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A cross-sectional and spatial analysis of the prevalence of multimorbidity and its association with socioeconomic disadvantage in South Africa: A comparison between 2008 and 2012

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  • Weimann, Amy
  • Dai, Dajun
  • Oni, Tolu

Abstract

This study utilised data from the National Income Dynamics Study, a longitudinal study with a sample of approximately 28 000 people, to investigate the cross-sectional and spatial distribution of multimorbidity and the association with socioeconomic disadvantage in South Africa for 2008 and 2012. Multimorbidity increased in prevalence from 2.73% to 2.84% in adults between 2008 and 2012 and was associated with age, socioeconomic deprivation, obesity and urban areas. Hypertension was found frequently coexisting with diabetes. Spatial analysis showed clusters (hot spots) of higher multimorbidity prevalence in parts of KwaZulu-Natal and the Eastern Cape, which compared with the socioeconomic disadvantage spatial pattern. Although these results were limited to a district level analysis, this study has provided a platform for future local level research and has provided insight into the socioeconomic determinants of disease multimorbidity within a developing country.

Suggested Citation

  • Weimann, Amy & Dai, Dajun & Oni, Tolu, 2016. "A cross-sectional and spatial analysis of the prevalence of multimorbidity and its association with socioeconomic disadvantage in South Africa: A comparison between 2008 and 2012," Social Science & Medicine, Elsevier, vol. 163(C), pages 144-156.
    • Handle: RePEc:eee:socmed:v:163:y:2016:i:c:p:144-156
    DOI: 10.1016/j.socscimed.2016.06.055
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    References listed on IDEAS

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    1. Michael Noble & Gemma Wright, 2013. "Using Indicators of Multiple Deprivation to Demonstrate the Spatial Legacy of Apartheid in South Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(1), pages 187-201, May.
    2. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
    3. Sabina Alkire, Maria Emma Santos, 2010. "Acute Multidimensional Poverty: A New Index for Developing Countries," OPHI Working Papers 38, Queen Elizabeth House, University of Oxford.
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    1. Jinfeng Zhao & Lisa Mackay & Kevin Chang & Suzanne Mavoa & Tom Stewart & Erika Ikeda & Niamh Donnellan & Melody Smith, 2019. "Visualising Combined Time Use Patterns of Children’s Activities and Their Association with Weight Status and Neighbourhood Context," IJERPH, MDPI, vol. 16(5), pages 1-16, March.
    2. Anastasia A. Lam & Katherine Keenan & Mikko Myrskylä & Hill Kulu, 2022. "Multimorbid life expectancy across race, socioeconomic status, and gender in South Africa," MPIDR Working Papers WP-2022-024, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Héctor Pifarré i Arolas & Christian Dudel, 2019. "An Ordinal Measure of Population Health," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(3), pages 1219-1243, June.
    4. Urgent Tsuro & Kelechi E. Oladimeji & Guillermo-Alfredo Pulido-Estrada & Teke R. Apalata, 2022. "Risk Factors Attributable to Hypertension among HIV-Infected Patients on Antiretroviral Therapy in Selected Rural Districts of the Eastern Cape Province, South Africa," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    5. Jyoti Rajan Sharma & Sihle E. Mabhida & Bronwyn Myers & Teke Apalata & Edward Nicol & Mongi Benjeddou & Christo Muller & Rabia Johnson, 2021. "Prevalence of Hypertension and Its Associated Risk Factors in a Rural Black Population of Mthatha Town, South Africa," IJERPH, MDPI, vol. 18(3), pages 1-17, January.

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