Fig. 4

Association between patients’ characteristics and the risk of cardiovascular diseases in the multivariable Bayesian survival spatial, Cox, and Weibull models. This figure shows the association between patients’ characteristics (i.e., demographics, drug treatments, diabetes status/severity, comorbidities, and air pollution exposure) and the risk of composite cardiovascular disease events, estimated by the multivariable Bayesian survival spatial, Cox, and Weibull models. And, in the Bayesian survival spatial model, the estimated prior parameters α, λ, σ, and φ are 0.932, 0.0001, 1.2829, and 0.440, respectively. For the Weibull model, α and λ are 0.8971 and 0.0007, respectively. Abbreviations: aDCSI, adapted Diabetes Complication Severity Index; CVD, cardiovascular disease; GLA, glucose-lowering agent; SU, sulfonylurea; DPP-4i, dipeptidyl-peptidase 4 inhibitor; TZD, thiazolidinedione; CrI, credible interval; CI, confidence interval