Barriers and Facilitators Impacting Lung Cancer Screening Uptake Among Black Veterans: A Qualitative Study

Abstract: Background: Racial disparities in lung cancer screening (LCS) are well established. Black Veterans are among those at the highest risk for developing lung cancer but are less likely to complete LCS. We sought to identify barriers and facilitators to LCS uptake among Black Veterans. Patients and Methods: A qualitative study using semistructured interviews was conducted with 32 Black Veterans to assess for barriers, facilitators, and contextual factors for LCS and strategies to improve screening. Veterans were purposively sampled by age, sex, and LCS participation status (ie, patients who received a low-dose CT [LDCT], patients who contacted the screening program but did not receive an LDCT, and patients who did not connect with the screening program nor receive an LDCT). Interview guides were developed using the Theoretical Domains Framework and Health Belief Model. Data were analyzed using rapid qualitative analysis. Results: Barriers of LCS uptake among Black Veterans include self-reported low LCS knowledge and poor memory, attention, and decision processes associated with the centralized LCS process. Facilitators of LCS uptake among Black Veterans include social/professional role; identity and social influences; perceived susceptibility, threat, and consequences due to smoking status and military or occupational exposures; emotion, behavioral regulation, and intentions; and high trust in providers. Environmental context and resources (eg, transportation) and race and racism serve as contextual factors that did not emerge as having a major impact on LCS uptake. Strategies to improve LCS uptake included increased social messaging surrounding LCS, various forms of information dissemination, LCS reminders, balanced and repeated shared decision-making discussions, and streamlined referrals. Conclusions: We identified addressable barriers and facilitators for LCS uptake among Black Veterans that can help focus efforts to improve disparities in screening. Future studies should explore provider perspectives and test interventions to improve equity in LCS.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.