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Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study

OBJECTIVE: The objective is to investigate the prevalence of pre-diabetes in Namibia and South Africa and to determine sociodemographic correlates of disease using population data. DESIGN: Cross-sectional study. SETTING: Demographic and Health Survey for emerging (Namibia) and established (South Afr...

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Autores principales: Walker, Rebekah J, Thorgerson, Abigail M, Yan, Alice, Williams, Joni S, Campbell, Jennifer A, Dawson, Aprill Z, Renta, Vincent, Egede, Leonard E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603485/
https://www.ncbi.nlm.nih.gov/pubmed/37852767
http://dx.doi.org/10.1136/bmjopen-2022-069640
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author Walker, Rebekah J
Thorgerson, Abigail M
Yan, Alice
Williams, Joni S
Campbell, Jennifer A
Dawson, Aprill Z
Renta, Vincent
Egede, Leonard E
author_facet Walker, Rebekah J
Thorgerson, Abigail M
Yan, Alice
Williams, Joni S
Campbell, Jennifer A
Dawson, Aprill Z
Renta, Vincent
Egede, Leonard E
author_sort Walker, Rebekah J
collection PubMed
description OBJECTIVE: The objective is to investigate the prevalence of pre-diabetes in Namibia and South Africa and to determine sociodemographic correlates of disease using population data. DESIGN: Cross-sectional study. SETTING: Demographic and Health Survey for emerging (Namibia) and established (South Africa) economies in Sub-Saharan Africa collected laboratory data that allowed determination of pre-diabetes status. PARTICIPANTS: 3141 adults over age 18 from the 2013 Namibia survey, weighted to a population of 2176, and 4964 adults over age 18 from the 2016 South Africa survey, weighted to a population of 4627 had blood glucose/glycated haemoglobin (HbA1c) and diabetes information were included in the analysis. OUTCOME MEASURES: Pre-diabetes was defined as not being diagnosed with diabetes and having a blood sugar measurement of 100–125 mg/dL in Namibia or an HbA1c measurement of 5.7%–6.4%. Logistic models were run for each country separately, with pre-diabetes as the outcome and a series of sociodemographic variables (age, gender, urban/rural residence, number of children, employment status, wealth index, education level, and ethnicity (in South Africa) or religion (in Namibia)) entered as variables to investigate the independent relationship of each. RESULTS: The weighted prevalence of pre-diabetes was 18.7% in Namibia and 70.1% in South Africa. Rural residence was independently associated with higher odds of pre-diabetes in Namibia (1.47, 95% CI 1.05 to 2.06), while both younger age (0.98, 95% CI 0.97 to 0.99) and urban residence (0.80, 95% CI 0.66 to 0.99) were independently associated with odds of pre-diabetes in South Africa. CONCLUSIONS: The prevalence of pre-diabetes was 18.7% in Namibia and 70.1% in South Africa. Correlates of pre-diabetes differed between the two countries with rural residents having higher odds of pre-diabetes in Namibia and urban residents with higher odds in South Africa. Aggressive interventions, including population level education and awareness programmes, and individual level education and lifestyle interventions that account for country-specific contextual factors are urgently needed to prevent progression to diabetes.
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spelling pubmed-106034852023-10-28 Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study Walker, Rebekah J Thorgerson, Abigail M Yan, Alice Williams, Joni S Campbell, Jennifer A Dawson, Aprill Z Renta, Vincent Egede, Leonard E BMJ Open Diabetes and Endocrinology OBJECTIVE: The objective is to investigate the prevalence of pre-diabetes in Namibia and South Africa and to determine sociodemographic correlates of disease using population data. DESIGN: Cross-sectional study. SETTING: Demographic and Health Survey for emerging (Namibia) and established (South Africa) economies in Sub-Saharan Africa collected laboratory data that allowed determination of pre-diabetes status. PARTICIPANTS: 3141 adults over age 18 from the 2013 Namibia survey, weighted to a population of 2176, and 4964 adults over age 18 from the 2016 South Africa survey, weighted to a population of 4627 had blood glucose/glycated haemoglobin (HbA1c) and diabetes information were included in the analysis. OUTCOME MEASURES: Pre-diabetes was defined as not being diagnosed with diabetes and having a blood sugar measurement of 100–125 mg/dL in Namibia or an HbA1c measurement of 5.7%–6.4%. Logistic models were run for each country separately, with pre-diabetes as the outcome and a series of sociodemographic variables (age, gender, urban/rural residence, number of children, employment status, wealth index, education level, and ethnicity (in South Africa) or religion (in Namibia)) entered as variables to investigate the independent relationship of each. RESULTS: The weighted prevalence of pre-diabetes was 18.7% in Namibia and 70.1% in South Africa. Rural residence was independently associated with higher odds of pre-diabetes in Namibia (1.47, 95% CI 1.05 to 2.06), while both younger age (0.98, 95% CI 0.97 to 0.99) and urban residence (0.80, 95% CI 0.66 to 0.99) were independently associated with odds of pre-diabetes in South Africa. CONCLUSIONS: The prevalence of pre-diabetes was 18.7% in Namibia and 70.1% in South Africa. Correlates of pre-diabetes differed between the two countries with rural residents having higher odds of pre-diabetes in Namibia and urban residents with higher odds in South Africa. Aggressive interventions, including population level education and awareness programmes, and individual level education and lifestyle interventions that account for country-specific contextual factors are urgently needed to prevent progression to diabetes. BMJ Publishing Group 2023-10-18 /pmc/articles/PMC10603485/ /pubmed/37852767 http://dx.doi.org/10.1136/bmjopen-2022-069640 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Diabetes and Endocrinology
Walker, Rebekah J
Thorgerson, Abigail M
Yan, Alice
Williams, Joni S
Campbell, Jennifer A
Dawson, Aprill Z
Renta, Vincent
Egede, Leonard E
Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study
title Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study
title_full Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study
title_fullStr Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study
title_full_unstemmed Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study
title_short Prevalence and correlates of pre-diabetes in Sub-Saharan Africa using Demographic and Health Survey Data: a cross-sectional study
title_sort prevalence and correlates of pre-diabetes in sub-saharan africa using demographic and health survey data: a cross-sectional study
topic Diabetes and Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603485/
https://www.ncbi.nlm.nih.gov/pubmed/37852767
http://dx.doi.org/10.1136/bmjopen-2022-069640
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