Spatio-Temporal Analysis Of The Association Between Ambient Air Pollution And Breast Cancer Incidence In Southern India: A Bayesian Modeling Approach

Abstract

Breast cancer (BC) is the most common cancer among women in India, and in Thiruvananthapuram, Kerala, its incidence has shown a steady increase over recent decades. While reproductive and lifestyle factors remain well-established determinants, environmental exposures such as air pollution are emerging as potential contributors, yet limited research in India has investigated this relationship using advanced spatial epidemiological approaches. In this study, data on BC cases from the Population-Based Cancer Registry between 2005 and 2019 were analyzed and aggregated into five 3-year periods. Air pollutant concentrations were estimated using inverse distance weighting, and spatial clustering was examined through Global and Local Moran’s I, Getis-Ord Gi*, and Kulldorff’s spatio-temporal scan statistic. To evaluate associations, Bayesian spatio-temporal models incorporating Conditional Autoregressive priors were applied, and competing models, including AR(1), AR(2), and a separated spatial structure, were compared using deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC). A total of 4,390 BC cases were reported over the study period, with crude incidence rates rising from 36.8 to 60.8 per 100,000. Spatial clustering was particularly evident in urban areas. Significant associations were found for NO₂ and PM₂.₅, with relative risks of 1.22 and 1.20, respectively. The findings highlight the importance of environmental monitoring and targeted interventions.