings revealed the following: ( i ) the PHL, PHS, and PHR indices show increasing trends in China; ( ii ) both EL and PHL exhibit high-high clustering and low-low clustering states; ( iii ) the PHL in the area has a positive spatial spillover effect on the surrounding area; ( iv ) EL will result in the siphoning effect of PHL; and ( v ) EL can enhance PHL through urbanization, PH, and PHS. Conclusions The PHL system constructed in this paper demonstrates multiple levels, pluralism, spatio-temporal comparability, and robustness. It can reflect not only the input and output of public health initiatives but also the interconnectedness and autonomy within the public health system. Therefore, it can be widely utilized in other areas of public health research.
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