Multimorbidity Patterns and Socioeconomic Determinants in a resource-limited setting: A Clustering Analysis

ImportanceMultimorbidity, the coexistence of multiple long-term conditions, is a growing public health challenge in South Africa. Understanding the patterns of multimorbidity and their socioeconomic determinants is crucial for developing prevention and control solutions. ObjectiveTo identify and characterize multimorbidity clusters and their socioeconomic determinants in the South African population using data from the Demographic and Health Survey (DHS). Design, Setting, and ParticipantsThis cross-sectional study used data from the recent South Africa DHS, a nationally representative household survey. The study included 5,342 individuals aged 18 years and above who participated in the adult health module of the survey. Data were collected through interviews and biomarker measurements between June and November 2016. Main Outcome and MeasuresThe primary outcome was multimorbidity, defined as the presence of two or more chronic conditions. Twelve chronic conditions were considered: tuberculosis, hypertension, stroke, high blood cholesterol, anaemia, chronic bronchitis, diabetes, asthma, cancer, heart disease, HIV, and chronic pain. Socioeconomic determinants included wealth index, education level, occupation, health insurance, marital status, age, sex, ethnicity, and media access. ResultsOf the 5,342 participants, 2,382 (44.6%) had multimorbidity. Four distinct multimorbidity clusters were identified: "Low Morbidity Group" (low prevalence of chronic conditions), "Cardiometabolic Cluster" (high prevalence of hypertension and diabetes), "Chronic Infectious Disease Cluster" (high prevalence of tuberculosis and HIV), and "Complex Chronic Disease Cluster" (high prevalence of multiple chronic conditions, including cancer, stroke, and heart attack). Multinomial logistic regression analysis revealed socioeconomic disparities in multimorbidity patterns, with lower levels of education, unemployment, and poverty associated with membership in the clusters characterized by a higher burden of chronic diseases. Conclusions and RelevanceThis study identified four distinct multimorbidity clusters in the South African population, each characterized by unique patterns of chronic disease co-occurrence and socioeconomic determinants. The findings highlight the need for tailored interventions and policies that address the specific needs of each multimorbidity cluster while also tackling the underlying social and economic determinants of health.