英格兰国家医疗服务体系中等待择期手术对健康的影响:应用于冠状动脉旁路移植术和全髋关节置换术的建模框架。

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2024-07-01 Epub Date: 2024-06-10 DOI:10.1177/0272989X241256639
Naomi Kate Gibbs, Susan Griffin, Nils Gutacker, Adrián Villaseñor, Simon Walker
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引用次数: 0

摘要

简介本研究旨在展示一个实用框架,该框架可用于估算英国国民健康服务系统(NHS)中一系列择期手术的等待时间变化对健康的影响。我们通过对冠状动脉旁路移植术(CABG)和全髋关节置换术(THR)这两种手术的建模来应用这一框架:我们建立了一个马尔可夫模型,以捕捉手术前后的健康状况,包括手术前转入急性 NHS 护理或自费私人护理的可能性。我们估算了按性别和多重贫困指数五分位数组定义的 10 个亚组以及 7 种备选方案的终生质量调整生命年(QALYs)的变化。我们将 18 周作为基线等待时间,这与英国国家医疗服务体系的现行政策一致。该模型尽可能使用常规收集的数据集(医院事件统计、患者报告结果测量和国家统计局死亡率记录)中的数据,并以学术文献作为补充:与 18 周相比,将等待时间延长至 36 周会导致平均折算 QALY 损失,CABG 为 0.034 至 0.043,THR 为 0.193 至 0.291。对于那些生活在较贫困地区的人来说,国家医疗服务系统较长的等待时间对其 QALY 的影响更大,部分原因是较少患者转而接受私人医疗服务:建议的框架适用于两种不同的手术和患者群体。如果将其应用于更多的手术,它可以为决策者提供信息,帮助他们确定候诊名单的优先次序。关于择期手术候诊的常规数据存在一些局限性,主要是缺乏关于仍在候诊的患者的信息:我们提出了一个建模框架,可用于估算英格兰国家医疗服务体系中等待择期手术对健康的影响(以质量调整生命年衡量)。增加全髋关节置换术的等待时间比增加冠状动脉旁路移植术的等待时间造成的健康损失更大。该模型可用于估算对更多手术的影响,为确定减少等待时间活动的优先次序提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Health Impact of Waiting for Elective Procedures in the NHS in England: A Modeling Framework Applied to Coronary Artery Bypass Graft and Total Hip Replacement.

Introduction: The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR).

Methods: We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature.

Results: Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care.

Discussion/conclusion: The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting.

Highlights: We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England.We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG.This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times.

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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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