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Breast Cancer Research and Treatment

, Volume 171, Issue 2, pp 477–488 | Cite as

The relationship between patient and tumor characteristics, patterns of breast cancer care, and 5-year survival among elderly women with incident breast cancer

  • Amanda L. KongEmail author
  • Ann B. Nattinger
  • Emily McGinley
  • Liliana E. Pezzin
Epidemiology
  • 166 Downloads

Abstract

Purpose

To examine the relationship between patient and tumor characteristics, patterns of breast cancer care, and 5-year survival among a population-based cohort of elderly women with incident breast cancer, with a special focus on identifying sources of socioeconomic (SES) disparities in outcomes.

Methods

We identified women with newly diagnosed breast cancer in 2006–2009 from the Surveillance and Epidemiology End Result study linked with Medicare claims. A Classification and Regression Tree (CART) model was applied to 13 individual indicators of neoadjuvant and adjuvant breast cancer treatment, tumor characteristics, and patient sociodemographic variables to identify patterns with the greatest discriminant value in predicting 5-year survival. We subsequently examined the extent to which these patterns varied by the patient’s SES.

Results

Survival probabilities associated with the 18 unique CART-identified patterns ranged from 22 to 87%. The number of positive axillary nodes was the best single discriminator between high and lower survival outcomes. The most common discriminant factor among patterns with poor (< 25%) survival was the absence of radiation treatment, followed by the presence of comorbidities, tumor size > 2 cm, and no breast surgery. Relative to high SES women, poor women were nearly four times (12.3% vs. 3.2%, p < 0.001) as likely to be classified in the pattern associated with worse survival, and less likely (31.7% vs. 52.9%, p = 0.04) to receive the pattern associated with the greatest survival.

Conclusions

Greater adoption of effective patterns of care could improve survival of elderly women with incident breast cancer overall, and reduce SES disparities therein.

Keywords

Patterns Care Breast Cancer Elderly Survival 

Notes

Acknowledgements

The authors gratefully acknowledge funding by NCI under grant R01-CA 170945 and the American Cancer Society (RSG-13-070-01-CPHPS). The authors acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database. The ideas and opinions expressed herein are solely those of the author(s); any endorsement by the National Cancer Institute, the Centers for Disease Control and Prevention, or their Contractors and Subcontractors is not intended nor should be inferred. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement # U58DP003862-01 awarded to the California Department of Public Health. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred. The authors acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database. The authors acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database.

Authors’ Contributions

AK and LEP are jointly responsible for the planning, conducting, and reporting of results. ELM conducted the statistical analyses. ABN provided clinical insights into variable construction and interpretation of results. As a senior author, LEP is responsible for the overall content of the manuscript.

Funding

The authors gratefully acknowledge funding by NCI under grant R01-CA 170945 and the American Cancer Society (RSG-13-070-01-CPHPS). No conflicts of interest or disclosures from any authors.

Compliance with ethical standards

Conflicts of interest

There are no conflicts of interest to disclose.

Ethical approval

This study has received ethical approval by the Medical College of Wisconsin/Froedtert Hospital Institutional Review Board #5 as it satisfies requirements of 45 CFR 46.111.

References

  1. 1.
    Singh GK, Miller B, Hankey B, Edwards BK (2003) Area socioeconomic variations in U.S. Cancer Incidence, Mortality, Stage, Treatment, and Survival, 1975–1999. In. National Cancer Institute, BethesdaGoogle Scholar
  2. 2.
    Gordon NH, Crowe JP, Brumberg DJ, Berger NA (1992) Socioeconomic factors and race in breast cancer recurrence and survival. Am J Epidemiol 135(6):609–618CrossRefPubMedGoogle Scholar
  3. 3.
    Gorey KM, Luginaah IN, Holowaty EJ, Fung KY, Hamm C (2009) Breast cancer survival in Ontario and California, 1998–2006: socioeconomic inequity remains much greater in the United States. Ann Epidemiol 19(2):121–124CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Sprague BL, Trentham-Dietz A, Gangnon RE, Ramchandani R, Hampton JM, Robert SA, Remington PL, Newcomb PA (2011) Socioeconomic status and survival after an invasive breast cancer diagnosis. Cancer 117(7):1542–1551CrossRefPubMedGoogle Scholar
  5. 5.
    Harper S, Lynch J, Meersman SC, Breen N, Davis WW, Reichman MC (2009) Trends in area-socioeconomic and race-ethnic disparities in breast cancer incidence, stage at diagnosis, screening, mortality, and survival among women ages 50 years and over (1987–2005). Cancer Epidemiol Biomark Prev 18(1):121–131CrossRefGoogle Scholar
  6. 6.
    Byers TE, Wolf HJ, Bauer KR, Bolick-Aldrich S, Chen VW, Finch JL, Fulton JP, Schymura MJ, Shen T, Van Heest S et al (2008) The impact of socioeconomic status on survival after cancer in the United States: findings from the National Program of Cancer Registries Patterns of Care Study. Cancer 113(3):582–591CrossRefPubMedGoogle Scholar
  7. 7.
    Yu XQ (2009) Socioeconomic disparities in breast cancer survival: relation to stage at diagnosis, treatment and race. BMC Cancer 9:364CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kish JK, Yu M, Percy-Laurry A, Altekruse SF (2014) Racial and ethnic disparities in cancer survival by neighborhood socioeconomic status in Surveillance, Epidemiology, and End Results (SEER) Registries. J Natl Cancer Inst Monogr 2014(49):236–243CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Feinglass J, Rydzewski N, Yang A (2015) The socioeconomic gradient in all-cause mortality for women with breast cancer: findings from the 1998 to 2006 National Cancer Data Base with follow-up through 2011. Ann Epidemiol 25(8):549–555CrossRefPubMedGoogle Scholar
  10. 10.
    Gilligan MA, Kneusel RT, Hoffmann RG, Greer AL, Nattinger AB (2002) Persistent differences in sociodemographic determinants of breast conserving treatment despite overall increased adoption. Med Care 40(3):181–189CrossRefPubMedGoogle Scholar
  11. 11.
    Nattinger AB, Wozniak EM, McGinley EL, Li J, Laud P, Pezzin LE (2017) Socioeconomic disparities in mortality among women with incident breast cancer before and after implementation of medicare Part D. Med Care 55(5):463–469CrossRefPubMedGoogle Scholar
  12. 12.
  13. 13.
    Reeder-Hayes K, Peacock Hinton S, Meng K, Carey LA, Dusetzina SB (2016) Disparities in use of human epidermal growth hormone receptor 2-targeted therapy for early-stage breast cancer. J Clin Oncol 34(17):2003–2009CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Lautner M, Lin H, Shen Y, Parker C, Kuerer H, Shaitelman S, Babiera G, Bedrosian I (2015) Disparities in the use of breast-conserving therapy among patients with early-stage breast cancer. JAMA Surg 150(8):778–786CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Royak-Schaler R, Pelser C, Langenberg P, Hayes J, Gardner L, Nesbitt K, Citron W, Drogula CL, Dwyer D (2012) Characteristics associated with the initiation of radiation therapy after breast-conserving surgery among African American and white women diagnosed with early-stage breast cancer in Maryland, 2000–2006. Ann Epidemiol 22(1):28–36CrossRefPubMedGoogle Scholar
  16. 16.
    Parise CA, Bauer KR, Caggiano V (2012) Disparities in receipt of adjuvant radiation therapy after breast-conserving surgery among the cancer-reporting regions of California. Cancer 118(9):2516–2524CrossRefPubMedGoogle Scholar
  17. 17.
    Chen AY, Halpern MT, Schrag NM, Stewart A, Leitch M, Ward E (2008) Disparities and trends in sentinel lymph node biopsy among early-stage breast cancer patients (1998–2005). J Natl Cancer Inst 100(7):462–474CrossRefPubMedGoogle Scholar
  18. 18.
    Reeder-Hayes KE, Bainbridge J, Meyer AM, Amos KD, Weiner BJ, Godley PA, Carpenter WR (2011) Race and age disparities in receipt of sentinel lymph node biopsy for early-stage breast cancer. Breast Cancer Res Treat 128(3):863–871CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    American Community Survey 2005–2009. http://factfinder2.census.gov
  20. 20.
    Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks/Cole Advanced Books & Software, MontereyGoogle Scholar
  21. 21.
    Klabunde CN, Potosky AL, Legler JM, Warren JL (2000) Development of a comorbidity index using physician claims data. J Clin Epidemiol 53(12):1258–1267CrossRefPubMedGoogle Scholar
  22. 22.
    Chavez-MacGregor M, Clarke CA, Lichtensztajn DY, Giordano SH (2016) Delayed initiation of adjuvant chemotherapy among patients with breast cancer. JAMA Oncol 2(3):322–329CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hershman DL, Wang X, McBride R, Jacobson JS, Grann VR, Neugut AI (2006) Delay in initiating adjuvant radiotherapy following breast conservation surgery and its impact on survival. Int J Radiat Oncol Biol Phys 65(5):1353–1360CrossRefPubMedGoogle Scholar
  24. 24.
    Hershman DL, Wang X, McBride R, Jacobson JS, Grann VR, Neugut AI (2006) Delay of adjuvant chemotherapy initiation following breast cancer surgery among elderly women. Breast Cancer Res Treat 99(3):313–321CrossRefPubMedGoogle Scholar
  25. 25.
    Saadatmand S, Bretveld R, Siesling S, Tilanus-Linthorst MM (2015) Influence of tumour stage at breast cancer detection on survival in modern times: population based study in 173,797 patients. BMJ 351:h4901CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Michaelson JS, Silverstein M, Sgroi D, Cheongsiatmoy JA, Taghian A, Powell S, Hughes K, Comegno A, Tanabe KK, Smith B (2003) The effect of tumor size and lymph node status on breast carcinoma lethality. Cancer 98(10):2133–2143CrossRefPubMedGoogle Scholar
  27. 27.
    Say CC, Donegan WL (1974) Invasive carcinoma of the breast: prognostic significance of tumor size and involved axillary lymph nodes. Cancer 34(2):468–471CrossRefPubMedGoogle Scholar
  28. 28.
    Braithwaite D, Moore DH, Satariano WA, Kwan ML, Hiatt RA, Kroenke C, Caan BJ (2012) Prognostic impact of comorbidity among long-term breast cancer survivors: results from the LACE study. Cancer Epidemiol Biomarkers Prev 21(7):1115–1125CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Zhao XB, Ren GS (2016) Diabetes mellitus and prognosis in women with breast cancer: A systematic review and meta-analysis. Medicine (Baltimore) 95(49):e5602CrossRefGoogle Scholar
  30. 30.
    Chen L, Linden HM, Anderson BO, Li CI (2014) Trends in 5-year survival rates among breast cancer patients by hormone receptor status and stage. Breast Cancer Res Treat 147(3):609–616CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Early Breast Cancer Trialists’ Collaborative G, Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, Cutter D, Darby S, McGale P et al (2012) Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 379(9814):432–444CrossRefGoogle Scholar
  32. 32.
    Early Breast Cancer Trialists’ Collaborative G (2015) Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet 386(10001):1341–1352CrossRefGoogle Scholar
  33. 33.
    Early Breast Cancer Trialists’ Collaborative G, Davies C, Godwin J, Gray R, Clarke M, Cutter D, Darby S, McGale P, Pan HC, Taylor C (2011) Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 378(9793):771–784CrossRefGoogle Scholar
  34. 34.
    Early Breast Cancer Trialists' Collaborative G, Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, Cutter D, Davies C, Ewertz M (2011) M et al: Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 378(9804):1707–1716CrossRefGoogle Scholar
  35. 35.
    Ebctcg McGaleP, Taylor C, Correa C, Cutter D, Duane F, Ewertz M, Gray R, Mannu G, Peto R et al (2014) Effect of radiotherapy after mastectomy and axillary surgery on 10-year recurrence and 20-year breast cancer mortality: meta-analysis of individual patient data for 8135 women in 22 randomised trials. Lancet 383(9935):2127–2135CrossRefGoogle Scholar
  36. 36.
    Hughes KS, Schnaper LA, Bellon JR, Cirrincione CT, Berry DA, McCormick B, Muss HB, Smith BL, Hudis CA, Winer EP et al (2013) Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer: long-term follow-up of CALGB 9343. J Clin Oncol 31(19):2382–2387CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Cuzick J, Sestak I, Baum M, Buzdar A, Howell A, Dowsett M, Forbes JF, Investigators AL (2010) Effect of anastrozole and tamoxifen as adjuvant treatment for early-stage breast cancer: 10-year analysis of the ATAC trial. Lancet Oncol 11(12):1135–1141CrossRefPubMedGoogle Scholar
  38. 38.
    Wang Y, Witten I (1997) Inducing model trees for continuous classes. In: Proceedings of the ninth european conference machine learningGoogle Scholar
  39. 39.
    Gornick M (2000) Vulnerable populations and medicare services: why do disparities exist? Century Foundation Press, New York, NYGoogle Scholar
  40. 40.
    Du XL, Fang S, Meyer TE (2008) Impact of treatment and socioeconomic status on racial disparities in survival among older women with breast cancer. Am J Clin Oncol 31(2):125–132CrossRefPubMedGoogle Scholar
  41. 41.
    Nattinger AB, Pezzin LE, McGinley EL, Charlson JA, Yen TW, Neuner JM (2015) Patient costs of breast cancer endocrine therapy agents under Medicare Part D vs with generic formulations. Springerplus 4:54CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Dreyer MS, Nattinger AB, McGinley EL, Pezzin LE (2018) Socioeconomic status and breast cancer treatment. Breast Cancer Res Treat 167(1):1–8CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Surgical Oncology, Department of SurgeryMedical College of WisconsinMilwaukeeUSA
  2. 2.Center for Patient Care and Outcomes ResearchMedical College of WisconsinMilwaukeeUSA
  3. 3.Department of MedicineMedical College of WisconsinMilwaukeeUSA

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