Abstract
BACKGROUND: Colorectal cancer poses a major health challenge in Gansu Province, being one of the top causes of both incidence and mortality among gastrointestinal cancers. This study aims to analyze the epidemiological patterns of colorectal cancer in Gansu Province, while exploring potential associations with environmental determinants. METHOD: We analyzed clinical records of all colorectal cancer cases from 2013 to 2023, retrieved from hospital information systems across 87 counties in Gansu Province, encompassing municipal, district, county, and township-level medical institutions. A thorough analysis was conducted employing various methods, including Joinpoint regression, spatial autocorrelation, spatiotemporal scanning, and Geo-detector analysis, using specialized software (Joinpoint 5.0, ArcGIS 10.8, and SaTScan). Our study explored the relationship between colorectal cancer incidence in Gansu Province and 14 Environmental factors. RESULT: The results indicate a steady rise in colorectal cancer incidence over the 11-year period and the highest age-standardized incidence rates of colorectal cancer occurred in Jinchuan, Chengguan, Suzhou, Baiyin, and Liangzhou districts, contrasting sharply with the significantly lower rates documented in Liangdang, Kang, Heshui, Huining, Zhengning, and Cheng counties. Spatial and spatiotemporal analyses identified several significant high- and low-risk clusters of colorectal cancer throughout Gansu Province, demonstrating both spatial and temporal variability in disease distribution. The Geo-detector indicated that colorectal cancer incidence was significantly linked to the distribution of climatic conditions (precipitation and temperature), ecological factor, and certain air pollutants. Multivariate spatial analysis was used to further explore the relationship between environmental factors and the incidence of colorectal cancer. CONCLUSION: Our research highlighted notable spatial variability in colorectal cancer incidence across Gansu Province, with geospatial and spatiotemporal analyses uncovering high-risk clusters and important environmental factors.