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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

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 Afric...

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Autores principales: Chidumwa, Glory, Maposa, Innocent, Corso, Barbara, Minicuci, Nadia, Kowal, Paul, Micklesfield, Lisa K, Ware, Lisa Jayne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849898/
https://www.ncbi.nlm.nih.gov/pubmed/33514578
http://dx.doi.org/10.1136/bmjopen-2020-041604
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author Chidumwa, Glory
Maposa, Innocent
Corso, Barbara
Minicuci, Nadia
Kowal, Paul
Micklesfield, Lisa K
Ware, Lisa Jayne
author_facet Chidumwa, Glory
Maposa, Innocent
Corso, Barbara
Minicuci, Nadia
Kowal, Paul
Micklesfield, Lisa K
Ware, Lisa Jayne
author_sort Chidumwa, Glory
collection PubMed
description 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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spelling pubmed-78498982021-02-02 Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2 Chidumwa, Glory Maposa, Innocent Corso, Barbara Minicuci, Nadia Kowal, Paul Micklesfield, Lisa K Ware, Lisa Jayne BMJ Open Public Health 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. BMJ Publishing Group 2021-01-29 /pmc/articles/PMC7849898/ /pubmed/33514578 http://dx.doi.org/10.1136/bmjopen-2020-041604 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Public Health
Chidumwa, Glory
Maposa, Innocent
Corso, Barbara
Minicuci, Nadia
Kowal, Paul
Micklesfield, Lisa K
Ware, Lisa Jayne
Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
title Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
title_full Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
title_fullStr Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
title_full_unstemmed Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
title_short Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
title_sort identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from sage south africa wave 2
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849898/
https://www.ncbi.nlm.nih.gov/pubmed/33514578
http://dx.doi.org/10.1136/bmjopen-2020-041604
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