Cargando…

Optimal allocation of HIV resources among geographical regions

BACKGROUND: Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the ques...

Descripción completa

Detalles Bibliográficos
Autores principales: Kedziora, David J., Stuart, Robyn M., Pearson, Jonathan, Latypov, Alisher, Dierst-Davies, Rhodri, Duda, Maksym, Avaliani, Nata, Wilson, David P., Kerr, Cliff C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849208/
https://www.ncbi.nlm.nih.gov/pubmed/31718603
http://dx.doi.org/10.1186/s12889-019-7681-5
_version_ 1783469160463859712
author Kedziora, David J.
Stuart, Robyn M.
Pearson, Jonathan
Latypov, Alisher
Dierst-Davies, Rhodri
Duda, Maksym
Avaliani, Nata
Wilson, David P.
Kerr, Cliff C.
author_facet Kedziora, David J.
Stuart, Robyn M.
Pearson, Jonathan
Latypov, Alisher
Dierst-Davies, Rhodri
Duda, Maksym
Avaliani, Nata
Wilson, David P.
Kerr, Cliff C.
author_sort Kedziora, David J.
collection PubMed
description BACKGROUND: Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the question of how resources should be distributed among different geographic regions. METHODS: We describe a novel geographical optimization algorithm, which has been implemented as an extension to the Optima HIV model. This algorithm identifies an optimal funding of services and programs across regions, such as multiple countries or multiple districts within a country. The algorithm consists of three steps: (1) calibrating the model to each region, (2) determining the optimal allocation for each region across a range of different budget levels, and (3) finding the budget level in each region that minimizes the outcome (such as reducing new HIV infections and/or HIV-related deaths), subject to the constraint of fixed total budget across all regions. As a case study, we applied this method to determine an illustrative allocation of HIV program funding across three representative oblasts (regions) in Ukraine (Mykolayiv, Poltava, and Zhytomyr) to minimize the number of new HIV infections. RESULTS: Geographical optimization was found to identify solutions with better outcomes than would be possible by considering region-specific allocations alone. In the case of Ukraine, prior to optimization (i.e. with status quo spending), a total of 244,000 HIV-related disability-adjusted life years (DALYs) were estimated to occur from 2016 to 2030 across the three oblasts. With optimization within (but not between) oblasts, this was estimated to be reduced to 181,000. With geographical optimization (i.e., allowing reallocation of funds between oblasts), this was estimated to be further reduced to 173,000. CONCLUSIONS: With the increasing availability of region- and even facility-level data, geographical optimization is likely to play an increasingly important role in health economic decision making. Although the largest gains are typically due to reallocating resources to the most effective interventions, especially treatment, further gains can be achieved by optimally reallocating resources between regions. Finally, the methods described here are not restricted to geographical optimization, and can be applied to other problems where competing resources need to be allocated with constraints, such as between diseases.
format Online
Article
Text
id pubmed-6849208
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-68492082019-11-15 Optimal allocation of HIV resources among geographical regions Kedziora, David J. Stuart, Robyn M. Pearson, Jonathan Latypov, Alisher Dierst-Davies, Rhodri Duda, Maksym Avaliani, Nata Wilson, David P. Kerr, Cliff C. BMC Public Health Technical Advance BACKGROUND: Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the question of how resources should be distributed among different geographic regions. METHODS: We describe a novel geographical optimization algorithm, which has been implemented as an extension to the Optima HIV model. This algorithm identifies an optimal funding of services and programs across regions, such as multiple countries or multiple districts within a country. The algorithm consists of three steps: (1) calibrating the model to each region, (2) determining the optimal allocation for each region across a range of different budget levels, and (3) finding the budget level in each region that minimizes the outcome (such as reducing new HIV infections and/or HIV-related deaths), subject to the constraint of fixed total budget across all regions. As a case study, we applied this method to determine an illustrative allocation of HIV program funding across three representative oblasts (regions) in Ukraine (Mykolayiv, Poltava, and Zhytomyr) to minimize the number of new HIV infections. RESULTS: Geographical optimization was found to identify solutions with better outcomes than would be possible by considering region-specific allocations alone. In the case of Ukraine, prior to optimization (i.e. with status quo spending), a total of 244,000 HIV-related disability-adjusted life years (DALYs) were estimated to occur from 2016 to 2030 across the three oblasts. With optimization within (but not between) oblasts, this was estimated to be reduced to 181,000. With geographical optimization (i.e., allowing reallocation of funds between oblasts), this was estimated to be further reduced to 173,000. CONCLUSIONS: With the increasing availability of region- and even facility-level data, geographical optimization is likely to play an increasingly important role in health economic decision making. Although the largest gains are typically due to reallocating resources to the most effective interventions, especially treatment, further gains can be achieved by optimally reallocating resources between regions. Finally, the methods described here are not restricted to geographical optimization, and can be applied to other problems where competing resources need to be allocated with constraints, such as between diseases. BioMed Central 2019-11-12 /pmc/articles/PMC6849208/ /pubmed/31718603 http://dx.doi.org/10.1186/s12889-019-7681-5 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Kedziora, David J.
Stuart, Robyn M.
Pearson, Jonathan
Latypov, Alisher
Dierst-Davies, Rhodri
Duda, Maksym
Avaliani, Nata
Wilson, David P.
Kerr, Cliff C.
Optimal allocation of HIV resources among geographical regions
title Optimal allocation of HIV resources among geographical regions
title_full Optimal allocation of HIV resources among geographical regions
title_fullStr Optimal allocation of HIV resources among geographical regions
title_full_unstemmed Optimal allocation of HIV resources among geographical regions
title_short Optimal allocation of HIV resources among geographical regions
title_sort optimal allocation of hiv resources among geographical regions
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849208/
https://www.ncbi.nlm.nih.gov/pubmed/31718603
http://dx.doi.org/10.1186/s12889-019-7681-5
work_keys_str_mv AT kedzioradavidj optimalallocationofhivresourcesamonggeographicalregions
AT stuartrobynm optimalallocationofhivresourcesamonggeographicalregions
AT pearsonjonathan optimalallocationofhivresourcesamonggeographicalregions
AT latypovalisher optimalallocationofhivresourcesamonggeographicalregions
AT dierstdaviesrhodri optimalallocationofhivresourcesamonggeographicalregions
AT dudamaksym optimalallocationofhivresourcesamonggeographicalregions
AT avalianinata optimalallocationofhivresourcesamonggeographicalregions
AT wilsondavidp optimalallocationofhivresourcesamonggeographicalregions
AT kerrcliffc optimalallocationofhivresourcesamonggeographicalregions