Abstract
Individuals with rheumatoid arthritis (RA), particularly those with a history of smoking, are at increased risk for lung cancer. However, real-world data on the use of low dose computed tomography (LDCT) screening for lung cancer in this population are lacking. Therefore, we assessed the rates, trends, and predictors of LDCT screening among patients with a smoking history and RA. A retrospective cohort study using the IQVIA PharMetrics® Plus for Academics Closed Health Plan claims database was conducted. Patients with RA diagnosed in 2014-2023, aged 50-77 years, and with a history of smoking and no prior cancer or advanced respiratory illness were identified. LDCT screening was identified using Healthcare Common Procedure Coding System codes. Among 15,341 eligible patients, 997 (6.5%) received LDCT screening; median time from RA diagnosis to first LDCT claim was 452 days. In a sensitivity analysis of 12,301 patients with continuous insurance coverage during the 12 months after RA diagnosis, only 367 (3%) underwent LDCT within the first year after RA diagnosis. Demographic factors such as male sex (aOR=1.24; p=0.003), age 55-69 (aOR=~1.90; p<0.0001), and residence outside the West region (p<0.0001) increased uptake. Clinical characteristics and healthcare utilization such as chronic obstructive pulmonary disease (COPD) (aOR=2.85; p<0.0001), more outpatient visits (aOR=2.15-2.41; p< 0.001) and disease-modifying antirheumatic drug (DMARD) use (aOR=1.27-1.52; p≤ 0.001) also increased odds, whereas greater comorbidity burden reduced screening (aOR=0.79-0.39; p≤0.006). In conclusion, LDCT rates in patients with RA remained low overall and varied according to demographic and clinical factors. Targeted efforts may be needed to improve screening uptake in this high-risk population.
Keywords: Lung cancer screening, rheumatoid arthritis, smoking, claims data, retrospective study
INTRODUCTION
Patients with rheumatoid arthritis (RA) have a markedly higher risk of lung cancer than the general population (1-3). The epidemiology of lung cancer in patients with RA is complex, as excess risk may reflect the combined effects of smoking, chronic systemic inflammation, RA-associated lung disease, and demographic factors (2-5). Although smoking remains the dominant risk factor for lung cancer, it also occurs in patients with RA who would not necessarily meet current screening criteria, including never-smokers, and patients whose risk may be mediated by RA-related pulmonary disease rather than cumulative tobacco exposure alone. However, these proportions are not well defined. In addition, available evidence suggests that lung cancer in patients with RA may be diagnosed at advanced stages and associated with worse outcomes than in patients without RA, underscoring the need to better understand screening patterns in this population (6, 7).
Lung cancer screening (LCS) with low-dose computed tomography (LDCT) has demonstrated clear mortality benefits in high-risk groups. The U.S. Preventive Services Task Force (USPSTF) recommend annual LDCT screening for lung cancer for older adults with significant smoking histories (8). However, current screening recommendations rely almost exclusively on age and smoking history, with RA and other chronic inflammatory conditions not considered independent risk factors (8-11). To date, no authors have estimated the rate of LCS in the RA population, so the extent of screening among at-risk individuals with RA under current recommendations remains unknown. In our previous work (12), after surveying 348 participants in the FORWARD databank (rheumatology registry) with detailed smoking exposure data, we found that about 10.6% of patients with RA met current USPSTF LDCT screening criteria suggesting that only a minority of patients at elevated risk for lung cancer are identified using current guidelines. Understanding present screening patterns could inform whether targeted interventions or tailored guidelines aimed at improving lung cancer outcomes in patients with RA are needed.
We undertook the present study to characterize LCS practices among patients with RA through an analysis of U.S. healthcare claims data. Our specific objectives were to 1) determine the rate of LDCT screening in these patients, stratified by key demographic and clinical characteristics; 2) identify patient characteristics associated with receipt of LDCT; and 3) evaluate temporal trends in LDCT uptake over the study period.
MATERIALS AND METHODS
This study is reported in accordance with the REporting of studies Conducted using Observational Routinely-collected health Data checklist, which extends the Strengthening the Reporting of Observational Studies in Epidemiology statement for observational studies using routinely collected health data (13, 14).
Study design and setting
This retrospective cohort study was a secondary data analysis using the 2006-2023 IQVIA PharMetrics® Plus for Academics Closed Health Plan database (RRID:SCR_021377), a large U.S. proprietary claims database. Frequently used in health services research, this database captures medical information across the full continuum of care, including inpatient and outpatient services and outpatient prescription drugs, covering about 116 million unique patients since 2006.
Participants
Individuals in the database were included in our study if they had at least two claims with International Classification of Diseases diagnosis codes for RA (ICD-9 714.x and ICD-10 M05.x, M06.x) at least 60 days apart from January 1, 2014, to December 31, 2023 (15). The index date was the date of the first RA diagnosis claim. The patients included were aged 50-77 years at RA diagnosis, which was consistent with the Centers for Medicare & Medicaid Services age eligibility criterion for LCS with LDCT (Supplementary Table S1) (16), and were continuous enrolled in insurance coverage for at least 3 months before and 6 months after their RA diagnosis date.
Individuals in the database who formerly or currently smoked were identified using ICD-9 and ICD-10 diagnosis codes and Healthcare Common Procedure Coding System (HCPCS) codes related to tobacco use or cessation or tobacco treatment in the 3 months before and 6 months after RA diagnosis (Supplementary Table S2) (17). The cohort selection procedure is shown in Figure 1.
Figure 1.
Flow diagram of study participant selection. The figure shows the sequential application of inclusion and exclusion criteria used to define the final study cohort.
Individuals were excluded from the study if they had a history of lung cancer, lung transplantation, active malignancy (other than non-melanoma skin cancer), or end-stage renal disease before RA diagnosis. To ensure the cohort was a screening-eligible population (18), patients were excluded if they underwent long-term oxygen therapy, were hospitalized for respiratory failure requiring mechanical ventilation, or had a CT scan of the thorax (other than LDCT) in the 3 months before and 6 months after RA diagnosis or were hospitalized three or more times within 3 months before and after RA diagnosis.
Variables and data sources
The primary outcome variable was LDCT screening for lung cancer, which was indicated by claims at any time after RA diagnosis with the HCPCS code S8032 or G0297 or the Current Procedural Terminology (CPT) code 71271 (19). Demographic characteristics of the patients, including diagnosis year, age, sex, and U.S. geographic region (South, Midwest, East, and West), were examined at RA diagnosis. RA treatment was used as a proxy for RA status (e.g., disease-modifying antirheumatic drug (DMARD) monotherapy suggesting milder disease and combination of DMARDs with biologic or targeted therapy indicating more severe disease). National drug codes for dispensed medications and HCPCS codes for injection or infusion biologic DMARDs were identified among the study patients (Supplementary Table S3). The total number of outpatient visits in the 6 months after RA diagnosis for the entire cohort was determined. Lung comorbidities at any outpatient diagnosis or inpatient discharge diagnosis 3 months before and 6 months after RA diagnosis were also identified. They included asthma, asbestosis, silicosis, tuberculosis, lung fibrosis, sarcoidosis, pneumonia, interstitial lung disease, and chronic obstructive pulmonary disease (COPD). Other comorbidities were also assessed in the same time period; these included moderate to severe liver disease, diabetes, hypertension, stroke, congestive heart failure, peripheral vascular disease, and cerebrovascular disease (Supplementary Table S4). With the exception of COPD, the total number of all comorbidities (including lung and others) was calculated and categorized as 0 (for no comorbidity), 1, 2, or 3 or higher.
Quantitative variables and statistical methods
Summary statistics for demographic and clinical characteristics were calculated, such as frequencies and percentages for categorical variables and medians with IQRs for continuous variables. The rates of LDCT were estimated by RA diagnosis year. The temporal trend was assessed using a joinpoint model (RRID:SCR_018129), and the annual percentage change (APC) was estimated (20). The median values and IQRs for time from RA diagnosis to LDCT were reported. The demographic and clinical characteristics of patients receiving LDCT were compared using a chi-square test with those not receiving LDCT. Multivariable logistic regression models were used to evaluate the associations of LDCT screening as the dependent variable and the prespecified covariates of RA diagnosis year, age, sex, geographic region, COPD, total number of comorbidities, RA treatment, and number of outpatient visits. Adjusted odds ratios (aORs) and 95% CIs also were reported. Sensitivity analyses were performed for patients with continuous insurance coverage during the 12 months after RA diagnosis, with LDCT receipt assessed within that period, and for patients with longer follow-up times. In addition, the change in age criterion (55 to 77 years before 2021 and 50 to 77 years thereafter) for years prior to 2021 was accounted for by excluding noneligible patients from the analysis (Supplementary Table S1). All statistical tests were two-sided, with the significance level set at 0.05. Data analysis was performed using SAS Enterprise Guide 7.1 (SAS Institute, Cary, NC).
Ethics approval
This study was reviewed and approved by The University of Texas MD Anderson Cancer Center IRB (Protocol No. 2024-1899). The requirement for informed consent was waived because the study used de-identified claims data and posed minimal risk to participants. All procedures were conducted in accordance with institutional guidelines and the ethical principles embodied in the U.S. Common Rule (45 CFR 46).
RESULTS
Participants
We identified 374,952 individuals having RA from 2014 to 2023 in the database. After restricting this group to those aged 50-77 years with continuous insurance coverage, 163,223 patients remained. Exclusions for prior lung cancer, lung transplantation, or other active malignancies reduced the sample to 142,648. Further exclusions because of serious respiratory or renal conditions brought the total to 126,789. After removing 111,448 patients without codes indicating smoking status, the final cohort consisted of 15,341 individuals with former or current smoking history and RA (Fig. 1).
Descriptive data
More than 60% of the patients were younger than 65 years (Table 1). Female patients accounted for 69.6% of the cohort. Patients were geographically distributed across all major U.S. regions, with the Midwest (33.7%) and South (33.3%) being most represented. COPD was present in 15.6% of the patients. In terms of comorbidity burden, 2.7% had three or more comorbidities. Regarding RA treatment, 44% of the patients were prescribed conventional synthetic DMARDs within 6 months of diagnosis. A smaller proportion received biologics or targeted synthetic DMARDs (5.2%) or a combination of treatments. After RA diagnosis, 22.6% of the patients had only one outpatient visit, whereas 32.1% had 10 or more visits.
Table 1.
Participant characteristics (n = 15.341)
| Characteristic | n (%) |
|---|---|
| RA diagnosis year | |
| 2014 | 2,024 (13.2) |
| 2015 | 1,270 (8.3) |
| 2016 | 2,500 (16.3) |
| 2017 | 1,482 (9.7) |
| 2018 | 1,389 (9.1) |
| 2019 | 1,544 (10.1) |
| 2020 | 1,375 (9.0) |
| 2021 | 1,539 (10.0) |
| 2022 | 1,477 (9.6) |
| 2023 | 741 (4.8) |
| Sex | |
| Female | 10,673 (69.6) |
| Male | 4,668 (30.4) |
| Age, years | |
| 50-54 | 2,793 (18.2) |
| 55-59 | 3,558 (23.2) |
| 60-64 | 3,273 (21.3) |
| 65-69 | 2,958 (19.3) |
| 70-77 | 2,759 (18.0) |
| U.S. region | |
| West | 2,363 (15.4) |
| East | 2,413 (15.7) |
| Midwest | 5,168 (33.7) |
| South | 5,105 (33.3) |
| Unknown | 292 (1.9) |
| COPD | |
| No | 12,949 (84.4) |
| Yes | 2,392 (15.6) |
| Comorbiditiesa | |
| 0 | 10,721 (69.9) |
| 1 | 3,223 (21.0) |
| 2 | 982 (6.4) |
| 3+ | 415 (2.7) |
| Treatment type within 6 months of RA diagnosis | |
| None | 6,460 (42.1) |
| Conventional synthetic DMARDs | 6,744 (44.0) |
| Biologics or targeted synthetic DMARDs | 805 (5.2) |
| Combination of conventional synthetics and biologics or targeted synthetic DMARDs | 1,332 (8.7) |
| Outpatient visits within 6 months of RA diagnosis | |
| 1 | 3,467 (22.6) |
| 2-5 | 3,270 (21.3) |
| 6-10 | 3,686 (24.0) |
| ≥10 | 4,918 (32.1) |
Number of comorbid conditions. We assessed lung comorbidities including asthma, asbestosis, silicosis, tuberculosis, lung fibrosis, sarcoidosis, pneumonia, interstitial lung disease, and chronic obstructive pulmonary disease (COPD). Other comorbidities were moderate to severe liver disease, diabetes, hypertension, stroke, congestive heart failure, peripheral vascular disease, and cerebrovascular disease
LCS rates and time trends
Among the patients with RA who were eligible for LCS, only 997 (6.5%) received LDCT screening (95% CI, 6.11-6.89%). The median time from RA diagnosis to the first LDCT claim was 452 days (IQR, 218-894 days). Among the patients who received LDCT, 215 (21.6%) underwent a second LDCT screening in 10-18 months after their first LDCT claim. In our full cohort with continuous insurance coverage, the LDCT rates were higher in patients with longer follow-up times (Supplementary Table S5), increasing from 1.8% within 6-12 months to 10.4% by 37-60 months before plateauing thereafter. In a sensitivity analysis with 12,301 patients with full insurance coverage in the 12 months after RA diagnosis, 367 (3%) patients received LDCT during the year after RA diagnosis.
Figure 2 shows the temporal trends in LDCT rates in the study cohort. We observed significantly different trends before and after 2020. Specifically, the screening rate increased from 1.1% in 2014 to a peak of 13.2% in 2020 (APC, 45.1%; p = .002). However, after 2020, the rate declined to 9.4% in 2021, 7.4% in 2022, and 1.9% in 2023 (APC, -46.7%; p = .005). We observed similar trends even when accounting for the change in age criteria for the years prior to 2021 (overall LCS rate, 7.0%), with patients diagnosed in later years having lower LDCT rates due to shorter follow-up.
Figure 2.
Temporal trends in LDCT rates among the study patients. The figure shows annual LDCT rates according to RA diagnosis year. The solid line represents LDCT rates among patients aged 50-77 years throughout 2014-2023. The dashed line represents LDCT rates using the age criteria corresponding to USPSTF recommendations during the study period: ages 55-77 years for 2014-2020 and ages 50-77 years for 2021-2023.
LCS rates by demographic and clinical characteristics
LDCT screening rates varied significantly by demographic and clinical characteristics (Table 2). Screening was slightly more common in male patients (7.4%) than in female patients (6.1%; p = .003). Age-stratified analysis demonstrated the highest screening uptake among individuals aged 62-69 years (8.1%), whereas the lowest was among those aged 50-54 years (3.9%), with a significant difference across the age groups (p < .001). We also observed geographic differences in LCS rate. Specifically, the Midwest had the highest screening rate (8.5%), whereas the West had the lowest rate (2.8%; p < .001). Furthermore, diagnosis of COPD was strongly associated with screening uptake (12.6% vs. 5.4% for no COPD diagnosis; p < .001). LDCT use also declined as the comorbidity burden increased (p = .030). Regarding treatment, patients receiving a combination of conventional synthetic and biologic/targeted DMARDs, who were assumed to have the worst disease control, had the highest screening rate (7.8%), whereas those receiving no treatment had the lowest rate (5.6%; p < .001). Finally, LCS was more common in patients with more than one outpatient visit after RA diagnosis than in those with one or no visits afterward (p < .001).
Table 2.
Rates of LCS by demographic and clinical characteristics of the study patients
| n (%) | |||
|---|---|---|---|
| Characteristic | Received LDCT (n = 997) |
Did not receive LDCT (n = 14,334) |
ρ-value |
| Sex | |||
| Female | 652 (6.11%) | 10,021 (93.89%) | .0030 |
| Male | 345 (7.39%) | 4,323 (92.61%) | |
| Age, years | |||
| 50-54 | 110 (3.94) | 2,683 (96.06) | <.0001 |
| 55-59 | 261 (7.34) | 3,297 (92.66) | |
| 60-64 | 253 (7.73) | 3,020 (92.27) | |
| 65-69 | 240 (8.11) | 2,718 (91.89) | |
| 70-77 | 133 (4.82) | 2,626 (95.18) | |
| U.S. region | |||
| West | 66 (2.79) | 2,297 (97.21) | <.0001 |
| East | 167 (6.92) | 2,246 (97.26) | |
| Midwest | 439 (8.49) | 4,729 (91.51) | |
| South | 317 (6.21) | 4,788 (93.79) | |
| Unknown | 8 (2.74) | 284 (97.26) | |
| COPD | |||
| No | 695 (5.37) | 12,254 (94.6) | <.0001 |
| Yes | 302 (12.63) | 2,090 (87.37) | |
| Comorbiditiesa | |||
| 0 | 730 (6.81) | 9,991 (93.19) | .0300 |
| 1 | 199 (6.17) | 3,024 (93.83) | |
| 2 | 51 (5.19) | 931 (94.81) | |
| ≥3 | 17 (4.10) | 398 (95.90) | |
| Treatment type within 6 months of RA diagnosis | |||
| None | 359 (5.56) | 6,101 (94.44) | <.0010 |
| Conventional synthetic DMARDs | 489 (7.25) | 6,255 (92.75) | |
| Biologics or targeted synthetic DMARDs | 45 (5.59) | 760 (94.41) | |
| Combination of conventional synthetics and biologics or targeted synthetic DMARDs | 104 (7.81) | 1,228 (92.19) | |
| Outpatient visits within 6 months of RA diagnosis | <.0010 | ||
| 1 | 85 (2.45) | 3,382 (97.55) | |
| 2-5 | 257 (7.86) | 3,013 (92.14) | |
| 6-10 | 294 (7.98) | 3,392 (92.02) | |
| ≥10 | 361 (7.34) | 4,557 (92.66) | |
Comorbidities included moderate to severe liver disease, congestive heart failure, diabetes, hypertension, stroke, peripheral vascular disease, cerebrovascular disease, asthma, asbestosis, silicosis, tuberculosis, lung fibrosis, sarcoidosis, pneumonia, and interstitial lung disease.
Determinants of LCS in patients with RA
In our multivariable logistic regression analysis, several factors were significantly associated with increased odds of receiving LDCT screening (Table 3). Compared with patients diagnosed with RA in 2014, those diagnosed in subsequent years had progressively higher odds of screening, peaking in 2020 (aOR, 9.11 [95% CI, 5.77-14.37]; p < .0001). Also, male patients had higher odds of screening than female patients (aOR, 1.24 [95% CI, 1.08-1.42]; p = .003). Age was also a significant factor, as individuals aged 55-69 years had nearly double the odds of screening than those aged 50-54 years (aOR, 1.90-1.91; all p < .0001). Compared with those in the West, patients living in other geographical areas had significantly greater odds of undergoing screening (all p < .0001). COPD was one of the strongest predictors of LDCT uptake (aOR, 2.85 [95% CI, 2.43-3.34]; p < .0001). In addition, comorbidity burden was inversely associated with screening. Patients with one, two, or three or more non-COPD comorbidities had significantly lower odds of receiving LCS than those without these comorbidities (aOR, 0.79, 0.60, and 0.39, respectively; all p ≤ .006). Treatment also influenced screening likelihood. Patients receiving conventional synthetic DMARDs (aOR, 1.27; p = .001) or combination therapy (aOR, 1.52; p < .001) were more likely to be screened than were untreated patients. Finally, compared with patients with only one outpatient visit in the 6 months after RA diagnosis, those with more outpatient visits (2-5, 6-10, and 10+) had significantly higher odds of receiving LCS (aOR, 2.38, 2.41, and 2.15, respectively; all p < .001).
Table 3.
Multivariable logistic regression model used to identify factors associated with LCS uptake
| Factor | aOR (95% CI) | ρ-value |
|---|---|---|
| RA diagnosis year | ||
| 2014 | Ref. | |
| 2015 | 3.77 (2.25, 6.32) | <.0001 |
| 2016 | 4.72 (2.97, 7.51) | <.0001 |
| 2017 | 5.55 (3.47, 8.88) | <.0001 |
| 2018 | 6.34 (3.98, 10.12) | <.0001 |
| 2019 | 6.72 (4.24, 10.65) | <.0001 |
| 2020 | 9.11 (5.77, 14.37) | <.0001 |
| 2021 | 6.67 (4.21, 10.57) | <.0001 |
| 2022 | 4.94 (3.09, 7.91) | <.0001 |
| 2023 | 1.20 (0.61, 2.37) | .5980 |
| Sex | ||
| Female | Ref. | |
| Male | 1.24 (1.08, 1.42) | .0030 |
| Age, years | ||
| 50-54 | Ref. | |
| 55-59 | 1.90 (1.50, 2.39) | <.0001 |
| 60-64 | 1.89 (1.49, 2.39) | <.0001 |
| 65-69 | 1.91 (1.50, 2.43) | <.0001 |
| 70-77 | 1.16 (0.89, 1.51) | .2850 |
| U.S. region | ||
| West | Ref. | |
| East | 2.70 (2.01, 3.63) | <.0001 |
| Midwest | 2.44 (1.87, 3.20) | <.0001 |
| South | 2.07 (1.57, 2.72) | <.0001 |
| Unknown | 1.49 (0.70, 3.18) | .3010 |
| COPD | ||
| No | Ref. | |
| Yes | 2.85 (2.43, 3.34) | <.0001 |
| Comorbiditiesa | ||
| 0 | Ref. | |
| 1 | 0.79 (0.66, 0.94) | .0080 |
| 2 | 0.60 (0.44, 0.82) | .0010 |
| ≥3 | 0.39 (0.24, 0.66) | <.0001 |
| Treatment type within 6 months of RA diagnosis | ||
| None | Ref. | |
| Conventional synthetic DMARDs | 1.27 (1.10, 1.47) | .0020 |
| Biologics or targeted synthetic DMARDs | 1.00 (0.72, 1.38) | .9870 |
| Combination of conventional synthetics and biologics or targeted synthetic DMARDs | 1.52 (1.20, 1.92) | .0010 |
| Outpatient visits within 6 months of RA diagnosis | ||
| 1 | Ref. | |
| 3-5 | 2.38 (1.81, 3.15) | <.0010 |
| 6-10 | 2.41 (1.84, 3.16) | <.0010 |
| ≥10 | 2.15 (1.64, 2.81) | .0010 |
Comorbidities included moderate to severe liver disease, congestive heart failure, diabetes, hypertension, stroke, peripheral vascular disease, cerebrovascular disease, asthma, asbestosis, silicosis, tuberculosis, lung fibrosis, sarcoidosis, pneumonia, and interstitial lung disease.
DISCUSSION
In this retrospective claims-based study, we found that LDCT screening for lung cancer was underused among patients with RA (6.5% or 7.0% of patients according to the eligibility criteria used), observing notable time trends and disparities in screening according to demographic and clinical factors. Whereas prior studies explored LDCT in general populations, to our knowledge, none have focused on patients with RA, a group with known pulmonary risks and underrecognized cancer screening needs.
Screening rates in the study population increased steadily starting in 2014, with the largest year-over-year gains occurring from 2016 to 2020, suggesting a growing awareness of screening guidelines. The proportion of patients who eventually received LDCT increased as the follow-up time increased, peaking at 10.4% at 37-60 months of follow-up. After 60 months, the rate declined slightly to 9.1%, suggesting that most LDCT screenings occurred within the first 3-5 years after RA diagnosis. However, even with 5+ years of follow-up, fewer than 1 in 10 patients underwent LDCT, indicating low adoption/implementation despite having continuous insurance coverage. These uptake rates are considerably lower than expected as suggested by established implementation programs of LCS (30-70%) (21), or those reported in current clinical practice (16%) (22-24). Some of the apparent temporal differences in LDCT screening rate may reflect shorter follow-up among patients diagnosed in later years or broader disruptions in preventive care during the COVID-19 pandemic; however, our sensitivity analysis restricted to patients with uniform follow-up similarly demonstrated low uptake.
Although lung cancer risk increases with age and cumulative smoking exposure, older patients (70-77 years) and those with higher comorbidity burdens were less likely to be screened. This finding contrasts with prior studies in which greater comorbidity burden has been associated with higher screening rates, potentially due to more frequent healthcare encounters (25, 26). This discrepancy raises concerns that some high-risk patients may be overlooked due to competing health priorities, risk-benefit concerns, or appropriate adherence of providers to LCS eligibility criteria. The USPSTF guidelines recommend excluding individuals from screening for lung cancer if they have significant health conditions that increase the risks of harm from follow-up procedures or make them poor candidates for curative lung cancer treatment. These conditions include advanced heart failure, severe COPD, end-stage renal disease, cirrhosis with decompensation, and other life-limiting conditions. Many of these conditions are more common among patients with multiple comorbidities, which may explain the lower screening rates observed in patients with more than 3 comorbid conditions.
Similar to our finding of a low LDCT screening rate in the RA population, a nationally representative analysis using 2022 Behavioral Risk Factor Surveillance System data demonstrated that only 18.1% of U.S. adults eligible for LCS had undergone LDCT, with substantial variation across states and particularly lower rates of LCS in regions with higher lung cancer mortality rates. Disparities in adoption were primarily driven by healthcare access factors (i.e., insurance status, usual source of care, Medicaid expansion, local screening capacity) rather than clinical eligibility alone (27). Prior evidence also suggests state-level variation in LCS resources and implementation (e.g., greater investment in states with high tobacco use than in states with lower use, such as California) (24).
Other studies highlighted the role of chronic pulmonary disease in facilitating screening uptake and the potential of LDCT screening to benefit well-selected high-risk populations. For example, Metwally et al. (8) found that more than half of individuals undergoing LDCT screening had a lung-related comorbidity, and de-Torres et al. (28) showed that patients with COPD enrolled in structured screening programs were often diagnosed with lung cancer at earlier stages with favorable long-term survival. These findings align with current guidelines that prioritize pulmonary risk (e.g., COPD, emphysema) when determining eligibility for LDCT screening, and with our own observation of a strong association between COPD and LDCT receipt. We found that patients with RA receiving more RA medications (a proxy for disease severity) were more likely to be screened. These findings are similar to those described by Rustagi et al. (29), who reported greater screening among individuals with poorer health. Our finding that 93.5% of LCS-eligible patients with RA did not receive LDCT, particularly when combined with lower screening rates among female than male patients, aligns with prior studies identifying disparities in screening uptake according to sex. This observation is especially concerning given the high proportion of women in this population and aligns with work by Raju et al. (30), who found that LCS-eligible nonparticipants in screening were markedly more likely to be female than male and identified multiple barriers to LCS, including lack of awareness, fear of results, and logistical challenges such as travel distance.
Our study had several strengths, including that it uses a large, nationally representative claims database, and several strategies were employed to minimize potential sources of bias. RA diagnoses, smoking status, comorbidities, and receipt of LDCT scans were identified using previously validated diagnosis and procedure codes. To address potential confounding, multivariable logistic regression models with adjustment for covariates selected a priori based on clinical relevance were used. Patients were observed from the RA index date until the earliest date of LDCT receipt, disenrollment from the health plan, death, or the end of the study period. As disenrollment may result in informative censoring, especially if it is related to the patient’s health status, this was considered a potential source of bias. To account for changes in coding practices over time, including the transition from ICD-9-CM to ICD-10-CM in 2015, we used definitions that incorporated both code sets when applicable. However, our study had limitations. Our analysis was designed to assess annual LDCT utilization among RA patients rather than incident initiation of screening, therefore, we did not apply a washout period. Some patients may have undergone LDCT before RA diagnosis; however, this number was small (n=97, 0.63%), and our results were not affected when considering these patients separately. Similarly, congestive heart failure (CHF) was present in 568 patients (5.11% of the cohort), but lung cancer screening rates did not differ significantly between patients with and without CHF (5.11% versus 6.55%, respectively). Smoking status was ascertained using diagnostic codes, therefore, the absence of a smoking-related diagnostic code should not be interpreted as confirmation of never smoking status. In addition, diagnostic codes do not capture smoking intensity, duration, pack-years or quit date. Consequently, we were unable to confirm whether individual patients met all current LCS eligibility criteria. The direction of bias in estimating the screening-eligible RA population is uncertain. Our approach may have underestimated eligibility by missing patients with uncoded smoking history while also overestimating eligibility among identified smokers because pack-years and quit date could not be confirmed. Claims data may also not fully distinguish LDCT performed for LCS from thoracic imaging performed for diagnostic evaluation because billing codes do not capture the complete clinical indication for imaging. Although we used claims-based LDCT definitions and exclusion criteria intended to reduce the capture of diagnostic or surveillance imaging, residual misclassification may remain. Therefore, the observed LDCT rates should be interpreted as rates of billed LDCT screening rather than screening confirmed by through radiology report or medical record review. In addition, although we contextualized LDCT uptake in the RA cohort using published estimates from the general screening-eligible population, these comparisons should be interpreted cautiously because prior studies used different data sources, eligibility definitions, study periods, and LDCT ascertainment methods. Finally, we did not evaluate incident lung cancer diagnoses, cancer stage, pulmonary nodule findings, follow-up imaging after abnormal screening, or downstream diagnostic procedures. Therefore, we could not determine whether observed LDCT utilization patterns translated into differences in cancer detection, stage shift, or clinical outcomes. Although we adjusted for key potential confounders such as age, sex, comorbidities, and markers of disease severity using multivariable logistic regression models, residual confounding may have remained. Important factors such as socioeconomic status, race/ethnicity, healthcare access, disease duration, physician awareness of the guidelines for LCS, referral and ordering practices, and patient decision-making were not available. Therefore, we could not determine which patient, provider, or health-system factors contributed to LDCT uptake or the low screening rate (31).
In the future, studies should directly compare LDCT uptake among screening-eligible individuals with and without RA. Also, researchers should evaluate targeted strategies to improve LCS rates in RA populations at high-risk for lung cancer, particularly those with complex health profiles who may be underserved by current screening strategies. Clinical decision support tools, patient decision aids, and multidisciplinary care models may help bridge this gap by facilitating more personalized and informed screening discussions. To inform such strategies, additional research is needed to assess providers’ awareness of LCS recommendations and explore barriers and facilitators that influence decision-making about LCS among patients with RA and clinicians. Comparative studies are also needed to evaluate the performance of existing lung cancer risk models in RA populations and to assess the impact of RA-specific factors (e.g., treatments, pulmonary complications, comorbidities) on screening outcomes. Furthermore, the potential ability of circulating biomarkers to complement LDCT in early lung cancer detection warrants investigation, along with implementation research to examine the feasibility, acceptability, and practicality of integrating risk models and biomarkers into RA clinical care. Importantly, research strategies should also address not only the initial adoption of but also adherence to annual screening over time to maximize the effectiveness of LCS programs in RA populations.
Our findings demonstrate that patients with RA remain underrepresented in LCS despite their known elevated risk of this cancer. We identified several factors associated with lack of LDCT, including female sex, residence in the western United States, age older than 69 years or younger than 55 years, absence of COPD, presence of multiple comorbidities, and indicators of low RA severity (e.g., limited use of RA therapies). These patterns highlight the complex interplay among demographic, clinical, and healthcare system factors that may contribute to gaps in LCS uptake. Addressing the unique challenges faced by individuals with RA will be essential to improving the equitable implementation of LCS in this high-risk population.
Supplementary Material
PREVENTION RELEVANCE.
This study is relevant to cancer prevention because lung cancer screening is an established strategy for reducing lung cancer mortality, yet many high-risk patients do not receive timely or consistent screening. Our study is groundwork for future efforts to improve early lung cancer detection and prevention in high-risk populations.
ACKNOWLEDGEMENTS
We thank Donald R. Norwood, Scientific Editor in the Research Medical Library at The University of Texas MD Anderson Cancer Center, for editing this article.
Funding
M.A. Lopez-Olivo’s work is supported by the NIH/NCI (K08 CA237619). S. Giordano is funded by a Susan G. Komen grant (SAC 150051). R.J. Volk is funded in part by the Cancer Prevention & Research Institute of Texas (RP240508) and by the NIH/NCI under award number P30CA016672.
Footnotes
Conflicts of Interest: The authors declare no potential conflicts of interest
Data availability
The data underlying this study are derived from IQVIA PharMetrics® Plus for Academics Closed Health Plan claims database and are subject to restrictions. The investigators do not have permission to share the data. Access to the data can be requested directly from IQVIA PharMetrics® Plus (https://www.iqvia.com/solutions/real-world-evidence/real-world-data-and-insights), subject to their data use agreements and approval processes.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this study are derived from IQVIA PharMetrics® Plus for Academics Closed Health Plan claims database and are subject to restrictions. The investigators do not have permission to share the data. Access to the data can be requested directly from IQVIA PharMetrics® Plus (https://www.iqvia.com/solutions/real-world-evidence/real-world-data-and-insights), subject to their data use agreements and approval processes.