Publications & Reports

Inferring HIV incidence from case surveillance with CD4+ cell counts.

James Jansson, Cliff C Kerr, Kylie-Ann Mallitt, Jianyun Wu, Richard T Gray, David P Wilson
aKirby Institute, UNSW Australia bComplex Systems Group, School of Physics, University of Sydney, Sydney, New South Wales, Australia.

Abstract

BACKGROUND: In some countries, HIV surveillance is based on case-reporting of newly diagnosed infections. We present a new back-projection method for estimating HIV-incidence trends using individuals' CD4 cell counts at diagnosis. METHODS: On the basis of a review of CD4 cell count distributions among HIV-uninfected people, CD4 cell count following primary infection, and rates of CD4 cell count decline over time among people with HIV, we simulate the expected distribution in time between infection and diagnosis. Applying this to all diagnosed individuals provides a distribution of likely infection times and estimates for population incidence, level of undiagnosed HIV, and the average time from infection to diagnosis each year. We applied this method to the national HIV case surveillance data of Australia for 1983-2013. RESULTS: The estimated number of new HIV infections in Australia in 2013 was 912 (95% uncertainty bound 835-1002). We estimate that 2280 (95% uncertainty bound 1900-2830) people were living with undiagnosed HIV at the end of 2013, corresponding to approximately 9.4% (95% uncertainty bound 7.8-10.1%) of all people living with HIV. With increases in the average CD4 count at diagnosis, the inferred HIV testing rate has been increasing over time and the estimated mean and median times between infection and diagnosis have decreased substantially. However, the estimated mean time between infection and diagnosis is considerably greater than the median, indicating that some people remain undiagnosed for long periods. Differences were found between cases attributable to male homosexual exposure versus other cases. CONCLUSION: This methodology provides a novel way of estimating population incidence by combining diagnosis dates and CD4 cell counts at diagnosis.

Publication

  • Journal: AIDS
  • Published: 31/07/2015
  • Volume: 29
  • Issue: 12
  • Pagination: 1517-1525

Author

Health Issue