UCLA

Lung cancer is the leading cause of cancer-related death in both sexes. Large, randomized clinical trials have demonstrated that low-dose computed tomography screening reduces mortality from lung cancer, as opposed to chest X-ray or no screening, when participants adhere to follow-up recommendations. However, low adherence rates in post-trial clinical lung cancer screening (LCS) programs have been reported across the United States (US). Low adherence to LCS in real-world clinical practice diminishes the mortality benefit of annual screening derived from clinical trials. Thus far, limited studies have examined factors affecting the patient’s decision (not) to adhere to screening guidelines. This dissertation examines the factors that may predict patient non-adherence to LCS recommendations. First, we performed a systematic review and meta-analysis of 24 studies published between 2014 and 2020 that mentioned adherence to Lung-RADS recommendations, identifying factors contributing to adherence rates. Second, using the Carter-Harris conceptual model, which enumerates psychosocial variables (e.g., smoking stigma, cancer fear, cancer fatalism) related to LCS participation and adherence, we examined these variables’ availability and completeness in our medical records. Next, using a subset of variables where data were consistently available, we identified factors of non-adherence over multiple screening time points using logistic regression and mixed effects models. Lastly, we used statistical and machine learning-based methods to examine how well we could predict patient non-adherence using longitudinal data across three screening time points. This dissertation advances our understanding of factors contributing to patient non-adherence to LCS recommendations. It provides a basis for identifying patient groups that could benefit from individualized interventions to improve LCS adherence.