Interplay of regional economic development, income, gender and type 2 diabetes: evidence from half a million Chinese

J Epidemiol Community Health. 2019 Sep;73(9):867-873. doi: 10.1136/jech-2018-211091. Epub 2019 Jun 4.

Abstract

Background: Following the rapid economic growth, there has been a strong disparity of regional development and personal income in China. Type 2 diabetes mellitus (T2DM) may be influenced by socioeconomic status at both the societal and individual levels. This study examines the associations of regional economic development, household income and gender on T2DM.

Method: Data from the baseline of a Chinese population-based study of approximately 500 000 adults from 10 areas were analysed. Clinically identified and screen-detected T2DM were examined. Regional economic development was indicated by gross domestic product (GDP) per capita. A logistic regression-based method was used to calculate the adjusted prevalence.

Result: The prevalence of T2DM was significantly higher in medium GDP per capita areas for both males (7.04%, 95% CI 6.82% to 7.26%) and females (6.04%, 95% CI 5.86% to 6.22%) compared with areas of other levels of economic development. The different shapes of associations between household income and T2DM prevalence were observed in different GDP per capita areas. There were strong gender differences in terms of both the trend and strength of association between household income and T2DM prevalence.

Conclusions: Findings from this study underscore the importance of economic conditions and gender difference on T2DM. It suggests that strategies for diabetes prevention should address social-economic differences besides a person-centred approach.

Keywords: China; GDP per capita; T2DM; gender; health disparity; income.

MeSH terms

  • Adult
  • Aged
  • Asian People / statistics & numerical data*
  • China / epidemiology
  • Diabetes Mellitus, Type 2 / epidemiology
  • Diabetes Mellitus, Type 2 / ethnology*
  • Economic Development / statistics & numerical data*
  • Female
  • Gross Domestic Product / statistics & numerical data*
  • Humans
  • Income / statistics & numerical data*
  • Life Style
  • Male
  • Middle Aged
  • Prevalence
  • Sex Factors*
  • Social Class*
  • Socioeconomic Factors