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  • One strength of the present


    One strength of the present study is that we considered incidence data together with mortality data, the combined results better expressing the relationship between the risk of exposure and its adverse health effects [60]. Given the role of air pollution in the development of cancer, as evidenced in recent years, mainly in cohort studies, we understand that there is a need for ecological studies to show effects that are invisible at the individual level. Another strength of our study is the fact that no spatial analysis to date was conducted in the city of São Paulo to evaluate respiratory cancer risks in relation to residential proximity to traffic. Furthermore, much of the evidence investigating air pollution and cancer risk have been derived from high-income countries. We employed Besag–York–Mollié models, which explicitly consider the spatial structure of the data and allow the risks to be estimated for each area under analysis. The likelihood was modeled with a Poisson distribution because of the better fit in relation to a negative binomial distribution. The classic Poisson model, normally used in most studies using similar approaches, not considers the spatial variability of risk. The use of the INLA method with weak priors best reproduces the spatial random effects, the results being comparable to those obtained with the use of a Monte Carlo Markov Chain [34]. Our study has certain limitations. We used traffic density as a proxy for exposure to traffic-related air pollution, because there is a limited number of municipal air quality monitoring stations, making it impossible to characterize exposure on a small scale. Another limitation is that we could not calculate the traffic density for previous years with similar precision. Nevertheless, the evaluation of traffic density allows exposure to traffic-related air pollution to be assessed throughout the entire city and might be used as a proxy when more accurate measurements are not available [61]. However, as respiratory cancers are chronic diseases, the specific exposures responsible for the measured outcomes might have occurred before the traffic density GSK1838705A used in this study, requiring us to assume that these exposures remained stable over preceding decades. Although the number of cars in use was lower in previous years, the car fleet was less technologically advanced and the policies on the emissions of pollutants were less rigorous. The historical air quality data corroborate this as high levels of pollutants were registered. For example, the PM10 concentrations decreased by around 60 μg/m3 to 40 μg/m3 from 1987 to 2008 [56], leading us to assume that the exposure scenario to traffic pollutants in previous years was worse than the exposure period used in this study.
    Declarations of interest
    Authorship contributions
    This work was supported by the National Council for Scientific and Technological Development (CNPq) – Process number 475362/2012-8, and the São Paulo Research Foundation (FAPESP) – FAPESP/PPP-SUS 2006/61616-5.
    Introduction In Canada in 2017, nearly half of Canadians were expected to develop cancer over a lifetime, most after age 50, and cancer was the leading cause of death [1]. Physical inactivity is a known, modifiable risk factor for cancer [2]. A recent pooled analysis of 1.44 million men and women estimated that women reporting high versus low levels of leisure-time physical activity experienced lower risks of 13 types of cancer [3]. Despite strong epidemiologic evidence of an association, the biologic mechanisms are not well understood. One hypothesized mechanism relates to telomere length. Telomeres are nucleoprotein structures located at the ends of chromosomes that protect cells from chromosomal instability and shorten naturally with every cellular division in normal cells. Telomere attrition reflects cellular aging, cellular damage, chronological age [4], and has been linked to cancer susceptibility. Over time, telomeres can become critically shortened and dysfunctional. If important cell cycle checkpoint genes are compromised, premalignant cells may continue to grow, eventually leading to genomic instability and potentially cancer [5]. Genetic determinants of telomere length (e.g., variants of the TERT gene which encodes telomerase, a reverse transcriptase enzyme that maintains telomere length) have been associated with cancer risk [[6], [7], [8], [9]]. Observational studies show that shorter leukocyte telomere length (LTL) may be associated with higher risks of cancer [[10], [11], [12]], type 2 diabetes [13], and all-cause mortality [14].