Inference of Long-Term Screening Outcomes for Individuals with Screening Histories

We develop a probability model for evaluating long-term outcomes due to regular screening that incorporates the effects of prior screening examinations. Previous models assume that individuals have no prior screening examinations at their current ages. Due to current widespread medical emphasis on screening, the consideration of screening histories is essential, particularly in assessing the benefit of future screening examinations given a certain number of previous negative screens. Screening participants are categorized into four mutually exclusive groups: symptom-free-life, no-early-detection, true-early-detection, and overdiagnosis. For each case, we develop models that incorporate a person’s current age, screening history, expected future screening frequency, screening test sensitivity, and other factors, and derive the probabilities of occurrence for the four groups. The probability of overdiagnosis among screen-detected cases is derived and estimated. The model applies to screening for any disease or condition; for concreteness, we focus on female breast cancer and use data from the study conducted by the Health Insurance Plan of Greater New York (HIP) to estimate these probabilities and corresponding credible intervals. The model can provide policy makers with important information regarding ranges of expected lives saved and percentages of true-early-detection and overdiagnosis among the screen-detected cases.本研究构建了一款概率模型，用于评估常规筛查带来的长期结局，该模型纳入了既往筛查检查的影响。既往相关模型均假设，研究对象在当前年龄时未接受过任何既往筛查。鉴于当前医学界对筛查的重视程度日益提升，考虑筛查史已成为必要环节，尤其在针对既往接受过一定次数阴性筛查的人群评估未来筛查收益时。本研究将筛查参与者划分为四组互斥人群：无症状生存组（symptom-free-life）、未早期检出组（no-early-detection）、真早期检出组（true-early-detection）以及过度诊断组（overdiagnosis）。针对每一类人群，本研究构建了纳入个体当前年龄、筛查史、预期未来筛查频率、筛查试验灵敏度及其他相关因素的模型，并推导了四类人群的发生概率。本研究推导并估算了筛查检出病例中的过度诊断概率。该模型适用于任意疾病或病症的筛查研究；为具体说明应用场景，本研究以女性乳腺癌筛查为对象，采用大纽约健康保险计划（Health Insurance Plan of Greater New York, HIP）的研究数据，估算了上述概率及对应的可信区间。该模型可为政策制定者提供重要参考信息，包括预期挽救生命的区间范围，以及筛查检出病例中真早期检出率与过度诊断率的相关数据。