Geographical variation in diabetes prevalence and detection in China: multilevel spatial analysis of 98,058 Adults

Maigeng Zhou, Thomas Astell-Burt, Yufang Bi, Xiaoqi Feng, Yong Jiang, Yichong Li, Andrew Page, Limin Wang, Yu Xu, Linhong Wang, Wenhua Zhao, Guang Ning*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    99 Citations (Scopus)

    Abstract

    OBJECTIVE

    To investigate the geographic variation in diabetes prevalence and detection in China.

    RESEARCH DESIGN AND 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.

    Original languageEnglish
    Pages (from-to)72-81
    Number of pages10
    JournalDiabetes Care
    Volume38
    Issue number1
    Early online date28 Oct 2014
    DOIs
    Publication statusPublished - Jan 2015

    Keywords

    • Risk-factors
    • Health
    • Population
    • Provinces
    • Awareness
    • Mainland
    • Diseases
    • Obesity
    • Burden

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