Anisotropic patterns of liver cancer prevalence in Guangxi in Southwest China: is local climate a contributing factor?

Asian Pac J Cancer Prev. 2015;16(8):3579-86. doi: 10.7314/apjcp.2015.16.8.3579.

Abstract

Geographic information system (GIS) technology has useful applications for epidemiology, enabling the detection of spatial patterns of disease dispersion and locating geographic areas at increased risk. In this study, we applied GIS technology to characterize the spatial pattern of mortality due to liver cancer in the autonomous region of Guangxi Zhuang in southwest China. A database with liver cancer mortality data for 1971-1973, 1990-1992, and 2004-2005, including geographic locations and climate conditions, was constructed, and the appropriate associations were investigated. It was found that the regions with the highest mortality rates were central Guangxi with Guigang City at the center, and southwest Guangxi centered in Fusui County. Regions with the lowest mortality rates were eastern Guangxi with Pingnan County at the center, and northern Guangxi centered in Sanjiang and Rongshui counties. Regarding climate conditions, in the 1990s the mortality rate of liver cancer positively correlated with average temperature and average minimum temperature, and negatively correlated with average precipitation. In 2004 through 2005, mortality due to liver cancer positively correlated with the average minimum temperature. Regions of high mortality had lower average humidity and higher average barometric pressure than did regions of low mortality. Our results provide information to benefit development of a regional liver cancer prevention program in Guangxi, and provide important information and a reference for exploring causes of liver cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Atmospheric Pressure*
  • China / epidemiology
  • Climate*
  • Databases, Factual
  • Geographic Information Systems
  • Humans
  • Liver Neoplasms / epidemiology
  • Liver Neoplasms / mortality*
  • Prevalence
  • Rain*
  • Retrospective Studies
  • Spatial Analysis
  • Temperature*