Uncertainty Quantification in Cost-effectiveness Analysis for Stochastic-based Infectious Disease Models: Insights from Surveillance on Lymphatic Filariasis

Mary Chriselda Antony Oliver, Matthew Graham, Ioanna Manolopoulou, Graham Medley, Lorenzo Pellis, Koen B Pouwels, Matthew Thorpe, Deirdre Hollingsworth
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Abstract

Cost-effectiveness analyses (CEA) typically involve comparing effectiveness and costs of one or more interventions compared to standard of care, to determine which intervention should be optimally implemented to maximise population health within the constraints of the healthcare budget. Traditionally, cost-effectiveness evaluations are expressed using incremental cost-effectiveness ratios (ICERs), which are compared with a fixed willingness-to-pay (WTP) threshold. Due to the existing uncertainty in costs for interventions and the overall burden of disease, particularly with regard to diseases in populations that are difficult to study, it becomes important to consider uncertainty quantification whilst estimating ICERs. To tackle the challenges of uncertainty quantification in CEA, we propose an alternative paradigm utilizing the Linear Wasserstein framework combined with Linear Discriminant Analysis (LDA) using a demonstrative example of lymphatic filariasis (LF). This approach uses geometric embeddings of the overall costs for treatment and surveillance, disability-adjusted lifeyears (DALYs) averted for morbidity by quantifying the burden of disease due to the years lived with disability, and probabilities of local elimination over a time-horizon of 20 years to evaluate the cost-effectiveness of lowering the stopping thresholds for post-surveillance determination of LF elimination as a public health problem. Our findings suggest that reducing the stopping threshold from <1% to <0.5% microfilaria (mf) prevalence for adults aged 20 years and above, under various treatment coverages and baseline prevalences, is cost-effective. When validated on 20% of test data, for 65% treatment coverage, a government expenditure of WTP ranging from $500 to $3,000 per 1% increase in local elimination probability justifies the switch to the lower threshold as cost-effective. Stochastic model simulations often lead to parameter and structural uncertainty in CEA. Uncertainty may impact the decisions taken, and this study underscores the necessity of better uncertainty quantification techniques within CEA for making informed decisions.
基于随机的传染病模型成本效益分析中的不确定性量化:淋巴丝虫病监测的启示
成本效益分析(CEA)通常是将一种或多种干预措施的效果和成本与标准护理进行比较,以确定在医疗预算的限制范围内,应最佳实施哪种干预措施,从而最大限度地提高人口健康水平。传统上,成本效益评估使用增量成本效益比(ICER)来表示,并与固定的支付意愿(WTP)阈值进行比较。由于干预成本和总体疾病负担存在不确定性,特别是对于难以研究的人群中的疾病,因此在估算 ICER 时考虑不确定性量化变得非常重要。为了应对 CEA 中不确定性量化的挑战,我们以淋巴丝虫病(LF)为例,提出了一种利用线性 Wasserstein 框架与线性判别分析(LDA)相结合的替代模式。这种方法利用治疗和监测总成本的几何内嵌、通过量化残疾生活年限造成的疾病负担而避免的残疾调整寿命年 (DALYs),以及在 20 年的时间跨度内消除局部淋巴丝虫病的概率,来评估降低监测后确定消除淋巴丝虫病这一公共卫生问题的终止阈值的成本效益。我们的研究结果表明,在不同的治疗覆盖率和基线流行率条件下,将 20 岁及以上成年人的微丝蚴流行率从 1%降至 0.5%的终止阈值具有成本效益。在对 20% 的测试数据进行验证时,对于 65% 的治疗覆盖率,当地消除概率每增加 1%,政府的 WTP 支出从 500 美元到 3,000 美元不等,这说明改用较低的阈值是符合成本效益的。在成本效益分析中,随机模型模拟通常会导致参数和结构的不确定性。不确定性可能会影响所做的决策,本研究强调了在成本效益分析中采用更好的不确定性量化技术以做出明智决策的必要性。
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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