Geographic Accessibility and Completion of Initial Low-Dose CT-Based Lung Cancer Screening in an Urban Safety-Net Population

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Sofia Yi School of Medicine, UT Southwestern Medical Center, Dallas, TX

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Rutu A. Rathod Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX

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Vijaya Subbu Natchimuthu Parkland Health, Dallas, TX

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Sheena Bhalla Division of Hematology-Oncology, UT Southwestern Medical Center, Dallas, TX
Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX

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Jessica L. Lee Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX

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Travis Browning Department of Radiology, UT Southwestern Medical Center, Dallas, TX

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Joyce O. Adesina Parkland Health, Dallas, TX

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Minh Do Parkland Health, Dallas, TX

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David Balis Parkland Health, Dallas, TX
Division of General Internal Medicine, UT Southwestern Medical Center, Dallas, TX

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Juana Gamarra de Wiliams Parkland Health, Dallas, TX

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Ellen Kitchell Parkland Health, Dallas, TX

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Noel O. Santini Parkland Health, Dallas, TX
Division of General Internal Medicine, UT Southwestern Medical Center, Dallas, TX

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David H. Johnson Division of Hematology-Oncology, UT Southwestern Medical Center, Dallas, TX
Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX

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Heidi A. Hamann Departments of Psychology and Family and Community Medicine, University of Arizona, Tucson, AZ

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Simon J. Craddock Lee Department of Population Sciences, University of Kansas, Kansas City, KS

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Amy E. Hughes Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX

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David E. Gerber Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX
Parkland Health, Dallas, TX
Division of Hematology-Oncology, UT Southwestern Medical Center, Dallas, TX
Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX

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

Background

Despite a clear mortality benefit,1 US Preventive Services Task Force (USPSTF) endorsement, and Centers for Medicare & Medicaid coverage, uptake of lung cancer screening with annual low-dose CT (LDCT) remains remarkably limited. As of 2021, it is estimated that only 6% of eligible individuals had undergone LDCT-based screening.2 This rate lags critically below those for breast and colorectal cancers, which both exceeded 60% in 2021.3,4 Social, economic, and psychological factors may contribute to the limited uptake of lung cancer screening.5 Other proposed explanations include lack of patient and clinician awareness, associated stigma and fear, and lack of available programs.

In 2021, the USPSTF expanded eligibility for lung cancer screening by lowering the minimum age from 55 to 50 years and the minimum smoking history from 30 to 20 pack years.6 Broadened LDCT eligibility is projected to have major beneficial public health effects, including more cancers detected and more lives saved.7,8 These revised recommendations are also anticipated to disproportionately increase eligibility among diverse populations that may be socioeconomically disadvantaged, including groups with less health literacy, lower rates of private health insurance, and lower education and income levels. In general, these individuals have lower rates of cancer screening participation and adherence.912

The non-White US population newly reached by revised USPSTF criteria is overrepresented in urban areas. Overall, racial and ethnic minorities make up 43% of the nation’s urban population compared with 22% in rural areas.13 Although some rural regions with high smoking rates have a clear shortage of established lung cancer screening programs and LDCT-certified radiology centers, larger cities are more likely to have major medical centers with these resources. Rather than lacking available screening sites altogether, the racial and ethnic minorities as well as the socioeconomically disadvantaged individuals living in urban areas instead face challenges accessing these existing programs.14

To address this issue central to the success of expanding lung cancer screening programs nationwide, and to explore a potential barrier to screening adherence for these populations, we analyzed initial LDCT completion according to patient residence and transportation requirements in a major metropolitan area.

Methods

This study was approved by the UT Southwestern Medical Center Institutional Review Board (STU#122015-046).

Study Setting and Population

This study analyzed patients for whom an initial LDCT-based lung cancer screening was ordered at Parkland Memorial Hospital, which is the flagship location for the integrated safety-net health system for Dallas County, Texas (population of 2.6 million; 41% Hispanic, 24% Black). Parkland includes a main campus encompassing a 982-bed tertiary care hospital and specialty clinics, as well as 12 community-based primary care clinics dispersed throughout the county. An enterprise-wide electronic health record (EHR) system (Epic) allows electronic tracking of a wide array of patient characteristics and outcomes.15

Since its inception in 2017, lung cancer screening at Parkland is generally initiated and overseen by primary care providers (physicians and advanced practice providers), who order LDCT through an EHR order set that incorporates both eligibility assessment (eg, age and smoking history) and process requirements (eg, shared decision-making). LDCT results are reported to the ordering clinician in template form using the Lung-RADS scoring system. Although most primary care medical services in the system occur in the community-based clinics, LDCT for lung cancer screening is available only at the main campus.

For this analysis, our cohort comprised patients for whom initial LDCT-based lung cancer screening was ordered in March 2017 (first availability of LDCT-based lung cancer screening at Parkland) through December 2022. Eligible patients were those for whom we were able to calculate public transit times from their residence to the clinical site at which the first LDCT order was placed (hereafter referred to as “LDCT ordering clinic”) and the LDCT performance site (hereafter referred to as “LDCT screening facility”).

Data Collection

We collected the patient residential address at the time of the initial LDCT order placement, the address for each LDCT ordering clinic, and the address for the LDCT screening facility from the EHR. We defined the outcome of our study—initial LDCT screening completion—as “yes” if the patient completed the first LDCT order placed. We did not designate a specific timeframe for completion. Using the EHR, we captured the following patient-level characteristics: age, race and ethnicity, gender, insurance type, and marital status. To measure general health status, we calculated the Charlson comorbidity index score (no significant comorbidity [0–1], moderate [2–4], and severe [≥5]) in the year prior to LDCT ordering.

Data Analysis

We geocoded addresses for patient residence, the LDCT ordering clinic, and the LDCT screening facility in ArcGIS Pro 3.0.2 using Esri StreetMap Premium Q3 2022 as reference data. We then calculated travel time by private vehicle and public transportation, in minutes, from the patient’s residence to the LDCT ordering clinic and from the patient’s residence to the LDCT screening facility. We downloaded public transit data in general transit feed specification for Dallas County and 2 neighboring counties from online repositories. Travel time by public transportation was calculated using the r5r package16 in R version 4.1.2 (R Foundation for Statistical Computing) with Open StreetMap as reference and network data.

To obtain data for a representative weekday and time, we calculated the public transit travel time for all trips starting between 9 am and 11 am on Wednesday, January 25, 2023, and took the median of these theoretical trips as the travel times of interest. We selected a 2-hour transit window because a 1-hour window provided less-stable results due to infrequent components of public transportation. We selected the specific time window to avoid rush-hour effects, which were not incorporated into our estimates of travel time by vehicle. Similarly, we selected the specific day of the week (Wednesday) because it may be less subject to differential traffic delays than the beginning or end of the workweek. We limited walking segments to 30 minutes and assumed a walking pace of 3.6 km/h (∼27 min/mi) such that walking segments were limited to approximately 1.125 miles.

We described patient characteristics and travel times using means (standard deviations) and medians (interquartile ranges) for continuous variables and numbers (percents) for categorical variables. We mapped mean private vehicle and public transportation travel times from patient residence to the LDCT ordering clinic and to the LDCT screening facility. We used unadjusted (univariable) and adjusted (multivariable) logistic regressions to evaluate the associations between projected travel time measures (log-transformed) and LDCT screening completion for 4 different models: patient residence to the LDCT ordering clinic by private vehicle, patient residence to the LDCT screening facility by private vehicle, patient residence to the LDCT ordering clinic by public transportation, and patient residence to the LDCT screening facility by public transportation. We included all covariates in the multivariable model due to their conceptual relevance. We considered statistical significance as P<.05. We also evaluated the correlation between patient characteristics and travel time measures using Spearman’s rank correlation for continuous variables (eg, age), Wilcoxon rank-sum test/Mann-Whitney test for sex, and Kruskal-Wallis rank-sum test for other categorical characteristics with >2 groups. We conducted all analyses in R version 4.1.2 (R Foundation for Statistical Computing).

To account for potential effects of COVID-19 on travel times and general access to health care, we conducted a sensitivity analysis to explore whether results varied according to COVID period. Because Dallas County pandemic-related shelter-in-place orders began on March 23, 2020, and ended on March 10, 2021, we divided our study into 3 time periods based on the order date for a patient’s first LDCT: prepandemic (ordered before February 1, 2020), pandemic restriction period (ordered between February 1, 2020, and March 31, 2021), and postpandemic restriction period (ordered after March 31, 2021). We ran our fully adjusted models with robust standard errors on these 3 subsets.

Because we were unable to determine whether an individual patient would have taken a private vehicle or public transportation to a given scheduled appointment, we conducted a simulation experiment with 10,000 random selections to determine whether our results are robust to a mix of transportation modes (see Supplemental Methods in the supplementary materials, available online with this article).

Results

Among 3,081 patients for whom an initial LDCT order was placed during the study period, 2,287 (74%) had available public transit times to the LDCT ordering clinic and to the LDCT screening facility. For this cohort, the median age was 63 years, 42% were women, 73% were underrepresented minorities, 50% were partnered or previously partnered, and almost all (99%) had either moderate or severe comorbidity. Additional demographic characteristics are shown in Table 1. Consistent with the mission of the health care system as the safety-net provider for Dallas County, 2,267 (99%) patients resided within Dallas County.

Table 1.

Patient and Travel Time Characteristics According to LDCT Completion

Table 1.

Median street network distance between patient residence and LDCT ordering clinic was 6.7 miles and between patient residence and LDCT screening facility was 10.7 miles. Median projected travel time from patient residence to the LDCT ordering clinic was 12 minutes by private vehicle and 63 minutes by public transportation. From patient residence to the LDCT screening facility, median travel time was 17 minutes by private vehicle and 67 minutes by public transportation. Figure 1 displays projected travel times according to trip (patient residence to LDCT ordering clinic and to LDCT screening facility) and modality (private vehicle; public transportation).

Figure 1.
Figure 1.

Distribution of travel times across the study area for patient residence to the (A) LDCT ordering clinic by private vehicle, (B) LDCT screening facility by private vehicle, (C) LDCT ordering clinic by public transportation, and (D) LDCT screening facility by public transportation.

Abbreviation: LDCT, low-dose CT.

Citation: Journal of the National Comprehensive Cancer Network 22, 5; 10.6004/jnccn.2023.7112

Overall, 1,553 (68%) patients completed the initial ordered LDCT. Figure 2 displays the proportion of patients completing the initial LDCT according to patient residence location. Univariate associations of patient characteristics and projected travel times with initial LDCT completion are shown in Table 1. Older individuals and those with severe comorbidity were more likely to complete screening than were individuals with moderate or no significant comorbidity. Travel time measures were not associated with LDCT completion. Specifically, median projected travel time by private vehicle from patient residence to LDCT screening facility was 16.7 and 16.6 minutes, respectively, for patients who completed LDCT and those who did not (P=.67). Via public transportation, travel time was 67 and 66 minutes, respectively, for patients who completed LDCT and those who did not (P=.04).

Figure 2.
Figure 2.

Distribution of patient residence and proportion of patients completing the initial LDCT screening across the study area.

Abbreviation: LDCT, low-dose CT.

Citation: Journal of the National Comprehensive Cancer Network 22, 5; 10.6004/jnccn.2023.7112

Multivariable analysis of associations between initial LDCT completion and both patient and travel time characteristics is shown in Table 2. Projected travel times were not significantly associated with LDCT completion. Increasing age was associated with greater likelihood of LDCT completion. Black individuals were 29% less likely to complete an initial LDCT compared with White individuals.

Table 2.

Multivariable Regression Showing the Association Between Screening Completion and Patient and Travel Characteristics

Table 2.

Although projected travel times were not associated with initial LDCT completion rates, they correlated with patient characteristics (Table 3). Older age was associated with longer travel times by private vehicle, whereas females had longer travel times by public transportation. White individuals and those of unknown race/ethnicity had longer travel times across scenarios. Marital status, comorbidity burden, and insurance type were also correlated with travel times.

Table 3.

Associations Between Travel Time Measures and Patient Characteristics

Table 3.

Our sensitivity analysis examining the potential effect of COVID-19 on our findings found that travel time coefficient estimates and their significance did not vary among prepandemic, pandemic, and postpandemic periods (Supplementary Table S1). In our simulation experiment of 10,000 random selections of private vehicle/public transportation mix, only 7% of replications for patient residence to LDCT screening facility and 4% of replications for patient residence to LDCT ordering clinic were statistically significant. These had small effect sizes (see Supplementary Results and Supplementary Figure S1).

Finally, because the distribution of travel time measures was skewed, we performed the following: analysis of the longest 10% and 25% of travel times and analysis of projected travel time measures by quartiles and quintiles as primary independent variables. As shown in Supplementary Tables S2 and S3, we found no significant associations with initial LDCT completion.

Discussion

We studied the association between completion of a patient’s first LDCT order and their estimated travel time in a large, urban safety-net patient population. Although travel times to ordering clinics and screening facilities were associated with numerous patient characteristics—including age, race/ethnicity, comorbidities, marital status, and insurance type—initial LDCT completion was robustly associated only with patient age and race/ethnicity. We found no evidence that longer projected travel times to the LDCT ordering clinics or the LDCT screening facilities are associated with LDCT completion.

Transportation-related challenges represent a potential barrier to accessing health care services, particularly for older adults and underserved populations.5,14,17 Earlier studies examining the association between travel requirements and screening behaviors have demonstrated mixed findings. Aligning with our results, some mammography and colonoscopy studies found no link with travel time,1820 whereas others have revealed an association with uptake.21 A recent analysis of lung cancer screening in an integrated health care system in South Carolina found that longer travel time to the screening site was associated with lower screening utilization.22 These discordant results may reflect differences in the geographic unit under study (county vs census tract), population density (rural vs urban areas), outcome variable (screening initiation vs follow-up), and travel calculations (patient residence vs centroid of zip code area).

Providing access to cancer screening programs in urban areas is critical to achieving equity in health care. More than 90% of individuals who are Latino live in urban areas (nearly 50% in a central city) compared with <70% (20% in a central city) of individuals who are White.23 Residents who are Black make up only 8% of the rural but 13% of the urban US population.13 We found that Black individuals were less likely to complete LDCT screening compared with White individuals in an urban safety-net health care system, consistent with previous studies that have linked lung screening uptake with race and ethnicity.22,24,25 Among others, potential reasons for this observation include education level, health literacy and beliefs, and smoking status. Notably, a recent study examining travel time to treatment facilities for lung cancer found that a 15-minute increase in driving and public transit time increased the risk of undertreatment and delayed treatment for non-Hispanic Black individuals but not for non-Hispanic White individuals.26

Recent years have seen major growth in the number of programs featuring mobile cancer screening units (mammography, LDCT, Papanicolaou testing), not only in rural regions but also in heavily populated urban centers.27 Yet, our results suggest that—in one of the largest, most diverse cities in the United States—distance and required time to reach a single, brick-and-mortar screening facility do not seem to affect screening completion rates. The nature of screening referral and performance may underlie these findings. In the health care system we studied, LDCT for lung cancer screening could only be ordered by a treating clinician. Accordingly, LDCT order placement implied a previously demonstrated ability to access the health care system. Our observed LDCT completion rate of nearly 70% may also indicate specific characteristics of the study setting, including the “closed” hospital system and assurance that screening would be paid for. In considering whether a mobile screening unit is worthwhile for a given health care system, it is important to recognize potential values beyond access, such as the increase in general screening and specific program awareness that comes with a region’s citizens seeing mobile units during their everyday activities.

To our knowledge, this is the largest study to examine the association between travel time and lung cancer screening adherence, and the first to incorporate public transportation–based data. This second point is key to studying underrepresented populations; 18% of Black households in the United States do not have a car compared with only 5% of White households.28 Additionally, we used precise locations of patient residences and clinical facilities rather than census tract centroids as surrogates of proximity, permitting more accurate projections of travel time. Another strength of our study lies in the robustness of our results given that our findings for travel time persisted throughout several distinct approaches for analysis, including accounting for potential COVID effects, focused analysis of outlying travel time values, and examination of associations across 10,000 permutations of transportation type.

This last approach is critical to addressing a limitation inherent to most studies of patient travel—lack of information on the type of transportation a patient used or would use to reach a clinical facility.29,30 Nearly 1 in 10 urban-dwelling older adults report recently using public transportation, and >20% of these individuals rely on public transportation to see their primary doctor.31 Although we were unable to incorporate historic traffic information into both measures of travel time, we were able to create comparable measures of travel time. Another limitation is the single-center urban setting, which differs substantially from rural areas that face a unique set of challenges, such as a lower number of screening sites and a lack of public transportation.32 Lastly, because the lung cancer screening program under study is relatively new, we examined only the initial ordered LDCT. With effective lung cancer screening requiring longitudinal surveillance over years, future studies will need to analyze not only initial but also subsequent LDCT as well as other diagnostic tests recommended to follow up on abnormal screens.

Potential reasons why certain patients do not complete intended lung cancer screening are complex and multifactorial. Social, psychological, or economic obstacles could play a role. Unmeasured confounding variables or factors such as higher reported perceptions of patient–physician mistrust in non-Hispanic black patients, physician uncertainty regarding the benefit of LDCT-based lung cancer screening, or medical adherence rates among patients of older age could all potentially contribute to the overall rates of screening adherence.3335

Conclusions

We found that travel times to either the LDCT ordering or performance facility do not appear to affect screening completion in an urban safety-net health care system. The near-70% LDCT completion rate overall may reflect the integrated delivery of clinical care, diagnostic procedures, and therapeutic interventions. Nevertheless, LDCT completion rates were lower among Black individuals. Examination of other potential barriers to lung cancer screening in such settings is critical to addressing persistently low screening rates in the United States.

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Submitted May 27, 2023; final revision received October 11, 2023; accepted for publication November 21, 2023. Published online April 26, 2024.

S. Yi and R.A. Rathod contributed equally to this study.

Author contributions: Conceptualization: Hughes, Gerber. Data curation: Rathod, Hughes. Formal analysis: Rathod, Hughes. Funding acquisition: Craddock Lee, Hughes, Gerber. Investigation: Yi, Rathod, Bhalla, Hughes, Gerber. Methodology: Hughes. Project administration: Lee. Supervision: Hughes, Gerber. Visualization: Hughes. Writing—original draft: Yi, Rathod, Hughes, Gerber. Writing—review & editing: Natchimuthu, Bhalla, Lee, Browning, Adesina, Do, Balis, de Wiliams, Kitchell, Santini, Johnson, Hamann, Craddock Lee.

Disclosures: Dr. Bhalla has disclosed serving on a data safety monitoring board for Mirati Therapeutics; and serving on an advisory board for AstraZeneca, Merus, Novocure, and Takeda. Dr. Browning has disclosed serving as a consultant for Change Healthcare. Dr. Gerber has disclosed receiving grant/research support from AstraZeneca, BerGenBio, Karyopharm, and Novocure; serving as a consultant for BeiGene, Catalyst, Daiichi-Sankyo, Elevation Oncology, Janssen, Mirati Therapeutics, Regeneron, and Sanofi; owning stock/having ownership interest in Gilead Sciences, Medtronic, and Walgreens; and holding an executive position at OncoSeer Diagnostics. The remaining authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: This work was supported by the Cancer Prevention and Research Institute of Texas (RP160030, PP190052, PP230041; H.A. Hamann, S.J. Craddock Lee, D.E. Gerber) and a Texas Health Resources Scholar Award (A.E. Hughes). Additional support was received through the Biostatistics Shared Resource in the Harold C. Simmons Comprehensive Cancer Center (5P30 CA142543) and UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418).

Supplementary material: Supplementary material associated with this article is available online at https://doi.org/10.6004/jnccn.2023.7112. The supplementary material has been supplied by the author(s) and appears in its originally submitted form. It has not been edited or vetted by JNCCN. All contents and opinions are solely those of the author. Any comments or questions related to the supplementary materials should be directed to the corresponding author.

Correspondence: Amy E. Hughes, PhD, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Mail Code 9066, Dallas, TX 75390-9066. Email: amye.hughes@utsouthwestern.edu; and
David E. Gerber, MD, Division of Hematology-Oncology, Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Mail Code 8852, Dallas, TX 75390-8852. Email: david.gerber@utsouthwestern.edu

Supplementary Materials

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  • Figure 1.

    Distribution of travel times across the study area for patient residence to the (A) LDCT ordering clinic by private vehicle, (B) LDCT screening facility by private vehicle, (C) LDCT ordering clinic by public transportation, and (D) LDCT screening facility by public transportation.

    Abbreviation: LDCT, low-dose CT.

  • Figure 2.

    Distribution of patient residence and proportion of patients completing the initial LDCT screening across the study area.

    Abbreviation: LDCT, low-dose CT.

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