Abstract
Objective:
Lung cancer screening (LCS) efficacy is highly dependent on adherence to annual screening, but little is known about real-world adherence determinants. We used insurance claims data to examine associations between LCS annual adherence and demographic, comorbidity, healthcare usage, and geographic factors.
Materials and Methods:
Insurance claims data for all individuals with a LCS low dose CT scan was obtained from the Colorado All Payer Claims Dataset. Adherence was defined as a second claim for a screening CT 10-18 months after the index claim. Cox proportional hazards regression was used to define the relationship between annual adherence and age, sex, insurance type, residence location, outpatient healthcare usage, and comorbidity burden.
Results:
After exclusions, the final dataset consisted of 9,056 records with 3,072 adherent, 3,570 non-adherent, and 2,414 censored (unclassifiable) individuals. Less adherence was associated with ages 55-59 (hazard ratio (HR)=0.80, 99% confidence interval (CI)=0.67-0.94), 60-64 (HR=0.83, 99% CI=0.71-0.97) and 75-79 (HR=0.79, 99% CI=0.65-0.97), rural residence (HR=0.56, 99% CI=0.43-0.73), Medicare Fee-for-Service (HR=0.45, 99% CI=0.39-0.51), and Medicaid (HR=0.50, 99% CI=0.40-0.62). A significant interaction between outpatient healthcare usage and comorbidity was also observed. Increased outpatient usage was associated with increased adherence and was most pronounced for individuals without comorbidities.
Conclusions:
This population-based description of LCS adherence determinants provides insight into populations that might benefit from specific interventions targeted toward improving adherence and maximizing LCS benefit. Quantifying population-based adherence rates and understanding factors associated with annual adherence is critical to improving screening adherence and reducing lung cancer death.
Summary Sentence
Our findings that age, rural residence, insurance type, and health care use/comorbidity burden influence LCS adherence provides insight into populations that may benefit from interventions to improve adherence and maximize LCS benefit.
Introduction
Lung cancer is the leading cause of cancer death and kills more people than breast, colon, and prostate cancer combined (1). Lung cancer lethality is largely driven by the preponderance of metastatic disease at diagnosis. Most patients present with advanced disease and despite recent treatment advances, the 5-year survival of metastatic lung cancer remains under 10% (2). Though the 5-year survival of early stage lung cancer exceeds 55%, only 17% of patients present with localized disease (2). Lung cancer screening (LCS) by annual low dose computed tomography (LDCT) reduces lung cancer mortality by identifying surgically curable early stage disease (3, 4). The Centers for Medicare and Medicaid Services (CMS) recommend LCS for current and former smokers 55-77 years old with a ≥ 30 pack year smoking history who are still smoking or have quit within the last 15 years (5). Based on modeling data, the US Preventive Services Task Force recommends screening through age 80 (6) and recently released a draft recommendation to expand screening by lowering eligibility to 50 years of age and tobacco exposure to 20 pack years (7).
LCS efficacy is highly dependent on both screening uptake and adherence to annual screening. Uptake refers to the number of eligible people that have an (index or initial) screening LDCT, while annual adherence is the proportion of individuals that continue to receive subsequent interval screening LDCTs. In the National Lung Screening Trial (NLST), 58% of early stage lung cancers were detected on annual (as opposed to initial) LDCT, suggesting that a substantial fraction of the reduced lung cancer mortality was attributable to the >95% adherence to serial imaging in the trial (3). Similar stage I lung cancer detection (58%), reduced lung cancer mortality (25% at 10 years) and adherence to screening (90%) were recently reported in another large study (4). Microsimulation modeling predicts a halving of screening benefit when annual screening adherence falls below 50% (8). Similarly, stage shift from late to early stage disease is reduced as the screening interval is increased (9).
Determinants of cancer screening adherence are multifaceted and include both individual factors (demographics, income, beliefs) and health care system factors (insurance, accessibility, clinician recommendation) (10-21). Early reports of real-world screening cohorts describe LCS annual adherence rates outside clinical trials between 37 and 66% (22-27). Recent systemic reviews and meta-analyses found higher adherence in participants in their 60s and former (as opposed to current) smokers and lower adherence in racial minorities, individuals with less education, and individuals who lived further from the screening facility (20, 21). However, the quality of evidence supporting most of these associations was moderate or low and was largely derived from cohort studies performed in academic settings before the CMS decision to cover screening in the high-risk population defined by the NLST (20, 21). To this point, determinants of LCS adherence in a real-world, unselected United States population, outside of a study have not been described. We used a state-based, administrative claims dataset to quantify population level LCS adherence and identify demographic, comorbidity, healthcare usage, and geographic factors associated with adherence.
Materials and Methods
Data Source:
The Colorado All Payer Claims Dataset (CO APCD) was queried to identify all claims for LCS specific LDCT scans identified with procedural codes G0297 and S8032. The extracted dataset included all health claims with dates of service between 1/2012 and 12/2018, diagnosis or procedural codes, and associated demographic and insurance information available for these claims. CO APCD covers approximately 68% of insured lives in Colorado and captures data from >40 commercial (private insurance) payers, Medicare fee-for-service (FFS) (public insurance for Americans >65 years of age or individuals with a disability), Medicare Advantage (Medicare contracted through a private insurance company), and Medicaid (government assistance program for low income Americans regardless of age), CO APCD does not include data from the majority of self-funded health care plans regulated by the Employee Retirement Income Security Act or health care plans administered by the federal government (TriCare, Veterans Administration, Indian Health Service, Federal Employee Health Benefits). Within CO APCD, individuals are assigned a unique composite ID that provides continuity of claims across payer types. The study was reviewed by the Colorado Multiple Institutional Review Board and determined to be exempt for use of secondary data. Although the CO APCD dataset includes personal health information (dates associated with health claims), the study met criteria for a full waiver of HIPAA authorization.
Measures and definitions:
We defined annual adherence as a second claim for a screening LDCT 10-18 months after the index LDCT claim. This definition captures 87% of all returning individuals (22). Time (in months) between the index screening LDCT and any subsequent screening specific LDCTs was calculated using the dates of service associated with the LDCT claim. We classified individuals as adherent if there was a second screening LDCT claim within 10-18 months of the first claim and non-adherent if there was not a second LDCT claim and more than 18 months had elapsed since the first claim. Individuals with 10-18 months of follow-up time after the index LDCT and no second screening specific LDCT claim were censored as they could not be classified as adherent or non-adherent since incomplete time had passed to meet the definition of adherence. For the primary analysis, individuals who returned >18 months after the index LDCT were classified as non-adherent.
The eligible population included anyone 55-79 years old with at least one claim for a lung cancer screening LDCT. We excluded individuals with less than 10 months of follow-up after the index LDCT, individuals with a second LDCT claim 3-9 months after the index LDCT, individuals with a lung cancer diagnosis using International Classification of Disease (ICD) 9 and 10 codes, out-of-state residents, and individuals with missing statistical model data.
We assessed the following associations with LCS adherence: sex, age at index LDCT, residence (urban vs rural/frontier as designated by the Colorado Rural Health Center) at index LDCT, insurance type (commercial, Medicare FFS, Medicare Advantage, or Medicaid) linked with the index LDCT claim, health care utilization (defined by the number of outpatient visits excluding the LDCT in the 3.5 years before through 1.5 years after the index CT date), and comorbidity burden (by Charlson Comorbidity Index (CCI) score (28) based on clinician (SPM) review of ICD 9 and 10 codes found in the CO APCD and categorized as 0, 1, or ≥2). In calculating the CCI, we utilized any data available in the CO APCD and excluded non-melanoma skin cancers and HIV/AIDS as the CCI was validated in a period before effective antiretroviral therapy (29, 30).
Statistical Analysis:
Normal distribution of continuous variables was evaluated with quantile-quantile plots. Continuous variables were categorized and tested to determine if they showed a linear or nonlinear trend in the log hazard. Univariate differences between the three population groups were assessed with a χ2 test. Cox proportional hazards regression was performed to characterize the relationship between annual adherence and subject variables. Utilization of a cox proportional hazards regression accounted for the time element of our adherence definition and allowed for a prospective approach to the study. The proportional hazards assumption was tested with weighted Schoenfeld residuals and we utilized an alpha level of 0.01 for statistical significance of all hypothesis tests. The primary analysis focused on enumerating adherence rates and associations for adherence for only the first annual LDCT scan. We performed three sensitivity analyses: one assessing a 15-month definition of adherence, one defining any return for screening as adherent, and one excluding late returning (>18 month) individuals. We additionally calculated continued adherence rates to the second annual screening among individuals adherent to the first annual LDCT. Data analysis was generated using SAS/STAT software, Version 9.4 of the SAS System for Windows (SAS Institute Inc., Cary, NC, USA).
Results
There were 27,332 claims for LCS LDCT between 10/1/2014 and 12/31/2018 (Fig. 1). Second LDCT claims within 2 months were assumed to be duplicate claims; removing these claims left 19,416 unique LDCT claims for 14,563 individuals. We excluded 4,530 individuals with <10 months of follow-up time after the index LDCT claim as insufficient time had elapsed for determination of annual adherence. We excluded individuals with second LDCT claims between 3-9 months as these were assumed to be diagnostic (not screening) studies; a distribution of times from index to second LDCT claims is shown in Fig. 2. We excluded individuals <55 or >79 years of age and individuals with a lung cancer diagnosis (ICD 9 or 10 codes (1623, 1624, 1625, 1628, 1629, C341, C343, C349) as screening is not recommended in these individuals. After excluding non-Colorado residents and records with missing subject variable data, we had a final dataset of 9,056 records with 3,072 adherent individuals, 3,570 non-adherent individuals, and 2,414 individuals who were censored because they could not be accurately classified with <18 months of follow-up time.
Figure 1. Study cohort.
Study population was derived as described in the text. Four individuals had multiple exclusion criteria. Censored individuals had 10-18 months of follow-up since index CT but no claim for a second LDCT and hence could not be classified as adherent or non-adherent. The duplicate claims are explained by the procedure (LDCT) and professional fee (radiology interpretation) being billed separately to insurance.
Figure 2. Time between index and second LDCT.
Defining adherence as having a second LDCT 10-18 months after the index CT scan captures 87% of returning individuals. Claims 3-9 months after the index LDCT were assumed to be diagnostic studies for monitoring pulmonary nodules.
Overall adherence in this cohort using an 18-month definition was 46%; this increased to 53% if any return for screening was considered adherent. Adherence to the first annual LDCT increased over the duration of this study (p = 0.0009) (Fig. 3A). Of individuals who were adherent to the first round of screening, adherence to the second annual LDCT was approximately 50% (Fig. 3B); due to data maturity, adherence to additional screening rounds could not be assessed.
Figure 3. Screening adherence by year of index LDCT.
(A) Adherence to first annual LDCT by year of index claim. *For 2017, individuals who could not be classified due to data maturity are not included. †Overall numbers do not include unclassifiable individuals screened in 2017 or any individuals screened in 2018. (B) Adherence to the second annual LDCT by year of index claim. Only individuals who were adherent to the first annual screening are included.
Characteristics of the study population are presented in Table 1. The adherent group had more males (p = 0.003), individuals aged 65-69 (p = 0.01), urban residents (p < 0.0001), individuals with commercial or Medicare Advantage insurance (p < 0.0001), outpatient visits (p < 0.0001), and comorbidities (p < 0.0001). After adjustment for covariates, age, residence, and insurance were significantly associated with annual lung cancer screening adherence (Fig. 4). Compared to individuals aged 65-69, individuals 55-64, and 75-79 had a 20% reduction in adherence. Individuals with a rural residence had a 44% reduction in adherence compared to urban residents, and individuals with Medicare FFS and Medicaid had a 45% reduction in adherence when compared to individuals with Medicare Advantage.
Table 1. Characteristics of study population.
Over half of the study population had unknown or missing race and ethnicity data, making this variable unusable for regression analysis. The adherent group has a higher proportion of missing data because >95% of missing data is from Medicare Advantage and commercial payers, which comprise a higher percentage of the adherent group. Data are presented as count (%). Definition of abbreviations: LDCT = low dose computed tomography, FFS = fee-for-service, CCI = Charlson Comorbidity Index.
| Adherent n = 3,072 |
Non-adherent n = 3,570 |
Censored n = 2,414 |
|
|---|---|---|---|
| Sex | |||
| Male | 1694 (55) | 1847 (52) | 1228 (51) |
| Female | 1378 (45) | 1723 (48) | 1186 (49) |
| Race | |||
| White | 767 (25) | 1542 (43) | 1140 (47) |
| Black | 38 (1) | 94 (3) | 52 (2) |
| Other | 57 (2) | 83 (2) | 42 (2) |
| Unknown | 800 (26) | 1108 (31) | 730 (30) |
| Missing | 1410 (46) | 743 (21) | 450 (19) |
| Hispanic Ethnicity | |||
| Yes | 11 (0.3) | 35 (1) | 24 (1) |
| No | 826 (27) | 1761 (49) | 1339 (56) |
| Unknown | 182 (6) | 395 (11) | 275 (11) |
| Missing | 2053 (67) | 1379 (39) | 776 (32) |
| Age at index LDCT | |||
| 55-59 | 496 (16) | 676 (19) | 446 (19) |
| 60-64 | 676 (22) | 769 (21) | 552 (23) |
| 65-69 | 1026 (33) | 1079 (30) | 752 (31) |
| 70-74 | 670 (22) | 772 (22) | 486 (20) |
| 75-79 | 204 (7) | 274 (8) | 178 (7) |
| Residence | |||
| Urban | 2973 (97) | 3307 (93) | 2162 (90) |
| Rural/Frontier* | 99 (3) | 263 (7) | 252 (10) |
| Insurance at index LDCT† | |||
| Commercial | 939 (30) | 770 (22) | 479 (20) |
| Medicare Advantage | 1340 (44) | 1023 (29) | 698 (29) |
| Medicare FFS | 571 (19) | 1269 (35) | 840 (35) |
| Medicaid | 222 (7) | 508 (14) | 397 (16) |
| Number of outpatient visits | |||
| 0 | 521 (17) | 1184 (33) | 862 (36) |
| 1 | 1060 (34) | 1206 (34) | 911 (38) |
| 2 | 842 (27) | 668 (19) | 376 (16) |
| ≥3 | 649 (21) | 512 (14) | 265 (11) |
| CCI score | |||
| 0 | 1227 (40) | 1611 (45) | 1133 (47) |
| 1 | 951 (31) | 1142 (32) | 768 (32) |
| ≥2 | 894 (29) | 817 (23) | 513 (21) |
Rural/Frontier is based on designations contained in the CO APCD by the Colorado Rural Health Network. Counties not designated as part of a Metropolitan Statistical Area as defined by the United States Office of Management and Budget are designated as rural. Counties that additionally have population density of six or fewer persons per square mile are designated as frontier.
Dual enrolled individuals were classified according to the primary insurance type associated with the index LDCT claim.
Figure 4. Variables associated with lung cancer screening adherence.
Analysis was performed as described in Methods. After adjustment for other covariates, female sex, ages 70-74, and commercial insurance (compared to Medicare Advantage) did not have a statistically significant relationship with screening adherence. † There was a significant interaction between number of outpatient visits and CCI score, therefore these results are presented in Figure 5.
Within the multivariable model, outpatient visits and CCI had a significant interaction (p < 0.0001), the effect of number of outpatient visits at each CCI level are presented in Fig. 5. At each CCI level there was a gradient increase in adherence with increasing outpatient visits; this trend was most pronounced for individuals with no comorbidities. In a secondary analysis, a significant interaction (p = 0.01) was observed between residence and insurance type (Fig. 6); all the resulting hazard ratios are consistent with reduced adherence among rural patients. All sensitivity analyses (using a 15-month definition of adherence, defining any return for screening as adherent, excluding late returning individuals) yielded no differences in results.
Figure 5. Influence of comorbidities and heath care utilization on screening adherence.
After adjustment for other covariates, increased screening adherence is seen with increasing number of outpatient visits across all comorbidity burdens.
Figure 6. Interaction between residence and insurance at index LDCT.
There is a statistically significant reduction in adherence for rural residents with commercial and Medicare Advantage insurance, however this is likely driven by the small number of rural residents with each insurance type (commercial n = 85, Medicare Advantage n = 67, Medicare FFS n = 372, and Medicaid n = 90).
Discussion
Early descriptions of LCS adherence determinants (31, 32) came from clinical trials performed prior to clear proof that LCS reduced mortality and are difficult to contextualize in an environment where LCS is a preventive service delivered in community settings and is now covered by most insurance plans. More recent studies in academic (23 - 27), community (22) or federal health (33) settings have emerged, but no prior studies have used insurance claims to assess LCS adherence (20, 21). Using longitudinal claims data, we found that ages 55-64 and 75-79, rural residence, and Medicare FFS and Medicaid insurance are associated with reduced adherence to annual LCS. While higher healthcare usage and increased comorbidity burden are both associated with increased LCS adherence, the effects of these two variables are inter-dependent.
Our observation that individuals 65-70 years old are most adherent to LCS is relatively consistent with recent LCS adherence meta-analyses that found that individuals 60-75 years old are most adherent (20, 21). Reduced adherence amongst older individuals is likely explained by multiple recommendations to stop LCS as individuals reach their mid-70s (5, 34 - 37) and an increasing proportion of patients who may have a shorter life expectancy and hence derive less screening benefit (38). Younger individuals are less likely to receive preventive services than individuals over 65 (39) and may have competing time priorities and misperceptions about screening cost.
Consistent with other studies (20, 21), we did not observe an association between sex and adherence. While reduced LCS adherence has been observed in racial minorities (32), the amount of missing data on race and ethnicity in our study precluded assessing that important relationship. Rural populations have higher smoking rates, lower socioeconomic status (SES), and use less preventive care than their urban counterparts (40, 41). Our finding that rural residence is associated with reduced adherence is consistent with these demographic characteristics and is supported by the observation in our cohort that ~75% of rural residents have Medicare FFS or Medicaid compared to ~40% of urban residents. (Fig. 7A). Insurance coverage is strongly associated with increased adherence in breast and colon cancer screenings (11, 14, 42); however, the CO APCD is limited to insurance claims, hence we cannot compare insured and uninsured populations. We did find that coverage with Medicare FFS or Medicaid was associated with markedly less adherence compared to coverage by Medicare Advantage or commercial insurance. Medicare Advantage is offered by private insurance companies and there is selection of healthier patients into this program (43). In addition, Medicaid insurance is likely a surrogate for lower SES. A secondary analysis found that adherence of rural patients varies by insurance (Fig. 6), though this is mostly driven by the small number of rural patients with each insurance type.
Figure 7.
(A) Insurance type by residence. (B) Association between comorbidity burden and health care utilization.
We observed a complex relationship between increased adherence and heath care system usage (measured by number of outpatient visits) and comorbidity burden (measured by CCI). Not surprisingly, heath care use and comorbidity burden are closely linked (Fig. 7B) as individuals with co-morbidities are expected to have more health care usage and contact with the health care system is required to obtain the diagnoses that drive comorbidity index measures. That the association between adherence and health care utilization was most pronounced in individuals with no co-morbidities, may suggest that health care utilization alone is associated with higher adherence to preventive services. In breast and colon cancer screening, increased contact with the healthcare system has been associated with improved adherence (14, 44). While higher comorbidity burden has been associated with reduced adherence in breast cancer screening (45), a recent study failed to identify an association between LCS adherence and comorbidity burden in a veteran population (33).
Our 18-month adherence rate was 46%; this is consistent with the 37-66% reported LCS adherence outside of clinical trials (22-27). This may be related to our CO APCD sample being biased by clinicians or programs that are early LCS adopters as overall LCS uptake remains low with only 2-16% of eligible individuals currently receiving LCS (46-50). This notion is supported by the observation that in the CO APCD LCS data set, approximately 20% of all screening was performed by the Kaiser Permanente system. Interestingly, the increased adherence by index screening year (Fig. 3) is consistent with increasing LCS uptake over time (51).
The CO APCD study population is representative of the Colorado and US populations with respect to sex and age (52). Direct comparisons of race and ethnicity are not possible as a large amount of this information is missing in the CO APCD; however, the population of Colorado is less racially diverse than the US population (Colorado population is 84% white and 4% black compared to 72% white and 13% black in the US) (52). Our observation that adherence is highest between ages 65-69, is consistent with prior studies showing that LCS uptake is highest between ages 65-74 (51, 53).
In addition to the intrinsic issues of using claims data for research (54), our study has several limitations. First, due to missing data, we could not include race and ethnicity in our regression model. Not only do minority populations probably have reduced LCS adherence (20, 21), race and ethnicity are likely underrepresented in the CO APCD and probably confound other subject variables such as residence, insurance, and SES. A second limitation is the inability to capture CT results via claims data. Because of this, we were forced to exclude individuals with short interval imaging and lung cancer diagnosis as these individuals could not be accurately classified. A third limitation is that we assumed that all screened individuals met standard smoking history eligibility criteria, however screening of ineligible individuals remains an issue (48, 55); the impact of this is difficult to predict. Fourth, there was missing or incomplete data on enrollment status and length of health insurance coverage period. This could lead to misclassification if an individual were to move out state or no longer be eligible for health insurance coverage in Colorado. Finally, we utilized patient information associated with the index LDCT claim to classify variable information (residence, insurance type) that could potentially change over time leading to misclassification if individuals moved out of state or changed insurance.
This is the first report using insurance claims to identify population-based determinants of LCS adherence. Our findings that age, rural residence, insurance type, and health care use/comorbidity burden influence LCS adherence provides insight into populations that might benefit from specific interventions targeted toward improving adherence and maximizing LCS benefit. Interventions described to increase lung cancer screening adherence include dedicated program coordinators, patient reminders, and mobile screening of rural residents (20, 23, 24, 56). Similar interventions have increased adherence to breast, colon, cervical, and prostate cancer screenings (57-62). Quantifying population-based adherence rates and understanding factors associated with annual adherence is a critical first step in improving screening adherence and ultimately reducing lung cancer death.
Take home points.
Using claims data from the Colorado All Payer Claims Database we observed a 46% population-based adherence rate to annual lung cancer screening guidelines.
Ages 55-64 and 75-79, rural residence, and Medicare FFS and Medicaid insurance are associated with reduced adherence to annual LCS.
While higher healthcare usage and increased comorbidity burden are both associated with increased LCS adherence, the effects of these two variables are inter-dependent.
Acknowledgements:
We thank the Center for Improving Value in Health Care, administrator of the CO APCD, for assistance in acquisition of the dataset.
Sources of support: This research was supported by the University of Colorado Cancer Center Support Grant (NCI/NIH P30CA046934) and by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. Content is the authors’ sole responsibility and does not necessarily represent official NIH views.
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Statement of Data Access and Integrity: The authors declare that they had full access to all of the data in this study and the authors take complete responsibility for the integrity of the data and the accuracy of the data analysis.
Disclosures: Ms. Hirsch reports grants from NIH/ National Cancer Institute, non-financial support from NIH/ National Center for Advancing Translational Sciences, during the conduct of the study. Dr. Studts reports personal fees from Lung Ambition Alliance, outside the submitted work; and Dr. Studts volunteers on the Scientific Leadership Board of the GO2 Foundation for Lung Cancer. Dr. Baron, Dr. Risendal, Dr. New, and Dr. Malkoski have no conflicts of interest to disclose
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