Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa
Objective To determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).Design We used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.Participants T...
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BMJ Publishing Group
2023-02-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/2/e064275.full |
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| author | Charles Agyemang Frederick Wekesah Calistus Wilunda Gershim Asiki Peter Otieno Richard E Sanya Welcome Wami |
| author_facet | Charles Agyemang Frederick Wekesah Calistus Wilunda Gershim Asiki Peter Otieno Richard E Sanya Welcome Wami |
| author_sort | Charles Agyemang |
| collection | DOAJ |
| description | Objective To determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).Design We used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.Participants The participants comprised 39, 658 respondents aged 15–69 years randomly selected from nine SSA countries using a multistage stratified sampling design.Primary outcome measure Using latent class analysis and agglomerative hierarchical clustering algorithms, we analysed the clustering of cardiometabolic diseases (CMDs) including high blood sugar, hypercholesterolaemia, hypertension and cardiovascular diseases (CVDs) such as heart attack, angina and stroke. Clusters of lifestyle risk factors: harmful salt intake, physical inactivity, obesity, tobacco and alcohol use were also computed. Prevalence ratios (PR) from modified Poisson regression were used to assess the association of cardiometabolic multimorbidity with sociodemographic and lifestyle risk factors.Results Two distinct classes of CMDs were identified: relatively healthy group with minimal CMDs (95.2%) and cardiometabolic multimorbidity class comprising participants with high blood sugar, hypercholesterolaemia, hypertension and CVDs (4.8%). The clusters of lifestyle risk factors included alcohol, tobacco and harmful salt consumption (27.0%), and physical inactivity and obesity (5.8%). The cardiometabolic multimorbidity cluster exhibited unique sociodemographic and lifestyle risk profiles. Being female (PR=1.7, 95% CI (1.5 to 2.0), middle-aged (35–54 years) (3.9 (95% CI 3.2 to 4.8)), compared with age 15–34 years, employed (1.2 (95% CI 1.1 to 1.4)), having tertiary education (2.5 (95% CI 2.0 to 3.3)), vs no formal education and clustering of physical inactivity and obesity (2.4 (95% CI 2.0 to 2.8)) were associated with a higher likelihood of cardiometabolic multimorbidity.Conclusion Our findings show that cardiometabolic multimorbidity and lifestyle risk factors cluster in distinct patterns with a disproportionate burden among women, middle-aged, persons in high socioeconomic positions, and those with sedentary lifestyles and obesity. These results provide insights for health systems response in SSA to focus on these clusters as potential targets for integrated care. |
| format | Article |
| id | doaj-art-6f952483f83a4551bd5708d3a12ca8bb |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-6f952483f83a4551bd5708d3a12ca8bb2025-08-20T03:01:22ZengBMJ Publishing GroupBMJ Open2044-60552023-02-0113210.1136/bmjopen-2022-064275Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan AfricaCharles Agyemang0Frederick Wekesah1Calistus Wilunda2Gershim Asiki3Peter Otieno4Richard E Sanya5Welcome Wami6Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA2 African Population and Health Research Center, Nairobi, KenyaDivision of Epidemiology and Prevention, National Cancer Center Japan, Chuo-ku, JapanAfrican Population and Health Research Center, Nairobi, KenyaChronic Diseases Management Unit, African Population and Health Research Center, Nairobi, KenyaChronic Disease Management Unit, African Population and Health Research Center, Nairobi, KenyaAmsterdam Institute for Global Health and Development, Amsterdam, The NetherlandsObjective To determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).Design We used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.Participants The participants comprised 39, 658 respondents aged 15–69 years randomly selected from nine SSA countries using a multistage stratified sampling design.Primary outcome measure Using latent class analysis and agglomerative hierarchical clustering algorithms, we analysed the clustering of cardiometabolic diseases (CMDs) including high blood sugar, hypercholesterolaemia, hypertension and cardiovascular diseases (CVDs) such as heart attack, angina and stroke. Clusters of lifestyle risk factors: harmful salt intake, physical inactivity, obesity, tobacco and alcohol use were also computed. Prevalence ratios (PR) from modified Poisson regression were used to assess the association of cardiometabolic multimorbidity with sociodemographic and lifestyle risk factors.Results Two distinct classes of CMDs were identified: relatively healthy group with minimal CMDs (95.2%) and cardiometabolic multimorbidity class comprising participants with high blood sugar, hypercholesterolaemia, hypertension and CVDs (4.8%). The clusters of lifestyle risk factors included alcohol, tobacco and harmful salt consumption (27.0%), and physical inactivity and obesity (5.8%). The cardiometabolic multimorbidity cluster exhibited unique sociodemographic and lifestyle risk profiles. Being female (PR=1.7, 95% CI (1.5 to 2.0), middle-aged (35–54 years) (3.9 (95% CI 3.2 to 4.8)), compared with age 15–34 years, employed (1.2 (95% CI 1.1 to 1.4)), having tertiary education (2.5 (95% CI 2.0 to 3.3)), vs no formal education and clustering of physical inactivity and obesity (2.4 (95% CI 2.0 to 2.8)) were associated with a higher likelihood of cardiometabolic multimorbidity.Conclusion Our findings show that cardiometabolic multimorbidity and lifestyle risk factors cluster in distinct patterns with a disproportionate burden among women, middle-aged, persons in high socioeconomic positions, and those with sedentary lifestyles and obesity. These results provide insights for health systems response in SSA to focus on these clusters as potential targets for integrated care.https://bmjopen.bmj.com/content/13/2/e064275.full |
| spellingShingle | Charles Agyemang Frederick Wekesah Calistus Wilunda Gershim Asiki Peter Otieno Richard E Sanya Welcome Wami Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa BMJ Open |
| title | Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa |
| title_full | Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa |
| title_fullStr | Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa |
| title_full_unstemmed | Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa |
| title_short | Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa |
| title_sort | multimorbidity of cardiometabolic diseases a cross sectional study of patterns clusters and associated risk factors in sub saharan africa |
| url | https://bmjopen.bmj.com/content/13/2/e064275.full |
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