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AIDS. 2019 Jan 16. doi: 10.1097/QAD.0000000000002155. [Epub ahead of print]

Applying the 'no-one worse off' criterion to design Pareto efficient HIV responses in Sudan and Togo.

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Burnet Institute, Melbourne, Victoria, Australia.
Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.
Institute for Global Health.
Departments of Applied Health Research and Mathematics, University College London.
Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
School of Physics, University of Sydney, Sydney, New South Wales.
Monash University, Melbourne, Victoria, Australia.



Globally, there is increased focus on getting the greatest impact from available health funding. However, the pursuit of overall welfare maximization may mean some are left worse off than before. Pareto efficiency takes welfare shifts into account by ruling out funding reallocations that worsen outcomes for any person or group.


Using the Optima HIV model, studies of HIV response efficiency were conducted in Sudan in 2014 and Togo in 2015. In this article, we estimate the welfare maximizing and Pareto efficient allocations for these two national HIV budgets, using data from the original studies.


We estimate that if the 2013 HIV budget for Sudan was annually available to 2020 but with funds reallocated according to the welfare maximizing allocation, a 36% reduction in cumulative new infections could be achieved between 2014 and 2020. We also find that this is Pareto efficient. In Togo, however, we find that it is possible to reduce overall new infections but applying the Pareto efficiency criterion means that shifts in emphases cannot occur in the HIV response without additional resources.


Protecting service coverage for key population groups is not necessarily equivalent to protecting health outcomes. In some cases, requiring Pareto efficiency may reduce the potential for population-wide welfare gains, but this is not always the case.


Pareto efficiency may be an appropriate addition to the quantitative toolset for evaluating HIV responses.

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