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Article

Allocating Treatment Resources for Hepatitis C in the UK: A Constrained Optimization Modelling Approach

by
Ru Han
1,2,*,
Shuyao Liang
1,2,
Clément François
1,2,
Samuel Aballea
1,3,
Emilie Clay
1,2 and
Mondher Toumi
1,2
1
Public Health Department - Research Unit, University of Aix-Marseille, Marseille, France
2
HEOR, Creativ-Ceutical, Paris, France
3
Creativ-Ceutical, HEOR, Rotterdam, The Netherlands
*
Author to whom correspondence should be addressed.
J. Mark. Access Health Policy 2021, 9(1), 1887664; https://doi.org/10.1080/20016689.2021.1887664
Submission received: 24 August 2020 / Revised: 3 February 2021 / Accepted: 4 February 2021 / Published: 25 March 2021

Abstract

Background and objective: Although the treatment of chronic hepatitis C (CHC) has significantly evolved with the introduction of direct-acting antivirals, the treatment uptake rates have been low especially among marginalized groups in the UK, such as people who inject drug (PWID) and men who have sex with men (MSM). Cutting health inequality is a major focus of healthcare agencies. This study aims to identify the optimal allocation of treatment budget for chronic hepatitis CHC among populations and treatments in the UK so that liver-related mortality in patients with CHC is minimized, given the constraint of treatment budget and equity issue. Methods: A constrained optimization modelling of resource allocation for the treatment of CHC was developed in Excel from the perspective of the UK National Health System over a lifetime horizon. The model was designated with the objective function of minimizing liver-related deaths by varying the decision variables, representing the number of patients receiving each treatment (elbasvir-grazoprevir, ombitasvir-paritaprevir-ritonavir-dasabuvir, sofosbuvir-ledipasvir, and pegylated interferon-ribavirin) in each population (the general population, PWID, and MSM). Two main constraints were formulated including treatment budget and the issue of equity. The model was populated with UK local data applying linear programming and underwent internal and external validation. Scenario analyses were performed to assess the robustness of model results. Results: Within the constraints of no additional funding over original spending in status quo and the consideration of the issue of equity among populations, the optimal allocation from the constrained optimization modelling (treating 13,122 PWID, 160 MSM, and 904 general patients with ombitasvir-paritaprevir-ritonavir-dasabuvir) was found to treat 2,430 more patients (relative change: 20.7%) and avert 78 liver-related deaths (relative change: 0.3%) compared with the current allocation. The number of patients receiving treatment increased 4,928 (relative change: 60.1%) among PWID and 42 (relative change: 35.8%) among MSM. Conclusion: The current allocation of treatment budget for CHC is not optimal in the UK. More patients would be treated, and more liver-related deaths would be avoided using a new allocation from a constrained optimization modelling without incurring additional spending and considering the issue of equity.
Keywords: constrained optimization modelling; hepatitis C; resource allocation constrained optimization modelling; hepatitis C; resource allocation

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MDPI and ACS Style

Han, R.; Liang, S.; François, C.; Aballea, S.; Clay, E.; Toumi, M. Allocating Treatment Resources for Hepatitis C in the UK: A Constrained Optimization Modelling Approach. J. Mark. Access Health Policy 2021, 9, 1887664. https://doi.org/10.1080/20016689.2021.1887664

AMA Style

Han R, Liang S, François C, Aballea S, Clay E, Toumi M. Allocating Treatment Resources for Hepatitis C in the UK: A Constrained Optimization Modelling Approach. Journal of Market Access & Health Policy. 2021; 9(1):1887664. https://doi.org/10.1080/20016689.2021.1887664

Chicago/Turabian Style

Han, Ru, Shuyao Liang, Clément François, Samuel Aballea, Emilie Clay, and Mondher Toumi. 2021. "Allocating Treatment Resources for Hepatitis C in the UK: A Constrained Optimization Modelling Approach" Journal of Market Access & Health Policy 9, no. 1: 1887664. https://doi.org/10.1080/20016689.2021.1887664

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