分配治疗资源的丙型肝炎在英国:一个约束优化建模方法。

Ru Han, Shuyao Liang, Clément François, Samuel Aballea, Emilie Clay, Mondher Toumi
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引用次数: 1

摘要

背景和目的:虽然慢性丙型肝炎(CHC)的治疗随着直接作用抗病毒药物的引入有了显著的发展,但治疗的接受率一直很低,特别是在英国的边缘群体,如注射吸毒者(PWID)和男男性行为者(MSM)。减少保健不平等是保健机构的一个主要重点。本研究旨在确定英国慢性肝炎CHC治疗预算在人群和治疗中的最佳分配,以最大限度地降低CHC患者的肝脏相关死亡率,同时考虑到治疗预算的约束和公平性问题。方法:从英国国家卫生系统的角度出发,在Excel中建立了CHC治疗资源配置的约束优化模型。该模型的目标函数是通过改变决策变量来最小化肝脏相关死亡,决策变量代表每个人群(普通人群、PWID和MSM)中接受每种治疗(elbasvir-grazoprevir、ombitasvir-paritaprevir- ritonvir -dasabuvir、sofosbuvir-ledipasvir和聚乙二醇化干扰素-利巴韦林)的患者数量。制定了治疗预算和公平问题两个主要制约因素。该模型采用线性规划填充英国本地数据,并进行了内部和外部验证。进行情景分析以评估模型结果的稳健性。结果:在没有额外资金的限制下,在考虑人群公平问题的情况下,约束优化模型的最佳分配(使用ombitasvir-paritaprevir- ritonvir -dasabuvir治疗13122名PWID, 160名MSM和904名普通患者)发现,与目前的分配相比,治疗了2430名患者(相对变化:20.7%),避免了78例肝脏相关死亡(相对变化:0.3%)。接受治疗的PWID患者增加4928例(相对变化60.1%),MSM患者增加42例(相对变化35.8%)。结论:目前英国CHC的治疗预算分配并不理想。使用约束优化模型的新分配,更多的患者将得到治疗,更多的肝脏相关死亡将避免,而不会产生额外的支出和考虑到公平问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Allocating treatment resources for hepatitis C in the UK: a constrained optimization modelling approach.

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.

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4.90
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