Disease Burden Evaluation of Injury and Poisoning in China from 2009 to 2019

Xiuli Hu; Miao Qi; Ping Yuan; Guojia Qi; Xiahong Li; Yanna Zhou; Xiuquan Shi

doi:10.18502/ijph.v52i5.12717

Xiuli Hu Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
Miao Qi Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
Ping Yuan Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
Guojia Qi Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
Xiahong Li Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
Yanna Zhou Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
Xiuquan Shi Department of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China

DOI: https://doi.org/10.18502/ijph.v52i5.12717

Keywords: Mortality; Injury and poisoning; Gender difference; Urban and rural areas

Abstract

Background: We aimed to analyze the differences and changing trends of mortality of Injury and Poisoning (IP) between urban and rural areas and gender in China to find out the influencing factors and to propose improvement measures.

Methods: IP mortality, population, economy, medical and health information data came from the official website of the National Bureau of Statistics, and basic data on education level came from the Chinese Ministry of Education. Then the differences of the mortality of IP were compared between different areas and gender in China from 2009 to 2019, and the relationships between the mortality changes of IP and education level, GDP per capita, the numbers of practicing physicians, health institutions and urbanization rate were also explored by establishing a ridge regression model.

Results: The mortality of IP in rural areas was significantly higher than that of urban areas, and in male was higher than that of female (both P<0.001). Primary school graduates, GDP per capita, the number of practicing physicians, health institutions and urbanization rate had strong correlations (r_min=-0.622) with the mortality of IP. Ridge regression model showed that there was a quantitative relationship between primary school graduates, GDP per capita, the number of practising physicians, health institutions, urbanization rate and the mortality of IP in China.

Conclusion: As the difference of working nature, economic development imbalance, psychological and gender, the mortality of IP was significantly different, so the state should take more effective measures to develop the urban and rural areas balanced, and reduce the IP risk in some particular occupations.