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Treatment quality and outcomes vary with hospital burden of uninsured and Medicaid patients with cancer in early non–small cell lung cancer

Open AccessPublished:July 11, 2022DOI:https://doi.org/10.1016/j.xjon.2022.05.020

      Abstract

      Objectives

      Safety-net hospitals deliver a significant level of care to uninsured patients, Medicaid-enrolled patients, and other vulnerable patients. Little is known about the impact of safety-net hospital status on outcomes in non–small cell lung cancer. We aimed to compare treatment characteristics and outcomes between hospitals categorized according to their relative burden of uninsured or Medicaid-enrolled patients with non–small cell lung cancer.

      Methods

      We queried the National Cancer Database for patients with clinical stage I and II non–small cell lung cancer presenting from 2004 to 2018. We categorized hospitals on the basis of their relative burden of uninsured or Medicaid-enrolled patients with non–small cell lung cancer into low-burden (<8.2%), medium-burden (8.2%-12.0%), high-burden (12.1%-16.8%), and highest burden (>16.8%) quartiles. We investigated the impact of care at these hospitals on outcomes while controlling for sociodemographic, clinical, and facility characteristics.

      Results

      We identified 204,189 patients treated at 1286 facilities. There were 592 low-burden, 297 medium-burden, 219 high-burden, and 178 highest burden hospitals. Patients at highest burden hospitals were more likely to be younger, male, Black, and Hispanic (P < .01), and to reside in rural, low-income, and low-educated regions (P < .01). Patients at these facilities had a greater likelihood of not receiving surgery, undergoing an open procedure, undergoing a regional lymph node examination involving less than 10 lymph nodes, having a length of stay more than 4 days, and not receiving treatment (P < .05).

      Conclusions

      Our results indicate reduced treatment quality and higher mortality in patients undergoing surgery for early non–small cell lung cancer at hospitals with an increased burden of uninsured or Medicaid-enrolled patients with non–small cell lung cancer. There is a need to raise the standard of care to improve outcomes in vulnerable populations.

      Graphical abstract

      Key Words

      Abbreviations and Acronyms:

      AJCC (American Joint Committee on Cancer), aOR (adjusted odds ratio), IRB (Institutional Review Board), NCDB (National Cancer Database), NSCLC (non–small cell lung cancer)
      Figure thumbnail fx2
      Treatment characteristics by hospital burden of uninsured or Medicaid-enrolled patients.
      We observed reduced quality of care and higher mortality in patients with early lung cancer receiving surgery at centers with an increased burden of uninsured patients or Medicaid-enrolled patients with lung cancer.
      Safety-net hospitals are crucial to providing access to care for the underprivileged, and our findings emphasize the need to raise the standard of care in patients undergoing early lung cancer treatment at these facilities to ultimately improve outcomes in medically marginalized populations.
      Social determinants of health impact treatment quality and survival in patients with non–small cell lung cancer (NSCLC).
      • Muslim Z.
      • Stroever S.
      • Baig M.Z.
      • Weber J.F.
      • Connery C.P.
      • Bhora F.Y.
      Social determinants and facility type impact adherence to best practices in operable IIIAN2 lung cancer.
      ,
      • Namburi N.
      • Timsina L.
      • Ninad N.
      • Ceppa D.
      • Birdas T.
      The impact of social determinants of health on management of stage I non–small cell lung cancer.
      Safety-net hospitals have previously been grouped with other proposed patient- and facility-specific contributors of these healthcare disparities.
      • Brandel M.G.
      • Rennert R.C.
      • Lopez Ramos C.
      • Santiago-Dieppa D.R.
      • Steinberg J.A.
      • Sarkar R.R.
      • et al.
      Management of glioblastoma at safety-net hospitals.
      The Institute of Medicine defines safety-net hospitals as “those providers that organize and deliver a significant level of health care and other related services to uninsured, Medicaid, and other vulnerable patients.”
      Institute of Medicine Committee on the changing market, managed care, and the future viability of safety net providers.
      Safety-net hospitals have been associated with reduced adherence to quality-of-care measures and a lower use of curative-intent surgery in patients with NSCLC.
      • Sarkar R.R.
      • Courtney P.T.
      • Bachand K.
      • Sheridan P.E.
      • Riviere P.J.
      • Guss Z.D.
      • et al.
      Quality of care at safety-net hospitals and the impact on pay-for-performance reimbursement.
      ,
      • Virgo K.S.
      • Little A.G.
      • Fedewa S.A.
      • Chen A.Y.
      • Flanders W.D.
      • Ward E.M.
      Safety-net burden hospitals and likelihood of curative-intent surgery for non–small cell lung cancer.
      These findings may be partially explained by limited resources and services that hinder adequate provision of costly and complex treatments
      • Go D.E.
      • Abbott D.E.
      • Wima K.
      • Hanseman D.J.
      • Ertel A.E.
      • Chang A.L.
      • et al.
      Addressing the high costs of pancreaticoduodenectomy at safety-net hospitals.
      ,
      • Hoehn R.S.
      • Wima K.
      • Vestal M.A.
      • Weilage D.J.
      • Hanseman D.J.
      • Abbott D.E.
      • et al.
      Effect of hospital safety-net burden on cost and outcomes after surgery.
      because these hospitals at baseline are subject to lower compensation due to the underinsured nature of their patient population
      • McAlearney A.S.
      • Murray K.
      • Sieck C.
      • Lin J.J.
      • Bellacera B.
      • Bickell N.A.
      The challenge of improving breast cancer care coordination in safety-net hospitals: barriers, facilitators, and opportunities.
      and are susceptible to further financial penalties in light of ongoing revisions to healthcare policy.
      • Gilman M.
      • Hockenberry J.M.
      • Adams E.K.
      • Milstein A.S.
      • Wilson I.B.
      • Becker E.R.
      The financial effect of value-based purchasing and the hospital readmissions reduction program on safety-net hospitals in 2014: a cohort study.
      We noted that there is a paucity of literature investigating the impact of safety-net hospital status on treatment and outcomes in NSCLC. With an anticipated increase in demand for care at safety-net hospitals,
      • Fung V.
      • Liang C.Y.
      • Shi J.
      • Seo V.
      • Overhage L.
      • Dow W.H.
      • et al.
      Potential effects of eliminating the individual mandate penalty in California.
      understanding how outcomes may differ in patients treated at such institutions will become even more pressing.
      The National Cancer Database (NCDB) is the largest cancer registry in the world that currently captures 72% of all newly diagnosed malignancies annually in the United States from more than 1500 Commission on Cancer–approved facilities.
      • Lerro C.C.
      • Robbins A.S.
      • Phillips J.L.
      • Stewart A.K.
      Comparison of cases captured in the national cancer data base with those in population-based central cancer registries.
      The objective of our study was to use this contemporary nationwide patient cohort to compare treatment characteristics and perioperative outcomes between hospitals categorized according to their relative burden of uninsured or Medicaid-enrolled patients with NSCLC. We also aimed to elucidate factors associated with care at a hospital with a high burden of uninsured or Medicaid-enrolled patients with NSCLC. We hypothesized that care at such facilities would be associated with reduced treatment quality and inferior outcomes.

      Materials and Methods

      Data Source

      The NCDB is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The Institutional Review Board (IRB) or equivalent ethics committee (Biomedical Research Alliance of New York) approved the study protocol and publication of data. Patient written consent for the publication of the study data was waived by the IRB. IRB exemption was granted due to nonhuman subjects determination (Investigator Initiated Protocol 21-511/BRANY File # 21-15-264-337 [06/24/2021]).

      Selection of Study Population

      In this retrospective cohort study, we queried the NCDB for patients diagnosed with American Joint Committee on Cancer (AJCC) clinical stage I or II NSCLC from 2004 to 2018. Clinical stage was reported using the AJCC clinical staging edition prevalent at the time of diagnosis (AJCC 6: 2004-2009, AJCC 7: 2010-2017, AJCC 8: 2018). We excluded patients who were diagnosed and treated at separate facilities. We also excluded those patients whose insurance status was not known.

      Variables

      Covariates

      We included the following variables in our analysis: age at diagnosis, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, ZIP code level education, county of residence, facility type, facility region, distance to treatment facility, Charlson-Deyo comorbidity score, clinical stage, clinical T stage, clinical N stage, pathologic stage, tumor size, tumor histology, type of treatment provided, and type of surgery performed.

      Hospital burden of uninsured or Medicaid-enrolled patients with non–small cell lung cancer

      We categorized hospitals on the basis of their percentage of uninsured or Medicaid-enrolled patients with stage I to IV NSCLC treated into low-burden (<8.2%), medium-burden (8.2%-12.0%), high-burden (12.1%-16.8%), and highest burden (>16.8%) quartiles. A similar categorization has been used in prior studies to investigate hospital safety-net burden.
      • Sridhar P.
      • Bhatt M.
      • Qureshi M.M.
      • Asokan S.
      • Truong M.T.
      • Suzuki K.
      • et al.
      Esophageal cancer presentation, treatment, and outcomes vary with hospital safety-net burden.
      ,
      • Gamble C.R.
      • Huang Y.
      • Tergas A.I.
      • Khoury-Collado F.
      • Hou J.Y.
      • St Clair C.M.
      • et al.
      Quality of care and outcomes of patients with gynecologic malignancies treated at safety-net hospitals.
      We were unable to calculate safety-net burden inclusive of all cancer and noncancer diagnoses at a particular facility because our dataset was limited to only NSCLC cases.

      Outcome measures

      The outcomes of interest included performing surgery, open thoracotomy, conversion to thoracotomy, anatomic resection, examination of 10 or more regional lymph nodes, pathologic nodal upstaging, positive surgical margins, length of stay more than 4 days, 30-day mortality, unplanned readmission, recommending adjuvant chemotherapy for pathologic stage II disease or higher, and providing no treatment. For the outcomes of performing surgery and recommending chemotherapy, hospitals were not included in the “not performed” and “not recommended” groups, respectively, in the event of nontreatment, if patients were documented not to be candidates due to risk factors or patient refusal. Likewise, for the outcome of providing no treatment, hospitals were not included in the “not provided” group in the event of nontreatment if patients refused treatment and the refusal was documented.

      Statistical Analysis

      We used SPSS Statistical software version 25 (IBM SPSS Statistics for Macintosh, Version 25.0; IBM Corp) for statistical analyses. We determined univariate differences among low-, medium-, high-, and highest burden hospitals using the Pearson chi-square test for categorical variables.
      To understand the patient population affected by any potential differences in treatment quality at higher burden facilities, we used multivariable logistic regression to determine factors that were independently associated with treatment at highest burden facilities. We included the following variables in the regression model on the basis of a priori hypotheses: age at diagnosis, sex, ethnicity, year of diagnosis, insurance status, ZIP code level income, ZIP code level education, county of residence, facility type, facility region, distance to treatment facility, Charlson-Deyo comorbidity score, tumor size, clinical N stage, and tumor histology.
      We also fitted multivariable logistic regression models with the outcome of interest as the dependent variable to determine the effect of hospital burden of uninsured or Medicaid-enrolled patients with NSCLC on each outcome. Hospital burden was included as a predictor. Other predictors were included in the models on the basis of a priori hypotheses and varied by outcome measure. These are listed in the footnote of Table 1. We performed this analysis in the entire cohort for the following 2 outcome measures: performing surgery and providing no treatment. We performed this analysis only in patients undergoing surgery for the following outcome measures: performing an open procedure, performing an anatomic resection, examination of 10 or more regional lymph nodes, pathologic nodal upstaging, positive surgical margins, length of stay more than 4 days, 30-day mortality, and unplanned readmission. This analysis was performed in patients undergoing minimally invasive surgery for the conversion-to-thoracotomy outcome measure and in patients with pathologic stage II disease or higher for the outcome measure of appropriate adjuvant chemotherapy recommendation.
      Table 1Results of the multivariable regression models showing adjusted odds ratios for various outcomes of interest associated with hospital burden of uninsured or Medicaid-enrolled patients with lung cancer
      OutcomeaOR (95% CI)P value
      Surgery performed
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, and tumor histology.
       Low burdenReference
       Medium burden0.88 (0.76-1.03).10
       High burden0.96 (0.80-1.15).70
       Highest burden0.58 (0.46-0.73)<.01
      Open thoracotomy
      Adjusted for age, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, type of treatment received, and type of surgery performed.
       Low burdenReference
       Medium burden1.00 (0.97-1.03)1.00
       High burden1.36 (1.31-1.41)<.01
       Highest burden1.47 (1.40-1.56)<.01
      Conversion to thoracotomy
      Adjusted for age, year of diagnosis, facility type, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, type of treatment received, and type of surgery performed.
       Low burdenReference
       Medium burden1.06 (0.99-1.13).10
       High burden1.14 (1.05-1.24)<.01
       Highest burden1.19 (1.05-1.34).01
      Anatomic resection
      Adjusted for age, year of diagnosis, facility type, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, and type of treatment received.
       Low burdenReference
       Medium burden0.94 (0.90-0.97)<.01
       High burden0.95 (0.91-0.99).03
       Highest burden1.04 (0.97-1.11).30
      ≥10 regional lymph nodes examined
      Year of diagnosis, facility type, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, type of treatment received, and type of surgery performed.
       Low burdenReference
       Medium burden0.85 (0.83-0.88)<.01
       High burden0.77 (0.75-0.80)<.01
       Highest burden0.87 (0.83-0.92)<.01
      Pathologic nodal upstaging for clinical N0
      Adjusted for age, year of diagnosis, facility type, facility region, Charlson score, tumor size, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden1.04 (0.98-1.09).20
       High burden1.03 (0.97-1.10).30
       Highest burden1.02 (0.93-1.11).70
      Pathologic nodal upstaging for clinical N1
      Adjusted for age, year of diagnosis, facility type, facility region, Charlson score, tumor size, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden0.87 (0.72-1.06).20
       High burden0.74 (0.59-0.93).01
       Highest burden0.59 (0.41-0.85)<.01
      Positive surgical margins
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, tumor size, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden1.14 (1.05-1.25)<.01
       High burden1.25 (1.13-1.38)<.01
       Highest burden1.05 (0.90-1.23).50
      >4 d length of stay
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden1.08 (1.05-1.12)<.01
       High burden1.34 (1.29-1.40)<.01
       Highest burden1.47 (1.39-1.55)<.01
      30-d mortality
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden1.09 (0.97-1.23).20
       High burden1.23 (1.07-1.42)<.01
       Highest burden1.39 (1.39-1.13)<.01
      Unplanned readmission
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden0.99 (0.91-1.06).70
       High burden0.83 (0.76-1.20).80
       Highest burden1.04 (0.91-1.19).60
      Adjuvant chemotherapy for pathologic stage ≥ II
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, tumor size, tumor histology, and type of surgery performed.
       Low burdenReference
       Medium burden1.00 (0.93-1.07).90
       High burden1.00 (0.92-1.09)1.00
       Highest burden0.98 (0.87-1.10).70
      No treatment provided
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, and tumor histology.
       Low burdenReference
       Medium burden1.43 (1.04-1.97).04
       High burden1.08 (0.74-1.60).70
       Highest burden2.11 (1.31-3.40)<.01
      Bold indicates statistical significance.
      aOR, Adjusted odds ratio; CI, confidence interval.
      Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, and tumor histology.
      Adjusted for age, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, type of treatment received, and type of surgery performed.
      Adjusted for age, year of diagnosis, facility type, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, type of treatment received, and type of surgery performed.
      § Adjusted for age, year of diagnosis, facility type, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, and type of treatment received.
      Year of diagnosis, facility type, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, type of treatment received, and type of surgery performed.
      Adjusted for age, year of diagnosis, facility type, facility region, Charlson score, tumor size, tumor histology, and type of surgery performed.
      # Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, tumor size, tumor histology, and type of surgery performed.
      ∗∗ Adjusted for age, sex, race, ethnicity, year of diagnosis, insurance status, ZIP code level income, education level, residence county, facility type, facility region, distance to facility, Charlson score, pathologic stage, tumor size, clinical N stage, tumor histology, and type of surgery performed.
      We assessed collinearity in the multivariable regression models by examining variance inflation factors. These were examined for each variable in the model with a value of more than 5 indicating collinearity. We also ran collinearity diagnostics in which we considered dimensions with 2 or more variables having variance proportions more than 0.5 to be indicative of collinearity. Independent variables were included in the multivariable models unless they were highly correlated with 1 or more other independent variables. The facility identification variable was used as a cluster-level variable in order to account for clustering within a facility. Clustering was accounted for in all the multivariable models.

      Results

      There were a total of 204,189 patients treated at 1286 facilities. There were 592 (46.0%) low-burden hospitals, 297 (23.1%) medium-burden hospitals, 219 (17.0%) high-burden hospitals, and 178 (13.9%) highest burden hospitals. Table 2 shows significant differences in baseline demographic and clinical characteristics among the 4 groups. Higher burden hospitals had a greater proportion of patients with more advanced clinical stage. Figures 1 and 2 illustrate differences in overall and surgical treatment characteristics, respectively, by hospital burden. Higher burden hospitals had higher rates of adjuvant therapy, nonsurgical treatment, and nontreatment.
      Table 2Baseline demographic and clinical characteristics of patients with stage I and II non–small cell lung cancer according to the relative burden of uninsured or Medicaid-enrolled patients with lung cancer at a facility
      VariableLow burden N (cases) = 101,793 (49.9%) N (facilities) = 592 (46.0%)Medium burden N (cases) = 53,022 (26.0%) N (facilities) = 297 (23.1%)High burden N (cases) = 34,808 (17.0%) N (facilities) = 219 (17.0%)Highest burden N (cases) = 14,566 (7.1%) N (facilities) = 178 (13.9%)P value
      Age/y
       Median71707067
      Age/y<.01
       <556167 (6.1%)3752 (7.1%)2630 (7.6%)1659 (11.4%)
       55-6419,080 (18.7%)11,060 (20.9%)7824 (22.5%)4256 (29.2%)
       65-7439,118 (38.4%)20,229 (38.2%)13,476 (38.7%)4974 (34.1%)
       ≥7537,428 (36.8%)17,981 (33.9%)10,878 (31.3%)3677 (25.2%)
      Sex<.01
       Male45,320 (44.5%)24,420 (46.1%)16,568 (47.6%)6920 (47.5%)
       Female56,473 (55.5%)28,602 (53.9%)18,240 (52.4%)7646 (52.5%)
      Race<.01
       White91,669 (90.1%)46,337 (87.4%)29,241 (84.0%)10,116 (69.4%)
       Black6811 (6.7%)5119 (9.7%)4401 (12.6%)3477 (23.9%)
       Other3313 (3.3%)1566 (3.0%)1166 (3.3%)973 (6.7%)
      Ethnicity<.01
       Non-Hispanic100,239 (98.5%)52,216 (98.5%)34,422 (98.9%)14,082 (96.7%)
       Hispanic1554 (1.5%)806 (1.5%)386 (1.1%)484 (3.3%)
      Insurance status<.01
       Not insured792 (0.8%)594 (1.1%)694 (2.0%)822 (5.6%)
       Private insurance24,940 (24.5%)11,367 (21.4%)6837 (19.6%)3159 (21.7%)
       Medicaid2953 (2.9%)2996 (5.7%)2512 (7.2%)2207 (15.2%)
       Medicare72,227 (71.0%)37,382 (70.5%)24,257 (69.7%)8226 (56.5%)
       Other government831 (0.8%)683 (1.3%)508 (1.5%)152 (1.0%)
      ZIP code level income<.01
       <$40,227/y10,836 (12.0%)10,244 (22.3%)9463 (30.6%)4353 (33.9%)
       $40,227-$50,353/y17,179 (19.0%)11,474 (25.0%)8275 (26.7%)2986 (23.3%)
       $50,354-$63,332/y22,722 (25.2%)11,297 (24.6%)6478 (20.9%)2512 (19.6%)
       ≥$63,333/y39,526 (43.8%)12,876 (28.1%)6744 (21.8%)2987 (23.3%)
      % without high school degree<.01
       ≥17.6%12,940 (14.3%)8640 (18.8%)9036 (29.1%)4787 (37.2%)
       10.9%-17.5%21,009 (23.2%)14,281 (31.1%)10,248 (33.0%)3819 (29.7%)
       6.3%-10.8%28,229 (31.2%)13,506 (29.4%)7497 (24.2%)2676 (20.8%)
       <6.3%28,213 (31.2%)9563 (20.8%)4237 (13.7%)1580 (12.3%)
      Residence county<.01
       Metropolitan86,226 (87.2%)42,566 (81.8%)26,881 (79.0%)12,038 (84.4%)
       Urban11,069 (11.2%)8485 (16.3%)6425 (18.9%)1943 (13.6%)
       Rural1560 (1.6%)1013 (1.9%)742 (2.2%)287 (2.0%)
      Facility type<.01
       CCP3846 (3.8%)2275 (4.3%)2437 (7.0%)774 (5.4%)
       CCCP48,720 (48.1%)19,706 (37.4%)14,074 (40.6%)1839 (12.7%)
       ARP27,085 (26.7%)19,520 (37.0%)14,112 (40.8%)10,151 (70.3%)
       INCP21,650 (21.4%)11,244 (21.3%)4005 (11.6%)1671 (11.6%)
      Facility region<.01
       New England7221 (7.1%)4863 (9.2%)2837 (8.2%)446 (3.1%)
       Atlantic37,204 (36.7%)18,476 (35.0%)16,081 (46.4%)5115 (35.4%)
       Central41,977 (41.4%)23,798 (45.1%)13,409 (38.7%)6734 (46.7%)
       Mountain3989 (3.9%)1771 (3.4%)437 (1.3%)250 (1.7%)
       Pacific10,910 (10.8%)3837 (7.3%)1864 (5.4%)1890 (13.1%)
      Distance from facility<.01
       <5 miles26,883 (29.5%)14,272 (30.8%)9230 (29.5%)4909 (37.8%)
       5-15 miles33,639 (36.9%)15,235 (32.9%)10,372 (33.1%)3993 (30.8%)
       >15 miles30,550 (33.5%)16,855 (36.4%)11,687 (37.4%)4079 (31.4%)
      Charlson-Deyo score<.01
       052,586 (51.7%)26,852 (50.6%)17,699 (50.8%)7766 (53.3%)
       130,136 (29.6%)15,585 (29.4%)10,288 (29.6%)4193 (28.8%)
       212,072 (11.9%)6675 (12.6%)4313 (12.4%)1701 (11.7%)
       ≥36999 (6.9%)3910 (7.4%)2508 (7.2%)906 (6.2%)
      Clinical stage<.01
       I85,563 (84.1%)43,893 (82.8%)28,045 (80.6%)11,703 (80.3%)
       II16,230 (15.9%)9129 (17.2%)6763 (19.4%)2863 (19.7%)
      Tumor size<.01
       <3 cm69,124 (69.1%)35,463 (68.5%)22,506 (66.2%)9297 (65.9%)
       3-5 cm22,666 (22.7%)11,883 (22.9%)8210 (24.1%)3452 (24.5%)
       5.1-7 cm5651 (5.7%)3062 (5.9%)2353 (6.9%)916 (6.5%)
       >7 cm2576 (2.6%)1376 (2.7%)929 (2.7%)447 (3.2%)
      Clinical T stage<.01
       T170,269 (69.6%)35,952 (68.3%)22,399 (65.3%)9429 (65.3%)
       T224,175 (23.9%)12,934 (24.6%)9214 (26.9%)3785 (26.2%)
       T36571 (6.5%)3743 (7.1%)2692 (7.8%)1224 (8.5%)
      Clinical N stage<.01
       N095,410 (95.0%)49,490 (94.4%)31,937 (93.6%)13,479 (93.8%)
       N15035 (5.0%)2913 (5.6%)2171 (6.4%)893 (6.2%)
      Tumor histology<.01
       Squamous cell carcinoma28,172 (29.2%)16,355 (32.5%)11,202 (33.6%)4220 (30.4%)
       Adenocarcinoma50,358 (52.2%)25,317 (50.2%)15,723 (47.2%)7108 (51.3%)
       Neuroendocrine2752 (2.9%)1365 (2.7%)1022 (3.1%)388 (2.8%)
       Other15,112 (15.7%)7351 (14.6%)5389 (16.2%)2151 (15.5%)
      Bold indicates statistical significance.
      CCP, Community Cancer Program; CCCP, Comprehensive Community Cancer Program; ARP, Academic/Research Program; INCP, Integrated Network Cancer Program.
      Figure thumbnail gr1
      Figure 1Hospitals with a higher burden of uninsured or Medicaid-enrolled patients with NSCLC had higher rates of adjuvant therapy, nonsurgical treatment, and nontreatment in patients with clinical stage I or II NSCLC, P < .01.
      Figure thumbnail gr2
      Figure 2Comparison of the types of operations performed according to the relative burden of uninsured or Medicaid-enrolled patients with NSCLC. Lobectomy and wedge resection were performed most frequently in patients with clinical stage I or II NSCLC, P < .01.
      Table 3 shows the results of the multivariable regression model outlining the factors associated with care at highest burden hospitals. Associated characteristics included younger age, male sex, black race, and Hispanic ethnicity (P < .01). Residence in nearby (<5 miles to hospital), lower-income, lower-education, and rural areas was also associated with care at highest burden hospitals (P < .01). These hospitals were more likely to be academic/research programs (adjusted odds ratio [aOR], 3.09, P < .01).
      Table 3Results of the multivariable regression model showing the factors associated with care at a facility with the highest burden of uninsured or Medicaid-enrolled patients with lung cancer
      VariableaOR (95% CI)P value
      Age/y
       <55Reference
       55-640.96 (0.88-1.04).30
       65-740.82 (0.75-0.89)<.01
       ≥750.74 (0.68-0.81)<.01
      Sex
       MaleReference
       Female0.89 (0.85-0.93)<.01
      Race
       WhiteReference
       Black1.93 (1.83-2.03)<.01
       Other1.57 (1.45-1.71)<.01
      Ethnicity
       Non-HispanicReference
       Hispanic1.52 (1.36-1.71)<.01
      Year of diagnosis
       2004Reference
       20052.00 (0.24-16.4).50
       20062.26 (0.26-19.7).50
       20072.26 (0.27-18.7).50
       20083.12 (0.39-24.8).20
       20094.70 (0.62-35.4).30
       20103.18 (0.43-23.5).30
       20113.28 (0.44-24.2).30
       20123.22 (0.44-23.8).20
       20133.42 (0.46-25.3).20
       20143.42 (0.46-25.3).20
       20153.61 (0.49-26.7).20
       20163.71 (0.50-27.4).20
       20173.93 (0.53-29.1).20
       20184.04 (0.55-29.8).20
      Insurance status
       Not insuredReference
       Private insurance0.23 (0.20-0.25)<.01
       Medicaid0.56 (0.50-0.63)<.01
       Medicare0.23 (0.21-0.26)<.01
       Other government0.26 (0.21-0.32)<.01
      ZIP code level income
       <$40,227/yReference
       $40,227-$50,353/y0.93 (0.87-0.98).01
       $50,354-$63,332/y0.84 (0.79-0.90)<.01
       ≥$63,333/y0.84 (0.78-0.91)<.01
      % without high school degree
       ≥17.6%Reference
       10.9%-17.5%0.71 (0.68-0.75)<.01
       6.3%-10.8%0.55 (0.51-0.58)<.01
       <6.3%0.35 (0.32-0.38)<.01
      Residence county
       MetropolitanReference
       Urban1.28 (1.20-1.36)<.01
       Rural1.42 (1.23-1.65)<.01
      Facility type
       CCPReference
       CCCP0.35 (0.35-0.36)<.01
       ARP3.09 (3.00-3.19)<.01
       INCP0.81 (0.79-0.83)<.01
      Facility region
       New EnglandReference
       Atlantic2.14 (2.08-2.21)<.01
       Central2.92 (2.82-3.01)<.01
       Mountain3.22 (3.05-3.41)<.01
       Pacific6.08 (5.88-6.28)<.01
      Distance from facility
       <5 milesReference
       5-15 miles0.79 (0.75-0.83)<.01
       >15 miles0.61 (0.58-0.65)<.01
      Charlson-Deyo score
       0Reference
       11.06 (1.01-1.10).03
       21.06 (0.99-1.13).09
       ≥30.92 (0.84-1.00).05
      Tumor size
       <3 cmReference
       3-5 cm1.10 (1.05-1.16)<.01
       5.1-7 cm1.10 (1.01-1.20).03
       >7 cm1.22 (1.08-1.38)<.01
      Clinical N stage
       N0Reference
       N11.04 (0.96-1.13).40
      Tumor histology
       Squamous cell carcinomaReference
       Adenocarcinoma0.94 (0.90-0.99).01
       Neuroendocrine0.91 (0.80-1.04).20
       Other0.95 (0.89-1.01).09
      Bold indicates statistical significance.
      aOR, Adjusted odds ratio; CI, confidence interval; CCP, Community Cancer Program; CCCP, Comprehensive Community Cancer Program; ARP, Academic/Research Program; INCP, Integrated Network Cancer Program.
      Table 4 shows group differences in the outcomes of interest among the 4 comparison groups. There were significant differences across all outcomes except conversion to thoracotomy (P = .06) and unplanned readmission (P = .08).
      Table 4Outcomes of interest according to the relative burden of uninsured or Medicaid-enrolled patients with lung cancer at a facility
      VariableLow burdenMedium burdenHigh burdenHighest burdenP value
      Surgery<.01
       Not performed24,185 (23.8%)14,617 (27.6%)10,438 (30.0%)4143 (28.4%)
       Performed68,299 (67.1%)33,583 (63.3%)21,139 (60.7%)9098 (62.5%)
       Patient refused/not indicated9130 (9.0%)4702 (8.9%)3137 (9.0%)1258 (8.6%)
       Unknown179 (0.2%)120 (0.2%)94 (0.3%)67 (0.5%)
      Surgical approach<.01
       Minimally invasive34,928 (51.8%)16,962 (51.0%)9250 (44.5%)4211 (47.0%)
       Open thoracotomy32,505 (48.2%)16,270 (49.0%)11,525 (55.5%)4747 (53.0%)
      Conversion to thoracotomy.06
       No31,730 (90.8%)15,394 (90.8%)8319 (89.9%)3812 (90.5%)
       Yes3198 (9.2%)1568 (9.2%)931 (10.1%)399 (9.5%)
      Type of resection<.01
       Nonanatomic14,541 (21.3%)7485 (22.3%)4627 (21.9%)1913 (21.0%)
       Anatomic53,758 (78.7%)26,098 (77.7%)16,512 (78.1%)7185 (79.0%)
      Regional lymph nodes examined<.01
       <1038,600 (59.7%)19,654 (62.0%)12,934 (63.5%)5125 (57.7%)
       ≥1026,103 (40.3%)12,063 (38.0%)7427 (36.5%)3764 (42.3%)
      Pathologic N stage (clinical N0).02
       N054,722 (91.6%)26,745 (91.3%)16,288 (91.1%)7251 (90.9%)
       N13391 (5.7%)1678 (5.7%)1032 (5.8%)472 (5.9%)
       N21613 (2.7%)883 (3.0%)554 (3.1%)255 (3.2%)
      Pathologic N stage (clinical N1).02
       N0755 (27.7%)424 (28.6%)306 (28.8%)137 (30.2%)
       N11585 (58.1%)877 (59.1%)639 (60.2%)276 (60.8%)
       N2390 (14.3%)184 (12.4%)116 (10.9%)41 (9.0%)
      Surgical margins<.01
       Negative65,393 (97.1%)31,930 (96.6%)19,973 (96.2%)8660 (96.9%)
       Positive1940 (2.9%)1112 (3.4%)794 (3.8%)276 (3.1%)
      Length of stay<.01
       ≤4 d34,805 (52.1%)15,953 (49.4%)8889 (44.6%)4096 (46.8%)
       >4 d32,023 (47.9%)16,361 (50.6%)11,030 (55.4%)4652 (53.2%)
      30-d mortality<.01
       No58,375 (98.3%)28,713 (98.0%)17,968 (97.7%)7639 (98.1%)
       Yes1023 (1.7%)582 (2.0%)420 (2.3%)151 (1.9%)
      Unplanned readmission.08
       No65,344 (96.0%)32,165 (95.9%)20,251 (96.0%)8545 (95.8%)
       Yes2738 (4.0%)1367 (4.1%)844 (4.0%)377 (4.2%)
      Adjuvant chemotherapy (pathologic stage ≥ II).04
       Not recommended5198 (38.4%)2601 (37.3%)1655 (36.5%)723 (36.0%)
       Recommended or reason for no recommendation documented8345 (61.6%)4369 (62.7%)2883 (63.5%)1288 (64.0%)
      Treatment<.01
       Provided95,336 (94.3%)49,347 (93.6%)31,951 (92.6%)13,171 (91.1%)
       Not provided3893 (3.9%)2397 (4.5%)1875 (5.4%)990 (6.8%)
       Active surveillance739 (0.7%)384 (0.7%)225 (0.7%)102 (0.7%)
       Patient refused762 (0.8%)376 (0.7%)303 (0.9%)102 (0.7%)
       Unknown350 (0.3%)217 (0.4%)151 (0.4%)88 (0.6%)
      Bold indicates statistical significance.
      Table 1 shows the results of the multivariable regression models outlining the association between hospital burden of uninsured or Medicaid-enrolled patients with lung cancer and various outcomes of interest. Compared with patients treated at low-burden hospitals, those treated at medium-, high-, and highest burden hospitals were associated with a reduced likelihood of undergoing a regional lymph node examination involving 10 or more nodes during surgery (P < .01). Patients at these facilities had increased odds of having a length of stay more than 4 days (P < .01). Compared with patients treated at low-burden hospitals, those treated at high- and highest burden hospitals were more likely to undergo an open procedure and had increased odds of death within 30 days of their operation (P < .01). Such patients also had increased odds of undergoing a conversion to thoracotomy and reduced odds of pathologic nodal upstaging for clinical N1 disease (P < .05). Compared with patients undergoing operation at low-burden hospitals, those receiving surgical treatment at medium- and high-burden hospitals had a greater likelihood of positive surgical margins (P < .01). Finally, those at highest burden hospitals had a reduced likelihood of undergoing surgery and a greater likelihood of not receiving any treatment (P < .01).

      Discussion

      There is a paucity of data assessing the impact of hospital burden of uninsured or Medicaid-enrolled patients on perioperative outcomes in NSCLC. Hospital safety-net burden has previously been investigated in patients with other types of cancers with varying results. Hoehn and colleagues
      • Hoehn R.S.
      • Hanseman D.J.
      • Dhar V.K.
      • Go D.E.
      • Edwards M.J.
      • Shah S.A.
      Opportunities to improve care of hepatocellular carcinoma in vulnerable patient populations.
      concluded that vulnerable patients with hepatocellular carcinoma are less likely to receive curative surgery at safety-net hospitals and have worse short-term outcomes. Others have also reported inferior outcomes in patients with cancers of the colon, larynx, and esophagus.
      • Sridhar P.
      • Bhatt M.
      • Qureshi M.M.
      • Asokan S.
      • Truong M.T.
      • Suzuki K.
      • et al.
      Esophageal cancer presentation, treatment, and outcomes vary with hospital safety-net burden.
      ,
      • Hrebinko K.A.
      • Rieser C.
      • Nassour I.
      • Tohme S.
      • Sabik L.M.
      • Khan S.
      • et al.
      Patient factors limit colon cancer survival at safety-net hospitals: a national analysis.
      ,
      • Qureshi M.M.
      • Oladeru O.T.
      • Lam C.M.
      • Dyer M.A.
      • Mak K.S.
      • Hirsch A.E.
      • et al.
      Disparities in laryngeal cancer treatment and outcomes: an analysis by hospital safety-net burden.
      In contrast, studies investigating this relationship in patients with cancers of the rectum, pancreas, and head and neck have reported equivalent outcomes irrespective of hospital payer mix.
      • Hoehn R.S.
      • Go D.E.
      • Hanseman D.J.
      • Shah S.A.
      • Paquette I.M.
      Hospital safety-net burden does not predict differences in rectal cancer treatment and outcomes.
      • Genther D.J.
      • Gourin C.G.
      The effect of hospital safety-net burden status on short-term outcomes and cost of care after head and neck cancer surgery.
      • Dhar V.K.
      • Hoehn R.S.
      • Kim Y.
      • Xia B.T.
      • Jung A.D.
      • Hanseman D.J.
      • et al.
      Equivalent treatment and survival after resection of pancreatic cancer at safety-net hospitals.
      It is possible that the factors that affect treatment quality and outcomes differ by type of cancer and procedure and should be the focus of subsequent studies.
      We observed lower surgery rates in medium- (63%) and high-burden (60%) hospitals compared with low-burden hospitals (67%), which may not be indicative of guideline noncompliance in the presence of appropriate referrals for stereotactic body radiation therapy. Our results suggest that this was the case for these hospitals, which had higher rates of definitive radiation treatment (22%) than low-burden hospitals (20%). However, highest burden quartile hospitals were approximately half as likely to perform surgery for stage I and II NSCLC compared with low-burden hospitals even after accounting for those who were documented not to be candidates for resection (aOR, 0.58, P < .01). In addition, these hospitals also had a lower definitive radiation treatment rate (19%) compared with low-burden hospitals and were twice as likely to not provide patients with any treatment (aOR, 2.11, P < .01). Other differences in outcomes included a longer length of stay in higher burden hospitals, which is likely linked to a greater propensity for open thoracotomy seen at these hospitals, as has been well documented.
      • Giambrone G.P.
      • Smith M.C.
      • Wu X.
      • Gaber-Baylis L.K.
      • Bhat A.U.
      • Zabih R.
      • et al.
      Variability in length of stay after uncomplicated pulmonary lobectomy: is length of stay a quality metric or a patient metric?.
      Likewise, a higher likelihood of pathologic nodal upstaging in clinical N1 disease at low-burden hospitals correlates well with a higher likelihood of examining 10 or more regional lymph nodes at these institutions. Others have reported a linear relationship between the number of lymph nodes examined and the odds of upstaging.
      • Bott M.J.
      • Patel A.P.
      • Crabtree T.D.
      • Colditz G.A.
      • Kreisel D.
      • Krupnick A.S.
      • et al.
      Pathologic upstaging in patients undergoing resection for stage I non-small cell lung cancer: are there modifiable predictors?.
      Hospital differences in rates of open thoracotomy, lymph node harvest, and length of stay may also be reflective of differences in surgeon training and subspecialty between these institutions. For instance, Virgo and colleagues
      • Virgo K.S.
      • Little A.G.
      • Fedewa S.A.
      • Chen A.Y.
      • Flanders W.D.
      • Ward E.M.
      Safety-net burden hospitals and likelihood of curative-intent surgery for non–small cell lung cancer.
      noted that high safety-net burden hospitals were less likely to have a dedicated general thoracic surgeon on staff, who in turn have been observed to perform a greater number of minimally invasive lobectomies than cardiac surgeons and general surgeons.
      • Cooke D.T.
      • Wisner D.H.
      Who performs complex noncardiac thoracic surgery in United States academic medical centers?.
      Finally, although medium- (n = 1112) and high-burden (n = 794) hospitals had a greater likelihood of positive surgical margins, this trend did not continue in highest burden hospitals. We hypothesize that this is because this quartile may have been relatively underpowered (n = 276) to demonstrate a medium to small absolute difference.
      Our findings suggest an association among Black race, higher burden hospital care, and reduced treatment quality, which is in line with robust literature evidencing racial healthcare disparities in lung cancer treatment. For instance, Namburi and colleagues
      • Namburi N.
      • Timsina L.
      • Ninad N.
      • Ceppa D.
      • Birdas T.
      The impact of social determinants of health on management of stage I non–small cell lung cancer.
      observed Black patients to be significantly more likely than White patients to be subject to lower treatment quality in the form of lower surgery use for stage I NSCLC. The data clearly suggest that racial differences in care leave much to be desired. Other factors associated with higher burden care included academic facility teaching status. Others have also reported this previously.
      • Qureshi M.M.
      • Oladeru O.T.
      • Lam C.M.
      • Dyer M.A.
      • Mak K.S.
      • Hirsch A.E.
      • et al.
      Disparities in laryngeal cancer treatment and outcomes: an analysis by hospital safety-net burden.
      ,
      • Genther D.J.
      • Gourin C.G.
      The effect of hospital safety-net burden status on short-term outcomes and cost of care after head and neck cancer surgery.
      This finding is unexpected and interesting given that academic centers are widely reported to have superior survival outcomes in NSCLC by virtue of being more guideline concordant than other center types.
      • Muslim Z.
      • Stroever S.
      • Baig M.Z.
      • Weber J.F.
      • Connery C.P.
      • Bhora F.Y.
      Social determinants and facility type impact adherence to best practices in operable IIIAN2 lung cancer.
      ,
      • Samson P.
      • Patel A.
      • Crabtree T.D.
      • Morgensztern D.
      • Robinson C.G.
      • Colditz G.A.
      • et al.
      Multidisciplinary treatment for stage IIIA non-small cell lung cancer: does institution type matter?.
      This suggests that achieving better outcomes in this group of patients is an exceedingly complex and multifactorial process and one that clearly needs further study.
      Another noteworthy finding of our study was the association of higher burden hospitals with a more advanced disease stage at presentation. Underinsured populations have been reported to present with a higher disease stage resulting from reduced participation in screening programs and delays in presenting to a care provider after the onset of symptoms.
      • Bradley C.J.
      • Neumark D.
      • Shickle L.M.
      • Farrell N.
      Differences in breast cancer diagnosis and treatment: experiences of insured and uninsured women in a safety-net setting.
      This in turn has been due to various financial, social, and healthcare access–related hurdles.
      • Ward E.M.
      • Fedewa S.A.
      • Cokkinides V.
      • Virgo K.
      The association of insurance and stage at diagnosis among patients aged 55 to 74 years in the National Cancer Database.
      These findings underscore the important role that safety-net hospitals must play in increasing screening rates and reducing the percentage of patients presenting with advanced disease. We noted increased adjuvant therapy rates in higher burden hospitals, which may also be a function of a more advanced disease stage at presentation, particularly because our results suggest that hospital burden was not associated with an appropriate recommendation of adjuvant chemotherapy for pathologic stage II disease or higher after controlling for other clinical characteristics.

      Study Limitations

      Our analysis is limited by its retrospective nature, and there were several unmeasured clinical and social confounders that we were unable to account for. For instance, we did not have information pertaining to cardiopulmonary status, specific comorbidities, smoking status, the number of surgeons present at a hospital, individual surgeon training, and access to follow-up care. It must also be noted that the NCDB, similar to other large databases, has missing data (Table E1), which may introduce a degree of bias into the results. The NCDB does not include information on cause of death, which precludes any assessment of lung cancer-specific survival. Additionally, because of our large sample size, our results frequently reached statistical significance despite small absolute differences, which may not be clinically relevant. An example of this was the anatomic resection outcome measure, which was interpreted as not being clinically significant. We were also unable to determine the true safety-net burden for facilities, the calculation of which requires information on all cancer and noncancer diagnoses at a particular facility, because our dataset was limited to only NSCLC cases. Finally, it is important to consider the effect of a lack of high-volume centers in a particular hospital burden quartile on outcomes. We have made a note of case load information according to hospital burden in Table E2, which shows that medium-burden hospitals had the highest median number of cases/hospital (747), whereas highest burden hospitals had the lowest (566).

      Conclusions

      Our results indicate reduced quality of care and higher mortality particularly among patients undergoing surgery for early lung cancer at hospitals with an increased burden of uninsured or Medicaid-enrolled patients with lung cancer (Figure 3). Safety-net hospitals are crucial to providing access to care for the underprivileged, and our findings emphasize the need to raise the standard of care in patients undergoing early lung cancer treatment at these facilities to ultimately improve outcomes in medically marginalized populations.
      Figure thumbnail gr3
      Figure 3Graphical depiction of varying treatment characteristics according to the relative burden of uninsured or Medicaid-enrolled patients with NSCLC in the NCDB from 2004 to 2018 treated at hospitals categorized into low-, medium-, high-, and highest burden quartiles. Higher burden hospitals had higher rates of adjuvant therapy, nonsurgical treatment, and nontreatment in patients with clinical stage I or II NSCLC. These findings emphasize the need to raise the standard of care at these facilities to ultimately improve outcomes in medically marginalized populations. NSCLC, Non–small cell lung cancer; tx, treatment.

      Conflict of Interest Statement

      C.C.: Commission on Cancer: Consultant. All other authors reported no conflicts of interest.
      The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest.

      Appendix E1

      Table E1Percentages of nonmissing and missing data for each variable
      VariableNonmissing data n (%)Missing data n (%)
      Exposure variable
       Burden of uninsured or Medicaid-enrolled204,189 (100%)0 (0%)
      Covariates
       Age/y204,189 (100%)0 (0%)
       Sex204,189 (100%)0 (0%)
       Race204,189 (100%)0 (0%)
       Ethnicity204,189 (100%)0 (0%)
       Year of diagnosis204,189 (100%)0 (0%)
       Insurance status204,189 (100%)0 (0%)
       ZIP code level income179,952 (88.1%)24,237 (11.9%)
       % without high school degree180,261 (88.3%)23,928 (11.7%)
       Residence county199,235 (97.6%)4954 (2.4%)
       Facility type203,109 (99.5%)1080 (0.5%)
       Facility region203,109 (99.5%)1080 (0.5%)
       Distance from facility181,704 (89.0%)22,485 (11.0%)
       Charlson-Deyo score204,189 (100%)0 (0%)
       Tumor size199,911 (97.9%)4278 (2.1%)
       Clinical stage204,189 (100%)0 (0%)
       Clinical T202,387 (99.1%)1802 (0.9%)
       Clinical N201,328 (98.6%)2861 (1.4%)
       Pathologic stage120,675 (91.3%)11,444 (8.7%)
       Tumor histology193,985 (95.0%)10,204 (5.0%)
       Treatment type202,759 (99.3%)1430 (0.7%)
       Surgery type132,119 (100%)0 (0%)
      Outcome variables
       Surgery204,189 (100%)0 (0%)
       Surgical approach130,398 (99.3%)1721 (0.7%)
       Conversion to thoracotomy65,351 (100%)0 (0%)
       Type of resection132,119 (100%)0 (0%)
       Regional lymph nodes examined125,670 (95.1%)6449 (4.9%)
       Pathologic N stage119,479 (90.4%)12,640 (9.6%)
       Surgical margins130,078 (98.5%)2041 (1.5%)
       Length of stay127,809 (96.7%)4310 (3.3%)
       30-d mortality114,871 (86.9%)17,248 (13.1%)
       Unplanned readmission131,631 (99.6%)488 (0.4%)
       Adjuvant chemotherapy (pathologic stage ≥ II)27,062 (99.5%)146 (0.5%)
       Treatment202,759 (99.3%)1430 (0.7%)
       Vital status178,594 (87.5%)25,595 (12.5%)
       Months between diagnosis and last contact/death178,548 (87.4%)25,641 (12.6%)
      The default listwise deletion was used for missing data.
      Table E2Stage I to IV non–small cell lung cancer case load information according to hospital burden of uninsured or Medicaid-enrolled patients with non–small cell lung cancer
      VariableLow burdenMedium burdenHigh burdenHighest burden
      No. of cases per hospital
       Mean402431430232
       Median657747726566
      No. of hospitals
       <10 anatomic resections/y443227172158
       10-20 anatomic resections/y98463111
       >20 anatomic resections/y5227179

      References

        • Muslim Z.
        • Stroever S.
        • Baig M.Z.
        • Weber J.F.
        • Connery C.P.
        • Bhora F.Y.
        Social determinants and facility type impact adherence to best practices in operable IIIAN2 lung cancer.
        Interact Cardiovasc Thorac Surg. 2022; 34: 49-56
        • Namburi N.
        • Timsina L.
        • Ninad N.
        • Ceppa D.
        • Birdas T.
        The impact of social determinants of health on management of stage I non–small cell lung cancer.
        Am J Surg. 2022; 223: 1063-1066
        • Brandel M.G.
        • Rennert R.C.
        • Lopez Ramos C.
        • Santiago-Dieppa D.R.
        • Steinberg J.A.
        • Sarkar R.R.
        • et al.
        Management of glioblastoma at safety-net hospitals.
        J Neurooncol. 2018; 139: 389-397
      1. Institute of Medicine Committee on the changing market, managed care, and the future viability of safety net providers.
        in: Ein Lewin M. Altman S. America's Health Care Safety Net: Intact but Endangered. National Academies Press (US), 2000
        • Sarkar R.R.
        • Courtney P.T.
        • Bachand K.
        • Sheridan P.E.
        • Riviere P.J.
        • Guss Z.D.
        • et al.
        Quality of care at safety-net hospitals and the impact on pay-for-performance reimbursement.
        Cancer. 2020; 126: 4584-4592
        • Virgo K.S.
        • Little A.G.
        • Fedewa S.A.
        • Chen A.Y.
        • Flanders W.D.
        • Ward E.M.
        Safety-net burden hospitals and likelihood of curative-intent surgery for non–small cell lung cancer.
        J Am Coll Surg. 2011; 213: 633-643
        • Go D.E.
        • Abbott D.E.
        • Wima K.
        • Hanseman D.J.
        • Ertel A.E.
        • Chang A.L.
        • et al.
        Addressing the high costs of pancreaticoduodenectomy at safety-net hospitals.
        JAMA Surg. 2016; 151: 908-914
        • Hoehn R.S.
        • Wima K.
        • Vestal M.A.
        • Weilage D.J.
        • Hanseman D.J.
        • Abbott D.E.
        • et al.
        Effect of hospital safety-net burden on cost and outcomes after surgery.
        JAMA Surg. 2016; 151: 120-128
        • McAlearney A.S.
        • Murray K.
        • Sieck C.
        • Lin J.J.
        • Bellacera B.
        • Bickell N.A.
        The challenge of improving breast cancer care coordination in safety-net hospitals: barriers, facilitators, and opportunities.
        Med Care. 2016; 54: 147-154
        • Gilman M.
        • Hockenberry J.M.
        • Adams E.K.
        • Milstein A.S.
        • Wilson I.B.
        • Becker E.R.
        The financial effect of value-based purchasing and the hospital readmissions reduction program on safety-net hospitals in 2014: a cohort study.
        Ann Intern Med. 2015; 163: 427-436
        • Fung V.
        • Liang C.Y.
        • Shi J.
        • Seo V.
        • Overhage L.
        • Dow W.H.
        • et al.
        Potential effects of eliminating the individual mandate penalty in California.
        Health Aff (Millwood). 2019; 38: 147-154
        • Lerro C.C.
        • Robbins A.S.
        • Phillips J.L.
        • Stewart A.K.
        Comparison of cases captured in the national cancer data base with those in population-based central cancer registries.
        Ann Surg Oncol. 2013; 20: 1759-1765
        • Sridhar P.
        • Bhatt M.
        • Qureshi M.M.
        • Asokan S.
        • Truong M.T.
        • Suzuki K.
        • et al.
        Esophageal cancer presentation, treatment, and outcomes vary with hospital safety-net burden.
        Ann Thorac Surg. 2019; 107: 1472-1479
        • Gamble C.R.
        • Huang Y.
        • Tergas A.I.
        • Khoury-Collado F.
        • Hou J.Y.
        • St Clair C.M.
        • et al.
        Quality of care and outcomes of patients with gynecologic malignancies treated at safety-net hospitals.
        JNCI Cancer Spectr. 2019; 3: pkz039
        • Hoehn R.S.
        • Hanseman D.J.
        • Dhar V.K.
        • Go D.E.
        • Edwards M.J.
        • Shah S.A.
        Opportunities to improve care of hepatocellular carcinoma in vulnerable patient populations.
        J Am Coll Surg. 2017; 224: 697-704
        • Hrebinko K.A.
        • Rieser C.
        • Nassour I.
        • Tohme S.
        • Sabik L.M.
        • Khan S.
        • et al.
        Patient factors limit colon cancer survival at safety-net hospitals: a national analysis.
        J Surg Res. 2021; 264: 279-286
        • Qureshi M.M.
        • Oladeru O.T.
        • Lam C.M.
        • Dyer M.A.
        • Mak K.S.
        • Hirsch A.E.
        • et al.
        Disparities in laryngeal cancer treatment and outcomes: an analysis by hospital safety-net burden.
        Laryngoscope. 2021; 131: E1987-E1997
        • Hoehn R.S.
        • Go D.E.
        • Hanseman D.J.
        • Shah S.A.
        • Paquette I.M.
        Hospital safety-net burden does not predict differences in rectal cancer treatment and outcomes.
        J Surg Res. 2018; 221: 204-210
        • Genther D.J.
        • Gourin C.G.
        The effect of hospital safety-net burden status on short-term outcomes and cost of care after head and neck cancer surgery.
        Arch Otolaryngol Head Neck Surg. 2012; 138: 1015-1022
        • Dhar V.K.
        • Hoehn R.S.
        • Kim Y.
        • Xia B.T.
        • Jung A.D.
        • Hanseman D.J.
        • et al.
        Equivalent treatment and survival after resection of pancreatic cancer at safety-net hospitals.
        J Gastrointest Surg. 2018; 22: 98-106
        • Giambrone G.P.
        • Smith M.C.
        • Wu X.
        • Gaber-Baylis L.K.
        • Bhat A.U.
        • Zabih R.
        • et al.
        Variability in length of stay after uncomplicated pulmonary lobectomy: is length of stay a quality metric or a patient metric?.
        Eur J Cardiothorac Surg. 2016; 49: e65-e71
        • Bott M.J.
        • Patel A.P.
        • Crabtree T.D.
        • Colditz G.A.
        • Kreisel D.
        • Krupnick A.S.
        • et al.
        Pathologic upstaging in patients undergoing resection for stage I non-small cell lung cancer: are there modifiable predictors?.
        Ann Thorac Surg. 2015; 100: 2048-2053
        • Cooke D.T.
        • Wisner D.H.
        Who performs complex noncardiac thoracic surgery in United States academic medical centers?.
        Ann Thorac Surg. 2012; 94: 1060-1064
        • Samson P.
        • Patel A.
        • Crabtree T.D.
        • Morgensztern D.
        • Robinson C.G.
        • Colditz G.A.
        • et al.
        Multidisciplinary treatment for stage IIIA non-small cell lung cancer: does institution type matter?.
        Ann Thorac Surg. 2015; 100: 1773-1779
        • Bradley C.J.
        • Neumark D.
        • Shickle L.M.
        • Farrell N.
        Differences in breast cancer diagnosis and treatment: experiences of insured and uninsured women in a safety-net setting.
        Inquiry. 2008; 45: 323-339
        • Ward E.M.
        • Fedewa S.A.
        • Cokkinides V.
        • Virgo K.
        The association of insurance and stage at diagnosis among patients aged 55 to 74 years in the National Cancer Database.
        Cancer J. 2010; 16: 614-621