Comparing geospatial clustering methods to study spatial patterns of lung cancer rates in urban areas: A case study in Mashhad, Iran

Geospatial clustering methods play a significant role in identifying high-risk areas in terms of cancers rate. This study aims to compare three different spatial clustering approaches in identifying geospatial clusters of lung cancer in an urban area. Data of diagnosed lung cancer cases between 2017-2018 were extracted from the population-based cancer registry of Mashhad in Iran. Getis-Ord G*, Anselin Local Moran's I and spatial scan statistics were used to explore any geographical pattern behind the data. All analyses were conducted at the significant level of .95 (p <.05). Arc GIS 10.3 and SaTScan were used for spatial analyses. There were 225 lung cancer cases in the city in the study period. Lung cancer cases were higher among >45 years of age and more in men than women. All three methods revealed persistent clustering of lung cancer in the southern and central parts of the city around the central business district with slight variations. Among the three methods, spatial scan statistics revealed more geographical areas as lung cancer high-risk areas. All the methods used can explain spatial heterogeneity of lung cancer in the study area; however, a combination of methods is suggested examining spatial disease patterns for better epidemiological and etiological understanding.

Automated summary

This study aimed to compare three different spatial clustering approaches in identifying geospatial clusters of lung cancer in an urban area. The results showed persistent clustering of lung cancer in the southern and central parts of the city around the central business district. Spatial scan statistics revealed more geographical areas as lung cancer high-risk areas.