Association of ambient air pollution with cardiovascular disease risks in people with type 2 diabetes: a Bayesian spatia
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RESEARCH
Open Access
Association of ambient air pollution with cardiovascular disease risks in people with type 2 diabetes: a Bayesian spatial survival analysis Pei-Fang Su1, Fei-Ci Sie1, Chun-Ting Yang2, Yu-Lin Mau1, Shihchen Kuo3 and Huang-Tz Ou2,4,5*
Abstract 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. PM2.5 and SO2 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/m3 increase in the monthly PM2.5 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 SO2 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. Keywords: Time-to-event, Survival, Spatial correlation, Bayesian approach, Cardiovascular disease, Type 2 diabetes
* Correspondence: [email protected] 2 Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan 4 Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the materia
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