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Geospatial assessment of the convergence of communicable and non-communicable diseases in South Africa.
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- Author(s): Cuadros, Diego F; Moreno, Claudia M; Tomita, Andrew; Singh, Urisha; Olivier, Stephen; Castle, Alison; Moosa, Yumna; Edwards, Johnathan A; Kim, Hae-Young; Siedner, Mark J; Wong, Emily B; Tanser, Frank
- Source:
Journal of Multimorbidity & Comorbidity; 9/28/2023, p1-12, 12p- Subject Terms:
HIV infection epidemiology; TUBERCULOSIS epidemiology; COMMUNICABLE disease epidemiology; HIV infection risk factors; NON-communicable diseases; RESPIRATORY diseases; GLOBAL Positioning System; DISEASE clusters; STATISTICS; CARDIOVASCULAR diseases risk factors; MIDDLE-income countries; CONFIDENCE intervals; CHRONIC diseases; MULTIPLE regression analysis; MULTIVARIATE analysis; CARDIOVASCULAR diseases; DIABETES; METABOLIC disorders; RISK assessment; HYPERLIPIDEMIA; AIDS serodiagnosis; PEARSON correlation (Statistics); RESEARCH funding; LOW-income countries; DISEASE prevalence; ENZYME-linked immunosorbent assay; STATISTICAL models; ODDS ratio; CLUSTER analysis (Statistics); COMORBIDITY; DISEASE risk factors - Source:
- Additional Information
- Subject Terms:
- Abstract: Introduction: Several low-and middle-income countries are undergoing rapid epidemiological transition with a rising burden of non-communicable diseases (NCDs). South Africa (SA) is a country with one of the largest HIV epidemics worldwide and a growing burden of NCDs where the collision of these epidemics poses a major public health challenge. Methods: Using data from a large nationally representative survey, the South Africa Demographic and Health Survey (SADHS 2016), we conducted a geospatial analysis of several diseases including HIV, tuberculosis (TB), cardiovascular, respiratory, and metabolic diseases to identify areas with a high burden of co-morbidity within the country. We explored the spatial structure of each disease and associations between diseases using different spatial and visual data methodologies. We also assessed the individual level co-occurrence of HIV and the other diseases included in the analysis. Results: The spatial distribution for HIV prevalence showed that this epidemic is most intense in the eastern region of the country, mostly within the Gauteng, Mpumalanga, and Kwazulu-Natal provinces. In contrast, chronic diseases had their highest prevalence rates the southern region of the country, particularly in the Eastern and Western Cape provinces. Individual-level analyses were consistent with the spatial correlations and found no statistically significant associations between HIV infection and the presence of any NCDs. Conclusions: We found no evidence of geospatial overlap between the HIV epidemic and NCDs in SA. These results evidence the complex epidemiological landscape of the country, characterized by geographically distinct areas exhibiting different health burdens. The detailed description of the heterogenous prevalence of HIV and NCDs in SA reported in this study could be a useful tool to inform and direct policies to enhance targeted health service delivery according to the local health needs of each community. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Journal of Multimorbidity & Comorbidity is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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