Dynamic evolution and spatial difference of public health service supply in economically developed provinces of China: typical evidence from Guangdong Province.

OBJECTIVE: The outbreak of the COVID-19 pandemic has drawn attention from all sectors of society to the level of public health services. This study aims to investigate the level of public health service supply in the four major regions of Guangdong Province, providing a basis for optimizing health resource allocation. METHODS: This article uses the entropy method and panel data of 21 prefecture-level cities in Guangdong Province from 2005 to 2021 to construct the evaluation index system of public health service supply and calculate its supply index. On this basis, the standard deviation ellipse method, kernel density estimation, and Markov chain are used to analyze the spatiotemporal evolution trend of the public health service supply level in Guangdong Province. The Dagum Gini coefficient and panel regression model are further used to analyze the relative differences and the key influencing factors of difference formation. Finally, the threshold effect model is used to explore the action mechanism of the key factors. RESULTS: Overall, the level of public health service supply in Guangdong Province is on an upward trend. Among them, polarization and gradient effects are observed in the Pearl River Delta and Eastern Guangdong regions; the balance of public health service supply in Western Guangdong and Northern Mountainous areas has improved. During the observation period, the level of public health services in Guangdong Province shifted towards a higher level with a smaller probability of leapfrogging transition, and regions with a high level of supply demonstrated a positive spillover effect. The overall difference, intra-regional difference and inter-regional difference in the level of public health service supply in Guangdong Province during the observation period showed different evolutionary trends, and spatial differences still exist. These differences are more significantly positively affected by factors such as the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate. Under different economic development threshold values, the degree of fiscal decentralization and urbanization rate both have a double threshold effect on the role of public health service supply level. CONCLUSION: The overall level of public health service supply in Guangdong Province has improved, but spatial differences still exist. Key factors influencing these differences include the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate, all of which exhibit threshold effects. It is suggested that, in view of the actual situation of each region, efforts should be made to build and maintain their own advantages, enhance the spatial linkage of public health service supply, and consider the threshold effects of key factors in order to optimize the allocation of health resources.

Impact of digital economic development and environmental pollution on residents' health: an empirical analysis based on 279 prefecture-level cities in China.

BACKGROUND: The digital economy based on the internet and IT is developing rapidly in China, which makes a profound impact on urban environmental quality and residents' health activities. Thus, this study introduces environmental pollution as a mediating variable based on Grossman's health production function to explore the impact of digital economic development on the health of the population and its influence path. METHODS: Based on the panel data of 279 prefecture-level cities in China from 2011 to 2017, this paper investigates the acting mechanism of digital economic development on residents' health by employing a combination of mediating effects model and spatial Durbin model. RESULTS: The development of digital economy makes direct improvement on residents' health condition, which is also obtained indirectly by means of environmental pollution mitigation. Besides, from the perspective of spatial spillover effect, the development of digital economy also has a significant promoting effect on the health of adjacent urban residents, and further analysis reveals that the promoting effect in the central and western regions of China is more pronounced than that in the eastern region. CONCLUSIONS: Digital economy can have a direct promoting effect on the health of residents, and environmental pollution has an intermediary effect between digital economy and residents' health; At the same time, there is also a regional heterogeneity among the three relationships. Therefore, this paper believes that the government should continue to formulate and implement scientific digital economy development policies at the macro and micro levels to narrow the regional digital divide, improve environmental quality and enhance the health level of residents.

Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020.

BACKGROUND: With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies. METHODS: Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA). RESULTS: From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year. CONCLUSIONS: The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.

A study of self-precaution against the background of the COVID-19 pandemic from the perspective of risk perception attitude theory and social support.

BACKGROUND: In this research, the factors that influence the self-precautionary behavior during the pandemic are explored with the combination of social support and a risk perception attitude framework. METHODS: An online survey was conducted among 429 members to collect information on demographic data, social support, perceptions of outbreak risk, health self-efficacy, and self-precautionary behaviors with the guide of the Social Support Scale, the COVID-19 Risk Perception Scale, the Health Self-Efficacy Scale and the Self-precautionary Behavior Scale. RESULTS: The research shows that among the three dimensions of social support, both objective support and support utilization negatively predict risk perception, while subjective support positively predicts health self-efficacy; health self-efficacy and risk perception significantly predict self-precautionary behavior; the relationship between risk perception and self-precautionary behavior is significantly moderated by health self-efficacy. CONCLUSIONS: The combined influence of social capital and risk perception attitudinal frameworks on self-precautionary behavior is highlighted in this study, with the relationship between the public's risk perception, health self-efficacy, and self-precautionary behavior intentions examined against the background of coronavirus disease 2019 (COVID-19). These findings contribute to understanding the impact of social capital factors on risk perception and health self-efficacy, which provides insight into the current status and influencing factors of the public's precautionary behavior and facilitates early intervention during a pandemic.