Background:

Management of advanced-stage non–small cell lung cancer (NSCLC) has changed significantly over the past two decades with the development of numerous systemic treatments, including targeted therapies. However, a high proportion of advanced-stage patients are untreated. The role that health insurance plays in receipt of systemic treatments is unclear.

Methods:

Using California Cancer Registry data (2012–2014), we developed multivariable Poisson regression models to assess the independent effect of health insurance type on systemic treatment utilization among patients with stage IV NSCLC. Systemic treatment information was manually abstracted from treatment text fields.

Results:

A total of 17,310 patients were evaluated. Patients with Medicaid/other public insurance were significantly less likely to receive any systemic treatments [risk ratio (RR), 0.78; 95% confidence interval (CI), 0.75–0.82], bevacizumab combinations (RR, 0.57; 95% CI, 0.45–0.71), or tyrosine kinase inhibitors (RR, 0.70; 95% CI, 0.60–0.82) compared with the privately insured. Patients with Medicare or dual Medicare–Medicaid insurance were not significantly different from the privately insured in their likelihood of receiving systemic treatments.

Conclusions:

Substantial disparities in the use of systemic treatments for stage IV NSCLC exist by source of health insurance in California. Patients with Medicaid/other public insurance were significantly less likely to receive systemic treatments compared with their privately insured counterparts.

Impact:

Source of health insurance influences care received. Further research is warranted to better understand barriers to treatment that patients with Medicaid face.

This article is featured in Highlights of This Issue, p. 1001

Treatment of advanced-stage non–small cell lung cancer (NSCLC) has changed dramatically over the past two decades as driver mutations have been identified, molecular tests developed, and new drugs introduced (1–3). Many systemic treatment options now exist for patients with stage IV disease. Patients can receive standard chemotherapy with platinum or nonplatinum agents, bevacizumab (a VEGF inhibitor) combinations, targeted therapy with tyrosine kinase inhibitors (TKI), or immunotherapy, depending on histology and biomarker results (National Comprehensive Cancer Network Guidelines, www.nccn.org). Although all types of systemic therapy extend survival, a large proportion of patients with NSCLC remain untreated (4–7) and use of targeted treatments and bevacizumab combinations have remained fairly low (3, 8–12).

Disparities in quality of cancer treatment and cancer outcomes by health insurance status have been documented (13–15). Studies have shown that patients with Medicaid insurance or no insurance are less likely to receive recommended cancer treatments than patients with private insurance (16–18). Compared to patients with private insurance, patients with lung cancer with no insurance or public insurance have higher mortality, more treatment delays, and are less likely to receive molecular testing, chemotherapy, or radiation (9, 19–22).

Recommendations for molecular testing of nonsquamous NSCLC and treatment with targeted agents have been in place for a number of years (23). Bevacizumab combinations were approved as first-line treatment for nonsquamous NSCLC in 2006 (24, 25). Survival advantages have been noted for TKIs and bevacizumab combinations (12, 26). A population-based study using recent years of data (2012–2014) to assess the impact of health insurance type on receipt of these agents has not been conducted.

The purpose of this study is to use population data to assess systemic therapy utilization by source of health insurance, with a focus on bevacizumab combinations and TKIs, among patients with stage IV NSCLC in California. Findings from this study can provide insight into dissemination of these treatments and possible barriers to utilization.

Study population

We identified patients with first primary, stage IV NSCLC from 2012 to 2014 who were age 20 years or older through the California Cancer Registry (CCR). Patients under age 20 were excluded because of the small number of cases (n = 3). The CCR is a population-based cancer surveillance system that collects incidence reports on more than 160,000 cases of cancer diagnosed annually in California. The CCR has collected data on tumor characteristics, treatment, and patient demographic information for incident cancers diagnosed since 1988 with annual follow-up for vital status. Data are collected through a network of regional registries, which are affiliated with the National Cancer Institute's (NCI) Surveillance Epidemiology and End Results program.

Individual patients with NSCLC were selected using the International Classification of Diseases (ICD) for Oncology, 3rd edition, World Health Organization (WHO) site recode (ICD-O-3/WHO) 2008 definition, and the 2015 WHO classification of lung tumors (27). Included in the analysis were squamous cell carcinoma (ICD-O-3 codes: 8070, 8071, 8072, 8073, 8083, 8084, 8052, and 8123), adenocarcinoma (ICD-O-3 codes: 8140, 8250, 8551, 8260, 8265, 8230, 8253, 8254, 8480, 8333, 8144, 8256, 8257, 8550, 8255, 8251, 8252, 8470, 8481, and 8490), and non–small cell carcinoma not otherwise specified (NOS) including large cell, adenosquamous, and sarcomatoid tumors (ICD-O-3 codes: 8012, 8560, 8022, 8032, 8031, 8980, 8972, 8033, 8046, 8310, 8014, 8082, 8200, and 8430). Groupings of squamous versus nonsquamous (adenocarcinoma, non–small cell carcinoma NOS, large cell, adenosquamous, and sarcomatoid) histologies were used in the analysis as treatments differ for these groups. Stage at diagnosis was assigned using the American Joint Committee on Cancer staging system rules.

Patients with neuroendocrine tumors (ICD-O-3 codes: 8041, 8045, 8013, 8240, 8249, 8040, and 8246), and other histologies (composed mostly of malignant neoplasm NOS, ICD-O-3 code 8000, and carcinoma NOS, ICD-O-3 code 8010) were excluded from the analysis. Additional exclusions included autopsy-only and death certificate–only cases and other values for sex (other, transsexual/transgender, or not otherwise specified; Fig. 1).

Figure 1.

Study inclusion/exclusion flow diagram, which describes the lung cancer cases that were excluded from the study.

Figure 1.

Study inclusion/exclusion flow diagram, which describes the lung cancer cases that were excluded from the study.

Close modal

This study received an exempt determination from the University of California, Davis Institutional Review Board.

Baseline covariates

Patient characteristics collected in the CCR and used in the analysis include health insurance type, sex, race/ethnicity, neighborhood socioeconomic status (SES), rural/urban residence, age at diagnosis, comorbidity score, treatment at a NCI-designated cancer center, and tumor histology.

Health insurance type corresponds to the latest primary source of payment recorded during the patient's initial treatment, or at diagnosis if there was no further treatment. It was categorized as private (managed care, private fee for service, health maintenance organization, preferred provider organization, insurance not otherwise specified, Medicare with unspecified supplement, Medicare with private supplement), Medicare (Medicare through managed care plan), Medicaid/other public (Medicaid, Medicaid/managed care, county funded not otherwise specified, Indian/Public Health Service), Medicare–Medicaid dual eligibility (Medicare with Medicaid eligibility, Medicare/Medicaid not otherwise specified), military-related [Veterans Affairs, Department of Defense funded (Tricare, military treatment facilities)], uninsured (not insured, not insured/self-pay), and unknown.

Sex was defined as male or female. Race/ethnicity was classified as non-Hispanic white, non-Hispanic black, Hispanic, Asian/Pacific Islander (API), and other/unknown.

Neighborhood SES in the CCR is classified using an established aggregate measure based on patients' block group of residence at the time of diagnosis. The index score, grouped into quintiles, incorporates 2006–2010 American Community Survey data on education, occupation, unemployment, household income, poverty, rent, and home values of census tracts. Rural/urban residence is based on Medical Service Study Area designations and on the 2010 U.S. Census.

Patient comorbidities were assessed using the Charlson comorbidity index described by Deyo and colleagues (28). Categories of 0, 1, and 2 or more comorbidities are based on 16 medical conditions, excluding cancer diagnoses, reported in the Office of Statewide Health Planning and Development patient discharge files linked to CCR data (29). Treatment at a NCI-designated cancer center was determined by reviewing all reporting facilities where a patient was seen and identifying NCI-designated centers.

Outcome variable

The primary outcomes of interest were (i) any first-line systemic treatment, (ii) first-line use of bevacizumab combinations, and (iii) first-line use of TKIs. First-line systemic treatment was defined as the initiation of systemic or oral chemotherapy, reported by each treating facility where the patient was seen. The CCR collects first-line treatments only. The reported treatment information comes from the patient's medical record and is summarized and entered into text fields by certified tumor registrars. We manually reviewed the systemic treatment text fields and grouped the treatments into six mutually exclusive categories that align with National Comprehensive Cancer Network treatment guidelines for the diagnosis years used in this study. The groupings were as follows: (i) platinum doublets (any platinum chemotherapy in combination with another chemotherapy drug, excluding pemetrexed and bevacizumab); (ii) pemetrexed-based combinations (pemetrexed alone or combined with a platinum agent); (iii) bevacizumab-based combinations (bevacizumab alone or combined with platinum chemotherapy or another chemotherapeutic drug excluding pemetrexed; (iv) pemetrexed plus bevacizumab-based combinations (used together with or without a platinum agent); (v) single agent (platinum or nonplatinum); and (vi) TKIs. If text fields were blank or noninformative, then treatment was categorized as unknown. If text fields indicated that treatment was administered, but the drug was not listed, then treatment was categorized as chemotherapy not otherwise specified. Treatment was categorized as “none” when there was indication that none was given such as “patient refused treatment,” “patient opted for hospice instead of treatment,” “no treatment given,” or “patient died before any treatment given”.

Patients receiving any systemic treatment (platinum doublets, pemetrexed-based combinations, bevacizumab-based combinations, pemetrexed plus bevacizumab-based combinations, single agents, TKIs, chemotherapy not otherwise specified) were classified as having received systemic treatment. Patients who received bevacizumab-based combinations or pemetrexed plus bevacizumab-based combinations were classified as having received bevacizumab and patients who received TKIs were classified as having received TKIs.

Statistical analyses

Sociodemographic and clinical characteristics of patients with NSCLC are presented by receipt of bevacizumab, TKIs, and any systemic treatment. Multivariable Poisson regression models with robust SEs were used to analyze the association of receipt of any systemic treatment, bevacizumab, or TKIs and source of health insurance. Robust SEs were estimated to account for the incorrect assumption of Poisson distributed outcomes in the regression approach (30). Models were adjusted for race/ethnicity, neighborhood SES, comorbidity score, sex, rural/urban residence, treatment at NCI-designated cancer centers, and age. The model considering any systemic treatment was also adjusted for histology. Results are presented as adjusted RRs and their associated 95% confidence intervals (CI). Analyses were conducted using SAS software version 9.4 (SAS Institute Inc.).

Demographic and clinical characteristics of all 17,310 patients in the study population are summarized in Table 1. Nearly half of all patients had private insurance (53%). The next largest insurance grouping was Medicare–Medicaid dual eligible (22%). The majority of patients were non-Hispanic white (62%) and lived in urban areas (86%). The average age was 70 years.

Table 1.

Characteristics of patients with stage IV NSCLC by treatment type, 2012–2014, California

Nonsquamous (n = 14,145)aSquamous and nonsquamous (n = 17,310)
BevacizumabTKIsAny systemic treatmentNo systemic treatmentUnknownAll
n = 1,137 (8)n = 1,683 (12)n = 8,828 (51)n = 5,486 (32)n = 2,996 (17)n = 17,310 (100)
Characteristicn (%)n (%)n (%)n (%)n (%)n (%)
Insurance type 
 Private 705 (62) 1,028 (61) 5,085 (58) 2,618 (48) 1,468 (49) 9,171 (53) 
 Medicare 90 (8) 130 (8) 709 (8) 570 (10) 266 (9) 1,545 (9) 
 Medicaid/other public 80 (7) 142 (8) 821 (9) 543 (10) 311 (10) 1,675 (10) 
 Medicare–Medicaid dual eligible 218 (19) 319 (19) 1,744 (20) 1,389 (25) 741 (25) 3,874 (22) 
 Military-related 17 (2) 15 (1) 194 (2) 122 (2) 63 (2) 379 (2) 
 Uninsured 12 (1) 27 (2) 132 (2) 129 (2) 69 (2) 330 (2) 
 Unknown 15 (1) 22 (1) 143 (2) 115 (2) 78 (3) 336 (1) 
Sex 
 Male 564 (50) 627 (37) 4,535 (51) 2,945 (54) 1,653 (55) 9,133 (53) 
 Female 573 (50) 1,056 (63) 4,293 (49) 2,541 (46) 1,343 (45) 8,177 (47) 
Race/ethnicity 
 NH White 785 (69) 681 (40) 5,361 (61) 3,607 (66) 1,808 (60) 10,776 (62) 
 NH Black 71 (6) 62 (4) 672 (8) 477 (9) 291 (10) 1,440 (8) 
 Hispanic 115 (10) 212 (13) 1,068 (12) 712 (13) 445 (15) 2,225 (13) 
 API 158 (14) 715 (42) 1,652 (19) 653 (12) 412 (14) 2,717 (16) 
 Other/unknown 8 (1) 13 (1) 75 (1) 37 (1) 40 (1) 152 (1) 
Neighborhood SES (quintiles) 
 Lowest 145 (13) 157 (9) 1,210 (14) 1,089 (20) 600 (20) 2,899 (17) 
 Lower-middle 194 (17) 279 (17) 1,676 (19) 1,204 (22) 665 (22) 3,545 (21) 
 Middle 235 (21) 335 (20) 1,917 (22) 1,202 (22) 600 (20) 3,719 (22) 
 Higher-middle 267 (24) 408 (24) 2,037 (23) 1,132 (21) 611 (20) 3,780 (22) 
 Highest 296 (26) 504 (30) 1,988 (23) 859 (16) 520 (17) 3,367 (19) 
Rural/urban residence 
 Rural 171 (15) 151 (9) 1,253 (14) 838 (15) 409 (14) 2,500 (14) 
 Urban 966 (85) 1,532 (91) 7,575 (86) 4,648 (85) 2,587 (86) 14,810 (86) 
Age       
 Mean ± SD (years) 64.6 ± 10.1 66.8 ± 13.0 66.6 ± 10.9 73.2 ± 10.9 71.5 ± 11.1 69.6 ± 11.4 
Charlson comorbidity score 
 Mean ± SD 0.85 ± 1.18 0.81 ± 1.26 1.09 ± 1.38 1.74 ± 1.72 1.59 ± 1.67 1.39 ± 1.58 
 Unknown 132 (12) 305 (18) 1,342 (15) 733 (13) 474 (16) 2,549 (15) 
Histology 
 Squamous 0 (0) 0 (0) 1,413 (16) 1,196 (22) 556 (19) 3,165 (18) 
 Nonsquamous 1,137 (100) 1,683 (100) 7,415 (84) 4,290 (78) 2,440 (81) 14,145 (82) 
NCI-designated cancer center 
 Yes 180 (16) 360 (21) 1,471 (17) 436 (8) 244 (8) 2,151 (12) 
 No 957 (84) 1,323 (79) 7,357 (83) 5,050 (92) 2,752 (92) 15,159 (88) 
Nonsquamous (n = 14,145)aSquamous and nonsquamous (n = 17,310)
BevacizumabTKIsAny systemic treatmentNo systemic treatmentUnknownAll
n = 1,137 (8)n = 1,683 (12)n = 8,828 (51)n = 5,486 (32)n = 2,996 (17)n = 17,310 (100)
Characteristicn (%)n (%)n (%)n (%)n (%)n (%)
Insurance type 
 Private 705 (62) 1,028 (61) 5,085 (58) 2,618 (48) 1,468 (49) 9,171 (53) 
 Medicare 90 (8) 130 (8) 709 (8) 570 (10) 266 (9) 1,545 (9) 
 Medicaid/other public 80 (7) 142 (8) 821 (9) 543 (10) 311 (10) 1,675 (10) 
 Medicare–Medicaid dual eligible 218 (19) 319 (19) 1,744 (20) 1,389 (25) 741 (25) 3,874 (22) 
 Military-related 17 (2) 15 (1) 194 (2) 122 (2) 63 (2) 379 (2) 
 Uninsured 12 (1) 27 (2) 132 (2) 129 (2) 69 (2) 330 (2) 
 Unknown 15 (1) 22 (1) 143 (2) 115 (2) 78 (3) 336 (1) 
Sex 
 Male 564 (50) 627 (37) 4,535 (51) 2,945 (54) 1,653 (55) 9,133 (53) 
 Female 573 (50) 1,056 (63) 4,293 (49) 2,541 (46) 1,343 (45) 8,177 (47) 
Race/ethnicity 
 NH White 785 (69) 681 (40) 5,361 (61) 3,607 (66) 1,808 (60) 10,776 (62) 
 NH Black 71 (6) 62 (4) 672 (8) 477 (9) 291 (10) 1,440 (8) 
 Hispanic 115 (10) 212 (13) 1,068 (12) 712 (13) 445 (15) 2,225 (13) 
 API 158 (14) 715 (42) 1,652 (19) 653 (12) 412 (14) 2,717 (16) 
 Other/unknown 8 (1) 13 (1) 75 (1) 37 (1) 40 (1) 152 (1) 
Neighborhood SES (quintiles) 
 Lowest 145 (13) 157 (9) 1,210 (14) 1,089 (20) 600 (20) 2,899 (17) 
 Lower-middle 194 (17) 279 (17) 1,676 (19) 1,204 (22) 665 (22) 3,545 (21) 
 Middle 235 (21) 335 (20) 1,917 (22) 1,202 (22) 600 (20) 3,719 (22) 
 Higher-middle 267 (24) 408 (24) 2,037 (23) 1,132 (21) 611 (20) 3,780 (22) 
 Highest 296 (26) 504 (30) 1,988 (23) 859 (16) 520 (17) 3,367 (19) 
Rural/urban residence 
 Rural 171 (15) 151 (9) 1,253 (14) 838 (15) 409 (14) 2,500 (14) 
 Urban 966 (85) 1,532 (91) 7,575 (86) 4,648 (85) 2,587 (86) 14,810 (86) 
Age       
 Mean ± SD (years) 64.6 ± 10.1 66.8 ± 13.0 66.6 ± 10.9 73.2 ± 10.9 71.5 ± 11.1 69.6 ± 11.4 
Charlson comorbidity score 
 Mean ± SD 0.85 ± 1.18 0.81 ± 1.26 1.09 ± 1.38 1.74 ± 1.72 1.59 ± 1.67 1.39 ± 1.58 
 Unknown 132 (12) 305 (18) 1,342 (15) 733 (13) 474 (16) 2,549 (15) 
Histology 
 Squamous 0 (0) 0 (0) 1,413 (16) 1,196 (22) 556 (19) 3,165 (18) 
 Nonsquamous 1,137 (100) 1,683 (100) 7,415 (84) 4,290 (78) 2,440 (81) 14,145 (82) 
NCI-designated cancer center 
 Yes 180 (16) 360 (21) 1,471 (17) 436 (8) 244 (8) 2,151 (12) 
 No 957 (84) 1,323 (79) 7,357 (83) 5,050 (92) 2,752 (92) 15,159 (88) 

Abbreviations: API, Asian Pacific Islander; NCI, National Cancer Institute; NH, non-Hispanic; SD, standard deviation; SES, socioeconomic status.

aOther treatment categories are as follows: 3,855 (27%) received other systemic treatments, 4,290 (30%) did not receive treatment, 3,180 (22%) had unknown systemic treatment (treatment given but not specified, n = 740; unknown if treatment given, n = 2,440).

Overall, 51% of patients received systemic treatment, 32% did not, and 17% had unknown treatment status (Table 1). More patients receiving systemic treatment had private insurance (58% vs. 48%) and fewer had dual Medicare–Medicaid or Medicaid/other public (29% vs. 35%) compared with the untreated group. The systemic treatment group had more APIs (19% vs. 12%), fewer people in lowest neighborhood SES quintile (14% vs. 20%), were younger (mean age 67 years vs. 73 years), had lower average comorbidity scores (1.09 vs. 1.74), and were more frequently seen at NCI-designated cancer centers (17% vs. 8%) compared with the untreated group. Similar associations were seen with patients receiving bevacizumab (8%) or TKIs (12%). The TKI-treated group had high percentages of females (63%) and APIs (42%; Table 1).

Table 2 shows the results of the multivariable Poisson regression analysis of factors associated with systemic treatment use. Compared with those with private insurance, the likelihood of receiving any systemic therapy was significantly lower for patients with insurance coverage by Medicaid/other public (RR, 0.78; 95% CI, 075–0.82) and for the uninsured (RR, 0.68; 95% CI, 0.60–0.76). Those with Medicare, dual Medicare–Medicaid, and military-related insurance were not significantly different from the privately insured with respect to receiving any systemic treatment. API race/ethnicity (vs. non-Hispanic white), nonsquamous histology, and treatment at a NCI-designated cancer center were found to be associated with increased likelihood of receiving any systemic treatment, whereas decreasing neighborhood SES, increasing comorbidity score, and increasing age were associated with decreased likelihood of receiving systemic treatment.

Table 2.

Multivariable adjusteda RR and 95% CI estimates for characteristics associated with receipt of bevacizumabb, TKIsb, or any systemic treatment among patients with stage IV NSCLC, 2012–2014, California

BevacizumabTKIsAny systemic treatment
CharacteristicsRR (95% CI)PRR (95% CI)PRR (95% CI)P
Insurance 
 Private (reference) 1.00  1.00  1.00  
 Medicare 1.10 (0.89–1.36) 0.36 1.02 (0.86–1.20) 0.83 1.01 (0.96–1.06) 0.77 
 Medicaid/other public 0.57 (0.45–0.71) <0.001 0.70 (0.60–0.82) <0.001 0.78 (0.75–0.82) <0.001 
 Medicare–Medicaid dual eligible 1.13 (0.97–1.31) 0.11 0.90 (0.80–1.01) 0.08 0.98 (0.94–1.02) 0.22 
 Military-related 0.68 (0.43–1.09) 0.11 0.51 (0.32–0.82) 0.006 0.93 (0.85–1.01) 0.08 
 Uninsured 0.41 (0.24–0.71) 0.002 0.73 (0.53–1.01) 0.06 0.68 (0.60–0.76) <0.001 
 Unknown 0.58 (0.40–0.85) 0.006 0.71 (0.48–1.04) 0.08 0.84 (0.76–0.94) 0.002 
Race/ethnicity 
 NH White (reference) 1.00  1.00  1.00  
 NH Black 0.71 (0.56–0.91) 0.006 0.86 (0.67–1.10) 0.23 0.99 (0.94–1.04) 0.79 
 Hispanic 0.72 (0.60–0.87) <0.001 1.75 (1.52–2.02) <0.001 1.03 (0.98–1.07) 0.22 
 API 0.62 (0.52–0.73) <0.001 3.37 (3.06–3.70) <0.001 1.12 (1.08–1.15) <0.001 
 Other/unknown 0.76 (0.40–1.48) 0.43 1.59 (0.97–2.61) 0.06 1.07 (0.96–1.20) 0.22 
Neighborhood SES (quintile) 
 Highest (reference) 1.00  1.00  1.00  
 Higher-middle 0.88 (0.75–1.02) 0.09 0.83 (0.74–0.92) <0.001 0.94 (0.91–0.97) <0.001 
 Middle 0.80 (0.68–0.95) 0.008 0.73 (0.65–0.83) <0.001 0.90 (0.87–0.93) <0.001 
 Lower-middle 0.74 (0.62–0.88) <0.001 0.73 (0.64–0.83) <0.001 0.86 (0.83–0.89) <0.001 
 Lowest 0.75 (0.62–0.92) 0.005 0.53 (0.45–0.63) <0.001 0.78 (0.74–0.82) <0.001 
Histology 
 Squamous (reference) N/A  N/A  1.00  
 Nonsquamous N/A (N/A) N/A N/A (N/A) N/A 1.06 (1.02–1.10) 0.002 
Charlson comorbidity score 
 0 (reference) 1.00  1.00  1.00  
 1 0.86 (0.75–0.98) 0.03 0.69 (0.61–0.77) <0.001 0.95 (0.92–0.98) <0.001 
 ≥2 0.58 (0.49–0.68) <0.001 0.61 (0.53–0.69) <0.001 0.80 (0.77–0.83) <0.001 
 Unknown 0.65 (0.54–0.78) <0.001 0.95 (0.85–1.06) 0.33 0.95 (0.92–0.99) 0.007 
Sex 
 Male (reference) 1.00  1.00  1.00  
 Female 0.93 (0.83–1.03) 0.17 1.60 (1.46–1.74) <0.001 0.99 (0.98–1.02) 0.96 
Rural/urban residence 
 Rural (reference) 1.00  1.00  1.00  
 Urban 0.91 (0.78–1.07) 0.27 1.09 (0.94–1.28) 0.26 0.97 (0.93–1.01) 0.11 
NCI Program 
 No (reference) 1.00  1.00  1.00  
 Yes 0.94 (0.81–1.09) 0.40 1.29 (1.16–1.44) <0.001 1.16 (1.13–1.20) <0.001 
Age (1-year increments) 0.97 (0.96–0.97) <0.001 0.99 (0.98–0.99) 0.001 0.98 (0.980–0.983) <0.001 
BevacizumabTKIsAny systemic treatment
CharacteristicsRR (95% CI)PRR (95% CI)PRR (95% CI)P
Insurance 
 Private (reference) 1.00  1.00  1.00  
 Medicare 1.10 (0.89–1.36) 0.36 1.02 (0.86–1.20) 0.83 1.01 (0.96–1.06) 0.77 
 Medicaid/other public 0.57 (0.45–0.71) <0.001 0.70 (0.60–0.82) <0.001 0.78 (0.75–0.82) <0.001 
 Medicare–Medicaid dual eligible 1.13 (0.97–1.31) 0.11 0.90 (0.80–1.01) 0.08 0.98 (0.94–1.02) 0.22 
 Military-related 0.68 (0.43–1.09) 0.11 0.51 (0.32–0.82) 0.006 0.93 (0.85–1.01) 0.08 
 Uninsured 0.41 (0.24–0.71) 0.002 0.73 (0.53–1.01) 0.06 0.68 (0.60–0.76) <0.001 
 Unknown 0.58 (0.40–0.85) 0.006 0.71 (0.48–1.04) 0.08 0.84 (0.76–0.94) 0.002 
Race/ethnicity 
 NH White (reference) 1.00  1.00  1.00  
 NH Black 0.71 (0.56–0.91) 0.006 0.86 (0.67–1.10) 0.23 0.99 (0.94–1.04) 0.79 
 Hispanic 0.72 (0.60–0.87) <0.001 1.75 (1.52–2.02) <0.001 1.03 (0.98–1.07) 0.22 
 API 0.62 (0.52–0.73) <0.001 3.37 (3.06–3.70) <0.001 1.12 (1.08–1.15) <0.001 
 Other/unknown 0.76 (0.40–1.48) 0.43 1.59 (0.97–2.61) 0.06 1.07 (0.96–1.20) 0.22 
Neighborhood SES (quintile) 
 Highest (reference) 1.00  1.00  1.00  
 Higher-middle 0.88 (0.75–1.02) 0.09 0.83 (0.74–0.92) <0.001 0.94 (0.91–0.97) <0.001 
 Middle 0.80 (0.68–0.95) 0.008 0.73 (0.65–0.83) <0.001 0.90 (0.87–0.93) <0.001 
 Lower-middle 0.74 (0.62–0.88) <0.001 0.73 (0.64–0.83) <0.001 0.86 (0.83–0.89) <0.001 
 Lowest 0.75 (0.62–0.92) 0.005 0.53 (0.45–0.63) <0.001 0.78 (0.74–0.82) <0.001 
Histology 
 Squamous (reference) N/A  N/A  1.00  
 Nonsquamous N/A (N/A) N/A N/A (N/A) N/A 1.06 (1.02–1.10) 0.002 
Charlson comorbidity score 
 0 (reference) 1.00  1.00  1.00  
 1 0.86 (0.75–0.98) 0.03 0.69 (0.61–0.77) <0.001 0.95 (0.92–0.98) <0.001 
 ≥2 0.58 (0.49–0.68) <0.001 0.61 (0.53–0.69) <0.001 0.80 (0.77–0.83) <0.001 
 Unknown 0.65 (0.54–0.78) <0.001 0.95 (0.85–1.06) 0.33 0.95 (0.92–0.99) 0.007 
Sex 
 Male (reference) 1.00  1.00  1.00  
 Female 0.93 (0.83–1.03) 0.17 1.60 (1.46–1.74) <0.001 0.99 (0.98–1.02) 0.96 
Rural/urban residence 
 Rural (reference) 1.00  1.00  1.00  
 Urban 0.91 (0.78–1.07) 0.27 1.09 (0.94–1.28) 0.26 0.97 (0.93–1.01) 0.11 
NCI Program 
 No (reference) 1.00  1.00  1.00  
 Yes 0.94 (0.81–1.09) 0.40 1.29 (1.16–1.44) <0.001 1.16 (1.13–1.20) <0.001 
Age (1-year increments) 0.97 (0.96–0.97) <0.001 0.99 (0.98–0.99) 0.001 0.98 (0.980–0.983) <0.001 

Abbreviations: API, Asian Pacific Islander; N/A, not applicable; NCI, National Cancer Institute; NH, non-Hispanic; SES, socioeconomic status.

aAdjusted for all variables in the table.

bRestricted to nonsquamous patients.

Results of the multivariable regression analysis of factors associated with bevacizumab or TKI use among patients with nonsquamous histology are shown in Table 2. Compared with those with private insurance, the likelihood of receiving bevacizumab or TKIs was significantly lower for patients with insurance coverage by Medicaid/other public (RR = 0.57, 95% CI = 0.45–0.71 bevacizumab; RR = 0.70, 95%CI = 0.60–0.82 TKIs). The uninsured were significantly less likely to receive bevacizumab (RR, 0.41; 95% CI, 0.24–0.71), whereas those with military-related insurance were significantly less likely to receive TKIs (RR, 0.51; 95% CI, 0.32–0.82). Those with Medicare and Medicare–Medicaid dual eligibility were not significantly different from the privately insured with respect to receiving bevacizumab or TKIs. API, Hispanic, and Black race/ethnicity (vs. non-Hispanic white) were associated with decreased likelihood of receiving bevacizumab, while API, Hispanic race/ethnicity (vs. non-Hispanic white), female sex, and treatment at an NCI-designated cancer center were associated with an increased likelihood of receiving TKIs. Treatment at NCI-designated cancer centers was found to be associated with an increased likelihood of receiving TKIs, but the same association was not found for bevacizumab. Decreasing neighborhood SES, increasing comorbidity score, and increasing age were associated with a decreased likelihood of receiving bevacizumab or TKIs.

In this diverse population-based sample of 17,310 patients with stage IV NSCLC, we found significant disparities in (i) receipt of any systemic treatment, (ii) receipt of bevacizumab, and (iii) receipt of TKIs by source of health insurance after accounting for demographic and clinical factors. Compared with the privately insured, patients with NSCLC with Medicaid/other public or no insurance were less likely to receive any systemic treatment. More specifically, patients with nonsquamous NSCLC with Medicaid/other public or no insurance were less likely to receive bevacizumab, whereas patients with Medicaid/other public or military-related insurance were less likely to receive TKIs. Our findings are consistent with studies that have noted that Medicaid and uninsured patients with NSCLC and other cancers are less likely to receive recommended treatments than patients with private insurance (20, 31–34). Our findings also are consistent with two studies that found fewer patients with NSCLC with public insurance received molecular testing or TKIs compared with patients with private insurance (9, 35).

However, because TKI use is restricted to patients with certain mutations (EGFR, ALK, ROS1; refs. 2, 36), it is difficult to assess appropriate utilization without knowing about molecular testing results which are not available in CCR data. Patient factors that can contribute to the incidence of mutations, such as smoking (not available in CCR data), likely vary by insurance grouping. Smoking prevalence is higher among those in the military and those with low SES (37). There is evidence that smoking is associated with a lower incidence of EGFR mutations (38), so our findings of decreased likelihood of TKI use among those with military-related or Medicaid/other public insurance compared with the privately insured could reflect more smokers with fewer TKI actionable mutations in these categories.

Few studies exist on cancer in military-affiliated populations. Some studies have been done on incidence rates of selected cancers among military members (39, 40), but the quality of treatments provided through military-associated insurance compared with other insurance types has not been well studied. Lin and colleagues found that patients with NSCLC in the U.S. military health system had better survival than those in the U.S. general population (41). Shekelle and colleagues found that care provided at the Veterans Affairs (VA), including cancer care, mostly compares favorably with non-VA care (42). Parikh-Patel and colleagues found that VA patients with selected cancers had comparable or better outcomes and quality of care measures compared with persons with other sources of health insurance but patients with lung cancer did wait longer for initial treatment (15). Patients with military-related insurance in our study did not differ from the privately insured in their likelihood of receiving any systemic treatment or bevacizumab. However, the study was underpowered to detect differences for this group because of its small size (2% of the study population).

There are multiple reasons why patients with Medicaid or no insurance might be less likely to receive systemic treatments. Reasons for restricted care among Medicaid patients noted by other studies include low reimbursement levels resulting in delayed or limited care, insufficient number of Medicaid providers resulting in long wait times to see one, and rising cancer drug costs especially of novel medications with formulary restrictions (19, 31, 43). Patient characteristics may also be a factor. Patients with no insurance or Medicaid have a low SES, more comorbidities, and more difficulty interacting with the healthcare system (44–46). Deleterious health behaviors, such as smoking, are higher among low SES groups, as mentioned previously, and higher among patients with lung cancer (47). Because smoking contributes to comorbidities and poor patient performance status, cancer treatments can be less effective and more prone to complications among smokers resulting in fewer smokers receiving treatment (48–51). In our study, as patient comorbidity score increased, the likelihood of receiving treatment decreased. This is consistent with other studies that showed that patients with higher comorbidity burden are less likely to receive systemic treatment (20, 52, 53).

Notably, patients with Medicare or dual Medicare–Medicaid were not significantly different from patients with private insurance with regard to receiving bevacizumab combinations, TKIs, or any systemic treatment. Approximately 20% of Medicare beneficiaries are eligible for Medicaid (Kaiser Family Foundation, https://www.kff.org/medicaid/slide/dually-eligible-beneficiaries-comprise-20-of-the-medicare-population-and-15-of-the-medicaid-population-2008/). These patients are both elderly and low income making them a particularly vulnerable population. Unlike our findings, other studies have found dual Medicare–Medicaid patients to be less likely to receive cancer treatments (32, 54). Perhaps the dual coverage in this group of patients reduced barriers, described previously, that patients insured with Medicaid alone face (low reimbursement levels, insufficient number of providers) and helped defray the impact of out-of-pocket costs associated with needed care and treatments.

Other factors found to be associated with receipt of systemic therapy included older age, as found previously (5, 55, 56), race/ethnicity, female sex, and treatment at NCI-designated cancer centers. Our finding, that blacks were less likely to receive bevacizumab is consistent with other studies that have shown that blacks are less likely to undergo newer treatments and have higher cancer-related mortality (57, 58). Asians and women are known to have a higher prevalence of EGFR mutations (59, 60), which explains our finding of an increased likelihood of TKI use in these groups. We also observed that patients treated at NCI-designated centers were more likely to receive any systemic treatment and TKIs, consistent with studies that have observed better adherence to guideline concordant care at NCI-designated cancer centers (61, 62). However, NCI designation did not affect the likelihood of receiving treatment with bevacizumab.

This study has several limitations. First, patient health insurance can change over time and over the course of treatment. Classifications used for this study correspond to the time of diagnosis or initial treatment, whichever is the most recent record for the patient. Thus, changes in insurance coverage or the length of time a patient had their insurance coverage were not captured. Patients may have enrolled in a health plan around the time of diagnosis. Studies have shown that approximately 40% of patients with cancer with Medicaid enrolled at the time of their diagnosis (13, 63). These patients, who may have been previously uninsured, present with later-stage disease and have worse survival (45, 63). Our Medicaid grouping is unable to distinguish those who enrolled at diagnosis from those who had continuous Medicaid coverage prior to diagnosis. It is possible that patients with Medicaid coverage prior to diagnosis are more likely to receive systemic treatment compared with those who enrolled at the time of diagnosis.

Second, we do not have information on patient preference regarding treatments or discussions that went into treatment decisions. It is possible that factors associated with patient preference vary by type of health insurance. Third, there was a high percentage of patients with unknown systemic treatment information. These patients had similar distributions of insurance types, sex, and neighborhood SES compared with the untreated group and sensitivity analyses including the unknown treatment records with the untreated group yielded similar results. These findings suggest that the unknown group may be largely composed of untreated patients. Fourth, 15% of patients had an unknown Charlson comorbidity score. This score is derived from hospital discharge data and only available for patients who had an inpatient admission or encounter at the emergency department or ambulatory surgery center in the year prior to through 6 months following diagnosis.

Fifth, we lack information on performance status and biomarker testing which are both used in determining patient eligibility for treatment. There may be differences in these measures across insurance categories that account for some of the differences seen in treatment. Sixth, we did not have patients' smoking history. As mentioned previously, smokers may be less likely to have TKI actionable mutations or to receive treatment because of comorbidities and complications. Seventh, the study was underpowered to detect differences in the military-related insurance group. Finally, the study years used for this analysis predate approval of immunotherapy drugs so we were unable to assess use and differences by health insurance source for this newest class of drugs.

This study adds to the body of research showing that source of health insurance influences care received and patients with advanced-stage NSCLC with Medicaid or no insurance are less likely to receive recommended treatments. The findings from this study highlight the need to better understand access (low reimbursement levels, insufficient providers, and formulary restrictions) and patient (deleterious health behaviors, comorbidities, and difficulty interacting with healthcare system) barriers to optimal treatment among patients with Medicaid to reduce these treatment disparities. As treatments for lung cancer continue to evolve, future studies describing patterns of use at the population level should be conducted to identify groups not benefiting from available treatments and to guide efforts to improve their uptake.

No potential conflicts of interest were disclosed.

The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California, Department of Public Health, the NCI, the Centers for Disease Control and Prevention, or their contractors and subcontractors is not intended nor should be inferred.

Conception and design: F.B. Maguire, C.R. Morris, T.H.M. Keegan, C.-S. Li

Development of methodology: F.B. Maguire, C.R. Morris, C.-S. Li

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Parikh-Patel, K.W. Kizer

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): F.B. Maguire, C.R. Morris, A. Parikh-Patel, P.S. Lin, K.W. Kizer

Writing, review, and/or revision of the manuscript: F.B. Maguire, C.R. Morris, A. Parikh-Patel, R.D. Cress, T.H.M. Keegan, C.-S. Li, P.S. Lin, K.W. Kizer

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): K.W. Kizer

Study supervision: A. Parikh-Patel, R.D. Cress, C.-S. Li, K.W. Kizer

This work was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the NCI's Surveillance Epidemiology and End Results Program under contracts awarded to the University of California San Francisco, the University of Southern California, and the Public Health Institute; and the Centers for Disease Control and Prevention's National Program of Cancer Registries, under agreement awarded to the California Department of Public Health. Researchers at California Cancer Reporting and Epidemiologic Surveillance, Institute for Population Health Improvement, UC Davis Health (F.B. Maguire, C.R. Morris, A. Parikh-Patel, and K.W. Kizer) partner with the California Department of Public Health to manage the operations of the state-mandated California Cancer Registry through a contract (17–10097) awarded to UC Davis Health with K.W. Kizer serving as principal investigator.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hensing
T
,
Chawla
A
,
Batra
R
,
Salgia
R
. 
A personalized treatment for lung cancer: molecular pathways, targeted therapies, and genomic characterization
.
Adv Exp Med Biol
2014
;
799
:
85
117
.
2.
Hirsch
FR
,
Suda
K
,
Wiens
J
,
Bunn
PA
 Jr
. 
New and emerging targeted treatments in advanced non-small-cell lung cancer
.
Lancet
2016
;
388
:
1012
24
.
3.
Adamson
RT
. 
Biomarkers and molecular profiling in non-small cell lung cancer: an expanding role and its managed care implications
.
Am J Manag Care
2013
;
19
s398
404
.
4.
David
EA
,
Daly
ME
,
Li
CS
,
Chiu
CL
,
Cooke
DT
,
Brown
LM
, et al
Increasing rates of no treatment in advanced-stage non-small cell lung cancer patients: a propensity-matched analysis
.
J Thorac Oncol
2017
;
12
:
437
45
.
5.
Sacher
AG
,
Le
LW
,
Lau
A
,
Earle
CC
,
Leighl
NB
. 
Real-world chemotherapy treatment patterns in metastatic non-small cell lung cancer: are patients undertreated?
Cancer
2015
;
121
:
2562
9
.
6.
Brule
SY
,
Al-Baimani
K
,
Jonker
H
,
Zhang
T
,
Nicholas
G
,
Goss
G
, et al
Palliative systemic therapy for advanced non-small cell lung cancer: investigating disparities between patients who are treated versus those who are not
.
Lung Cancer
2016
;
97
:
15
21
.
7.
Small
AC
,
Tsao
CK
,
Moshier
EL
,
Gartrell
BA
,
Wisnivesky
JP
,
Godbold
JH
, et al
Prevalence and characteristics of patients with metastatic cancer who receive no anticancer therapy
.
Cancer
2012
;
118
:
5947
54
.
8.
Ko
JJ
,
Tudor
R
,
Li
H
,
Liu
M
,
Skolnik
K
,
Boland
WK
, et al
Reasons for lack of referral to medical oncology for systemic therapy in stage IV non-small-cell lung cancer: comparison of 2003–2006 with 2010–2011
.
Current Oncology
2017
;
24
:
e486
e93
.
9.
Enewold
L
,
Thomas
A
. 
Real-world patterns of EGFR testing and treatment with erlotinib for non-small cell lung cancer in the United States
.
PLoS One
2016
;
11
:
e0156728
.
10.
Shen
C
,
Kehl
KL
,
Zhao
B
,
Simon
GR
,
Zhou
S
,
Giordano
SH
. 
Utilization patterns and trends in epidermal growth factor receptor (EGFR) mutation testing among patients with newly diagnosed metastatic lung cancer
.
Clin Lung Cancer
2017
;
18
:
e233
e41
.
11.
Bittoni
MA
,
Arunachalam
A
,
Li
H
,
Camacho
R
,
He
J
,
Zhong
Y
, et al
Real-world treatment patterns, overall survival, and occurrence and costs of adverse events associated with first-line therapies for medicare patients 65 years and older with advanced non-small-cell lung cancer: a retrospective study
.
Clin Lung Cancer
2018
;
19
:
e629
e45
.
12.
Spence
MM
,
Hui
RL
,
Chang
JT
,
Schottinger
JE
,
Millares
M
,
Rashid
N
. 
Treatment patterns and overall survival associated with first-line systemic therapy for patients with advanced non-small cell lung cancer
.
J Manage Care Spec Pharm
2017
;
23
:
195
205
.
13.
Cress
RD
,
Chen
YS
,
Morris
CR
,
Chew
H
,
Kizer
KW
. 
Underutilization of gene expression profiling for early-stage breast cancer in California
.
Cancer Causes Control
2016
;
27
:
721
7
.
14.
Walker
GV
,
Grant
SR
,
Guadagnolo
BA
,
Hoffman
KE
,
Smith
BD
,
Koshy
M
, et al
Disparities in stage at diagnosis, treatment, and survival in nonelderly adult patients with cancer according to insurance status
.
J Clin Oncol
2014
;
32
:
3118
25
.
15.
Parikh-Patel
A
,
Morris
CR
,
Kizer
KW
. 
Disparities in quality of cancer care: the role of health insurance and population demographics
.
Medicine
2017
;
96
:
e9125
.
16.
Rhoads
KF
,
Ngo
JV
,
Ma
Y
,
Huang
L
,
Welton
ML
,
Dudley
RA
. 
Do hospitals that serve a high percentage of Medicaid patients perform well on evidence-based guidelines for colon cancer care?
J Health Care Poor Underserved
2013
;
24
:
1180
93
.
17.
Aizer
AA
,
Falit
B
,
Mendu
ML
,
Chen
MH
,
Choueiri
TK
,
Hoffman
KE
, et al
Cancer-specific outcomes among young adults without health insurance
.
J Clin Oncol
2014
;
32
:
2025
30
.
18.
Coburn
N
,
Fulton
J
,
Pearlman
DN
,
Law
C
,
DiPaolo
B
,
Cady
B
. 
Treatment variation by insurance status for breast cancer patients
.
Breast J
2008
;
14
:
128
34
.
19.
Ellis
L
,
Canchola
AJ
,
Spiegel
D
,
Ladabaum
U
,
Haile
R
,
Gomez
SL
. 
Trends in cancer survival by health insurance status in California from 1997 to 2014
.
JAMA Oncol
2018
;
4
:
317
23
.
20.
Pezzi
TA
,
Schwartz
DL
,
Mohamed
ASR
,
Welsh
JW
,
Komaki
RU
,
Hahn
SM
, et al
Barriers to combined-modality therapy for limited-stage small-cell lung cancer
.
JAMA Oncol
2018
;
4
:
e174504
.
21.
McMillan
MT
,
Ojerholm
E
,
Verma
V
,
Higgins
KA
,
Singhal
S
,
Predina
JD
, et al
Radiation treatment time and overall survival in locally advanced non-small cell lung cancer
.
Int J Radiat Oncol Biol Phys
2017
;
98
:
1142
52
.
22.
Lynch
JA
,
Berse
B
,
Rabb
M
,
Mosquin
P
,
Chew
R
,
West
SL
, et al
Underutilization and disparities in access to EGFR testing among Medicare patients with lung cancer from 2010 - 2013
.
BMC Cancer
2018
;
18
:
306
.
23.
Keedy
VL
,
Temin
S
,
Somerfield
MR
,
Beasley
MB
,
Johnson
DH
,
McShane
LM
, et al
American Society of Clinical Oncology provisional clinical opinion: epidermal growth factor receptor (EGFR) mutation testing for patients with advanced non-small-cell lung cancer considering first-line EGFR tyrosine kinase inhibitor therapy
.
J Clin Oncol
2011
;
29
:
2121
7
.
24.
Cohen
MH
,
Gootenberg
J
,
Keegan
P
,
Pazdur
R
. 
FDA drug approval summary: bevacizumab (Avastin) plus carboplatin and paclitaxel as first-line treatment of advanced/metastatic recurrent nonsquamous non-small cell lung cancer
.
Oncologist
2007
;
12
:
713
8
.
25.
Russo
AE
,
Priolo
D
,
Antonelli
G
,
Libra
M
,
McCubrey
JA
,
Ferrau
F
. 
Bevacizumab in the treatment of NSCLC: patient selection and perspectives
.
Lung Cancer
2017
;
8
:
259
69
.
26.
Sandler
A
,
Gray
R
,
Perry
MC
,
Brahmer
J
,
Schiller
JH
,
Dowlati
A
, et al
Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer
.
N Engl J Med
2006
;
355
:
2542
50
.
27.
Travis
WD
,
Brambilla
E
,
Nicholson
AG
,
Yatabe
Y
,
Austin
JHM
,
Beasley
MB
, et al
The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification
.
J Thorac Oncol
2015
;
10
:
1243
60
.
28.
Deyo
RA
,
Cherkin
DC
,
Ciol
MA
. 
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
.
J Clin Epidemiol
1992
;
45
:
613
9
.
29.
Lichtensztajn
DY
,
Giddings
BM
,
Morris
CR
,
Parikh-Patel
A
,
Kizer
KW
. 
Comorbidity index in central cancer registries: the value of hospital discharge data
.
Clin Epidemiol
2017
;
9
:
601
9
.
30.
Zou
G
. 
A modified poisson regression approach to prospective studies with binary data
.
Am J Epidemiol
2004
;
159
:
702
6
.
31.
Slatore
CG
,
Au
DH
,
Gould
MK
,
American Thoracic Society Disparities in Healthcare Group
. 
An official American Thoracic Society systematic review: insurance status and disparities in lung cancer practices and outcomes
.
Am J Respir Crit Care Med
2010
;
182
:
1195
205
.
32.
Bradley
CJ
,
Dahman
B
,
Given
CW
. 
Treatment and survival differences in older Medicare patients with lung cancer as compared with those who are dually eligible for Medicare and Medicaid
.
J Clin Oncol
2008
;
26
:
5067
73
.
33.
Harlan
LC
,
Greene
AL
,
Clegg
LX
,
Mooney
M
,
Stevens
JL
,
Brown
ML
. 
Insurance status and the use of guideline therapy in the treatment of selected cancers
.
J Clin Oncol
2005
;
23
:
9079
88
.
34.
Roetzheim
RG
,
Gonzalez
EC
,
Ferrante
JM
,
Pal
N
,
Van Durme
DJ
,
Krischer
JP
. 
Effects of health insurance and race on breast carcinoma treatments and outcomes
.
Cancer
2000
;
89
:
2202
13
.
35.
MacLean
E
,
Louder
A
,
Saverno
K
,
Smith
G
,
Mardekian
J
,
Brunis
C
, et al
Molecular testing patterns in metastatic non-small cell lung cancer
.
Am J Manag Care
2016
;
22
:
e60
7
.
36.
Chan
BA
,
Hughes
BG
. 
Targeted therapy for non-small cell lung cancer: current standards and the promise of the future
.
Transl Lung Cancer Res
2015
;
4
:
36
54
.
37.
Drope
J
,
Liber
AC
,
Cahn
Z
,
Stoklosa
M
,
Kennedy
R
,
Douglas
CE
, et al
Who's still smoking? Disparities in adult cigarette smoking prevalence in the United States
.
CA Cancer J Clin
2018
;
68
:
106
15
.
38.
Tseng
CH
,
Chiang
CJ
,
Tseng
JS
,
Yang
TY
,
Hsu
KH
,
Chen
KC
, et al
EGFR mutation, smoking, and gender in advanced lung adenocarcinoma
.
Oncotarget
2017
;
8
:
98384
93
.
39.
Zhu
K
,
Devesa
SS
,
Wu
H
,
Zahm
SH
,
Jatoi
I
,
Anderson
WF
, et al
Cancer incidence in the U.S. military population: comparison with rates from the SEER program.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
1740
5
.
40.
Riemenschneider
K
,
Liu
J
,
Powers
JG
. 
Skin cancer in the military: a systematic review of melanoma and non-melanoma skin cancer incidence, prevention, and screening among active duty and veteran personnel
.
J Am Acad Dermatol
2018
;
78
:
1185
92
.
41.
Lin
J
,
Kamamia
C
,
Brown
D
,
Shao
S
,
McGlynn
KA
,
Nations
JA
, et al
Survival among lung cancer patients in the U.S. Military Health System: a comparison with the SEER population.
Cancer Epidemiol Biomarkers Prev
2018
;
27
:
673
9
.
42.
Asch
S
,
Glassman
P
,
Matula
S
,
Trivedi
A
,
Miake-Lye
I
,
Shekelle
P
. 
VA Evidence-based synthesis program reports. Comparison of quality of care in VA and non-VA settings: a systematic review.
VA-ESP Project #05-226
.
Washington, DC: Department of Veterans Affairs (US)
; 
2010
.
Available from:
https://www.hsrd.research.va.gov/publications/esp/quality.pdf.
43.
Spencer
CS
,
Gaskin
DJ
,
Roberts
ET
. 
The quality of care delivered to patients within the same hospital varies by insurance type
.
Health Aff
2013
;
32
:
1731
9
.
44.
Allen
H
,
Wright
BJ
,
Baicker
K
. 
New Medicaid enrollees in Oregon report health care successes and challenges
.
Health Aff
2014
;
33
:
292
9
.
45.
Bradley
CJ
,
Given
CW
,
Roberts
C
. 
Late stage cancers in a Medicaid-insured population
.
Med Care
2003
;
41
:
722
8
.
46.
Bradley
CJ
,
Gardiner
J
,
Given
CW
,
Roberts
C
. 
Cancer, Medicaid enrollment, and survival disparities
.
Cancer
2005
;
103
:
1712
8
.
47.
National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health
.
The health consequences of smoking-50 years of progress: a report of the surgeon general
.
Atlanta, GA
:
Centers for Disease Control and Prevention (US)
; 
2014
.
48.
Zon
RT
,
Goss
E
,
Vogel
VG
,
Chlebowski
RT
,
Jatoi
I
,
Robson
ME
, et al
American Society of Clinical Oncology policy statement: the role of the oncologist in cancer prevention and risk assessment
.
J Clin Oncol
2009
;
27
:
986
93
.
49.
Park
ER
,
Japuntich
SJ
,
Traeger
L
,
Cannon
S
,
Pajolek
H
. 
Disparities between blacks and whites in tobacco and lung cancer treatment
.
Oncologist
2011
;
16
:
1428
34
.
50.
Pawlik
TM
,
Olver
IN
,
Storm
CD
,
Rodriguez
MA
. 
Can physicians refuse treatment to patients who smoke?
J Oncol Pract
2009
;
5
:
250
1
.
51.
Craig
BM
,
Kraus
CK
,
Chewning
BA
,
Davis
JE
. 
Quality of care for older adults with chronic obstructive pulmonary disease and asthma based on comparisons to practice guidelines and smoking status
.
BMC Health Services Res
2008
;
8
:
144
.
52.
Tabchi
S
,
Kassouf
E
,
Florescu
M
,
Tehfe
M
,
Blais
N
. 
Factors influencing treatment selection and survival in advanced lung cancer
.
Curr Oncol
2017
;
24
:
e115
e22
.
53.
Stavrou
EP
,
Lu
CY
,
Buckley
N
,
Pearson
S
. 
The role of comorbidities on the uptake of systemic treatment and 3-year survival in older cancer patients
.
Ann Oncol
2012
;
23
:
2422
8
.
54.
Warren
JL
,
Butler
EN
,
Stevens
J
,
Lathan
CS
,
Noone
AM
,
Ward
KC
, et al
Receipt of chemotherapy among Medicare patients with cancer by type of supplemental insurance
.
J Clin Oncol
2015
;
33
:
312
8
.
55.
Bakirhan
K
,
Sharma
J
,
Perez-Soler
R
,
Cheng
H
. 
Medical treatment in elderly patients with non-small cell lung cancer
.
Curr Treat Options Oncol
2016
;
17
:
13
.
56.
Foster
JA
,
Salinas
GD
,
Mansell
D
,
Williamson
JC
,
Casebeer
LL
. 
How does older age influence oncologists' cancer management?
Oncologist
2010
;
15
:
584
92
.
57.
Onega
T
,
Duell
EJ
,
Shi
X
,
Demidenko
E
,
Goodman
DC
. 
Race versus place of service in mortality among medicare beneficiaries with cancer
.
Cancer
2010
;
116
:
2698
706
.
58.
Escarce
JJ
,
Goodell
S
. 
Racial and ethnic disparities in access to and quality of health care
.
Synth Proj Res Synth Rep
2007
pii: 20651
.
59.
Midha
A
,
Dearden
S
,
McCormack
R
. 
EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII)
.
Am J Cancer Res
2015
;
5
:
2892
911
.
60.
Bell
DW
,
Brannigan
BW
,
Matsuo
K
,
Finkelstein
DM
,
Sordella
R
,
Settleman
J
, et al
Increased prevalence of EGFR-mutant lung cancer in women and in East Asian populations: analysis of estrogen-related polymorphisms
.
Clin Cancer Res
2008
;
14
:
4079
84
.
61.
Ho
G
,
Wun
T
,
Muffly
L
,
Li
Q
,
Brunson
A
,
Rosenberg
AS
, et al
Decreased early mortality associated with the treatment of acute myeloid leukemia at National Cancer Institute-designated cancer centers in California
.
Cancer
2018
;
124
:
1938
45
.
62.
Bristow
RE
,
Chang
J
,
Ziogas
A
,
Campos
B
,
Chavez
LR
,
Anton-Culver
H
. 
Impact of National Cancer Institute Comprehensive Cancer Centers on ovarian cancer treatment and survival
.
J Am Coll Surg
2015
;
220
:
940
50
.
63.
Koroukian
SM
,
Bakaki
PM
,
Raghavan
D
. 
Survival disparities by Medicaid status: an analysis of 8 cancers
.
Cancer
2012
;
118
:
4271
9
.