Lung cancer risk and its potential association with PM2.5 in Bagmati province, Nepal—A spatiotemporal study from 2012 to 2021

• Published in: Frontiers in public health 2024-12, Vol.12, p.1490973
• Main Authors: Neupane, Basanta Kumar, Acharya, Bipin Kumar, Cao, Chunxiang, Xu, Min, Taylor, Pornpimol Kodsup, Wang, Shaohua, Yang, Yujie
• Format: Article
• Language: English
• Subjects: Bagmati province; lung cancer; PM2.5; pollution; Public Health; spatiotemporal distribution
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2024.1490973

Despite examining the role of an association between particulate matter and lung cancer in low-income countries, studies on the association between long-term exposure to particulate matter and lung cancer risk are still contradictory. This study investigates the spatiotemporal distribution patterns of lung cancer incidence and potential association with particulate matter (PM2.5) in Bagmati province, Nepal.BackgroundDespite examining the role of an association between particulate matter and lung cancer in low-income countries, studies on the association between long-term exposure to particulate matter and lung cancer risk are still contradictory. This study investigates the spatiotemporal distribution patterns of lung cancer incidence and potential association with particulate matter (PM2.5) in Bagmati province, Nepal.We performed a spatiotemporal study to analyze the LC - PM2.5 association, using LC and annual mean PM2.5 concentration data from 2012 to 2021. The study assessed the global spatial autocorrelation test using global Moran's I, applied hotspot analysis. A bivariate statistical analysis was performed to evaluate the association, we also applied the geographically weighted regression model (GWR) to look for possible relationships.MethodsWe performed a spatiotemporal study to analyze the LC - PM2.5 association, using LC and annual mean PM2.5 concentration data from 2012 to 2021. The study assessed the global spatial autocorrelation test using global Moran's I, applied hotspot analysis. A bivariate statistical analysis was performed to evaluate the association, we also applied the geographically weighted regression model (GWR) to look for possible relationships.The annual mean crude incidence rate (CIR) and standardized incidence rate (SIR) were 5.16, and 6.09 respectively. The study reveals an increasing trend with notable municipal-level spatial variations. Bhaktapur municipality exhibits the highest CIR (243.88), followed by Panchkhal and Sunapati. Males consistently exhibit higher rates, particularly in middle-aged and older adult populations. Bhaktapur displayed the highest CIR in males (171.9) but very low in females (72). The spatial analysis identified concentration trends and hotspots developed in the Bhaktapur, Panchkhal, and Sunapati municipalities. The SIR showed fluctuating patterns of continuous rise until 2019, decrease in 2020, and rise again thereafter. Similar fluctuation association patterns were observed with PM2.5, the r-squar