Browsing by Author "Wilson, David"
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- ItemCost-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(International AIDS Society, 2019) Phillips, Andrew N.; Cambiano, Valentina; Nakagawa, Fumiyo; Bansi-Matharu, Loveleen; Wilson, David; Jani, Ilesh; Apollo, Tsitsi; Sculpher, Mark; Hallett, Timothy; Kerr, Cliff; Van Oosterhout, J.; Eaton, Jeffrey W.; Estill, Janne; Williams, Brian; Doi, Naoko; Cowan, Frances; Keiser, Olivia; Ford, Deborah; Hatzold, Karin; Barnabas, Ruanne; Ayles, Helen; Meyer-Rath, Gesine; Nelson, Lisa; Johnson, Cheryl; Baggaley, Rachel; Fakoya, Ade; Jahn, Andreas; Revill, PaulIntroduction: 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 beyond this (“additionaltesting”), 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. The discount rate used in the base case was 3% per annum (costs in 2018 U.S. dollars). 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 per diagnosis for testing in men to be cost-effective 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.
- ItemHIV treatment as prevention : principles of good HIV epidemiology modelling for public health decision- making in all modes of prevention and evaluation(Public Library of Science (PLOS), 2012-07) Delva, Wim; Wilson, David P.; Abu-Raddad, Laith; Gorgens, Marelize; Wilson, David; Hallett, Timothy B.; Welte, AlexPublic health responses to HIV epidemics have long relied on epidemiological modelling analyses to help prospectively project and retrospectively estimate the impact, cost-effectiveness, affordability, and investment returns of interventions, and to help plan the design of evaluations. But translating model output into policy decisions and implementation on the ground is challenged by the differences in background and expectations of modellers and decision-makers. As part of the PLoS Medicine Collection ‘‘Investigating the Impact of Treatment on New HIV Infections’’—which focuses on the contribution of modelling to current issues in HIV prevention—we present here principles of ‘‘best practice’’ for the construction, reporting, and interpretation of HIV epidemiological models for public health decision-making on all aspects of HIV. Aimed at both those who conduct modelling research and those who use modelling results, we hope that the principles described here will become a shared resource that facilitates constructive discussions about the policy implications that emerge from HIV epidemiology modelling results, and that promotes joint understanding between modellers and decision-makers about when modelling is useful as a tool in quantifying HIV epidemiological outcomes and improving prevention programming.