Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Apr;43(4):767-775.
doi: 10.2337/dc19-1782. Epub 2020 Feb 12.

Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries

Affiliations

Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries

Jacqueline A Seiglie et al. Diabetes Care. 2020 Apr.

Abstract

Objective: Diabetes is a rapidly growing health problem in low- and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk.

Research design and methods: We pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ≥25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR).

Results: Overall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1-8.0) and of undiagnosed diabetes 4.9% (4.6-5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5-8.1), lower-middle-income economies (LMIs) 7.1% (6.6-7.6), and upper-middle-income economies (UMIs) 8.2% (7.5-9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (LICs RR 1.47 [95% CI 1.22-1.78], LMIs 1.14 [1.06-1.23], and UMIs 1.28 [1.02-1.61]).

Conclusions: Among 29 LMICs, diabetes prevalence was substantial and increased with increasing WBIG. In contrast to the association seen in high-income countries, diabetes risk was highest among those with greater educational attainment, independent of BMI. LMICs included in this analysis may be at an advanced stage in the nutrition transition but with no reversal in the socioeconomic gradient of diabetes risk.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Country-level prevalence of total diabetes and the subset with undiagnosed diabetes in 29 population-based surveys conducted between 2008 and 2016, by World Bank income classification. Country classification at time of survey according to World Bank gross national income per capita in USD (Atlas methodology). Prevalence accounts for complex survey sampling design. Values are not adjusted for age and sex. Overall prevalence estimated with sampling weights scaled such that each country contributed proportionally to its population size. Unable to estimate undiagnosed diabetes prevalence for Ecuador, given no information available on diabetes diagnosis self-report in the Ecuador ENSANUT 2012 survey.

References

    1. NCD Risk Factor Collaboration (NCD-RisC) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants [published correction appears in Lancet 2017;389:e2]. Lancet 2016;387:1513–1530 - PMC - PubMed
    1. International Diabetes Federation IDF Diabetes Atlas, 9th edition [Internet], 2019. Available from https://diabetesatlas.org/en/. Accessed 18 November 2019
    1. Davies J, Yudkin JS, Atun R. Liberating data: the crucial weapon in the fight against NCDs. Lancet Diabetes Endocrinol 2016;4:197–198 - PubMed
    1. Boerma T, Victora C, Abouzahr C. Monitoring country progress and achievements by making global predictions: is the tail wagging the dog? Lancet 2018;392:607–609 - PubMed
    1. Hosseinpoor AR, Bergen N, Mendis S, et al. . Socioeconomic inequality in the prevalence of noncommunicable diseases in low- and middle-income countries: results from the World Health Survey. BMC Public Health 2012;12:474. - PMC - PubMed

Publication types

MeSH terms