| Background & Objective: Esophageal cancer (EC) is a common malignancy with high mortality and ranks as the fourth most frequent cause of cancer death in China. However, EC has a striking variation in geographical distribution, which is a reflection of exposure to specific environmental factors. It is generally recognized that EC is the result of multiple risk factors, such as environmental factors, biologic factors and genetic factors. Known risk environmental factors for EC include lacking of fresh fruits and vegetables, nutrient and micronutrient intake, nitrosamine, smoking and alcohol consumption, and so on. However, ecologic environment factors such as atmosphere, water, biology and vegetation, geologic environment factors such as soil and mineral, climatic environment factors such as precipitation, evaporation, temperature, wind speed, etc. are also important environmental factors as well as a basic environment in which human live, so it is important to study the relation between soil, vegetation, ambient climate and EC mortality for etiology study. The objective of our study is to explore the relationship between EC mortality of one tenth of nationwide population sampling areas and ambient climate, soil and vegetation types, soil organic carbon densities (SOCD), vegetation fraction, altitude and socioeconomic factors in China by using Geographic Information System and Remote Sensing, so as to provide some supplements to the etiology study of EC.Methods: Database of EC mortality rate of 237 sampling areas surveyed in 1990-1992 in mainland China was established in Excel. And a GIS for EC mortality was set up by linking it with the digital polygon maps of study areas which were created in Arc/Info 9.0. Soil and vegetation types of high- and low-risk areas of EC were compared by using overlay analysis based on Chinese Soil Dataset, Chinese Vegetation Dataset provided by Chinese Natural Resources Scientific Database and digital polygon maps of study areas, and mean SOCD of these areas were also calculated and analyzed. Geographic and climate data of 237 sampling areas were extracted from the raster or monitoring station dataset such as altitude, climate (including precipitation, evaporation, temperature, wind speed etc.) and socioeconomic data provided by Data-sharing network of China earth system science, and finished in Arc/Info 9.0 software by using IDW interpolation and zonal statistics. Normalized difference vegetation indexes (NDVI) of the 237 sampling areas were extracted from the NDVI maps provided by the same database as above and finished in ENVI 4.2 software. Spearman correlation analysis was used to analyze the relationship between EC mortality of these areas and the spatial environment factors. The 237 sampling areas were classified as 4 death ranks according to their EC mortality by cluster analysis. Nonparametric test was used to compare geographic and climatic factors between the 4 death ranks. In order to control multicollinearity, multiple regression analysis after factor analysis between esophageal cancer and geographic and climate factors were also carried out to confirm the risk factors for EC. All the statistic analyses were finished in SPSS 13.0.Results: The counties that have the highest morality of EC show significant aggregation. The results of the digital distribution maps of EC mortality, drought index (DI) and water-heat index (WHI) show that high EC mortality mostly occurred in areas with high DI and low WHI. Overlay analysis shows that there are clear differences of soil and vegetation types between high- and low-risk areas of EC, and mean SOCD of high EC mortality areas is lower than that of low EC mortality areas (t=-2.47, p less than 0.05). Kruskal-Wallis and Jonckheere-Terpstra test show that mean precipitation, DI, WHI, mean wind speed of June (p less than 0.01 respectively) and mean temperature, mean highest/lowest temperature, mean wind speed of 4, 6, 8, 12 months (p less than 0.05 respectively) of the four EC death ranks are different. Spearman correlation analysis show weak negative correlation between precipitation, WHI, highest and lowest temperature and EC mortality (r=-0.233, -0.404, -0.143, -0.128 respectively; and p less than 0.001, 0.001, 0.05, 0.05 respectively), and weak positive correlation between DI, wind speed of 6, 8, 12 months, population per 1km~2 and EC mortality (r=0.345, 0.189, 0.170, 0.132, 0.140 respectively; and p less than 0.001, 0.01, 0.01, 0.05, 0.05 respectively). Through the factor analysis, the original twenty-four spatial environment variables were reduced to six new factors. The six factors accounted for 91.463% of the original variability in the data. The variables that enter the multiple stepwise regression equation finally are mean elevation, DI, WHI and NDVI value of July. Conclusions: (1) Soil and vegetation types of high- and low-risk areas of EC in China are obviously different. Soil organic matter and soil reduction ability of high-risk areas of EC is relatively low. (2) The high-risk areas of EC have some climatic features such as short of precipitation, high evaporation, high DI, that is to say, they are drought areas relatively. (3) The high-risk areas of EC have high annual mean temperature and low WHI relatively. (4) NDVI value of high-risk areas of EC in summer (July) is relatively low, which means there are low vegetation fraction and severe water and soil loss. (5) There are relatively high monthly mean wind speeds (especially June, August and December) in high-risk areas of EC. (6) GIS/RS can be applied to cancer epidemiology study and will exert active effect, which should be further explored. (7) Spatial and health monitoring is needed to get more accurate data to determine the association between spatial environment factors and EC. In addition, other known EC risk factors such as life style, biologic and genetic factors should be taken into account in future study, which may control some ecological confoundings, and this is the target of our next study.
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