Prevalence of Diabetes and the Relationship Between Wealth and Social Demographic Characteristics Across 6 Low-and-Middle Income Countries.

Gifty Marfowaa, Jennifer A Campbell, Sneha Nagavally, Aprill Z Dawson, Rebekah J Walker, Leonard E Egede
Author Information
  1. Gifty Marfowaa: Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA.
  2. Jennifer A Campbell: Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA.
  3. Sneha Nagavally: Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA.
  4. Aprill Z Dawson: Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA.
  5. Rebekah J Walker: Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA.
  6. Leonard E Egede: Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA.

Abstract

Background: As the global burden of diabetes persists, research is needed to understand the role of wealth and correlates of diabetes across regions of the world. The purpose of this study is to examine the prevalence and role of wealth and diabetes across 6 low- and middle- income countries while also accounting for independent correlates of diabetes by country.
Methods: Data from the Study on Global Ageing and Adult Health (SAGE), SAGE Wave 1 was used. Self-reported diabetes status was the primary dependent variable and wealth quintile, number of dwelling characteristics and possession of a set of assets, was the independent variable. Logistic regression models examined the relationship between wealth and presence of diabetes across 6 countries with the highest wealth quintile, quintile 1, serving as the reference group.
Results: Sample size by country included Ghana N = 5573, South Africa N = 4227, Russia N = 4947, Mexico N = 5448, India N = 12198, and China N = 15050. Average age across country ranged from 49 to 63 years of age. Prevalence of diabetes across country included 3.4% and 9.2% for Ghana and South Africa, respectively. In Russia, 8.3%; Mexico, 18.1%; India, 4.9%; and China, 5.9% of the sample reported having diabetes. In the adjusted logistic model, wealth was associated with higher odds of diabetes in Ghana (OR 2.26; CI 1.28; 4.13), South Africa (OR 4.57; CI 2.25; 10.32), Mexico (OR 2.00; CI 1.14; 3.60), India (OR 2.45; CI 1.60; 3.86), and China (OR 2.16; CI 1.62, 2.93).
Conclusions: These findings add to the growing body of evidence in our understanding between wealth and diabetes. As diabetes persists as a leading cause of death globally, future work should focus on mechanisms underlying the relationship between wealth and diabetes while also developing interventions to mitigate his burgeoning disease affecting communities across low- and middle-income countries.

Keywords

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Grants

  1. K01 DK131319/NIDDK NIH HHS
  2. L60 MD001574/NIMHD NIH HHS
  3. R01 MD018012/NIMHD NIH HHS
  4. R01 DK120861/NIDDK NIH HHS
  5. R01 MD013826/NIMHD NIH HHS
  6. R01 DK118038/NIDDK NIH HHS
  7. L30 DK130040/NIDDK NIH HHS
  8. R01 MD017574/NIMHD NIH HHS

Word Cloud

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