Economic and health implications from earlier detection of HIV infection in the United Kingdom
Authors Zah V, Toumi M
Received 21 September 2015
Accepted for publication 29 December 2015
Published 15 March 2016 Volume 2016:8 Pages 67—74
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Venkata Atluri
Peer reviewer comments 5
Editor who approved publication: Professor Bassel Sawaya
Vladimir Zah,1,2 Mondher Toumi1
1Ecole Doctoral Interdisciplinaire Sciences-Santé (EDISS), University of Lyon, Lyon, France; 2ZRx Outcomes Research Inc., Mississauga, Canada
Purpose: To model the budget and survival impact of implementing interventions to increase the proportion of HIV infections detected early in a given UK population.
Patients and methods: A Microsoft Excel decision model was designed to generate a set of outcomes for a defined population. Survival was modeled on the Collaboration of Observational HIV Epidemiological Research Europe (COHERE) study extrapolated to a 5-year horizon as a constant hazard. Hazard rates were specific to age, sex, and whether detection was early or late. The primary outcomes for each year up to 5 years were: annual costs, numbers of infected cases, hospital admissions, and surviving cases. Three locations in the UK were chosen to model outcomes across a range of HIV prevalence areas: Lambeth, Southwark, and Lewisham (LSL), Greater Manchester Cluster (GMC), and Kent and Medway (K&M).
Results: In LSL, the projected cumulative cost savings over 5 years were £3,210,206 or £5,290,206 when including the value of the 104 life-years saved. Savings were insensitive to transmission rates, but sensitive in direct proportion to the percentage shift from late to early detection. In GMC, savings were in a similar proportion to LSL, but the magnitude was smaller, as a consequence of the lower base-case HIV prevalence. In K&M, with a smaller population and lower HIV prevalence than GMC, savings were commensurately smaller (£733,202 cumulatively over 5 years).
Conclusion: The results strengthen the rationale for implementing increased testing in high prevalence areas. However, in areas of low prevalence, it is unlikely that costs will be returned over a 5-year period.
Keywords: HIV, testing, costs, savings, model
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