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Improving communication of breast cancer recurrence risk

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Abstract

Doctors commonly use genomic testing for breast cancer recurrence risk. We sought to assess whether the standard genomic report provided to doctors is a good approach for communicating results to patients. During 2009–2010, we interviewed 133 patients with stages I or II, node-negative, hormone receptor–positive breast cancer and eligible for the Oncotype DX genomic test. In a randomized experiment, patients viewed six vignettes that presented hypothetical recurrence risk test results. Each vignette described a low, intermediate, or high chance of breast cancer recurrence in 10 years. Vignettes used one of five risk formats of increasing complexity that we derived from the standard report that accompanies the commercial assay or a sixth format that used an icon array. Among women who received the genomic recurrence risk test, 63% said their doctors showed them the standard report. The standard report format yielded among the most errors in identification of whether a result was low, intermediate, or high risk (i.e., the gist of the results), whereas a newly developed risk continuum format yielded the fewest errors (17% vs. 5%; OR 0.23; 95% CI 0.10–0.52). For high recurrence risk results presented in the standard format, women made errors 35% of the time. Women rated the standard report as one of the least understandable and least-liked formats, but they rated the risk continuum format as among the most understandable and most liked. Results differed little by health literacy, numeracy, prior receipt of genomic test results during clinical care, and actual genomic test results. The standard genomic recurrence risk report was more difficult for women to understand and interpret than the other formats. A less complex report, potentially including the risk continuum format, would be more effective in communicating test results to patients.

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References

  1. Oakman C, Bessi S, Zafarana E et al (2009) New diagnostics and biological predictors of outcomes in early breast cancer. Breast Cancer Res 11:205–216

    Article  PubMed  Google Scholar 

  2. Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826

    Article  PubMed  CAS  Google Scholar 

  3. Wolf I, Baruch NB, Shapira-Frommer R et al (2008) Association between standard clinical and pathologic characteristics and the 21-gene recurrence score in breast cancer patients. Cancer 112:731–736

    Article  PubMed  Google Scholar 

  4. Paik S, Tang G, Shak S et al (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726–3734

    Article  PubMed  CAS  Google Scholar 

  5. Morris SR, Carey LA (2007) Gene expression profiling in breast cancer. Curr Opin Oncol 19:547–551

    Article  PubMed  CAS  Google Scholar 

  6. Henry LR, Stojadinovic A, Swain SM et al (2009) The influence of a gene expression profile on breast cancer decisions. J Surg Oncol 99:319–323

    Article  PubMed  CAS  Google Scholar 

  7. Asad J, Jacobson AF, Estabrook A et al (2008) Does Oncotype DX recurrence score affect the management of patients with early-stage breast cancer? Am J Surg 196:527–529

    Article  PubMed  Google Scholar 

  8. Brewer NT, Edwards AS, O’Neill SC, Tzeng JP, Carey LA, Rimer BK (2009) When genomic and standard test results diverge: implications for breast cancer patients’ preference for chemotherapy. Breast Cancer Res Treat 117:25–29

    Article  PubMed  Google Scholar 

  9. Buyse M, Loi S, van’t Veer L et al (2006) Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98:1183–1192

    Article  PubMed  CAS  Google Scholar 

  10. Foekens JA, Atkins D, Zhang Y et al (2006) Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 24:1665–1671

    Article  PubMed  CAS  Google Scholar 

  11. van’t Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536

    Article  Google Scholar 

  12. van de Vijver MJ, He YD, van’t Veer LJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009

    Article  PubMed  Google Scholar 

  13. Peters E, Dieckmann N, Dixon A et al (2007) Less is more in presenting quality information to consumers. Med Care Res Rev 64:169–190

    Article  PubMed  Google Scholar 

  14. Zikmund-Fisher BJ, Fagerlin A, Ubel PA (2008) Improving understanding of adjuvant therapy options by using simpler risk graphics. Cancer 113:3382–3390

    Article  PubMed  Google Scholar 

  15. Zikmund-Fisher BJ, Fagerlin A, Ubel PA (2010) A demonstration of “less can be more” in risk graphics. Med Decis Mak 30:661–671

    Article  Google Scholar 

  16. Belia S, Fidler F, Williams J et al (2005) Researchers misunderstand confidence intervals and standard error bars. Psychol Methods 10:389–396

    Article  PubMed  Google Scholar 

  17. Reyna VF (2008) A theory of medical decision making and health: fuzzy trace theory. Med Decis Mak 28:850–865

    Article  Google Scholar 

  18. Bass PF, Wilson JF, Griffith CH (2003) A shortened instrument for literacy screening. J Gen Intern Med 18:1036–1038

    Article  PubMed  Google Scholar 

  19. Schwartz LM, Woloshin S, Black WC et al (1997) The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med 127:966–973

    PubMed  CAS  Google Scholar 

  20. Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. J Pers Soc Psychol 51:1173–1182

    Article  PubMed  CAS  Google Scholar 

  21. Tzeng JP, Mayer D, Richman AR et al (2010) Women’s experiences with genomic testing for breast cancer recurrence risk. Cancer 116:1992–2000

    Article  PubMed  Google Scholar 

  22. Zikmund-Fisher BJ, Angott AM, Ubel PA (2011) The benefits of discussing adjuvant therapies one at a time instead of all at once. Breast Cancer Res Treat 129:79–87

    Article  PubMed  Google Scholar 

  23. Reyna VF, Lloyd FJ (2006) Physician decision making and cardiac risk: effects of knowledge, risk perception, risk tolerance, and fuzzy processing. J Exp Psychol Appl 12:179–195

    Article  PubMed  Google Scholar 

  24. Gurmankin AD, Domchek S, Stopfer J et al (2005) Patients’ resistance to risk information in genetic counseling for BRCA1/2. Arch Intern Med 165:523–529

    Article  PubMed  Google Scholar 

  25. O’Neill SC, Brewer NT, Lillie SE et al (2007) Women’s interest in gene expression analysis for breast cancer recurrence risk. J Clin Oncol 25:4628–4634

    Article  PubMed  Google Scholar 

  26. Chao C, Studts JL, Abell T et al (2003) Adjuvant chemotherapy for breast cancer: how presentation of recurrence risk influences decision-making. J Clin Oncol 21:4299–4305

    Article  PubMed  Google Scholar 

  27. Ancker JS, Senathirajah Y, Kukafka R et al (2006) Design features of graphs in health risk communication: a systematic review. J Am Med Inform Assoc 13:608–618

    Article  PubMed  Google Scholar 

  28. Cuite CL, Weinstein ND, Emmons K et al (2008) A test of numeric formats for communicating risk probabilities. Med Decis Mak 28:377–384

    Article  Google Scholar 

  29. Han PK, Klein WM, Lehman T et al (2011) Communication of uncertainty regarding individualized cancer risk estimates: effects and influential factors. Med Decis Mak 31:354–366

    Article  CAS  Google Scholar 

  30. Schapira MM, Nattinger AB, McAuliffe TL (2006) The influence of graphic format on breast cancer risk communication. J Health Commun 11:569–582

    Article  PubMed  Google Scholar 

  31. Reyna VF, Mills B (2007) Converging evidence supports fuzzy-trace theory’s nested sets hypothesis, but not the frequency hypothesis. Behav Brain Sci 30:278–280

    Article  Google Scholar 

  32. Reyna VF, Brainerd CJ (2008) Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learn Individ Differ 18:89–107

    Article  Google Scholar 

  33. Slovic P, Finucane ML, Peters E, MacGregor DG (2004) Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality. Risk Anal 24:311–322

    Article  PubMed  Google Scholar 

  34. Reyna VF, Nelson WL, Han PK, Dieckmann NF (2009) How numeracy influences risk comprehension and medical decision making. Psychol Bull 135:943–973

    Article  PubMed  Google Scholar 

  35. Brewer NT, Tzeng JP, Lillie SE, Edwards A, Peppercorn JM, Rimer BK (2009) Health literacy and cancer risk perception: implications for genomic risk communication. Med Decis Mak 29:157–166

    Article  Google Scholar 

  36. Lillie SE, Brewer NT, Rimer BK et al (2007) Retention and use of breast cancer recurrence risk information from genomic tests: the role of health literacy. Cancer Epidemiol Biomarkers Prev 16:249–255

    Article  PubMed  Google Scholar 

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Acknowledgments

We are grateful to the physicians and nurses of the University of North Carolina Breast Center for their assistance with this study. Most importantly, we thank the women who participated in this study. We thank Laura van ‘t Veer for sharing ideas that inspired the study design, Janice Tzeng for her work on the questionnaires, and Paul Gilbert, Rebecca Sink, and clinic interviewers for their work on the study. The study received generous financial support from the American Cancer Society (MSRG-06-259-01-CPPB). Jessica T. DeFrank was funded by the UNC Cancer Care and Quality Training Program (NCI R25 Grant, CA116339).

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The authors have no financial disclosures or conflicts of interest to declare.

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Correspondence to Noel T. Brewer.

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Brewer, N.T., Richman, A.R., DeFrank, J.T. et al. Improving communication of breast cancer recurrence risk. Breast Cancer Res Treat 133, 553–561 (2012). https://doi.org/10.1007/s10549-011-1791-9

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  • DOI: https://doi.org/10.1007/s10549-011-1791-9

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