Skip to main content

Advertisement

Log in

Time-to-surgery and overall survival after breast cancer diagnosis in a universal health system

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

It is unclear whether time between breast cancer diagnosis and surgery is associated with survival and whether this relationship is affected by access to care. We evaluated the association between time-to-surgery and overall survival among women in the universal-access U.S. Military Health System (MHS).

Methods

Women aged 18–79 who received surgical treatment for stages I–III breast cancer between 1998 and 2010 were identified in linked cancer registry and administrative databases with follow-up through 2015. Multivariable Cox regression models were used to estimate risk of all-cause death associated with time-to-surgery intervals.

Results

The study included 9669 women with 93.1% survival during the study period. The hazards ratios (95% confidence intervals) of all-cause death associated with time-to-surgery were 1.15 (0.93, 1.42) for 0 days, 1.00 (reference) for 1–21 days, 0.97 (0.78, 1.21) for 22–35 days, and 1.30 (1.04, 1.61) for ≥ 36 days. The higher risk of mortality associated with time-to-surgery ≥ 36 days tended to be consistent when analyzed by surgery type, age at diagnosis, and tumor stage.

Conclusions

In the MHS, longer time-to-surgery for breast cancer was associated with poorer overall survival, suggesting the importance of timeliness in receiving surgical treatment for breast cancer in relation to overall survival.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Data availability

The data that support the findings of this study are not publicly available due to the sensitive nature of the data and presence of protected health information (PHI). The Department of Defense Central Cancer Registry (CCR) data may be requested from the Joint Pathology Center; and the Military Health System Data Repository (MDR) data may be requested from the Defense Health Agency. Restrictions apply to the access and use of these data.

Abbreviations

CCR:

Central Cancer Registry

CI:

Confidence interval

CPT:

Current Procedural Terminology

ER:

Estrogen receptor

FORDS:

Facility Oncology Registry Data Standards

HCPCS:

Healthcare Common Procedure Coding System

HR:

Hazards ratio

ICD:

International Classification of Diseases

MDR:

Military Health System Data Repository

MHS:

Military Health System

PR:

Progesterone receptor

SEER:

Surveillance, Epidemiology, and End Results Program

TTS:

Time-to-surgery

References

  1. Kaufman CS, Shockney L, Rabinowitz B et al (2010) National quality measures for breast centers (NQMBC): a robust quality tool: Breast center quality measures. Ann Surg Oncol 17(2):377–385

    CAS  PubMed  Google Scholar 

  2. Del Turco MR, Ponti A, Bick U et al (2010) Quality indicators in breast cancer care. Eur J Cancer 46(13):2344–2356

    PubMed  Google Scholar 

  3. Comber H, Cronin DP, Deady S et al (2005) Delays in treatment in the cancer services: impact on cancer stage and survival. Ir Med J 98(8):238–239

    CAS  PubMed  Google Scholar 

  4. Brazda A, Estroff J, Euhus D et al (2010) Delays in time to treatment and survival impact in breast cancer. Ann Surg Oncol 17(Suppl 3):291–296

    PubMed  Google Scholar 

  5. Neal RD, Tharmanathan P, France B et al (2015) Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. Br J Cancer 112(Suppl 1):S92–S107

    PubMed  PubMed Central  Google Scholar 

  6. Bleicher RJ, Ruth K, Sigurdson ER et al (2016) Time to surgery and breast cancer survival in the United States. JAMA Oncol 2(3):330–339

    PubMed  PubMed Central  Google Scholar 

  7. Smith EC, Ziogas A, Anton-Culver H (2013) Delay in surgical treatment and survival after breast cancer diagnosis in young women by race/ethnicity. JAMA Surg 148(6):516–523

    PubMed  Google Scholar 

  8. Mariella M, Kimbrough CW, McMasters KM, Ajkay N (2018) Longer time intervals from diagnosis to surgical treatment in breast cancer: associated factors and survival impact. Am Surg 84(1):63–70

    PubMed  Google Scholar 

  9. Gu J, Groot G, Boden C et al (2018) Review of factors influencing women’s choice of mastectomy versus breast conserving therapy in early stage breast cancer: a systematic review. Clin Breast Cancer 18(4):e539–e554

    PubMed  Google Scholar 

  10. Lizarraga I, Schroeder MC, Weigel RJ, Thomas A (2015) Surgical management of breast cancer in 2010-2011 SEER registries by hormone and her2 receptor status. Ann Surg Oncol 22(Suppl 3):S566–S572

    PubMed  PubMed Central  Google Scholar 

  11. Nijenhuis MV, Rutgers EJ (2013) Who should not undergo breast conservation? Breast 22(Suppl 2):S110–S114

    PubMed  Google Scholar 

  12. Bleicher RJ, Ruth K, Sigurdson ER et al (2012) Preoperative delays in the us medicare population with breast cancer. J Clin Oncol 30(36):4485–4492

    PubMed  PubMed Central  Google Scholar 

  13. McGee SA, Durham DD, Tse CK, Millikan RC (2013) Determinants of breast cancer treatment delay differ for african american and white women. Cancer Epidemiol Biomark Prev 22(7):1227–1238

    Google Scholar 

  14. Golshan M, Losk K, Kadish S et al (2014) Understanding process-of-care delays in surgical treatment of breast cancer at a comprehensive cancer center. Breast Cancer Res Treat 148(1):125–133

    PubMed  Google Scholar 

  15. Freedman RA, Partridge AH (2013) Management of breast cancer in very young women. Breast 22(Suppl 2):S176–S179

    PubMed  Google Scholar 

  16. Dietz JR, Partridge AH, Gemignani ML et al (2015) Breast cancer management updates: young and older, pregnant, or male. Ann Surg Oncol 22(10):3219–3224

    PubMed  Google Scholar 

  17. Wang J, Kollias J, Boult M et al (2010) Patterns of surgical treatment for women with breast cancer in relation to age. Breast J 16(1):60–65

    CAS  PubMed  Google Scholar 

  18. Yancik R, Wesley MN, Ries LA et al (2001) Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. JAMA 285(7):885–892

    CAS  PubMed  Google Scholar 

  19. Coughlin SS, Calle EE, Teras LR et al (2004) Diabetes mellitus as a predictor of cancer mortality in a large cohort of US adults. Am J Epidemiol 159(12):1160–1167

    PubMed  Google Scholar 

  20. Calip GS, Malone KE, Gralow JR et al (2014) Metabolic syndrome and outcomes following early-stage breast cancer. Breast Cancer Res Treat 148(2):363–377

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Walker GV, Grant SR, Guadagnolo BA et al (2014) Disparities in stage at diagnosis, treatment, and survival in nonelderly adult patients with cancer according to insurance status. J Clin Oncol 32(28):3118–3125

    PubMed  PubMed Central  Google Scholar 

  22. Abdelsattar ZM, Hendren S, Wong SL (2017) The impact of health insurance on cancer care in disadvantaged communities. Cancer 123(7):1219–1227

    PubMed  Google Scholar 

  23. Zhang Y, Franzini L, Chan W et al (2015) Effects of health insurance on tumor stage, treatment, and survival in large cohorts of patients with breast and colorectal cancer. J Health Care Poor Underserved 26(4):1336–1358

    PubMed  Google Scholar 

  24. Lee-Feldstein A, Feldstein PJ, Buchmueller T, Katterhagen G (2000) The relationship of HMOs, health insurance, and delivery systems to breast cancer outcomes. Med Care 38(7):705–718

    CAS  PubMed  Google Scholar 

  25. Rosenberg AR, Kroon L, Chen L et al (2015) Insurance status and risk of cancer mortality among adolescents and young adults. Cancer 121(8):1279–1286

    PubMed  Google Scholar 

  26. Hsu CD, Wang X, Habif DV Jr et al (2017) Breast cancer stage variation and survival in association with insurance status and sociodemographic factors in US women 18 to 64 years old. Cancer 123(16):3125–3131

    PubMed  Google Scholar 

  27. Niu X, Roche LM, Pawlish KS, Henry KA (2013) Cancer survival disparities by health insurance status. Cancer Med 2(3):403–411

    PubMed  PubMed Central  Google Scholar 

  28. Mayberry RM, Mili F, Ofili E (2000) Racial and ethnic differences in access to medical care. Med Care Res Rev 57(Suppl 1):108–145

    PubMed  Google Scholar 

  29. Barnett JC, Berchick ER (2017) Current population reports: Health insurance coverage in the United States, 2016, U.S. Census Bureau, Editor. U.S. Government Printing Office, Washington, DC

  30. Akinyemiju TF, Soliman AS, Johnson NJ et al (2013) Individual and neighborhood socioeconomic status and healthcare resources in relation to black-white breast cancer survival disparities. J Cancer Epidemiol. https://doi.org/10.1155/2013/490472

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wheeler SB, Reeder-Hayes KE, Carey LA (2013) Disparities in breast cancer treatment and outcomes: biological, social, and health system determinants and opportunities for research. Oncologist 18(9):986–993

    PubMed  PubMed Central  Google Scholar 

  32. Defense Health Agency Decision Support Division (2018) Evaluation of the TRICARE program: Fiscal year 2018 report to congress, Defense Health Agency and Office of the Assistant Secretary of Defense (Health Affairs) (OASD[HA]), Editors. pp 1–206. http://health.mil

  33. The Department of Defense Joint Pathology Center (2014) DoD Cancer Registry Program. 2014. https://www.jpc.capmed.mil

  34. Defense Health Agency: Military health system data repository (2017) https://www.health.mil/Military-Health-Topics/Technology/Clinical-Support/Military-Health-System-Data-Repository

  35. Commission on Cancer (2016) Facility oncology registry data standards. American College of Surgeons, Chicago, IL

    Google Scholar 

  36. Gradishar WJ, Anderson BO, Balassanian R et al (2018) Breast cancer, version 4.2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 16(3):310–320

    PubMed  Google Scholar 

  37. American Joint Committee on Cancer (2002) American Joint Committee on Cancer: Part VII: Breast. In: Greene FL, Page DL, Fleming ID, Fritz AG et al (eds) AJCC Cancer Staging Manual. Springer, Chicago, IL, pp 223–240

    Google Scholar 

  38. Edge SB, Byrd DR, Compton CC, Fritz AG et al (eds) (2010) AJCC cancer staging manual, 7th edn. Springer, Chicago

    Google Scholar 

  39. Eaglehouse YL, Manjelievskaia J, Shao S et al (2018) Costs for breast cancer care in the military health system: an analysis by benefit type and care source. Mil Med 183(11–12):e500–e508

    PubMed  Google Scholar 

  40. Slade C, Talbot R (2007) Sustainability of cancer waiting times: the need to focus on pathways relevant to the cancer type. J R Soc Med 100(7):309–313

    PubMed  PubMed Central  Google Scholar 

  41. Crawford SC, Davis JA, Siddiqui NA et al (2002) The waiting time paradox: population based retrospective study of treatment delay and survival of women with endometrial cancer in Scotland. BMJ 325(7357):196

    PubMed  PubMed Central  Google Scholar 

  42. Defense Health Agency (2019) Tricare. www.tricare.mil

  43. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383

    CAS  PubMed  Google Scholar 

  44. Mihalcik SA, Rawal B, Braunstein LZ et al (2017) The impact of reexcision and residual disease on local recurrence following breast-conserving therapy. Ann Surg Oncol 24(7):1868–1873

    PubMed  Google Scholar 

  45. Fredriksson I, Liljegren G, Palm-Sjovall M et al (2003) Risk factors for local recurrence after breast-conserving surgery. Br J Surg 90(9):1093–1102

    CAS  PubMed  Google Scholar 

  46. Bilimoria KY, Ko CY, Tomlinson JS et al (2011) Wait times for cancer surgery in the United States: trends and predictors of delays. Ann Surg 253(4):779–785

    PubMed  Google Scholar 

  47. Murphy AE, Hussain L, Ho C et al (2017) Preoperative panel testing for hereditary cancer syndromes does not significantly impact time to surgery for newly diagnosed breast cancer patients compared with brca1/2 testing. Ann Surg Oncol 24(10):3055–3059

    PubMed  Google Scholar 

  48. National Cancer Institute (2018) Her2’s genetic link to breast cancer spurs development of new treatments. Stories of Discovery. https://www.cancer.gov/research/progress/discovery/her2

  49. Haslam A, Prasad V (2019) Estimation of the percentage of us patients with cancer who are eligible for and respond to checkpoint inhibitor immunotherapy drugs. JAMA Netw Open 2(5):e192535–e192535

    PubMed  PubMed Central  Google Scholar 

  50. Goddard KAB, Weinmann S, Richert-Boe K et al (2011) Her2 evaluation and its impact on breast cancer treatment decisions. Public Health Genomics 15(1):1–10

    PubMed  PubMed Central  Google Scholar 

  51. Krishnamurti U, Silverman JF (2014) Her2 in breast cancer: a review and update. Adv Anat Pathol 21(2):100–107

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the Joint Pathology Center and Defense Health Agency for providing the data used in this study.

Disclaimer

The contents of this publication are the sole responsibility of the authors and do not reflect the views, assertions, opinions or policies of the Uniformed Services University of the Health Sciences (USUHS), the Department of Defense (DoD), or the Departments of the Army, Navy, or Air Force, or any other agency of the U.S. Government, or the Henry M. Jackson Foundation (HJF). Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government.

Funding

This project was supported by the Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center under the auspices of the Henry M. Jackson Foundation for the Advancement of Military Medicine.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kangmin Zhu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The data linkage project was reviewed and approved by the institutional review boards of the Walter Reed National Military Medical Center and the Defense Health Agency for compliance with ethical standards. All study activities were conducted in accordance with the ethical standards of the institutional review boards and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. It was determined by the institutional review boards that formal consent was not required for this type of study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eaglehouse, Y.L., Georg, M.W., Shriver, C.D. et al. Time-to-surgery and overall survival after breast cancer diagnosis in a universal health system. Breast Cancer Res Treat 178, 441–450 (2019). https://doi.org/10.1007/s10549-019-05404-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-019-05404-8

Keywords

Navigation