OBJECTIVE: To detect the spatial distribution characteristics of iodine in drinking water of residents in Shandong province with spatial autocorrelation analysis. METHODS: The county-based study set Shandong province as a research site. A total of 108 164 water samples from 140 counties were collected. The drinking water iodine data in county-level city between 2008 to 2010 were obtained from Shandong Institute of Prevention and Control for Endemic Disease and was merged with an electronic map to build a spatial database. Global and local Moran's I index were calculated, respectively, and spatial autocorrelation and cluster range of iodine distribution in drinking water in Shandong province were studied by SaTScan software. RESULTS: All counties were further grouped according to the "criteria of delimitation for IDD endemic areas" and "determination and classification of the areas of high water iodine and the endemic areas of iodine excess goiter", and 90 counties were iodine deficiency (< 10 µg/L), 31 were iodine suitable (10 - 150 µg/L), and 19 (> 150 µg/L) were high iodine. For the overall study area, the iodine distribution in drinking water in Shandong province existed spatial autocorrelation (Moran's I = 0.52, Z = 7.4, P < 0.01). For the local scale, the drinking water iodine in 18 counties of Dezhou, Liaocheng and Heze city in western Shandong province was clustered, the local Moran's I were between 0.22 - 1.00 (P < 0.01), which were all high-high clusters, indicating the positive spatial correlation. Spatial analysis using SaTScan software detected two cluster areas including 20 counties, which the centers located in Xiajin and Dingtao county, the cluster radiuses were 57.47 km and 65.58 km respectively. The analysis results were consistent with the results of local spatial autocorrelation. CONCLUSION: There are apparent spatial autocorrelation and strong spatial heterogeneity existed in the iodine distribution in drink water in Shandong province.
OBJECTIVE: To detect the spatial distribution characteristics of iodine in drinking water of residents in Shandong province with spatial autocorrelation analysis. METHODS: The county-based study set Shandong province as a research site. A total of 108 164 water samples from 140 counties were collected. The drinking wateriodine data in county-level city between 2008 to 2010 were obtained from Shandong Institute of Prevention and Control for Endemic Disease and was merged with an electronic map to build a spatial database. Global and local Moran's I index were calculated, respectively, and spatial autocorrelation and cluster range of iodine distribution in drinking water in Shandong province were studied by SaTScan software. RESULTS: All counties were further grouped according to the "criteria of delimitation for IDD endemic areas" and "determination and classification of the areas of high wateriodine and the endemic areas of iodineexcess goiter", and 90 counties were iodine deficiency (< 10 µg/L), 31 were iodine suitable (10 - 150 µg/L), and 19 (> 150 µg/L) were high iodine. For the overall study area, the iodine distribution in drinking water in Shandong province existed spatial autocorrelation (Moran's I = 0.52, Z = 7.4, P < 0.01). For the local scale, the drinking wateriodine in 18 counties of Dezhou, Liaocheng and Heze city in western Shandong province was clustered, the local Moran's I were between 0.22 - 1.00 (P < 0.01), which were all high-high clusters, indicating the positive spatial correlation. Spatial analysis using SaTScan software detected two cluster areas including 20 counties, which the centers located in Xiajin and Dingtao county, the cluster radiuses were 57.47 km and 65.58 km respectively. The analysis results were consistent with the results of local spatial autocorrelation. CONCLUSION: There are apparent spatial autocorrelation and strong spatial heterogeneity existed in the iodine distribution in drink water in Shandong province.
Authors: Jie Gao; Zhijie Zhang; Yi Hu; Jianchao Bian; Wen Jiang; Xiaoming Wang; Liqian Sun; Qingwu Jiang Journal: Int J Environ Res Public Health Date: 2014-05-19 Impact factor: 3.390