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The superposition effects of air pollution on government health expenditure in China— spatial evidence from GeoDetector

BACKGROUND: As the fifth-largest global mortality risk factor, air pollution has caused nearly one-tenth of the world’s deaths, with a death toll of 5 million. 21% of China’s disease burden was related to environmental pollution, which is 8% higher than the US. Air pollution will increase the demand...

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Detalles Bibliográficos
Autores principales: Xia, Qi, Zhang, Xiyu, Hu, Yanmin, Tian, Wanxin, Miao, Wenqing, Wu, Bing, Lai, Yongqiang, Meng, Jia, Fan, Zhixin, Zhang, Chenxi, Xin, Ling, Miao, Jingying, Wu, Qunhong, Jiao, Mingli, Shan, Linghan, Wang, Nianshi, Shi, Baoguo, Li, Ye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310420/
https://www.ncbi.nlm.nih.gov/pubmed/35879697
http://dx.doi.org/10.1186/s12889-022-13702-y
Descripción
Sumario:BACKGROUND: As the fifth-largest global mortality risk factor, air pollution has caused nearly one-tenth of the world’s deaths, with a death toll of 5 million. 21% of China’s disease burden was related to environmental pollution, which is 8% higher than the US. Air pollution will increase the demand and utilisation of Chinese residents’ health services, thereby placing a greater economic burden on the government. This study reveals the spatial impact of socioeconomic, health, policy and population factors combined with environmental factors on government health expenditure. METHODS: Spearman’s correlation coefficient and GeoDetector were used to identify the determinants of government health expenditure. The GeoDetector consist of four detectors: factor detection, interaction detection, risk detection, and ecological detection. One hundred sixty-nine prefecture-level cities in China are studied. The data sources are the 2017 data from China’s Economic and Social Big Data Research Platform and WorldPOP gridded population datasets. RESULTS: It is found that industrial sulfur dioxide attributed to government health expenditure, whose q value (explanatory power of X to Y) is 0.5283. The interaction between air pollution factors and other factors will increase the impact on government health expenditure, the interaction value (explanatory power of × 1∩× 2 to Y) of GDP and industrial sulfur dioxide the largest, whose values is 0.9593. There are 96 simple high-risk areas in these 169 areas, but there are still high-risk areas affected by multiple factors. CONCLUSION: First, multiple factors influence the spatial heterogeneity of government health expenditure. Second, health and socio-economic factors are still the dominant factors leading to increased government health expenditure. Third, air pollution does have an important impact on government health expenditure. As a catalytic factor, combining with other factors, it will strengthen their impact on government health expenditure. Finally, an integrated approach should be adopted to synergisticly governance the high-risk areas with multi-risk factors.