Lung cancer and particulate pollution: A critical review of spatial and temporal analysis evidence

Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs.

We conducted a critical review of spatial and temporal analyses of PM and lung cancer. The databases of PubMed, Web of Science and Scopus were searched for potential articles published until September 30, 2017. We included studies that applied spatial and temporal analyses to evaluate the associations of PM_2.5 (inhalable particles with diameters that are 2.5 µm and smaller) and PM₁₀ (inhalable particles with diameters that are 10 µm and smaller) with lung cancer.

We identified 17 articles eligible for the review. Of these, 11 focused on PM_2.5, five on PM₁₀, and one on both. These studies suggested a significant positive association between PM_2.5 exposure and the risk of lung cancer. Relative risks of lung cancer mortality ranged from 1.08 (95% confidence interval (CI): 1.07-1.09) to 1.60 (95%CI: 1.09-2.33) for 10 µg/m³ increase in PM_2.5 exposure. The association between PM₁₀ and lung cancer had been less well researched and the results were not consistent. In terms of the analysis methods, 16 papers undertook spatial analysis and one paper employed temporal analysis. No paper included spatial and temporal analyses simultaneously and considered spatiotemporal uncertainty into model predictions. Among the 16 papers with spatial analyses, thirteen studies presented maps, while only five and 11 studies utilized spatial exploration and modeling methods, respectively.

Advanced spatial and temporal epidemiological methods were seldom applied to PM-lung cancer associations. Further research is urgently needed to develop and employ robust and comprehensive spatiotemporal analysis methods for the evaluation of PM-lung cancer associations and the support of lung cancer prevention strategies.