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Background: Although low-dose, CT–based lung cancer screening (LCS) can decrease lung cancer mortality in high-risk individuals, the process may be complex and pose challenges to patients, particularly those from minority underinsured and uninsured populations. We conducted a randomized controlled trial of telephone-based navigation for LCS within an integrated, urban, safety-net health care system. Patients and Methods: Patients eligible for LCS were randomized (1:1) to usual care with or without navigation at Parkland Health in Dallas, Texas. The primary endpoint was completion of the first 3 consecutive steps in a patient’s LCS process. We explored differences in completion of LCS steps between navigation and usual care groups, controlling for patient characteristics using the chi-square test. Results: Patients (N=447) were randomized to either navigation (n=225) or usual care (n=222). Mean patient age was 62 years, 46% were female, and 69% were racial/ethnic minorities. There was no difference in completion of the first 3 steps of the LCS algorithm between arms (12% vs 9%, respectively; P=.30). For ordered LCS steps, completion rates were higher among patients who received navigation (86% vs 79%; P=.03). The primary reason for step noncompletion was lack of order placement. Conclusions: In this study, lack of order placement was a key reason for incomplete LCS steps. When orders were placed, patients who received navigation had higher rates of completion. Clinical team education and enhanced electronic health record processes to simplify order placement, coupled with patient navigation, may improve LCS in safety-net health care systems.

Background: Large segments of the US population do not receive quality cancer care due to pervasive and systemic inequities, which can increase morbidity and mortality. Multicomponent, multilevel interventions can address inequities and improve care, but only if they reach communities with suboptimal access. Intervention studies often underenroll individuals from historically excluded groups. Methods: The Alliance to Advance Patient-Centered Cancer Care includes 6 grantees across the United States who implemented unique multicomponent, multilevel intervention programs with common goals of reducing disparities, increasing engagement, and improving the quality of care for targeted populations. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework informed the evaluation efforts across sites. Each Alliance site identified their intended populations, which included underrepresented minorities (eg, Black and Latinx persons), individuals who prefer a language other than English, and rural residents. We evaluated the demographic characteristics of participants to determine program reach. Results: Between 2018 and 2020, a total of 2,390 of 5,309 potentially eligible participants were enrolled across the 6 sites. The proportion of enrolled individuals with selected characteristics included 38% (n=908) Black adults, 24% (n=574) Latinx adults, 19% (n=454) preferring a language other than English, and 30% (n=717) rural residents. The proportion of those enrolled who were the intended population was commensurate to the proportion with desired characteristics in those identified as potentially eligible. Conclusions: The grantees met or exceeded enrollments from their intended populations who have been underserved by quality cancer care into patient-centered intervention programs. Intentional application of recruitment/engagement strategies is needed to reach individuals from historically underserved communities.

Background: Recent modifications to low-dose CT (LDCT)–based lung cancer screening guidelines increase the number of eligible individuals, particularly among racial and ethnic minorities. Because these populations disproportionately live in metropolitan areas, we analyzed the association between travel time and initial LDCT completion within an integrated, urban safety-net health care system. Methods: Using Esri’s StreetMap Premium, OpenStreetMap, and the r5r package in R, we determined projected private vehicle and public transportation travel times between patient residence and the screening facility for LDCT ordered in March 2017 through December 2022 at Parkland Memorial Hospital in Dallas, Texas. We characterized associations between travel time and LDCT completion in univariable and multivariable analyses. We tested these associations in a simulation of 10,000 permutations of private vehicle and public transportation distribution. Results: A total of 2,287 patients were included in the analysis, of whom 1,553 (68%) completed the initial ordered LDCT. Mean age was 63 years, and 73% were underrepresented minorities. Median travel time from patient residence to the LDCT screening facility was 17 minutes by private vehicle and 67 minutes by public transportation. There was a small difference in travel time to the LDCT screening facility by public transportation for patients who completed LDCT versus those who did not (67 vs 66 min, respectively; P=.04) but no difference in travel time by private vehicle for these patients (17 min for both; P=.67). In multivariable analysis, LDCT completion was not associated with projected travel time to the LDCT facility by private vehicle (odds ratio, 1.01; 95% CI, 0.82–1.25) or public transportation (odds ratio, 1.14; 95% CI, 0.89–1.44). Similar results were noted across travel-type permutations. Black individuals were 29% less likely to complete LDCT screening compared with White individuals. Conclusions: In an urban population comprising predominantly underrepresented minorities, projected travel time is not associated with initial LDCT completion in an integrated health care system. Other reasons for differences in LDCT completion warrant investigation.