Abstract
Many states require radiologists to notify women of dense breast status. Our aim was to elicit women’s response to state mandated dense breast notification language. Of respondents, 82% report that current notification does not inform them of additional screening studies available, and 41% report notification does not inform them of next steps. Open-ended responses indicate three main areas of patient concern: decisional needs, decision quality, and decision support. We modified an existing decision support framework to capture additional themes specific to dense breast decisions. The developed framework can be used to revise and improve current breast density reporting methods.
Keywords: Mammography, Breast Density, Dense Breast Notification, Legislation, Shared Decision Making
Introduction:
Approximately 40–50% of women in the United States have dense breast tissue1. Dense breast tissue decreases mammographic sensitivity and specificity for breast cancer detection and serves as an independent risk factor for developing breast cancer2–4. Recently, state-level legislation has mandated that women are informed of their breast density. In 2015, at the time of this study, 24 states mandated Dense Breast Notification (DBN), 19 of which required use of specific state-mandated language5. Our aim was to elicit women’s feedback to the Tennessee state mandated DBN language including their understanding of next steps.
Materials and Methods:
The Vanderbilt University Medical Center institutional review board (IRB) approved (IRB# 151230) this study. Participants provided informed consent and were offered the opportunity to enter a drawing to win an iPad mini. Using the Vanderbilt University research recruitment tool, MyResearch, we identified patients with interest in research participation. We then used the Research Derivative, a database of clinical and administrative data derived from our medical center’s clinical systems and restructured for research to select eligible patients. Inclusion criteria included encounter for screening mammography (ICD 9-CM V76.11 and V76.12) between January 1, 2014 and November 30, 2015, female sex, and alive at the time of the search.
Between December 1, 2015 and January 18, 2016, we emailed eligible women an invitation containing study information including risks and benefits. Women were assured of anonymity and confidentiality. The email invitation included a unique code to access and complete the web–based survey through the Research Electronic Data Capture (REDCap™) program6. An interdisciplinary team developed the survey items, and reviewed them for appropriateness of content, item comprehension, and readability (Flesch-Kincaid Grade Level 6.7). We elicited demographic information and provided participants with the state-mandated DBN language then queried their understanding of the state DBN language and asked an open-ended question that allowed for free text response.
Descriptive statistics including means, medians, and cross-tabulations were conducted to analyze patient demographics and quantitative item responses. For qualitative analysis, we created an a priori list of codes and organized the free-text responses to the open-ended question. Responses were imported into the computer software program Excel Statistical Package for the Social Sciences (SPSS). The constant comparison method was used to capture additional themes emerging in the data. Two members of the research team (KB, DS) reviewed the data independently, identified codes based on themes presented in the data, and reconciled until consensus was reached. The research team used the coded data to sort and summarize the quotes.
We compared our qualitative data to existing conceptual frameworks. Our results aligned well with the Ottawa Decision Support Framework (ODSF). The ODSF is an evidence based guide aimed to improve the development of interventions to prepare patients and physicians for shared decision making7. The ODSF is organized into 3 main constructs according to determinants of decision: Decisional Needs; Decision Quality; and Decision Support. We nested coded responses within the ODSF main constructs and sub-themes. Responses containing information pertinent to more than one sub-theme were nested within all applicable constructs and sub-themes. We modified the ODSF sub-themes to meet dense breast decision making needs.
Results:
Of the 18,656 patients in the MyResearch database, 1,924 met study eligibility criteria and were sent an email invitation. Thirty-three emails could not be delivered. Of the 1,891 receiving email invitations, 920 responded (48.6%); and 864 were complete for analysis. There were 199 free text responses to the open-ended question for the qualitative analysis. Demographic characteristics of respondents who answered the free text question were similar to those who did not and are seen in Table 1.
Table 1.
Demographics
| Participants not completing open-ended response (N=665) | Participants completing open-ended response (N=199) | |
|---|---|---|
| Variable | n (%) | n (%) |
| Age | ||
| <40 | 9 (1) | 1 (0.5) |
| 40–49 | 129 (19) | 33 (17) |
| 50–59 | 194 (29) | 51 (26) |
| 60–69 | 164 (25) | 60 (30) |
| ≥70 | 42 (6) | 25 (13) |
| Missing Data | 127 (19) | 29 (15) |
| Race/Ethnicity | ||
| White | 610 (92) | 185 (92) |
| Black/African American | 33 (5) | 9 (4) |
| Asian | 8 (1) | 4 (2) |
| Pacific Islander | 1 (0.2) | 0 (0) |
| American Indian | 5 (0.8) | 1 (0.5) |
| Hispanic/Latino | 9 (1) | 2 (1) |
| Education | ||
| High School/GED or less | 42 (6) | 7 (4) |
| Some College | 159 (24) | 50 (25) |
| Bachelors degree | 222 (33) | 49 (25) |
| Graduate or professional | 236 (35) | 92 (46) |
| Missing Data | 6 (0.9) | 1 (0.5) |
| Income | ||
| Less than $24,999 | 24 (4) | 9 (4) |
| $25,000 to $49,000 | 98 (15) | 32 (16) |
| $50,000 to $74,999 | 128 (19) | 36 (18) |
| $75,000 to $99,999 | 132 (20) | 40 (20) |
| $100,000 to $199,000 | 175 (26) | 61 (31) |
| $200,000 or more | 89 (13) | 12 (6) |
| Missing Data | 19 (3) | 9 (4) |
| Employment | ||
| Employed for wages | 426 (64) | 110 (55) |
| Self-employed | 39 (6) | 12 (6) |
| Out of work | 11 (2) | 5 (3) |
| A Homemaker | 45 (7) | 7 (4) |
| A Student | 1 (0.2) | 0 (0) |
| Retired | 127 (19) | 58 (29) |
| Unable to work | 12 (2) | 5 (3) |
| Missing Data | 4 (0.6) | 2 (1) |
Quantitative survey results are shown in Table 2. Of 864 respondents, 792 (92%) reported that the state mandated DBN language was not confusing. However, only 501/864 (58%) of women reported that they knew next steps to take if they have dense breast tissue. Additionally, only 150/864 (17%) reported that they knew what additional screening options are available for patients with dense breast tissue. If insurance did not cover additional screening tests, 523/864 (61%) reported that they would pursue additional screening if recommended.
Table 2.
Quantitative Survey Data
| QUESTION | “YES” n (% total*) | “NO” n(% total*) | Missing n(% total*) |
|---|---|---|---|
| Is this statement regarding breast density confusing? | 68(8) | 792 (92) | 4(0) |
| Do you think this statement adequately informs patients: | |||
| that they have dense breast tissue? | 822 (95) | 38(5) | 4(0) |
| that dense breast tissue can affect the interpretation of a mammogram? | 825 (96) | 35(4) | 4(0) |
| that breast density affects their risk of developing breast cancer? | 707 (82) | 151(17) | 6(1) |
| what they should do if they have dense breast tissue? | 501 (58) | 354(41) | 9(1) |
| what additional screening options are available for patients with dense breast tissue? | 150(17) | 708 (82) | 6(1) |
| If recommended, would you pursue additional screening tests if insurance did not pay for them? | 523 (61) | 329 (38) | 12(1) |
total study participants = 864
We extracted 440 individual codes from the 199 open-ended responses. We retained sub-themes in the ODSF for our coded participant responses. Retained sub-themes in the Decisional Needs construct included Knowledge & Expectations and Support & Resources. A single sub-theme, Actions, was retained within the Decision Quality construct. Retained sub-themes within the Decision Support construct included Guide in Deliberation & Communication and Counseling. As many of our patient responses indicated an emotional response to DBN, we further modified the Decisional Needs construct to contain a sub-theme for Emotional Responses. Our dense breast decision making framework, modified from the ODSF, can be seen in Figure 1.
Figure 1.
Dense Breast Decision Support Framework
The main constructs and subthemes are reported in Table 3.
Table 3.
Coding Counts
| Main Construct - n (% total codes)* Sub-theme - n (% of main construct) |
Individual Code | n (% of sub-theme) |
|---|---|---|
| Decisional Needs - 194 (44) | ||
| Knowledge & Expectations - 137 (70) | ||
| Definition of breast density | 10(7) | |
| Cancer risk | 25 (18) | |
| Severity of breast density | 7(5) | |
| Next steps | 29 (21) | |
| Causation of breast density | 1 (0.7) | |
| Relationship to mammogram | 65 (47) | |
| Emotional Response - 21 (11) | ||
| Fear/Anxiety | 17(81) | |
| Stress | 0(0) | |
| Coping with emotions | 1(5) | |
| Other emotions | 3(14) | |
| Support & Resources - 36 (19) | ||
| Insurance status | 16 (44) | |
| Cost | 13 (36) | |
| Scheduling additional tests | 7(19) | |
| Decision Quality - 52 (12) | ||
| Actions - 52 (100) | ||
| Seek information | 29 (56) | |
| Pursue additional testing | 16(31) | |
| Uncertainty | 7(19) | |
| Decision Support - 194 (44) | ||
| Guide in Deliberation & Communication – 149 (77) | ||
| Postal letter | 29 (19) | |
| Electronic | 13(9) | |
| Telephone | 9(6) | |
| Reading level | 10(7) | |
| Delivery style | 57 (38) | |
| Adequate information | 31(21) | |
| Counseling – 45 (23) |
total codes = 440
Of the 440 codes, 194 (44%) corresponded to Decisional Needs, 52 (12%) corresponded to Decision Quality, and 194 (44%) corresponded to Decision Support. Three sample responses are below:
“In my case I wasn’t informed of the high risk of breast cancer for patients with dense breasts, otherwise I would have insisted on further screening.” Participant 192
(Decisional Needs)
“Not much use to know if nothing will be done about it; seems like a ploy to avoid liability.” Participant 133
(Decisional Quality)
“Cancer and in particular breast cancer can cause panic in a patient. This type of information should be discussed in person so that the patient can ask questions and fully understand the situation rather than a random letter to scare them.” Participant 3
(Decisional Support)
Discussion:
Our study demonstrates that despite a high proportion of women reporting that they understood the DBN language, fewer than half knew the next steps or options for screening among those who had dense breasts. Many women remain unaware of their breast density and its relationship to cancer detection and cancer risk8–10. A high percentage of women have expressed interest in knowing their density and in understanding follow up options9.
Study limitations include the analysis of a small number of predominantly white, well-educated women receiving care at a single institution, the qualitative nature of the research, and the potential for non-response bias. These may contribute to limited generalizability of the study results.
Allowing for these limitations, this study is an important preliminary step in documenting patient perspectives on DBN language and patient needs for dense breast decision support. Future work should elicit information from a larger more geographically, racially, and socially diverse sample of women. Our dense breast decision support framework can be used as a guide to revise and improve current breast density reporting methods to better meet patient needs.
ACKNOWLEDGEMENTS:
Funding Sources:
This study is supported in part by the Vanderbilt CTSA grant UL1 TR000445 from NCATS/NIH. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health.
This material is based upon work supported by the Office of Academic Affiliations, Department of Veterans Affairs, VA National Quality Scholars Program and with resources and the use of facilities at VA Tennessee Valley Healthcare System, Nashville TN.
REFERENCES:
- 1.Kerlikowske K, Zhu W, Hubbard RA, et al. Outcomes of screening mammography by frequency, breast density, and postmenopausal hormone therapy. JAMA Intern Med 2013;173(9):807–816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mandelson MT, Oestreicher N, Porter PL, et al. Breast density as a predictor of mammographic detection: comparison of interval and screen-detected cancers. J Natl Cancer Inst 2000;92(13):1081–7. [DOI] [PubMed] [Google Scholar]
- 3.Van Gils CH, Otten JD, Verbeek Al, et al. Effect of mammographic breast density on breast cancer screening performance: a study in Nijmegen, the Netherlands. J Epidemiol Community Health 1998;52:267–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Boyd NF, Guo H, Martin LJ et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007;356(3):227–36. [DOI] [PubMed] [Google Scholar]
- 5.Slanetz PJ, Freer PE, Birdwell RL, et al. Breast-density legislation – practical considerations. N Engl J Med. 2015;372(7):593–595. [DOI] [PubMed] [Google Scholar]
- 6.Harris PA, Taylor R, Thielke R et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42(2):377–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.O′Connor AM, Drake ER, Fiset V, et al. The Ottawa patient decision aids. Eff Clin Prac 1999;2(4):163–170. [PubMed] [Google Scholar]
- 8.Manning MA, Duric N, Littrup P et al. Knowledge of Breast Density and Awareness of Related Breast Cancer Risk. J Cancer Educ 2013. 28(2):270–274. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Trinh L, Ikeda DM, Miyake KK et al. Patient Awareness of Breast Density and Interest in Supplemental Screening Tests: Comparison of an Academic Facility and a County Hospital. J Am Coll Radiol 2015. 12(3):249–255. [DOI] [PubMed] [Google Scholar]
- 10.Guterbock TM, Cohn WF, Rexrode DL, et al. What do women know about breast density? Results from a population survey of Virginia women. J Am Coll Radiol 2017;14:34–44. [DOI] [PubMed] [Google Scholar]