Epidemiology of laboratory-confirmed mumps infections in South Africa, 2012–2017: a cross-sectional study

Abstract

Abstract Background Data on the burden of mumps in South Africa are limited and the epidemiology of mumps in this setting is not well understood. We present an analysis of mumps data in South Africa from 2012 to 2017. Methods This cross-sectional study included secondary data on laboratory-confirmed mumps infections from 2012 to 2017, archived at the South African National Health Laboratory Services’ data repository as well as from four private laboratories. Mumps-specific immunoglobulin M (IgM) and/or viral nucleic acid positive results represented acute infections. We used age-specific mid-year population estimates for each study year as denominators when calculating annual cumulative incidence. Seasonality was based on the season that showed a peak in infections. Results Out of 48,580 records obtained from the public and private sectors, 46,713 (96.2%) were from the private sector. Over the study period, there were 7494 acute infections, 7085 (94.5%) of which were recorded in the private sector. Of these 7494 infections, 3924 (52.4%) occurred in males. The proportion of samples tested that were IgM positive was 18.6% (1058/5682) in 2012, 15% (1016/6790) in 2013, 15.8% (1280/8093) in 2014, 15.5% (1384/8944) in 2015, 13.1% (1260/9629) in 2016 and 15.8% (1496/9442) in 2017. The cumulative incidence rate per 100,000 was highest in children between one and 9 years throughout the study period. The cumulative incidence of infections was highest in the Western Cape, Gauteng and the Northern Cape. Infections peaked in June and November. Conclusion Laboratory-confirmed mumps infections predominantly occurred in spring, affecting children below 10 years of age and individuals who were male. There were fewer tests performed in the public sector compared to the private sector. Since only laboratory data was analysed our results represent and underestimate of disease burden. Further studies that include clinical data are required to provide better estimates of disease burden in South Africa.