Sydney School of Public Health, Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia.
Centre for Longitudinal and Life Course Research, School of Public Health, University of Queensland, Herston, Queensland 4006, Australia.
J Clin Epidemiol. 2022 Aug;148:146-159. doi: 10.1016/j.jclinepi.2022.04.022. Epub 2022 Apr 25.
Methods to quantify overdiagnosis of screen detected cancer have been developed, but methods for quantifying overdiagnosis of noncancer conditions (whether symptomatic or asymptomatic) have been lacking. We aimed to develop a methodological framework for quantifying overdiagnosis that may be used for asymptomatic or symptomatic conditions and used gestational diabetes mellitus as an example of how it may be applied.
We identify two earlier definitions for overdiagnosis, a narrower prognosis-based definition and a wider utility-based definition. Building on the central importance of the concepts of prognostic information and clinical utility of a diagnosis, we consider the following questions: within a target population, do people found to have a disease using one diagnostic strategy but found not to have the disease using another diagnostic strategy (so called 'additional diagnoses'), have an increased risk of adverse clinical outcomes without treatment (prognosis evidence), and/or a decreased risk of adverse outcomes with treatment (utility evidence)?
Using Causal Directed Acyclic Graphs and fair umpires, we illuminate the relationships between diagnostics strategies and the frequency of overdiagnosis. We then use the example of gestational diabetes mellitus to demonstrate how the Fair Umpire framework may be applied to estimate overdiagnosis.
Our framework may be used to quantify overdiagnosis in noncancer conditions (and in cancer conditions) and to guide further studies on this topic.
已经开发出用于量化筛查检出癌症过度诊断的方法，但缺乏用于量化非癌症疾病（无论是有症状还是无症状）过度诊断的方法。我们旨在开发一种用于量化过度诊断的方法框架，该框架可用于无症状或有症状的疾病，并以妊娠糖尿病为例说明其应用方法。
我们确定了过度诊断的两个早期定义，即更狭义的基于预后的定义和更广义的基于效用的定义。基于诊断的预后信息和临床实用性这两个概念的核心重要性，我们考虑以下问题：在目标人群中，使用一种诊断策略发现患有疾病但使用另一种诊断策略未发现患有疾病的人（所谓的“附加诊断”），是否有未经治疗就出现不良临床结局的风险增加（预后证据），以及/或接受治疗后出现不良结局的风险降低（效用证据）？
我们使用因果无环图和公平裁判，阐明了诊断策略与过度诊断频率之间的关系。然后，我们以妊娠糖尿病为例，演示了公平裁判框架如何用于估计过度诊断。
我们的框架可用于量化非癌症疾病（以及癌症疾病）中的过度诊断，并指导该主题的进一步研究。
