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  • There is evidence of associations between exposure

    2019-08-16

    There is evidence of associations between exposure to traffic-related air pollution and various health outcomes, as outlined in a review study conducted by the Health Effects Institute [6], which identified the zones of exposure to such pollution as areas located 300–500 m from a major road. A number of studies have found that long-term exposure to particulate matter or other constituents of traffic-related air pollution correlates with the incidence of lung cancer [[7], [8], [9], [10], [11], [12], [13]], as well as with the associated mortality [[14], [15], [16], [17], [18], [19], [20]]. Most studies that have addressed the impacts of air pollution and the occurrence of cancer have been cohort studies, few studies having performed spatial analyses to investigate associations between the two [21,22]. Within a spatial analysis, the use of Bayesian modeling has been shown to be an effective tool for mapping diseases, providing more accurate risk estimates, using a combination of covariate data and a set of spatial random effects to model any overdispersion or spatial correlation in the disease data [[23], [24], [25]]. Therefore, the objective of this study was to perform a Bayesian analysis to test the AMG-176 that the incidence of and mortality related to respiratory tract cancer are higher in areas with higher traffic density in the city of São Paulo, Brazil.
    Methods
    Results The total traffic density and the MHDI values for the city of São Paulo are shown in Fig. 1. The roadways and traffic are considerably more dense in the central region of the city, areas of low and very low traffic intensity being seen in its extreme northern, eastern, and southern regions. The socioeconomic indicator (MHDI) also showed higher values in the central regions. However, in some regions, affluent areas are adjacent to poorer areas. Table 1 shows the modeling results for the incidence of and mortality associated with respiratory tract cancer. Collinearity was not considered problematic because the VIF for the traffic density and MHDI was 1.29 (incidence) and 1.31 (mortality). In the analysis of the incidence of respiratory tract cancer, the ecological model had a better fit (DIC = 2267) than did the intercept-only model (DIC = 2292) and both covariates were identified as risk factors. For each 1-unit standard-deviation increase in traffic density and in the MHDI, the incidence increased by 7% and 25%, respectively. In the analysis of the mortality associated with respiratory tract cancer, the ecological model also presented a better fit (DIC = 2369) than did the intercept-only model (DIC = 2392). For each 1-unit standard-deviation increase in traffic density and in the MHDI, the associated mortality increased by 4% and 23%, respectively. The effect of traffic density was marginally significant: its posterior distribution had a 0.025 quantile of 0.99; and there was a 0.05 probability Primosome the effect size was ≤ 1. The RRs for respiratory tract cancer incidence estimated by the intercept-only model presented a gradient in which the values were highest for the weighting areas in the center of the city and lowest for those on the periphery, especially in the extreme northern, eastern, and southern regions (Fig. 2a). Fig. 2b shows the spatial variability, after adjustment for the covariates, representing the incidence that could not be explained by the influence of traffic density and the MHDI. Note that weighting areas with an RR of 2.0–4.0 disappeared in the most central region and that the number of weighting areas with an RR of 1.3–2.0 has increased in the northern and eastern regions of the city. Areas with a low RR (0.3–1.0) came to present values between 1.1 and 1.3.