Abstract
BackgroundEvidence 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.MethodsTaiwan’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.ResultsThere 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.ConclusionsAgainst 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.Graphical abstract
Highlights
Mounting evidence indicates that elevated exposure to air pollution has been linked to increased risks of cardiovascular diseases (CVDs) [1,2,3,4,5,6,7,8,9] and reduction in the ambient PM2.5 concentrations may be associated with improved life expectancy [10]
Compared to substantial evidence of adverse cardiovascular effects of air pollution in the general population, research on this topic for type 2 diabetes (T2D) population is in a great demand, for those with established CVDs who are more vulnerable to deleterious cardiovascular outcomes [14, 15]
The Kaplan-Meier curve for composite CVD events is presented in Supplementary Figure 2, with a censoring rate of 73.8% from a total of 2072 study patients with 542 CVD events occurring during the followup
Summary
Mounting evidence indicates that elevated exposure to air pollution has been linked to increased risks of cardiovascular diseases (CVDs) [1,2,3,4,5,6,7,8,9] and reduction in the ambient PM2.5 concentrations may be associated with improved life expectancy [10]. Despite growing research on adverse health effects of environmental pollution, these studies typically face three major challenges Survival outcomes such as time to CVD events or death have become popular. Individual exposure to pollution is assessed using the average concentration of pollution within an area, with all residents assigned the same exposure concentrations [12, 17,18,19] Such an aggregated assessment of environmental pollution may be subjected to measurement errors, affecting the estimation precision of the adverse pollution effects on health outcomes. The pattern of pollution exposure and adverse pollution effects might be similar for individuals who are near each other This is referred to as the spatial correlation between potential health effects and environmental hazards. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach
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