Screening for chronic diseases, such as cancer, is an important public health priority, but traditionally only the frequency or rate of screening has received attention. In this work, we study the importance of adhering to recommended screening policies and develop new methodology to better optimize screening policies when adherence is imperfect. We consider a progressive disease model with four states (healthy, undetectable preclinical, detectable preclinical, clinical), and overlay this with a stochastic screening–behavior model using the theory of renewal processes that allows us to capture imperfect adherence to screening programs in a transparent way. We show that decreased adherence leads to reduced efficacy of screening programs, quantified here using elements of the lead time distribution (i.e., the time between screening diagnosis and when diagnosis would have occurred clinically in the absence of screening). Under the assumption of an inverse relationship between prescribed screening frequency and individual adherence, we show that the optimal screening frequency generally decreases with increasing levels of non-adherence. We apply this model to an example in breast cancer screening, demonstrating how accounting for imperfect adherence affects the recommended screening frequency.

筛查癌症等慢性疾病是一项重要的公共卫生优先事项，但传统上只有筛查频率或比率受到关注。在这项工作中，我们研究了遵守推荐的筛查政策的重要性，并开发了新的方法，以便在遵守不完善的情况下更好地优化筛查政策。我们考虑具有四种状态（健康、临床前检测不到、临床前检测可检测、临床）的进行性疾病模型，并使用更新过程理论将其与随机筛查行为模型叠加，使我们能够以透明的方式捕捉对筛查计划的不完全依从性方法。我们表明，依从性降低会导致筛选计划的功效降低，这里使用提前期分布的元素（即，筛查诊断之间的时间和在没有筛查的情况下临床诊断的时间）。在规定的筛查频率与个体依从性之间存在反比关系的假设下，我们发现最佳筛查频率通常随着不依从性水平的增加而降低。我们将此模型应用于乳腺癌筛查的示例，展示了不完全依从性如何影响推荐的筛查频率。