Geospatial hot spot analysis of lung cancer patients correlated to fine particulate matter (PM2.5) and industrial wind in Eastern Thailand

摘要

Lung cancer is the most common type of cancer and is the major cause of death first among males and second among females in Thailand. Lung cancer is highly related to particulate matter (PM)-especially fine particulates with a diameter of 2.5 mu m or less (PM2.5). Recent studies have indicated a strong correlation between fine particulate matter (PM25) and lung function diseases. Therefore, this study aims to investigate and explore the phenomenon of lung cancer and its spatial correlation to mortality and PM2.5 in Eastern Thailand from 2008 to 2012 using multidisciplinary techniques. The cancer registry was utilized as data inventory and geographical information system (GIS), Global Moran's I, Getis-Ord G statistics, Average Nearest Neighbor (ANN) tool, Inverse Distance Weighting (IDW), Local Indicators of Spatial Autocorrelation (LISA), and ordinary least square (OLS) methods to generate the PM2.5 maps to create hot spots in Eastern Thailand. The results visualize and analyze lung cancer hot spots and are adjusted for known factors such as sex and age of lung cancer patients. Choropleth maps of lung cancer incidence and mortality rates, generated for the first time, revealed that the number of male cancer patients is higher than that of females in Eastern Thailand. Global autocorrelation demonstrated considerable spatial clustering of lung cancer incidence and mortality. 91.56% of the lung cancer patients belonged to the age group of above 50 in both sexes. Significant relationships were found between the PM2.5 variable and the spatial patterns of lung cancer incidence and mortality. The Chonburi and Chanthaburi provinces were found to be the major hot spots for lung cancer incidence, which are close to industrial areas. These findings are useful in identifying the cancer registry information globally as well as locally. This study also provides a useful set of tools to identify and create hot spots in the developing countries where data and resources are major limitations. (C) 2017 Elsevier Ltd. All rights reserved.