A model-based approach to estimating the prevalence of disease combinations in South Africa
Background The development of strategies to better detect and manage patients with multiple long-term conditions requires estimates of the most prevalent condition combinations. However, standard meta-analysis tools are not well suited to synthesising heterogeneous multimorbidity data.Methods We dev...
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BMJ Publishing Group
2024-02-01
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| Series: | BMJ Global Health |
| Online Access: | https://gh.bmj.com/content/9/2/e013376.full |
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| author | Naomi S Levitt Sarah Bennett Kirsty Bobrow Lara R Fairall Rifqah A Roomaney Max O Bachmann Andrew Boulle Robyn Curran Leigh F Johnson Reshma Kassanjee Naomi Folb |
| author_facet | Naomi S Levitt Sarah Bennett Kirsty Bobrow Lara R Fairall Rifqah A Roomaney Max O Bachmann Andrew Boulle Robyn Curran Leigh F Johnson Reshma Kassanjee Naomi Folb |
| author_sort | Naomi S Levitt |
| collection | DOAJ |
| description | Background The development of strategies to better detect and manage patients with multiple long-term conditions requires estimates of the most prevalent condition combinations. However, standard meta-analysis tools are not well suited to synthesising heterogeneous multimorbidity data.Methods We developed a statistical model to synthesise data on associations between diseases and nationally representative prevalence estimates and applied the model to South Africa. Published and unpublished data were reviewed, and meta-regression analysis was conducted to assess pairwise associations between 10 conditions: arthritis, asthma, chronic obstructive pulmonary disease (COPD), depression, diabetes, HIV, hypertension, ischaemic heart disease (IHD), stroke and tuberculosis. The national prevalence of each condition in individuals aged 15 and older was then independently estimated, and these estimates were integrated with the ORs from the meta-regressions in a statistical model, to estimate the national prevalence of each condition combination.Results The strongest disease associations in South Africa are between COPD and asthma (OR 14.6, 95% CI 10.3 to 19.9), COPD and IHD (OR 9.2, 95% CI 8.3 to 10.2) and IHD and stroke (OR 7.2, 95% CI 5.9 to 8.4). The most prevalent condition combinations in individuals aged 15+ are hypertension and arthritis (7.6%, 95% CI 5.8% to 9.5%), hypertension and diabetes (7.5%, 95% CI 6.4% to 8.6%) and hypertension and HIV (4.8%, 95% CI 3.3% to 6.6%). The average numbers of comorbidities are greatest in the case of COPD (2.3, 95% CI 2.1 to 2.6), stroke (2.1, 95% CI 1.8 to 2.4) and IHD (1.9, 95% CI 1.6 to 2.2).Conclusion South Africa has high levels of HIV, hypertension, diabetes and arthritis, by international standards, and these are reflected in the most prevalent condition combinations. However, less prevalent conditions such as COPD, stroke and IHD contribute disproportionately to the multimorbidity burden, with high rates of comorbidity. This modelling approach can be used in other settings to characterise the most important disease combinations and levels of comorbidity. |
| format | Article |
| id | doaj-art-d140b67b069f4aa4852a200fcd92e2ba |
| institution | DOAJ |
| issn | 2059-7908 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Global Health |
| spelling | doaj-art-d140b67b069f4aa4852a200fcd92e2ba2025-08-20T02:41:11ZengBMJ Publishing GroupBMJ Global Health2059-79082024-02-019210.1136/bmjgh-2023-013376A model-based approach to estimating the prevalence of disease combinations in South AfricaNaomi S Levitt0Sarah Bennett1Kirsty Bobrow2Lara R Fairall3Rifqah A Roomaney4Max O Bachmann5Andrew Boulle6Robyn Curran7Leigh F Johnson8Reshma Kassanjee9Naomi Folb10Chronic Disease Initiative for Africa, Faculty of Medicine and Health Sciences, University of Cape Town, Cape Town, Western Cape, South AfricaMedscheme, Cape Town, South AfricaDepartment of Medicine, University of Cape Town, Cape Town, South AfricaKing`s Global Health Institute, King`s College London, London, UKBurden of Disease Research Unit, South African Medical Research Council, Cape Town, Western Cape, South AfricaNorwich Medical School, University of East Anglia, Faculty of Medicine and Health Sciences, Norwich, UKCentre for Infectious Disease Epidemiology and Research (CIDER), University of Cape Town, Cape Town, South AfricaKnowledge Translation Unit, University of Cape Town, Cape Town, Western Cape, South AfricaCentre for Infectious Disease Epidemiology and Research (CIDER), University of Cape Town, Cape Town, South AfricaCentre for Infectious Disease Epidemiology and Research (CIDER), University of Cape Town, Cape Town, South AfricaMedscheme, Cape Town, South AfricaBackground The development of strategies to better detect and manage patients with multiple long-term conditions requires estimates of the most prevalent condition combinations. However, standard meta-analysis tools are not well suited to synthesising heterogeneous multimorbidity data.Methods We developed a statistical model to synthesise data on associations between diseases and nationally representative prevalence estimates and applied the model to South Africa. Published and unpublished data were reviewed, and meta-regression analysis was conducted to assess pairwise associations between 10 conditions: arthritis, asthma, chronic obstructive pulmonary disease (COPD), depression, diabetes, HIV, hypertension, ischaemic heart disease (IHD), stroke and tuberculosis. The national prevalence of each condition in individuals aged 15 and older was then independently estimated, and these estimates were integrated with the ORs from the meta-regressions in a statistical model, to estimate the national prevalence of each condition combination.Results The strongest disease associations in South Africa are between COPD and asthma (OR 14.6, 95% CI 10.3 to 19.9), COPD and IHD (OR 9.2, 95% CI 8.3 to 10.2) and IHD and stroke (OR 7.2, 95% CI 5.9 to 8.4). The most prevalent condition combinations in individuals aged 15+ are hypertension and arthritis (7.6%, 95% CI 5.8% to 9.5%), hypertension and diabetes (7.5%, 95% CI 6.4% to 8.6%) and hypertension and HIV (4.8%, 95% CI 3.3% to 6.6%). The average numbers of comorbidities are greatest in the case of COPD (2.3, 95% CI 2.1 to 2.6), stroke (2.1, 95% CI 1.8 to 2.4) and IHD (1.9, 95% CI 1.6 to 2.2).Conclusion South Africa has high levels of HIV, hypertension, diabetes and arthritis, by international standards, and these are reflected in the most prevalent condition combinations. However, less prevalent conditions such as COPD, stroke and IHD contribute disproportionately to the multimorbidity burden, with high rates of comorbidity. This modelling approach can be used in other settings to characterise the most important disease combinations and levels of comorbidity.https://gh.bmj.com/content/9/2/e013376.full |
| spellingShingle | Naomi S Levitt Sarah Bennett Kirsty Bobrow Lara R Fairall Rifqah A Roomaney Max O Bachmann Andrew Boulle Robyn Curran Leigh F Johnson Reshma Kassanjee Naomi Folb A model-based approach to estimating the prevalence of disease combinations in South Africa BMJ Global Health |
| title | A model-based approach to estimating the prevalence of disease combinations in South Africa |
| title_full | A model-based approach to estimating the prevalence of disease combinations in South Africa |
| title_fullStr | A model-based approach to estimating the prevalence of disease combinations in South Africa |
| title_full_unstemmed | A model-based approach to estimating the prevalence of disease combinations in South Africa |
| title_short | A model-based approach to estimating the prevalence of disease combinations in South Africa |
| title_sort | model based approach to estimating the prevalence of disease combinations in south africa |
| url | https://gh.bmj.com/content/9/2/e013376.full |
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