Choice of time horizon critical in estimating costs and effects of changes to HIV programmes
Authored by Nicky McCreesh, Mark Strong, Richard G White, Ian Vernon, Trevelyan J McKinley, Jeremy E Oakley, Rebecca N Nsubuga, Michael Goldstein, Ioannis Andrianakis, Richard Hayes
Date Published: 2018
DOI: 10.1371/journal.pone.0196480
Sponsors:
Bill and Melinda Gates Foundation
United States National Institutes of Health (NIH)
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Abstract
Background
Uganda changed its antiretroviral therapy guidelines in 2014, increasing
the CD4 threshold for antiretroviral therapy initiation from 350
cells/mu l to 500 cells/mu l. We investigate what effect this change in
policy is likely to have on HIV incidence, morbidity, and programme
costs, and estimate the cost-effectiveness of the change over different
time horizons.
Methods
We used a complex individual-based model of HIV transmission and
antiretroviral therapy scale-up in Uganda. 100 model fits were generated
by fitting the model to 51 demographic, sexual behaviour, and
epidemiological calibration targets, varying 96 input parameters, using
history matching with model emulation. An additional 19 cost and
disability weight parameters were varied during the analysis of the
model results. For each model fit, the model was run to 2030, with and
without the change in threshold to 500 cells/mu l.
Results
The change in threshold led to a 9.7\% (90\% plausible range:
4.3\%-15.0\%) reduction in incidence in 2030, and averted 278,944
(118,452-502,790) DALYs, at a total cost of \$28M (-\$142M to +\$195M).
The cost per disability adjusted life year (DALY) averted fell over
time, from \$3238 (-\$125 to +\$29,969) in 2014 to \$100 (-\$499 to
+\$785) in 2030. The change in threshold was cost-effective (cost
<3xUganda's per capita GDP per DALY averted) by 2018, and highly
cost-effective (cost <Uganda's per capita GDP per DALY averted) by 2022,
for more than 50\% of parameter sets.
Conclusions
Model results suggest that the change in threshold is unlikely to have
been cost-effective to date, but is likely to be highly cost-effective
in Uganda by 2030. The time horizon needs to be chosen carefully when
projecting intervention effects. Large amounts of uncertainty in our
results demonstrates the need to comprehensively incorporate
uncertainties in model parameterisation.
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Africa
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