Evidence map›Paper›PMID 30699173›Full record

ArticlePloS one2019

Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: A decomposition analysis.

Chipo Mutyambizi, Frederik Booysen, Andrew Stokes, Milena Pavlova, Wim Groot

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Prevalence and Determinants of Household Self-Reported Diabetes Mellitus in Gauteng, South Africa.International journal of environmental research and public health · 2024
    Article
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  12. Diabetes is affecting everyone everywhere.World journal of diabetes · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chipo MutyambiziPopulation Health, Health Systems and Innovation, Human Sciences Research Council, Pretoria, South Africa.ORCID 0000-0003-4749-7280
Frederik BooysenUniversity of the Witwatersrand, Johannesburg, South Africa.
Andrew StokesDepartment of Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America.
Milena PavlovaDepartment of Health Services Research; CAPHRI, Maastricht University Medical Centre, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Wim GrootDepartment of Health Services Research; CAPHRI, Maastricht University Medical Centre, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInequalities in diabetes are widespread and are exacerbated by differences in lifestyle. Many studies that have estimated inequalities in diabetes make use of self-reported diabetes which is often biased by differences in access to health care and diabetes awareness. This study adds to this literature by making use of a more objective standardised measure of diabetes in South Africa. The study estimates socio-economic inequalities in undiagnosed diabetes, diagnosed diabetes (self-reported), as well as total diabetes (undiagnosed diabetics + diagnosed diabetics). The study also examines the contribution of lifestyle factors to diabetes inequalities in South Africa.

methodsThis cross sectional study uses data from the 2012 South African National Health and Nutrition Examination Survey (SANHANES-1) and applies the Erreygers Concentration Indices to assess socio-economic inequalities in diabetes. Contributions of lifestyle factors to inequalities in diabetes are assessed using a decomposition method.

resultsSelf-reported diabetes and total diabetes (undiagnosed diabetics + diagnosed diabetics) were significantly concentrated amongst the rich (CI = 0.0746; p < 0.05 and CI = 0.0859; p < 0.05). The concentration index for undiagnosed diabetes was insignificant but pro-poor. The decomposition showed that lifestyle factors contributed 22% and 35% to socioeconomic inequalities in self-reported and total diabetes, respectively.

conclusionDiabetes in South Africa is more concentrated amongst higher socio-economic groups when measured using self-reported diabetes or clinical data. Our findings also show that the extent of inequality is worse in the total diabetes outcome (undiagnosed diabetics + diagnosed diabetics) when compared to the self-reported diabetes outcome. Although in comparison to other determinants, the contribution of lifestyle factors was modest, these contributions are important in the development of policies that address socio-economic inequalities in the prevalence of diabetes.

Indexed as

Health Status DisparitiesAdolescentAdultAge DistributionCross-Sectional StudiesDiabetes MellitusFemaleHealth Services AccessibilityHumansLife StyleMaleMiddle AgedPrevalenceSelf ReportSocioeconomic FactorsSouth Africa

Identifiers

PMID30699173
PMCPMC6353159

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.