HIV estimates through 2018: data for decision-making

AIDS. 2019 Dec 15;33 Suppl 3(Suppl 3):S203-S211. doi: 10.1097/QAD.0000000000002321.

Abstract

Background: Global targets call for a 75% reduction in new HIV infections and AIDS deaths between 2010 and 2020. UNAIDS supports countries to measure progress towards these targets. In 2019, this effort resulted in revised national, regional and global estimates reflecting the best available data.

Methods: Spectrum software was used to develop estimates for 170 countries. Country teams from 151 countries developed HIV estimates directly and estimates for an additional 19 country were developed by UNAIDS based on available evidence. 107 countries employed models using HIV prevalence data from sentinel surveillance, routinely collected HIV testing and household surveys while the remaining 63 countries applied models using HIV case surveillance and/or reported AIDS deaths. Model parameters were informed by the UNAIDS Reference Group on Estimates, Modeling and Projections.

Results: HIV estimates were available for 170 countries representing 99% of the global population. An estimated 37.9 million (uncertainty bounds 32.7-44.0 million) people were living with HIV in 2018. There were 1.7 million (1.4-2.3 million) new infections and 770 000 (570 000-1.1 million) AIDS-related deaths. New HIV infections declined in five of eight regions and AIDS deaths were declining in six of eight regions between 2010 and 2018.

Conclusion: The estimates demonstrate progress towards ending the AIDS epidemic by 2030, however, through 2018 declines in new HIV infections and AIDS-related deaths were not sufficient to meet global interim targets. The UNAIDS estimates have made important contributions to guide decisions about the HIV response at global, regional and country level.

MeSH terms

  • Acquired Immunodeficiency Syndrome / epidemiology*
  • Acquired Immunodeficiency Syndrome / mortality*
  • Decision Making
  • Global Health / statistics & numerical data*
  • HIV Infections / epidemiology*
  • HIV Infections / mortality*
  • Humans
  • Models, Theoretical
  • Population Surveillance