A geographically weighted regression approach to investigate air pollution effect on lung cancer: A case study in Portugal

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Diogo Cardoso *
Marco Painho
Rita Roquette
(*) Corresponding Author:
Diogo Cardoso | diogo.luzcardoso@gmail.com

Abstract

The risk of developing lung cancer might to a certain extent be attributed to tobacco. Nevertheless, the role of air pollution, both form urban and industrial sources, needs to be addressed. Numerous studies have concluded that long-term exposure to air pollution is an important environmental risk factor for lung cancer mortality. Still, there are only a few studies on air pollution and lung cancer in Portugal and none addressing its spatial dimension. The goal was to determine the influence of air pollution and urbanization rate on lung cancer mortality. A geographically weighted regression (GWR) model was performed to evaluate the relation between particle matter10 (PM10) emissions and lung cancer mortality relative risk (RR) for males and females in Portugal between 2007 and 2011. RR was computed with the BYM model. For a more in-depth analysis, the urbanization rate and the percentage of industrial area in each municipality were added. GWR efforts led to identifying three variables that were statistically significant in explaining lung cancer relative risk mortality, PM10 emissions, urbanization rate and the percentage of industrial area with an adjusted R2 of 0,63 for men and 0,59 for women. A small set of 8 municipalities with high correlation values was also identified (local R2 above 0,70). Stronger relationships were found in the north-western part of mainland Portugal. The local R2 tends to be higher when the emissions of PM10 are joined by urbanization and industrial areas. However, when assessing the industrial areas alone, it was noted that its impact was lower overall. As one of the first communications on this subject in Portugal, we have identified municipalities where possible impacts of air pollution on lung cancer mortality RR are higher thereby highlighting the role of geography and spatial analysis in explaining the associations between a disease and its determinants.


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