A Comparison of South African National HIV Incidence Estimates: A Critical Appraisal of Different Methods

PLoS One. 2015 Jul 31;10(7):e0133255. doi: 10.1371/journal.pone.0133255. eCollection 2015.

Abstract

Background: The interpretation of HIV prevalence trends is increasingly difficult as antiretroviral treatment programs expand. Reliable HIV incidence estimates are critical to monitoring transmission trends and guiding an effective national response to the epidemic.

Methods and findings: We used a range of methods to estimate HIV incidence in South Africa: (i) an incidence testing algorithm applying the Limiting-Antigen Avidity Assay (LAg-Avidity EIA) in combination with antiretroviral drug and HIV viral load testing; (ii) a modelling technique based on the synthetic cohort principle; and (iii) two dynamic mathematical models, the EPP/Spectrum model package and the Thembisa model. Overall, the different incidence estimation methods were in broad agreement on HIV incidence estimates among persons aged 15-49 years in 2012. The assay-based method produced slightly higher estimates of incidence, 1.72% (95% CI 1.38 - 2.06), compared with the mathematical models, 1.47% (95% CI 1.23 - 1.72) in Thembisa and 1.52% (95% CI 1.43 - 1.62) in EPP/Spectrum, and slightly lower estimates of incidence compared to the synthetic cohort, 1.9% (95% CI 0.8 - 3.1) over the period from 2008 to 2012. Among youth aged 15-24 years, a declining trend in HIV incidence was estimated by all three mathematical estimation methods.

Conclusions: The multi-method comparison showed similar levels and trends in HIV incidence and validated the estimates provided by the assay-based incidence testing algorithm. Our results confirm that South Africa is the country with the largest number of new HIV infections in the world, with about 1 000 new infections occurring each day among adults aged 15-49 years in 2012.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Algorithms
  • Female
  • HIV Infections / epidemiology*
  • Humans
  • Incidence
  • Male
  • Middle Aged
  • Models, Theoretical
  • South Africa / epidemiology
  • Young Adult