Using allocative efficiency analysis to inform health benefits package design for progressing towards Universal Health Coverage - Proof-of-concept studies

Jul 19, 2026

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Countries increasingly define health benefits packages (HBPs) as a pathway toward Universal Health Coverage (UHC), while health resources remain constrained. This proof-of-concept work applies allocative efficiency analysis using HIPtool to estimate costs and impacts of alternative HBPs in Armenia, Cote d’Ivoire, and Zimbabwe. The study uses preloaded essential UHC interventions and global burden data, adapted with local inputs. Optimized allocations quantify DALYs averted and inform priority-setting discussions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

Abstract

Background

Methods and findings

Conclusions

Introduction

Public Disclosure Authorized Public Disclosure Authorized PLOS ONE OPEN ACCESS Citation: Fraser-Hurt N, Hou X, Wilkinson T, Duran D, Abou Jaoude GJ, Skordis J, et al. (2021) Using allocative efficiency analysis to inform health benefits package design for progressing towards Universal Health Coverage: Proof-of-concept studies in countries seeking decision support. PLoS ONE 16(11): e0260247 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0260247 Editor: M. Mahmud Khan, University of Georgia, UNITED STATES Received: May 11, 2021 Accepted: November 5, 2021 Published: November 29, 2021 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal.pone.0260247](https://doi.org/10.1371/journal.pone.0260247) [Copyright:](Copyright:) © [2021](2021) Fraser-Hurt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying the Armenia analysis are available in a databook on: RESEARCH ARTICLE Using allocative efficiency analysis to inform health benefits package design for progressing towards Universal Health Coverage: Proof-of-concept studies in countries seeking decision support Nicole Fraser-Hurt1, Xiaohui Hou1 *, Thomas Wilkinson1, Denizhan Duran1, Gerard J. Abou Jaoude2, Jolene Skordis2, Adanna Chukwuma1, Christine Lao Pena1, Opope O. Tshivuila Matala1, Marelize Gorgens1, David P. Wilson3 1 The World Bank Group, Washington, DC, United States of America, 2 University College London Institute for Global Health, London, United Kingdom, 3 Bill & Melinda Gates Foundation, Seattle, Washington, United States of America * [xhou@worldbank.org](xhou@worldbank.org) Abstract Background Countries are increasingly defining health benefits packages (HBPs) as a way of progressing towards Universal Health Coverage (UHC) . Resources for health are commonly constrained, so it is imperative to allocate funds as efficiently as possible. We conducted allocative efficiency analyses using the Health Interventions Prioritization tool (HIPtool) to estimate the cost and impact of potential HBPs in three countries. These analyses explore the usefulness of allocative efficiency analysis and HIPtool in particular, in contributing to priority setting discussions. Methods and findings HIPtool is an open-access and open-source allocative efficiency modelling tool. It is preloaded with publicly available data, including data on the 218 cost-effective interventions comprising the Essential UHC package identified in the 3rd Edition of Disease Control Priorities, and global burden of disease data from the Institute for Health Metrics and Evaluation. For these analyses, the data were adapted to the health systems of Armenia, Cˆote d’Ivoire and Zimbabwe. Local data replaced global data where possible. Optimized resource allocations were then estimated using the optimization algorithm. In Armenia, optimized spending on UHC interventions could avert 26% more disability-adjusted life years (DALYs), but even highly cost-effective interventions are not funded without an increase in the current health budget. In Cˆote d’Ivoire, surgical interventions, maternal and child health and health promotion interventions are scaled up under optimized spending with an estimated 22% increase in DALYs averted–mostly at the primary care level. In Zimbabwe, the estimated gain was even higher at 49% of additional DALYs averted through optimized spending. [https://dataverse.harvard.edu/dataset.xhtml?](https://dataver

FAQs

What problem does the study address regarding Universal Health Coverage?

It addresses how constrained health resources can be allocated efficiently while countries define health benefits packages to progress toward UHC.

What tool and data does the study use to evaluate health benefits packages?

The study uses HIPtool, an open-access and open-source allocative efficiency modeling tool preloaded with essential UHC interventions and global burden of disease data, adapted to each country with local inputs when possible.

What do the optimized allocations show for the three countries?

Optimized spending could avert additional DALYs in all settings: 26% more in Armenia, about 22% more in Cote d’Ivoire (mostly at the primary care level), and 49% more in Zimbabwe.

Jul 19, 2026

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