AFRICA CARDIOVASCULAR JOURNAL OF AFRICA • SAHS Biennial Congress 16-18 September 2022 60 Submission ID: 1381 Objectives To use generalized structural equation modelling to understand the inter-relationships between socio-economic, socio-demographic, and environmental factors with multimorbidity. Design Nationally representative (for ≥50 years old adults) cross-sectional WHO South Africa wave 2 study. Participants 1,967 individuals (men: 623, and women: 1,344) aged ≥45 years for whom data from the World Health Organization Study on global AGEing and adult health (WHO SAGE) South Africa Wave 2 between 2014 and 2015 were available. This data includes 7 health conditions to determine multimorbidity and socioeconomic, demographic, behavioral, and anthropological information. Measures Multimorbidity and depression. Statistical analysis Descriptive statistics were completed for the descriptive characteristics and tested for statistically significant associations and differences between multimorbidity categories using chi-squared tests for categorical data, and Mann-Whitney tests for the continuous characteristics, given the non-normal distributions as shown by the Shapiro Wilk test. Multivariable logistic regression analyses were conducted for the association of all the characteristics with multimorbidity and depression. Multivariable analyses performed in gSEM were based and guided by an a priori conceptual model, and gSEM analysis was used to assess associations on all pathways. Results Of the 1,967 participants, 21% (n=415) had multimorbidity and this was higher among women, and those living in urban areas. In the unadjusted logistic regression analyses, feeling “unsafe” [aOR =2.04 ; 95% Confidence Interval: 1.25; 3.42], being female, [aOR=1.93; 95% Confidence Interval: 1.02; 3.62], and older age [aOR=1.05; 95% Confidence Interval: 1.02; 1.08] were associated with higher odds for multimorbidity. In addition, being female, belonging to the highest wealth tertile relative to those in the lowest tertile, and living in an urban area were significantly associated with higher odds of depression [OR =1.39; 95% Confidence Interval: 0.59; 3.29]. Similarly, in the gSEM model, where models are estimated concurrently, demographic factors [older age (aOR=1.03, 95% Confidence Interval: 1.01; 1.05) and being female (aOR= 3.02; 95% Confidence Interval: 1.88; 4.86)] and behavioural factors [individuals with history of tobacco avoidance (aOR=0.46; 95% Confidence Interval: 0.27;0.75), and good sleep quality (aOR=0.59; 95% Confidence Interval: 0.39;0.91)] were significantly associated with multimorbidity. Moreover, using the gSEM approach, multimorbidity was associated with two-fold greater odds of depression (aOR=2.41; 95% Confidence Interval: 1.36;4.28). Conclusion The results indicate that multimorbidity in middle-aged and older adults is a relatively common condition that is influenced by behavioral, demographic, and environmental factors. Given that the primary health (PHC) system in South Africa remains single-disease-focused in the treatment of patients, efforts should be made to manage multiple conditions concurrently at PHC centers. In addition, these inform policymakers to prioritize the older population, females, and tobacco users and enhance environmental safety in prevention programs. Name: Presenting Author Information Article Category Abstract Title Centre of Excellence in Human Development glory.chidumwa@gmail.com English Abstract Basic Sciences Understanding the inter-relationships between socio-economic, socio-demographic, behavioural, and environmental factors for multimorbidity: a structural equation modelling approach Author Affiliation: Email: Glory Chidumwa Author Name & Surname Title Expertise Affiliation Email Country Glory Chidumwa Dr Biostatistics and Epidemiology Centre of Excellence in Human Development glory.chidumwa@gmail.com South Africa POSTER PRESENTATION
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