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Cost-per-diagnosis as a metric for monitoring cost effectiveness of HIV testing programmes in low income settings in southern Africa: health economic and modelling analysis

  • Andrew N. Phillips
  • , Valentina Cambiano
  • , Fumiyo Nakagawa
  • , Loveleen Bansi-Matharu
  • , David Wilson
  • , Ilesh Jani
  • , Tsitsi Apollo
  • , Mark Sculpher
  • , Timothy Hallett
  • , Cliff Kerr
  • , Joep J. van Oosterhout
  • , Jeffrey W. Eaton
  • , Janne Estill
  • , Brian Williams
  • , Naoko Doi
  • , Frances Cowan
  • , Olivia Keiser
  • , Deborah Ford
  • , Karin Hatzold
  • , Ruanne Barnabas
  • Helen Ayles, Gesine Meyer-Rath, Lisa Nelson, Cheryl Johnson, Rachel Baggaley, Ade Fakoya, Andreas Jahn, Paul Revill
  • University College London
  • Burnet Institute
  • Instituto Nacional de Saude Maputo
  • Ministry of Health and Child Care, Zimbabwe
  • University of York
  • Imperial College London
  • University of Sydney
  • Dignitas International
  • Kamuzu University of Health Sciences
  • University of Geneva
  • University of Bern
  • Stellenbosch University
  • Clinton Health Access Initiative, Inc.
  • Centre for Sexual Health and HIV/AIDS Research
  • PSI Zimbabwe
  • University of Washington
  • Zambart, Zambia
  • University of the Witwatersrand
  • Boston University
  • CDC Uganda
  • World Health Organization
  • The Global Fund
  • Ministry of Health, Malawi

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Introduction: As prevalence of undiagnosed HIV declines, it is unclear whether testing programmes will be cost effective. To guide their HIV testing programmes, countries require appropriate metrics that can be measured. The cost-per-diagnosis is potentially a useful metric.

Methods: We simulated a series of setting-scenarios for adult HIV epidemics and ART programmes typical of settings in southern Africa using an individual-based model and projected forward from 2018 under two policies: (i) a minimum package of “core” testing (i.e. testing in pregnant women, for diagnosis of symptoms, in sex workers, and in men coming forward for circumcision) is conducted, and (ii) “core” testing as above plus “additional-testing”, for which we specify different rates of testing and various degrees to which those with HIV are more likely to test than those without HIV. We also considered a plausible range of unit test costs. The aim was to assess the relationship between cost-per-diagnosis and the incremental cost-effectiveness ratio (ICER) of the additional-testing policy. Discount rate 3%; costs in 2018 $US.

Results: There was a strong graded relationship between the cost-per-diagnosis and the ICER. Overall, the ICER was below $500 per-DALY-averted (the cost effectiveness threshold used in primary analysis) so long as the cost-per-diagnosis was below $315. This threshold cost-per-diagnosis was similar according to epidemic and programmatic features including the prevalence of undiagnosed HIV, the HIV incidence and a measure of HIV programme quality (the proportion of HIV diagnosed people having a viral load <1000 copies/mL). However, restricting to women, additional-testing did not appear cost-effective even at a cost-per-diagnosis of below $50, while restricting to men additional-testing was cost effective up to a cost-per-diagnosis of $585. The threshold cost for testing in men fell to $256 when the cost effectiveness threshold was $300 instead of $500, and to $81 when considering a discount rate of 10% per annum.

Conclusions: For testing programmes in low income settings in southern African there is an extremely strong relationship between the cost-per-diagnosis and the cost per DALY averted, indicating that the cost-per-diagnosis can be used to monitor the cost effectiveness of testing programmes.

Original languageEnglish
Article numbere25325
Pages (from-to)e25325
JournalJournal of the International AIDS Society
Volume22
Issue number7
Early online date9 Jul 2019
DOIs
Publication statusPublished - 22 Jul 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • cost-effectiveness
  • health systems
  • HIV
  • modelling
  • testing

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