Skip to main content

Advertisement

SpringerLink
Log in

Absenteeism and short-term disability associated with breast cancer

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

Abstract

Few data exist related to the impact of breast cancer on work absenteeism and short-term disability. This retrospective study estimated the extent and costs of breast cancer-associated production loss using a large medical and pharmacy claims database from a US commercially insured population between January 2003 and December 2007. Women aged ≥18 years with ≥2 breast cancer diagnoses within 90 days were selected. Controls were matched to cases based on index date (first breast cancer diagnosis), age, region, employer, and health insurance type. Outcomes were days absent from work and days with short-term disability. Costs were estimated using daily wage rates. 856 and 2,668 patients were selected for absenteeism and short-term disability, respectively, with a mean age of 49 and 50 years. Average number of absenteeism days was 35 and 21, and short-term disability days were 51 and 5, for cases and controls, respectively, within the post-index year (both P < 0.001). Adjusted incremental costs for absenteeism and short-term disability were $1,911 and $6,157 (P < 0.001), respectively, per breast cancer patient per year. This study suggests that breast cancer is associated with work-related productivity loss within the first year of diagnosis that may be a substantial cost to employers.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. American Cancer Society. Cancer facts & figures 2009. http://www.cancer.org/downloads/STT/500809web.pdf. Accessed 5 Feb 2010

  2. Cancer Stat Fact Sheets. National Cancer Institute. http://seer.cancer.gov/statfacts/html/breast.html. Accessed 23 Feb 2010

  3. A snapshot of breast cancer. National Cancer Institute, 2009 Sept. http://www.cancer.gov/aboutnci/servingpeople/snapshots/breast.pdf. Accessed 1 Mar 2010

  4. Campbell JD, Ramsey SD (2009) The costs of treating breast cancer in the US: a synthesis of published evidence. Pharmacoeconomics 27:199–209

    Article  PubMed  Google Scholar 

  5. Berkowitz N, Gupta S, Silberman G (2000) Estimates of the lifetime direct costs of treatment for metastatic breast cancer. Value Health 3:23–30

    Article  PubMed  CAS  Google Scholar 

  6. Barron JJ, Quimbo R, Nikam PT, Amonkar MM (2008) Assessing the economic burden of breast cancer in a US managed care population. Breast Cancer Res Treat 109:367–377

    Article  PubMed  Google Scholar 

  7. Rao S, Kubisiak J, Gilden D (2004) Cost of illness associated with metastatic breast cancer. Breast Cancer Res Treat 83:25–32

    Article  PubMed  CAS  Google Scholar 

  8. Lamerato L, Havstad S, Gandhi S, Jones D, Nathanson D (2006) Economic burden associated with breast cancer recurrence: findings from a retrospective analysis of health system data. Cancer 106:1875–1882

    Article  PubMed  Google Scholar 

  9. Hensley ML, Dowell J, Herndon JE 2nd, Winer E, Stark N, Weeks JC, Paskett E (2005) Economic outcomes of breast cancer survivorship: CALGB study 79804. Breast Cancer Res Treat 91:153–161

    Article  PubMed  Google Scholar 

  10. Legorreta AP, Brooks RJ, Leibowitz AN, Solin LJ (1996) Cost of breast cancer treatment. A 4 year longitudinal study. Arch Intern Med 156:2197–2201

    Article  PubMed  CAS  Google Scholar 

  11. Bradley CJ, Yabroff KR, Dahman B, Feuer EJ, Mariotto A, Brown ML (2008) Productivity costs of cancer mortality in the United States: 2000–2020. J Natl Cancer Inst 100:1763–1770

    Article  PubMed  Google Scholar 

  12. Max W, Sung HY, Stark B (2009) The economic burden of breast cancer in California. Breast Cancer Res Treat 116:201–207

    Article  PubMed  Google Scholar 

  13. Barnett A, Birnbaum H, Cremieux PY, Fendrick AM, Slavin M (2000) The costs of cancer to a major employer in the United States: a case–control analysis. Am J Manag Care 6:1243–1251

    PubMed  CAS  Google Scholar 

  14. Sasser AC, Rousculp MD, Birnbaum HG, Oster EF, Lufkin E, Mallet D (2005) Economic burden of osteoporosis, breast cancer, and cardiovascular disease among postmenopausal women in an employed population. Women’s Health Issues 15:97–108

    Article  PubMed  Google Scholar 

  15. Bloom BS (2004) Prevalence and economic effects of depression. Manag Care 13(6 suppl):9–16

    PubMed  Google Scholar 

  16. Greenberg PE, Kessler RC, Birnbaum HG, Leong SA, Lowe SW, Berglund PA, Corey-Lisle PK (2003) The economic burden of depression in the United States: how did it change between 1990 and 2000. J Clin Psychiatry 64:1465–1475

    Article  PubMed  Google Scholar 

  17. Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L (2005) Predicting pharmacy costs and other medical costs using diagnosis and drug claims. Public Health Rep 116:45–50

    Google Scholar 

  18. United States Bureau Of Labor Statistics. Usual weekly earnings of wage and salary workers by age and sex, 2nd quarter 2009 averages. http://www.bls.gov/news.release/wkyeng.toc.htm. Accessed 10 Nov 2009

  19. United States Bureau Of Labor Statistics. Consumer Price Index. http://www.bls.gov/cpi/cpi_dr.htm. Accessed 15 Jun 2009

  20. Elixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity measures for use with administrative data. Med Care 36:8–27

    Article  PubMed  CAS  Google Scholar 

  21. Comorbidity software version 3.4 (2009) Rockville, MD: Agency for Healthcare Research and Quality

  22. Box GEP, Cox DR (1964) An analysis of transformation. J R Stat Soc B 26:211–253

    Google Scholar 

  23. Duan N (1983) Smearing estimate: a nonparametric retransformation method. J Am Stat Assoc 78:605–610

    Article  Google Scholar 

  24. Manning WG (1998) The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ 17:283–295

    Article  PubMed  CAS  Google Scholar 

  25. Duan N, Manning WG, Morris CN, Newhouse JP (1983) A comparison of alternative models for the demand for medical care. J Bus Econ Stat 1:115–126

    Article  Google Scholar 

  26. Fu AZ, Kattan MW (2006) Racial and ethnic differences in preference-based health status measure. Curr Med Res Opin 22:2439–2448

    Article  PubMed  Google Scholar 

  27. McCullagh P, Nelder JA (1989) Generalized linear model. New York, Chapman and Hall

    Google Scholar 

  28. Diehr P, Yanez D, Ash A, Hornbrook M, Lin DY (1999) Methods for analyzing health care utilization and costs. Annu Rev Public Health 20:125–144

    Article  PubMed  CAS  Google Scholar 

  29. Park RE (1966) Estimation with heteroskedastic error terms. Econometrica 34:888

    Article  Google Scholar 

  30. Manning WG, Mullahy J (2001) Estimating log models: to transform or not to transform? J Health Econ 20:461–494

    Article  PubMed  CAS  Google Scholar 

  31. Fu AZ, Wang N (2008) Healthcare expenditure and patient satisfaction: cost and quality from the consumer’s perspective in the United States. Curr Med Res Opin 24:1385–1394

    Article  PubMed  Google Scholar 

  32. Fu AZ, Qiu Y, Radican L, Wells BJ (2009) Healthcare and productivity costs associated with diabetic patients with macrovascular comorbid conditions. Diab Care 32:2187–2192

    Article  Google Scholar 

  33. Broekx S, Hond ED, Torfs R, Remacle A, Mertens R, D’Hooghe T, Neven P, Christiaens MR, Simoens S (2010) The costs of breast cancer prior to and following diagnosis. Eur J Health Econ. doi:10.1007/s10198-010-0237-3

  34. Gordon L, Scuffham P, Hayes S, Newman B (2007) Exploring the economic impact of breast cancers during the 18 months following diagnosis. Psychooncology 16:1130–1139

    Article  PubMed  Google Scholar 

  35. Lidgren M, Wilking N, Jönsson B (2007) Cost of breast cancer in Sweden in 2002. Eur J Health Econ 8:5–15

    Article  PubMed  Google Scholar 

  36. Albain KS, Paik S, van’t Veer L (2009) Prediction of adjuvant chemotherapy benefit in endocrine responsive, early breast cancer using multigene assays. Breast 18(suppl 3):S141–S145

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This study was supported by funding from sanofi-aventis US. Editorial support was provided by David Pechar, PhD at Phase Five Communications and funded by sanofi-aventis US.

Author information

Authors and Affiliations

  1. Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue/JJN3-01, Cleveland, OH, 44195, USA

    Alex Z. Fu

  2. Sanofi-aventis US, Bridgewater, NJ, USA

    Lei Chen

  3. University of Washington, Seattle, WA, USA

    Sean D. Sullivan

  4. Medical University of South Carolina, Charleston, SC, USA

    Neal P. Christiansen

Authors
  1. Alex Z. Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sean D. Sullivan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neal P. Christiansen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Z. Fu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fu, A.Z., Chen, L., Sullivan, S.D. et al. Absenteeism and short-term disability associated with breast cancer. Breast Cancer Res Treat 130, 235–242 (2011). https://doi.org/10.1007/s10549-011-1541-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-011-1541-z

Keywords

Search

Navigation