AUTHOR=Peng Qian , Zhang Na , Yu Hongjie , Shao Yueqin , Ji Ying , Jin Yaqing , Zhong Peisong , Zhang Yiying , Jiang Honglin , Li Chunlin , Shi Ying , Zheng Yingyan , Xiong Ying , Wang Zhengzhong , Jiang Feng , Chen Yue , Jiang Qingwu , Zhou Yibiao
TITLE=Geographical Variation of COPD Mortality and Related Risk Factors in Jiading District, Shanghai
JOURNAL=Frontiers in Public Health
VOLUME=9
YEAR=2021
URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.627312
DOI=10.3389/fpubh.2021.627312
ISSN=2296-2565
ABSTRACT=Background: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in China. Although numerous studies have been conducted to determine the risk factors for COPD mortality such as ambient air pollution, the results are not fully consistent.
Methods: This study included mortality analysis and a case-control design by using the data extracted from the Mortality Registration System in Jiading District, Shanghai. Traditional logistic regression, geographically weighted logistic regression (GWLR), and spatial scan statistical analysis were performed to explore the geographic variation of COPD mortality and the possible influencing factors.
Results: Traditional logistic regression showed that extreme lower temperature in the month prior to death, shorter distance to highway, lower GDP level were associated with increased COPD mortality. GWRL model further demonstrated obvious geographical discrepancies for the above associations. We additionally identified a significant cluster of low COPD mortality (OR = 0.36, P = 0.002) in the southwest region of Jiading District with a radius of 3.55 km by using the Bernoulli model. The geographical variation in age-standardized mortality rate for COPD in Jiading District was explained to a certain degree by these factors.
Conclusion: The risk of COPD mortality in Jiading District showed obvious geographical variation, which were partially explained by the geographical variations in effects of the extreme low temperature in the month prior to death, residential proximity to highway, and GDP level.