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Ann Thorac Surg 2011;92:434-439. doi:10.1016/j.athoracsur.2011.04.048
© 2011 The Society of Thoracic Surgeons
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Original Articles: General Thoracic

Gender, Race, and Socioeconomic Status Affects Outcomes After Lung Cancer Resections in the United States

Damien J. LaPar, MD, Castigliano M. Bhamidipati, DO, David A. Harris, BA, Benjamin D. Kozower, MD, MPH, David R. Jones, MD, Irving L. Kron, MD, Gorav Ailawadi, MD, Christine L. Lau, MD*

Department of Surgery, Division of Thoracic and Cardiovascular Surgery, University of Virginia Health System, Charlottesville, Virginia

Accepted for publication April 11, 2011.

* Address correspondence to Dr Lau, Department of Surgery, University of Virginia School of Medicine, PO Box 800679, Charlottesville, VA 22908 (Email: cll2y{at}virginia.edu).

Presented at the Poster Session of the Forty-seventh Annual Meeting of The Society of Thoracic Surgeons, San Diego, CA, Jan 31–Feb 2, 2011.


   Abstract
Top
 Abstract
Introduction
 Material and Methods
 Results
 Comment
 Acknowledgments
 References
 
Background: The effect of gender, race, and socioeconomic status on contemporary outcomes after lung cancer resections has not been comprehensively evaluated across the United States. We hypothesized that risk-adjusted outcomes for lung cancer resections would not be influenced by these factors.

Methods: From 2003 to 2007, 129,207 patients undergoing lung cancer resections were evaluated using the Nationwide Inpatient Sample (NIS) database. Multiple regression analysis was used to estimate the effects of gender, race, and socioeconomic status on risk-adjusted outcomes.

Results: Average patient age was 66.8 ± 10.5 years. Women accounted for 5.0% of the total study population. Among racial groups, whites underwent the largest majority of operations (86.2%), followed by black (6.9%) and Hispanic (2.8%) races. Overall the incidence of mortality was 2.9%, postoperative complications were 30.4%, and pulmonary complications were 22.0%. Female gender, race, and mean income were all multivariate correlates of adjusted mortality and morbidity. Black patients incurred decreased risk-adjusted morbidity and mortality compared with white patients. Hispanics and Asians demonstrated decreased risk-adjusted complication rates. Importantly low income status independently increased the adjusted odds of mortality.

Conclusions: Female gender is associated with decreased mortality and morbidity after lung cancer resections. Complication rates are lower for black, Hispanic, and Asian patients. Low socioeconomic status increases the risk of in-hospital death. These factors should be considered during patient risk stratification for lung cancer resection.


   Introduction
Top
 Abstract
 Introduction
Material and Methods
 Results
 Comment
 Acknowledgments
 References
 
In the United States, lung cancer is the leading cause of cancer-related deaths among both men and women [1]. Despite recent advances in oncologic therapy, surgical resection for early-stage lung cancer remains the predominant treatment strategy. As a result continued emphasis on improving postoperative outcomes is critical. Operative mortality rates for lung cancer resections currently approach 2%, and complication rates are approximately 8% [2, 3]. In an effort to further improve patient outcomes and quality of care, continued examination of potential risk factors for morbidity and mortality is warranted at a nationwide level.

Disparities in medical and surgical outcomes are often influenced by several patient- and health system–related factors. Patient outcomes after surgical lung cancer resection may be related to inherent differences in gender, race, or socioeconomic status. Although other reported series have identified these factors as potential determinants of patient outcomes and survival [3–13], many of these reports are limited by single institutional experiences or statewide databases. In addition many published analyses lack critical social- and hospital-related data required for rigorous risk adjustment and are subject to biases that limit their generalizability to patients nationwide.

The present study used a nationwide administrative database to examine the influence of gender, race, and socioeconomic status on risk-adjusted morbidity and mortality after appropriate adjustment for various demographic, social, operation, and institutional factors. Understanding the independent influence of these variables is critical to reducing disparities in lung cancer care and identifying methods to improve patient outcomes.


   Material and Methods
Top
 Abstract
 Introduction
 Material and Methods
Results
 Comment
 Acknowledgments
 References
 
Data Source
Data was obtained from the 2002 to 2007 Nationwide Inpatient Sample (NIS) datasets. NIS data represents the largest, all-payer, publicly available inpatient care database in the United States, providing a 20% random sample of US hospital discharges. The hospitals represented within these datasets are designated as "community hospitals" within the American Hospital Association annual survey. Data reported herein represents in-patient admissions for patients of all ages, races, income levels, and sources of insurance.

The University of Virginia Institutional Review Board (IRB) exempted this study from formal review because it failed to meet the regulatory definition of human subject research because of the lack of controlled patient identifiers and because the data is not collected for research purposes only.

Patients and Hospitals
A total of 26,189 discharge records representing a weighted estimate of 129,207 patients undergoing lung cancer resections was identified by querying the first 5 diagnosis and procedure categories with the NIS using the following International Classification of Diseases—Ninth Revision, Clinical Modifications (ICD-9-CM) procedure and diagnostic codes: lung resection (ICD-9-CM codes 323, 3230, 3239, 324, 3241, 3249, 325, 3250, 3259) and primary diagnosis of lung cancer (ICD-9-CM codes 162, 1622, 1623, 1624, 1625, 1628, 1629). The presence of patient admission-level comorbid disease was assessed using available Agency for Health Research and Quality comorbidity categories within the NIS datasets developed by Elixhauser and colleagues [14]. Hospital-related details were available within the NIS database. Thoracic surgery teaching hospital status was determined by linking the American Hospital Association identification numbers of all hospitals within the NIS study dataset with hospital reports from the Association of American Medical College's Graduate Medical Education tracking system. Hospital operative volume was categorized into quartiles: low (< 25th percentile), medium (26th to 49th percentile), high (50th to 74th percentile], and very high (> 75th percentile].

Outcomes Measured
All measured outcomes were established a priori. The primary outcomes in this study were the effects of gender, race, and socioeconomic status on risk-adjusted mortality and morbidity after lung cancer resections. Secondary outcomes of interest included observed differences in the overall incidence of mortality and postoperative complication rates. The incidence of postoperative and pulmonary complications was determined using previously described methodology [15, 16].

Statistical Analysis
All statistical methodology used in this study was designed to test the null hypothesis that risk-adjusted outcomes after lung cancer resections in the United States are not significantly different with respect to gender, race, and socioeconomic status. Statistical significance for all analyses was defined by an alpha of less than 0.05. Because of the complex sampling methods used by the NIS, all data analyses were performed using Predictive Analytics SoftWare (PASW) Statistics version 18.0.0 complex samples module (IBM Corporation, Somers, NY).

Descriptive Statistics and Univariate Analyses
Descriptive and inferential statistics were used to compare observed differences in the incidence of mortality, composite postoperative complication rate, and pulmonary complication rate as a function of gender, race, and mean income. Continuous variables with normal distributions are reported as means ± standard deviation, whereas the median (interquartile range) is used to express nonnormally distributed data. Continuous variables were compared using either the Student's t test or the Mann-Whitney U test. Comparisons of categorical variables used the Pearson's {chi}2 or Fisher's exact test where appropriate. All categorical variables are expressed as a percentage of the total study population or respective study group. Independent sample group comparisons were unpaired. All calculated test statistics were used to derive reported 2-tailed p values. Two additional effect size statistics were calculated to provide an estimate of the strength of the relationship between 2 variables within a given population and to provide a clinically practical interpretation of the reported results The phi coefficient was calculated for all univariate comparisons with 1 degree of freedom, and the Cramer's V statistic was computed for comparisons of categorical ordinal variables with greater than 1 degree of freedom.

Multivariable Analysis
Because of the complex structure of this study dataset, hierarchical multiple logistic regression was used to estimate risk-adjusted associations between female gender, race, and mean income quartile and the outcomes of in-hospital death, composite incidence of postoperative complications, and pulmonary complications for patients undergoing lung cancer resections. Three separate logistic regression models were used for each outcome. Missing data for individual covariates accounted for less than 5% of the total study dataset. All covariates considered potential confounders for model outcomes were selected a priori and were retained in each final model. The predictive strength and relative contribution of each model covariate was assessed by the Wald {chi}2 statistic. Results of each logistic regression model are reported as confounder adjusted odds ratio (AOR) with 95% confidence interval (CI). Model performance was assessed by the area under the receiver operating characteristics curve (AUC) and the Nagelkerke Pseudo R 2 statistic. Sensitivity analyses were performed by reestimating each model after removing the strongest individual predictor as determined by the Wald statistic [17]. Using this technique, model performance is validated if the observed effects remain statistically significant and are not substantially attenuated (> 10%) after reestimation.


   Results
Top
 Abstract
 Introduction
 Material and Methods
 Results
Comment
 Acknowledgments
 References
 
Patient, Hospital, and Operative Characteristics
Descriptive statistics for select model covariates are presented in Table 1. Average patient age was 66.8 ± 10.5 years. Women accounted for 5.0% of the total study population. Among racial groups, whites underwent the large majority of operations (86.2%), followed by black (6.9%) and Hispanic (2.8%) races. The most frequent mean income quartile represented those earning more than $63,000 per year (mean income quartile IV). With respect to lung cancer resections, lobectomy was the most common operation performed, and the large majority of operations were elective. Resections were performed at thoracic surgery teaching hospitals 16.5% of the time, and the large majority of operations were performed at hospitals with very high (> 75th percentile) operative volume (73.9%). The overall incidence of in-hospital mortality was 2.9%, the composite incidence of any postoperative complication was 30.4%, and pulmonary complications occurred after 22.0% of lung cancer resections.


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  		Table 1 Descriptive Statistics of Select Patient Risk Factors Entered as Model Covariates
 
Univariate Analyses for Mortality and Morbidity
Univariate associations for the outcomes of hospital mortality, composite postoperative complication rate, and pulmonary complications as a function of gender, race, and income after lung cancer resections are detailed in Table 2. For the outcome of mortality, female gender (46.2% vs. 32.0%; p < 0.001), black race (8.3% vs. 4.1%; p = 0.03), and higher income (34.9% vs. 25.9%; p = 0.01) were more commonly observed among survivors compared with decedents. Alternatively, low income status (24.5% vs. 18.85, p = 0.04) was more commonly associated with mortality compared with survivors. For the composite incidence of any postoperative complication, female gender (42.3% vs. 47.0%; p = 0.002) and Hispanic race (2.8% vs. 4.0%; p = 0.04) were less commonly associated with postoperative complications, whereas white (86.1% vs. 83.3%; p = 0.02) or Native American (0.5% vs. 0.2%; p = 0.04) race was more commonly associated with complications. Regarding the incidence of pulmonary complications, white race was more frequent among patients with pulmonary complications (87.2% vs. 83.5%; p = 0.01), whereas Hispanics less commonly encountered pulmonary complications (2.5% vs. 3.9%; p = 0.03).


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  		Table 2 Univariate Associations of Gender, Race, and Income on the Unadjusted Odds of In-Hospital Mortality, Composite Incidence of Postoperative Complications, and Pulmonary Complications
 
Adjusted effects of gender, race, and income on mortality and morbidity
The AORs for the effects of female gender, race, and mean income on mortality and postoperative complications appear in Table 3. Within the mortality model, female gender was associated with a 24% (p < 0.001) reduction in the odds of mortality compared with men. Among racial groups, black race was the only multivariate correlate of mortality. Mean income was a significant predictor of mortality (p < 0.001), and lower income categories were associated with significantly increased odds of death compared with mean income quartile IV. Importantly female gender and mean income proved to be stronger predictors in this model, with higher Wald {chi}2 statistics when compared with the effect of race. With respect to postoperative complications, female gender was a significant positive predictor of any postoperative complication (AOR = 0.83) as well as pulmonary complications (AOR = 0.93) after lung cancer resection. Among racial groups, black, Hispanic, and Asian patients incurred decreased risk-adjusted complication rates compared with white patients. No significant associations were observed for the effect of income on the odds of postoperative complications.


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  		Table 3 Hierarchical Logistic Regression Models: Effect of Gender, Race, and Income
 

   Comment
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
Acknowledgments
 References
 
The present study reports on contemporary nationwide lung cancer resection outcomes in the United States as they relate to differences in gender, race, and socioeconomic status. These results suggest that after accounting for the potential confounding influence of more than 50 different variables, female gender was a significant, independent correlate of postoperative morbidity and mortality and was associated with reduced odds of death and postoperative complications compared with male gender. Race was also a significant predictor of postoperative complications. Moreover risk-adjusted mortality was significantly influenced by socioeconomic status, and the odds of death increased with declining mean income. Importantly among these factors, gender proved to be the strongest predictor of postoperative death and morbidity. To our knowledge, these findings represent the most comprehensive report of current nationwide outcomes after lung cancer resections to address the contribution of important demographic factors that have been implicated in health disparities within the United States. Thus these data provide an analysis of valuable patient-related factors to be considered by thoracic surgeons and patients in the preoperative setting.

In this study, the effect of gender was a significant correlate of postoperative mortality and morbidity and is in agreement with other reported series [3, 4, 8, 10, 11]. In 1 recent series reporting on outcomes from the national Society of Thoracic Surgeons (STS) General Thoracic Surgery Database, male gender was associated with elevated odds of mortality (OR = 1.37; p = 0.013) as well as the composite outcome of mortality and major morbidity (OR = 1.12; p = 0.031) after lung cancer resections [3]. Furthermore the beneficial effects of female gender on 5-year survival rates for women with stage I/III tumors were noted in another prospective series of 1,085 patients with non-small cell lung carcinoma [8]. Important to consider in the results of the present study is the relative disproportion of females to males within this NIS dataset as well as the relative strength of female gender as an independent risk factor for the primary outcomes of interest. Considering the estimated associations between female gender and outcomes, we would expect that the effect of female gender on risk-adjusted outcomes may be even more dramatic in datasets with more equal distribution of gender. Moreover although not directly assessed in the present study, the influences of tumor type and disease stage must be considered as contributing to the improved perioperative outcomes for women undergoing lung cancer resections.

The present results provide a valuable extension to accumulated data regarding the influence of race and socioeconomic status on lung cancer treatment and outcomes [5–7, 13, 18]. In a recent population-based study of 76,086 lung cancer resection patients (1998 to 2002) within a Florida cancer registry, black patients were diagnosed with lung cancer at an earlier age and with more advanced disease. They composed the largest proportion of low-income patients and were less likely to undergo surgical resection, which resulted in reduced median survival times compared with whites (7.5 years vs. 8.8 years; p < 0.001) [13]. However after risk-factor adjustment, race failed to be a multivariate correlate of survival in this series, whereas severe poverty was an independent predictor of worse survival (HR = 1.05; p = 0.001). Importantly in this series no significant differences were observed for patients undergoing surgical resections, and the study is limited by the fact that only 22% of their cohort underwent surgical resection, their analysis failed to address postoperative morbidity, and the results reflect trends that may not be current. Other series however corroborate these findings among single institutional experiences and various cancer registries and are complementary to those of the present analysis [5, 18].

The potential explanations for disparities in outcomes related to gender, race, and socioeconomic status in this study are complex and multifactorial. Substantial evidence exists describing the interaction of various factors on patient outcomes, including ethnicity, education level, language barriers, socioeconomic status, cultural values, poor physician-patient communication, provider bias, disparities in hospital resource use, and access to specialized care [16, 19–24]. In this large observational analysis, we also demonstrate the independent influence of several of these factors. Specifically these results indicate that many of the racial and socioeconomic influences that have been documented as potential culprits for disparities in patient outcomes appear related. When individually accounted for through regression analysis, various modifiable social, health system, and economic factors largely account for the observed differences. In fact these data, as well as those presented elsewhere [5, 18], demonstrate that many ethnic disparities in lung cancer outcomes could be reduced, and even improved, with appropriate use of operative intervention and adjuvant therapy.

The presented results are subject to select limitations. Because of the retrospective study design, selection bias must be considered. In addition we are unable to account for certain data, including tumor type, pathologic or clinical stage, preoperative performance status, or predicted pulmonary function. Use of community-level income status, such as mean income by ZIP code, is admittedly imperfect, and this definition of socioeconomic status may differ compared with other studies. However previous research has supported the use of such definitions as a valid proxy for socioeconomic status [25–28]. The use of de-identified data and the lack of long-term follow-up within the NIS limits the ability to scrutinize the data further, and this study also did not directly examine the effects of insurance or primary payer status on risk-adjusted outcomes. The impact of varying insurance types on risk-adjusted outcomes however has been documented in other recent surgical series [16, 29]. Despite these limitations, use of the NIS provides important benefits because the data represented is broadly applicable to patients nationwide and allows for the effective adjustment for certain social and economic influences that are often poorly captured or unavailable in other institutional or registry datasets.

Conclusions
The results reported herein demonstrate important differences in lung cancer resection outcomes as they relate to disparate differences in gender, race, and socioeconomic status. Based on these analyses, female gender is associated with decreased risk-adjusted mortality and morbidity after lung cancer resection, whereas the odds of postoperative complications are lower for black, Hispanic, and Asian patients. Low socioeconomic status increases the risk of in-hospital death. These factors should be considered during individual patient risk stratification for lung cancer resection, and optimization of modifiable patient-, provider-, and system-related factors may help to reduce health disparities and outcomes for this patient population.


   Acknowledgments
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Acknowledgments
References
 
This study was supported by Award Number 2T32HL007849-11A1 (DJL, CMB) from the National Heart, Lung, and Blood Institute and the Thoracic Surgery Foundation for Research and Education Research Grant (CLL).


   References
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Acknowledgments
 References
 

  1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, Thun MJ. Cancer statistics, 2008 CA Cancer J Clin 2008;58:71-96.[Medline]
  2. Ginsberg RJ. Lung cancer surgery: acceptable morbidity and mortality, expected results and quality control Surg Oncol 2002;11:263-266.[Medline]
  3. Kozower BD, Sheng S, O'Brien SM, et al. STS database risk models: predictors of mortality and major morbidity for lung cancer resection Ann Thorac Surg 2010;90:875-881discussion 881–3.[Abstract/Free Full Text]
  4. Agudo A, Ahrens W, Benhamou E, et al. Lung cancer and cigarette smoking in women: a multicenter case-control study in Europe Int J Cancer 2000;88:820-827.[Medline]
  5. Bach PB, Cramer LD, Warren JL, Begg CB. Racial differences in the treatment of early-stage lung cancer N Engl J Med 1999;341:1198-1205.[Medline]
  6. Blackstock AW, Herndon 2nd JE, Paskett ED, et al. Similar outcomes between African American and non–African American patients with extensive-stage small-cell lung carcinoma: report from the Cancer and Leukemia Group B J Clin Oncol 2006;24:407-412.[Abstract/Free Full Text]
  7. Blackstock AW, Herndon 2nd JE, Paskett ED, et al. Outcomes among African-American/non-African-American patients with advanced non-small-cell lung carcinoma: report from the Cancer and Leukemia Group B J Natl Cancer Inst 2002;94:284-290.[Abstract/Free Full Text]
  8. Cerfolio RJ, Bryant AS, Scott E, et al. Women with pathologic stage I, II, and III non-small cell lung cancer have better survival than men Chest 2006;130:1796-1802.[Medline]
  9. Chang JW, Asamura H, Kawachi R, Watanabe S. Gender difference in survival of resected non-small cell lung cancer: histology-related phenomenon? J Thorac Cardiovasc Surg 2009;137:807-812.[Abstract/Free Full Text]
  10. Ferguson MK, Skosey C, Hoffman PC, Golomb HM. Sex-associated differences in presentation and survival in patients with lung cancer J Clin Oncol 1990;8:1402-1407.[Abstract]
  11. Ferguson MK, Wang J, Hoffman PC, et al. Sex-associated differences in survival of patients undergoing resection for lung cancer Ann Thorac Surg 2000;69:245-249discussion 249–50.[Abstract/Free Full Text]
  12. Radzikowska E, Glaz P, Roszkowski K. Lung cancer in women: age, smoking, histology, performance status, stage, initial treatment and survival. Population-based study of 20 561 cases. Ann Oncol 2002;13:1087-1093.[Abstract/Free Full Text]
  13. Yang R, Cheung MC, Byrne MM, et al. Do racial or socioeconomic disparities exist in lung cancer treatment? Cancer 2010;116:2437-2447.[Medline]
  14. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data Med Care 1998;36:8-27.[Medline]
  15. Guller U, Hervey S, Purves H, et al. Laparoscopic versus open appendectomy: outcomes comparison based on a large administrative database Ann Surg 2004;239:43-52.[Medline]
  16. LaPar DJ, Bhamidipati CM, Mery CM, et al. Primary payer status affects mortality for major surgical operations Ann Surg 2010;252:544-550discussion 550–1.[Medline]
  17. Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression results to unmeasured confounders in observational studies Biometrics 1998;54:948-963.[Medline]
  18. Cheung MC, Perez EA, Molina MA, et al. Defining the role of surgery for primary gastrointestinal tract melanoma J Gastrointest Surg 2008;12:731-738.[Medline]
  19. Hodgson N, Koniaris LG, Livingstone AS, Franceschi D. Gastric carcinoids: a temporal increase with proton pump introduction Surg Endosc 2005;19:1610-1612.[Medline]
  20. Perez EA, Koniaris LG, Snell SE, et al. 7201 carcinoids: increasing incidence overall and disproportionate mortality in the elderly World J Surg 2007;31:1022-1030.[Medline]
  21. Perez EA, Livingstone AS, Franceschi D, et al. Current incidence and outcomes of gastrointestinal mesenchymal tumors including gastrointestinal stromal tumors J Am Coll Surg 2006;202:623-629.[Medline]
  22. Bryant AS, Cerfolio RJ. Impact of race on outcomes of patients with non-small cell lung cancer J Thorac Oncol 2008;3:711-715.[Medline]
  23. Ayanian JZ, Zaslavsky AM, Guadagnoli E, et al. Patients' perceptions of quality of care for colorectal cancer by race, ethnicity, and language J Clin Oncol 2005;23:6576-6586.[Abstract/Free Full Text]
  24. Breitkopf CR, Catero J, Jaccard J, Berenson AB. Psychological and sociocultural perspectives on follow-up of abnormal Papanicolaou results Obstet Gynecol 2004;104:13474.
  25. Johnson RL, Roter D, Powe NR, Cooper LA. Patient race/ethnicity and quality of patient-physician communication during medical visits Am J Public Health 2004;94:2084-2090.[Medline]
  26. Johnson RL, Saha S, Arbelaez JJ, Beach MC, Cooper LA. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care J Gen Intern Med 2004;19:101-110.[Medline]
  27. Liu JH, Zingmond DS, McGory ML, SooHoo NF, Ettner SL, Brook RH, Ko CY. Disparities in the utilization of high-volume hospitals for complex surgery JAMA 2006;296:1973-1980.[Medline]
  28. Schulman KA, Berlin JA, Harless W, et al. The effect of race and sex on physicians' recommendations for cardiac catheterization N Engl J Med 1999;340:618-626.[Medline]
  29. Lapar DJ, Bhamidipati CM, Walters DM, et al. Primary payer status affects outcomes for cardiac valve operations J Am Coll Surg 2011;212:7597.



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 Ann. Thorac. Surg.Home page
D. J. LaPar, C. M. Bhamidipati, C. L. Lau, D. R. Jones, and B. D. Kozower
The Society of Thoracic Surgeons General Thoracic Surgery Database: Establishing Generalizability to National Lung Cancer Resection Outcomes
Ann. Thorac. Surg., July 1, 2012; 94(1): 216 - 221.
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