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

ObjectivesTo 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...

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Published in:BMJ open 2021-01, Vol.11 (1), p.e041604-e041604
Main Authors: Chidumwa, Glory, Maposa, Innocent, Corso, Barbara, Minicuci, Nadia, Kowal, Paul, Micklesfield, Lisa K, Ware, Lisa Jayne
Format: Article
Language:English
Subjects:
Adult
Adults
Age
Aged
Aged, 80 and over
Aging
Angina pectoris
Arthritis
Asthma
Blood pressure
Chronic Disease
Chronic illnesses
Chronic obstructive pulmonary disease
Cluster Analysis
Cross-Sectional Studies
Diabetes
Female
Heart rate
Households
Humans
Hypertension
Latent class analysis
Low income groups
Lung diseases
Male
Measurement techniques
Middle Aged
Prevalence
Principal components analysis
Public Health
Risk Factors
Rural areas
Socioeconomic Factors
South Africa - epidemiology
statistics & research methods
Tobacco
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creator Chidumwa, Glory
Maposa, Innocent
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Kowal, Paul
Micklesfield, Lisa K
Ware, Lisa Jayne
description ObjectivesTo 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.DesignNationally representative (for ≥50-year-old adults) cross-sectional study.SettingAdults in South Africa between 2014 and 2015.Participants1967 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.MeasuresMM latent classes.ResultsThe 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
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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&lt;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.ConclusionNCDs 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.</description><identifier>ISSN: 2044-6055</identifier><identifier>EISSN: 2044-6055</identifier><identifier>DOI: 10.1136/bmjopen-2020-041604</identifier><identifier>PMID: 33514578</identifier><language>eng</language><publisher>England: British Medical Journal Publishing Group</publisher><subject>Adult ; Adults ; Age ; Aged ; Aged, 80 and over ; Aging ; Angina pectoris ; Arthritis ; Asthma ; Blood pressure ; Chronic Disease ; Chronic illnesses ; Chronic obstructive pulmonary disease ; Cluster Analysis ; Cross-Sectional Studies ; Diabetes ; Female ; Heart rate ; Households ; Humans ; Hypertension ; Latent class analysis ; Low income groups ; Lung diseases ; Male ; Measurement techniques ; Middle Aged ; Prevalence ; Principal components analysis ; Public Health ; Risk Factors ; Rural areas ; Socioeconomic Factors ; South Africa - epidemiology ; statistics &amp; research methods ; Tobacco</subject><ispartof>BMJ open, 2021-01, Vol.11 (1), p.e041604-e041604</ispartof><rights>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.</rights><rights>2021 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/ 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/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>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. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b578t-cc5aaf3976129c53d0c49fbaa925659b8e57e8b55d3e134232ac9a7d83d7ae7b3</citedby><cites>FETCH-LOGICAL-b578t-cc5aaf3976129c53d0c49fbaa925659b8e57e8b55d3e134232ac9a7d83d7ae7b3</cites><orcidid>0000-0002-8743-9045 ; 0000-0002-9762-4017</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2483548574/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2483548574?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>112,113,230,314,727,780,784,885,3194,25753,27549,27550,27924,27925,37012,37013,44590,53791,53793,55341,55350,74998,77466,77467,77468,77469,77473,77504,77532,77558</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33514578$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chidumwa, Glory</creatorcontrib><creatorcontrib>Maposa, Innocent</creatorcontrib><creatorcontrib>Corso, Barbara</creatorcontrib><creatorcontrib>Minicuci, Nadia</creatorcontrib><creatorcontrib>Kowal, Paul</creatorcontrib><creatorcontrib>Micklesfield, Lisa K</creatorcontrib><creatorcontrib>Ware, Lisa Jayne</creatorcontrib><title>Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2</title><title>BMJ open</title><addtitle>BMJ Open</addtitle><addtitle>BMJ Open</addtitle><description>ObjectivesTo 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.DesignNationally representative (for ≥50-year-old adults) cross-sectional study.SettingAdults in South Africa between 2014 and 2015.Participants1967 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.MeasuresMM latent classes.ResultsThe 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&lt;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.ConclusionNCDs 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.</description><subject>Adult</subject><subject>Adults</subject><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aging</subject><subject>Angina pectoris</subject><subject>Arthritis</subject><subject>Asthma</subject><subject>Blood pressure</subject><subject>Chronic Disease</subject><subject>Chronic illnesses</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Cluster Analysis</subject><subject>Cross-Sectional Studies</subject><subject>Diabetes</subject><subject>Female</subject><subject>Heart rate</subject><subject>Households</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Latent class analysis</subject><subject>Low income groups</subject><subject>Lung diseases</subject><subject>Male</subject><subject>Measurement techniques</subject><subject>Middle Aged</subject><subject>Prevalence</subject><subject>Principal components analysis</subject><subject>Public Health</subject><subject>Risk Factors</subject><subject>Rural areas</subject><subject>Socioeconomic Factors</subject><subject>South Africa - epidemiology</subject><subject>statistics &amp; 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Maposa, Innocent ; Corso, Barbara ; Minicuci, Nadia ; Kowal, Paul ; Micklesfield, Lisa K ; Ware, Lisa Jayne</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b578t-cc5aaf3976129c53d0c49fbaa925659b8e57e8b55d3e134232ac9a7d83d7ae7b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Adults</topic><topic>Age</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aging</topic><topic>Angina pectoris</topic><topic>Arthritis</topic><topic>Asthma</topic><topic>Blood pressure</topic><topic>Chronic Disease</topic><topic>Chronic illnesses</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Cluster Analysis</topic><topic>Cross-Sectional Studies</topic><topic>Diabetes</topic><topic>Female</topic><topic>Heart rate</topic><topic>Households</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Latent class analysis</topic><topic>Low income groups</topic><topic>Lung diseases</topic><topic>Male</topic><topic>Measurement techniques</topic><topic>Middle Aged</topic><topic>Prevalence</topic><topic>Principal components analysis</topic><topic>Public Health</topic><topic>Risk Factors</topic><topic>Rural areas</topic><topic>Socioeconomic Factors</topic><topic>South Africa - epidemiology</topic><topic>statistics &amp; research methods</topic><topic>Tobacco</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chidumwa, Glory</creatorcontrib><creatorcontrib>Maposa, Innocent</creatorcontrib><creatorcontrib>Corso, Barbara</creatorcontrib><creatorcontrib>Minicuci, Nadia</creatorcontrib><creatorcontrib>Kowal, Paul</creatorcontrib><creatorcontrib>Micklesfield, Lisa K</creatorcontrib><creatorcontrib>Ware, Lisa Jayne</creatorcontrib><collection>BMJ Journals (Open Access)</collection><collection>BMJ Journals:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing and Allied Health Journals</collection><collection>ProQuest Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>BMJ Journals</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Consumer Health Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Consumer Database (Proquest)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database (ProQuest)</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>BMJ open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chidumwa, Glory</au><au>Maposa, Innocent</au><au>Corso, Barbara</au><au>Minicuci, Nadia</au><au>Kowal, Paul</au><au>Micklesfield, Lisa K</au><au>Ware, Lisa Jayne</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2</atitle><jtitle>BMJ open</jtitle><stitle>BMJ Open</stitle><addtitle>BMJ Open</addtitle><date>2021-01-29</date><risdate>2021</risdate><volume>11</volume><issue>1</issue><spage>e041604</spage><epage>e041604</epage><pages>e041604-e041604</pages><issn>2044-6055</issn><eissn>2044-6055</eissn><abstract>ObjectivesTo 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.DesignNationally representative (for ≥50-year-old adults) cross-sectional study.SettingAdults in South Africa between 2014 and 2015.Participants1967 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.MeasuresMM latent classes.ResultsThe 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&lt;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.ConclusionNCDs 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.</abstract><cop>England</cop><pub>British Medical Journal Publishing Group</pub><pmid>33514578</pmid><doi>10.1136/bmjopen-2020-041604</doi><orcidid>https://orcid.org/0000-0002-8743-9045</orcidid><orcidid>https://orcid.org/0000-0002-9762-4017</orcidid><oa>free_for_read</oa></addata></record>
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source BMJ; PMC (PubMed Central); BMJ Journals (Open Access); Publicly Available Content (ProQuest)
subjects Adult
Adults
Age
Aged
Aged, 80 and over
Aging
Angina pectoris
Arthritis
Asthma
Blood pressure
Chronic Disease
Chronic illnesses
Chronic obstructive pulmonary disease
Cluster Analysis
Cross-Sectional Studies
Diabetes
Female
Heart rate
Households
Humans
Hypertension
Latent class analysis
Low income groups
Lung diseases
Male
Measurement techniques
Middle Aged
Prevalence
Principal components analysis
Public Health
Risk Factors
Rural areas
Socioeconomic Factors
South Africa - epidemiology
statistics & research methods
Tobacco
title Identifying co-occurrence and clustering of chronic diseases using latent class analysis: cross-sectional findings from SAGE South Africa Wave 2
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