How to do (or not to do)… health resource allocations using constrained mathematical optimization
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How to do (or not to do)… health resource allocations using constrained mathematical optimization. / Stuart, Robyn M.; Fraser-Hurt, Nicole; Shubber, Zara; Vu, Lung; Cheik, Nejma; Kerr, Cliff C.; Wilson, David P.
In: Health Policy and Planning, Vol. 38, No. 1, 2023, p. 122-128.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - How to do (or not to do)… health resource allocations using constrained mathematical optimization
AU - Stuart, Robyn M.
AU - Fraser-Hurt, Nicole
AU - Shubber, Zara
AU - Vu, Lung
AU - Cheik, Nejma
AU - Kerr, Cliff C.
AU - Wilson, David P.
N1 - Publisher Copyright: © The Author(s) 2022. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.
PY - 2023
Y1 - 2023
N2 - Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health systems are to serve people best, then they must operate efficiently and equitably, and appropriate valuation methods are needed to determine how to do this. With the advances in computing power over the past few decades, advanced mathematical optimization algorithms can now be run on personal computers and can be used to provide comprehensive, evidence-based recommendations for policymakers on how to prioritize health spending considering policy objectives, interactions of interventions, real-world system constraints and budget envelopes. Such methods provide an invaluable complement to traditional or extended cost-effectiveness analyses or league tables. In this paper, we describe how such methods work, how policymakers and programme managers can access them and implement their recommendations and how they have changed health spending in the world to date.
AB - Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health systems are to serve people best, then they must operate efficiently and equitably, and appropriate valuation methods are needed to determine how to do this. With the advances in computing power over the past few decades, advanced mathematical optimization algorithms can now be run on personal computers and can be used to provide comprehensive, evidence-based recommendations for policymakers on how to prioritize health spending considering policy objectives, interactions of interventions, real-world system constraints and budget envelopes. Such methods provide an invaluable complement to traditional or extended cost-effectiveness analyses or league tables. In this paper, we describe how such methods work, how policymakers and programme managers can access them and implement their recommendations and how they have changed health spending in the world to date.
KW - cost-effectiveness analysis
KW - Resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85145955809&partnerID=8YFLogxK
U2 - 10.1093/heapol/czac096
DO - 10.1093/heapol/czac096
M3 - Journal article
C2 - 36398991
AN - SCOPUS:85145955809
VL - 38
SP - 122
EP - 128
JO - Health Policy and Planning
JF - Health Policy and Planning
SN - 0268-1080
IS - 1
ER -
ID: 332827034