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Identifying co-occurrence and clustering of chronic diseases using latent class analysis: Cross-sectional findings from SAGE South Africa Wave 2

Chidumwa, G.
Maposa, I.
Corso, B.
Minicuci, N.
Kowal, P.
Micklesfield, L.K.
Ware, L.J.
Objectives To classify South African adults with chronic health conditions for multimorbidity (MM) risk, and to determine sociodemographic, anthropometric and behavioural factors associated with identified patterns of MM, using data from the WHO’s Study on global AGEing and adult health South Africa Wave 2. Design Nationally representative (for ≥50-year-old adults) cross-sectional study. Setting Adults in South Africa between 2014 and 2015. Participants 1967 individuals (men: 623 and women: 1344) aged ≥45 years for whom data on all seven health conditions and socioeconomic, demographic, behavioural, and anthropological information were available. Measures MM latent classes. Results The prevalence of MM (coexistence of two or more non-communicable diseases (NCDs)) was 21%. The latent class analysis identified three groups namely: minimal MM risk (83%), concordant (hypertension and diabetes) MM (11%) and discordant (angina, asthma, chronic lung disease, arthritis and depression) MM (6%). Using the minimal MM risk group as the reference, female (relative risk ratio (RRR)=4.57; 95% CI (1.64 to 12.75); p =0.004) and older (RRR=1.08; 95% CI (1.04 to 1.12); p<0.001) participants were more likely to belong to the concordant MM group, while tobacco users (RRR=8.41; 95% CI (1.93 to 36.69); p=0.005) and older (RRR=1.09; 95% CI (1.03 to 1.15); p=0.002) participants had a high likelihood of belonging to the discordant MM group. Conclusion NCDs with similar pathophysiological risk profiles tend to cluster together in older people. Risk factors for MM in South African adults include sex, age and tobacco use.
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Hypertension , Public health , Statistics & research methods , SDG-03 Good health and well-being
Chidumwa G, Maposa I, Corso B, Minicuci N, Kowal P, Micklesfield LK, Ware LJ. Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2. BMJ Open. 2021 Jan 29;11(1):e041604. doi: 10.1136/bmjopen-2020-041604.
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