Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Patient Educ Couns. Author manuscript; available in PMC 2013 March 20.
Published in final edited form as:
PMCID: PMC3603851

Latina Patient Perspectives about Informed Treatment Decision Making for Breast Cancer



To evaluate Latina breast cancer patient perspectives regarding informed decision making related to surgical treatment decision making for breast cancer.


2030 women with non-metastatic breast cancer diagnosed from 8/05-5/06 and reported to the Los Angeles metropolitan SEER registries were mailed a survey shortly after surgical treatment. Latina and African American women were over-sampled. We conducted regression of four decision outcome to evaluate associations between race/ethnicity, demographic and clinical factors, and mechanistic variables (i.e., health literacy) and decision outcomes.


Our analytic sample was 877 women: 24.5% Latina Spanish speaking (Latina-SP), 20.5% Latina English speaking, 24% African American and 26.6% Caucasian. Approximately 28% of women in each ethnic group reported a surgeon-based, 36% a shared, and 36% a patient-based surgery decision. Spanish-preferent Latina women had the greatest odds of high decision dissatisfaction and regret controlling for other factors (OR 5.5, 95% CI:2.9, 10.5 and OR 4.1, 95% CI: 2.2,8.0, respectively). Low health literacy was independently associated with dissatisfaction and regret (OR 5.6, 95% CI:2.9, 11.1 and OR 3.5, 95% CI 1.8,7.1, respectively) and slightly attenuated associations between Latina-SP ethnicity and decision outcomes,.


Despite similar clinical outcomes, patients report very different experiences with treatment decision making. Latina women, especially those who prefer Spanish, are vulnerable to poor breast cancer treatment decision outcomes.

Practice implications

Providers need to be aware of the role of ethnicity, acculturation and literacy in breast cancer treatment discussions.

Keywords: Latina, breast cancer, surgical treatment, decision making, involvement

1. Introduction

There is a growing body of literature related to patterns of breast cancer treatment decision making. However little of it has evaluated racial/ethnic differences in involvement and virtually none has included a large proportion of Latina patients.1-2. Research has found that not all patients desire an active role in all decisions.3-7 Furthermore, a substantial proportion of patients report a mismatch between their actual versus preferred role in surgical treatment decisions, with patients reporting having both too little and too much involvement.6-10 This mismatch is associated with lower patient satisfaction with both the decision making process and choice of treatment.6-7 At the same time, research suggests that informed decision making for breast cancer treatment is lacking for African American women and possibly other racial minorities.11-12 These results imply that racial minorities may be at particular risk for making poor quality breast cancer treatment decisions. However, a major limitation of the current literature is the paucity of reports focusing on the treatment experience of Latina women with breast cancer.

Latinas comprise a growing proportion of the general population and are increasingly represented among women diagnosed with breast cancer.13 Although increasing informed decision making has been highlighted as a mechanism for improving patient centered care- especially for racial/ethnic minorities14-16- few published reports have addressed Latina perspectives regarding cancer decision involvement and outcomes. Some studies suggest that Latina breast cancer patients may experience barriers to informed treatment decision making related to literacy, language and acculturation.1-2,17-18 However, the reports have been limited by small samples in selected clinical settings. Thus, more research is needed with larger samples of Latinas in comparison to other racial/ethnic groups in more generalizable settings.1

To address the gap in the literature, this paper addresses two research objectives: 1) to examine the degree to which Latina women (Spanish and English preferent) with breast cancer participate in informed surgical breast cancer treatment decision making compared to other race/ethnic groups; and 2) to evaluate factors associated with the desired amount of involvement and informed decision making for surgical breast cancer treatment across racial/ethnic groups. In addition, we assessed some potential mechanisms for racial/ethnic differences in decision outcomes.

2. Methods

2.1. Study Population

Los Angeles County resident women aged 20-79 years diagnosed with primary ductal carcinoma in-situ (DCIS) or invasive breast cancer (Stage I, II or III) from August 2005 through May 2006 were eligible for sample selection for the study. We excluded patients with stage 4 breast cancer, those who died prior the deployment of the survey, and those who could not complete a questionnaire in English or Spanish.

2.2 Sampling

Patients were selected shortly after diagnosis on a monthly basis as they were reported to the Los Angeles Cancer Surveillance Program (LA-CSP), the Surveillance, Epidemiology, and End Results (SEER) Cancer Registry for Los Angeles County. This method of selection can yield a sample representative of the County population. We oversampled African American (AA) women and Latinas to increase their representation in the dataset. To do so, the LA-CSP identified all women meeting the eligibility criteria of AA race based on the demographic information available from the SEER data. Because Latina status is not accurately collected by the treating hospital at the time of diagnosis, and therefore not reliably included in the SEER data, we selected all women who were designated as Hispanic by the hospital as well as all women whose surname indicated a high probability of being Latina based on a list generated from the 1980 U.S. Census.19 We then selected an approximately 11% random sample of the remaining white (non-Spanish Surnamed) patients meeting eligibility criteria. Asian women were excluded because these women were already being enrolled in other studies.

Over the study period, 1766 eligible patients were accrued including 796 Latinas, 459 African-Americans, 478 Caucasians, and 33 patients of other race/ethnicity. The accrued sample included approximately two thirds of the Latina and AA patients and 14% of White patients diagnosed with breast cancer in LA County during the study period. Sixty-eight patients were excluded because: 1) physician refused permission to contact (7); 2) the patient did not speak English or Spanish (8), 3) the patient was too ill or incompetent to participate (30), and 4) the patient denied having cancer (23). Of the 1,698 patients included in the final accrued sample, 179 (10.5%) could not be located or contacted and 296 (17.5%) did not participate in the survey. A final sample of 1,224 (72.0%) completed the survey. Our power calculations were based on extensive modeling using a prior population-based dataset of breast cancer patients, and the numbers of available Latina and AA breast cancer patients in the LA SEER catchment area. Our approach to overampling by race/ethnicity resulted in one of the largest and most racially/ethnically diverse samples (N=1,224) with sufficient power to test the study hypotheses.

2.3 Data Collection and Management

Physicians were notified of our intent to contact patients. If the physician did not refuse,, we applied a modified Dillman survey method to encourage response.20 This method involves the mailing of an introductory letter, risk and benefit informed consent sheet, survey materials with return envelope, and $10 cash gift. All patients who were likely to be Latina based on hospital or surname-based census information were sent bilingual materials that were translated into Spanish using a standard approach.21 The correspondence between those identified with a Spanish surname and self-reporting Latina ethnicity was 98.2%. The Dillman method then involves telephone follow up to encourage response and post-card reminders for those without telephones, with a telephone survey option offered as a last report. Twenty-seven of the 1,224 participants completed a telephone survey. Of these, 21 were Latinas, and 15 were conducted in Spanish. The survey procedures followed protocols established by the LA County SEER site. The study protocol was approved by the Institutional Review Boards of the University of Michigan and the University of Southern California.

Information from the survey was merged to SEER data for all patients in the final sample. An analysis of non-respondents vs. respondents showed no significant differences by age at diagnosis, or race/ethnicity. However, compared to respondents, non-respondents were less likely to have ever married (18.9% vs. 25.2 %, p=.005), more likely to live in lower SES census tracts (48.6% vs. 40.2%, p=.030), more likely to have stage II or III disease (45.7% vs. 40.3% p<.001), and less likely to have received breast conserving surgery (BCS) (56.0% vs. 64.4%, p<.001). .

For this analysis, we included women whose documented summary stage in SEER was DCIS, I, II or IIIa based on treatment guidelines of both the National Cancer Institute Physician Data Query (NCI-PDQ) database and the National Comprehensive Cancer Network (NCCN) that describe both mastectomy and BCS with radiation as viable options for these stages of cancer.22-23 We excluded women with a self-reported clinical contraindication to BCS (about 20% of the sample).

2.4. Measures

The patient survey was developed based on extensive pilot testing and prior work by our team surveying breast cancer patients.6-8,11-12,18,24-25

2.4.1. Dependent variables

We used four dependent variables for this analysis: 1) actual involvement in the breast cancer surgery decision (actual involvement); 2) match between actual and preferred involvement in this decision (match); 3) decision satisfaction; and 4) decision regret. The first dependent variable was measured using the Control Preferences Scale (CPS) developed by Degner26 where respondents were asked to report their actual involvement in a medical decision on a 5 point scale (see Appendix). We re-coded this variable into three categories: doctor-based; shared/collaborative; or patient-based, consistent with prior work evaluating patient involvement in breast cancer treatment decision making.6,9, 27 To evaluate match, patients were asked to report whether they felt they had too much, the right amount, or too little involvement in the surgery decision (see Appendix). Decision satisfaction and regret were both measured using separate 5-item scales that are based on components of existing scales28-29 (see Appendix). Higher scores on both scales represented higher levels of both decision satisfaction and decision regret. Each scale was categorized into three levels based on tertiles representing low, medium and high amounts of decision satisfaction and regret, respectively. For logistic regression, these variables were dichotomized. For decision regret, 1=a lot of regret vs. 0=none or some regret. Decision satisfaction was reverse-coded to allow for direct comparison with decision regret: 1 = a lot of decision dissatisfaction vs. 0 = none or some dissatisfaction. Applying different categorizations of decision dissatisfaction and regret did not significantly alter the results.

2.4.2. Patient-related independent variables

Our primary independent variable was patient race/ethnicity which we categorized into four groups: 1) Latina-Spanish preferent (L-SP); 2) Latina-English preferent (L-EP); 3) African American (AA); and 4) Caucasian (C). The determination of whether a Latina respondent was Spanish or English preferent was made based on an algorithm used to assess language preference and degree of acculturation, developed by Marin and colleagues and used to measure degree of Latina acculturation.21,30

Other demographic variables included age, education (less than high school, high school graduate, some college or more), and marital status (married, not married). We evaluated age both as a continuous and categorical (29-44, 45-64, 65-79) variable. The clinical variables used in the analysis were from the SEER record and included the tumor size in centimeters, tumor histology (well differentiated, moderately differentiated, poorly differentiated or unknown) and tumor behavior (DCIS or invasive). We chose these variables because this information is generally available at the time of surgery consultation, while pathologic stage is not available until after the biopsy.31 We also evaluated type of surgical treatment received (mastectomy vs. BCS).

2.4.3. Mechanistic variable(s)

We included three variables to assess possible underlying mechanisms for associations between race/ethnicity and decision outcomes. These included a measure of health literacy, need for translation services, and whether women reported having a family member or friend with them during their surgical consultation(s). These measures were included based on research suggesting that health literacy, need for translation, and involvement of friends/ family are particularly important to Latina women making health-related treatment decisions.1-2,18,32-33 Our health literacy measure was developed from two questions in the patient survey reflecting how often respondents had 1) trouble understanding written information about breast cancer and 2) someone help them read written information from the hospital or clinic. These measures were combined to create a three-level measure of health literacy based on tertiles; low, moderate or high. Need for translation and having someone present were categorized as yes vs. no.

2.5. Analysis

We first generated descriptive statistics for the variables. We then calculated an adjusted proportion of women in each of the decision outcome categories by race/ethnicity, controlling for other demographic and clinical variables. We compared differences between racial/ethnic groups for each decision outcome using Wald tests. Point estimates were adjusted for design effects by using a sample population weight that accounted for differential selection by race, ethnicity, and nonresponse.

2.5.1. Analysis for involvement in decision making

We conducted multinomial logistic regression for both of our three-level dependent variables, actual involvement and match. For the first model—patient actual involvement in the surgery decision—we used the shared decision as the base category (i.e., reference category). We regressed the 3-level actual involvement outcome on all variables described above in a forward stepwise manner: 1) race/ethnicity, plus other demographic and clinical variables; 2) race/ethnicity, demographics, clinical variables, plus mechanistic variables. For the second model—match between preferred and actual involvement in the surgery decision—we used the right amount of involvement as the reference category. We followed the same procedures as in the first model. For each model, we also conducted a Hausman test for independence of irrelevant alternatives (IIA test) to ensure that our three-level comparison for each outcome was correctly specified.34

2.5.2. Analysis for decision dissatisfaction and decision regret

We conducted logistic regression of decision dissatisfaction and decision regret following the steps described for the models above. All analyses were conducted in STATA version 10.0 for Windows.35

3. Results

The final analytic sample size for this analysis was 925 patients with stage 0, I, II, or IIIa and no clinical contraindications to BCS. The mean time between the date of definitive surgical treatment and response to the survey was 9 months. There were no significant differences in tumor behavior (DCIS vs. invasive) or treatment received by race/ethnicity (Table 1). About 18% of all women received mastectomy. About two-thirds of women reported that they had completed radiation and chemotherapy treatment by the time they participated in the survey.

Table 1
Characteristics of the patient sample by race/ethnicity (N=877)1

3.1. Decision Outcomes

Overall, 37% of women reported a shared surgical treatment decision, 36% reported a patient-based decision, and 27% reported a surgeon-based decision. The majority (93%) reported a match between actual and preferred involvement in the breast cancer surgery decision, while 3% reported having too much involvement and 4% reported having too little. Thirty three percent of the sample reported having high decision satisfaction, while 38% had moderate satisfaction and 30% had low decision satisfaction (i.e., high decision dissatisfaction). About thirty-nine percent had a lot of decision regret, 30% a moderate amount of regret and another 31% reported very little regret.

3.2. Patient Characteristics

The race/ethnic distribution of the sample was: 25.2% Latina-SP, 20.6% Latina-EP, 24% African American, 26.1% Caucasian and 4.1% other. For purposes of these analyses those indicating ‘other’ race/ethnic group were excluded (N=48). The mean age of patients was 59 years (range 29 -79). Table 1 shows the proportion of women with different demographic and clinical factors by race/ethnicity. Latina women, both Spanish and English preferent, were significantly younger than Caucasian women (P<0.001). Latina-SP women were significantly less likely to have some college or more education compared to other groups (P<0.001). Latina-SP women were significantly more likely to have low health literacy as measured in our study than any of the other racial/ethnic groups (28.1%, 8.0%, 6.2%, and 4.5% for Latina-SP, Latina-EP, AA and Caucasian women, respectively, p<0.001). As expected, Latina-SP women used more translation services than other racial/ethnic groups. About three-quarters of all women reported having someone with them during treatment discussions.

3.3 Decision Outcomes by Race/ethnicity

Figure 1 provides the adjusted proportion of respondents reporting each of the three categories of actual involvement (surgeon-based, shared, patient-based) by racial/ethnic group, controlling for demographic and clinical factors. Latina-SP women were slightly less likely to report a patient-based decision than the other groups (P<0.10). We also found that Latina-SP women were slightly more likely to report too little involvement compared to other groups (results not shown). Figure 2 shows the proportion of women in each racial/ethnic group who reported high decision dissatisfaction and/or a lot of decision regret. Latina-SP women reported having both high dissatisfaction and a lot of regret significantly more often than women of other racial/ethnic groups (P<0.001).

Figure 1
Actual decision involvement by race/ethnicity1
Figure 2
Decision dissatisfaction and regret, by race/ethnicity1

3.4. Factors Associated with Decision Dissatisfaction and Regret

Tables Tables22 and and33 provide the logistic regression results for correlates of high decision dissatisfaction (Table 2) and a lot of decision regret (Table 3). The first column of both tables provides the unadjusted results for race/ethnicity. The second column provides the results, controlling for other demographic and clinical factors, and the third column shows results controlling for the mechanistic variables.

Table 2
Logistic regression of decision dissatisfaction (N=877)
Table 3
Logistic regression of decision regret (N=877)

As seen in Tables Tables22 and and3,3, without adjusting for other factors, Latina-SP women were 8.7 times as likely as Caucasian women to report high levels of decision dissatisfaction and 9.4 times as likely to report decision regret. The unadjusted column shows that Latina-EP and African American women were also significantly more likely than Caucasian women to report high decision dissatisfaction and regret (P<0.01). When other factors were controlled, Latina-SP women continued to be the most likely to report high decision dissatisfaction and regret though the odds were reduced in both cases (OR: 5.6; 95% CI 3.2-9.8 for dissatisfaction; OR: 5,6; 95%CI 3.2-9.9 for decision regret) compared to Caucasian women. The other racial/ethnic groups were also more likely than Caucasian women to report dissatisfaction and regret controlling for other factors (P<0.05 across racial/ethnic groups). While women with less than a high school education were more likely than those with some college to report decision dissatisfaction (OR: 1.8; 95% CI 1.1-2.8), there was no significant association between low education and decision regret. However including education in the model did reduce the difference in decision regret between Latina-SP and Caucasian women (Unadjusted OR: 9.4 vs. adjusted OR: 5.6) None of the clinical variables, including type of surgical treatment, were significantly associated with high decision dissatisfaction or regret.

The third column of both tables shows the results for race/ethnicity when controlling for the three mechanistic variables. Women with low health literacy were significantly more likely than those with high health literacy to report both decision dissatisfaction and regret (OR: 5.6; 95%CI 2.9-11.1 for dissatisfaction; OR 3.5; 95% CI 1.8-7.1 for regret). Those with moderate health literacy were also more likely to report dissatisfaction and regret (P<0.01) than those with high literacy. Having someone with you during treatment discussions was negatively associated with decision dissatisfaction (OR: 0.65; 95% CI 0.44-0.96); while it was also negatively associated with regret this association was not significant (Table 3). Translation was not associated with either dissatisfaction or regret. Although including the mechanistic variables, specifically health literacy, did not attenuate the association between race/ethnicity and decision outcomes, it did significantly reduce differences in decision regret between Latina-SP and Caucasian women (from an adjusted OR of 5.6 to an adjusted OR of 4.1 including mechanistic variables).

4. Discussion and Conclusion

4.1. Discussion

To our knowledge, this is the first analysis of breast cancer treatment decision making in a large population-based sample with substantial representation by Latinas, especially with low acculturation. Our results are consistent with what has been suggested in previous work. In one of the first papers about breast cancer treatment decision making to include Latinas, Maly and colleagues found that Latina ethnicity was negatively associated with the provision of interactive information support by physicians (i.e., provision of information by physician with elicitation of patient feedback).2 This study also found that racial/ethnic minority patients more often preferred interpersonal sources of information to written information, yet were less likely than Caucasians to have received this type of interaction.2 Another paper by Maly and colleagues also showed that interactive informational support from providers could serve as a mechanism for reducing disparities in informed decision making33, such as those seen in our results. A small qualitative study found that although Latina breast cancer patients perceived the physician-patient interaction to be an important element of quality care, Latinas had considerable distrust regarding information received about breast cancer from providers.36 These results suggest that providers should try to convey information in a culturally acceptable manner.

While Latinas in our study reported the same amount of shared decision making as other groups, they were slightly more likely to report a mismatch between actual and preferred involvement. Other research has shown that this match is important for achieving decision and treatment satisfaction.6-7,10 Our results suggest that matching preferred to actual involvement may be especially important for Latinas with low acculturation. Prior work by our team, and others, has found that patient-physician communication was associated with achieving this match,7,9 providing further evidence to support that the provider plays an important role in ensuring patients obtain their desired amount of involvement in decisions.

A possible explanation for the findings related to poor decision outcomes in the less acculturated, Latina-SP, group lies with the decision process; specifically with language barriers and poor communication with providers regarding treatment options. Studies have demonstrated the importance of physician-patient communication in general,37-38 and for breast cancer treatment specifically.9,39-41 However, none have included sufficient samples of Latinas to evaluate the impact of language and/or culture on decision making. Many studies have found that language and acculturation are important factors in Latina patients’ utilization of services, such as cancer screening and diabetes care.42-44 It would follow that these issues are important to Latinas faced with breast cancer treatment decisions. Only two papers to date have assessed the impact of acculturation on breast cancer treatment decision making.17-19 In a prior study with fewer Latinas, our team found that less acculturated Latinas had the highest odds of decision dissatisfaction.18 Maly and colleagues found that the role of family in decision making was greatest for Latinas with low acculturation.17 We found in this study that many women reported including others in treatment discussions. While we did not specifically ask whether a family member helped with treatment decision-making, we did find that having someone with you during treatment discussions was associated with better decision outcomes across race/ethnicity. This result is consistent with other research documenting the importance of companions in cancer communications.45

Ongoing work by our team provides another possible reason for the poor decision outcomes reported by less acculturated Latinas, supporting the idea that Latinas may be less satisfied with the decision making process compared to other groups. We found that despite reporting receiving similar amounts of information about surgical treatments as Caucasians, Latinas with low acculturation reported a strong desire for more information.46 These results, coupled with those above, suggest that additional effort may be needed by clinicians to ensure that information is understandable and culturally appropriate and improve the decision making process for all breast cancer patients.

Our limited measure of health literacy was independently associated with decision dissatisfaction and regret, and slightly reduced the association between race/ethnicity and decision regret. Thus, despite Latina-SP women having significantly less education than other women, low literacy did not fully account for all poor decision outcomes. Simply directing providers to target discussion to a lower literacy level may not be sufficient to improve decision satisfaction among Latina-SP women. Moreover, a recent study found that providers overestimated the literacy levels of their patients, particularly for minority patients, suggesting a possible source of disparities in certain health outcomes.47 However, as recommended the Institute of Medicine, language and literacy issues need to be addressed by providers in any healthcare decision.48

Our study has some limitations. First, the measures used in our analysis came from patient self-report and may be subject to recall bias. Second, the study was cross sectional and we can only determine associations between independent variables and our outcomes of interest. Third, our study was conducted in one geographic location and despite the racial/ethnic diversity may not be generalizable to other women with breast cancer. Though we did achieve high representation of Latinas, we were not able to evaluate decision making among Latina sub-groups. Our outcomes may have also been subject to measurement error. Our measures may not be best-suited for evaluating decision making among Latinas. For example, the Control Preferences Scale does not allow for the influence of family in the decision making process. Finally, our measure of language preferences was a proxy for acculturation and further work assessing the role of culture in decision making is needed.

4.2. Conclusion

Latina women, especially those who prefer Spanish, appear to be at increased risk for poor breast cancer treatment decision outcomes. Although there were no racial/ethnic differences in tumor behavior (DCIS vs. invasive), or in subsequent treatment (mastectomy vs. BCS), Latinas in our study were significantly more likely than Caucasian patients to experience decision dissatisfaction and regret. The finding that health literacy was independently associated with poor decision outcomes indicates that this is an important contributor to poor decision outcomes among racial/ethnic breast cancer patients. Overall, our results suggest that despite similar clinical outcomes, patient experiences with decision making were very different.

4.3. Practice implications

Providers need to be aware that minority and low literacy patients, especially less acculturated Latinas, are vulnerable to poor decision outcomes. Clinicians should be attentive to patient preferences for involvement in decision making and to the inclusion of others (e.g., family) in decision making. Interventions to improve the ability of providers to communicate in a culturally appropriate manner, and to accurately assess the health literacy of their patients, may improve the process of breast cancer treatment decision making.


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 N01-PC-35139 awarded to the University of Southern California, contract N01-PC-54404 awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement 1U58DP00807-01 awarded to the Public Health Institute. 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. We acknowledge the outstanding work of our research coordinator, data analyst and project staff, Barbara Salem, Paul Abrahamse and Ashley Gay. We would also like to acknowledge the breast cancer patients who responded to our survey.


This work was funded by grant number R01-CA109696 from the National Cancer Institute to the University of Michigan

Sources of Support: This work was funded by grant number R01-CA109696 from the National Cancer Institute to the University of Michigan


Latina Patient Perspectives about Informed Treatment Decision Making for Breast Cancer

Decision Outcomes

I. Patient Actual Involvement in Surgical Treatment Decision

[Control Preferences Scale from Degner, 1992]

Please describe how your surgical treatment decision was made (choose one):

  • My doctor(s) made the surgery decision with little input from me
  • My doctor(s) made the surgery decision but seriously considered my opinion
  • My doctor(s) and I made the surgery decision together
  • I made the surgery decision after seriously considered my doctor(s) opinions
  • I made the surgery decision with little input from my doctor(s)

II. Match Between Actual-Preferred Involvement

Thinking back over your surgical breast cancer decision, would you say:

  • I wish I had been more involved in the surgical treatment decision
  • My level of involvement was about right for me
  • I wish my surgeon had been more involved in the surgical treatment decision

III. Decision Satisfaction 5-item Scale

[all 5-point Likert Scale response options from strongly agree to strongly disagree]

  • I am satisfied I was adequately informed about the issues important to the decision about what kind of surgery to have
  • I am satisfied with the decision about what kind of surgery to have
  • I wish I had given more consideration to other surgical treatment options
  • I would have liked more information when the decision about surgery was made
  • I would like to have participated more in making the decision about what kind of surgery to have

IV. Decision Regret 5-item Scale

[all 5-point Likert Scale response options from strongly agree to strongly disagree]

If I had to do it over…

  • I would make a different decision about what type of surgery to have
  • I would choose a different surgeon for my surgery
  • I would take more time to make decisions about my treatment
  • I would consult more doctors about my treatment before making a decision
  • I would do everything the same


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest

The authors do not have any conflict of interest to disclose, including any financial, personal or other relationship within three years of beginning the submitted work that could inappropriately influence, or be perceived to influence, this work.


1. Polacek GN, Ramos MC, Ferrer RL. Breast cancer disparities and decision-making among U.S. women. Patient Educ Couns. 2007;65:158–65. [PubMed]
2. Maly RC, Leake B, Silliman RA. Health care disparities in older patients with breast carcinoma: informational support from physicians. Cancer. 2003;97:1517–27. [PubMed]
3. Levinson W, Kao A, Kuby A, Thisted RA. Not all patients want to participate in decision making. A national study of public references. J Gen Intern Med. 2005;20:531–5. [PMC free article] [PubMed]
4. Ford S, Schofield T, Hope T. Are patients’ decision-making preferences being met? Health Expect. 2003;6:72–80. [PMC free article] [PubMed]
5. Janz NK, Wren PA, Copeland LA, Lowery J, Goldfarb SL, Wilkins E. Patient-physician concordance: preferences, perceptions, and factors influencing the breast cancer surgical decision. J Clin Oncol. 2004;22:3091–8. [PubMed]
6. Lantz PM, Janz NK, Fagerlin A, Schwartz K, Liu L, Lakhani I, Salem B, Katz SJ. Satisfaction with surgery outcomes and the decision process in a population-based sample of women with breast cancer. Health Serv Res. 2005;40:745–67. [PMC free article] [PubMed]
7. Vogel BA, Helmes AW, Hasenburg A. Concordance between patients’ desired and actual decision making roles in breast cancer care. Psychooncology. 2007;17:182–9. [PubMed]
8. Katz SJ, Lantz PM, Janz NK, Fagerlin A, Schwartz K, Liu L, Deapen D, Salem B, Lakhani I, Morrow M. Patient involvement in surgical treatment decisions for breast cancer. J Clin Oncol. 2005;23:5526–33. [PubMed]
9. Hawley ST, Lantz PM, Janz NK, Salem B, Morrow M, Schwartz K, Liu L, Katz SJ. Factors associated with patient involvement in surgical treatment decision making for breast cancer Patient. Educ Couns. 2007;65:387–95. [PMC free article] [PubMed]
10. Keating NL, Guadagnoli E, Landrum MB, Borbas C, Weeks JC. Treatment decision making in early-stage breast cancer: should surgeons match patients’ desired level of involvement? J Clin Oncol. 2002;20:1473–9. [PubMed]
11. Fagerlin A, Lakhani I, Lantz PM, Janz NK, Morrow M, Schwartz K, Deapen D, Salem B, Liu L, Katz SJ. An Informed Decision? Breast cancer patients’ knowledge about treatment. Patient Educ Couns. 2006;64:303–12. [PubMed]
12. Hawley ST, Fagerlin A, Janz NK, Katz SJ. Racial/ethnic differences in knowledge about the risks and benefits of breast cancer treatment: does it matter where you go? Health Serv Res. 2008 epub ahead of print. [PMC free article] [PubMed]
13. Living Beyond Cancer: President’s Cancer Panel Report 2003-2004. National Cancer Institute, National Institutes of Health; [accessed on 7/14/08]. Available online at:
14. Institute of Medicine Report . Ensuring Quality Cancer Care. In: Hewitt M, Simone JV, editors. National Cancer Policy Board, Institute of Medicine Report and National Research Council. National Academy Press; Washington, DC: 1999. p. 97.
15. Betancourt JR, Maina AW. The Institute of Medicine Report: Unequal Treatment: Implications for Academic Health Centers. The Mount Sinai Journal of Medicine. 2004;71:314–21. [PubMed]
16. National Healthcare Disparities Report . Agency for Healthcare Research and Quality. U.S. Department of Health and Human Services. AHRQ; [accessed on 7/14/08]. 2005. pub. #06-0017. Available online at:
17. Maly RC, Umezawa Y, Ratliff CT, Leake B. Racial/ethnic group differences in treatment decision-making and treatment received among older breast carcinoma patients. Cancer. 2006;106:957–65. [PubMed]
18. Katz SJ, Lantz PM, Parades Y, Janz NK, Fagerlin A, Liu L, Deapen D. Breast cancer treatment experiences of Latinas in Los Angeles County. Am J Public Health. 2005;95:2225–30. [PubMed]
19. Word D, Perkins JR. Building a Spanish surname list for the 1990’s—a new approach to an old problem. U.S. Census Bureau; 1996. Technical Working Paper No. 13.
20. Dillman DA. Mail and Telephone Surveys. John Wiley and Sons, Inc.; New York: 1978.
21. Marin G, Sabogal F, Vanoos Marin B. [accessed on 7/14/08];Development of a short acculturation for Hispanics (SASH) scale. Hisp J Behav Sic. 1987 9:183–205. Available online at
22. National Comprehensive Cancer Network (NCCN) [accessed 7/14/08];Treatment guidelines. Available online at:
23. National Cancer Institute [Accessed on 7/14/08];Physician Data Query (PDQ): Breast cancer treatment guidelines for health professionals. Available online at:
24. Katz SJ, Lantz P, Janz NK, Fagerlin A, Schwartz K, Liu L, Deapen D, Salem B, Lakhani I, Morrow M. Patterns and correlates of local therapy for women with ductal carcinoma-in-situ. J Clin Oncol. 2005;23:3001–7. [PMC free article] [PubMed]
25. Janz NK, Mujahid M, Lantz PM, Fagerlin A, Salem B, Morrow M, Deapen D, Katz SJ. Population-based study of the relationship of treatment and sociodemographics on quality of life for early stage breast cancer. Qual Life Res. 2005;14:1467–79. [PubMed]
26. Degner LF, Sloan JA, Venkatesh P. The Control Preferences Scale. Can J Nurs Res. 1997;29:21–43. [PubMed]
27. Davison BJ, Degner LF. Feasibility of using a computer-assisted intervention to enhance the way women with breast cancer communicate with their physicians. Cancer Nursing. 2002;25:417–24. [PubMed]
28. Holmes-Rovner M, Kroll J, Schmitt N, et al. Patient satisfaction with health care decisions: the satisfaction with decision scale. Med Decis Making. 1996;16:58–64. [PubMed]
29. Brehaut JC, O’Connor AM, Wood TJ, Hack TF, Siminoff L, Gordon E, Feldman-Stewart D. Validation of a decision regret scale. Med Decis Making. 2003;23:281–92. [PubMed]
30. Marin G, VanOss Marin B. Research with Hispanic populations. Applied Social Research Methods Series. 1991:23.
31. Staradub VL, Hsieh YC, Clauson J, Langerman A, Rademaker AW, Morrow M. Factors that influence surgical choices in women with breast carcinoma. Cancer. 2002;95:1185–90. [PubMed]
32. Maly RC, Leake B, Silliman RA. Breast cancer treatment in older women: impact of the patient-physician relationship. J Am Geriatr. 2004;52:1138–45. [PubMed]
33. Maly RC, Umezawa Y, Leake B, Silliman RA. Determinants of participation in treatment decision-making by older breast cancer patients. Breast Cancer Res Treat. 2004;85:201–9. [PubMed]
34. Tse YK. A Diagnostic Test for the Multinomial Logit Model. Journal of Business & Economic Statistics. 1987;5:283–6.
35. STATA Reference Manual: Release 10.0. STATA Press; College Station, TX: 2007.
36. Ashing-Giwa KT, Padilla GV, Bohorquez DE, Tejero JS, Garcia M, Myers EA. Understanding the breast cancer experience of Latina women. J Psychosocial Oncology. 2006;24:19–35. [PubMed]
37. Gordon HS, Street RL, Sharf BF, Souchek J. Racial differences in doctors’ information-giving and patients’ participation. Cancer. 2006;107:1313–20. [PubMed]
38. Street RL, Gordon HS, Ward MM, Krupat E, Kravitz RL. Patient participation in medical consultations: why some patients are more involved than others. Med Care. 2005;43:960–9. [PubMed]
39. Liang W, Burnett CB, Rowland JH, Merepol N, Eggert L, Hwang YT, Silliman RA, Weeks JC, Mandelblatt JS. Communication between physicians and older women with localized breast cancer: implications for treatment and patient satisfaction. J Clin Oncol. 2002;20:1008–1016. [PubMed]
40. Oskay-Ozcelik G, Lehmacher W, Konsgen D, Christ H, Kaufmann M, Lichtenegger W, Bamberg M, Wallwiener D, Overkamp F, Diedrich K, von Minckwitz G, Höffken K, Seeber S, Mirz R, Sehouli J. Breast cancer patients’ expectations in respect of the physicina-patient relationship and treatment management: results of a survey of 617 patients. Ann Oncol. 2007;18:479–84. [PubMed]
41. Thind A, Maly R. The surgeon-patient interaction in older women with breast cancer: what are the determinants of a helpful discussion? Ann Surg Oncol. 2006;13:788–93. [PubMed]
42. Larkey L. Las Mujeres Saludables: reaching Latinas for breast, cervical and colorectal cancer prevention and screening. J Community Health. 2006;31:69–77. [PubMed]
43. Warren AG, Londono GE, Wessel LA, Warren RD. Breaking down barriers to breast and cervical cancer screening: a university-based prevention program for Latinas. J Health Care Poor Underserved. 2006;17:512–21. [PubMed]
44. Campos C. Addressing cultural barriers to the successful use of insulin in Hispanics with type 2 diabetes. South Med J. 2007;100:812–20. [PubMed]
45. Street RL, Gordon HS. Companion participation in cancer consultations. Psychooncology. 2007;17:244–51. [PubMed]
46. Janz NK, Mujahid M, Hawley ST, Hamilton A, Griggs J, Katz SJ. Racial/ethnic differences in adequacy of information and support for women with breast cancer. Cancer. In press. [PMC free article] [PubMed]
47. Kelly PA, Haidet P. Physician overestimation of patient literacy: a potential source of health care disparities. Patient Educ Couns. 2007;66:119–22. [PubMed]
48. Institute of Medicine . Health Literacy: A Prescription to End Confusion. National Academies Press; Washington DC: 2004.