Abstract
Rationale: Although lung cancer screening (LCS) with low-dose computed tomography (LDCT) is now recommended for those meeting standard risk factor–based eligibility criteria, the role of comorbidity in the uptake of LCS with LDCT in an older real-world U.S. population is not well established.
Objectives: To examine the relationships between comorbidity, functional status, and LCS utilization in the United States.
Methods: Using population-based data from the 2017–2019 Behavioral Risk Factor Surveillance System, we examined the association of comorbid conditions and functional limitations regarding activities of daily living with LCS utilization among participants that met the LCS criteria based on the U.S. Preventive Service Taskforce guidelines. We employed multivariable weighted logistic regression models to evaluate these associations, both overall and within subgroups defined by age (<65 yr vs. ⩾65 yr), sex, and smoking history.
Results: Of 11,214 participants who met the eligibility criteria for LCS, 1,731 (16%) underwent LCS with LDCT. The majority were White (90%), male (55%), former smokers (52%), and living with at least one chronic comorbid condition (77%). More than 28% had three or more comorbid conditions, and approximately 40% of participants reported having some form of functional limitations. In the multivariable models, the likelihood of undergoing LCS with LDCT within the past year was positively associated with higher amount of comorbidity (⩾5 vs. 0: adjusted odds ratio, 2.34; 95% confidence interval [CI], 1.22–4.48) but not with functional limitations (⩾3 vs. 0: adjusted odds ratio, 1.00; 95% CI, 0.66–1.50).
Conclusions: The presence of comorbid conditions is associated with a higher likelihood of undergoing LCS with LDCT. Because poor health status may diminish the benefits of screening, future research is needed to precisely characterize the health status of LCS-eligible individuals.
Lung cancer is the leading cause of cancer death in the United States and worldwide (1), largely because most patients have advanced, incurable disease at the time of diagnosis (2). According to the most recent estimates, lung cancer will account for 235,760 new cases in the United States and will be responsible for 131,880 deaths in 2021 (3). However, lung cancer screening (LCS) with low-dose computed tomography (LDCT) has the potential to improve lung cancer outcomes through early detection (4–9). In 2011, the National Lung Screening Trial showed that, compared with chest radiography, three rounds of annual screening with LDCT reduced the risk of lung cancer death by 20% among high-risk current and former smokers (10). Furthermore, results from the NELSON (Nederlands–Leuvens Longkanker Screenings Onderzoek) LCS trial have found that screening with computed tomographic (CT) scans among high-risk individuals reduced lung cancer–specific mortality. In addition, individuals who underwent screening also had low rates of follow-up invasive procedures (11). Consequently, LCS with LDCT is now recommended for those meeting standard age- and smoking-based eligibility criteria (12, 13). However, compared with the National Lung Screening Trial participants, U.S. adults eligible for LCS are nearly twice as likely to be over the age of 70 years and are substantially more likely to be a current smoker (10, 14). Furthermore, of the nearly 8.6 million LCS-eligible adults in the United States, ∼3 million have chronic coexisting conditions that may decrease the net benefit of screening for early-stage disease (15).
The U.S. Preventive Services Taskforce (USPSTF) guidelines on LCS recommend excluding persons unable to tolerate surgical resection (13), whereas other guidelines state that screening should be restricted to those “in reasonably good health” (16) or those able to “tolerate cancer treatment” (17–19). Notably, all these statements are qualitative and subjective; they do not provide objective or transparent criteria for assessing a patient’s suitability for LCS. Clinicians may wish to ensure that higher-risk patients benefit from the detection of localized disease that LDCT offers; however, to date, there is inadequate evidence to guide selection when patients present with multimorbidity (i.e., chronic coexisting conditions) or functional limitations. This presents challenges for clinicians who must assess and communicate risk. One recent study among U.S. veterans found that Charlson Comorbidity Index was not associated with either receiving or agreeing to receive LCS (20).
Consistent with evidence that individuals with comorbidities are less likely to undergo cancer screening (21, 22), we hypothesized that LCS-eligible individuals with high comorbidity burden and functional limitations would have lower utilization of LCS. Hence, we set out to examine the relationships between comorbidity, functional status, and LCS utilization. Toward this end, we used the data from the 2,017–2019 Behavioral Risk Factor Surveillance System (BRFSS) to examine the influence of these factors on the LCS uptake in the U.S. general population.
Methods
Study Population
The BRFSS is a nationwide cross-sectional telephone survey initiated in the United States in 1984 and implemented by the U.S. Centers for Disease Control and Prevention (CDC) Population Health Service Branch. BRFSS collects self-reported, nonidentifiable sociodemographic information, health-related characteristics, and histories of medical service use from adult residents (⩾18 yr) across all 50 states, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, Guam, American Samoa, and Palau (23). Participants are chosen randomly from a selected list of telephone and cell phone numbers from a noninstitutionalized target population. For our current analysis, we selected participants on the basis of the 2013 update to the USPSTF LCS guidelines (13). Specifically, the 2017–2019 BRFSS measured age at smoking initiation and age of last smoking if the smoker had quit smoking. The questionnaire also measured how many cigarettes participants smoked per day, and we assumed that each pack contained 20 cigarettes (24). We took these measures into account and calculated each participant’s pack-year smoking history. Finally, participants aged 55–80 years who had a 30 pack-year smoking history and currently smoked or had quit smoking within the past 15 years were considered to be eligible for LCS.
Exposure Variables
Nine types of comorbid conditions were measured by self-report in the 2017–2019 BRFSS. Particularly, participants self-reported whether they had myocardial infarction, coronary heart disease, stroke, diabetes mellitus, chronic kidney disease, arthritis, asthma, chronic obstructive pulmonary disease (COPD), or a personal history of malignancy other than lung cancer (24). We developed a composite score of these comorbidities and categorized it as an ordinal variable (0, 1–2, 3–4, and ⩾5). Furthermore, the 2017–2019 BRFSS used four questions to measure the presence of functional impairment (24). These questions measured whether the participants had cognitive impairment and difficulty in walking, dressing, and completing errands alone. We summed the number of functional limitations to create a composite score and treated it as an ordinal variable (0, 1–2, and ⩾3).
Outcome Variable
The outcome of interest was measured by the following question in Module 21 of BRFSS questionnaire that was added in 2017 questionnaire on LCS and was later repeated in 2018 and 2019: “The next question is about CT or CAT scans. During this test, you lie flat on your back on a table. While you hold your breath, the table moves through a donut shaped X-ray machine while the scan is done. In the last 12 months, did you have a CT or CAT scan?” Participants answering “Yes to check for lung cancer” were treated as having a history of screening utilization within the past year. People reporting “No” or “Had a CT scan, but for some other reason” were treated as not having screening. Participants who refused to answer this question or reported “Don’t know/Not sure” were classified as missing and not considered into our analysis. Although this question was included in the LCS module, the participants were not specifically asked if the CT scan was part of a screening program, which might limit its specificity. In 2017, Florida, Georgia, Kansas, Maine, Maryland, Missouri, Nevada, Oklahoma, Vermont, and Wyoming incorporated this module. In 2018, Delaware, Maryland, New Jersey, South Dakota, Texas, West Virginia, Oklahoma, and Maine incorporated the LCS in their survey, whereas Arizona, Idaho, Kentucky, Maine, Minnesota, Missouri, Montana, North Carolina, North Dakota, Pennsylvania, Rhode Island, South Carolina, Utah, Vermont, West Virginia, Wisconsin, Maryland, Kansas, Nebraska, and Oklahoma incorporated the module in 2019.
Covariates
Other variables of interest included self-reported age (categorized as 55–64 yr, 65–74 yr, and 75–79 yr), sex, race (White, Black, and other), education (high school or less, some college, and college graduate or higher) and marital status (married vs. other). Marital status was treated as an important factor in our analysis because of evidence suggesting that marital status could be associated with health and might impact patients’ obligation to their spouses, which can subsequently influence screening behaviors (25–27). In the current analysis, a recent history of a clinical checkup and health coverage were used to represent access to health care, which may affect the risk of chronic diseases and the likelihood of screening utilization (28). Participants were categorized as having health insurance if they reported having any coverage for prepaid plans (e.g., HMO), government plans (e.g., Medicare), or Indian Health Service. Self-rated health was treated as a binary variable (excellent, very good, and good vs. fair and poor) based on the cutoff validated by researchers at the CDC. The following four lifestyle-related factors were considered in the analysis because they reflected health behavior patterns and awareness of disease prevention: 1) patients with a body mass index (BMI) ⩾30 kg/m2 were considered to have obesity, 2) patients who had no physical activities or exercises (e.g., running, calisthenics, golf, gardening, or walking for exercise) during past month were considered physically inactive, 3) smoking status was treated as current or former smoker on the basis of self-reported information, and 4) heavy alcohol consumption was defined as ⩾14 drinks/wk for men and ⩾7 drinks/wk for women. Selection of covariates was based on a priori knowledge regarding their associations with exposures and the outcome of interest.
Statistical Analysis
Our analysis included 11,214 respondents in BRFSS. We took into account the complex survey design structure through a modification of the BRFSS-provided survey (29). All estimates were corrected for sampling weights (30). The statistical procedure of using weights to analyze survey data helps generalize study findings beyond the study sample. We produced weighted frequencies for sociodemographic and health-related characteristics. Unadjusted and multivariable weighted logistic regression models were used to calculate crude and adjusted odds ratios (aORs) for LCS uptake within the past 12 months (odds ratio) by multimorbidity and functional limitations while adjusting for other sociodemographic and clinical factors. The multivariable logistic regression model included both the number of comorbidities and the functional limitations and were adjusted for age, sex, race, education background, marital status, clinical checkup, health coverage, obesity, heavy alcohol intake, physical activity, smoking status, and health status. We adjusted for these variables a priori, as they have been shown to impact cancer screening behaviors (31, 32). Tests for trends were conducted by treating number of comorbidities and functional limitations as continuous variables in the models. In addition, we performed subgroup analysis by age at interview (<65 yr vs. ⩾65 yr), sex (female vs. male), and smoking status (former smoker vs. current smoker) to assess whether the impact of comorbidities and functional impairment differed across these groups. More specifically, we added interaction terms in addition to main effect terms between exposures and these covariates into the multivariable model, and Wald tests were used to examine whether the interactions were statistically significant. For current analysis, two-sided P values <0.05 were considered to be statistically significant. All statistical analyses were conducted using STATA version 15.0.
Results
Characteristics of the Study Population
We identified 11,214 respondents who were LCS eligible as per USPSTF criteria. Of these, 1,731 (16.3%) underwent LCS within the past year. The majority of respondents were men (55%), White (90%), and married (54%). Most participants reported an education of high school or less (55%), had healthcare coverage (93%), and had a clinical checkup within the past year (85%). The majority of respondents (52%) were former smokers, had a BMI <30 kg/m2 (66%), participated in physical activity within past month (56%), and were not heavy drinkers (90%). Compared with nonscreeners, those who had LCS within the past year were more likely to be older, have a graduate degree, have a clinical checkup last year, have healthcare coverage, be physically inactive, and have lower self-rated health.
Distribution of Exposures of Interest
The prevalence of each individual comorbidity and functional impairment is summarized in Table 1, including myocardial infarction (16%), coronary heart disease (14%), stroke (9%), diabetes (22%), chronic kidney disease (6%), arthritis (50%), asthma (15%), COPD (34%), and personal history of cancer (14%). Twenty-three percent of participants in the screening-eligible population reported having no comorbidities, whereas 22% were living with three to four comorbidities and almost 6% had five or more comorbidities. The most prevalent functional limitations included limitations in walking (35%), cognitive impairment (16%), and performing errands alone (14%). Almost 41% of participants reported not having any functional limitations, whereas 10% of our screening-eligible population reported three or more functional limitations. The participants who underwent LCS within the past year had a higher prevalence of myocardial infarction, coronary heart disease, stroke, arthritis, asthma, COPD, prior history of cancer, and higher overall comorbidity score. Furthermore, screened participants were more likely to have functional limitations, including cognitive impairments, as compared with nonscreeners. (Table 1)
Table 1.
Overall distribution of study characteristics in BRFSS 2019 eligible for lung cancer screening
| Overall (N = 11,214) [n (%)] | Nonscreener (n = 9,483; 83.7%) [n (%)] | Screener (n = 1,731; 16.3%) [n (%)] | |
|---|---|---|---|
| Covariates | |||
| Age at interview (n = 11,214) | |||
| 55–64 yr | 5,502 (57.34) | 4,798 (58.52) | 704 (51.28) |
| 65–74 yr | 4,585 (35.31) | 3,738 (34.14) | 847 (41.32) |
| 75–79 yr | 1,127 (7.35) | 947 (7.34) | 180 (7.39) |
| Sex (n = 11,211) | |||
| Female | 5,211 (44.57) | 4,376 (43.5) | 835 (50.1) |
| Male | 6,000 (55.42) | 5,106 (56.5) | 894 (49.9) |
| Race (n = 11,214) | |||
| White | 10,213 (89.54) | 8,626 (89.32) | 1,587 (91.09) |
| Black | 435 (5.89) | 373 (6.19) | 62 (4.38) |
| Other | 477 (4.57) | 408 (4.57) | 69 (4.53) |
| Education (n = 11,200) | |||
| High school or less | 5,517 (55.09) | 4,681 (54.93) | 836 (55.91) |
| Attended college | 3,563 (33.14) | 3,036 (33.47) | 527 (31.42) |
| Graduated from college | 2,120 (11.77) | 1,755 (11.59) | 365 (12.67) |
| Marital status (n = 11,177) | |||
| Unmarried | 5,928 (46.43) | 4,982 (45.84) | 946 (49.43) |
| Married | 5,249 (53.57) | 4,467 (54.16) | 782 (50.57) |
| Clinical checkup last year (n = 11,165) | |||
| No | 1,724 (15.44) | 1,623 (17.58) | 101 (4.48) |
| Yes | 9,441 (84.56) | 7,818 (82.42) | 1,724 (95.52) |
| Health coverage (n = 11,198) | |||
| No | 667 (7.09) | 630 (8.13) | 37 (1.79) |
| Yes | 10,531 (92.91) | 8837 (91.87) | 1,684 (98.21) |
| Obesity (BMI ⩾ 30 kg/m2) (n = 10,864) | |||
| No | 7,182 (65.65) | 6,063 (65.90) | 1,119 (64.35) |
| Yes | 3,682 (34.35) | 3,110 (34.10) | 572 (35.65) |
| Physical activity in last month (n = 11,189) | |||
| No | 4,793 (43.92) | 3,981 (42.84) | 812 (49.5) |
| Yes | 6,396 (56.08) | 5,484 (57.16) | 912 (50.5) |
| Heavy alcohol intake* (n = 11,038) | |||
| No | 9,981 (90.17) | 8,433 (89.99) | 1,548 (91.04) |
| Yes | 1,057 (9.83) | 902 (10.01) | 155 (8.96) |
| Smoking status (n = 11,214) | |||
| Former | 5,520 (51.51) | 4,644 (51.96) | 876 (49.18) |
| Current | 5,694 (48.49) | 4,839 (48.04) | 855 (50.82) |
| Low general health† (n = 11,186) | |||
| No | 6,866 (62.81) | 6,010 (65.36) | 856 (49.75) |
| Yes | 4,320 (37.19) | 3,451 (34.64) | 869 (50.25) |
| Exposures | |||
| Myocardial infarction (n = 11,160) | |||
| No | 9,366 (84.45) | 7,973 (84.77) | 1,393 (82.8) |
| Yes | 1,794 (15.55) | 1,463 (15.23) | 331 (17.2) |
| Coronary heart disease (n = 11,057) | |||
| No | 9,456 (86.48) | 8,061 (86.77) | 1,395 (84.99) |
| Yes | 1,601 (13.52) | 1,291 (13.23) | 310 (15.01) |
| Stroke (n = 11,185) | |||
| No | 10,070 (90.58) | 8,557 (90.74) | 1,513 (89.77) |
| Yes | 1,115 (9.42) | 803 (9.26) | 212 (10.23) |
| Diabetes mellitus (n = 11,202) | |||
| No | 8,759 (78.26) | 7,422 (79.56) | 1,337 (71.58) |
| Yes | 2,443 (21.74) | 2,051 (20.44) | 392 (28.42) |
| Chronic kidney disease (n = 11,181) | |||
| No | 10,482 (93.85) | 8,887 (94.13) | 1,595 (92.42) |
| Yes | 699 (6.15) | 575 (5.87) | 124 (7.58) |
| Arthritis (n = 11,174) | |||
| No | 5,358 (50.08) | 4,633 (50.82) | 725 (46.31) |
| Yes | 5,816 (49.92) | 4,816 (49.18) | 1,000 (53.69) |
| Asthma (n = 11,166) | |||
| No | 9,412 (84.86) | 8,054 (86.25) | 1,358 (77.69) |
| Yes | 1,754 (15.14) | 1,392 (13.75) | 362 (22.31) |
| COPD (n = 11,144) | |||
| No | 7,085 (66.03) | 6,320 (70.59) | 765 (42.71) |
| Yes | 4,059 (33.97) | 3,100 (29.41) | 959 (57.29) |
| Personal history of cancer (n = 11,214) | |||
| No | 9,468 (85.68) | 8,241 (88.23) | 1,227 (72.56) |
| Yes | 1,746 (14.32) | 1,242 (11.77) | 504 (27.44) |
| Number of comorbidities (n = 10,814) | |||
| 0 | 2,209 (22.7) | 2,039 (24.8) | 170 (11.92) |
| 1–2 | 5,406 (49.22) | 4,635 (50.16) | 771 (44.33) |
| 3–4 | 2,471 (21.93) | 1,936 (19.72) | 535 (33.33) |
| ⩾5 | 728 (6.15) | 541 (5.32) | 187 (10.43) |
| Cognitive impairment (n = 11,148) | |||
| No | 9,255 (83.75) | 7,885 (84.3) | 1,370 (80.93) |
| Yes | 1,893 (16.25) | 1,542 (15.7) | 351 (19.07) |
| Difficulty in walking (n = 11,182) | |||
| No | 7,172 (64.86) | 6,211 (66.22) | 961 (57.84) |
| Yes | 4,010 (35.14) | 3,244 (33.78) | 766 (42.15) |
| Difficulty in dressing (n = 11,191) | |||
| No | 10,071 (89.98) | 8,576 (90.8) | 1,495 (85.76) |
| Yes | 1,120 (10.02) | 889 (9.2) | 231 (14.24) |
| Difficulty in doing errand alone (n = 11,185) | |||
| No | 9,543 (86.09) | 8,158 (87.32) | 1,385 (79.79) |
| Yes | 1,642 (13.91) | 1,302 (12.68) | 340 (20.21) |
| Number of functional impairments (new) (n = 11,070) | |||
| 0 | 6,313 (58.58) | 5,496 (60.21) | 817 (50.2) |
| 1-2 | 3,687 (31.76) | 3,034 (30.87) | 653 (36.34) |
| ⩾3 | 1,070 (9.66) | 832 (8.92) | 238 (13.46) |
| Year of screening (n = 11,214) | |||
| 2017 | 3,047 (29.21) | 2,604 (30.0) | 443 (25.16) |
| 2018 | 2,413 (23.03) | 2,067 (21.97) | 346 (28.48) |
| 2019 | 5,754 (48.76) | 4,812 (48.03) | 942 (46.36) |
Definition of abbreviations: BMI = body mass index; BRFSS = Behavioral Risk Factor Surveillance System; COPD = chronic obstructive pulmonary disease.
Screeners were defined as people who had a computed tomographic or computed axial tomographic scan to detect for lung cancer in the past 12 mo. P value was calculated using χ2 test. P < 0.05 indicated statistical significance.
Adult men having more than 14 drinks/wk and adult women having more than 7 drinks/wk were treated as heavy alcohol consumers
Participants reporting general health condition as fair or poor were considered as having low general health.
Impact of Comorbidities and Functional Impairment on Screening Utilization
Table 2 summarizes the association of comorbidity burden and functional impairment on LCS within the last year using unadjusted and multivariable models. LCS frequency increased with the increasing number of comorbidities and functional limitations (Table 2). In multivariable models, participants with a higher comorbidity burden (⩾5 vs. 0: aOR, 2.34; 95% confidence interval [CI], 1.22–1.48; P trend < 0.001; P global = 0.0001) had a higher likelihood of undergoing LCS within the past year compared with participants with no comorbidity burden. We did not find any association between increasing functional impairment and LCS utilization (⩾3 vs. 0: aOR, 1.00; 95% CI, 0.66–1.50; P trend = 0.80; P global = 0.28). We saw evidence of differential association of functional limitations with LCS uptake when stratified by age groups (i.e., higher burden of functional limitations was associated with higher uptake of LCS among participants less than 65 years of age and an inverse association was observed among participants 65 years and older (P interaction = 0.02) (Table E1 in the online supplement). Further subgroup analyses suggested that association between comorbidities or functional impairment and screening uptake did not vary by sex or smoking history (Tables E2 and E3).
Table 2.
Associations of lung cancer screening with comorbidity and functional impairment
| Number of Comorbidities * |
Screening rate (95% CI) [%]
N = 10,814 |
cOR (95% CI)
N = 10,814 |
aOR (95% CI)
§
N = 10,039 |
|---|---|---|---|
| Comorbidities | 7.69 (6.65–8.88) | Ref | Ref |
| 1–2 | 14.26 (13.35–15.21) | 1.83 (1.11–3.05) | 1.43 (0.88–2.34) |
| 3–4 | 21.65 (20.07–23.31) | 3.51 (2.07–5.95) | 2.35 (1.38–4.01) |
| ⩾5 | 25.68 (22.64–28.98) | 4.07 (2.26–7.35) | 2.34 (1.22–4.48) |
| P trend < 0.001 | P trend < 0.001 | ||
| Number of functional impairments † | N = 11,070 | N = 11,070 | N = 10,039 |
| Functional impairments | 12.94 (12.13–13.79) | Ref | Ref |
| 1–2 | 17.71 (16.51–18.97) | 1.41 (1.05–1.89) | 0.89 (0.65, 1.23) |
| ⩾3 | 22.24 (19.85–24.83) | 1.80 (1.27–2.59) | 1.00 (0.66, 1.50) |
| P trend < 0.001 | P trend = 0.80 |
Definition of abbreviations: aOR = adjusted odds ratio, CI = confidence interval, cOR = crude odds ratio; Ref = reference.
Screeners were defined as people who had a computed tomographic or computed axial tomographic scan to detect for lung cancer in the past 12 mo.
Weighted multivariable logistic regression model included the summed number of comorbidities and functional impairments simultaneously and adjusted for age, sex, race, education background, marital status, clinical checkup, health coverage, obesity, heavy alcohol intake, physical activity, smoking status, general health, and year of screening.
The following nine comorbidities were considered: myocardial infarction, coronary heart disease, stroke, asthma, COPD, diabetes mellitus, chronic kidney disease, arthritis, and personal history of cancer.
The following four types of functional impairment were considered: difficulty in thinking, walking, dressing, or doing errands alone.
Discussion
The results of this study point to the importance of comorbidity status and functional limitations in the utilization of LCS in a nationally representative sample of the screening-eligible population. Comparing the nonscreeners with screeners in our study, the latter had a higher overall prevalence of comorbidities, including specific conditions such as myocardial infarction, coronary heart disease, stroke, asthma, COPD, and cancer, as well as higher amounts of functional limitations, both cognitive and physical. Moreover, a higher comorbidity burden was associated with a higher likelihood of undergoing LCS in the previous year. In contrast, there was no association observed between functional limitations and receipt of LCS.
LCS within the prior year was reported by 16.3% of respondents in this survey, which is higher than the previously reported estimates of 4% from the National Health Interview Survey of 2015 (33). Mixed-methods analyses from the United Kingdom have identified barriers to the uptake of LCS, including older age, current smoking, and lower socioeconomic status (34). Our results fill an important knowledge gap by examining the uptake of LCS among vulnerable older subpopulations with comorbid conditions. In our analysis, more than half of the participants of our sample were aged 65 years and older, had no more than a high school education, and had at least one chronic comorbid condition. This gives us a snapshot of a real-world screening-eligible population that is older and sicker in contrast to National Lung Screening Trial participants (35), but we do not know how this may affect the risk of harms (e.g., the potential for false-positive results, unnecessary diagnostic procedures, and complication rates from downstream diagnostic or surgical procedures).
Screened versus nonscreened participants in this study reported a higher prevalence of smoking-related comorbid conditions, including myocardial infarction, coronary heart disease, stroke, asthma, and COPD. Our study identified the prevalence of COPD to be approximately 34% in the LCS-eligible population. Crucially, previous studies suggest that LCS candidates with COPD face a twofold higher risk of lung cancer than those without COPD and may be more likely to benefit from LCS (36–42). Although prior studies also suggest that COPD impacts downstream outcomes after lung cancer diagnosis, including greater risk of complications during assessment of pulmonary nodules and higher morbidity and mortality after surgical resection for lung cancer including all-cause mortality, its impact on LCS-eligible population warrants further investigation (43–46).
Notably, individuals with higher overall comorbidity burden than their counterparts without comorbidity were more likely to undergo LCS in this analysis. This is in contrast to one study performed among veterans in the Veterans Affairs LCS demonstration project, which found that neither lung cancer risk nor comorbidity score was associated with the uptake of LCS (20). The impact of comorbidity profiles on cancer screening uptake has shown differential results when studied in the context of colorectal, breast, and prostate cancer (21). Though pooled measurement of comorbidity was associated with a decrease in overall screening for colorectal and prostate cancer, individual comorbidities were inconsistently associated with the uptake of screening mammography (21). Also, because patients with significant comorbidities might be more likely to have greater access to the healthcare system, including more contact with their physicians, they might be more likely to undergo LCS. Furthermore, as LCS eligibility focuses on individuals with a smoking history, it is highly likely that these participants will have a coexisting burden of smoking-related comorbidities, as observed in our analysis. Because comorbidity profiles impact not only LCS utilization but also remain independent predictors of treatment and survival in patients with early-stage lung cancer (47) and LCS disseminates into practice, there is a critical need to better understand this interplay by quantifying the impact of comorbidity on LCS outcomes.
In the context of functional limitations, we observed that more limitations were not associated with higher uptake of LCS in multivariable models. Prior research on the impact of functional limitations on cancer screening behaviors point to inconsistent findings. For example, in a nationally representative sample from the U.S. National Health Interview Survey, functional limitations were associated with higher rates of colorectal cancer screening among women but not men, and no association was observed with screening mammography (48). In contrast, women with long-term limitations in their activities of daily living had lower odds of undergoing screening mammography (49, 50). Our findings also underscore the need to illuminate the impact of functional limitations on LCS utilization and downstream outcomes in future studies. Finally, though different limitations have differential impact on overall screening and life expectancy, studying the impact of these limitations individually and as a composite measure need further evaluation. Furthermore, much of the cost-effectiveness analysis of LCS does not consider the impact of comorbidity or functional limitations or does not accurately describe comorbidity relationships and model them correctly.
Our study has several limitations and strengths. A notable limitation of this study is its cross-sectional nature that precludes causal inference. Even though the lack of medical records–based measures of comorbidity may be perceived as a limitation of the current analysis, evidence suggests that self-reported assessments of comorbidity are valid and reliable (51). Further, the BRFSS questionnaire does not specifically address whether the CT scan was performed as part of LCS program or whether was performed for diagnostic purposes, which might have resulted in an overestimation of the uptake of LCS in community setting and its association with multimorbidity and functional limitations. Also, the way the question was phrased might have left out individuals who had been screened in the year before the last 12 months. Furthermore, given the complexity around the question, patients with comorbidities might be undergoing diagnostic CT scan and might not understand the underlying difference between regular CT scans and LDCT. Finally, we could not use existing measures of comorbidity burden or functional limitations, and our measures were based on the BRFSS questionnaire. Furthermore, given the cross-sectional nature of our study, it might also be possible that sicker patients—with comorbidities associated with lung cancer such as COPD and a history of cancer—are more likely to receive diagnostic chest imaging and to be worked-up for lung cancer. One of the major strengths of this study includes the utilization of a nationally representative sample from the BRFSS, which collects self-reported data across the United States. We also evaluated the impact of both individual comorbid conditions and functional limitations as well as summary measures. Our analysis also considered key sociodemographic and lifestyle factors that have previously been associated with cancer screening outcomes.
In conclusion, we observed that the presence of comorbid conditions was associated with a higher likelihood of undergoing LCS with LDCT in a nationally representative sample from the United States. Furthermore, revised USPSTF guidelines extend LCS inclusion criteria to include individuals aged 50–55 years and lowered smoking history from 30 pack-years to 20 pack-years, which may lead to an increase in screening rates among vulnerable and high-risk populations (52, 53). Because poor health status may diminish the benefits of screening, future research is needed to precisely characterize the health status of LCS-eligible individuals as well as the benefit:harm ratio for older and sicker individuals who undergo LCS in real-world settings.
Footnotes
Supported by the Georgetown University Medical Center Partners in Research Award and the National Institutes of Health/National Cancer Institute award R01-CA249506.
Author Contributions: Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: S.A., D.Z., and D.B. Final approval of the version submitted for publication: S.A., D.Z., M.T., T.A., M.K.G., G.A.S., and D.B. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: S.A., D.Z., M.T., T.A., M.K.G., G.A.S., and D.B.
This article has an online supplement, which is accessible from this issue’s table of contents online at www.atsjournals.org.
Author disclosures are available with the text of this article at www.atsjournals.org.
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