Zamora Bernarda, Towse Adrian
Department of Surgery and Cancer, Imperial College, London, United Kingdom.
Office of Health Economics, London, United Kingdom.
Front Health Serv. 2023 Jan 19;2:936774. doi: 10.3389/frhs.2022.936774. eCollection 2022.
There are increasing numbers of estimates of opportunity cost to inform the setting of thresholds as ceiling cost-per-quality-adjusted life year (QALY) ratios. To understand their ability to inform policy making, we need to understand the degree of uncertainty surrounding these estimates. In particular, do estimates provide sufficient certainty that the current policy "rules" or "benchmarks" need revision? Does the degree of uncertainty around those estimates mean that further evidence generation is required?
We analyse uncertainty and methods from three papers that focus on the use of data from the NHS in England to estimate opportunity cost. All estimate the impact of expenditure on mortality in cross-sectional regression analyses and then translate the mortality elasticities into cost-per-QALY thresholds using the same assumptions. All three discuss structural uncertainty around the regression analysis, and report parameter uncertainty derived from their estimated standard errors. However, only the initial, seminal, paper explores the structural uncertainty involved in moving from the regression analysis to a threshold. We discuss the elements of structural uncertainty arising from the assumptions that underpin the translation of elasticities to thresholds and seek to quantify the importance of some of the effects.
We find several sets of plausible structural assumptions that would place the threshold estimates from these studies within the current National Institute for Health and Care Excellence (NICE) range of £20,000 to £30,000 per QALY. Heterogeneity, an additional source of uncertainty from variability, is also discussed and reported.
Lastly, we discuss how decision uncertainty around the threshold could be reduced, setting out what sort of additional research is required, notably in improving estimates of disease burden and of the impact of health expenditure on quality of life. Given the likely value to policy makers of this research it should be a priority for health system research funding.
为了确定成本效益阈值(如每质量调整生命年的最高成本比率),对机会成本的估计数量日益增多。为了解这些估计在为政策制定提供信息方面的能力,我们需要了解这些估计周围的不确定性程度。特别是,这些估计是否提供了足够的确定性,表明当前的政策“规则”或“基准”需要修订?这些估计周围的不确定性程度是否意味着需要进一步生成证据?
我们分析了三篇论文中的不确定性和方法,这些论文侧重于使用英格兰国民健康服务体系(NHS)的数据来估计机会成本。所有论文都在横断面回归分析中估计了支出对死亡率的影响,然后使用相同的假设将死亡率弹性转化为每质量调整生命年的成本阈值。所有三篇论文都讨论了回归分析中的结构不确定性,并报告了从估计的标准误差得出的参数不确定性。然而,只有最初的开创性论文探讨了从回归分析到阈值转变过程中涉及的结构不确定性。我们讨论了从弹性转化为阈值的假设中产生的结构不确定性因素,并试图量化其中一些影响的重要性。
我们发现了几组合理的结构假设,这些假设会使这些研究中的阈值估计值处于当前国家卫生与临床优化研究所(NICE)每质量调整生命年20,000至30,000英镑的范围内。还讨论并报告了异质性,这是变异性带来的另一个不确定性来源。
最后,我们讨论了如何减少围绕阈值的决策不确定性,列出了需要何种额外研究,特别是在改善疾病负担估计以及卫生支出对生活质量的影响方面。鉴于这项研究对政策制定者可能具有的价值,它应该成为卫生系统研究资金的优先事项。