Obstetrics & Gynecology:
Disparities in the Allocation of Treatment in Advanced Ovarian Cancer: Are There Certain Patient Characteristics Associated With Nonstandard Therapy?
Chase, Dana M. MD; Fedewa, Stacey PhD; Chou, Tatiana Stanisic MD; Chen, Amy MD, PhD; Ward, Elizabeth PhD; Brewster, Wendy R. MD, PhD
From the Creighton University School of Medicine at St Joseph's Hospital and Medical Center, Phoenix, Arizona; the American Cancer Society, Atlanta, Georgia; Kaiser Permanente, Oakland, California; Emory University, Atlanta, Georgia; and the University of North Carolina, Chapel Hill, Chapel Hill, North Carolina.
Corresponding author: Wendy R. Brewster, MD, PhD, UNC Chapel Hill Gynecologic Oncology, 3027 Old Clinic Building, CB 7570, Chapel Hill, NC 27599; e-mail: email@example.com.
Financial Disclosure The authors did not report any potential conflicts of interest.
OBJECTIVE: To explore data from the National Cancer Database to identify pretreatment patient characteristics associated with receipt of nonstandard treatment for advanced ovarian cancer.
METHODS: Between 2003 and 2006, there were 47,390 patients with ovarian cancer registered with the National Cancer Database. Variables included demographics, insurance, Charlson comorbidity score, zip income, and facility characteristics. Multivariable log binomial regression analyses were performed to assess factors associated with nonstandard care.
RESULTS: Among the 47,390 patients, 27,045 (81%) were stage IIIC or IV. After excluding patients with missing treatment information (n=1,129 [2.38%]), 13,789 (53.21%) had received standard treatment. In multivariable analyses, uninsured and Medicaid-insured patients were less likely to receive standard treatment as compared with privately insured patients (relative risk 0.88, 95% confidence interval [CI] 0.83–0.93 and relative risk 0.91, 95% CI 0.86–0.95, respectively). African Americans and Hispanics were also less likely to receive standard treatment (relative risk 0.87, 95% CI 0.83–0.92 and relative risk 0.89, 95% CI 0.84–0.94, respectively). Patients with a Charlson comorbidity score of 2+ were less likely to receive standard care (relative risk 0.74, 95% CI 0.68–0.80). Treatment at a community cancer hospital compared with a teaching hospital was also less likely to be associated with standard treatment (relative risk 0.83, 95% CI 0.80–0.87).
CONCLUSION: In this large multi-institutional cohort, approximately 47% of patients with stage IIIC and IV ovarian cancer did not receive standard treatment. Pretreatment patient characteristics such as race, insurance status, age, Charlson comorbidity score, and facility type were associated with nonstandard treatment.
LEVEL OF EVIDENCE: II
Survival disparities after cancer diagnosis are recognized in the United States in multiple disease sites. Specifically, these racial disparities have been described among a variety of cancer types with nonwhite ethnicity being a risk factor for poorer survival outcomes.1 In the setting of gynecologic cancers, the factors contributing to these disparities are still debated. More recent literature reports conflicting data regarding the status of ethnic, racial, or both disparities on treatment outcomes.2,3
With 21,990 estimated cases of ovarian cancer in 2011,4 15,460 deaths in 2011 are projected.4 Approximately 80% of patients with ovarian cancer present with advanced disease because screening strategies have yet to be developed.5 Although the standard of care in the United States has been a combination of primary surgical debulking followed by adjuvant chemotherapy, some have recently argued for the use of neoadjuvant chemotherapy followed by interval debulking as a reasonable alternative.6–10 Most recently, Vergote et al performed a prospective randomized trial in Europe and Canada, which supported the use of neoadjuvant chemotherapy as equivalent to standard treatment specifically in patients with suboptimal debulking.11 In this group of patients, neoadjuvant chemotherapy was associated with a greater chance of no gross residual at the time of surgery in addition to less postoperative rates of adverse events and mortality.
Regardless of outcomes from the chosen treatment course, women presenting with advanced ovarian cancer are typically in a clinically challenging situation because this disease often manifests itself with massive ascites, pleural effusions, and debilitating and aggressive tumor growths. The decision-making in these situations is complex and perhaps biased by physician or patient preference compared with being driven by the patient's medical history, comorbidities, or access to care. The objective of this study was to analyze a large registry database for patient characteristics associated with certain treatment choices in advanced ovarian cancer management.
MATERIALS AND METHODS
Data from the National Cancer Database, a hospital-based cancer registry jointly sponsored by the American Cancer Society and the American College of Surgeons, was used in this study.12–15 The National Cancer Database is unique in that it includes approximately 70% of all individuals diagnosed with malignant cancer in the United States and collects data from more than 1,400 hospitals that have cancer treatment programs approved by the American College of Surgeons Commission on Cancer. The National Cancer Database contains standardized data elements on patient demographics, insurance status, county of residence, tumor characteristics (including stage and histopathology), first course of treatment, and facility characteristics are also available in the National Cancer Database. The American Cancer Society uses the Morehouse Institutional Review Board for protocols involving the National Cancer Database. This study was exempt from review secondary to the anonymous nature of the database.
Women diagnosed with their first primary invasive epithelial ovarian cancer (International Classification of Diseases for Oncology, Tenth Revision code C56.9)16 between January 1, 2003, and December 31, 2006, who received all or part of their first treatment at the reporting facility were selected from the National Cancer Database (n=47,390). Data were abstracted using coding guidelines documented in the Facility Oncology Registry Data Standards manual.17 Performance status was not available within the National Cancer Database.
Analysis was restricted to women 18–99 years of age diagnosed with clinical or pathological stage IIIC or IV according to the American Joint Commission on Cancer Staging Manual (6th edition) with complete treatment information (Fig. 1). Pathologic stage was relied on; if pathologic stage was missing, clinical stage was used.
Factors associated with standard compared with nonstandard treatment were examined. Standard treatment was defined as surgery followed by chemotherapy. Patients receiving neoadjuvant chemotherapy, chemotherapy alone, surgery alone, and no treatment were considered to have received nonstandard treatment.
The key explanatory variables included patient, area, and facility characteristics, which are all detailed subsequently. Patient characteristics included insurance status, race and ethnicity, Charlson Deyo comorbidity score, age, and clinical stage. Patient race was categorized as non-Hispanic white, African American, Hispanic, other or Asian, and unknown. Primary payer or insurance type were grouped into the following categories: Medicaid, Medicare (including Medicare alone and with supplement), uninsured (which includes Facility Oncology Registry Data Standards codes for not insured–not otherwise specified, not insured–charity write-off, and not insured–self-pay), private insurance plans (health maintenance organizations, preferred provider organizations, managed-care not otherwise specified), and other or unknown. Because Medicare is available to essentially all U.S. residents aged 65 years and older, but only for permanently disabled individuals younger than 65 years, the Medicare category was dichotomized for the analyses into Medicare for patients 18–64 years of age and Medicare 65–99 years of age. Patient age at diagnosis was categorized into four age groups based on pre-exclusion quartiles. The Charlson Deyo comorbidity index was also included and divided into three categories: none documented, score of one, and a score of two or more.18 In the National Cancer Database, cancer is not counted as a comorbidity because all patients would have a point for this diagnosis. Only noncancer comorbidities (eg, diabetes, chronic obstructive pulmonary disease) are counted to avoid this inflation. For this data set, a Charlson Deyo comorbidity index score of 0 was compared with 1 or greater and 0–1 was compared with 2+.
Because individual-level indicators of socioeconomic status were not available in the National Cancer Database, we used an area-based indicator of income, which was derived from merging patient's zip codes with 2000 U.S. Census zip code data on income. The median income of the population in a patient's zip code of residence was categorized into the following groups, $29,999 or less, $30,000–$35,999, $36,000–$45,999, $46,000 or greater, or missing, which was based on quartiles of the observed distribution in the general U.S. population.
Facility characteristics were based on the Commission on Cancer-approved classification and were thus categorized into the following groups: community cancer programs, comprehensive community cancer programs, teaching or research centers, and missing or unknown. By definition, community hospitals treat at least 300 patients with cancer a year and have a full range of services for cancer care, but patients need a referral for portions of their treatment. Comprehensive community cancer centers are facilities that offer the same range of services as the community hospitals but treat at least 750 patients with cancer annually and conduct weekly cancer conferences; this includes National Cancer Institute-designated comprehensive cancer centers. Teaching or research facilities have residency programs and ongoing cancer research. A separate category was created for patients missing treatment facility type.
Analyses were performed with SAS 9.2. Chi-square tests were used to analyze the relationship between race and all other covariates. Additionally, chi-square tests (α=.05) were used to examine associations between each of the aforementioned independent variables with receipt of standard treatment. Multivariable log binomial models (PROC GENMOD) were used to generate risk ratio estimates and to assess factors associated with nonstandard care.19
Inclusion in this study relied on a recorded clinical or pathologic stage, which was missing on 5,199 (10.97%) of ovarian cases originally selected from the National Cancer Database. After removing those with missing stage information, the final analytic cohort contained 25,916 patients. In this cohort of 25,916 patients (Fig. 1), 13,789 (53.21%) women received standard treatment. Among the 12,127 patients who did not receive standard treatment, 3,034 (25.02%) received chemotherapy without surgery, 2,081 (17.16%) received no treatment, 4,748 (39.15%) received surgery without chemotherapy, and the remaining 2,264 (18.66%) received neoadjuvant chemotherapy followed by surgery (Table 1).
Surgery followed by chemotherapy was the most frequent type of treatment across all insurance types; however, the rate of treatment varied by insurance type (Table 1). Chemotherapy alone was more common among older Medicare patients, whereas neoadjuvant chemotherapy was more common among Medicaid patients. African American patients were disproportionately treated with chemotherapy alone or received no treatment. Patients with stage IV disease and higher comorbidity rates were more likely to receive no treatment or chemotherapy alone. Patients treated at community cancer programs were more likely to receive no treatment or chemotherapy alone, whereas patients treated at teaching research facilities were more likely to receive adjuvant chemotherapy.
Of note, more specifically related to the comorbidity scores, 55.46%, 47.18%, and 32.58% of patients with Charlson score of 0, 1, and 2+, respectively, received standard treatment. Patients with a Charlson comorbidity score of 2 or more were less likely to receive standard care (risk ratio 0.74; 95% confidence interval [CI] 0.68–0.80). Despite the variation in standard treatment by Charlson score, when multivariable models using Charlson score of 2+ compared with 0–1 are run, the risk ratios do not change. However, in univariable analysis when the rate of standard treatment among Charlson score of 0 and 1 is not homogenous, the rate of standard treatment for one comorbidity is approximately 47%.
Table 2 displays patient, area level, and facility characteristics by insurance status. Uninsured and Medicaid patients were disproportionately Hispanic and privately insured patients were disproportionately white and Asian. Older Medicare patients were more likely to have a higher Charlson comorbidity score and later stage at diagnosis. Uninsured and Medicaid patients were also more likely to reside in areas with lower socioeconomic status and to receive treatment at teaching or research hospitals.
Table 3 displays adjusted risk ratios for receipt of standard treatment by patient demographic, clinical factors, area-level socioeconomic status, and facility-level characteristics. Stage of diagnosis was an important predictor of standard treatment; stage IV patients were less likely to receive standard treatment compared with stage IIIC patients (risk ratio 0.63, 95% CI 0.61–0.64). For example, stage IV patients' risk of standard treatment was 0.51 compared with 0.81 for stage IIIC patients in referent categories (privately insured, white patients aged 18–53 years without Charlson comorbidities, residing in the highest income zip code, and receiving treatment at teaching or research facilities) Age of diagnosis was strongly associated with the receipt of surgery and chemotherapy; patients in the 75- to 99-year age group were 42% less likely to receive standard treatment compared with patients aged 18–53 years. Uninsured and Medicaid patients were approximately 9%–12% less likely to receive standard treatment and younger Medicare patients were 10% less likely to have received standard treatment relative to privately insured patients. There were no differences in receipt of standard treatment observed between privately insured and older Medicare patients in adjusted models. Standard treatment was also less likely among African American (risk ratio 0.87, 95% CI 0.83–0.92) patients and patients with higher comorbidity. For example, the absolute risk of standard treatment is only 0.71 for African Americans compared with 0.81 in whites with other comparable characteristics (such as age, stage, and insurance). Treatment at comprehensive cancer centers and community cancer centers was negatively associated with receipt of standard therapy. There were no statistically significant associations between socioeconomic status and receipt of standard therapy in our cohort of patients.
To address the potential inclusion bias of eliminating a large proportion of women with missing stage information, we conducted an analysis among these 5,199 patients to determine factors associated with missing clinical stage. Results from an analysis of women missing clinical stage information indicate that women age 75–99 years were more likely to have missing stage information (risk ratio 1.56, 95% CI 1.41–1.74). There was a positive association between insurance and missing stage information; the risk ratio for uninsured and Medicaid insured, patients relative to privately insured patients, were 1.18 (95% CI 1.03–1.35) and 1.37 (95% CI 1.22–1.54), respectively. The likelihood of missing stage information was higher among African American patients (risk ratio 1.25, 95% CI 1.14–1.37) but not among Hispanic or other races. Patients treated at community cancer centers were more likely to have a stage not documented in the National Cancer Database compared with teaching or research facilities.
In the United States, the standard of care for advanced ovarian cancer as defined by recent phase III clinical trials is primary surgical debulking followed by adjuvant chemotherapy.20 Thus, the guidelines set forth by the National Comprehensive Cancer Network state that cytoreductive surgery performed by a gynecologic oncologist is the “standard recommendation” for the treatment of advanced ovarian cancer.10 The National Comprehensive Cancer Network advocates the neoadjuvant chemotherapy approach as an alternative approach for advanced disease that is “bulky,” in patients who are poor surgical candidates, or both. Furthermore, the National Comprehensive Cancer Network advocates that the patient consult with a specialist before making the decision to treat with neoadjuvant chemotherapy. In fact, as shown by Dewdney et al, the majority of members surveyed in the Society of Gynecologic Oncologists were found to not favor the neoadjuvant approach despite recent provocative data from the randomized trial by Vergote et al.10,21
Perhaps the gynecologic oncologists in the United States follow this approach because standard of care is defined as primary surgical debulking secondary to a survival advantage seen in the nonrandomized trials performed in the United States.9,22,23 Furthermore, the lower overall survival rate of 30 months in those patients given neoadjuvant chemotherapy in the recent Vergote trial is notably poor compared with previous trials and retrospective reviews in the United States showing an approximately 50-month overall survival rate for patients with primary surgical debulking.10,24 In addition, Chi et al recently reported a retrospective review of patients treated with neoadjuvant chemotherapy compared with upfront debulking with the same inclusion and exclusion criteria defined by the European trial and found a 13-month survival advantage for patients undergoing upfront debulking surgery.22 Albeit, some may argue that 1) nonrandomized retrospective trials cannot be compared with randomized data; 2) physicians enrolling patients in the Vergote trial excluded those patients who would benefit the most from upfront debulking (perhaps those patients most likely included in the nonrandomized retrospective U.S. trials); and 3) certain patient groups present with characteristics that direct and at times demand treatment with neoadjuvant chemotherapy.25
Certain exclusion criteria preclude the participation of some women to the trials performed in the United States with upfront surgical debulking secondary to medical comorbidities or surgical risk. This is likely due to poor performance status or disease distribution. For example, participation is usually restricted to women with performance status of 0–2 (in bed for less than 50% of the day) in addition to the absence of debilitating medical conditions precluding treatment with surgery.11 This is consistent with our data because the number of patients receiving standard treatment decreased with increasing age and increasing Charlson comorbidity score. Age and comorbidities are perhaps surrogates for poor performance status or poor surgical candidates. This demonstrates that there appears to be a cohort of women in whom the standard upfront surgery is not regarded as an option for the treating physician. Thus, the objective of the present investigation was to further investigate baseline patient characteristics that were correlated with nonstandard treatment and to define which treatment alternatives were most often used. The hypothesis was that there were likely disparities in the characteristics of patients given nonstandard treatment because certain patient groups present too ill to receive a primary surgical approach.
The heterogeneity of treatment type in this collection of registry data is striking. Despite an established standard treatment as outline by the National Comprehensive Cancer Network, almost half of those sampled in our study received nonstandard treatment, including neoadjuvant chemotherapy, chemotherapy alone, or no treatment at all, and the distribution among the substandard therapies listed is nearly even among them. This finding is consistent with previous studies examining standard treatments.26 Although it is well known that the majority of women present with advanced metastatic ovarian cancer,4 the finding that the majority of patients do not receive the presumed standard treatment is noteworthy. Although there has been discussion of neoadjuvant chemotherapy being equivalent in some patients to the current standard of care, the options of chemotherapy alone or no treatment at all are less well defined but perhaps just as appropriate for some patients.
Further findings presented here demonstrated that uninsured and publically insured patients, African American patients, and those treated at either a community cancer program or comprehensive community cancer program were more likely to be treated with either chemotherapy alone or to receive no treatment at all. Previous reports have indicated that socioeconomic factors such as nonwhite race and public insurance type were associated with neoadjuvant chemotherapy use, even in multivariable analysis.27 This may not reflect a bias on the treating physician's decision-making. Instead perhaps this indicates that certain racial, socioeconomic, or both groups are more likely to present with 1) disease that is too advanced; 2) comorbidities and performance status that are too poor; or 3) access only to community institutions ill-equipped to perform radical surgeries. One may speculate that this points toward disparities that certain groups may have in health education and access to medical care earlier in life.
With political discussions centering on health care and its distribution to a larger population, disparity in available treatment options has been a topic of recent investigation.28–30 However, findings vary with some studies reporting no evidence of racial disparity in treatment.30 Boyd et al reported data from New York that patients presenting to public hospitals, who were more likely to be African American or Asian, were less likely to receive surgery by a gynecologic oncologist, by surgeons with a high volume of cases, or both.31 In addition, Morris et al reported that African American women and women with public insurance were less likely to present with early-stage ovarian cancer.32 These studies combined with the findings reported here indicate some unfortunate inequalities in the presentation and eventual treatment of women with advanced ovarian cancer.
Age and facility type (community compared with teaching or research) were also found to be associated with treatment allocation. Age may play a role in treatment allocation because there may be a tendency to treat elderly patients with chemotherapy alone. Although comorbidity and overall health affect a patient's ability to endure standard treatment, the treatment decision is assumed to be individually based because not all elderly women have significant comorbidities and poor performance status. This finding suggests that age may be an independent factor determining treatment course and this could potentially be a source for physician bias. The assumption is that elderly women may be less likely to tolerate the initial aggressive surgical approach; however, some have demonstrated that women older than 65 years are not prone to an increase in surgical or chemotherapy complications compared with those given neoadjuvant chemotherapy.33 This is an area that requires further exploration.
Finally, studies have shown that institutions with increased ovarian cancer surgical volume are not only associated with a higher likelihood of receiving standard treatment, but also are significantly predictive of improved overall survival outcome.34 Unfortunately, the National Cancer Database does not have data on whether the patient was treated by a gynecologic oncologist, gynecologist, or general surgeon. However, it can be hypothesized that smaller community hospitals are less likely to have specialist. A study conducted by Bilimoria et al compared Commission on Cancer with non-Commission on Cancer–accredited facilities and reported that Commission on Cancer-accredited hospitals were larger, were more likely to be located in urban areas, and offer more cancer services than non-Commission on Cancer–accredited facilities.35 However, there has not been a formal analyses of how different Commission on Cancer-accredited facility types vary by region, urban or rural status, and demographic information. Of the 1,325 facilities included in our study, 470, 557, and 264 were community, comprehensive, and teaching or research facilities, respectively. Of note, community centers were more likely to be located in the Midwest; 32.1% of community centers were located in the Midwest compared with 23.5% of comprehensive cancer centers and 26.14% of teaching or research facilities. To ensure the best quality of care for all patients, access to specialists must be made available to those of any insurance status. This may be the result of accessibility of gynecologic oncologists at larger medical centers who are trained and experienced with standards of care in the field.34 To ensure the best quality of care for all patients, access to specialists must be made available to those of any insurance status.
The strengths of this study are its large sample size and wide range of patients. Certainly there are inherent weaknesses in the analysis of a large retrospective database such as the National Cancer Database. Similar to other large database reviews, a limitation of the study is missing or incomplete data. For example, of 47,000 women screened for the study, more than 10% were missing staging information. The aforementioned factors (uninsured, Medicaid, African American, and treatment at community cancer centers) associated with missing stage should be considered when interpreting the results of this study. Furthermore, the “no treatment” group was assumed to have absolutely no treatment based on documentation. However, there is no way to absolutely confirm the lack of treatment. Albeit, other studies in the literature have recognized disparities in care with some groups receiving strikingly less than “standard” treatment.31,34,36,37 Although database research is not optimal, it does allow for one to hypothesize as to potential barriers to standards of care in large diverse patient populations. Finally, survival analysis was unable to be performed because only those patients diagnosed in 2003 had updated survival information available.
Future trials should investigate the effect of these pretreatment patient characteristics on survival rates to study the effect of these treatment decisions. Furthermore, intervention trials should be initiated to attempt to target groups that are receiving nonstandard care in settings where comorbidity is not a factor. Perhaps there are subsets of women who do not receive adequate counseling. The true disparity here may exist in prediagnosis and needs to be better teased out. Finally, attention to quality of life during treatment for advanced ovarian cancer cannot be ignored. The quality-of-life measurements did not differ among the two arms in Vergote's randomized trial comparing neoadjuvant chemotherapy with upfront debulking. In this trial, however, less than half of the patients were optimally debulked, which indicates a tendency toward less radical surgery in this cohort.10 Conversely, it has been demonstrated that optimal debulking rates are higher in the United States among women with advanced disease. Thus, in a clinical trial designed with quality-of-life measurements as a primary outcome rather than overall survival, perhaps quality of life would be more significantly impaired in those patients with radical cytoreductive surgery such as that described by Chi et al.22
In summary, this large multi-institutional cohort, based on registry data, approximates the situation for patients with ovarian cancer nationwide. Approximately one half of patients with stage IIIC and IV ovarian cancer did not receive standard treatment. This means that the majority of women did not qualify or were not offered what has been defined by the National Comprehensive Cancer Network as standard care. Pretreatment patient characteristics such as race, insurance status, age, Charlson comorbidity score, and facility type all appear to be associated with the allocation of nonstandard treatment.
1. Jemal A, Clegg LX, Ward E, Ries LA, Wu X, Jamison PM, et al.. Annual report to the nation on the status of cancer, 1975–2001, with a special feature regarding survival. Cancer 2004;101:3–27.
2. Bryant CS, Kumar S, Shah JP, Mahdi H, Ali-Fehmi R, Munkarah AR, et al.. Racial disparities in survival among patients with germ cell tumors of the ovary—United States. Gynecol Oncol 2009;114:437–41.
3. Chan JK, Zhang M, Hu JM, Shin JY, Osann K, Kapp DS. Racial disparities in surgical treatment and survival of epithelial ovarian cancer in United States. J Surg Oncol 2008;97:103–7.
4. American Cancer Society. Cancer facts & figures 2011. Atlanta (GA): American Cancer Society; 2011.
5. Ozols RF, Rubin SC, Thomas GM, Robboy SJ. Epithelial ovarian cancer. In: Hoskins WJ, Perez CA, Young RC, editors. Principles and practice of gynecologic oncology. Philadelphia (PA): Lippincott Williams & Wilkins; 2000. p. 981–1058.
6. Le T, Alshaikh G, Hopkins L, Faught W, Fund MF. Prognostic significance of postoperative morbidities in patients with advanced epithelial ovarian cancer treated with neoadjuvant chemotherapy and delayed primary surgical debulking. Ann Surg Oncol 2006;13:1711–6.
7. Onda T, Kobayashi H, Nakanishi T, Hatae M, Iwasaka T, Konishi I, et al.. Feasibility study of neoadjuvant chemotherapy followed by interval debulking surgery for stage III/IV ovarian, tubal, and peritoneal cancers: Japan Clinical Oncology Group Study JCOG0206. Gynecol Oncol 2009;113:57–62.
8. Berman ML. Future directions in the surgical management of ovarian cancer. Gynecol Oncol 2003;90:S33–9.
9. Bristow RE, Eisenhauer EL, Santillan A, Chi DS. Delaying the primary surgical effort for advanced ovarian cancer: a systematic review of neoadjuvant chemotherapy and interval cytoreduction. Gynecol Oncol 2007;104:480–90.
10. The NCCN Clinical Practice Guidelines in Oncology for Guideline Name V.X. 201X. Available at: www.nccn.org
. Retrieved October 3, 2011.
11. Vergote I, Trope CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al.. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med 2010;363:943–53.
12. Bilimoria KY, Stewart AK, Winchester DP, Ko CY. The National Cancer Data Base: a powerful initiative to improve cancer care in the United States. Ann Surg Oncol 2008;15:683–90.
13. Sylvester M, Blankenship C, Carter A, Douglas L, Stewart A. Quality control: the American College of Surgeons Commission on Cancer Standards, National Cancer Data Base, and Cancer Liason Program. J Reg Mgmt 2001;28:68–74.
14. Eberle C, Phillips J, Tary P, Menck HR. Quality management in the National Cancer Data Base: a re-abstracting study of the Midwest region. J Reg Mgmt 1997;24:93–7.
15. Fremgen A, Jessup J, Menck HR. Hospital use of NCDB data to assess quality of cancer patient care. J Reg Mgmt 1995;22:69–71.
16. Fritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L, Parkin DM, et al., editors. International classification of disease for oncology. 3rd ed. Geneva (Switzerland): World Health Organization; 2000.
17. Commission on Cancer. Facility oncology registry data standards (FORDS); revised for 2007. Chicago (IL): American College of Surgeons; 2007.
18. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613–9.
19. Deddens JA, Petersen MR. Approaches for estimating prevalence ratios. Occup Environ Med 2008;65:481–6, 501–6.
20. Bookman MA, Brady MF, McGuire WP, Harper PG, Alberts DS, Friedlander M, et al.. Evaluation of new platinum-based treatment regimens in advanced-stage ovarian cancer: a Phase III Trial of the Gynecologic Cancer Intergroup. J Clin Oncol 2009;27:1419–25.
21. Dewdney SB, Rimel BJ, Reinhart AJ, Kizer NT, Brooks RA, Massad LS, et al.. The role of neoadjuvant chemotherapy in the management of patients with advanced stage ovarian cancer: survey results from members of the Society of Gynecologic Oncologists. Gynecol Oncol 2010;119:18–21.
22. Chi DS, Musa F, Dao F, Zivanovic O, Sonoda Y, Leitao MM, et al.. An analysis of patients with bulky advanced stage ovarian, tubal, and peritoneal carcinoma treated with primary debulking surgery (PDS) during an identical time period as the randomized EORTC-NCIC trial of PDS vs neoadjuvant chemotherapy (NACT). Gynecol Oncol 2011 [Epub ahead of print].
23. Bristow RE, Chi DS. Platinum-based neoadjuvant chemotherapy and interval surgical cytoreduction for advanced ovarian cancer: a meta-analysis. Gynecol Oncol 2006;103:1070–6.
24. Armstrong DK, Bundy B, Wenzel L, Huang HQ, Baergen R, Lele S, et al.. Intraperitoneal cisplatin and paclitaxel in ovarian cancer. N Engl J Med 2006;354:34–43.
25. Vergote I, Tropé CG, Amant F, Ehlen T, Reed NS, Casado A. Neoadjuvant chemotherapy is the better treatment option in some patients with stage IIIc to IV ovarian cancer. J Clin Oncol 2011 [Epub ahead of print].
26. Harlan LC, Clegg LX, Trimble EL. Trends in surgery and chemotherapy for women diagnosed with ovarian cancer in the United States. J Clin Oncol 2003;21:3488–94.
27. Chase DM, Rincon A, Deane M, Tewari KS, Brewster WR. Socioeconomic factors may contribute to neoadjuvant chemotherapy use in metastatic epithelial ovarian carcinoma. Gynecol Oncol 2009;115:339–42.
28. Cress RD, O'Malley CD, Leiserowitz GS, Campleman SL. Patterns of chemotherapy use for women with ovarian cancer: a population-based study. J Clin Oncol 2003;21:530–5.
29. Du X, Sun C, Milam M, Bodurka D, Fang S. Ethnic differences in socioeconomic status, diagnosis, treatment, and survival among older women with epithelial ovarian cancer. Int J Gynecol Cancer 2008;18:660–9.
30. Terplan M, Temkin S, Tergas A, Lengyel E. Does equal treatment yield equal outcomes? The impact of race on survival in epithelial ovarian cancer. Gynecol Oncol 2008;111:173–8.
31. Boyd LR, Novetsky AP, Curtin JP. Ovarian cancer care for the underserved: are surgical patterns of care different in a public hospital setting? Cancer 2011;117:777–83.
32. Morris CR, Sands MT, Smith LH. Ovarian cancer: predictors of early-stage diagnosis. Cancer Causes Control 2010;21:1203–11.
33. McLean KA, Shah CA, Thompson SA, Gray HJ, Swensen RE, Goff BA. Ovarian cancer in the elderly: outcomes with neoadjuvant chemotherapy or primary cytoreduction. Gynecol Oncol 2010;118:43–6.
34. Bristow RE, Palis BE, Chi DS, Cliby WA. The National Cancer Database report on advanced-stage epithelial ovarian cancer: impact of hospital surgical case volume on overall survival and surgical treatment paradigm. Gynecol Oncol 2010;118:262–7.
35. Bilimoria KY, Bentrem DJ, Stewart AK, Winchester DP, Ko CY. Comparison of commission on cancer-approved and -nonapproved hospitals in the United States: implications for studies that use the National Cancer Data Base. J Clin Oncol 2009;27:4177–81.
36. Bristow RE, Zahurak ML, Ibeanu OA. Racial disparities in ovarian cancer surgical care: a population-based analysis. Gynecol Oncol 2011;121:364–8.
37. Du XL, Lin CC, Johnson NJ, Altekruse S. Effects of individual-level socioeconomic factors on racial disparities in cancer treatment and survival: findings from the National Longitudinal Mortality Study, 1979–2003. Cancer 2011;117:3242–51.
This article has been cited 3 time(s).
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Gynecologic OncologyPrevalence and incidence of comorbidities in elderly women with ovarian cancerGynecologic Oncology
© 2012 The American College of Obstetricians and Gynecologists
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