Maigeng Zhou|Thomas Astell?Burt|Yufang Bi|Xiaoqi Feng|Yong Jiang|Yichong Li|Andrew Page|Limin Wang|Yu Xu|L?nhong Wang|Zhao Wang|Guang Ning
Geographical Variation in Diabetes Prevalence and Detection in China: Multilevel Spatial Analysis of 98,058 Adults
National Center for Chronic and Noncommunicable Disease Control and Prevention|Western Sydney University|University of St Andrews|Shanghai Jiao Tong University|Shanghai Clinical Research Center|Western Sydney University|National Center for Chronic and Noncommunicable Disease Control and Prevention|National Center for Chronic and Noncommunicable Disease Control and Prevention|Western Sydney University|National Center for Chronic and Noncommunicable Disease Control and Prevention|Shanghai Jiao Tong University|Shanghai Clinical Research Center|National Center for Chronic and Noncommunicable Disease Control and Prevention|Chinese Center For Disease Control and Prevention|Shanghai Jiao Tong University|Shanghai Clinical Research Center
objective investigate geographic variation diabetes prevalence detection china research design methods selfreport biomedical data collected 98058 adults aged 18 years 905 response 162 areas spanning mainland china diabetes status assessed using american diabetes association criteria among diabetes detection defined prior diagnosis choropleth maps used visually assess geographical variation outcome provincial level odds outcome assessed using multilevel logistic regression adjustment person arealevel characteristics results geographic visualization provincial level indicated widespread variation diabetes prevalence detection across china regional prevalence adjusted age sex urbanrural socioeconomic circumstances secs ranged 83 95 ci 72 97 northeast 127 111 146 north clear negative gradient diabetes prevalence observed 131 120 144 urban highsec 87 78 96 rural lowsec countiesdistricts adjusting health literacy personlevel characteristics partially attenuated geographic variations onethird participants living diabetes previously diagnosed also varied substantively geography regional detection adjusted age sex urbanrural sec example spanned 404 349 463 north 156 117 205 southwest compared detection 408 373 444 urban highsec counties detection poorest among rural lowsec counties 205 177 237 personlevel characteristics fully account geographic variations diabetes detection conclusions strategies addressing diabetes risk improving detection require geographical targeting