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BACKGROUND Lung cancer is the leading cause of cancer-related death in the United States, with the majority of lung cancer occurrence diagnosed after the disease has already metastasized. Lung cancer screening (LCS) with low-dose computed tomography can diagnose early-stage disease, especially when eligible individuals participate in screening on a yearly basis. Unfortunately, annual adherence has emerged as a challenge for academic and community screening programs, endangering the individual and population health benefits of LCS. Reminder messages have effectively increased adherence rates in breast, colorectal, and cervical cancer screenings but have not been tested with LCS participants who experience unique barriers to screening associated with the stigma of smoking and social determinants of health. OBJECTIVE This research aims to use a theory-informed, multiphase, and mixed methods approach with LCS experts and participants to develop a set of clear and engaging reminder messages to support LCS annual adherence. METHODS In aim 1, survey data informed by the Cognitive-Social Health Information Processing model will be collected to assess how LCS participants process health information aimed at health protective behavior to develop content for reminder messages and pinpoint options for message targeting and tailoring. Aim 2 focuses on identifying themes for message imagery through a modified photovoice activity that asks participants to identify 3 images that represent LCS and then participate in an interview about the selection, likes, and dislikes of each photo. A pool of candidate messages for multiple delivery platforms will be developed in aim 3, using results from aim 1 for message content and aim 2 for imagery selection. The refinement of message content and imagery combinations will be completed through iterative feedback from LCS experts and participants. RESULTS Data collection began in July 2022 and will be completed by May 2023. The final reminder message candidates are expected to be completed by June 2023. CONCLUSIONS This project proposes a novel approach to facilitate adherence to annual LCS through the development of reminder messages that embrace content and imagery representative of the target population directly in the design process. Developing effective strategies to increase LCS adherence is instrumental in achieving optimal LCS outcomes at individual and population health levels. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/46657

Objective Lung cancer screening (LCS) efficacy is highly dependent on adherence to annual screening, but little is known about real-world adherence determinants. We used insurance claims data to examine associations between LCS annual adherence and demographic, comorbidity, health care usage, and geographic factors. Materials and Methods Insurance claims data for all individuals with an LCS low-dose CT scan were obtained from the Colorado All Payer Claims Dataset. Adherence was defined as a second claim for a screening CT 10 to 18 months after the index claim. Cox proportional hazards regression was used to define the relationship between annual adherence and age, gender, insurance type, residence location, outpatient health care usage, and comorbidity burden. Results After exclusions, the final data set consisted of 9,056 records with 3,072 adherent, 3,570 nonadherent, and 2,414 censored (unclassifiable) individuals. Less adherence was associated with ages 55 to 59 (hazard ratio [HR] = 0.80, 99% confidence interval [CI] = 0.67-0.94), 60 to 64 (HR = 0.83, 99% CI = 0.71-0.97), and 75 to 79 (HR = 0.79, 99% CI = 0.65-0.97); rural residence (HR = 0.56, 99% CI = 0.43-0.73); Medicare fee-for-service (HR = 0.45, 99% CI = 0.39-0.51), and Medicaid (HR = 0.50, 99% CI = 0.40-0.62). A significant interaction between outpatient health care usage and comorbidity was also observed. Increased outpatient usage was associated with increased adherence and was most pronounced for individuals without comorbidities. Conclusions This population-based description of LCS adherence determinants provides insight into populations that might benefit from specific interventions targeted toward improving adherence and maximizing LCS benefit. Quantifying population-based adherence rates and understanding factors associated with annual adherence are critical to improving screening adherence and reducing lung cancer death.

Background Lung cancer is the leading cause of cancer-related death in the United States, with the majority of lung cancer occurrence diagnosed after the disease has already metastasized. Lung cancer screening (LCS) with low-dose computed tomography can diagnose early-stage disease, especially when eligible individuals participate in screening on a yearly basis. Unfortunately, annual adherence has emerged as a challenge for academic and community screening programs, endangering the individual and population health benefits of LCS. Reminder messages have effectively increased adherence rates in breast, colorectal, and cervical cancer screenings but have not been tested with LCS participants who experience unique barriers to screening associated with the stigma of smoking and social determinants of health. Objective This research aims to use a theory-informed, multiphase, and mixed methods approach with LCS experts and participants to develop a set of clear and engaging reminder messages to support LCS annual adherence. Methods In aim 1, survey data informed by the Cognitive-Social Health Information Processing model will be collected to assess how LCS participants process health information aimed at health protective behavior to develop content for reminder messages and pinpoint options for message targeting and tailoring. Aim 2 focuses on identifying themes for message imagery through a modified photovoice activity that asks participants to identify 3 images that represent LCS and then participate in an interview about the selection, likes, and dislikes of each photo. A pool of candidate messages for multiple delivery platforms will be developed in aim 3, using results from aim 1 for message content and aim 2 for imagery selection. The refinement of message content and imagery combinations will be completed through iterative feedback from LCS experts and participants. Results Data collection began in July 2022 and will be completed by May 2023. The final reminder message candidates are expected to be completed by June 2023. Conclusions This project proposes a novel approach to facilitate adherence to annual LCS through the development of reminder messages that embrace content and imagery representative of the target population directly in the design process. Developing effective strategies to increase LCS adherence is instrumental in achieving optimal LCS outcomes at individual and population health levels. International Registered Report Identifier (IRRID) DERR1-10.2196/46657

Background Lung cancer screening (LCS) implementation is complicated by the Centers for Medicare and Medicaid Services reimbursement requirements of shared decision-making and tobacco cessation counseling. LCS programs can utilize different structures to meet these requirements, but the impact of programmatic structure on provider behavior and screening outcomes is poorly described. Patients and Methods In a retrospective chart review of 624 patients in a hybrid structure, academic LCS program, we compared characteristics and outcomes of primary care provider (PCP)- and specialist-screened patients. We also assessed the impact of the availability of an LCS specialty clinic and best practice advisory (BPA) on PCP ordering patterns using electronic medical record generated reports. Results During the study period of July 1, 2014 through June 30, 2018, 48% of patients were specialist-screened and 52% were PCP-screened; there were no clinically relevant differences in patient characteristics or screening outcomes between these populations. PCPs demonstrate distinct practice patterns when offered the choice of specialist-driven or PCP-driven screening. Increased exposure to a LCS BPA is associated with increased PCP screening orders. The addition of a nurse navigator into the LCS program increased documentation of shared decision-making and tobacco cessation counseling to > 95% and virtually eliminated screening of ineligible patients. Conclusions Systematic interventions including a BPA and nurse navigator are associated with increased screening and improved program quality, as evidenced by reduced screening of ineligible patients, increased lung cancer risk of the screened population, and improved compliance with LCS guidelines. Individual PCPs demonstrate clear preferences regarding LCS that should be considered in program design.

Rationale- Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk ...

We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some domains; however, implementation of these methods is in early development. Often these methods are only directly compared to existing methods; here we present a framework for assessing the value of a machine learning model by assessing the incremental value. We developed a machine learning model to identify and classify lung nodules and assessed the incremental value added to existing risk prediction models. Multiple external datasets were used for validation. We found that our image model, trained on a dataset from The Cancer Imaging Archive (TCIA), improves upon existing models that are restricted to patient characteristics, but it was inconclusive about whether it improves on models that consider nodule features. Another interesting finding is the variable performance on different datasets, suggesting population generalization with machine learning models may be more challenging than is often considered.