Background: The U.S. military health system (MHS) provides universal health care access to its beneficiaries. However, whether the universal access has translated into improved patient outcome is unknown. This study compared survival of non–small cell lung cancer (NSCLC) patients in the MHS with that in the U.S. general population.

Methods: The MHS data were obtained from The Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR), and the U.S. population data were drawn from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program. The study subjects were NSCLC patients diagnosed between January 1, 1987, and December 31, 2012, in ACTUR and a sample of SEER patients who were matched to the ACTUR patients on age group, sex, race, and year of diagnosis group with a matching ratio of 1:4. Patients were followed through December 31, 2013.

Results: A total of 16,257 NSCLC patients were identified from ACTUR and 65,028 matched patients from SEER. Compared with SEER patients, ACTUR patients had significantly better overall survival (log-rank P < 0.001). The better overall survival among the ACTUR patients remained after adjustment for potential confounders (HR = 0.78, 95% confidence interval, 0.76–0.81). The survival advantage of the ACTUR patients was present regardless of cancer stage, grade, age group, sex, or race.

Conclusions: The MHS's universal care and lung cancer care programs may have translated into improved survival among NSCLC patients.

Impact: This study supports improved survival outcome among NSCLC patients with universal care access. Cancer Epidemiol Biomarkers Prev; 27(6); 673–9. ©2018 AACR.

Lung cancer is the leading cause of cancer death among both men and women, accounting for 26.5% of all cancer deaths in the United States. In 2018, it is estimated that there will be 234,030 new cases of lung cancer and 154,050 deaths (1). Non–small cell lung cancer (NSCLC) comprises 85% to 90% of lung cancers (2). Despite the slight decline in incidence and mortality over the last 20 years, in the U.S. general population, the 5-year survival rates of NSCLC remain a dismal 1% for advanced disease (3). Accessibility to health care reflected by health insurance and type of insurance is an important predictor of lung cancer survival (4–7). Research has found that lung cancer patients without health insurance or with Medicaid had a higher mortality than patients with private insurance or Medicare (4–7). These patients were less likely to receive cancer-directed therapies than those with non-Medicaid insurance (4, 6). Lack of or low access to health care affects the utilization of screening services, medical visits, receipt of treatments and quality of care delivered, lessening survival of lung cancer patients, as demonstrated in the general population (4–7).

The U.S. military health system (MHS) provides universal access to health care for 9.6 million beneficiaries, including service members of the seven uniformed services, National Guard and Reserve members, retirees, and their family members (8). In the MHS, all beneficiaries have access to health care defined by the access standards in the MHS policies and guidance (8). However, little is known about whether the universal access to health care has translated into improved patient outcomes among the MHS beneficiaries. To the best of our knowledge, there have been no studies comparing the MHS and the U.S. general populations in survival of lung cancer. In this study, we compared NSCLC patients in the MHS with those in the U.S. general population in overall survival and receipt of lung cancer treatments.

Data sources

This study was based on data from the Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR) and the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. ACTUR was described previously (9, 10). Briefly, ACTUR tracks cancer patients who are diagnosed and/or receive cancer treatment at military treatment facilities (MTFs). MTFs are required to report to ACTUR cancer information of DoD beneficiaries, including active-duty members, retirees, and their dependents. Data are collected on demographics, tumor characteristics, cancer treatment, and vital status of patients. ACTUR complies with the uniform data standards set by the North American Association of Central Cancer Registries (NAACCR; ref. 11). The SEER program is a nation-wide cancer registry that collects data on patient demographics, primary tumor site, tumor stage, grade, and size at diagnosis, first course of treatment, vital status, and other information (12). The population residing within the areas served by SEER cancer registries is comparable to the general population (13). We used SEER 18 (1973–2012) with catchments for the 18 SEER registries (Atlanta, Connecticut, Detroit, Hawaii, Iowa, New Mexico, San Francisco–Oakland, Seattle–Puget Sound, Utah, Los Angeles, San Jose–Monterey, Rural Georgia, the Alaska Native, Greater California, Greater Georgia, Kentucky, Louisiana, and New Jersey). The SEER records were obtained for this study based on the November 2014 submission public use file (14).

This study was based on nonidentifiable ACTUR data approved for our research by the institutional review board of Walter Reed National Military Medical Center and SEER data were deidentified for public use.

Study subjects

Patients diagnosed with histologically confirmed primary NSCLC between January 1, 1987, and December 31, 2012, and identified from ACTUR and SEER databases were included in this study. This time period was selected to ensure the same diagnosis time frame for patients from both registries. NSCLC was defined with the cancer site codes (C34.0 to C34.3, C34.8, C34.9) and morphology codes (8050–8078, 8083, 8084, 8250–8260, 8480–8490, 8570–8574, 8140, 8211, 8230, 8231, 8323, 8550, 8551, 8576, 8010–8012, 8014–8031, 8035, 8310) of the International Classification of Diseases for Oncology, third edition (ICD-O-3; ref. 15). Data on tumor stage were consolidated, and stages were defined as derived stages I to IV according to the Revised International System for Staging Lung Cancer, 6th edition (16). Patients with diagnosis from death certificate or autopsy were excluded from the analysis. To reduce differences in age, gender, race, and year of diagnosis, the ACTUR and SEER patients were matched on age group (<50, 50–64, 65–80, >80), sex, race, and year of diagnosis group (1987–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2012). For each ACTUR patient, four matched patients in the same matching stratum were randomly selected from SEER.

Statistical analysis

We first compared the distributions of demographic and tumor characteristics the among ACTUR and SEER patients using χ2 test. We then used Kaplan–Meier curve and log-rank test to compare overall survival between patients from ACTUR and SEER with all-cause death as the outcome. Multivariable Cox proportional hazards model for matched data was then used to estimate HRs and 95% confidence interval (CI) for ACTUR compared with SEER. Patients who did not die during follow-up were censored. In multivariable Cox regression modeling, we adjusted for the following variables: region at diagnosis (Northeast, South, Midwest, West and other, as defined by the U.S. Census Bureau), tumor stage, tumor grade, surgery (for stages I and II), and radiation (for stages III and IV). Matching variables were no longer needed to be adjusted in the models. The Cox analysis was further stratified by age group, sex, race, and cancer stage. In addition to survival, we also compared ACTUR and SEER patients in receipt of surgery and radiation treatment. Receipt of surgery was compared for stages I and II patients only and receipt of radiotherapy was compared for stages III and IV patients only according to the recommended treatment guidelines (17, 18). Multivariable logistic regression was used to estimate odds ratios (OR) and 95% CIs of treatment with SEER as the reference.

Statistical analyses were conducted using the SAS software version 9.3.0 (SAS Institute, Inc.). All reported P values are two sided, with the significance level set at P < 0.05.

A total of 16,257 patients were identified from ACTUR and 65,028 matched patients from SEER. Demographic and tumor characteristics of patients are shown in Table 1. The ACTUR and SEER patients were well matched on age group, sex, race, and diagnosis year group (P = 1.00 for all). However, compared with the SEER patients, the ACTUR patients were more likely to be diagnosed in the South region and less likely to be diagnosed in the Northeast, Midwest, or West regions (P < 0.0001). In regard to tumor stage, the ACTUR patients were less likely to be diagnosed at stage IV than SEER cases (28.65% vs. 33.39%) and more likely to be diagnosed at earlier stages (P < 0.0001). The ACTUR patients had a lower percentage of poorly differentiated tumor than the SEER patients (35.71% vs. 37.14%), and higher percentages of well-differentiated tumor (7.50% vs. 5.40%) and moderately differentiated tumor (21.78% vs. 19.80%; P < 0.0001).

Table 1.

Demographic and tumor characteristics of NSCLC patients diagnosed during 1987–2012 from the ACTUR and SEER registriesa

ACTURSEERP
N (%)N (%)
Age groups   1.00 
 <50 1,202 (7.39) 4,808 (7.39)  
 50–64 7,004 (43.08) 28,016 (43.08)  
 65–80 7,129 (43.85) 28,516 (43.85)  
 >80 922 (5.67) 3,688 (5.67)  
Sex   1.00 
 Male 10,879 (66.92) 43,516 (66.92)  
 Female 5,378 (33.08) 21,512 (33.08)  
Race   1.00 
 White 13,693 (84.23) 54,772 (84.23)  
 Black 1,683 (10.35) 6,732 (10.35)  
 Asian or Pacific Islander 818 (5.03) 3,272 (5.03)  
 American India/Alaska Native 19 (0.12) 76 (0.12)  
 Other 44 (0.27) 176 (0.27)  
Year of diagnosis   1.00 
 1987–1989 2,026 (12.46) 8,104 (12.46)  
 1990–1994 4,643 (28.56) 18,572 (28.56)  
 1995–1999 3,409 (20.97) 13,636 (20.97)  
 2000–2004 2,614 (16.08) 10,456 (16.08)  
 2005–2009 2,285 (14.06) 9,140 (14.06)  
 2010–2012 1,280 (7.87) 5,120 (7.87)  
Region of diagnosisb   <0.0001 
 Northeast 273 (1.68) 10,287 (15.82)  
 South 9,111 (56.04) 9,617 (14.79)  
 Midwest 1,432 (8.81) 14,749 (22.68)  
 West 4,916 (30.24) 30,375 (46.71)  
 Other 525 (3.23) 0 (0.00)  
Stage   <0.0001 
 Stage I 4,747 (29.20) 16,761 (25.78)  
 Stage II 1,059 (6.51) 3,384 (5.20)  
 Stage III 4,020 (24.73) 15,725 (24.18)  
 Stage IV 4,657 (28.65) 21,715 (33.39)  
 Unknown 1,774 (10.91) 7,443 (11.45)  
Grade   <0.0001 
 Well differentiated, grade I 1,220 (7.50) 3,510 (5.40)  
 Moderately differentiated, grade II 3,541 (21.78) 12,875 (19.80)  
 Poorly differentiated, grade III 5,806 (35.71) 24,151 (37.14)  
 Undifferentiated, grade IV 526 (3.24) 4,002 (6.15)  
 Unknown 5,164 (31.76) 20,490 (31.51)  
ACTURSEERP
N (%)N (%)
Age groups   1.00 
 <50 1,202 (7.39) 4,808 (7.39)  
 50–64 7,004 (43.08) 28,016 (43.08)  
 65–80 7,129 (43.85) 28,516 (43.85)  
 >80 922 (5.67) 3,688 (5.67)  
Sex   1.00 
 Male 10,879 (66.92) 43,516 (66.92)  
 Female 5,378 (33.08) 21,512 (33.08)  
Race   1.00 
 White 13,693 (84.23) 54,772 (84.23)  
 Black 1,683 (10.35) 6,732 (10.35)  
 Asian or Pacific Islander 818 (5.03) 3,272 (5.03)  
 American India/Alaska Native 19 (0.12) 76 (0.12)  
 Other 44 (0.27) 176 (0.27)  
Year of diagnosis   1.00 
 1987–1989 2,026 (12.46) 8,104 (12.46)  
 1990–1994 4,643 (28.56) 18,572 (28.56)  
 1995–1999 3,409 (20.97) 13,636 (20.97)  
 2000–2004 2,614 (16.08) 10,456 (16.08)  
 2005–2009 2,285 (14.06) 9,140 (14.06)  
 2010–2012 1,280 (7.87) 5,120 (7.87)  
Region of diagnosisb   <0.0001 
 Northeast 273 (1.68) 10,287 (15.82)  
 South 9,111 (56.04) 9,617 (14.79)  
 Midwest 1,432 (8.81) 14,749 (22.68)  
 West 4,916 (30.24) 30,375 (46.71)  
 Other 525 (3.23) 0 (0.00)  
Stage   <0.0001 
 Stage I 4,747 (29.20) 16,761 (25.78)  
 Stage II 1,059 (6.51) 3,384 (5.20)  
 Stage III 4,020 (24.73) 15,725 (24.18)  
 Stage IV 4,657 (28.65) 21,715 (33.39)  
 Unknown 1,774 (10.91) 7,443 (11.45)  
Grade   <0.0001 
 Well differentiated, grade I 1,220 (7.50) 3,510 (5.40)  
 Moderately differentiated, grade II 3,541 (21.78) 12,875 (19.80)  
 Poorly differentiated, grade III 5,806 (35.71) 24,151 (37.14)  
 Undifferentiated, grade IV 526 (3.24) 4,002 (6.15)  
 Unknown 5,164 (31.76) 20,490 (31.51)  

aPatients were matched by age group, sex, race, and year of diagnosis group with ACTUR: SEER ratio = 1:4.

bDefined following the U.S. Census Bureau definition.

The median survival times for ACTUR and SEER patients were 15.5 and 10.0 months, respectively. Kaplan–Meier survival curves showed significantly better survival for ACTUR patients than SEER patients (log-rank P < 0.001; Fig. 1). In Cox proportional hazards analysis, ACTUR patients exhibited significantly better overall survival than did their SEER counterparts (HR = 0.78, 95% CI, 0.76–0.81; Table 2). The survival advantage of ACTUR patients remained in subgroups stratified by age group, sex, race, tumor stage, or tumor grade (Table 2).

Figure 1.

Kaplan–Meier survival curves for ACTUR and SEER patients with NSCLC. The figure shows the comparison of survival probability over time for ACTUR and SEER patients. Patients were diagnosed between 1987 and 2012 and followed through December 31, 2013.

Figure 1.

Kaplan–Meier survival curves for ACTUR and SEER patients with NSCLC. The figure shows the comparison of survival probability over time for ACTUR and SEER patients. Patients were diagnosed between 1987 and 2012 and followed through December 31, 2013.

Close modal
Table 2.

All-cause mortality among NSCLC patients diagnosed between 1987 and 2012, the ACTUR and SEER registries

Numbers
VariablesAliveDeadAdjusted HRa95% CI
Overall 
 SEER 7,700 57,328 1.00 (Ref.) 1.00 (Ref.) 
 ACTUR 1,895 14,362 0.78 0.76–0.81 
By age 
 <50 
  SEER 877 3,931 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 272 930 0.66 0.60–0.74 
 50–64 
  SEER 3,534 24,482 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 916 6,088 0.78 0.75–0.81 
 65–80 
  SEER 2,921 25,595 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 645 6,484 0.81 0.78–0.85 
 >80 
  SEER 368 3,320 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 62 860 0.74 0.66–0.82 
By sex 
 Male 
  SEER 4,110 39,406 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 958 9,921 0.79 0.76–0.82 
 Female 
  SEER 3,590 17,922 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 937 4,441 0.77 0.73–0.81 
By race 
 White 
  SEER 6,050 48,722 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 1,433 12,260 0.80 0.77–0.82 
 Black 
  SEER 775 5,957 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 238 1,445 0.67 0.62–0.73 
 Asian or Pacific Islander 
  SEER 767 2,505 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 199 619 0.84 0.74–0.96 
By stageb 
 Stage I 
  SEER 4,372 12,389 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 1,197 3,550 0.87 0.80–0.95 
 Stage II 
  SEER 538 2,846 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 147 912 1.00 0.72–1.37 
 Stage III 
  SEER 1,290 14,435 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 239 3,781 0.76 0.70–0.83 
 Stage IV 
  SEER 930 20,785 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 147 4,510 0.72 0.67–0.76 
 Unknown 
  SEER 570 6,873 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 165 1,609 0.86 0.75–0.98 
By grade 
 Well differentiated 
  SEER 981 2,529 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 379 841 0.73 0.50–1.06 
 Moderately differentiated 
  SEER 2,287 10,588 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 537 3,004 0.76 0.69–0.85 
 Poorly differentiated 
  SEER 2,237 21,914 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 462 5,344 0.81 0.76–0.86 
 Undifferentiated 
  SEER 213 3,789 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 35 491 0.81 0.55–1.21 
 Unknown 
  SEER 1,982 18,508 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 482 4,682 0.73 0.68–0.78 
Numbers
VariablesAliveDeadAdjusted HRa95% CI
Overall 
 SEER 7,700 57,328 1.00 (Ref.) 1.00 (Ref.) 
 ACTUR 1,895 14,362 0.78 0.76–0.81 
By age 
 <50 
  SEER 877 3,931 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 272 930 0.66 0.60–0.74 
 50–64 
  SEER 3,534 24,482 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 916 6,088 0.78 0.75–0.81 
 65–80 
  SEER 2,921 25,595 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 645 6,484 0.81 0.78–0.85 
 >80 
  SEER 368 3,320 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 62 860 0.74 0.66–0.82 
By sex 
 Male 
  SEER 4,110 39,406 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 958 9,921 0.79 0.76–0.82 
 Female 
  SEER 3,590 17,922 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 937 4,441 0.77 0.73–0.81 
By race 
 White 
  SEER 6,050 48,722 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 1,433 12,260 0.80 0.77–0.82 
 Black 
  SEER 775 5,957 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 238 1,445 0.67 0.62–0.73 
 Asian or Pacific Islander 
  SEER 767 2,505 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 199 619 0.84 0.74–0.96 
By stageb 
 Stage I 
  SEER 4,372 12,389 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 1,197 3,550 0.87 0.80–0.95 
 Stage II 
  SEER 538 2,846 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 147 912 1.00 0.72–1.37 
 Stage III 
  SEER 1,290 14,435 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 239 3,781 0.76 0.70–0.83 
 Stage IV 
  SEER 930 20,785 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 147 4,510 0.72 0.67–0.76 
 Unknown 
  SEER 570 6,873 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 165 1,609 0.86 0.75–0.98 
By grade 
 Well differentiated 
  SEER 981 2,529 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 379 841 0.73 0.50–1.06 
 Moderately differentiated 
  SEER 2,287 10,588 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 537 3,004 0.76 0.69–0.85 
 Poorly differentiated 
  SEER 2,237 21,914 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 462 5,344 0.81 0.76–0.86 
 Undifferentiated 
  SEER 213 3,789 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 35 491 0.81 0.55–1.21 
 Unknown 
  SEER 1,982 18,508 1.00 (Ref.) 1.00 (Ref.) 
  ACTUR 482 4,682 0.73 0.68–0.78 

aAll HRs were adjusted for tumor stage, tumor grade, and region at diagnosis. In stratified analysis, tumor stage was not adjusted in the analysis stratified by tumor stage, and tumor grade was not adjusted in the analysis stratified by tumor grade.

bFor stages I and II patients, HRs were adjusted for tumor grade, region at diagnosis and surgery. For stages III and IV patients, HRs were adjusted for tumor stage, region at diagnosis, and radiation.

Table 3 shows the results on receipt of cancer treatments. The ACTUR patients were more likely to receive surgery for early-stage (stages I and II) tumors (OR = 1.41, 95% CI, 1.28–1.55; Table 3), and radiotherapy for late-stage (stages III to IV) tumors (OR = 1.09, 95% CI, 1.03–1.15). When the analysis was stratified by age, sex, race, or cancer stage, the higher likelihood of receiving surgery among early-stage patients in ACTUR compared with SEER remained in all subgroups, except for Asian or Pacific Island populations. While the same tendency was observed for radiation receipt, the association was not significant in some subgroups.

Table 3.

Cancer treatments among NSCLC patients diagnosed between 1987 and 2012, the ACTUR and SEER registries

Surgery (stages I and II)Radiation (stages III and IV)
VariablesSurgeryNo surgeryAdjusted OR (95% CI)aRadiationNo radiationAdjusted OR (95% CI)a
Overall 
 SEER 16,430 3,692 1.00 (ref) 21,504 15,154 1.00 (ref) 
 ACTUR 4,891 876 1.41 (1.28–1.55) 5,099 3,217 1.09 (1.03–1.15) 
By age 
 <50 
  SEER 975 91 1.00 (ref) 2,111 1,001 1.00 (ref) 
  ACTUR 297 23 1.34 (1.14–1.58) 478 215 1.13 (0.92–1.38) 
 50–64 
  SEER 7,071 1,112 1.00 (ref) 10,259 6,068 1.00 (ref) 
  ACTUR 2,096 266 1.35 (1.15–1.58) 2,437 1,263 1.09 (1.01–1.19) 
 65–80 
  SEER 7,751 1,996 1.00 (ref) 8,265 6,897 1.00 (ref) 
  ACTUR 2,286 443 1.46 (1.28–1.66) 1,975 1,482 1.08 (0.99–1.17) 
 >80 
  SEER 633 493 1.00 (ref) 869 1,188 1.00 (ref) 
  ACTUR 212 144 1.42 (1.05–1.91) 209 257 1.03 (0.82–1.30) 
By sex 
 Male 
  SEER 10,214 2,527 1.00 (ref) 14,780 10,256 1.00 (ref) 
  ACTUR 3,064 582 1.50 (1.34–1.69) 3,570 2,190 1.10 (1.03–1.18) 
 Female 
  SEER 6,216 1,165 1.00 (ref) 6,724 4,898 1.00 (ref) 
  ACTUR 1,827 294 1.24 (1.05–1.46) 1,529 1,027 1.05 (0.95–1.17) 
By race 
 White 
  SEER 14,298 3,080 1.00 (ref) 17,820 12,435 1.00 (ref) 
  ACTUR 4,151 751 1.30 (1.17–1.44) 4,229 2,662 1.09 (1.02–1.16) 
 Black 
  SEER 1,277 457 1.00 (ref) 2,517 1,711 1.00 (ref) 
  ACTUR 458 75 2.65 (1.95–3.62) 584 351 1.04 (0.89–1.22) 
 Asian or Pacific Islander 
  SEER 813 144 1.00 (ref) 1,106 921 1.00 (ref) 
  ACTUR 262 42 1.35 (0.86–2.13) 270 189 1.31 (1.02–1.67) 
Surgery (stages I and II)Radiation (stages III and IV)
VariablesSurgeryNo surgeryAdjusted OR (95% CI)aRadiationNo radiationAdjusted OR (95% CI)a
Overall 
 SEER 16,430 3,692 1.00 (ref) 21,504 15,154 1.00 (ref) 
 ACTUR 4,891 876 1.41 (1.28–1.55) 5,099 3,217 1.09 (1.03–1.15) 
By age 
 <50 
  SEER 975 91 1.00 (ref) 2,111 1,001 1.00 (ref) 
  ACTUR 297 23 1.34 (1.14–1.58) 478 215 1.13 (0.92–1.38) 
 50–64 
  SEER 7,071 1,112 1.00 (ref) 10,259 6,068 1.00 (ref) 
  ACTUR 2,096 266 1.35 (1.15–1.58) 2,437 1,263 1.09 (1.01–1.19) 
 65–80 
  SEER 7,751 1,996 1.00 (ref) 8,265 6,897 1.00 (ref) 
  ACTUR 2,286 443 1.46 (1.28–1.66) 1,975 1,482 1.08 (0.99–1.17) 
 >80 
  SEER 633 493 1.00 (ref) 869 1,188 1.00 (ref) 
  ACTUR 212 144 1.42 (1.05–1.91) 209 257 1.03 (0.82–1.30) 
By sex 
 Male 
  SEER 10,214 2,527 1.00 (ref) 14,780 10,256 1.00 (ref) 
  ACTUR 3,064 582 1.50 (1.34–1.69) 3,570 2,190 1.10 (1.03–1.18) 
 Female 
  SEER 6,216 1,165 1.00 (ref) 6,724 4,898 1.00 (ref) 
  ACTUR 1,827 294 1.24 (1.05–1.46) 1,529 1,027 1.05 (0.95–1.17) 
By race 
 White 
  SEER 14,298 3,080 1.00 (ref) 17,820 12,435 1.00 (ref) 
  ACTUR 4,151 751 1.30 (1.17–1.44) 4,229 2,662 1.09 (1.02–1.16) 
 Black 
  SEER 1,277 457 1.00 (ref) 2,517 1,711 1.00 (ref) 
  ACTUR 458 75 2.65 (1.95–3.62) 584 351 1.04 (0.89–1.22) 
 Asian or Pacific Islander 
  SEER 813 144 1.00 (ref) 1,106 921 1.00 (ref) 
  ACTUR 262 42 1.35 (0.86–2.13) 270 189 1.31 (1.02–1.67) 

aAdjusted for tumor stage, tumor grade, and region at diagnosis.

This study showed that NSCLC patients in the MHS had better survival than those in the U.S. general population despite age, gender, race, tumor stage or grade. In addition, the ACTUR patients tended to be more likely than the SEER patients to receive surgery for early stage tumor and radiotherapy for late stage tumor. Our results suggest that the access to universal care within the MHS has translated into improved survival, which might partly result from more timely treatment and treatment compliance to the treatment guidelines.

The MHS is one of the largest health care providers in the United States that combines resources to provide ready access to health care for 9.6 million beneficiaries and is dedicated to quality health care and performance improvements (8). Recent evidence showed that MHS outperformed or was equal to national benchmarks in cancer screening and other services (8). There has been a lack of comparison of cancer outcomes between MHS beneficiaries and the U.S. general population. Nevertheless, there were a few studies in the Veterans Health Administration (VHA) health system, which also provides universal access to care for its beneficiaries. But the results are inconsistent. Landrum and colleagues found that relative to SEER-Medicare patients, VHA patients had earlier stage at diagnosis and higher survival rates of NSCLC with an HR of 0.91 (95% CI, 0.88–0.95) after adjusting for performance status, comorbidity, smoking history and education. However, the higher survival was not significant after adjusting for stage at diagnosis (19). This suggested that the effects of earlier tumor stage might result from better preventive care in VHA, on survival (19). In a study by Zeliadt and colleagues, veterans with NSCLC, identified from the cancer registry for the Veterans Affairs Pacific Northwest Network, were more likely to be diagnosed with early-stage disease than the SEER patients, identified from the Puget Sound SEER cancer registry (20), but survival was similar between the two populations among older patients with early-stage tumors (20). However, among younger (younger than 65 years) patients with early-stage disease, veterans had lower age-adjusted survival than SEER cases (20). Nevertheless, factors other than age were not adjusted for in the analysis. Although the reasons for the lower survival among beneficiaries in the VHA are unknown, the authors suggested that the lower surgical rates in younger veterans may be a potential factor (20). In another study in Pennsylvania (21), the survival rates of lung cancer patients were significantly lower among white veterans than white civilians after adjusting for age and stage. Lower socioeconomic status, less patient awareness, worse support systems, lower education and more comorbid illness among VHA patients than civilians may be possible reasons for the worse survival among VHA beneficiaries (21).

To our knowledge, the current study is the first to compare MHS beneficiaries and the U.S. general population in NSCLC survival. In the general population, cancer patients without health insurance have a higher mortality than patients with health insurance (4–7, 22). The lower survival of cancer patients without insurance has been attributed to advanced stage at diagnosis (4, 6, 23), not receiving the same treatment (4–6, 22, 24, 25) and other factors, such as delivery and quality of care received (6). The universal access to health care services of all MHS beneficiaries presumably reduces these disadvantages. In particular, regarding tumor stage at diagnosis, our results showed that the ACTUR patients were more likely to be diagnosed at early stages than the SEER patients. Lung cancer screening was not a standard practice during the time period of the data. The earlier stage at diagnosis in lung cancer observed and the higher utilization of screening in other cancers among MHS beneficiaries (26, 27) may suggest the benefit of readily access to diagnosis and care in the MHS.

Our results showed that ACTUR patients had better survival than SEER patients in the overall analysis adjusting for cancer stage and other factors, and in the stratified analysis by stage. In the study conducted by Landrum and colleagues, however, the survival advantage of VHA patients was not observed after adjustment for tumor stage (19). The authors concluded that earlier tumor stage explained much of the survival advantage among VHA patients and that better preventive care provided in VHA resulted in earlier tumor stage and therefore improved patient outcomes (19). In our study, ACTUR patients exhibited better survival than SEER patients after adjustment for tumor stage and the association was observed despite cancer stage, suggesting that the survival advantage may also be explained by factors other than earlier stage at diagnosis. The beneficial roles of cancer care after diagnosis may also play a role in the improved survival for patients of all stages.

As shown in our results, early-stage and late-stage ACTUR patients were more likely to receive surgery and radiation treatment than their SEER counterparts, respectively. The higher rates of receiving cancer care in MHS than the general population was also reported in other cancers. A recent study of more than 3,000 MHS cancer patients also found that 74% of breast and 65% of prostate cancer patients received all minimum recommended care over 3 years after diagnosis, and the rates did not vary by age groups (28), while a SEER-Medicare study reported only 57% of breast cancer patients ages 65 or older receive minimum recommended care in 3 years after diagnosis (29). In contrast, the VHA study reported age-adjusted lower surgery rates among younger veterans than civilians (20). Treatment receipt was not compared in the other two studies (19, 21) comparing VHA patients and civilians.

Overall, the results of comparison between VHA and general population in terms of survival and receipt of cancer treatment are different from our comparison between MHS and the general population. While it is unclear why the results are different between MHS and VHA when comparing to the general populations, the two systems may differ in population characteristics (e.g., socioeconomic status, education, health behavior, and health status) and cancer treatment and care, which are associated with survival. Further research is warranted.

The strengths of this study include a large number of patients from the ACTUR and SEER registries and matching the patients from the two databases on key demographic characteristics, which reduced confounding by these variables. Previous comparative studies of VHA beneficiaries with the general population did not match on demographics (19–21). However, due to the nature of cancer registry data, the potential effects of comorbidity, life style factors (smoking in particular), performance status, and health care quality on survival differences cannot be assessed. In addition, chemotherapy could not be compared between SEER and ACTUR populations due to the lack of information on chemotherapy in the SEER data. Further, since all-cause death rather than lung cancer-specific death was used as the outcome, we do not exclude potential effects of comorbidities on the results. However, this may not be a major concern in lung cancer because the majority of patients die of the disease. Finally, we also noted a fair amount of unknown tumor grade in both SEER (31.51%) and ACTUR (31.76%). The reasons for the unknown grade with similar extent in both registries warrant further investigation.

In conclusion, our results suggest that survival was better, and the likelihood of receiving cancer treatments was higher in the MHS than in the SEER population, implying that universal access to health care within the MHS may have translated into improved patient survival. Future studies are warranted to investigate specific factors that may contribute to the improved survival among NSCLC patients in the MHS.

No potential conflicts of interest were disclosed.

The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views, assertions, opinions, or policies of the Uniformed Services University of the Health Sciences (USUHS), the Department of Defense (DoD), National Cancer Institute, or the Departments of the Army, Navy, or Air Force. Nothing in the presentation implies any Federal/DoD endorsement.

Conception and design: J. Lin, D. Brown, C.A. Carter, C.D. Shriver, K. Zhu

Development of methodology: J. Lin, D. Brown, S. Shao, C.A. Carter, K. Zhu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Lin, K. Zhu

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Lin, C. Kamamia, D. Brown, S. Shao, J.A. Nations, C.A. Carter, C.D. Shriver, K. Zhu

Writing, review, and/or revision of the manuscript: J. Lin, C. Kamamia, D. Brown, K.A. McGlynn, J.A. Nations, C.A. Carter, C.D. Shriver, K. Zhu

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): C.D. Shriver, K. Zhu

Study supervision: J. Lin, C.A. Carter, K. Zhu

This project was supported by John P. Murtha Cancer Center, Walter Reed National Military Medical Center via the Uniformed Services University of the Health Sciences under the auspices of the Henry M. Jackson Foundation for the Advancement of Military Medicine. The authors thank the Joint Pathology Center (formerly Armed Forces Institute of Pathology) for providing the ACTUR data.

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.

3.
American Cancer Society. Key statistics for lung cancer
: https://www.cancer.org/cancer/non-small-cell-lung-cancer/detection-diagnosis-staging/survival-rates.html.
Accessed March 27, 2017
.
4.
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
.
5.
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
.
6.
Slatore
CG
,
Au
DH
,
Gould
MK
. 
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
.
7.
Niu
X
,
Roche
LM
,
Pawlish
KS
,
Henry
KA
. 
Cancer survival disparities by health insurance status
.
Cancer Med
2013
;
2
:
403
11
.
8.
Final Report to the Secretary of Defense: Military Health System Review
: http://www.defense.gov/Portals/1/Documents/pubs/140930_MHS_Review_Final_Report_Main_Body.pdf.
August 29, 2014 Accessed February 2, 2017
.
9.
Lin
J
,
Zahm
SH
,
Shriver
CD
,
Purdue
M
,
McGlynn
KA
,
Zhu
K
. 
Survival among black and white patients with renal cell carcinoma in an equal-access health care system
.
Cancer Causes Control
2015
;
26
:
1019
26
.
10.
Zheng
L
,
Enewold
L
,
Zahm
SH
,
Shriver
CD
,
Zhou
J
,
Marrogi
A
, et al
Lung cancer survival among black and white patients in an equal access health system
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
1841
7
.
11.
Tryon
J
. 
User's guide for ACTUR cancer registry software system abstracting module
. 
2007
.
12.
Surveillance, Epidemiology, and End Results Program
: http://seer.cancer.gov/.
Accessed May 10, 2016
.
13.
Edwards
BK
,
Noone
AM
,
Mariotto
AB
,
Simard
EP
,
Boscoe
FP
,
Henley
SJ
, et al
Annual Report to the Nation on the status of cancer, 1975–2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer
.
Cancer
2014
;
120
:
1290
314
.
14.
SEER*Stat databases: survival, case listing, and frequency sessions
: http://seer.cancer.gov/data/seerstat/nov2014/.
Surveillance, Epidemiology, and End Results Program; Accessed May 10, 2016
.
15.
Fritz
A
,
Percy
C
,
Lack
A
, et al
International classification of diseases for oncology
. 3rd ed.
Geneva
:
World Health Organization
; 
2000
.
16.
Greene
FL
,
Page
DL
,
Fleming
ID
,
Fritz
AG
,
Balch
CM
,
Haller
DG
, et al
AJCC cancer staging manual, 6th edition
.
Springer-Verlag
,
New York
; 
2002
.
17.
Pisters
KM
,
Evans
WK
,
Azzoli
CG
,
Kris
MG
,
Smith
CA
,
Desch
CE
, et al
Cancer Care Ontario and American Society of Clinical Oncology adjuvant chemotherapy and adjuvant radiation therapy for stages I–IIIA resectable non–small cell lung cancer guideline
.
J Clin Oncol
2007
;
25
:
5506
18
.
18.
Masters
GA
,
Temin
S
,
Azzoli
CG
,
Giaccone
G
,
Baker
S
 Jr
,
Brahmer
JR
, et al
Systemic therapy for stage IV non-small-cell lung cancer: American Society of Clinical Oncology Clinical Practice Guideline Update
.
J Clin Oncol
2015
;
33
:
3488
515
.
19.
Landrum
MB
,
Keating
NL
,
Lamont
EB
,
Bozeman
SR
,
Krasnow
SH
,
Shulman
L
, et al
Survival of older patients with cancer in the Veterans Health Administration versus fee-for-service Medicare
.
J Clin Oncol
2012
;
30
:
1072
9
.
20.
Zeliadt
SB
,
Sekaran
NK
,
Hu
EY
,
Slatore
CC
,
Au
DH
,
Backhus
L
, et al
Comparison of demographic characteristics, surgical resection patterns, and survival outcomes for veterans and nonveterans with non-small cell lung cancer in the Pacific Northwest
.
J Thorac Oncol
2011
;
6
:
1726
32
.
21.
Campling
BG
,
Hwang
WT
,
Zhang
J
,
Thompson
S
,
Litzky
LA
,
Vachani
A
, et al
A population-based study of lung carcinoma in Pennsylvania: comparison of Veterans Administration and civilian populations
.
Cancer
2005
;
104
:
833
40
.
22.
Parikh
AA
,
Robinson
J
,
Zaydfudim
VM
,
Penson
D
,
Whiteside
MA
. 
The effect of health insurance status on the treatment and outcomes of patients with colorectal cancer
.
J Surg Oncol
2014
;
110
:
227
32
.
23.
Halpern
MT
,
Ward
EM
,
Pavluck
AL
,
Schrag
NM
,
Bian
J
,
Chen
AY
. 
Association of insurance status and ethnicity with cancer stage at diagnosis for 12 cancer sites: a retrospective analysis
.
Lancet Oncol
2008
;
9
:
222
31
.
24.
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
.
25.
Virgo
KS
,
Little
AG
,
Fedewa
SA
,
Chen
AY
,
Flanders
WD
,
Ward
EM
. 
Safety-net burden hospitals and likelihood of curative-intent surgery for non-small cell lung cancer
.
J Am Coll Surg
2011
;
213
:
633
43
.
26.
Brown
DS
,
Kurlantzick
VG
,
McCall
NT
,
Williams
TV
,
Gantt
CJ
,
Granger
E
. 
Use of six clinical preventive services in TRICARE Prime compared to insured, managed care, and all U.S. populations and healthy people 2010
.
Prev Med
2009
;
48
:
389
91
.
27.
Debarros
M
,
Steele
SR
. 
Colorectal cancer screening in an equal access healthcare system
.
J Cancer
2013
;
4
:
270
80
.
28.
Fox
JP
,
Jeffery
DD
,
Williams
TV
,
Gross
CP
. 
Quality of cancer survivorship care in the military health system (TRICARE)
.
Cancer J
2013
;
19
:
1
9
.
29.
Keating
NL
,
Landrum
MB
,
Guadagnoli
E
,
Winer
EP
,
Ayanian
JZ
. 
Factors related to underuse of surveillance mammography among breast cancer survivors
.
J Clin Oncol
2006
;
24
:
85
94
.