Rajput Vije Kumar, Dowie Jack, Kaltoft Mette Kjer
Stonydelph Health Centre, Tamworth, UK.
London School of Hygiene and Tropical Medicine.
Stud Health Technol Inform. 2020 Nov 23;275:172-176. doi: 10.3233/SHTI200717.
Population-level studies confirm the existence of significant rates of overdiagnosis and overtreatment in a number of conditions, particularly those for which the screening of asymptomatic individuals is routine. The implication is that the possibility of being overdiagnosed and/or overtreated must be mentioned as a possible harm in generating informed consent and participation from the individual invited to be screened. But how should the rates of such preference-insensitive population-level phenomena be introduced into preference-sensitive individual decision making? Three possible strategies are rejected, including the currently dominant one that involves presenting the rates relevant to overdiagnosis and overtreatment as discrete pieces of information about a single criterion (typically condition-specific mortality). Extensive quotation from a review of cancer decision aids confirms that processing this complex and isolated information is not a practical approach. However, the task is unnecessary, since an outcome-focused multicriteria decision support tool will incorporate the effects of overdiagnosis and overtreatment - along with the effects of any underdiagnosis and undertreatment.
人群层面的研究证实,在许多病症中存在着显著的过度诊断和过度治疗率,尤其是那些对无症状个体进行常规筛查的病症。这意味着,在征得被邀请进行筛查的个体的知情同意并促使其参与时,必须提及被过度诊断和/或过度治疗的可能性,将其作为一种可能的危害。但是,应该如何将这种对偏好不敏感的人群层面现象的发生率引入到对偏好敏感的个体决策中呢?三种可能的策略被否定了,包括当前占主导地位的那种策略,即把与过度诊断和过度治疗相关的发生率作为关于单一标准(通常是特定病症的死亡率)的离散信息呈现出来。对癌症决策辅助工具综述的大量引用证实,处理这种复杂且孤立的信息并非切实可行的方法。然而,这项任务是不必要的,因为以结果为导向的多标准决策支持工具将纳入过度诊断和过度治疗的影响——以及任何漏诊和治疗不足的影响。