CHENG Wen-wei, YAN Xiao-fang, SHI Jing-cheng, LIU Xiao-li, LIU Xiao-fang. Analysis on spatial distribution of diabetes and related factors among middle-aged and elderly population in China based on GIS[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2017, 21(11): 1082-1087. doi: 10.16462/j.cnki.zhjbkz.2017.11.002
Citation:
CHENG Wen-wei, YAN Xiao-fang, SHI Jing-cheng, LIU Xiao-li, LIU Xiao-fang. Analysis on spatial distribution of diabetes and related factors among middle-aged and elderly population in China based on GIS[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2017, 21(11): 1082-1087. doi: 10.16462/j.cnki.zhjbkz.2017.11.002
CHENG Wen-wei, YAN Xiao-fang, SHI Jing-cheng, LIU Xiao-li, LIU Xiao-fang. Analysis on spatial distribution of diabetes and related factors among middle-aged and elderly population in China based on GIS[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2017, 21(11): 1082-1087. doi: 10.16462/j.cnki.zhjbkz.2017.11.002
Citation:
CHENG Wen-wei, YAN Xiao-fang, SHI Jing-cheng, LIU Xiao-li, LIU Xiao-fang. Analysis on spatial distribution of diabetes and related factors among middle-aged and elderly population in China based on GIS[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2017, 21(11): 1082-1087. doi: 10.16462/j.cnki.zhjbkz.2017.11.002
Objective To explore the spatial distribution of diabetes and its main influencing factors among middle-aged and elderly population in China based on geographic information system(GIS) and to provide useful information for the improvement of regional prevention and control of diabetes. Methods Data came from the baseline survey of the China Health and Retirement Longitudinal Study in 2011, which included 11 538 participants in this study. A spatial analysis was conducted to analyze prevalence of diabetes and its main risk factors by Geoda and ArcGIS 10.2 software. Results The prevalence of diabetes and pre-diabetes among middle-aged and elderly population were 15.1% (range of prevalence of diabetes:7.9%-33.5%) and 40.0% (range of prevalence of pre-diabetes:17.6%-61.3%) in China except for Hong Kong, Macao, Taiwan, Hainan, Ningxia and Tibet in 2011. The local space analysis showed that the concentrated areas of prevalence were located in north of China including Tianjin and Hebei. The geographically weighted regression (GWR) revealed that the percentage of the highly educated population, the rate of overweight and high levels of c-reactive protein (CRP) were the main influencing factors for diabetes prevalence, and all the regression coefficients of these three factors showed spatial heterogeneity. Conclusions Over 50% of middle-aged and elderly Chinese are threatened by diabetes and pre-diabetes. Geographic visualization at the provincial level indicates widespread variation in diabetes prevalence and its main factors across China. The key areas for prevention and control are mainly located in northeast and north in China. Major measures should be regionalization.