20th International AIDS Conference - Melbourne, Australia


WEAE0105LB - Oral Abstract

Multi-country analysis of the cost implications of HIV treatment scale-up

Presented by Samantha Diamond (United States).

E. Tagar1, T. Bärnighausen2,3, D.E. Bloom2, S. Humair2, A. Jahn4, F. Mwila5, S. Nsanzimana6, V. Okello7, S. Zwane7, K. Callahan1, S. Diamond1, D. Gwinnell1, P. Haimbe8, R. Hurley9, C. Lejeune10, S. Phanitsiri1, A. Sabino11, A. Shields12, F. Walsh13

1Clinton Health Access Initiative, New York, United States, 2Harvard University, Global Health and Population, Boston, United States, 3University of KwaZulu-Natal, Africa Center for Health and Population Studies, KwaZulu-Natal, South Africa, 4Ministry of Health Malawi, Lilongwe, Malawi, 5Ministry of Health Zambia, Lusaka, Zambia, 6Ministry of Health Rwanda, Kigali, Rwanda, 7Ministry of Health Swaziland, Mbabane, Swaziland, 8Clinton Health Access Initiative, Lusaka, Zambia, 9Clinton Health Access Initiative, Addis Ababa, Ethiopia, 10Clinton Health Access Initiative, Mbabane, Swaziland, 11Clinton Health Access Initiative, Kigali, Rwanda, 12Clinton Health Access Initiative, Lilongwe, Malawi, 13Clinton Health Access Initiative, Boston, United States

Background: Scaling up HIV treatment under the 2013 WHO Guidelines will have a significant impact on the epidemic, but there is a lack of evidence on the resources required for implementation. Costs from current HIV programs are not generalizable given the increasing number of patients who are not yet experiencing symptoms. Ministries of Health require additional evidence to make decisions about how and when to increase eligibility and coverage. This research set out to inform decisions in four countries where the Guidelines are being adopted.
Methods: We developed an analytically derived epidemiological model, leveraging prior work, to estimate the number of deaths, new HIV infections, and distribution of patients by disease stage. We built a decision-making tool in Excel to examine the implications of different eligibility scenarios, models of care, retention rates and testing strategies on the costs of treatment scale-up. Secondary cost data were collected and applied in Zambia, Malawi, Rwanda and Swaziland.
Results: In Zambia, Rwanda and Swaziland the costs of treatment and care, testing, pre-ART, male circumcision and condoms at universal access in 2020 under the 2013 Guidelines, accounted for less than 60% of projected resources for HIV on average. Costs exceeded projected resources in Malawi. The incremental cost of universal access under the 2013 Guidelines, when compared to the 2010 Guidelines, ranged from 5% (Swaziland) to 21% (Malawi). This takes into account expected changes in the model of care for treating an increasing number of less complex patients, including task shifting and multiple month prescriptions in some contexts.
Conclusions: Results illustrate that if programs ran efficiently and there was no significant decline in available resources for HIV, scale-up should be affordable in three of four countries. Malawi would however require additional resources. Overall, this supports a shift in policy debates from whether to scale-up treatment to how to do so in the most efficient manner. The tool that we have developed will be used to inform the many critical resource allocation and implementation decisions required as policymakers adopt the 2013 Guidelines.

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