The use and disaggregation of survey data to study the cross-sectional and spatial distribution of multimorbidity and its association with socioeconomic disadvantage in South Africa

• Main Author: Weimann, Amy
• Other Authors: Oni, Tolu
• Format: Dissertation
• Language: English
• Published: University of Cape Town 2017
• Subjects: Environment and Geographical Science
• Online Access: http://hdl.handle.net/11427/22926

This study identified the need to provide a proof of concept of the use and disaggregation of existing health data in order to study the cross-sectional and spatial distribution of HIV, tuberculosis and noncommunicable disease multimorbidity and the association with socioeconomic disadvantage at a South African, Western Cape Province and urban/intra-urban scale for 2008 and 2012. This study was framed within a health geography context and draws attention to the reality of health variations which are influenced by place-based effects, including the surrounding social, cultural and economic structural factors and mechanisms that, together, constitute the social determinants of health. However, in order to identify and understand these variations in health, access to health data that is able to be disaggregated by key characteristic and spatial scales, is essential. Therefore, this study utilised existing health data from the National Income Dynamics Study, a longitudinal study with a sample of approximately 28 000 people, to perform secondary data analysis using a positivist approach to research. This study found that the coupling of geospatial and health data is able to produce new health information and the graphical representation of data provides unique insights in health variations. Secondly, the burden of disease is not consistent between spatial scales which suggests variations in epidemiological profiles between sub-national geographies, thereby supporting the argument for the need of data disaggregation. Finally, the cross-sectional analysis of this study found multimorbidity to be associated with age, socioeconomic deprivation, obesity and urban areas, while the 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. Therefore, this study provides an example of the research needed to provide information to support policy improvement and enable the urban planning and public health professions to work together.