Prevalence of multimorbidity of cardiometabolic conditions and associated risk factors in a population-based sample of South Africans: A cross-sectional study

Sewpaul, Ronel; Mbewu, Anthony; Fagbamigbe, Adeniyi Francis; Kandala, Ngianga‐Bakwin; Reddy, Priscilla

doi:10.1016/j.puhip.2021.100193

Cited by 7 publications

(15 citation statements)

References 39 publications

(50 reference statements)

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“…A shift away from fragmented care may also improve access to quality healthcare services, especially in SSA where persons living with CMDs remain undiagnosed for several years and the majority of those on treatment often remain uncontrolled. 74 Second, our study provides baseline estimates for future researchers to design longitudinal studies on the burden and aetiology of the most common clusters of CMDs in SSA.…”

Section: Discussionmentioning

confidence: 99%

Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa

et al. 2023

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ObjectiveTo determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).DesignWe used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.ParticipantsThe participants comprised 39, 658 respondents aged 15–69 years randomly selected from nine SSA countries using a multistage stratified sampling design.Primary outcome measureUsing 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.ResultsTwo 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.ConclusionOur 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.

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Section: Discussionmentioning

confidence: 99%

Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa

et al. 2023

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“…The lack of consistency in de ning CMM has resulted in variations in the estimated rates of multimorbidity in different studies. The overall prevalence of CMM in our study is slightly higher than the ndings of Sewpaul, R, Canoy, D and others (1,15). In addition to the aforementioned variations in de ning CMM, our study includes not only patient self-reported case data but also data from cases diagnosed during hospitalization.…”

Section: Discussionmentioning

confidence: 73%

“…Several countries worldwide have conducted extensive research on the prevalence and impact of CMM (1,8,(12)(13)(14)(15)(16)(17)(18). However, in China, only a limited number of studies have been undertaken on this subject.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study

Xu,

Wang,

Wang

et al. 2024

Preprint

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Background The widespread prevalence of Cardiometabolic Multimorbidity (CMM) presents significant challenges to global public health. While previous studies have primarily examined individual cardiometabolic diseases, there has been limited research on CMM. As such, we intend to assess the prevalence of CMM and identify predictive risk factors within the Chinese population which will hold considerable implications for the future management of CMM. Methods We employed data from The China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project (China-PEACE MPP), enrolling a total of 102,358 participants aged 35–75 years. CMM was defined as the simultaneous presence of two or more of the following diseases: diabetes, hypertension, stroke, and coronary heart disease. Univariate and multivariate logistic regression analyses were performed on demographic variables and modifiable factors associated with CMM to identify its risk predictive factors. Results The participants, with an average age of 54.27 years, comprised 60.5% of women. The overall prevalence of CMM was 11.6%, with hypertension and diabetes coexisting as the most common comorbid combination at 8.5%. Multifactor logistic regression analysis revealed that increasing age (45–54 years (OR = 2.62, 95%CI: 2.39–2.88), 55–64 years (OR = 5.27, 95%CI: 4.83–5.78), and 65–75 years (OR = 8.36, 95%CI: 7.62–9.18) compared to 35–44 years), current alcohol consumption (OR = 1.23, 95%CI: 1.12–1.34), TG ≥ 2.3mmol/L (OR = 1.69, 95%CI: 1.61–1.78), recent use of lipid-lowering medications (OR = 3.47, 95%CI: 3.21–3.74), and recent use of antiplatelet aggregators (OR = 3.67, 95%CI: 3.33–4.04) were associated with an increased risk of CMM. Conversely, a reduced occurrence of CMM was associated with being female (OR = 0.74, 95%CI: 0.70–0.78), other marital statuses (OR = 0.91, 95%CI: 0.85–0.97), education level of high school or above (OR = 0.90, 95%CI: 0.85–0.94), annual household income not less than 50,000 yuan (OR = 0.93, 95% CI: 0.89–0.98, p = 0.004), and HDL-C ≥ 1.0mmol/L (OR = 0.84, 95%CI: 0.79–0.90). Conclusions In the general population of China, over one-tenth of individuals are affected by CMM, indicating a high current prevalence of the condition. This highlights the imperative for China to develop targeted intervention measures focusing on the risk factors of CMM to prevent its occurrence and progression, effectively manage the condition, and reduce associated adverse outcomes and healthcare resource consumption.

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“…Many multimorbidity studies were conducted in rural areas where almost all participants were Black (Oni et al 2015;Wade et al 2021;Wong et al 2021), or race was not included in the analysis. Of the studies that examined racial differences, two found that Asian/Indian participants were more than twice as likely to have multimorbidity compared to African and Coloured participants (Sewpaul et al 2021;Weimann et al 2016). Another found that Asian/Indian and Coloured participants had about 1.5 greater odds of multimorbidity compared to White participants (Phaswana-Mafuya et al 2013).…”

Section: Racementioning

confidence: 99%

Multimorbid life expectancy across race, socioeconomic status, and gender in South Africa

Lam¹,

Keenan²,

Myrskylä³

et al. 2022

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Prevalence of multimorbidity of cardiometabolic conditions and associated risk factors in a population-based sample of South Africans: A cross-sectional study

Cited by 7 publications

References 39 publications

Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa

Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa

Epidemiological Survey of Cardiometabolic Multimorbidity and Related Risk Factors in Chinese Population: A Cross-Sectional Study

Multimorbid life expectancy across race, socioeconomic status, and gender in South Africa

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