Time-series analysis of air pollution effects on diabetes related mortality
10.13213/j.cnki.jeom.2021.21102
- VernacularTitle:大气污染与糖尿病相关疾病死亡的时间序列分析
- Author:
Xiaoting CHU
1
;
Jianjing XIONG
1
;
Xiaoming YANG
1
;
Xiaolie YIN
1
;
Guohui ZHANG
1
;
Qiuping WAN
1
;
Yunhui WANG
1
;
Lan WANG
2
Author Information
1. Department of Vital Statistics, Tumor and Injury Prevention and Control, Shanghai Jing'an District Center for Disease Control and Prevention, Shanghai 200072, China.
2. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China.
- Publication Type:Investigation
- Keywords:
air pollution;
diabetes;
mortality;
time-series analysis;
generalized additive model
- From:
Journal of Environmental and Occupational Medicine
2021;38(11):1237-1243
- CountryChina
- Language:Chinese
-
Abstract:
Background Diabetes mellitus is a major public health issue at present. Previous studies have shown that ambient air pollution is a risk factor for diabetes. Objective This study aims to explore the acute effects of ambient air pollution on diabetes related death in Shanghai Jing’an District. Methods Daily air pollution data, meteorological data, and diabetes related mortality data in 2013−2019 in Shanghai Jing’an District were collected. A generalized additive model (GAM) was established to conduct time-series analysis on the short-term effect of ambient air pollution on diabetes related mortality, and gender- and age-stratified analysis on susceptibility of various groups to ambient air pollution exposures. Results For every 10 μg·m−3 increase of the concentrations of PM2.5, PM10, SO2, and NO2, the diabetes related mortality increased by 2.47% (95%CI: 1.56%−3.38%), 2.02% (95%CI: 1.29%−2.75%), 5.75% (95%CI: 2.99%−8.58%), and 3.93% (95%CI: 2.49%−5.39%) at lag05 respectively (P<0.05). In the stratified analysis, exposures to increased concentrations of PM2.5, PM10, SO2, and NO2 raised the mortality risks from diabetes in male, female, and ≥65 years oldgroups (P<0.05). However, the differences in mortality risks from diabetes due to air pollution within gender and age groups were statistically insignificant. Conclusion In Shanghai Jing'an District, the elevated levels of ambient air pollutants, including PM2.5, PM10, SO2, and NO2, are significantly associated with the increase of diabetes related mortality, and there are lag effects and cumulative effects. The ≥65 years olds are more susceptible to the impact of air pollution on diabetes related deaths.
