Abstract
Purpose
Racial disparities in cardiovascular disease and cardiac dysfunction exist amongst breast cancer survivors. This study examined the prevalence of cardioprotective medication use in survivors and identified factors associated with use by race.
Methods
The analysis included women enrolled in the Women’s Hormonal Initiation and Persistence study, a longitudinal observational trial of breast cancer survivors. The study outcome, angiotensin converting enzyme inhibitor (ACEi) or ß-Blocker (BB) use, were ascertained from pharmacy records. Demographic, psychosocial, healthcare, and quality of life factors were collected from surveys and clinical data were abstracted from medical records. Bivariate associations by race and ACEi/BB use were tested using chi square and t tests; logistic regression evaluated multivariable-adjusted associations.
Results
Of the 246 survivors in the sample, 33.3% were Black and most were < 65 years of age (58.4%). Most survivors were hypertensive (57.6%) and one-third received ACEi/BBs. In unadjusted analysis, White women (vs. Black) (OR 0.33, 95% 0.19–0.58) and women with higher ratings of functional wellbeing (OR 0.94, 95% 0.89–0.99) were less likely to use ACEi/BBs. Satisfaction with provider communication was only significant for White women. In multivariable-adjusted analysis, ACEi/BB use did not differ by race. Correlates of ACEi/BB use included hypertension among all women and older age for Black women only.
Conclusions
After adjusting for age and comorbidities, no differences by race in ACEi/BB use were observed. Hypertension was a major contributor of ACEi/BB use in BC survivors.
Keywords: Breast cancer, Cardiovascular disease, Angiotensin converting enzyme inhibitors, Beta-blockers, Hypertension
Introduction
Breast cancer survivors are almost twice as likely to develop cardiovascular disease (CVD) and up to four times more likely to have CVD risk factors when compared to women without cancer [1, 2]. Risk factors for CVD include older age, smoking, hypertension, and having overweight or obesity [3, 4]. For cancer survivors specifically, certain cancer treatments may pose additional risks for CVD. For example, anthracycline chemotherapy, trastuzumab, a targeted therapy for human epidermal receptor 2 (HER-2), and radiotherapy directed at the chest have been shown to increase women’s risk of CVD. Furthermore, cardiac-related mortality represents the most prevalent non-cancer cause of death amongst breast cancer survivors [5].
Prevention of treatment-related cardiovascular dysfunction among breast cancer survivors can include cardioprotective medications such as angiotensin converting enzyme inhibitors (ACEIs) and β-adrenergic blockers (BBs). Both medications are standard treatments for patients with heart failure or hypertension, two chronic diseases that are prevalent amongst women newly diagnosed with breast cancer and for long-term survivors [6–10]. Recent data among HER-2 positive breast cancer patients receiving trastuzumab, demonstrated a 50% lower risk of treatment-related cardiovasuclar dysfunction associated with ACEi or BB use when compared to a control group [11]. Furthermore, recent studies have shown that while treatment-related cardiovascular dysfunction (e.g., cardiotoxicity) causes disruption in treatment, cardioprotective medications prevent severe dysfunction thereby allowing patients to complete life-saving treatment [11]. Although results supporting the use of ACEIs and BBs are emerging, there are currently no standardized guidelines recommending their use [12]. Therefore, additional evidence and data regarding their use in clinical practice are necessary.
ACEi/BBs have shown promise with regard to CVD and dysfunction prevention, yet racial disparities in CVD risk and mortality are well documented among breast cancer survivors [13], with Black women having higher prevalence of risk factors and being more likely to develop and subsequently die from CVD compared to their White counterparts [14]. Despite higher prevalence of hypertension and other comorbid diseases among Black as compared with White patients [15] little is known about their receipt of cardioprotective medication. One potential mechanism that might underlie this observation is disparities in care and disease management. Recent reports have shown that Black cancer patients are less likely to receive recommended cancer-directed treatment (e.g., surgery, chemotherapy) than White patients [16–18]. Recent data have also shown that Black patients in cancer and non-cancer settings are less likely than White patients to receive supportive medication, pain medication (e.g., opioids), and statins when indicated [19–22], even after controlling for insurance status and income. Although underexplored, few factors have been shown to be related to the disparity in medication use. Lack of insurance or underinsurance are associated with racial differences in mediation use; however, this does not fully explain disparities in CVD-related mortality [23]. Given the supportive nature of ACEi/BBs with regard to treatment-related cardiac dysfunction in breast cancer survivors, further investigation is warranted to understand patterns of use, particularly amongst women with heightened CVD risk.
The aims of this investigation were to: (a) examine the prevalence of ACEi/BB use utilization rates among breast cancer survivors, (b) test for racial differences in receipt/use of cardioprotective medication, and (c) explore factors that predict receipt of cardioprotective medications. We hypothesized that ACEi/BB use would be lower in Black women compared to White women. We also hypothesized that older age and patient-provider communication would be associated with ACEi/BB use. This study was guided by the expanded Andersen Healthcare Utilization Model, which parameterizes several constructs (e.g., predisposing factors, psychosocial factors, need factors) that may help to explain ACEI and BB utilization [24, 25].
Methods
Study population
This was a secondary analysis of the Women’s Hormonal Therapy Initiation and Persistence (WHIP) Study, where investigators sought to understand factors related to adherence and discontinuation of adjuvant endocrine therapy. Black and White breast cancer survivors who were ≥ 21 years of age, diagnosed with hormone receptor positive (HR+) breast cancer, and who initiated adjuvant endocrine therapy were recruited from three healthcare centers in Washington D.C., Atlanta, GA, and Detroit, MI. Additional details regarding WHIP study procedures can be found elsewhere [26]. All women provided informed consent prior to completing study activities. The Georgetown University institutional review board approved all study procedures.
Measures
Women completed surveys (via telephone or online via Redcap) and provided access to pharmacy and clinical records. Surveys included sociodemographic (e.g., race, education), psychosocial (e.g., perceived racial discrimination, self-efficacy in obtaining care), quality of life (e.g., emotional well-being, physical activity), and healthcare factors (e.g., provider communication, patient satisfaction). Pharmacy and clinical records were abstracted to gather information about adjuvant endocrine therapy, cardiac medication use, tumor stage, and types of cancer treatment (e.g., surgery, chemotherapy).
Cardioprotective medication use
Our analysis focused on ACEIs and BBs, as they are the most frequently referenced cardioprotective medications for breast cancer survivors. Medication utilization was based on pharmacy records that were collected following a breast cancer diagnosis and initiation of adjuvant endocrine therapy. Cardiac medication use was dichotomized as no vs. yes for analysis.
Predisposing factors
Demographics such as race, age, marital status (e.g., married, not married), work status (e.g., employed, not employed), income (e.g., < $100 k and ≥ $100 k), and education (e.g., high school or less) along with tumor characteristics (stage) were collected from medical records.
Psychosocial factors
To measure women’s self-efficacy in obtaining and understanding cancer information we used a 12-item four point-Likert scale, where higher scores indicated greater self-efficacy (Cronbach’s alpha = 0.80) [27]. Perceived discrimination was assessed using the Race-Based Experiences scale that asks women to consider their race/ethnicity when answering seven yes/no items about their experiences with healthcare (e.g., received poorer service than others because of your race) [28]. Scores were dichotomized as none versus any for this analysis. Medical mistrust was measured using seven items from LaVeist and colleagues (e.g., patients have sometimes been deceived or misled by healthcare organizations) and higher scores indicated greater medical mistrust (Cronbach’s alpha = 0.78) [29]. Women’s social support was assess using the Medical Outcomes Survey, a 12-item 5-point Likert scale that asks women the frequency of friend or family support (e.g., someone who understands your problems) (Cronbach’s alpha = 0.92) [30]. Higher scores indicated greater social support. To measure trust in providers we used the Primary Care Assessment Survey Trust Scale which includes seven four-point Likert scale items (e.g., I completely trust my doctor’s judgement about my medical care) (Cronbach’s alpha = 0.81) [31]. Higher scores indicated greater trust. We adapted the Makoul and colleagues’ communication scale to assess women’s communication with their oncologists (e.g., the doctor asked me to choose a treatment for my breast cancer) (Cronbach’s alpha = 0.81) [32]. Higher scores reflected greater communication. That Patient Satisfaction Questionnaire Short Form (PSQ-18) measured women’s satisfaction with their cancer care (Cronbach’s alpha = 0.89) [33]. We also assessed satisfaction with domains of the PSQ-18 (e.g., interpersonal communication, financial aspect). Higher scores indicated greater satisfaction. The Functional Assessment of Cancer Therapy—Breast Cancer (FACT-B) scale [34] measured four domains of quality of life, physical (Cronbach’s alpha = 0.77), emotional (Cronbach’s alpha = 0.77), social (Cronbach’s alpha = 0.84), and functional well-being (Cronbach’s alpha = 0.63). Higher scores on each domain indicated better quality of life.
Need factors
We used the Elixhauser Comorbidity index to classify comorbidities (e.g., hypertension, obesity). Comorbidities were totaled and categorized for analyses (1–2, 3–4, ≥ 5). We abstracted treatment types, including adjuvant endocrine therapy (e.g., tamoxifen, aromatase inhibitors), chemotherapy use, and radiation, from medical records. We also assessed individual comorbid conditions for which ACEi/BBs may be indicated. These conditions include hypertension, diabetes mellitus, and congestive heart failure; comorbid conditions were abstracted from medical records. Cardiovascular disease treatment risk factors included treatment modalities (e.g., radiation therapy, chemotherapy, aromatase inhibitors) that have been shown to cause treatment-related cardiotoxicity. These data were abstracted from medical records. Women who received one of the aforementioned treatments were coded as “1” and each treatment type was totaled. The variable was categorized as “0, 1, 2, or 3” risks. Body mass index (BMI) was available from the medical record. We captured psychological distress using the Distress Thermometer [35]. Women indicated their level of distress in the week leading up to the survey using a scale of 0 (no distress) to ten (extreme distress).
Statistical analyses
First, we compared sociodemographic, psychosocial, healthcare, and quality of life factors according to race (White vs. Black) and receipt of cardiac medication (none vs. use of ACEIs or BBs) using Chi-square tests. Univariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between these characteristics and odds of cardiac medication use (no vs. yes). We then built a multivariable-adjusted logistic regression model by including variables that were determined to be significantly associated (p < 0.05) with race and cardiac medication in the univariable models (age, race, and Elixhauser comorbidities). We repeated this analysis stratified by race to determine if predictors of cardiac medication use differed between Black and White breast cancer survivors. Goodness of fit for these logistic regression models were assessed using Hosmer–Lemeshow (H–L) test [36]. All analyses were performed using IBM SPSS 25.
Results
Our study population included 246 breast cancer survivors with detailed pharmacy records, a majority of whom were under the age of 65 (58.4%), married (59.8%), and reported at least some college education (80.7%). Black women comprised 33.3% of our sample. Most women were diagnosed with stage I breast cancer (64.3%), received radiation therapy (56.9%) and aromatase inhibitors (70.7%), and had hypertension (57.6%). Thirty percent received chemotherapy (Table 1).
Table 1.
Distributions of sociodemographic, psychosocial, healthcare, and quality of life factors according to race and cardiac medication use
| Characteristics | Total N = 246 N (%) | Black N = 82 N (%) | White N = 164 N (%) | p value | Cardiac medication use |
p value | |
|---|---|---|---|---|---|---|---|
| No N = 164 n (%) | Yes N = 82 n (%) | ||||||
| Age (mean ± SD) | 61.5(10.9) | 58.8 (10.7) | 62.8 (10.8) | 0.036* | 0.004** | ||
| < 65 years | 143 (58.4) | 56 (68.3) | 87 (53.4) | 106 (65.0) | 37 (45.1) | ||
| ≥ 65 years | 102 (41.6) | 26 (31.7) | 76 (46.6) | 57 (35.0) | 45 (54.9) | ||
| Race | – | ||||||
| Black | 82 (33.3) | – | – | 41 (25.0) | 41 (50.0) | < 0.001*** | |
| White | 164 (66.7) | – | – | 123 (75.0) | 41 (50.0) | ||
| Marital Status | 0.001** | 0.89 | |||||
| Married/living as married | 147 (59.8) | 36 (43.9) | 111 (67.7) | 99 (60.4) | 48 (58.5) | ||
| Not married | 99 (40.2) | 46 (56.1) | 53 (32.3) | 65 (39.6) | 34 (41.5) | ||
| Education | 0.353 | 0.513 | |||||
| High school or less | 47 (19.3) | 19 (23.2) | 28 (17.3) | 29 (17.8) | 18 (22.2) | ||
| More than high school | 197 (80.7) | 63 (76.8) | 134 (82.7) | 134 (82.2) | 63 (77.8) | ||
| Employment status | 0.118 | 0.216 | |||||
| Employed | 117 (50.6) | 43 (58.9) | 74 (46.8) | 89 (57.4) | 28 (36.8) | ||
| Unemployed | 114 (49.4) | 30 (41.1) | 84 (53.2) | 66 (42.6) | 48 (63.2) | ||
| Household income | 0.001** | 0.005** | |||||
| < 100 k | 142 (62.0) | 58 (77.3) | 84 (54.5) | 92 (59.0) | 50 (68.5) | ||
| ≥100 k | 87 (38.0) | 17 (22.7) | 70 (45.5) | 64 (41.0) | 23 (31.5) | ||
| Elixhauser comorbidity | 0.232 | 0.003** | |||||
| 0–1 | 87 (35.8) | 23 (28.4) | 64 (39.5) | 70 (43.2) | 17 (21.0) | ||
| 2 | 44 (18.1) | 16 (19.8) | 28 (17.3) | 27 (16.7) | 17 (21.0) | ||
| ≥ 3 | 112 (46.1) | 42 (51.9) | 70 (43.2) | 65 (40.1) | 47 (58.0) | ||
| Hypertension | 0.06 | < 0.001*** | |||||
| Yes | 140 (57.6) | 54 (66.7) | 86 (53.1) | 66 (40.7) | 74 (91.4) | ||
| No | 103 (42.4) | 27 (33.3) | 76 (46.9) | 96 (59.3) | 7 (8.6) | ||
| CHF | 0.752 | 0.527 | |||||
| Yes | 22 (9.1) | 8 (9.9) | 14 (8.6) | 16 (9.9) | 6 (7.4) | ||
| No | 221 (90.9) | 73 (90.1) | 148 (91.4) | 146 (90.1) | 75 (92.6) | ||
| Diabetes Mellitus | 0.002** | < 0.001*** | |||||
| Yes | 48 (19.8) | 25 (30.9) | 23 (14.2) | 19 (11.7) | 29 (35.8) | ||
| No | 195 (80.2) | 56 (69.1) | 139 (85.8) | 143 (88.3) | 52 (64.2) | ||
| BMI: m (SD) | 29.81 (7.00) | 32.87 (7.37) | 28.31 (6.31) | < 0.001*** | 29.26 (6.74) | 30.99 (7.44) | 0.105 |
| Cancer stage | 0.013* | 0.693 | |||||
| I | 148 (64.3) | 39 (51.3) | 109 (70.8) | 95 (62.5) | 53 (67.9) | ||
| II | 67 (29.1) | 31 (40.8) | 36 (23.4) | 47 (30.9) | 20 (25.6) | ||
| III | 15 (6.5) | 6 (7.9) | 9 (5.8) | 10 (6.6) | 5 (6.4) | ||
| Endocrine therapy | 0.23 | 0.105 | |||||
| Tamoxifen | 70 (29.3) | 27 (35.1) | 43 (26.5) | 53 (32.9) | 17 (21.8) | ||
| Aromatase Inhibitor | 169 (70.7) | 50 (64.9) | 119 (73.5) | 108 (67.1) | 61 (78.2) | ||
| Radiation | 0.262 | 0.494 | |||||
| No radiation | 47 (19.1) | 11 (13.4) | 36 (22.0) | 33 (20.1) | 14 (17.1) | ||
| Radiation | 140 (56.9) | 49 (59.8) | 91 (55.5) | 89 (54.3) | 51 (62.2) | ||
| Missing radiation | 59 (24.0) | 22 (26.8) | 37 (22.6) | 42 (25.6) | 17 (20.7) | ||
| Chemotherapy | 0.001** | 0.782 | |||||
| No Chemotherapy | 92 (37.4) | 21 (25.6) | 71 (43.3) | 63 (38.4) | 29 (35.4) | ||
| Chemotherapy | 74 (30.1) | 37 (45.1) | 37 (22.6) | 47 (28.7) | 27 (32.9) | ||
| Missing | 80 (32.5) | 24 (29.3) | 56 (34.1) | 54 (32.9) | 26 (31.7) | ||
| Treatment risks | 0.023** | 0.16 | |||||
| 0 | 10 (6.8) | 2 (4.2) | 8 (8.1) | 9 (9.2) | 1 (2.0) | ||
| 1 | 36 (24.5) | 10(20.8) | 26(26.3) | 27 (27.6) | 9 (18.4) | ||
| 2 | 68 (46.3) | 18(37.5) | 50(50.5) | 43 (43.9) | 25 (51.0) | ||
| 3 | 33 (22.4) | 18(37.5) | 15(15.2) | 19 (19.4) | 14 (28.6) | ||
| Distress: m (SD) | 3.72 (2.85) | 3.91 (3.02) | 3.63 (2.77) | 0.476 | 3.61 (2.78) | 3.94 (3.00) | 0.419 |
| Self-efficacy: m (SD) | 44.35 (3.71) | 44.04 (3.60) | 44.51 (3.76) | 0.344 | 44.68 (3.55) | 43.70 (3.94) | 0.059 |
| Perceived race-based discrimination | < 0.001*** | 0.008** | |||||
| None | 215 (87.8) | 56 (68.3) | 59 (97.5) | 150 (92.0) | 65 (79.3) | ||
| Any | 30 (12.2) | 26 (31.7) | 4 (2.5) | 13 (8.0) | 17 (20.7) | ||
| Medical mistrust | 20.68 (4.72) | 23.01 (4.73) | 19.53 (4.28) | < 0.001*** | 20.06 (4.67) | 21.93 (4.60) | 0.004** |
| Emotional social support: m (SD) | 81.97 (19.87) | 81.46 (19.33) | 82.22(20.19) | 0.778 | 82.42(20.75) | 81.07(18.09) | 0.6 |
| Tangible social support: m (SD) | 80.08 (23.93) | 79.67 (23.92) | 80.29(24.01) | 0.851 | 80.71(22.97) | 78.83 (25.87) | 0.583 |
| Trust in provider: m (SD) | 76.80 (15.83) | 74.93 (15.02) | 77.71(16.18) | 0.187 | 77.35(16.13) | 75.72 (15.27) | 0.439 |
| Provider communication: m (SD) | 33.25 (4.70) | 32.36 (4.41) | 33.70 (4.79) | 0.031 | 33.78 (4.95) | 32.20 (3.96) | 0.007** |
| Patient satisfaction total: m (SD) | 3.91 (0.78) | 3.85 (0.80) | 3.94 (0.77) | 0.409 | 3.98 (0.74) | 3.76 (0.85) | 0.065 |
| Communication | 4.04 (0.72) | 4.00 (0.68) | 4.06 (0.74) | 0.585 | 4.13 (0.64) | 3.83 (0.82) | 0.009** |
| Interpersonal manner | 4.27 (0.64) | 4.26 (0.61) | 4.28 (0.65) | 0.777 | 4.29 (0.62) | 4.24 (0.66) | 0.552 |
| Financial aspect | 3.74 (0.99) | 3.36 (1.11) | 3.93 (0.87) | < 0.001*** | 3.83 (0.96) | 3.55 (1.03) | 0.061 |
| Access to/convenience of care | 4.14 (0.62) | 4.10 (0.65) | 4.17 (0.61) | 0.448 | 4.18 (0.65) | 4.07 (0.55) | 0.182 |
| Physical well-being: m (SD) | 23.26 (4.44) | 22.40 (4.64) | 23.67 (4.29) | 0.053 | 23.20 (4.49) | 23.37 (4.36) | 0.79 |
| Social well-being: m (SD) | 22.32 (5.29) | 20.94 (5.86) | 22.99 (4.87) | 0.012* | 22.75 (5.22) | 21.39 (5.36) | 0.082 |
| Emotional well-being: m (SD) | 20.68 (3.16) | 20.17 (3.74) | 20.93 (2.82) | 0.128 | 20.72 (3.26) | 20.59 (2.97) | 0.754 |
| Functional well-being: m (SD) | 21.00 (5.06) | 18.93 (5.26) | 22.01 (4.65) | < 0.001*** | 21.52 (4.91) | 19.90 (5.22) | 0.031* |
p < 0.05
p < 0.01
p < 0.001
We observed significant differences between Black and White breast cancer survivors with respect to sociodemographic, psychosocial, healthcare, and quality of life factors (Table 1). Black women were younger, less likely to be married, more likely to have advanced stage disease, and had greater chemotherapy uptake compared to White survivors. Black breast cancer survivors reported significantly more perceived discrimination (p < 0.001) and higher medical mistrust (p < 0.001) than White breast cancer survivors. Regarding healthcare factors, White women reported better patient-provider communication (p = 0.031) and greater satisfaction with the financial aspects of care (p < 0.001) than Black women. Lastly, Black women reported poorer quality of life on two domains when compared to White women—social well-being (p = 0.012), and functional well-being (p < 0.001).
Thirty-three percent of women in our sample received BBs or ACEIs. Use of cardiac medication was more common among older women, Black women, women with a household income of < $100 k, and women with greater comorbidities (Table 1). Women with hypertension (vs. women without hypertension) (p < 0.001) and women with diabetes mellitus (vs. women without diabetes mellitus) (p < 0.001) were more likely to use ACEi/BBs. Cardiac medication use was also highest amongst women with higher medical mistrust (p = 0.004), lower ratings of patient-provider communication (p = 0.007), and lower functional well-being (p = 0.031).
Table 2 shows unadjusted relationships between predictor variables and cardiac medication use. Age was directly associated with cardiac medication use in the overall study population (≥ 65 vs. < 65 years of age OR: 2.26, 95% CI 1.32–3.90) as was a hypertension diagnosis. Cardiac medication use was least likely amongst White women (OR 0.33, 95% CI 0.19–0.58) and women who were employed (vs. unemployed). Women who were taking cardiac medication experienced perceived discrimination and reported higher medical mistrust than women who were not. Cardiac medication use was also more likely amongst women who reported higher rating of patient-provider communication and amongst those with greater satisfaction with their providers’ communication. Regarding quality-of-life factors, women who reported cardiac medication use also reported significantly lower function well-being than women who were not on cardiac medication. Amongst Black women only, those who were taking Tamoxifen (vs. aromatase inhibitors) were less likely to be on cardioprotective medication (OR 0.30, 95% CI 0.19–0.81). Amongst White women, those with stage II (vs. stage I) disease and those who reported higher ratings of self-efficacy, provider communication, and satisfaction with provider communication least likely to use cardioprotective medication.
Table 2.
Unadjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between patient characteristics and cardiac medication use overall and by race in multivariate models
| Predictors | All women (N = 246) | Black women (N = 82) | White women (N = 164) |
|---|---|---|---|
| Unadjusted OR (95% CI) | Unadjusted OR (95% CI) | Unadjusted OR (95% CI) | |
| 65 or older (Ref.: Younger than 65) | 2.26 (1.32, 3.9)*** | 5.56 (2.02, 17.27)*** | 2.18 (1.06, 4.55)* |
| White (Ref.: Black) | 0.33 (0.19, 0.58)*** | NA | NA |
| Married (Ref.: Single) | 0.93 (0.54, 1.6) | 1.49 (0.62,3.61) | 1.04 (0.49, 2.27) |
| College or higher (Ref.: some college or less) | 0.76 (0.39, 1.48) | 1.15 (0.41, 3.26) | 0.64 (0.27, 1.6) |
| Employed (Ref. unemployed) | 0.43 (0.24, 0.76)*** | 0.27 (0.10, 0.70)** | 0.43 (0.20, 0.89)*** |
| Income ≥ $100 k (Ref. < $100 k) | 0.93 (0.52, 1.66) | 1.46 (0.46, 4.87) | 1.11 (0.53, 2.28) |
| Elixhauser Comorbidity Score 2 (Ref: Score 0–1) | 2.59 (1.16,5.85)* | 3.64 (0.97, 15) | 1.93(0.66, 5.48) |
| Elixhauser Comorbidity Score 3 + (Ref: Score 0–1) | 2.98 (1.58, 5.82)*** | 4.6 (1.57, 15.08)** | 2.06 (0.92, 4.86) |
| Hypertension (Ref: No hypertension) | 15.38 (7.09, 38.6)*** | 8.8 (3.05, 29.86)*** | 29.29 (8.43, 185.29)*** |
| Congestive Heart Failure (Ref. No CHF) | 0.73 (0.27, 1.94) | 0.97 (0.23, 4.19) | 0.48 (0.10, 2.25) |
| Diabetes Mellitus (Ref. No Diabetes Mellitus) | 4.20 (2.17, 8.12)*** | 2.83 (1.05, 7.65)* | 4.33 (1.73, 10.82)*** |
| BMI | 1.03 (0.99, 1.08) | 0.98 (0.92, 1.05) | 1.04 (0.98, 1.1) |
| Stage 2 (Ref: Stage 1) | 0.76 (0.4, 1.41) | 1.28 (0.5, 3.32) | 0.2 (0.05, 0.61)* |
| Stage 3 (Ref: Stage 1) | 0.9 (0.27, 2.66) | 1.05 (0.18, 6.31) | 0.63 (0.09, 2.77) |
| Tamoxifen (Ref: aromatase inhibitor) | 0.57 (0.3, 1.05) | 0.3(0.11,0.81)* | 0.72 (0.3, 1.62) |
| Radiation (Ref.: no radiation)1 | 1.35 (0.67, 2.82) | 1.02 (0.26, 3.84) | 1.25 (0.52, 3.28) |
| Chemo (Ref.: no chemo)1 | 1.25 (0.65, 2.39) | 1.16 (0.4, 3.43) | 0.75 (0.28, 1.89) |
| Treatment risk 1 (Ref.: treatment risk 0) | 3 (0.46, 59.24) | NA | 2.58 (0.36, 52.73) |
| Treatment risk 2 (Ref.: treatment risk 0) | 5.23 (0.9, 99.38) | NA | 2.46 (0.38, 48.26) |
| Treatment risk 3 (Ref.: treatment risk 0) | 6.63 (1.06, 129.62) | NA | 2.55 (0.29, 55.45) |
| Distress | 1.04 (0.95, 1.14) | 1.01 (0.87, 1.17) | 1.05 (0.93, 1.2) |
| Self-efficacy | 0.93 (0.87, 1) | 1.03 (0.91, 1.17) | 0.89 (0.81, 0.97)* |
| Racial discrimination (Ref.: no racial discrimination) | 3.02 (1.39, 6.69)* | 1.98 (0.78, 5.26) | 0.99 (0.05, 7.99) |
| Medical mistrust | 1.09 (1.03, 1.16)*** | 1.02 (0.93, 1.13) | 1.08 (1, 1.18) |
| Emotional support | 1 (0.98, 1.01) | 1.01 (0.98, 1.03) | 0.99 (0.98, 1.01) |
| Tangible support | 1 (0.99, 1.01) | 0.99 (0.98, 1.01) | 1 (0.98, 1.01) |
| Trust in provider | 0.99 (0.98, 1.01) | 0.99 (0.96, 1.02) | 1 (0.98, 1.02) |
| Provider communication | 0.93 (0.87, 0.98)* | 0.99 (0.9, 1.09) | 0.9 (0.83, 0.98)* |
| Patient satisfaction | 0.7 (0.48, 1) | 0.71 (0.38, 1.28) | 0.71 (0.44, 1.15) |
| Communication Satisfaction | 0.56 (0.37, 0.84)* | 0.55 (0.24, 1.13) | 0.56 (0.33, 0.92)* |
| Interpersonal manner | 0.87 (0.56, 1.37) | 0.86 (0.38, 1.87) | 0.89 (0.51, 1.62) |
| Financial aspect | 0.76 (0.57, 1.01) | 0.65 (0.41, 1) | 1.19 (0.76, 1.96) |
| Convenience of care | 0.75 (0.47, 1.18) | 0.69 (0.31, 1.42) | 0.83 (0.45, 1.57) |
| Physical well-being | 1.01 (0.95, 1.08) | 1.1 (0.99, 1.24) | 0.98 (0.9, 1.07) |
| Social well-being | 0.95 (0.9, 1.01) | 1 (0.93, 1.09) | 0.94 (0.87, 1.01) |
| Emotional well-being | 0.99 (0.9, 1.08) | 1 (0.88, 1.13) | 1.02 (0.89, 1.18) |
| Functional well-being | 0.94 (0.89, 0.99)* | 0.99 (0.9, 1.08) | 0.94 (0.87, 1.02) |
Missing results were included in the model but estimates were not shown
p < 0.05
p < 0.01
p < 0.001
For some covariates, such as hypertension (Tables 2, 3), treatment risk (Table 2), wide 95% confidence intervals were observed after the logistic regression fit. This is primarily due to the frequency imbalance in the covariates, such as fewer patients without hypertension taking cardiac medication (n = 7, Table 1). However, the corresponding H–L goodness-of-fit p values from the multivariable logistic regression fits were 0.14, 0.80, and 0.12 for the overall, White, and Black patients’ models, respectively, revealing no significant concerns for lack of fit In the multivariable-adjusted logistic regression model controlling for age and comorbidities. There was no significant difference in cardiac medication use between Black and White women (Table 3). Women with hypertension were more likely to use cardiac medication for all women (OR 31.19, 95% CI 8.93, 153.39, p < 0.001) and for Black and White women individually. Women with diabetes mellitus were also more likely to use cardiac medication (OR 3.00, 95% CI 1.15, 8.05, p = 0.026). Among Black women only, older age was significantly related to cardiac medication use (≥ 65 vs. < 65 years of age) (OR 5.10, 95% CI 1.35–22.54, p < 0.05). No healthcare or quality of life factors were significant in the final adjusted models.
Table 3.
Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between patient characteristics and cardiac medication use overall and by race in multivariate models
| Predictors | All women (n = 209) | Black women (n = 76) | White women (n = 142) |
|---|---|---|---|
| Adjusted odds ratios (95% CI) | Adjusted odds ratios (95% CI) | Adjusted odds ratios (95% CI) | |
| 65 or older (reference: younger than 65) | 1.56 (0.64, 3.88) | 7.04 (1.61, 39.42)* | 0.83 (0.25, 2.73) |
| White (Ref.: black) | 0.62 (0.23, 1.61) | ||
| Employed (Ref. unemployed) | 0.71 (0.27, 1.80) | 0.51 (0.11, 2.16) | 1.08 (0.32, 3.59) |
| Elixhauser Comorbidity Score 2 (Ref.: 0–1) | 1.84 (0.54, 6.35) | 1.16 (0.13, 9.61) | |
| Elixhauser Comorbidity Score 3 + (Ref.: 0–1) | 0.42 (0.13, 1.29) | 0.29 (0.02, 2.45) | |
| Hypertension (Ref: no hypertension) | 31.19 (8.93, 153.39)*** | 33.05 (4.67, 458.77)** | 221.07 (5.32, 142.93)*** |
| Congestive Heart Failure (Ref. No CHF) | 0.20 (0.03, 0.95) | 0.55 (0.05, 5.00) | 0.18 (0.01, 1.22) |
| Diabetes Mellitus (Ref. no diabetes mellitus) | 3.00 (1.15, 8.05)* | 2.29 (0.47, 12.08) | 2.05 (0.70, 6.19) |
| Tamoxifen (Ref.: aromatase inhibitor) | 0.50 (0.09, 2.73) | ||
| Provider communication | 0.75 (0.38, 1.44) | 0.91 (0.45, 1.85) | |
| Self-efficacy | 0.97 (0.86, 1.10) | ||
| Discrimination (Ref.: no discrimination) | 1.59 (0.41, 6.32) | ||
| Medical mistrust | 1.05 (0.95, 1.16) | ||
| Communication satisfaction | 0.96 (0.86, 1.06) | 0.90 (0.78, 1.03) | |
| Functional well being | 0.98 (0.90, 1.06) | ||
| Goodness-of-fit (p value) | 0.14 | 0.12 | 0.80 |
p < 0.05
p < 0.01
p < 0.001
Discussion
This secondary analysis of cardioprotective medication use among breast cancer survivors did not find significant differences by race when comparing Black and White women. To our knowledge, this is one of the first studies to examine factors related to cardioprotective medication use among breast cancer survivors. Use of cancer-directed treatments that are associated with CVD risk (e.g., aromatase inhibitors, chemotherapy) were associated with increased use of cardioprotective medications. Although not significant in adjusted analyses, psychosocial factors including perceived discrimination, functional wellbeing, and satisfaction with provider communication were associated with cardioprotective medication use. Study findings enhance knowledge pertaining to CVD risks and CVD maintenance during survivorship and may offer insight to potential targets to improve breast cancer survivorship within the context of CVD.
The strongest predictor of cardioprotective medication use in our sample was a hypertension diagnosis. While it is reassuring to observe the high prevalence of treated hypertension in this study population, it is important to note that 47% of women with a history of hypertension were classified as untreated with ACEi/BBs; however, other medication types including diuretics and calcium channel blockers may have been used to treat hypertension, particularly in Black women. Given the high prevalence of hypertension among breast cancer survivors documented in this and other studies [37], it may be important to understand the role of hypertension/high blood pressure on treatment-related cardiac dysfunction prior to initiation of chemotherapy and other therapies. Older age was the only other significant predictor of cardioprotective medication use; however, this relationship was only significant for Black women. This finding is not surprising given risk factors for heart disease include Black race and older age. Our study supports the need to enhance surveillance of Black women throughout survival, particularly as prevalence of cardiotoxicity is higher in Black women than White women and cardiotoxicity tends to occur post-treatment.
Contrary to our hypothesis, after adjusting for hypertension and age, there was no significant difference in ACEi/BB use between Black and White women. Salient to patterns of ACEi/BB use are the varying recommendations of ACEi/BB use by race, particularly with regard to treatment for hypertension and heart failure. Highlighted in recent hypertension guidelines, a comparative effectiveness study suggested ACEis to be less optimal for treating heart failure in Black patients with hypertension when compared White patients [38, 39]. Additionally, Black patients were more likely to develop adverse effects as a result of ACEi use. Conversely, reduction in all-cause mortality was similar for Black and White patients with heart failure and reduced ejection fraction who received BBs [40]. Studies examining ACEi/BB use and efficacy in breast cancer survivors, specifically, those that include large numbers of Black women and women with comorbid conditions (e.g., hypertension) are limited yet warranted [41].
While it may appear promising that there were no differences observed by race, this finding suggests a need for deeper exploration with regard to CVD prevention and maintenance particularly for Black survivors given the higher rates of Black survivors who experience cardiotoxicity and higher cardiac disease amongst Black people overall. An analysis of administrative data showed that Black women were more likely to receive anthracycline chemotherapies when compared to White women. Furthermore, women who received anthracycline chemotherapies were less likely to undergo rigorous monitoring for cardiac complications than women who did not receive anthracycline chemotherapies [42]. The paradox of no racial difference in ACEi/BB use in our sample and the higher rates of CVD and cardiovascular dysfunction amongst Black survivors suggests a need to explore additional cancer care delivery factors that may be driving this disparity.
Our analyses revealed differences in the role of provider communication and ACEI/BB use between Black and White survivors. We did not observe any significant patterns with regard to Black women and provider communication. Interestingly, White women who had higher ratings of provider communication and satisfaction with their provider communication were less likely to use cardioprotective medication. Although seemingly counterintuitive, other studies have shown this inverse relationship between provider communication and treatment utilization. Sheppard et al. found lower chemotherapy initiation in White breast cancer survivors who reported higher ratings of provider communication [43]. In addition, the content of provider communication may vary for different women. Studies exploring cardiovascular risk communication between physicians and breast cancer survivors have shown that Black women report less communication from the provider about risk reduction practices (e.g., exercise, weight control) when compared to White survivors, although most survivors regardless of race desired this information [44–46]. These findings suggest a need to explore the potential relationship between provider communication and differences in approaches to CVD management and prevention.
Strengths of this study include the measurement of psychosocial and quality of life factors, a large number of Black breast cancer survivors, and ascertainment of ACEi/BB using pharmacy records, limiting the potential for misclassification bias to influence our results. However, important limitations include our use of a convenience sample; therefore, we were not able to exclusively assess women who were most at-risk for cardiac dysfunction. We also lacked information on the underlying reason for ACEi/BB prescription; as previously mentioned, off-label use of these medications is common and could potentially introduce confounding, particularly as our future work will focus on the use of these medications to prevent or treat treatment-related cardiotoxicity. Additionally, the cross-sectional design limited our ability to assert causation with regard to predictors and our outcome. There were few women who did not have hypertension yet received ACEi/BBs; this contributed to the wide confidence intervals. Incidence of breast cancer is similar between Black and White women; therefore, an ideal sample would have included an equal number of Black and White women. Lastly, it is well documented that amongst treatment types, chemotherapy such as anthracycline chemotherapies and HER-2 targeted therapies are most frequently implicated in treatment-related toxicity. We were unable to obtain that detailed information for this study; therefore, our analysis was limited to the broad range of all chemotherapies.
Conclusion
Findings from this study suggest future opportunities to explore potential survivorship issues among women with breast cancer, specifically the high prevalence of hypertension and its implications for CVD. Interventions are needed to reduce hypertension and other chronic illnesses in this population. In addition, as our study showed no racial difference in ACEi/BB use, future work should explore maintenance and prevention of CVD in Black women in addition to patterns of adherence to cardioprotective medication and patterns of ACEi/BB use on racial disparities in CVD and other breast cancer outcomes.
Acknowledgements
The authors would like to thank the women who participated in this study. This research was funded by the National Cancer Institute R01CA154848 (Sheppard). Support for publication was also provided from K99CA256038 (Sutton), 2T32CA093423 (Sheppard/Sutton), and K01CA21845701A1 (Felix). Services in support of this study were generated by the VCU Massey Cancer Center Biostatistics Shared Resource, supported, with funding from NIH-NCI Cancer Center Support Grant P30 CA016059 and through RED-Cap provided by the Clinical and Translational Sciences Award No. UL1TR002649 from the National Center for Advancing Translational Sciences.
Footnotes
Declarations
Conflict of interest The authors declare that they have no conflict of interest.
Ethical standards All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Institutional Review Board at Virginia Commonwealth University.
Consent to participate Informed consent was obtained from all individual participants included in the study.
Availability of data and material Data archiving is not mandated but will be made available on reasonable request.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Gernaat SAM, Boer JMA, van den Bongard DHJ, Maas A, van der Pol CC, Bijlsma RM, Grobbee DE, Verkooijen HM, Peeters PH (2018) The risk of cardiovascular disease following breast cancer by Framingham risk score. Breast Cancer Res Treat 170(1):119–127 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Buttros DAB, Branco MT, Orsatti CL, Almeida-Filho BS, Nahas-Neto J, Nahas EAP (2019) High risk for cardiovascular disease in postmenopausal breast cancer survivors. Menopause 26(9):1024–1030 [DOI] [PubMed] [Google Scholar]
- 3.Patnaik JL, Byers T, DiGuiseppi C, Dabelea D, Denberg TD (2011) Cardiovascular disease competes with breast cancer as the leading cause of death for older females diagnosed with breast cancer: a retrospective cohort study. Breast Cancer Res 13(3):R64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Mast ME, Heijenbrok MW, Petoukhova AL, Scholten AN, Schreur JH, Struikmans H (2012) Preradiotherapy calcium scores of the coronary arteries in a cohort of women with early-stage breast cancer: a comparison with a cohort of healthy women. Int J Radiat Oncol Biol Phys 83(3):853–858 [DOI] [PubMed] [Google Scholar]
- 5.Schairer C, Mink PJ, Carroll L, Devesa SS (2004) Probabilities of death from breast cancer and other causes among female breast cancer patients. J Natl Cancer Inst 96(17):1311–1321 [DOI] [PubMed] [Google Scholar]
- 6.Blanter JB, Frishman WH (2019) The Preventive role of angiotensin converting enzyme inhibitors/angiotensin-II receptor blockers and beta-adrenergic blockers in anthracycline- and trastuzumabinduced cardiotoxicity. Cardiol Rev 27(5):256–259 [DOI] [PubMed] [Google Scholar]
- 7.Wittayanukorn S, Qian J, Westrick SC, Billor N, Johnson B, Hansen RA (2018) Prevention of trastuzumab and anthracycline-induced cardiotoxicity using angiotensin-converting enzyme inhibitors or beta-blockers in older adults with breast cancer. Am J Clin Oncol 41(9):909–918 [DOI] [PubMed] [Google Scholar]
- 8.Cardinale D, Colombo A, Sandri MT, Lamantia G, Colombo N, Civelli M, Martinelli G, Veglia F, Fiorentini C, Cipolla CM (2006) Prevention of high-dose chemotherapy-induced cardiotoxicity in high-risk patients by angiotensin-converting enzyme inhibition. Circulation 114(23):2474–2481 [DOI] [PubMed] [Google Scholar]
- 9.Gulati G, Heck SL, Ree AH, Hoffmann P, Schulz-Menger J, Fagerland MW, Gravdehaug B, von Knobelsdorff-Brenkenhoff F, Bratland A, Storas TH et al. (2016) Prevention of cardiac dysfunction during adjuvant breast cancer therapy (PRADA): a 2 × 2 factorial, randomized, placebo-controlled, doubleblind clinical trial of candesartan and metoprolol. Eur Heart J 37(21):1671–1680 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Boekhout AH, Gietema JA, Milojkovic Kerklaan B, van Werkhoven ED, Altena R, Honkoop A, Los M, Smit WM, Nieboer P, Smorenburg CH et al. (2016) Angiotensin II-receptor inhibition with candesartan to prevent trastuzumab-related cardiotoxic effects in patients with early breast cancer: a randomized clinical trial. JAMA Oncol 2(8):1030–1037 [DOI] [PubMed] [Google Scholar]
- 11.Guglin M, Krischer J, Tamura R, Fink A, Bello-Matricaria L, McCaskill-Stevens W, Munster PN (2019) Randomized trial of lisinopril versus carvedilol to prevent trastuzumab cardiotoxicity in patients with breast cancer. J Am Coll Cardiol 73(22):2859–2868 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Armenian SH, Lacchetti C, Lenihan D (2017) Prevention and monitoring of cardiac dysfunction in survivors of adult cancers: American Society of Clinical Oncology Clinical Practice Guideline Summary. J Oncol Pract 13(4):270–275 [DOI] [PubMed] [Google Scholar]
- 13.Gallicchio L, Calhoun C, Riseberg D, Helzlsouer K (2017) Cardiovascular health among black and white breast cancer patients initiating aromatase inhibitor therapy. Breast J 23(2):206–209 [DOI] [PubMed] [Google Scholar]
- 14.Troeschel AN, Liu Y, Collin LJ, Bradshaw PT, Ward KC, Gogineni K, McCullough LE (2019) Race differences in cardiovascular disease and breast cancer mortality among US women diagnosed with invasive breast cancer. Int J Epidemiol 48(6):1897–1905 [DOI] [PubMed] [Google Scholar]
- 15.Highland KB, Hurtado-de-Mendoza A, Stanton CA, Dash C, Sheppard VB (2015) Risk-reduction opportunities in breast cancer survivors: capitalizing on teachable moments. Support Care Cancer 23(4):933–941 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Nocon CC, Ajmani GS, Bhayani MK (2020) A contemporary analysis of racial disparities in recommended and received treatment for head and neck cancer. Cancer 126(2):381–389 [DOI] [PubMed] [Google Scholar]
- 17.Hildebrand JS, Wallace K, Graybill WS, Kelemen LE (2019) Racial disparities in treatment and survival from ovarian cancer. Cancer Epidemiol 58:77–82 [DOI] [PubMed] [Google Scholar]
- 18.Tramontano AC, Chen Y, Watson TR, Eckel A, Hur C, Kong CY (2020) Racial/ethnic disparities in colorectal cancer treatment utilization and phase-specific costs, 2000–2014. PLoS ONE 15(4):e0231599. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lamba N, Mehanna E, Kearney RB, Catalano PJ, Haas-Kogan DA, Alexander BM, Cagney DN, Lee KA, Aizer AA (2020) Racial disparities in supportive medication use among older patients with brain metastases: a population-based analysis. Neuro Oncol 22(9):1339–1347 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lawrence AE, Deans KJ, Chisolm DJ, Wrona SK, Minneci PC, Cooper JN (2020) Racial disparities in receipt of postoperative opioids after pediatric cholecystectomy. J Surg Res 245:309–314 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Badreldin N, Grobman WA, Yee LM (2019) Racial disparities in postpartum pain management. Obstet Gynecol 134(6):1147–1153 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Mochari-Greenberger H, Liao M, Mosca L (2014) Racial and ethnic differences in statin prescription and clinical outcomes among hospitalized patients with coronary heart disease. Am J Cardiol 113(3):413–417 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Briesacher B, Limcangco R, Gaskin D (2003) Racial and ethnic disparities in prescription coverage and medication use. Health Care Financ Rev 25(2):63–76 [PMC free article] [PubMed] [Google Scholar]
- 24.Andersen R, Newman JF (2005) Societal and individual determinants of medical care utilization in the United States: societal and individual determinants of medical care utilization. Milbank Q 83(4) (Online-only-Online-only) [PubMed] [Google Scholar]
- 25.Andersen RM (1995) Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav 36(1):1–10 [PubMed] [Google Scholar]
- 26.Sheppard VB, Hurtado-de-Mendoza A, Zheng YL, Wang Y, Graves KD, Lobo T, Xu H, Jennings Y, Tolsma D, Trout M et al. (2018) Biospecimen donation among black and white breast cancer survivors: opportunities to promote precision medicine. J Cancer Surviv 12(1):74–81 [DOI] [PubMed] [Google Scholar]
- 27.Wolf MMS (2005) Development and validation of the Communication and Attitudinal Self-Efficacy scale for cancer (CASE-cancer). Patient Educ Couns 57(3):333–341 [DOI] [PubMed] [Google Scholar]
- 28.Bird ST, Bogart LM (2001) Perceived race-based and socioeconomic status(SES)-based discrimination in interactions with health care providers. Ethn Dis 11(3):554–563 [PubMed] [Google Scholar]
- 29.LaVeist TA, Isaac LA, Williams KP (2009) Mistrust of health care organizations is associated with underutilization of health services. Health Serv Res 44(6):2093–2105 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Sherbourne CD, Stewart AL (1991) The MOS social support survey. Soc Sci Med 32(6):705–714 [DOI] [PubMed] [Google Scholar]
- 31.Safran DG, Kosinski M, Tarlov AR, Rogers WH, Taira DH, Lieberman N, Ware JE (1998) The Primary Care Assessment Survey: tests of data quality and measurement performance. Med Care 36(5):728–739 [DOI] [PubMed] [Google Scholar]
- 32.Makoul G, Arntson P, Schofield T (1995) Health promotion in primary care: physician-patient communication and decision making about prescription medications. Soc Sci Med (1982) 41(9):1241–1254 [DOI] [PubMed] [Google Scholar]
- 33.Marshall GN (1994) The Patient Satisfaction Questionnaire short-form (PSQ-18). Santa Monica, Calif.: Santa Monica, Calif: RAND [Google Scholar]
- 34.Cella DF, Tulsky DS, Gray G, Sarafian B, Linn E, Bonomi A, Silberman M, Yellen SB, Winicour P, Brannon J (1993) The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol Off J Am Soc Clin Oncol 11(0732–183; 3):570–579 [DOI] [PubMed] [Google Scholar]
- 35.Snowden A, White CA, Christie Z, Murray E, McGowan C, Scott R (2011) The clinical utility of the distress thermometer: a review. Br J Nurs 20(4):220–227 [DOI] [PubMed] [Google Scholar]
- 36.Hosmer DW, Lemesbow S (1980) A goodness-of-fit test for the multiple logistic regression model. Commun Stat A10:1043–1069 [Google Scholar]
- 37.Coughlin SS, Ayyala D, Majeed B, Cortes L, Kapuku G (2020) Cardiovascular disease among breast cancer survivors. Cardiovasc Disord Med 2(1) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Ogedegbe G, Shah NR, Phillips C, Goldfeld K, Roy J, Guo Y, Gyamfi J, Torgersen C, Capponi L, Bangalore S (2015) Comparative effectiveness of angiotensin-converting enzyme inhibitor-based treatment on cardiovascular outcomes in hypertensive blacks versus whites. J Am Coll Cardiol 66(11):1224–1233 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Whelton PK, Carey RM, Aronow WS, Casey DE Jr, Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW et al. (2018) 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension 71(6):e13–e115 [DOI] [PubMed] [Google Scholar]
- 40.Luzum JA, Peterson E, Li J, She R, Gui H, Liu B, Spertus JA, Pinto YM, Williams LK, Sabbah HN et al. (2018) Race and betablocker survival benefit in patients with heart failure: an investigation of self-reported race and proportion of African genetic ancestry. J Am Heart Assoc 7(10):e00795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Kim HY, Jung YJ, Lee SH, Jung HJ, Pak K (2017) Is Beta-Blocker Use Beneficial in Breast Cancer? A Meta-Analysis Oncology 92(5):264–268 [DOI] [PubMed] [Google Scholar]
- 42.Pinder MC, Duan Z, Goodwin JS, Hortobagyi GN, Giordano SH (2007) Congestive heart failure in older women treated with adjuvant anthracycline chemotherapy for breast cancer. J Clin Oncol 25(25):3808–3815 [DOI] [PubMed] [Google Scholar]
- 43.Sheppard VB, Isaacs C, Luta G, Willey SC, Boisvert M, Harper FW, Smith K, Horton S, Liu MC, Jennings Y et al. (2013) Narrowing racial gaps in breast cancer chemotherapy initiation: the role of the patient-provider relationship. Breast Cancer Res Treat 139(1):207–216 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Christian AH, O’Malley D, Barac A, Miller SM, Hudson SV (2017) Cardiovascular risk and communication among early stage breast cancer survivors. Patient Educ Couns 100(7):1360–1366 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Royak-Schaler R, Passmore SR, Gadalla S, Hoy MK, Zhan M, Tkaczuk K, Harper LM, Nicholson PD, Hutchison AP (2008) Exploring patient-physician communication in breast cancer care for African American women following primary treatment. Oncol Nurs Forum 35(5):836–843 [DOI] [PubMed] [Google Scholar]
- 46.Weaver KE, Foraker RE, Alfano CM, Rowland JH, Arora NK, Bellizzi KM, Hamilton AS, Oakley-Girvan I, Keel G, Aziz NM (2013) Cardiovascular risk factors among long-term survivors of breast, prostate, colorectal, and gynecologic cancers: a gap in survivorship care? J Cancer Surviv 7(2):253–261 [DOI] [PMC free article] [PubMed] [Google Scholar]