Xiangtong Liu1, Zhiwei Li2, Jie Zhang3, Moning Guo4, Feng Lu5, Xiaolin Xu6, Aklilu Deginet7, Mengmeng Liu8, Zhaomin Dong9, Yaoyu Hu10, Mengyang Liu11, Yutong Li12, Mengqiu Wu13, Yanxia Luo14, Lixin Tao15, Hualiang Lin16, Xiuhua Guo17. 1. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: xiangtongl@ccmu.edu.cn. 2. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: 15128472546@163.com. 3. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: zhangjie@ccmu.edu.cn. 4. Beijing Municipal Health Commission Information Center, Beijing 100034, China. Electronic address: 13581627289@163.com. 5. Beijing Municipal Health Commission Information Center, Beijing 100034, China. Electronic address: lufeng@wjw.beijng.gov.cn. 6. The University of Queensland, Brisbane, Australia; School of Public Health, Zhejiang University, Hangzhou 310058, China. Electronic address: xiaolin.xu@uq.edu.au. 7. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: dgaklilu2006@gmail.com. 8. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: liumengmeng@ccmu.edu.cn. 9. School of Space and Environment, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China. Electronic address: dongzm@buaa.edu.cn. 10. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: huyaoyu499@163.com. 11. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: 18031864301@163.com. 12. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: 15210355177@163.com. 13. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: qiuwm00@163.com. 14. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: lyx100@ccmu.edu.cn. 15. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: taolixin@ccmu.edu.cn. 16. Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: linhualiang@mail.sysu.edu.cn. 17. School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China. Electronic address: statguo@ccmu.edu.cn.
Abstract
BACKGROUND: The association between ozone and ischemic stroke has been widely reported; however, the association among patients with type 2 diabetes (T2D) has remained largely unknown. METHODS: The time series data of daily morbidity and concentrations of ozone from 2014 to 2018 were collected in Beijing, China. A time-stratified case-crossover study combined with a distributed lag nonlinear model was used to estimate the ozone effect on stroke morbidity among T2D patients. Based on principal diagnosis, ischemic stroke cases were identified according to the International Classification of Diseases (I63), and a history of T2D was coded as E12. RESULTS: A total of 149,757 hospital admissions for ischemic stroke among T2D patients were recorded in Beijing. Approximately U-shaped exposure-response curves were observed for ozone and ischemic stroke morbidity among T2D patients. With a reference at 54.91 μg/m3, extreme-low (5th: 9.59 μg/m3) ozone was significantly associated with a decreased risk for ischemic stroke [RR = 0.88, 95% confidence interval (CI): 0.80-0.98]. Subgroup analysis showed that extremely low-ozone (5th) level only had a significant protective effect in males and elderly population, with a RR value of 0.86 (95% CI: 0.76-0.97) and 0.85 (95% CI: 0.75-0.96), respectively. Extreme-high ozone (99th: 157.06 μg/m3) was significantly associated with an increased risk for ischemic stroke (RR = 1.33, 95% CI: 1.12-1.57). The effect size was 1.34 (95% CI: 1.10-1.63) for males and 1.32 (95% CI: 1.07-1.63) for females, and the difference was not significant (Z = -0.29, P = 0.77). The effect size in younger adults was significantly higher than that in participants aged ≥65 years [1.52 (95% CI: 1.21-1.91) vs. 1.22 (95% CI: 1.01-1.47), Z = -1.62, P < 0.05]. CONCLUSIONS: U-shaped associations were observed between ozone and ischemic stroke morbidity in T2D patients. Men and elderly population are vulnerable to low-ozone level, and the younger adults are more susceptible to extremely high-ozone level than the elderly.
BACKGROUND: The association between ozone and ischemic stroke has been widely reported; however, the association among patients with type 2 diabetes (T2D) has remained largely unknown. METHODS: The time series data of daily morbidity and concentrations of ozone from 2014 to 2018 were collected in Beijing, China. A time-stratified case-crossover study combined with a distributed lag nonlinear model was used to estimate the ozone effect on stroke morbidity among T2D patients. Based on principal diagnosis, ischemic stroke cases were identified according to the International Classification of Diseases (I63), and a history of T2D was coded as E12. RESULTS: A total of 149,757 hospital admissions for ischemic stroke among T2D patients were recorded in Beijing. Approximately U-shaped exposure-response curves were observed for ozone and ischemic stroke morbidity among T2D patients. With a reference at 54.91 μg/m3, extreme-low (5th: 9.59 μg/m3) ozone was significantly associated with a decreased risk for ischemic stroke [RR = 0.88, 95% confidence interval (CI): 0.80-0.98]. Subgroup analysis showed that extremely low-ozone (5th) level only had a significant protective effect in males and elderly population, with a RR value of 0.86 (95% CI: 0.76-0.97) and 0.85 (95% CI: 0.75-0.96), respectively. Extreme-high ozone (99th: 157.06 μg/m3) was significantly associated with an increased risk for ischemic stroke (RR = 1.33, 95% CI: 1.12-1.57). The effect size was 1.34 (95% CI: 1.10-1.63) for males and 1.32 (95% CI: 1.07-1.63) for females, and the difference was not significant (Z = -0.29, P = 0.77). The effect size in younger adults was significantly higher than that in participants aged ≥65 years [1.52 (95% CI: 1.21-1.91) vs. 1.22 (95% CI: 1.01-1.47), Z = -1.62, P < 0.05]. CONCLUSIONS: U-shaped associations were observed between ozone and ischemic stroke morbidity in T2D patients. Men and elderly population are vulnerable to low-ozone level, and the younger adults are more susceptible to extremely high-ozone level than the elderly.
Authors: Carmen Adella Sîrbu; Ion Stefan; Rodica Dumitru; Marian Mitrica; Aida Mihaela Manole; Titus Mihai Vasile; Constantin Stefani; Aurelian Emil Ranetti Journal: Healthcare (Basel) Date: 2022-06-23