Background. Health information systems for monitoring chronic noncommunicable diseases (NCDs) in South Africa (SA) are relatively less advanced than those for infectious diseases (particularly tuberculosis and HIV) and for maternal and child health. NCDs are now the largest cause of premature mortality owing to exposure to risk factors arising from obesity that include physical inactivity and accessible, cheap but unhealthy diets. The National Strategic Plan for the Prevention and Control of NonCommunicable Diseases 2013 17 developed by the SA National Department of Health outlines targets and monitoring priorities. Objectives. To assess data sources relevant for monitoring NCDs and their risk factors by identifying the strengths and weaknesses, including usability and availability, of surveys and routine systems focusing at national and certain subnational levels. Methods. Publicly available survey and routine data sources were assessed for variables collected, their characteristics, frequency of data collection, geographical coverage and data availability. Results. Survey data sources were found to be quite different in the way data variables are collected, their geographical coverage and also availability, while the main weakness of routine data sources was poor quality of data. Conclusions. To provide a sound basis for monitoring progress of NCDs and related risk factors, we recommend harmonising and strengthening available SA data sources in terms of data quality, definitions, categories used, timeliness, disease coverage and biomarker measurement.

Objective To profile the prevalence of the three body mass index (BMI) categories by sociodemographic characteristics, and to calculate the percentage transitioning (or not) from one BMI category to another, to inform South African health policy for the control of obesity and noncommunicable diseases. Methods We used data from the National Income Dynamics Study, including sociodemographic characteristics and BMI measurements collected in 2008, 2010, 2012, 2014 and 2017. For each data collection wave and each population group, we calculated mean BMI and prevalence by category. We also calculated the percentage making an upwards transition (e.g. from overweight to obese), a downwards transition or remaining within a particular category. We used a multinomial logistic regression model to estimate transition likelihood. Findings Between 2008 and 2017, mean BMI increased by 2.3 kg/m 2 . We calculated an increased prevalence of obesity from 19.7% (3686/18 679) to 23.6% (3412/14 463), with the largest increases in prevalence for those aged 19–24 years and those with at least high school education. The percentages of upwards transitions to overweight or obese categories increased sharply between the ages of 19 and 50 years. Once overweight or obese, the likelihood of transitioning to a normal BMI is low, particularly for women, those of higher age groups, and those with a higher income and a higher level of education. Conclusion In the development of national strategies to control obesity and noncommunicable diseases, our results will allow limited public health resources to be focused on the relevant population groups.

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