Differences in lung cancer screening outcomes and follow-up by patient, provider and place-based characteristics in Missouri and Illinois: A cross-sectional study

Differences in lung cancer screening outcomes and follow-up by patient, provider and place-based characteristics in Missouri and Illinois: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differences in lung cancer screening outcomes and follow-up by patient, provider and place-based characteristics in Missouri and Illinois: A cross-sectional study Akila Anandarajah, Vaishnavi Mamillapalle, Benjamin Bowe, Isaac Che Ngang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7745656/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lung cancer remains the leading cause of cancer-related mortality in the United States, yet disparities in lung cancer screening (LCS) outcomes exist and remain understudied, particularly in the Midwestern region. Our objective was to investigate disparities in lung radiology outcomes and follow-up care based on patient, provider, and place-based characteristics (i.e., area deprivation index; ADI). Methods This cross-sectional study used data from the LCS program at Siteman Cancer Center (SCC) in St. Louis, Missouri, from January to December 2023. SCC’s catchment area includes 82 counties in Missouri and Illinois; approximately 15% of the population reside in a rural zip code, and 29% reside in medically underserved areas 80% are White. The study included 1,946 individuals aged 50–80, meeting LCS eligibility criteria based on smoking history and age. Lung radiology findings were assessed as primary outcomes, and timely follow-up adherence (i.e., return for follow-up visit) was analyzed among patients with high-risk findings (Lung-RADS 3 [”Probably Benign”] and 4 [”Suspicious”]) from January to June 2023, requiring follow-up by December 2023. Multivariable logistic regression was conducted, adjusting for patient and provider characteristics and ADI. Results Of the 1,946 individuals who accessed LCS, 57% were Black, 41% White, 1% Asian, and 1% of another race; 54% were male and 46% female. Lung findings were classified as "probably benign/suspicious" (high risk) for 14%. Annual visits were associated with higher likelihood of high-risk scores compared to baseline visits (AOR = 2.10 (1.40–3.15); p < 0.001). Racial differences were noted in the association between provider type and lung outcomes. Among White individuals only, specialist compared to primary care provider referral was associated with increased odds of being high risk (AOR = 1.58 (1.00–2.50); p = 0.048). Sex, insurance, smoking status, park-years and ADI were not associated with lung radiology outcomes. Timely adherence to return follow-up visit among high-risk patients was suboptimal, with only 20.0% returning within 3–6 months for their repeat LCS. Individuals residing in moderate ADI (distress) areas were less likely to have timely follow-up (for high-risk findings) compared to those in high distress (AOR = 0.393, 95% CI = 0.155–1.001, p = 0.050). There were no differences by sex, insurance, smoking status, and pack-years for being classified as high risk. Conclusions SCC’s LCS program successfully captured Black populations and individuals from highly distressed areas, in a predominantly White catchment area. There were no observable race disparities in timely follow-up of high-risk findings, reflecting progress toward equity in access and outcomes. Place-based disparities in follow-up were observed that warrant further characterization and risk assessment to improve follow-up of patients undergoing LCS. Figures Figure 1 Introduction Lung cancer is the leading cause of cancer-related death and second most common cancer in the United States. 1 Lung cancer screening (LCS) enables earlier detection of lung cancer, when the disease is more responsive to treatment and associated with better clinical outcomes. 2,3 In clinical trials, low-dose computed tomography (LDCT) scans significantly reduced lung cancer mortality by 20%. 4–6 Additionally, prior research found that more than half of lung cancers were detected at repeat annual screenings; therefore, adherence to ongoing follow-up screenings is critical for secondary cancer prevention. 7 Based on this evidence, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT scans for high-risk individuals, identified as adults aged 50–80 with a history of smoking at least 20 pack-years, who either currently smoke or quit within the last 15 years. 8 Despite evidence-based guidelines and implementation efforts, LCS usage remains low. Of the 8.5 million current and former smokers eligible for LCS, only 18% have received screening, 9 and adherence to annual re-screening is also low (37–51%). 10 Further, prior research found that only 12% of primary care physicians ordered LCS for any eligible patients in the past year. 11 This reflects multilevel cancer care gaps at the physician and patient levels. Physician ordering of LCS is insufficient, likely due in part to lack of patient-specific data to properly identify and indicate the urgency of LCS, and lack of knowledge about LCS effectiveness or how to discuss benefits and risks with patients. 12,13 Additionally, accessing and using guideline-based care is challenging among patients with lower socioeconomic status, less insurance coverage and lower levels of education. 12,13 In addition, women and Black men are more likely to be ineligible to meet the LCS criteria, being diagnosed at earlier age. 14,15 Additionally, people of low socioeconomic status may be less likely to be eligible for LCS. 16 The USPSTF explicitly identified a need for future research to determine whether LCS benefits differs when expanded to more diverse community settings of 1) racial and ethnic minorities, 2) socially and economically disadvantaged groups with higher smoking rates and lung cancer incidence, and 3) more women being screened. 5,8 LCS radiology findings and follow-up by patient demographics in a clinical setting are under-explored, especially in the Midwestern United States. 5 This paper will examine disparities associated with LCS findings and follow-up care by patient (e.g., race, sex), referring provider (e.g., specialty), and place-based (e.g., area deprivation index, rurality) factors in Missouri and Illinois. We leverage the Conceptual Model for Lung Cancer Screening Participation (Fig. 1 ) which illustrates the multilevel factors at patient, provider, and socio-environmental levels that are associated with participation in lung cancer screening uptake and adherence. 17 To our knowledge, few studies have investigated LCS radiology findings—captured as the Lung CT Screening Reporting and Data System (Lung-RADS) scores—by these patient, provider, and place-based factors to expose and further characterize established inequities in access and outcomes of LCS. We aim to evaluate differences and disparities in lung radiology findings and follow-up care by patient, provider, and place-based factors in Missouri and Illinois. Methods Study Design, Source, and Preparation We conducted a cross-sectional analysis using electronic health record data collected from January to December 2023 for the Siteman Cancer Center’s (SCC) LCS program at Barnes-Jewish Hospital in St. Louis, Missouri. The study involved 1,946 adults aged 50–80 who met LCS eligibility criteria according to USPSTF guidelines, which included a minimum of 20 pack-year smoking history and either being a current smoker or having quit within the past 15 years. SCC is a National Cancer Institute-designated Comprehensive Cancer Center that provides advanced cancer prevention, screening, diagnosis, and treatment services. It serves a diverse population from the St. Louis metropolitan region, extending its reach to urban and rural communities across eastern Missouri and southern Illinois. The racial distribution of the hospital’s catchment is 79.5% non-Hispanic White, 13.7% Black, 2% Asian, and 4.8% other groups. Approximately 3% of the population identifies as Hispanic/Latino ethnicity. SCC also plays a significant role in addressing healthcare needs across a wide socioeconomic spectrum, including medically underserved (29% of the population) and high-risk populations, and 15% reside in a rural zip code. Data Elements Our dataset encompasses patient demographics, smoking history, Lung-RADS scores, insurance provider type, physician type, and indications and frequency of LCS screening. Missing pack-years data were imputed using the mean value of documented smoking history (pack-years) among all eligible individuals in the cohort, preserving the overall distribution, classification of Lung-RADS categories, standardization of smoking status, and categorization of insurance types as Medicare, Medicaid, private, or unknown. In addition to patient-level clinical data collected from January to December 2023, we incorporated place-based variables sourced from external datasets. Specifically, Area Deprivation Index (ADI) scores—used to characterize socioeconomic disadvantage—were obtained from data last updated in 2020 by the Center for Health Disparities Research. 18,19 ADI is a validated metric originally developed by the Health Resources & Services Administration (HRSA) and ranks neighborhoods at the Census block group level based on income, education, employment, and housing conditions. We calculated mean ADI ranks at the ZIP code level and categorized socioeconomic deprivation as high deprivation (above 70), moderate deprivation (40–69) and low deprivation ( 1 – 39 ). Data on place-based characteristics (i.e., urban, rural), categorized by zip codes, were collected from the United States Department of Agriculture (USDA) database—most recently updated on August 17, 2020. 20 ZIP codes were categorized as urban (secondary RUCA [Rural-Urban Commuting Area] scores 1–3) or rural (scores 4–9). For ZIP codes with a secondary RUCA value of 99, we conducted a manual search to determine whether they were urban; otherwise, we labeled them as "Unidentified." Provider specialization was determined by manually assigning a clinical specialty to each provider based on their name. Providers with backgrounds in family medicine, internal medicine, physician assistant, and nurse practitioner were grouped under the category of primary care providers (PCPs). All others, including those not explicitly categorized, were grouped under specialists. This classification allowed analysis comparing outcomes between patients seen by PCPs versus specialists. Outcomes The Lung-RADS is used as a standardized system to report LCS results. Lung-RADS are graded in four categories ranging from negative (category 1) to suspicious (category 4). Higher scores indicate that nodules were found in the lungs that have a higher probability of being lung cancer. 21 Lung-RADS scores that are probably benign ( 3 ) or suspicious ( 4 ) (high-risk) necessitate returning for sooner follow-up LCS than the standard one-year timeframe. Our outcomes of interest were the Lung-RADS score; for those with high-risk Lung-RADS findings, we were interested in whether they had one or multiple visits (i.e., follow-up) within 2023. We stratified results by race, sex and ADI. Individuals with Probably Benign Lung-RADS findings should be scheduled to undergo repeat LCS in 6 months, and those with Suspicious Lung-RADS findings should return at least within 3 months, while those with negative or benign findings can wait until a year for their next screen ( 14 ). We reviewed patient appointment records to identify individuals who required follow-up LCS within 3–6 months based on their Lung-RADS scores, the primary outcome measure for LCS. For the purposes of this study, we defined high-risk patients as those with a Lung-RADS score of 3 or 4 (i.e., probably benign or suspicious findings requiring closer surveillance), and low-risk patients as those with a Lung-RADS score of 1 or 2 (i.e., negative or benign findings). We identified the earliest recorded LCS appointment between January 1 and June 30, 2023, and classified patients with high-risk scores as requiring follow-up within 3–6 months. Focusing on patients with such scores in the first half of 2023 ensured a sufficient 6-month window within our dataset to observe their return for follow-up imaging. We examined records to determine whether the same patients had a subsequent, follow-up LCS visit in 2023. We categorized high-risk patients who completed more than one visit in 2023 as "Multiple Visit" patients, and high-risk patients who had only one visit in 2023, without any follow-up visit, as "Single Visit" patients. Statistical Analysis We conducted descriptive analyses to summarize patient, provider, and place-based characteristics. We calculated frequencies and percentages for categorical variables and mean and standard deviation for pack-years. Age was dichotomized as below and above 65 years, reflecting the Medicare eligibility threshold, which may influence insurance coverage and access to LCS. Multivariable logistic regression was conducted to identify predictors of Lung-RADS outcomes, with the analysis stratified by race and ADI. Statistical significance was assessed at a two-sided alpha level of 0.05. We adjusted for age, smoking status (current, former) and pack-years, insurance type (Medicaid, Medicare, private, unknown), and provider type (e.g., PCP, specialist). Under race, we removed Asian and “Other race” categories from inferential analyses due to very low numbers. Handling Missing Data To ensure the integrity and completeness of the analytical dataset, we addressed missing data systematically. First, 20 records lacking ZIP code information or with ZIP codes outside Illinois (IL) and Missouri (MO) were excluded across the combined patient-level clinical and place-based datasets (i.e., SCC database, Area Deprivation Index, and USDA rurality data) to avoid inconsistencies in location-based analyses. For continuous variables such as smoking history (pack-years), missing values were imputed using the mean value calculated from documented cases within the eligible cohort. This approach preserved the distributional properties of the variable while minimizing bias. Categorical variables were handled through logical imputation or default classification to maintain consistency. For example, missing values in the socioeconomic distress index (ADI) were assigned to the "Low Distress" category to reduce misclassification. All analyses were conducted using SAS Version 9.4 (SAS Institute Inc., Cary, NC). Results Population Characteristics Among the 1,946 individuals (Table 1), 1880 individuals with data, were included in inferential analysis, 53.9% were under 65 years old, and 46.1% were 65 or older. The cohort consisted of 53.6% males and 46.4% females. Over half (56.7%) of the participants identified as Black, 40.9% as White, 1.2% as Asian, and 1.2% as other races. Most patients had Benign (59.1%) and Negative (29.2%) LungRADS category, i.e., 88.3% had Low-risk scores. Patients in the probably benign and suspicious LungRADS category, were 6.1% and 5.5%, respectively, for a total of 11.7% with High-risk scores. Most individuals were current smokers (71.1%), and 28.9% were former smokers. The average number of pack-years smoked was 36.51 (±10.95).The average number of pack-years smoked was 36.51 (±10.95). 52.1% underwent their first ever baseline LCS, while 47.9% attended a repeat annual LCS. A majority (98.3%) resided in urban areas, with only 1.7% from rural regions. Nearly half of the patients (48.8%) lived in areas with high distress, 25.0% in moderate distress, and 26.3% in low distress. In terms of insurance status, 53.1% were covered by Medicare, followed by nearly equal proportions with private insurance (23.0%) and Medicaid (22.9%), while 1.0% had unknown insurance. Most were seen by a PCP (85.0%), while 15.0% were seen by specialists. Unadjusted LCS Outcomes We examined the unadjusted distribution of low-risk (Benign or Negative) and high-risk (Probably Benign or Suspicious) across characteristics using cross-tabulations and chi-square tests (Table 3). Overall, 88.3% of participants had low-risk findings, and 11.7% were categorized as high-risk. Age group: There was a significant association between age group and RADS category (OR = 1.34; 95% CI: 1.00–1.80; p=0.05). Participants aged ≥65 years were more likely to have high risk scores compared to those <65 years. Sex: There was no association between sex and RADS category (p=0.117) Race: There was no association by race (p=0.157), although White patients had a higher proportion of high-risk findings (12.9%) compared to Black patients (10.8%). Insurance: There was no association between insurance and RADS category approached ( private vs Medicaid, p=0.766; and private vs Medicare, p=0.124 ). Screening indication (baseline vs annual): There was a strong and significant association between screening indication and RADS category. Patients undergoing annual screening had a higher odds of high-risk scores compared to those getting their first, baseline screening (OR = 1.80; 95% CI: 1.33–2.42; p < 0.001). Provider type: Provider type was significantly associated with RADS category. Patients referred by specialists were more likely to have high-risk findings compared to those seen by PCPs (OR=1.33; 95% CI:1.00-2.07; p=0.047). Smoking status: There was no association between being a current or former smoker and RADS category (p= 0.85). ADI: There was no association between RADS category and different levels of area level deprivation. Compared to patients in low ADI areas, those in moderate ADI (p=0.278) and high ADI areas (p=0.817) did not differ significantly in their likelihood of being classified as high risk. Adjusted associations for LCS Outcomes Among Black individuals, undergoing an annual (repeat) screen was significantly associated with high-risk scores (AOR = 1.85; 95% CI: 1.32–2.58; p < 0.001). Other variables—including age, sex, provider type, smoking status, insurance, ADI and pack-years—were not significantly associated with high-risk findings in Black individuals. Effect modification for race was significant (p=0.037). Among White individuals, undergoing an annual (repeat) screen was significantly associated with high-risk lung-RADS scores (AOR = 2.10; 95% CI: 1.40–3.15; p < 0.001). Additionally, being seen by a specialist compared to a primary care provider was associated with increased odds of high-risk findings (AOR = 1.58; 95% CI: 1.00–2.50; p = 0.048). Other factors—including age, sex, smoking status, insurance, ADI and pack-years—were not significantly associated with high-risk findings in White individuals. Effect modification for race was borderline significant (p=0.058) Adherence to follow-up for high-risk findings In Table 4, among the 220 patients identified as high-risk based on screening criteria, the majority (80.0%) completed only a single lung cancer screening (LCS) visit, while 20.0% had a timely return for the initial follow-up visit, i.e., within the recommended 3- to 6-month window. The only variable approaching statistical significance was ADI. Individuals residing in moderately distressed areas had lower odds of timely adherence to initial follow-up visit compared to those in highly distressed areas (AOR=0.393; 95% CI: 0.155–1.001, p = 0.050). There was no difference in adherence to multiple visits comparing individuals residing in low vs moderate distress, or those in low vs high distress. Age, sex, insurance, race, smoking, screening indication, provider type and park- years. Discussion This study evaluates Lung-RADS outcomes by patient, provider, and place-based characteristics from a LCS program based in MO and IL. Our analysis revealed statistically significant place-based and provider-specific (among White individuals) differences in LCS radiology findings. We observed that more than half of the patients were Black in this predominantly White catchment area, reflecting progress toward equitable access to the SCC program. Concerningly, we found that 70% of patients were current smokers, emphasizing a greater need for integration of tobacco cessation strategies into LCS programs, and potentially leveraging Lung-RADS findings as an opportunity to educate patients on smoking cessation. The emergence of point-of-care tobacco use treatment facilitated by electronic health records and embedded in routine oncology care visits may offer a targeted strategy to address this gap in care. 22 Among White individuals only, those referred by specialists were more likely to have high-risk findings compared to those referred by PCPs. As this was not the case for Black individuals, it may signal differences in provider referrals by patient characteristics like race. We also found that high area distress was not associated poorer access to follow up care for high-risk findings. We found no significant variations in lung radiology outcomes or follow-up by sex, age, insurance, smoking status, and pack-years. These findings suggest that concerted efforts—potentially including updated USPSTF 2021 guidelines and local cancer center strategies—to enhance access and care for marginalized racial groups may be yielding more equitable LCS outcomes. While there were no direct racial differences in lung-RADS, this does not alleviate concerns around the applicability of Lung-RADS classification to Black populations. 23 A recent meta-analysis showed that Lung-RADS demonstrated significant heterogeneity between study populations in sensitivity and specificity, 24 underscoring the need for more research on Lung-RADS performance in multiethnic populations. Given that Black individuals have higher lung cancer incidence than White individuals at a younger age, our findings of no association between race and likelihood of high-risk findings is interesting and may point to concerns raised about reliability of Lung-RADS in Black and other populations. This may also be explained by our high number of Black individuals attending LCS, as they may have been attending LCS more often. SCC serves a 20.5% minority population, yet 56.7% of participants screened were Black, highlighting the success of SCC’s community-oriented efforts to expand LCS access among the underserved. With regard to provider referrals, when PCPs refer patients rather than specialists, this may encourage earlier LCS with less suspicious results. PCPs can play an important role in encouraging LCS, 25,26 as individuals are much more likely to see a PCP rather than a specialist. Reserving time during a PCP appointment for shared decision making to discuss the risks and benefits of LCS could promote earlier uptake and more consistent adherence to detect lung cancers at more treatable stages. 27 Training and supporting PCPs to refer their patients for LCS could lead to better patient outcomes. 28 However, specialists may be more likely to see sick patients with high-risk scores. We found no association between age and the outcomes. A prior study showed that older patients were more likely to be assigned high-risk results and found no associations with race or sex. 29 The differences in findings may be attributed to the differing patient populations, as the prior study used a nationwide cohort from five different institutions located across the country. We found 14.3% of patients had high-risk scores, which is similar to estimates provided by most other academic and community institutions throughout the United States, ranging from 11.4% in academic centers in North Carolina to 24.6% in an inner-city cohort including federally qualified health centers in Chicago. 30–33 Additionally, when the Lung-RADS classification was retrospectively applied to the National Lung Screening Trial, they had a similar positivity rate of 13.6%. 34 Two of these studies had a majority Black population, similar to the current study. 31,33 This validates our findings and demonstrates that our over- and under-diagnosis rates are similar to other institutions. Our finding that a higher incidence of suspicious findings among individuals returning for annual LCS underscores the importance of encouraging patients to receive regular LCS and establishing potential measures to track patients and incentivize retention in the LCS pathway. This facilitates earlier detection, enabling more treatment options and superior patient outcomes. 5 LCS programs must monitor participants over time and minimize loss of follow-up to be most effective. Longitudinal tracking of multiple lung CT scans may also hold more data that could potentially be used to diagnose lung cancer earlier on. 35 In our LCS program, most patients were Black in a predominantly White catchment area. This is noteworthy, as other programs have struggled with reaching Black patients and those of low socioeconomic status. 36 People with low socioeconomic status are more likely to display fatalistic attitudes, seeing cancer as unpreventable, and are less likely to understand their cancer risk. 6,37 To successfully serve these populations, targeted interventions should be developed to specifically address societal drivers of these disparities. This could be done by directly engaging patients to co-develop culturally competent interventions. 38 SCC patient navigators also guide people through the LCS process. Navigators play an important role in educating patients about LCS, addressing patient concerns, reducing physician burden, and ensuring patients are not lost to follow-up. Implementing navigators has been recommended, especially for vulnerable populations, 39 and has been shown to increase LCS uptake in a randomized controlled trial. 40 Sending targeted invitations to eligible participants in areas of high deprivation may also increase LCS uptake among these populations. 41,42 We found individuals from high distress areas were more likely to return for the recommended follow-up for high-risk findings, possibly due to the influence of a SCC’s outreach strategies and robust patient navigation systems. The strengths of this study include our robust sample size, diverse patient population, and range of multilevel factors (e.g., patient, provider, area-levels) evaluated. Being conducted in an urban academic center in the Midwest, findings may be generalizable to other similar settings with similar population characteristics. However, this study has several limitations. Our cohort included a low number of individuals not identifying as White or Black, including those who identified as Asian or of another race. It is possible that patients with high-risk findings who did not have timely follow-up visits moved to other locations or passed away. Although we adjusted for various factors the possibility of residual confounding remains due to the nature of cross-sectional study designs. Given the relatively small sample size of high-risk patients with category 3 or 4 findings (high-risk), we may have been underpowered to detect significant differences and associations. Another limitation is that follow-up adherence was only analyzed among patients with high-risk findings. Evaluation of follow-up adherence in low-risk( Lung-RADS 1 and 2) patients may have yielded different patterns and associated factors. Conclusions Results Sex There was no association between sex and RADS category (p = 0.117) Race There was no association by race (p = 0.157), although White patients had a higher proportion of high-risk findings (12.9%) compared to Black patients (10.8%). Insurance There was no association between insurance and RADS category approached ( private vs Medicaid, p = 0.766; and private vs Medicare, p = 0.124 ). Smoking status There was no association between being a current or former smoker and RADS category (p = 0.85). ADI There was no association between RADS category and different levels of area level deprivation. Compared to patients in low ADI areas, those in moderate ADI (p = 0.278) and high ADI areas (p = 0.817) did not differ significantly in their likelihood of being classified as high risk. Conclusions Our findings identify patient, provider, and place-based factors that may be associated with LCS outcomes and timely adherence to follow-up care. Further research is needed to understand the contribution and impact of these factors on Lung-RADS scores in risk assessment. SCC’s LCS program successfully captured individuals from highly distressed areas and Black populations, in a predominantly White catchment area. There were no observable race disparities in timely follow-up of high-risk findings, reflecting progress toward equity in access and outcomes. Place-based disparities in follow-up were observed that warrant further characterization and risk assessment to improve follow-up of patients undergoing LCS. Tailoring LCS criteria and risk assessment based on these insights can help clinicians improve lung cancer detection and follow-up efforts for high-risk patients. Ethical approval: We obtained ethical approval from the Washington University Institutional Review Board and Siteman Cancer Center’s Protocol Review and Monitoring Committee. We received a waiver of consent for electronic record data extraction and were have been approved to obtain verbal consent from health providers. Declarations Ethical approval The Washington University Institutional Review Board (IRB) approved this study and granted a waiver of informed consent to access protected health information in the SCC database. Following data extraction from the SCC database, all identifying information was removed before data analysis to ensure participant confidentiality. Funding: This research was supported by the Alvin J. Siteman Cancer Center. We thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, MO., for the Research Program Catalyst Award and for the use of the Siteman Biostatistics and Qualitative Research Shared Resource. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant #P30 CA091842. This project, in part, was supported by The Foundation for Barnes-Jewish Hospital and their generous donors, and by the Washington University Institute of Clinical and Translational Sciences which is, in part, supported by the NIH/National Center for Advancing Translational Sciences (NCATS), CTSA grant #UL1TR002345. Author Contribution BO and AA was responsible for the acquisition, conception, design, analyses, interpretation of the data, and writing of the manuscript. VM and BB contributed to analyses and interpretation of the data, and writing. ICN contributed to the interpretation of data and writing of the manuscript. AR was contributed to interpretation of data, review and writing of the manuscript. All authors reviewed the manuscript and approved it for publication. Data Availability Data cannot be shared publicly because they contain protected health information from patients within the BJC HealthCare system and Washington University School of Medicine. Data are available from the Washington University Human Research Protection Office (HRPO) (contact: [email protected] ) and the Human Data Review Committee (HDRC) (contact: [email protected] ) for researchers who meet the criteria for access to confidential data. References Kratzer TB, Bandi P, Freedman ND et al (2024) Lung cancer statistics, 2023. Cancer 130(8):1330–1348. 10.1002/cncr.35128 Blandin Knight S, Crosbie PA, Balata H, Chudziak J, Hussell T, Dive C (2017) Progress and prospects of early detection in lung cancer. Open Biol 7(9):170070. 10.1098/rsob.170070 Potter AL, Rosenstein AL, Kiang MV et al (2022) Association of computed tomography screening with lung cancer stage shift and survival in the United States: quasi-experimental study. BMJ Published online March 30:e069008. 10.1136/bmj-2021-069008 de Koning HJ, van der Aalst CM, de Jong PA et al (2020) Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med 382(6):503–513. 10.1056/NEJMoa1911793 Jonas DE, Reuland DS, Reddy SM et al (2021) Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 325(10):971. 10.1001/jama.2021.0377 Aberle DR, Abtin F, Brown K (2013) Computed Tomography Screening for Lung Cancer: Has It Finally Arrived? Implications of the National Lung Screening Trial. J Clin Oncol 31(8):1002–1008. 10.1200/JCO.2012.43.3110 Hirsch EA, New ML, Brown SP, Barón AE, Malkoski SP (2019) Patient Reminders and Longitudinal Adherence to Lung Cancer Screening in an Academic Setting. Ann Am Thorac Soc 16(10):1329–1332. 10.1513/AnnalsATS.201902-152RL Potter AL, Bajaj SS, Yang CFJ (2021) The 2021 USPSTF lung cancer screening guidelines: a new frontier. Lancet Respir Med 9(7):689–691 Bandi P, Star J, Ashad-Bishop K, Kratzer T, Smith R, Jemal A (2024) Lung Cancer Screening in the US, 2022. JAMA Intern Med 184(8):882. 10.1001/jamainternmed.2024.1655 Cattaneo SM, Meisenberg BR, Geronimo MCM, Bhandari B, Maxted JW, Brady-Copertino CJ (2018) Lung Cancer Screening in the Community Setting. Ann Thorac Surg 105(6):1627–1632. 10.1016/j.athoracsur.2018.01.075 Lewis JA, Petty WJ, Tooze JA et al (2015) Low-Dose CT Lung Cancer Screening Practices and Attitudes among Primary Care Providers at an Academic Medical Center. Cancer Epidemiol Biomarkers Prev 24(4):664–670. 10.1158/1055-9965.EPI-14-1241 Rankin NM, McWilliams A, Marshall HM (2020) Lung cancer screening implementation: Complexities and priorities. Respirology 25(S2):5–23. 10.1111/resp.13963 Draucker CB, Rawl SM, Vode E, Carter-Harris L (2019) Understanding the decision to screen for lung cancer or not: A qualitative analysis. Health Expect 22(6):1314–1321. 10.1111/hex.12975 Fiscella K, Winters P, Farah S, Sanders M, Mohile SG (2015) Do Lung Cancer Eligibility Criteria Align with Risk among Blacks and Hispanics? Chang JS, ed. PLOS ONE . ;10(11):e0143789. 10.1371/journal.pone.0143789 Jemal A, Miller KD, Ma J et al (2018) Higher Lung Cancer Incidence in Young Women Than Young Men in the United States. N Engl J Med 378(21):1999–2009. 10.1056/NEJMoa1715907 Han SS, Chow E, Ten Haaf K et al (2020) Disparities of National Lung Cancer Screening Guidelines in the US Population. JNCI J Natl Cancer Inst 112(11):1136–1142. 10.1093/jnci/djaa013 Carter-Harris L, Davis LL, Rawl SM (2016) Lung Cancer Screening Participation: Developing a Conceptual Model to Guide Research. Res Theory Nurs Pract 30(4):333–352. 10.1891/1541-6577.30.4.333 Kind AJH, Buckingham WR (2018) Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas. N Engl J Med 378(26):2456–2458. 10.1056/NEJMp1802313 Landscape of Area-Level Deprivation Measures and Other Approaches to Account for Social Risk and Social Determinants of Health in Health Care Payments. ASPE. September 26 (2022) Accessed September 25, 2025. http://aspe.hhs.gov/reports/area-level-measures-account-sdoh Rural-Urban Commuting Area Codes | Economic Research Service. Accessed September 26 (2025) https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes Christensen J, Prosper AE, Wu CC et al (2024) ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. J Am Coll Radiol 21(3):473–488. 10.1016/j.jacr.2023.09.009 Ramsey AT, Baker TB, Stoneking F et al (2022) Increased Reach and Effectiveness With a Low-Burden Point-of-Care Tobacco Treatment Program in Cancer Clinics. J Natl Compr Canc Netw 20(5):488–495e4. 10.6004/jnccn.2021.7333 Yang R, Cheung MC, Byrne MM et al (2010) Do racial or socioeconomic disparities exist in lung cancer treatment? Cancer 116(10):2437–2447. 10.1002/cncr.24986 Mao Y, Cai J, Heuvelmans MA et al (2023) Performance of Lung-RADS in different target populations: a systematic review and meta-analysis. Eur Radiol 34(3):1877–1892. 10.1007/s00330-023-10049-9 Peterson EB, Ostroff JS, DuHamel KN et al (2016) Impact of provider-patient communication on cancer screening adherence: A systematic review. Prev Med 93:96–105. 10.1016/j.ypmed.2016.09.034 Emery JD, Shaw K, Williams B et al (2014) The role of primary care in early detection and follow-up of cancer. Nat Rev Clin Oncol 11(1):38–48. 10.1038/nrclinonc.2013.212 Richards TB, White MC, Caraballo RS (2014) Lung Cancer Screening with Low-Dose Computed Tomography for Primary Care Providers. Prim Care Clin Off Pract 41(2):307–330. 10.1016/j.pop.2014.02.007 Kanodra NM, Pope C, Halbert CH, Silvestri GA, Rice LJ, Tanner NT (2016) Primary Care Provider and Patient Perspectives on Lung Cancer Screening. A Qualitative Study. Ann Am Thorac Soc 13(11):1977–1982. 10.1513/AnnalsATS.201604-286OC Burnett-Hartman AN, Carroll NM, Honda SA et al (2022) Community-based Lung Cancer Screening Results in Relation to Patient and Radiologist Characteristics: The PROSPR Consortium. Ann Am Thorac Soc 19(3):433–441. 10.1513/AnnalsATS.202011-1413OC Henderson LM, Bacchus L, Benefield T, Velasquez RH, Rivera MP (2020) Rates of positive lung cancer screening examinations in academic versus community practice. Transl Lung Cancer Res 9(4):1528–1532. 10.21037/tlcr-19-673 Guichet PL, Liu BY, Desai B, Surani Z, Cen SY, Lee C (2018) Preliminary Results of Lung Cancer Screening in a Socioeconomically Disadvantaged Population. Am J Roentgenol 210(3):489–496. 10.2214/AJR.17.18853 Simmerman EL, Thomson NB, Dillard TA et al (2017) Free Lung Cancer Screening Trends Toward a Twofold Increase in Lung Cancer Prevalence in the Underserved Southeastern United States. South Med J 110(3):188–194. 10.14423/SMJ.0000000000000619 Pasquinelli MM, Kovitz KL, Koshy M et al (2018) Outcomes From a Minority-Based Lung Cancer Screening Program vs the National Lung Screening Trial. JAMA Oncol 4(9):1291. 10.1001/jamaoncol.2018.2823 Pinsky PF, Gierada DS, Black W et al (2015) Performance of Lung-RADS in the National Lung Screening Trial: A Retrospective Assessment. Ann Intern Med 162(7):485–491. 10.7326/M14-2086 Paez R, Kammer MN, Balar A et al (2023) Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep 13(1):6157. 10.1038/s41598-023-33098-y Sosa E, D’Souza G, Akhtar A et al (2021) Racial and socioeconomic disparities in lung cancer screening in the United States: A systematic review. CA Cancer J Clin 71(4):299–314. 10.3322/caac.21671 Quaife SL, Marlow LAV, McEwen A, Janes SM, Wardle J (2017) Attitudes towards lung cancer screening in socioeconomically deprived and heavy smoking communities: informing screening communication. Health Expect 20(4):563–573. 10.1111/hex.12481 Harper LJ, Kidambi P, Kirincich JM, Thornton JD, Khatri SB, Culver DA (2023) Health Disparities. Chest 164(1):179–189. 10.1016/j.chest.2023.02.033 Shusted CS, Barta JA, Lake M et al (2019) The Case for Patient Navigation in Lung Cancer Screening in Vulnerable Populations: A Systematic Review. Popul Health Manag 22(4):347–361. 10.1089/pop.2018.0128 Baggett TP, Sporn N, Barbosa Teixeira J et al (2024) Patient Navigation for Lung Cancer Screening at a Health Care for the Homeless Program: A Randomized Clinical Trial. JAMA Intern Med 184(8):892. 10.1001/jamainternmed.2024.1662 Goodley P, Balata H, Alonso A et al (2024) Invitation strategies and participation in a community-based lung cancer screening programme located in areas of high socioeconomic deprivation. Thorax 79(1):58–67. 10.1136/thorax-2023-220001 Rivera MP, Katki HA, Tanner NT et al (2020) Addressing Disparities in Lung Cancer Screening Eligibility and Healthcare Access. An Official American Thoracic Society Statement. Am J Respir Crit Care Med 202(7):e95–e112. 10.1164/rccm.202008-3053ST Tables Table 1 Descriptive Characteristics Variable Category N = 1946 (%) Age Above 65 897 (46.1) Below 65 1049 (53.9) Sex Female 903 (46.4) Male 1043 (53.6) Insurance Medicaid 445 (22.9) Medicare 1034 (53.1) Private 447 (23.0) Unknown 20 (1.0) Race Black 1104 (56.7) White 795 (40.9) Asian 24 (1.2) Other 23 (1.2) Smoking Status Current 1384 (71.1) Former 562 (28.9) Indication for LCS evaluation Annual LCS 933 (47.9) Baseline LCS 1013 (52.1) Rural/Urban Rural 34 (1.7) Urban 1912 (98.3) Provider Type PCP 1654 (85.0) Specialist 292 (15.0) Lung-RADS Category 1 = Negative 568 (29.2) 2 = Benign 1151 (59.1) 3 = Probably Benign 119.0 (6.1) 4 = Suspicious 108.0 (5.6) ADI High Distress (70–100) 949.0 (48.8) Moderate Distress (40–69) 486 (25.0) Low Distress ( 1 – 39 ) 511 (26.3) Pack-Years (Mean, SD) 36.51 ± 10.95 Abbreviations: PCP = primary care provider, RUCA = Rural-Urban Commuting Area, SD = standard deviation, ADI = Area deprivation index, LCS = Lung Cancer Screening, LungRADS = Lung Cancer Screening Reporting and Data System Table 2 Unadjusted distribution of low- and high-risk Lung RADS findings by patient characteristics (N = 1880*) Characteristic Category Low Risk Score (N = 1660) n (%) High Risk Score (N = 220) n (%) OR (95%CI) p-value Age Group < 65 901 (89.7) 104 (10.3) ≥ 65 759 (86.7) 116 (13.3) 1.34 (1.00-1.80) 0.050 Sex Female 795 (89.5) 93 (10.5) Male 865 (87.2) 127 (12.8) 0.80 (0.60–1.06) 0.117 Insurance Private 389 (90) 43 ( 10 ) Medicaid 388 (89.4) 46 (10.6) 1.07 (0.71–1.62) 0.766 Medicare 883 (87.1) 131 (12.9) 1.33 (0.93–1.92) 0.124 Race Black 981 (89.2) 119 (10.8) White 679 (87.1) 101 (12.9) 0.90 (0.67–1.21) 0.157 Indication Annual 766 (85) 135 ( 15 ) Baseline 894 (91.3) 85 (8.7) 1.80 (1.33–2.42) < 0.001 Provider type PCP 1420 (88.9) 177 (11.1) Specialist 240 (84.8) 43 (15.2) 0.70 (1.49–0.99) 0.047 Smoking status Current 1182 (88.2) 158 (11.8) Former 478 (88.5) 62 (11.5) 1.03 (0.75–1.42) 0.850 Area Deprivation Index (ADI) Low distress 438 (88.7) 56 (11.3) Moderate distress 402 (86.5) 63 (13.5) 1.23 (0.85–1.77) 0.278 High distress 820 (89) 101 ( 11 ) 0.96 (0.69–1.34) 0.817 *Excludes Race categories with small N (Asian and Other) and individuals with missing data. Table 3 Adjusted odds of being in the high-risk category by patient, provider, and place-based characteristics (N = 1880) Black (N = 1093) White (N = 787) Interaction Variable AOR (95% CI) p-value AOR (95% CI) p-value p-value Age (< 65 vs ≥ 65) 0.85 (0.58–1.25) 0.389 0.78 (0.50–1.22) 0.276 0.213 Sex (Female vs Male) 0.83 (0.58–1.18) 0.297 0.77 (0.50–1.18) 0.229 0.342 Insurance: Private (ref) 1.00 1.00 Medicaid 0.89 (0.57–1.38) 0.61 0.84 (0.49–1.44) 0.524 0.408 Medicare 1.16 (0.76–1.77) 0.492 1.28 (0.81–2.03) 0.295 0.312 Indication (Annual vs Baseline) 1.85 (1.32–2.58) < 0.001 2.10 (1.40–3.15) < 0.001 0.037 Provider (Specialist vs PCP) 1.32 (0.87–2.01) 0.188 1.58 (1.00–2.50) 0.048 0.058 Smoking Status (Current vs Former) 1.06 (0.72–1.57) 0.776 1.02 (0.63–1.65) 0.927 0.723 ADI: Low (ref) 1.00 1.00 Moderate 1.20 (0.78–1.85) 0.411 1.27 (0.76–2.13) 0.357 0.452 High 1.09 (0.71–1.66) 0.687 1.15 (0.68–1.95) 0.587 0.384 Pack-Years 1.01 (0.99–1.02) 0.312 1.01 (0.98–1.03) 0.372 0.309 Abbreviations: AOR = adjusted odds ratio, PCP = primary care provider, ADI = Area deprivation index Low-risk score (reference category- Negative or Benign Lung RADS): No nodules or nodules with benign characteristics. Routine annual screening is recommended. High-risk score (Probably Benign- requiring a 6-month follow-up CT, or Suspicious Lung RADS - requiring a follow-up within 3 months). All AORs are adjusted for the following covariates: age group, sex, insurance type, screening indication, provider type, smoking status, Area Deprivation Index (ADI), pack-years with effect modification assessed for race. Moderate vs High ADI was not significantly associated high-risk scores (p = 0.468). Statistically significant associations are those with p < 0.05. Effect modification for race was only significant for screening indication (p-value: 0.037)—the association between screening indication and high risk score was higher in White individuals—and provider type—the association between provider and high risk score was more pronounced and significant in White individuals. Table 4 Adherence to multiple visits for individuals with high-risk scores (N = 220) Variable Comparison OR (95% CI) AOR (95% CI) p-value* Age group < 65 vs ≥ 65 1.229 (0.632–2.392) 0.852 (0.383–1.896) 0.695 Sex Female vs Male 1.176 (0.605–2.286) 0.959 (0.472–1.951) 0.909 Insurance Medicaid vs Medicare 1.412 (0.723–2.759) 1.487 (0.576–3.835) 0.412 Private vs Medicare 1.412 (0.723–2.759) 1.157 (0.422–3.172) 0.777 Race Black vs White 1.412 (0.723–2.759) 0.924 (0.432–1.979) 0.839 Smoking Current vs Former 0.704 (0.347–1.429) 0.687 (0.324–1.458) 0.328 Screening indication Annual vs Baseline 1.449 (0.718–2.924) 1.338 (0.643–2.786) 0.436 Provider Specialist vs PCP 0.570 (0.264–1.230) 1.568 (0.687–3.582) 0.285 ADI Low vs High — 0.537 (0.214–1.347) 0.185 Moderate vs High — 0.393 (0.155–1.001) 0.050 Moderate vs Low 0.750 (0.342–1.642) 0.72 (0.27–1.89) 0.522 Park-years 1.01 (0.983–1.042) 1.00 (0.976–1.041) 0.441 * p-values for race-based interaction effects OR: Odds rations, AOR: Adjusted odds ratios (AOR), 95% CI: 95% confidence intervals (CI), PCP: Primary Care Provider, ADI: Area Deprivation Index, Abbreviations PCP = primary care provider, RUCA = Rural-Urban Commuting Area, SD = standard deviation, ADI = Area deprivation index, LCS = Lung Cancer Screening, LungRADS = Lung Cancer Screening Reporting and Data System Additional Declarations No competing interests reported. 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01:12:20","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143672,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7745656/v1/a00428ad7fd407bb218a5efb.html"},{"id":92682331,"identity":"a54a1de1-e9e5-4cb2-9006-4b67069066c0","added_by":"auto","created_at":"2025-10-03 01:12:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117852,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model of Lung Cancer Screening Participation \u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7745656/v1/4f9a29337e038e923e827cf8.png"},{"id":103504269,"identity":"4f6a611c-0e25-4315-b820-f76f0b7ca7e9","added_by":"auto","created_at":"2026-02-26 13:18:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1055535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7745656/v1/ac4cd73d-3c71-4158-88eb-9797460365d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differences in lung cancer screening outcomes and follow-up by patient, provider and place-based characteristics in Missouri and Illinois: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related death and second most common cancer in the United States.\u003csup\u003e1\u003c/sup\u003e Lung cancer screening (LCS) enables earlier detection of lung cancer, when the disease is more responsive to treatment and associated with better clinical outcomes.\u003csup\u003e2,3\u003c/sup\u003e In clinical trials, low-dose computed tomography (LDCT) scans significantly reduced lung cancer mortality by 20%.\u003csup\u003e4\u0026ndash;6\u003c/sup\u003e Additionally, prior research found that more than half of lung cancers were detected at repeat annual screenings; therefore, adherence to ongoing follow-up screenings is critical for secondary cancer prevention.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eBased on this evidence, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT scans for high-risk individuals, identified as adults aged 50\u0026ndash;80 with a history of smoking at least 20 pack-years, who either currently smoke or quit within the last 15 years.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e Despite evidence-based guidelines and implementation efforts, LCS usage remains low. Of the 8.5\u0026nbsp;million current and former smokers eligible for LCS, only 18% have received screening,\u003csup\u003e9\u003c/sup\u003e and adherence to annual re-screening is also low (37\u0026ndash;51%).\u003csup\u003e10\u003c/sup\u003e Further, prior research found that only 12% of primary care physicians ordered LCS for any eligible patients in the past year.\u003csup\u003e11\u003c/sup\u003e This reflects multilevel cancer care gaps at the physician and patient levels. Physician ordering of LCS is insufficient, likely due in part to lack of patient-specific data to properly identify and indicate the urgency of LCS, and lack of knowledge about LCS effectiveness or how to discuss benefits and risks with patients.\u003csup\u003e12,13\u003c/sup\u003e Additionally, accessing and using guideline-based care is challenging among patients with lower socioeconomic status, less insurance coverage and lower levels of education.\u003csup\u003e12,13\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn addition, women and Black men are more likely to be ineligible to meet the LCS criteria, being diagnosed at earlier age.\u003csup\u003e14,15\u003c/sup\u003e Additionally, people of low socioeconomic status may be less likely to be eligible for LCS.\u003csup\u003e16\u003c/sup\u003e The USPSTF explicitly identified a need for future research to determine whether LCS benefits differs when expanded to more diverse community settings of 1) racial and ethnic minorities, 2) socially and economically disadvantaged groups with higher smoking rates and lung cancer incidence, and 3) more women being screened.\u003csup\u003e5,8\u003c/sup\u003e LCS radiology findings and follow-up by patient demographics in a clinical setting are under-explored, especially in the Midwestern United States.\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThis paper will examine disparities associated with LCS findings and follow-up care by patient (e.g., race, sex), referring provider (e.g., specialty), and place-based (e.g., area deprivation index, rurality) factors in Missouri and Illinois. We leverage the \u003cem\u003eConceptual Model for Lung Cancer Screening Participation\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) which illustrates the multilevel factors at patient, provider, and socio-environmental levels that are associated with participation in lung cancer screening uptake and adherence.\u003csup\u003e17\u003c/sup\u003e To our knowledge, few studies have investigated LCS radiology findings\u0026mdash;captured as the Lung CT Screening Reporting and Data System (Lung-RADS) scores\u0026mdash;by these patient, provider, and place-based factors to expose and further characterize established inequities in access and outcomes of LCS. We aim to evaluate differences and disparities in lung radiology findings and follow-up care by patient, provider, and place-based factors in Missouri and Illinois.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design, Source, and Preparation\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional analysis using electronic health record data collected from January to December 2023 for the Siteman Cancer Center\u0026rsquo;s (SCC) LCS program at Barnes-Jewish Hospital in St. Louis, Missouri. The study involved 1,946 adults aged 50\u0026ndash;80 who met LCS eligibility criteria according to USPSTF guidelines, which included a minimum of 20 pack-year smoking history and either being a current smoker or having quit within the past 15 years. SCC is a National Cancer Institute-designated Comprehensive Cancer Center that provides advanced cancer prevention, screening, diagnosis, and treatment services. It serves a diverse population from the St. Louis metropolitan region, extending its reach to urban and rural communities across eastern Missouri and southern Illinois. The racial distribution of the hospital\u0026rsquo;s catchment is 79.5% non-Hispanic White, 13.7% Black, 2% Asian, and 4.8% other groups. Approximately 3% of the population identifies as Hispanic/Latino ethnicity. SCC also plays a significant role in addressing healthcare needs across a wide socioeconomic spectrum, including medically underserved (29% of the population) and high-risk populations, and 15% reside in a rural zip code.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Elements\u003c/h3\u003e\n\u003cp\u003eOur dataset encompasses patient demographics, smoking history, Lung-RADS scores, insurance provider type, physician type, and indications and frequency of LCS screening. Missing pack-years data were imputed using the mean value of documented smoking history (pack-years) among all eligible individuals in the cohort, preserving the overall distribution, classification of Lung-RADS categories, standardization of smoking status, and categorization of insurance types as Medicare, Medicaid, private, or unknown.\u003c/p\u003e\u003cp\u003eIn addition to patient-level clinical data collected from January to December 2023, we incorporated place-based variables sourced from external datasets. Specifically, Area Deprivation Index (ADI) scores\u0026mdash;used to characterize socioeconomic disadvantage\u0026mdash;were obtained from data last updated in 2020 by the Center for Health Disparities Research.\u003csup\u003e18,19\u003c/sup\u003e ADI is a validated metric originally developed by the Health Resources \u0026amp; Services Administration (HRSA) and ranks neighborhoods at the Census block group level based on income, education, employment, and housing conditions. We calculated mean ADI ranks at the ZIP code level and categorized socioeconomic deprivation as high deprivation (above 70), moderate deprivation (40\u0026ndash;69) and low deprivation (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData on place-based characteristics (i.e., urban, rural), categorized by zip codes, were collected from the United States Department of Agriculture (USDA) database\u0026mdash;most recently updated on August 17, 2020.\u003csup\u003e20\u003c/sup\u003e ZIP codes were categorized as urban (secondary RUCA [Rural-Urban Commuting Area] scores 1\u0026ndash;3) or rural (scores 4\u0026ndash;9). For ZIP codes with a secondary RUCA value of 99, we conducted a manual search to determine whether they were urban; otherwise, we labeled them as \"Unidentified.\"\u003c/p\u003e\u003cp\u003eProvider specialization was determined by manually assigning a clinical specialty to each provider based on their name. Providers with backgrounds in family medicine, internal medicine, physician assistant, and nurse practitioner were grouped under the category of primary care providers (PCPs). All others, including those not explicitly categorized, were grouped under specialists. This classification allowed analysis comparing outcomes between patients seen by PCPs versus specialists.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe Lung-RADS is used as a standardized system to report LCS results. Lung-RADS are graded in four categories ranging from negative (category 1) to suspicious (category 4). Higher scores indicate that nodules were found in the lungs that have a higher probability of being lung cancer.\u003csup\u003e21\u003c/sup\u003e Lung-RADS scores that are probably benign (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) or suspicious (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (high-risk) necessitate returning for sooner follow-up LCS than the standard one-year timeframe.\u003c/p\u003e\u003cp\u003eOur outcomes of interest were the Lung-RADS score; for those with high-risk Lung-RADS findings, we were interested in whether they had one or multiple visits (i.e., follow-up) within 2023. We stratified results by race, sex and ADI. Individuals with Probably Benign Lung-RADS findings should be scheduled to undergo repeat LCS in 6 months, and those with Suspicious Lung-RADS findings should return at least within 3 months, while those with negative or benign findings can wait until a year for their next screen (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe reviewed patient appointment records to identify individuals who required follow-up LCS within 3\u0026ndash;6 months based on their Lung-RADS scores, the primary outcome measure for LCS. For the purposes of this study, we defined high-risk patients as those with a Lung-RADS score of 3 or 4 (i.e., probably benign or suspicious findings requiring closer surveillance), and low-risk patients as those with a Lung-RADS score of 1 or 2 (i.e., negative or benign findings). We identified the earliest recorded LCS appointment between January 1 and June 30, 2023, and classified patients with high-risk scores as requiring follow-up within 3\u0026ndash;6 months. Focusing on patients with such scores in the first half of 2023 ensured a sufficient 6-month window within our dataset to observe their return for follow-up imaging. We examined records to determine whether the same patients had a subsequent, follow-up LCS visit in 2023. We categorized high-risk patients who completed more than one visit in 2023 as \"Multiple Visit\" patients, and high-risk patients who had only one visit in 2023, without any follow-up visit, as \"Single Visit\" patients.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe conducted descriptive analyses to summarize patient, provider, and place-based characteristics. We calculated frequencies and percentages for categorical variables and mean and standard deviation for pack-years. Age was dichotomized as below and above 65 years, reflecting the Medicare eligibility threshold, which may influence insurance coverage and access to LCS. Multivariable logistic regression was conducted to identify predictors of Lung-RADS outcomes, with the analysis stratified by race and ADI. Statistical significance was assessed at a two-sided alpha level of 0.05. We adjusted for age, smoking status (current, former) and pack-years, insurance type (Medicaid, Medicare, private, unknown), and provider type (e.g., PCP, specialist).\u003c/p\u003e\u003cp\u003eUnder race, we removed Asian and \u0026ldquo;Other race\u0026rdquo; categories from inferential analyses due to very low numbers.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHandling Missing Data\u003c/h3\u003e\n\u003cp\u003eTo ensure the integrity and completeness of the analytical dataset, we addressed missing data systematically. First, 20 records lacking ZIP code information or with ZIP codes outside Illinois (IL) and Missouri (MO) were excluded across the combined patient-level clinical and place-based datasets (i.e., SCC database, Area Deprivation Index, and USDA rurality data) to avoid inconsistencies in location-based analyses.\u003c/p\u003e\u003cp\u003eFor continuous variables such as smoking history (pack-years), missing values were imputed using the mean value calculated from documented cases within the eligible cohort. This approach preserved the distributional properties of the variable while minimizing bias. Categorical variables were handled through logical imputation or default classification to maintain consistency. For example, missing values in the socioeconomic distress index (ADI) were assigned to the \"Low Distress\" category to reduce misclassification.\u003c/p\u003e\u003cp\u003eAll analyses were conducted using SAS Version 9.4 (SAS Institute Inc., Cary, NC).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003ePopulation Characteristics\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 1,946 individuals (Table 1), 1880 individuals with data, were included in inferential analysis, 53.9% were under 65 years old, and 46.1% were 65 or older. The cohort consisted of 53.6% males and 46.4% females. Over half (56.7%) of the participants identified as Black, 40.9% as White, 1.2% as Asian, and 1.2% as other races.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost patients had Benign (59.1%) and Negative (29.2%) LungRADS category, i.e., 88.3% had Low-risk scores. Patients in the probably benign and suspicious LungRADS category, were 6.1% and 5.5%, respectively, for a total of 11.7% with High-risk scores.\u003c/p\u003e\n\u003cp\u003eMost individuals were current smokers (71.1%), and 28.9% were former smokers. The average number of pack-years smoked was 36.51 (\u0026plusmn;10.95).The average number of pack-years smoked was 36.51 (\u0026plusmn;10.95). 52.1% underwent their first ever baseline LCS, while 47.9% attended a repeat annual LCS. A majority (98.3%) resided in urban areas, with only 1.7% from rural regions. Nearly half of the patients (48.8%) lived in areas with high distress, 25.0% in moderate distress, and 26.3% in low distress. In terms of insurance status, 53.1% were covered by Medicare, followed by nearly equal proportions with private insurance (23.0%) and Medicaid (22.9%), while 1.0% had unknown insurance. Most were seen by a PCP (85.0%), while 15.0% were seen by specialists. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cu\u003eUnadjusted LCS Outcomes\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the unadjusted distribution of low-risk (Benign or Negative) and high-risk (Probably Benign or Suspicious) across characteristics using cross-tabulations and chi-square tests (Table 3). Overall, 88.3% of participants had low-risk findings, and 11.7% were categorized as high-risk.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAge group:\u0026nbsp;\u003c/em\u003eThere was a significant association between age group and RADS category (OR = 1.34; 95% CI: 1.00\u0026ndash;1.80; p=0.05). Participants aged \u0026ge;65 years were more likely to have high risk scores compared to those \u0026lt;65 years.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSex:\u003c/em\u003e There was no association between sex and RADS category (p=0.117)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRace:\u003c/em\u003e There was no association by race (p=0.157), although White patients had a higher proportion of high-risk findings (12.9%) compared to Black patients (10.8%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInsurance:\u003c/em\u003e There was no association between insurance and RADS category approached ( private vs Medicaid, p=0.766; and private vs Medicare, p=0.124 ).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreening indication (baseline vs annual):\u0026nbsp;\u003c/em\u003eThere was a strong and significant association between screening indication and RADS category. Patients undergoing annual screening had a higher odds of high-risk scores compared to those getting their first, baseline screening (OR = 1.80; 95% CI: 1.33\u0026ndash;2.42; p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProvider type:\u003c/em\u003e Provider type was significantly associated with RADS category. Patients referred by specialists were more likely to have high-risk findings compared to those seen by PCPs (OR=1.33; 95% CI:1.00-2.07; p=0.047).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSmoking status:\u003c/em\u003e There was no association between being a current or former smoker and RADS category (p= 0.85).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eADI:\u0026nbsp;\u003c/em\u003eThere was no association between RADS category and different levels of area level deprivation. Compared to patients in low ADI areas, those in moderate ADI (p=0.278) and high ADI areas (p=0.817) did not differ significantly in their likelihood of being classified as high risk.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cu\u003eAdjusted associations for LCS Outcomes\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAmong Black individuals, undergoing an annual (repeat) screen was significantly associated with high-risk scores (AOR = 1.85; 95% CI: 1.32\u0026ndash;2.58; p \u0026lt; 0.001). Other variables\u0026mdash;including age, sex, provider type, smoking status, insurance, ADI and pack-years\u0026mdash;were not significantly associated with high-risk findings in Black individuals. Effect modification for race was significant (p=0.037).\u003c/p\u003e\n\u003cp\u003eAmong White individuals, undergoing an annual (repeat) screen was significantly associated with high-risk lung-RADS scores (AOR = 2.10; 95% CI: 1.40\u0026ndash;3.15; p \u0026lt; 0.001). Additionally, being seen by a specialist compared to a primary care provider was associated with increased odds of high-risk findings (AOR = 1.58; 95% CI: 1.00\u0026ndash;2.50; p = 0.048). Other factors\u0026mdash;including age, sex, smoking status, insurance, ADI and pack-years\u0026mdash;were not significantly associated with high-risk findings in White individuals. Effect modification for race was borderline significant (p=0.058)\u003c/p\u003e\u003cp\u003e\u003cu\u003eAdherence to follow-up for high-risk findings\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn Table 4, among the 220 patients identified as high-risk based on screening criteria, the majority (80.0%) completed only a single lung cancer screening (LCS) visit, while 20.0% had a timely return for the initial follow-up visit, i.e., within the recommended 3- to 6-month window.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe only variable approaching statistical significance was ADI. Individuals residing in moderately distressed areas had lower odds of timely adherence to initial follow-up visit compared to those in highly distressed areas (AOR=0.393; 95% CI: 0.155\u0026ndash;1.001, p = 0.050). There was no difference in adherence to multiple visits comparing individuals residing in low vs moderate distress, or those in low vs high distress. Age, sex, insurance, race, smoking, screening indication, provider type and park- years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluates Lung-RADS outcomes by patient, provider, and place-based characteristics from a LCS program based in MO and IL. Our analysis revealed statistically significant place-based and provider-specific (among White individuals) differences in LCS radiology findings. We observed that more than half of the patients were Black in this predominantly White catchment area, reflecting progress toward equitable access to the SCC program. Concerningly, we found that 70% of patients were current smokers, emphasizing a greater need for integration of tobacco cessation strategies into LCS programs, and potentially leveraging Lung-RADS findings as an opportunity to educate patients on smoking cessation. The emergence of point-of-care tobacco use treatment facilitated by electronic health records and embedded in routine oncology care visits may offer a targeted strategy to address this gap in care.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAmong White individuals only, those referred by specialists were more likely to have high-risk findings compared to those referred by PCPs. As this was not the case for Black individuals, it may signal differences in provider referrals by patient characteristics like race. We also found that high area distress was not associated poorer access to follow up care for high-risk findings. We found no significant variations in lung radiology outcomes or follow-up by sex, age, insurance, smoking status, and pack-years.\u003c/p\u003e\u003cp\u003eThese findings suggest that concerted efforts—potentially including updated USPSTF 2021 guidelines and local cancer center strategies—to enhance access and care for marginalized racial groups may be yielding more equitable LCS outcomes. While there were no direct racial differences in lung-RADS, this does not alleviate concerns around the applicability of Lung-RADS classification to Black populations.\u003csup\u003e23\u003c/sup\u003e A recent meta-analysis showed that Lung-RADS demonstrated significant heterogeneity between study populations in sensitivity and specificity,\u003csup\u003e24\u003c/sup\u003e underscoring the need for more research on Lung-RADS performance in multiethnic populations. Given that Black individuals have higher lung cancer incidence than White individuals at a younger age, our findings of no association between race and likelihood of high-risk findings is interesting and may point to concerns raised about reliability of Lung-RADS in Black and other populations. This may also be explained by our high number of Black individuals attending LCS, as they may have been attending LCS more often. SCC serves a 20.5% minority population, yet 56.7% of participants screened were Black, highlighting the success of SCC’s community-oriented efforts to expand LCS access among the underserved.\u003c/p\u003e\u003cp\u003eWith regard to provider referrals, when PCPs refer patients rather than specialists, this may encourage earlier LCS with less suspicious results. PCPs can play an important role in encouraging LCS,\u003csup\u003e25,26\u003c/sup\u003e as individuals are much more likely to see a PCP rather than a specialist. Reserving time during a PCP appointment for shared decision making to discuss the risks and benefits of LCS could promote earlier uptake and more consistent adherence to detect lung cancers at more treatable stages.\u003csup\u003e27\u003c/sup\u003e Training and supporting PCPs to refer their patients for LCS could lead to better patient outcomes.\u003csup\u003e28\u003c/sup\u003e However, specialists may be more likely to see sick patients with high-risk scores.\u003c/p\u003e\u003cp\u003eWe found no association between age and the outcomes. A prior study showed that older patients were more likely to be assigned high-risk results and found no associations with race or sex.\u003csup\u003e29\u003c/sup\u003e The differences in findings may be attributed to the differing patient populations, as the prior study used a nationwide cohort from five different institutions located across the country.\u003c/p\u003e\u003cp\u003eWe found 14.3% of patients had high-risk scores, which is similar to estimates provided by most other academic and community institutions throughout the United States, ranging from 11.4% in academic centers in North Carolina to 24.6% in an inner-city cohort including federally qualified health centers in Chicago.\u003csup\u003e30–33\u003c/sup\u003e Additionally, when the Lung-RADS classification was retrospectively applied to the National Lung Screening Trial, they had a similar positivity rate of 13.6%.\u003csup\u003e34\u003c/sup\u003e Two of these studies had a majority Black population, similar to the current study.\u003csup\u003e31,33\u003c/sup\u003e This validates our findings and demonstrates that our over- and under-diagnosis rates are similar to other institutions.\u003c/p\u003e\u003cp\u003eOur finding that a higher incidence of suspicious findings among individuals returning for annual LCS underscores the importance of encouraging patients to receive regular LCS and establishing potential measures to track patients and incentivize retention in the LCS pathway. This facilitates earlier detection, enabling more treatment options and superior patient outcomes.\u003csup\u003e5\u003c/sup\u003e LCS programs must monitor participants over time and minimize loss of follow-up to be most effective. Longitudinal tracking of multiple lung CT scans may also hold more data that could potentially be used to diagnose lung cancer earlier on.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn our LCS program, most patients were Black in a predominantly White catchment area. This is noteworthy, as other programs have struggled with reaching Black patients and those of low socioeconomic status.\u003csup\u003e36\u003c/sup\u003e People with low socioeconomic status are more likely to display fatalistic attitudes, seeing cancer as unpreventable, and are less likely to understand their cancer risk.\u003csup\u003e6,37\u003c/sup\u003e To successfully serve these populations, targeted interventions should be developed to specifically address societal drivers of these disparities. This could be done by directly engaging patients to co-develop culturally competent interventions.\u003csup\u003e38\u003c/sup\u003e SCC patient navigators also guide people through the LCS process. Navigators play an important role in educating patients about LCS, addressing patient concerns, reducing physician burden, and ensuring patients are not lost to follow-up.\u003c/p\u003e\u003cp\u003eImplementing navigators has been recommended, especially for vulnerable populations,\u003csup\u003e39\u003c/sup\u003e and has been shown to increase LCS uptake in a randomized controlled trial.\u003csup\u003e40\u003c/sup\u003e Sending targeted invitations to eligible participants in areas of high deprivation may also increase LCS uptake among these populations.\u003csup\u003e41,42\u003c/sup\u003e We found individuals from high distress areas were more likely to return for the recommended follow-up for high-risk findings, possibly due to the influence of a SCC’s outreach strategies and robust patient navigation systems.\u003c/p\u003e\u003cp\u003eThe strengths of this study include our robust sample size, diverse patient population, and range of multilevel factors (e.g., patient, provider, area-levels) evaluated. Being conducted in an urban academic center in the Midwest, findings may be generalizable to other similar settings with similar population characteristics. However, this study has several limitations. Our cohort included a low number of individuals not identifying as White or Black, including those who identified as Asian or of another race. It is possible that patients with high-risk findings who did not have timely follow-up visits moved to other locations or passed away. Although we adjusted for various factors the possibility of residual confounding remains due to the nature of cross-sectional study designs. Given the relatively small sample size of high-risk patients with category 3 or 4 findings (high-risk), we may have been underpowered to detect significant differences and associations. Another limitation is that follow-up adherence was only analyzed among patients with high-risk findings. Evaluation of follow-up adherence in low-risk( Lung-RADS 1 and 2) patients may have yielded different patterns and associated factors.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003ch2\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere was no association between sex and RADS category (p = 0.117)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere was no association by race (p = 0.157), although White patients had a higher proportion of high-risk findings (12.9%) compared to Black patients (10.8%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInsurance\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere was no association between insurance and RADS category approached ( private vs Medicaid, p = 0.766; and private vs Medicare, p = 0.124 ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eSmoking status\u003c/h2\u003e\u003cp\u003eThere was no association between being a current or former smoker and RADS category (p = 0.85).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eADI\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere was no association between RADS category and different levels of area level deprivation. Compared to patients in low ADI areas, those in moderate ADI (p = 0.278) and high ADI areas (p = 0.817) did not differ significantly in their likelihood of being classified as high risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings identify patient, provider, and place-based factors that may be associated with LCS outcomes and timely adherence to follow-up care. Further research is needed to understand the contribution and impact of these factors on Lung-RADS scores in risk assessment. SCC’s LCS program successfully captured individuals from highly distressed areas and Black populations, in a predominantly White catchment area. There were no observable race disparities in timely follow-up of high-risk findings, reflecting progress toward equity in access and outcomes. Place-based disparities in follow-up were observed that warrant further characterization and risk assessment to improve follow-up of patients undergoing LCS. Tailoring LCS criteria and risk assessment based on these insights can help clinicians improve lung cancer detection and follow-up efforts for high-risk patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eEthical approval:\u003c/h2\u003e\u003cp\u003e We obtained ethical approval from the Washington University Institutional Review Board and Siteman Cancer Center’s Protocol Review and Monitoring Committee. We received a waiver of consent for electronic record data extraction and were have been approved to obtain verbal consent from health providers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003c/p\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e\u003cp\u003e The Washington University Institutional Review Board (IRB) approved this study and granted a waiver of informed consent to access protected health information in the SCC database. Following data extraction from the SCC database, all identifying information was removed before data analysis to ensure participant confidentiality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was supported by the Alvin J. Siteman Cancer Center. We thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, MO., for the Research Program Catalyst Award and for the use of the Siteman Biostatistics and Qualitative Research Shared Resource. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant #P30 CA091842.\u003c/p\u003e\u003cp\u003eThis project, in part, was supported by The Foundation for Barnes-Jewish Hospital and their generous donors, and by the Washington University Institute of Clinical and Translational Sciences which is, in part, supported by the NIH/National Center for Advancing Translational Sciences (NCATS), CTSA grant #UL1TR002345.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBO and AA was responsible for the acquisition, conception, design, analyses, interpretation of the data, and writing of the manuscript. VM and BB contributed to analyses and interpretation of the data, and writing. ICN contributed to the interpretation of data and writing of the manuscript. AR was contributed to interpretation of data, review and writing of the manuscript. All authors reviewed the manuscript and approved it for publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData cannot be shared publicly because they contain protected health information from patients within the BJC HealthCare system and Washington University School of Medicine. Data are available from the Washington University Human Research Protection Office (HRPO) (contact: [email protected]) and the Human Data Review Committee (HDRC) (contact: [email protected]) for researchers who meet the criteria for access to confidential data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKratzer TB, Bandi P, Freedman ND et al (2024) Lung cancer statistics, 2023. Cancer 130(8):1330\u0026ndash;1348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.35128\u003c/span\u003e\u003cspan address=\"10.1002/cncr.35128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlandin Knight S, Crosbie PA, Balata H, Chudziak J, Hussell T, Dive C (2017) Progress and prospects of early detection in lung cancer. Open Biol 7(9):170070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsob.170070\u003c/span\u003e\u003cspan address=\"10.1098/rsob.170070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePotter AL, Rosenstein AL, Kiang MV et al (2022) Association of computed tomography screening with lung cancer stage shift and survival in the United States: quasi-experimental study. BMJ Published online March 30:e069008. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj-2021-069008\u003c/span\u003e\u003cspan address=\"10.1136/bmj-2021-069008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Koning HJ, van der Aalst CM, de Jong PA et al (2020) Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med 382(6):503\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1911793\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1911793\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJonas DE, Reuland DS, Reddy SM et al (2021) Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 325(10):971. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2021.0377\u003c/span\u003e\u003cspan address=\"10.1001/jama.2021.0377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAberle DR, Abtin F, Brown K (2013) Computed Tomography Screening for Lung Cancer: Has It Finally Arrived? Implications of the National Lung Screening Trial. J Clin Oncol 31(8):1002\u0026ndash;1008. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2012.43.3110\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2012.43.3110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirsch EA, New ML, Brown SP, Bar\u0026oacute;n AE, Malkoski SP (2019) Patient Reminders and Longitudinal Adherence to Lung Cancer Screening in an Academic Setting. Ann Am Thorac Soc 16(10):1329\u0026ndash;1332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1513/AnnalsATS.201902-152RL\u003c/span\u003e\u003cspan address=\"10.1513/AnnalsATS.201902-152RL\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePotter AL, Bajaj SS, Yang CFJ (2021) The 2021 USPSTF lung cancer screening guidelines: a new frontier. Lancet Respir Med 9(7):689\u0026ndash;691\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBandi P, Star J, Ashad-Bishop K, Kratzer T, Smith R, Jemal A (2024) Lung Cancer Screening in the US, 2022. JAMA Intern Med 184(8):882. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamainternmed.2024.1655\u003c/span\u003e\u003cspan address=\"10.1001/jamainternmed.2024.1655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCattaneo SM, Meisenberg BR, Geronimo MCM, Bhandari B, Maxted JW, Brady-Copertino CJ (2018) Lung Cancer Screening in the Community Setting. Ann Thorac Surg 105(6):1627\u0026ndash;1632. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.athoracsur.2018.01.075\u003c/span\u003e\u003cspan address=\"10.1016/j.athoracsur.2018.01.075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewis JA, Petty WJ, Tooze JA et al (2015) Low-Dose CT Lung Cancer Screening Practices and Attitudes among Primary Care Providers at an Academic Medical Center. Cancer Epidemiol Biomarkers Prev 24(4):664\u0026ndash;670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.EPI-14-1241\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-14-1241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRankin NM, McWilliams A, Marshall HM (2020) Lung cancer screening implementation: Complexities and priorities. Respirology 25(S2):5\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/resp.13963\u003c/span\u003e\u003cspan address=\"10.1111/resp.13963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDraucker CB, Rawl SM, Vode E, Carter-Harris L (2019) Understanding the decision to screen for lung cancer or not: A qualitative analysis. Health Expect 22(6):1314\u0026ndash;1321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/hex.12975\u003c/span\u003e\u003cspan address=\"10.1111/hex.12975\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFiscella K, Winters P, Farah S, Sanders M, Mohile SG (2015) Do Lung Cancer Eligibility Criteria Align with Risk among Blacks and Hispanics? Chang JS, ed. \u003cem\u003ePLOS ONE\u003c/em\u003e. ;10(11):e0143789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0143789\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0143789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJemal A, Miller KD, Ma J et al (2018) Higher Lung Cancer Incidence in Young Women Than Young Men in the United States. N Engl J Med 378(21):1999\u0026ndash;2009. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1715907\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1715907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan SS, Chow E, Ten Haaf K et al (2020) Disparities of National Lung Cancer Screening Guidelines in the US Population. JNCI J Natl Cancer Inst 112(11):1136\u0026ndash;1142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jnci/djaa013\u003c/span\u003e\u003cspan address=\"10.1093/jnci/djaa013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarter-Harris L, Davis LL, Rawl SM (2016) Lung Cancer Screening Participation: Developing a Conceptual Model to Guide Research. Res Theory Nurs Pract 30(4):333\u0026ndash;352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1891/1541-6577.30.4.333\u003c/span\u003e\u003cspan address=\"10.1891/1541-6577.30.4.333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKind AJH, Buckingham WR (2018) Making Neighborhood-Disadvantage Metrics Accessible \u0026mdash; The Neighborhood Atlas. N Engl J Med 378(26):2456\u0026ndash;2458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMp1802313\u003c/span\u003e\u003cspan address=\"10.1056/NEJMp1802313\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandscape of Area-Level Deprivation Measures and Other Approaches to Account for Social Risk and Social Determinants of Health in Health Care Payments. ASPE. September 26 (2022) Accessed September 25, 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://aspe.hhs.gov/reports/area-level-measures-account-sdoh\u003c/span\u003e\u003cspan address=\"http://aspe.hhs.gov/reports/area-level-measures-account-sdoh\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRural-Urban Commuting Area Codes | Economic Research Service. Accessed September 26 (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes\u003c/span\u003e\u003cspan address=\"https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristensen J, Prosper AE, Wu CC et al (2024) ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. J Am Coll Radiol 21(3):473\u0026ndash;488. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacr.2023.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jacr.2023.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamsey AT, Baker TB, Stoneking F et al (2022) Increased Reach and Effectiveness With a Low-Burden Point-of-Care Tobacco Treatment Program in Cancer Clinics. J Natl Compr Canc Netw 20(5):488\u0026ndash;495e4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6004/jnccn.2021.7333\u003c/span\u003e\u003cspan address=\"10.6004/jnccn.2021.7333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang R, Cheung MC, Byrne MM et al (2010) Do racial or socioeconomic disparities exist in lung cancer treatment? Cancer 116(10):2437\u0026ndash;2447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.24986\u003c/span\u003e\u003cspan address=\"10.1002/cncr.24986\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMao Y, Cai J, Heuvelmans MA et al (2023) Performance of Lung-RADS in different target populations: a systematic review and meta-analysis. Eur Radiol 34(3):1877\u0026ndash;1892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-023-10049-9\u003c/span\u003e\u003cspan address=\"10.1007/s00330-023-10049-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeterson EB, Ostroff JS, DuHamel KN et al (2016) Impact of provider-patient communication on cancer screening adherence: A systematic review. Prev Med 93:96\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ypmed.2016.09.034\u003c/span\u003e\u003cspan address=\"10.1016/j.ypmed.2016.09.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmery JD, Shaw K, Williams B et al (2014) The role of primary care in early detection and follow-up of cancer. Nat Rev Clin Oncol 11(1):38\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrclinonc.2013.212\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2013.212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards TB, White MC, Caraballo RS (2014) Lung Cancer Screening with Low-Dose Computed Tomography for Primary Care Providers. Prim Care Clin Off Pract 41(2):307\u0026ndash;330. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pop.2014.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.pop.2014.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanodra NM, Pope C, Halbert CH, Silvestri GA, Rice LJ, Tanner NT (2016) Primary Care Provider and Patient Perspectives on Lung Cancer Screening. A Qualitative Study. Ann Am Thorac Soc 13(11):1977\u0026ndash;1982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1513/AnnalsATS.201604-286OC\u003c/span\u003e\u003cspan address=\"10.1513/AnnalsATS.201604-286OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurnett-Hartman AN, Carroll NM, Honda SA et al (2022) Community-based Lung Cancer Screening Results in Relation to Patient and Radiologist Characteristics: The PROSPR Consortium. Ann Am Thorac Soc 19(3):433\u0026ndash;441. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1513/AnnalsATS.202011-1413OC\u003c/span\u003e\u003cspan address=\"10.1513/AnnalsATS.202011-1413OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenderson LM, Bacchus L, Benefield T, Velasquez RH, Rivera MP (2020) Rates of positive lung cancer screening examinations in academic versus community practice. Transl Lung Cancer Res 9(4):1528\u0026ndash;1532. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/tlcr-19-673\u003c/span\u003e\u003cspan address=\"10.21037/tlcr-19-673\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuichet PL, Liu BY, Desai B, Surani Z, Cen SY, Lee C (2018) Preliminary Results of Lung Cancer Screening in a Socioeconomically Disadvantaged Population. Am J Roentgenol 210(3):489\u0026ndash;496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2214/AJR.17.18853\u003c/span\u003e\u003cspan address=\"10.2214/AJR.17.18853\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimmerman EL, Thomson NB, Dillard TA et al (2017) Free Lung Cancer Screening Trends Toward a Twofold Increase in Lung Cancer Prevalence in the Underserved Southeastern United States. South Med J 110(3):188\u0026ndash;194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14423/SMJ.0000000000000619\u003c/span\u003e\u003cspan address=\"10.14423/SMJ.0000000000000619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePasquinelli MM, Kovitz KL, Koshy M et al (2018) Outcomes From a Minority-Based Lung Cancer Screening Program vs the National Lung Screening Trial. JAMA Oncol 4(9):1291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2018.2823\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2018.2823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinsky PF, Gierada DS, Black W et al (2015) Performance of Lung-RADS in the National Lung Screening Trial: A Retrospective Assessment. Ann Intern Med 162(7):485\u0026ndash;491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7326/M14-2086\u003c/span\u003e\u003cspan address=\"10.7326/M14-2086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaez R, Kammer MN, Balar A et al (2023) Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep 13(1):6157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-33098-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-33098-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSosa E, D\u0026rsquo;Souza G, Akhtar A et al (2021) Racial and socioeconomic disparities in lung cancer screening in the United States: A systematic review. CA Cancer J Clin 71(4):299\u0026ndash;314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21671\u003c/span\u003e\u003cspan address=\"10.3322/caac.21671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuaife SL, Marlow LAV, McEwen A, Janes SM, Wardle J (2017) Attitudes towards lung cancer screening in socioeconomically deprived and heavy smoking communities: informing screening communication. Health Expect 20(4):563\u0026ndash;573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/hex.12481\u003c/span\u003e\u003cspan address=\"10.1111/hex.12481\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarper LJ, Kidambi P, Kirincich JM, Thornton JD, Khatri SB, Culver DA (2023) Health Disparities. Chest 164(1):179\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chest.2023.02.033\u003c/span\u003e\u003cspan address=\"10.1016/j.chest.2023.02.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShusted CS, Barta JA, Lake M et al (2019) The Case for Patient Navigation in Lung Cancer Screening in Vulnerable Populations: A Systematic Review. Popul Health Manag 22(4):347\u0026ndash;361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/pop.2018.0128\u003c/span\u003e\u003cspan address=\"10.1089/pop.2018.0128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaggett TP, Sporn N, Barbosa Teixeira J et al (2024) Patient Navigation for Lung Cancer Screening at a Health Care for the Homeless Program: A Randomized Clinical Trial. JAMA Intern Med 184(8):892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamainternmed.2024.1662\u003c/span\u003e\u003cspan address=\"10.1001/jamainternmed.2024.1662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoodley P, Balata H, Alonso A et al (2024) Invitation strategies and participation in a community-based lung cancer screening programme located in areas of high socioeconomic deprivation. Thorax 79(1):58\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/thorax-2023-220001\u003c/span\u003e\u003cspan address=\"10.1136/thorax-2023-220001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRivera MP, Katki HA, Tanner NT et al (2020) Addressing Disparities in Lung Cancer Screening Eligibility and Healthcare Access. An Official American Thoracic Society Statement. Am J Respir Crit Care Med 202(7):e95\u0026ndash;e112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202008-3053ST\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202008-3053ST\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cdiv class=\"SimplePara\"\u003eDescriptive Characteristics\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eCategory\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003eN\u0026thinsp;=\u0026thinsp;1946 (%)\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAge\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAbove 65\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e897 (46.1)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBelow 65\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1049 (53.9)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSex\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e903 (46.4)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1043 (53.6)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eInsurance\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicaid\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e445 (22.9)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicare\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1034 (53.1)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003ePrivate\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e447 (23.0)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eUnknown\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e20 (1.0)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eRace\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBlack\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1104 (56.7)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eWhite\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e795 (40.9)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAsian\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e24 (1.2)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e23 (1.2)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSmoking Status\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eCurrent\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1384 (71.1)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eFormer\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e562 (28.9)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIndication for LCS evaluation\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAnnual LCS\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e933 (47.9)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBaseline LCS\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1013 (52.1)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eRural/Urban\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eRural\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e34 (1.7)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eUrban\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1912 (98.3)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eProvider Type\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003ePCP\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1654 (85.0)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eSpecialist\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e292 (15.0)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eLung-RADS Category\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1\u0026thinsp;=\u0026thinsp;Negative\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e568 (29.2)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e2\u0026thinsp;=\u0026thinsp;Benign\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1151 (59.1)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e3\u0026thinsp;=\u0026thinsp;Probably Benign\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e119.0 (6.1)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e4\u0026thinsp;=\u0026thinsp;Suspicious\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e108.0 (5.6)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eADI\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eHigh Distress (70\u0026ndash;100)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e949.0 (48.8)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eModerate Distress (40\u0026ndash;69)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e486 (25.0)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eLow Distress (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e511 (26.3)\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePack-Years (Mean, SD)\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e36.51\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.95\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: PCP\u0026thinsp;=\u0026thinsp;primary care provider, RUCA\u0026thinsp;=\u0026thinsp;Rural-Urban Commuting Area, SD\u0026thinsp;=\u0026thinsp;standard deviation, ADI\u0026thinsp;=\u0026thinsp;Area deprivation index, LCS\u0026thinsp;=\u0026thinsp;Lung Cancer Screening, LungRADS\u0026thinsp;=\u0026thinsp;Lung Cancer Screening Reporting and Data System\u003c/td\u003e\u003c/tr\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003cdiv category=\"Completeness\" id=\"14\" ruleid=\"MissingTableCitation_01\" status=\"Neutral\" values=\"Table 2\" class=\"btn-xs-small Annotation tooltipped\" data-position=\"top\" data-tooltip=\"\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cdiv class=\"SimplePara\"\u003eUnadjusted distribution of low- and high-risk Lung RADS findings by patient characteristics (N\u0026thinsp;=\u0026thinsp;1880*)\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eCharacteristic\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eCategory\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003eLow Risk Score\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e(N\u0026thinsp;=\u0026thinsp;1660) n (%)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003eHigh Risk Score (N\u0026thinsp;=\u0026thinsp;220) n (%)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003eOR\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e(95%CI)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eAge Group\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;65\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e901 (89.7)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e104 (10.3)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026ge;\u0026thinsp;65\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e759 (86.7)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e116 (13.3)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.34 (1.00-1.80)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.050\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSex\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e795 (89.5)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e93 (10.5)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e865 (87.2)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e127 (12.8)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.80 (0.60\u0026ndash;1.06)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.117\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eInsurance\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003ePrivate\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e389 (90)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e43 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicaid\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e388 (89.4)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e46 (10.6)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.07 (0.71\u0026ndash;1.62)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.766\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicare\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e883 (87.1)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e131 (12.9)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.33 (0.93\u0026ndash;1.92)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.124\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eRace\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBlack\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e981 (89.2)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e119 (10.8)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eWhite\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e679 (87.1)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e101 (12.9)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.90 (0.67\u0026ndash;1.21)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.157\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eIndication\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAnnual\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e766 (85)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e135 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBaseline\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e894 (91.3)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e85 (8.7)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.80 (1.33\u0026ndash;2.42)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eProvider type\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003ePCP\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1420 (88.9)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e177 (11.1)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eSpecialist\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e240 (84.8)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e43 (15.2)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.70 (1.49\u0026ndash;0.99)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.047\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSmoking status\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eCurrent\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1182 (88.2)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e158 (11.8)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eFormer\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e478 (88.5)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e62 (11.5)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.03 (0.75\u0026ndash;1.42)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.850\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cdiv class=\"SimplePara\"\u003eArea Deprivation\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003eIndex (ADI)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eLow distress\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e438 (88.7)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e56 (11.3)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eModerate distress\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e402 (86.5)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e63 (13.5)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.23 (0.85\u0026ndash;1.77)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.278\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eHigh distress\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e820 (89)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e101 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.96 (0.69\u0026ndash;1.34)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.817\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Excludes Race categories with small N (Asian and Other) and individuals with missing data.\u003c/td\u003e\u003c/tr\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cdiv class=\"SimplePara\"\u003eAdjusted odds of being in the high-risk category by patient, provider, and place-based characteristics (N\u0026thinsp;=\u0026thinsp;1880)\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBlack (N\u0026thinsp;=\u0026thinsp;1093)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003eWhite (N\u0026thinsp;=\u0026thinsp;787)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003eInteraction\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAOR (95% CI)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003eAOR (95% CI)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eAge (\u0026lt;\u0026thinsp;65 vs\u0026thinsp;\u0026ge;\u0026thinsp;65)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.85 (0.58\u0026ndash;1.25)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.389\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.78 (0.50\u0026ndash;1.22)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.276\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.213\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSex (Female vs Male)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.83 (0.58\u0026ndash;1.18)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.297\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.77 (0.50\u0026ndash;1.18)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.229\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.342\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eInsurance: Private (ref)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.00\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.00\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicaid\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.89 (0.57\u0026ndash;1.38)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.61\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.84 (0.49\u0026ndash;1.44)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.524\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.408\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicare\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.16 (0.76\u0026ndash;1.77)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.492\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.28 (0.81\u0026ndash;2.03)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.295\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.312\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eIndication (Annual vs Baseline)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.85 (1.32\u0026ndash;2.58)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e2.10 (1.40\u0026ndash;3.15)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.037\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eProvider (Specialist vs PCP)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.32 (0.87\u0026ndash;2.01)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.188\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.58 (1.00\u0026ndash;2.50)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.048\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.058\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSmoking Status (Current vs Former)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.06 (0.72\u0026ndash;1.57)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.776\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.02 (0.63\u0026ndash;1.65)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.927\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.723\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eADI: Low (ref)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.00\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.00\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eModerate\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.20 (0.78\u0026ndash;1.85)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.411\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.27 (0.76\u0026ndash;2.13)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.357\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.452\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eHigh\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.09 (0.71\u0026ndash;1.66)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.687\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.15 (0.68\u0026ndash;1.95)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.587\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.384\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003ePack-Years\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.01 (0.99\u0026ndash;1.02)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.312\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.01 (0.98\u0026ndash;1.03)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.372\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.309\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: AOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio, PCP\u0026thinsp;=\u0026thinsp;primary care provider, ADI\u0026thinsp;=\u0026thinsp;Area deprivation index\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eLow-risk score (reference category- Negative or Benign Lung RADS): No nodules or nodules with benign characteristics. Routine annual screening is recommended.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eHigh-risk score (Probably Benign- requiring a 6-month follow-up CT, or Suspicious Lung RADS - requiring a follow-up within 3 months).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAll AORs are adjusted for the following covariates: \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eage group, sex, insurance type, screening indication, provider type, smoking status, Area Deprivation Index (ADI), pack-years with effect modification assessed for race.\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModerate vs High ADI was not significantly associated high-risk scores (p\u0026thinsp;=\u0026thinsp;0.468).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eStatistically significant associations are those with \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eEffect modification for race was only significant for screening indication (p-value: 0.037)\u0026mdash;the association between screening indication and high risk score was higher in White individuals\u0026mdash;and provider type\u0026mdash;the association between provider and high risk score was more pronounced and significant in White individuals.\u003c/td\u003e\u003c/tr\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cdiv class=\"SimplePara\"\u003eAdherence to multiple visits for individuals with high-risk scores (N\u0026thinsp;=\u0026thinsp;220)\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eComparison\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003eAOR (95% CI)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003ep-value*\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eAge group\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;65 vs\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;65\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.229 (0.632\u0026ndash;2.392)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.852 (0.383\u0026ndash;1.896)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.695\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSex\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eFemale vs Male\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.176 (0.605\u0026ndash;2.286)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.959 (0.472\u0026ndash;1.951)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.909\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eInsurance\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eMedicaid vs Medicare\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.412 (0.723\u0026ndash;2.759)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.487 (0.576\u0026ndash;3.835)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.412\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003ePrivate vs Medicare\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.412 (0.723\u0026ndash;2.759)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.157 (0.422\u0026ndash;3.172)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.777\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eRace\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eBlack vs White\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.412 (0.723\u0026ndash;2.759)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.924 (0.432\u0026ndash;1.979)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.839\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSmoking\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eCurrent vs Former\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.704 (0.347\u0026ndash;1.429)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.687 (0.324\u0026ndash;1.458)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.328\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eScreening indication\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAnnual vs Baseline\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.449 (0.718\u0026ndash;2.924)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.338 (0.643\u0026ndash;2.786)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.436\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eProvider\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eSpecialist vs PCP\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.570 (0.264\u0026ndash;1.230)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.568 (0.687\u0026ndash;3.582)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.285\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eADI\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eLow vs High\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.537 (0.214\u0026ndash;1.347)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.185\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eModerate vs High\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.393 (0.155\u0026ndash;1.001)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.050\u003c/span\u003e\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eModerate vs Low\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.750 (0.342\u0026ndash;1.642)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.72 (0.27\u0026ndash;1.89)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.522\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003ePark-years\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.01 (0.983\u0026ndash;1.042)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e1.00 (0.976\u0026ndash;1.041)\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.441\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p-values for race-based interaction effects\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR: Odds rations, AOR: Adjusted odds ratios (AOR), 95% CI: 95% confidence intervals (CI), PCP: Primary Care Provider, ADI: Area Deprivation Index,\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cbr/\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCP = primary care provider, RUCA = Rural-Urban Commuting Area, SD = standard deviation, ADI = Area deprivation index, LCS = Lung Cancer Screening, LungRADS = Lung Cancer Screening Reporting and Data System\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7745656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7745656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality in the United States, yet disparities in lung cancer screening (LCS) outcomes exist and remain understudied, particularly in the Midwestern region. Our objective was to investigate disparities in lung radiology outcomes and follow-up care based on patient, provider, and place-based characteristics (i.e., area deprivation index; ADI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study used data from the LCS program at Siteman Cancer Center (SCC) in St. Louis, Missouri, from January to December 2023. SCC\u0026rsquo;s catchment area includes 82 counties in Missouri and Illinois; approximately 15% of the population reside in a rural zip code, and 29% reside in medically underserved areas 80% are White. The study included 1,946 individuals aged 50\u0026ndash;80, meeting LCS eligibility criteria based on smoking history and age. Lung radiology findings were assessed as primary outcomes, and timely follow-up adherence (i.e., return for follow-up visit) was analyzed among patients with high-risk findings (Lung-RADS 3 [\u0026rdquo;Probably Benign\u0026rdquo;] and 4 [\u0026rdquo;Suspicious\u0026rdquo;]) from January to June 2023, requiring follow-up by December 2023. Multivariable logistic regression was conducted, adjusting for patient and provider characteristics and ADI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 1,946 individuals who accessed LCS, 57% were Black, 41% White, 1% Asian, and 1% of another race; 54% were male and 46% female. Lung findings were classified as \"probably benign/suspicious\" (high risk) for 14%. Annual visits were associated with higher likelihood of high-risk scores compared to baseline visits (AOR\u0026thinsp;=\u0026thinsp;2.10 (1.40\u0026ndash;3.15); p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Racial differences were noted in the association between provider type and lung outcomes. Among White individuals only, specialist compared to primary care provider referral was associated with increased odds of being high risk (AOR\u0026thinsp;=\u0026thinsp;1.58 (1.00\u0026ndash;2.50); p\u0026thinsp;=\u0026thinsp;0.048). Sex, insurance, smoking status, park-years and ADI were not associated with lung radiology outcomes. Timely adherence to return follow-up visit among high-risk patients was suboptimal, with only 20.0% returning within 3\u0026ndash;6 months for their repeat LCS. Individuals residing in moderate ADI (distress) areas were less likely to have timely follow-up (for high-risk findings) compared to those in high distress (AOR\u0026thinsp;=\u0026thinsp;0.393, 95% CI\u0026thinsp;=\u0026thinsp;0.155\u0026ndash;1.001, p\u0026thinsp;=\u0026thinsp;0.050). There were no differences by sex, insurance, smoking status, and pack-years for being classified as high risk.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSCC\u0026rsquo;s LCS program successfully captured Black populations and individuals from highly distressed areas, in a predominantly White catchment area. There were no observable race disparities in timely follow-up of high-risk findings, reflecting progress toward equity in access and outcomes. Place-based disparities in follow-up were observed that warrant further characterization and risk assessment to improve follow-up of patients undergoing LCS.\u003c/p\u003e","manuscriptTitle":"Differences in lung cancer screening outcomes and follow-up by patient, provider and place-based characteristics in Missouri and Illinois: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 01:12:15","doi":"10.21203/rs.3.rs-7745656/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3594e9c2-6160-4f51-9fbc-790a64bf01f4","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-21T21:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 01:12:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7745656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7745656","identity":"rs-7745656","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}