Four multimorbidity profiles found in South Africa

Multimorbidity—the coexistence of two or more long-term health conditions—is becoming a major public health challenge in South Africa. A team led by corresponding author Olalekan A. Uthman used nationally representative survey data to look for patterns in how diseases cluster together and how social and economic factors shape those patterns. The researchers analyzed data from the recent South Africa Demographic and Health Survey (DHS), which included 5,342 people aged 18 and older who took part in the adult health module. Data were collected through interviews and biomarker measurements between June and November 2016. The study focused on a set of twelve chronic conditions: tuberculosis, hypertension, stroke, high blood cholesterol, anaemia, chronic bronchitis, diabetes, asthma, cancer, heart disease, HIV, and chronic pain. By defining multimorbidity as having two or more of these conditions, the team set out to identify distinct groups of people with similar disease burdens and to link those groups to socioeconomic factors such as wealth, education and employment. The goal was practical: to provide information that could help design prevention and control approaches suited to the realities of a resource-limited setting.

This cross-sectional analysis used the Demographic and Health Survey (DHS) data to identify groups of people with similar combinations of conditions and then to test which social and economic factors were associated with those groups. Of the 5,342 participants, 2,382 (44.6%) met the study definition of multimorbidity. The researchers applied a clustering approach to the list of twelve conditions and identified four distinct multimorbidity clusters. The “Low Morbidity Group” showed generally low prevalence of chronic conditions. The “Cardiometabolic Cluster” was marked by a high prevalence of hypertension and diabetes. The “Chronic Infectious Disease Cluster” had a high prevalence of tuberculosis and HIV. The “Complex Chronic Disease Cluster” included people with multiple serious conditions such as cancer, stroke and heart attack. The team then used multinomial logistic regression analysis to examine socioeconomic determinants. They found clear disparities: lower education levels, unemployment and poverty were associated with membership in the clusters with heavier burdens of chronic disease. Other determinants examined included wealth index, education level, occupation, health insurance, marital status, age, sex, ethnicity and media access.

The findings point to practical opportunities for health planners and policymakers. Knowing that people fall into different multimorbidity clusters suggests that one-size-fits-all programs will miss important needs. For example, the Cardiometabolic Cluster could be reached through stronger screening and management for hypertension and diabetes in primary care, while the Chronic Infectious Disease Cluster will need continued emphasis on tuberculosis and HIV testing, treatment and integrated services. The Complex Chronic Disease Cluster, with cancer, stroke and heart disease, may require more coordinated specialist referral pathways and rehabilitation support. Importantly, the study links these health patterns to social and economic drivers: poverty, low education and unemployment. That suggests interventions beyond the clinic—such as social protection, education and job programs—could reduce the burden of multiple chronic conditions. The authors emphasize the need for tailored interventions and policies that both address the specific medical needs of each cluster and tackle the underlying social determinants of health in this resource-limited setting.