title: Geographical variation in diabetes prevalence and detection in china: multilevel spatial analysis of 98,058 adults.
abstract: OBJECTIVE
  To investigate the geographic variation in diabetes prevalence and detection in China.
  
  
  METHODS
  Self-report and biomedical data were collected from 98,058 adults aged ≥18 years (90.5% response) from 162 areas spanning mainland China. Diabetes status was assessed using American Diabetes Association criteria. Among those with diabetes, detection was defined by prior diagnosis. Choropleth maps were used to visually assess geographical variation in each outcome at the provincial level. The odds of each outcome were assessed using multilevel logistic regression, with adjustment for person- and area-level characteristics.
  
  
  RESULTS
  Geographic visualization at the provincial level indicated widespread variation in diabetes prevalence and detection across China. Regional prevalence adjusted for age, sex, and urban/rural socioeconomic circumstances (SECs) ranged from 8.3% (95% CI 7.2%, 9.7%) in the northeast to 12.7% (11.1%, 14.6%) in the north. A clear negative gradient in diabetes prevalence was observed from 13.1% (12.0%, 14.4%) in the urban high-SEC to 8.7% (7.8%, 9.6%) in rural low-SEC counties/districts. Adjusting for health literacy and other person-level characteristics only partially attenuated these geographic variations. Only one-third of participants living with diabetes had been previously diagnosed, but this also varied substantively by geography. Regional detection adjusted for age, sex, and urban/rural SEC, for example, spanned from 40.4% (34.9%, 46.3%) in the north to 15.6% (11.7%, 20.5%) in the southwest. Compared with detection of 40.8% (37.3%, 44.4%) in urban high-SEC counties, detection was poorest among rural low-SEC counties at just 20.5% (17.7%, 23.7%). Person-level characteristics did not fully account for these geographic variations in diabetes detection.
  
  
  CONCLUSIONS
  Strategies for addressing diabetes risk and improving detection require geographical targeting.
authors.affiliation: National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Maigeng
authors.initials: M
authors.lastname: Zhou
authors.affiliation: School of Science and Health, University of Western Sydney, Sydney, Australia School of Geography and Geosciences, University of St. Andrews, St. Andrews, U.K.
authors.forename: Thomas
authors.initials: T
authors.lastname: Astell-Burt
authors.affiliation: State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of the Ministry of Health, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, E-Institute of Shanghai Universities; Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.
authors.forename: Yufang
authors.initials: Y
authors.lastname: Bi
authors.affiliation: Centre for Health Research, School of Medicine, University of Western Sydney, Sydney, Australia.
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Feng
authors.affiliation: National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Yichong
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Science and Health, University of Western Sydney, Sydney, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: Page
authors.affiliation: National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Limin
authors.initials: L
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of the Ministry of Health, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, E-Institute of Shanghai Universities; Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Xu
authors.affiliation: National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China gning@sibs.ac.cn whzhao@ilsichina.org linhong@chinawch.org.cn.
authors.forename: Linhong
authors.initials: L
authors.lastname: Wang
authors.affiliation: Chinese Center for Disease Control and Prevention, Beijing, China gning@sibs.ac.cn whzhao@ilsichina.org linhong@chinawch.org.cn.
authors.forename: Wenhua
authors.initials: W
authors.lastname: Zhao
authors.affiliation: State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of the Ministry of Health, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, E-Institute of Shanghai Universities; Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China gning@sibs.ac.cn whzhao@ilsichina.org linhong@chinawch.org.cn.
authors.forename: Guang
authors.initials: G
authors.lastname: Ning
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002681:China / Q000453:epidemiology; D003625:Data Collection; D003920:Diabetes Mellitus / Q000453:epidemiology*; D005260:Female; D005843:Geography; D006801:Humans; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D012424:Rural Population; D012959:Socioeconomic Factors; D062206:Spatial Analysis; D014505:Urban Population; D055815:Young Adult