Association of ambient air pollution with cardiovascular disease risks in people with type 2 diabetes: a Bayesian spatial survival analysis.

Autor: Su PF; Department of Statistics, National Cheng Kung University, Tainan, Taiwan., Sie FC; Department of Statistics, National Cheng Kung University, Tainan, Taiwan., Yang CT; Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan., Mau YL; Department of Statistics, National Cheng Kung University, Tainan, Taiwan., Kuo S; Division of Metabolism, Endocrinology & Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA., Ou HT; Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan. huangtz@mail.ncku.edu.tw.; Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan. huangtz@mail.ncku.edu.tw.; Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan. huangtz@mail.ncku.edu.tw.
Jazyk: angličtina
Zdroj: Environmental health : a global access science source [Environ Health] 2020 Nov 05; Vol. 19 (1), pp. 110. Date of Electronic Publication: 2020 Nov 05.
DOI: 10.1186/s12940-020-00664-0
Abstrakt: Background: Evidence is limited on excess risks of cardiovascular diseases (CVDs) associated with ambient air pollution in diabetic populations. Survival analyses without considering the spatial structure and possible spatial correlations in health and environmental data may affect the precision of estimation of adverse environmental pollution effects. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach.
Methods: Taiwan's national-level health claims and air pollution databases were utilized. Fine individual-level latitude and longitude were used to determine pollution exposure. The exponential spatial correlation between air pollution and CVDs was analyzed in our Bayesian model compared to traditional Weibull and Cox models.
Results: There were 2072 diabetic patients included in analyses. PM 2.5 and SO 2 were significant CVD risk factors in our Bayesian model, but such associations were attenuated or underestimated in traditional models; adjusted hazard ratio (HR) and 95% credible interval (CrI) or confidence interval (CI) of CVDs for a 1 μg/m 3 increase in the monthly PM 2.5 concentration for our model, the Weibull and Cox models was 1.040 (1.004-1.073), 0.994 (0.984-1.004), and 0.994 (0.984-1.004), respectively. With a 1 ppb increase in the monthly SO 2 concentration, adjusted HR (95% CrI or CI) was 1.886 (1.642-2.113), 1.092 (1.022-1.168), and 1.091 (1.021-1.166) for these models, respectively.
Conclusions: Against traditional non-spatial analyses, our Bayesian spatial survival model enhances the assessment precision for environmental research with spatial survival data to reveal significant adverse cardiovascular effects of air pollution among vulnerable diabetic patients.
Databáze: MEDLINE
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