Literature DB >> 25609997

Factors associated with contralateral preventive mastectomy.

Danny Yakoub1, Eli Avisar1, Tulay Koru-Sengul2, Feng Miao3, Stacey L Tannenbaum3, Margaret M Byrne4, Frederick Moffat1, Alan Livingstone1, Dido Franceschi1.   

Abstract

INTRODUCTION: Contralateral prophylactic mastectomy (CPM) is an option for women who wish to reduce their risk of breast cancer or its local recurrence. There is limited data on demographic differences among patients who choose to undergo this procedure.
METHODS: The population-based Florida cancer registry, Florida's Agency for Health Care Administration data, and US census data were linked and queried for patients diagnosed with invasive breast cancer from 1996 to 2009. The main outcome variable was the rate of CPM. Primary predictors were race, ethnicity, socioeconomic status (SES), marital status and insurance status.
RESULTS: Our population was 91.1% White and 7.5% Black; 89.1% non-Hispanic and 10.9% Hispanic. Out of 21,608 patients with a single unilateral invasive breast cancer lesion, 837 (3.9%) underwent CPM. Significantly more White than Black (3.9% vs 2.8%; P<0.001) and more Hispanic than non-Hispanic (4.5% vs 3.8%; P=0.0909) underwent CPM. Those in the highest SES category had higher rates of CPM compared to the lowest SES category (5.3% vs 2.9%; P<0.001). In multivariate analyses, Blacks compared to Whites (OR =0.59, 95% CI =0.42-0.83, P=0.002) and uninsured patients compared to privately insured (OR =0.60, 95% CI =0.36-0.98, P=0.043) had significantly less CPM.
CONCLUSION: CPM rates were significantly different among patients of different race, socio-economic class, and insurance coverage. This observation is not accounted for by population distribution, incidence or disease stage. More in-depth study of the causes of these disparities in health care choice and delivery is critically needed.

Entities:  

Keywords:  bilateral mastectomy; breast cancer; cancer disparities; ethnic factors; social factors

Year:  2015        PMID: 25609997      PMCID: PMC4293214          DOI: 10.2147/BCTT.S72737

Source DB:  PubMed          Journal:  Breast Cancer (Dove Med Press)        ISSN: 1179-1314


Introduction

Contralateral prophylactic/preventive mastectomy (CPM) patients with unilateral invasive breast cancer (BC) increased in the US by 150% since 1988, with no evidence of a geographic difference in practice or plateau effect.1 The annual incidence of contralateral breast cancer (CBC) is 0.5% to 0.75%,1 but has now radically decreased with the recent use of newer therapies such as Tamoxifen, aromatase inhibitors, Trastuzumab and neo/adjuvant chemotherapy.2–5 Even in subgroups thought to be at higher risk for CBC, such as those younger than 45 years and those with lobular histologies, the actuarial CBC rate at 10 years remains <7%.2 At present, the only two groups of women at a substantially increased risk of CBC are those with BRCA mutations,6 and women with a history of mantle irradiation during childhood and adolescence.7 A Cochrane Review published by Lostumbo et al8 found that although CPM reduces the risk of developing CBC, there is insufficient evidence that CPM improves survival. Although it is unclear why the aggressive and irreversible procedure of CPM is growing in prevalence, speculation includes that greater awareness and availability of genetic testing may be responsible, but this is uncertain.1,9,10 In a recent study on 2,504 patients using multivariate logistic regression to identify independent predictors of CPM, 30.6% of patients <50 years of age underwent CPM compared with only 18.2% of women ≥50 years of age (odds ratio [OR] =2.2). They were more likely to be surgeon identified, White race (OR =3.3), have a family history of BC (OR =2.9), have invasive lobular histology, be able to have immediate reconstruction (OR =3.3), and have multicentric disease. Most of these women did not have positive genetic mutation findings.9 Another study suggested increased rates of CPM were associated with having a female surgeon.11,12 However, these previous studies were not performed using large comprehensive databases. Therefore, the current study explored factors associated with use of CPM in a large enriched population-based cancer registry database which included demographic, clinical and co-morbidity factors. The main aim of this study was to determine which demographic and social factors were associated with receipt of CPM. As previous research has shown that disparities in treatments and procedures in the care of BC patients (eg, time to initiation of chemotherapy13 or adjuvant radiation, and use of breast conserving surgery)14–16 are associated with race, ethnicity, socioeconomic status (SES) and insurance status,17–19 we were particularly interested in exploring the association of these variables with receipt of CPM. Our secondary aim was to investigate clinical (treatment and hospital characteristics) and comorbidity associations with CPM surgery.

Materials and methods

Our study used data from three sources to investigate CPM in patients with invasive BC. The Florida Cancer Data System (FCDS), a population-based Florida cancer registry, was used to identify BC patients diagnosed from 1996 to 2009. Florida’s Agency for Health Care Administration (AHCA) database provided procedure and diagnoses information from all in- and out-patient facilities, and data from the US census provided a proxy for individual SES. Female patients who were 18 years or older were included if they resided in Florida during the study period. Patients with carcinoma in situ or with missing data on surgery (bilateral or unilateral), race, ethnicity, SES, marital status or insurance status were excluded from the study. Patients with unilateral BC were identified by having a single record of malignant neoplasm of the breast with diagnostic code 174 (2012 version of International Classification of Diseases, Ninth Revision (ICD-9)). Patients with more than one 174 code either with the same date or different dates were assumed to be multifocal, bilateral, or recurrent BC, and were excluded from the study. As the majority of patients receive surgical treatment at the time of diagnosis, patients receiving CPM later in the course of their disease were not included in this study. The dichotomous primary outcome variable was whether the patient had CPM (yes/no). Patients’ sociodemographic variables were age at diagnosis, race (White, Black, other), ethnicity (Hispanic and non-Hispanic), neighborhood SES based on percent of individuals living below the federal poverty line from US census tract-level information (lowest SES ≥20%, middle-low ≥10% and <20%, middle-high [≥5% and <10%], or highest <5%), marital status (never married, married, or divorced/separated/widowed), primary payer at diagnosis (private insurance, Medicare, Medicaid, defense/military, Indian Health Service, other insurance, or uninsured), urban or rural geographic residence (by zip code), and characteristics of the treating facility (teaching vs non-teaching hospital and high vs low volume hospital). Clinical characteristics included tumor and treatment related variables such as the Surveillance, Epidemiology, and End Results (SEER) stage, histological differentiation grade, and history of chemotherapy or radiation. Finally, comorbidities were available for all patients based on ICD-9 diagnoses.

Statistical analysis

Demographic and clinical characteristics of patients were calculated as frequencies and percentages for categorical variables and means and standard deviations (Std) for continuous variables, and compared for all patients in the population, and then for patients with unilateral and CPM. To assess what demographics and clinical factors were significantly associated with having a CPM, multivariate logistic regression models using generalized estimation equations were fitted. Robust standard errors were calculated to take into account clustering of patients within facilities. The first multivariate model (Model 1) included covariates for sociodemographic and clinical variables; the second multivariate model (Model 2) included all diagnostic information to fully adjust for other co-morbidities. Adjusted ORs, corresponding 95% confidence intervals (95% CIs), and P-values were calculated from these models. Statistical significance was considered at P<0.05. All statistical analyses were performed using SAS v 9.3 for Windows (SAS Institute Inc., Cary, NC, USA). The study was approved by the Institutional Review Boards of both University of Miami and Florida Department of Health.

Results

Between 1996 and 2009, 54,275 women were diagnosed with invasive BC and underwent a unilateral or CPM. Of these, 32,667 records had no data on whether there was unilateral or bilateral disease, race, ethnicity, SES, marital status, or insurance status, and therefore these cases were excluded from the analyses, for a final sample of 21,608 patients with unilateral disease and a record of the type of surgery. Descriptive statistics for demographics characteristics are provided for overall sample and by laterality of procedure done (Table 1). Mean age at diagnosis of all patients was 67.5 years (Std =13.9). The majority of patients were White (19,684; 91.1%), while 1,625 (7.5%) were Black, and 299 (1.4%) were other races. Non-Hispanics (19,257; 89.1%) outnumbered the Hispanics (2,351; 10.9%). There were 719 (3.3%) uninsured patients, 642 (3%) on Medicaid compared to 6,342 (29.4%) with private insurance and 11,778 (54.5%) on Medicare.
Table 1

Sociodemographic characteristics of female breast cancer patients from the Florida Cancer Data System and Agency for Health Care Administration datasets (1996–2009)

All female patients
Laterality of procedure done
Unilateral
Bilateral
nColumn %nColumn %Row %nColumn %Row %
All21,608100.020,771100.096.1837100.03.9
Race
 White19,68491.118,91991.196.176591.43.9
 Black1,6257.51,5797.697.2465.52.8
 Other2991.42731.391.3263.18.7
Hispanic origin
 Non-Hispanic19,25789.118,52689.296.273187.33.8
 Hispanic2,35110.92,24510.895.510612.74.5
Race and ethnicity
 White/Non-Hispanic17,40880.616,74780.696.266179.03.8
 White/Hispanic2,27610.52,17210.595.410412.44.6
 Black/Non-Hispanic1,5777.31,5317.497.1465.52.9
 Black/Hispanic480.2480.2100.0
 Other/Non-Hispanic2721.32481.291.2242.98.8
 Other/Hispanic270.1250.192.620.27.4
SES
 Lowest2,62812.22,55312.397.1759.02.9
 Middle-low6,59730.56,37630.796.622126.43.4
 Middle-high7,92636.77,62336.796.230336.23.8
 Highest4,45720.64,21920.394.723828.45.3
Marital status
 Never married2,22710.32,12910.295.69811.74.4
 Married11,24052.010,70051.595.254064.54.8
 Divorced/separated/widowed8,14137.77,94238.297.619923.82.4
Primary payer at diagnosis
 Uninsured7193.36903.396.0293.54.0
 Private insurance6,34229.45,90628.493.143652.16.9
 Medicaid6423.06133.095.5293.54.5
 Medicare11,77854.511,53855.598.024028.72.0
 Defense/military/veteran2061.01960.995.1101.24.9
 Indian health services410.2390.295.120.24.9
 Insurance, NOS1,8808.71,7898.695.29110.94.8
Tobacco use
 Never11,14851.610,71951.696.242951.33.8
 History3,87617.93,70717.895.616920.24.4
 Current2,34110.82,24610.895.99511.44.1
 Unknown4,24319.64,09919.796.614417.23.4
Urban/rural living
 Rural1,3836.41,3516.597.7323.82.3
 Urban20,22593.619,42093.596.080596.24.0
Teaching hospital
 No19,47090.118,79290.596.567881.03.5
 Yes2,1389.91,9799.592.615919.07.4
Hospital volume
 Low13,47462.413,12563.297.434941.72.6
 High8,13437.67,64636.894.048858.36.0

Notes: SES, neighborhood SES was based on percent of individuals living below the federal poverty line from US census tract-level information: lowest (≥20%), middle-low (≥10% and <20%), middle-high (≥5% and <10%), or highest (<5%).

Abbreviations: SES, socioeconomic status; NOS, not otherwise specified.

Of the 21,608 patients in our population, all with a single unilaterally diagnosed lesion, 837 (3.9%) underwent bilateral mastectomy. Those who had CPM were significantly younger (mean =56.9 years, SD =14.2) than those who had a unilateral procedure (mean =68 years, SD =13.7). Only 2.8% of Black patients had CPM as compared to 3.9% of White patients (P<0.001). Lower rates of CPM were seen in non-Hispanic patients (3.8%) compared to Hispanics (4.5%). There was a monotonic relationship between SES and rate of CPM, with CPM increasing from 2.9% in the lowest SES up to 5.3% in the highest SES (P<0.001). Clinical characteristics of all patients and patients with unilateral and CPM are shown in Table 2. Patients undergoing CPM have lower rates of four or more comorbid conditions than those undergoing unilateral procedures (40.6% vs 58.1%) and are more likely to get adjuvant chemotherapy (23.7% vs 11.7%), but are similar on other clinical and pathological characteristics.
Table 2

Clinical and pathological characteristics of female breast cancer patients in Florida (1996–2009)

All female patients
Laterality of procedure done
Unilateral
Bilateral
nColumn %nColumn %Row %nColumn %Row %
All21,608100.020,771100.096.1837100.03.9
Elixhauser co-morbidity count
 None4542.14172.091.9374.48.1
 1–23,71917.23,47916.793.524028.76.5
 3–45,02723.34,80723.195.622026.34.4
 >412,40857.412,06858.197.334040.62.7
Surveillance, Epidemiology, and End Results stage
 Unknown/unstaged1,0725.01,0365.096.6364.33.4
 Localized15,38071.214,75071.095.963075.34.1
 Regional, direct extension ± lymph nodes1,0684.91,0395.097.3293.52.7
 Regional, lymph nodes only3,67517.03,54417.196.413115.73.6
 Distant4131.94021.997.3111.32.7
Histological grade
 Unknown/not stated3,84917.83,71017.996.413916.63.6
 Well-differentiated3,83617.83,67717.795.915919.04.1
 Moderately differentiated8,12137.67,80537.696.131637.83.9
 Poorly differentiated5,50725.55,29425.596.121325.43.9
 Undifferentiated2951.42851.496.6101.23.4
Histological type
 Ductal carcinoma15,83673.315,22373.396.161373.23.9
 Lobular carcinoma3,61216.73,44316.695.316920.24.7
 Other2,16010.02,10510.197.5556.62.5
Adjuvant chemotherapy
 Unknown1,0024.69694.796.7333.93.3
 No17,98883.217,38283.796.660672.43.4
 Yes2,61812.12,42011.792.419823.77.6
Adjuvant radiation therapy
 Unknown6212.96052.997.4161.92.6
 No16,67077.115,91276.695.575890.64.5
 Yes4,31720.04,25420.598.5637.51.5
Death within 30 days of surgery
 No21,48299.420,64799.496.183599.83.9
 Yes1260.61240.698.420.21.6
Survival status
 Dead4,87122.54,80323.198.6688.11.4
 Alive16,73777.515,96876.995.476991.94.6.
In the multivariate logistic regression model without co-morbidities (Table 3; Model 1), Black patients were less likely than Whites to have CPM (OR =0.55, 95% CI =0.39–0.78, P<0.001). Those in the highest SES category were more likely to have CPM compared with lowest SES category (OR =1.37, 95% CI =1.06–1.76, P=0.016). Uninsured patients had significantly lower rates of CPM as compared with privately insured patients (OR =0.58, 95% CI =0.36–0.95, P=0.029). As patients aged they were less likely to undergo this surgery (OR =0.95, 95% CI =0.94–0.96, P<0.001). The characteristics of the facility where patients were treated also played a significant role, as patients at non-teaching compared to teaching (OR =0.69, 95% CI =0.49–0.99, P=0.042) and low compared to high volume hospitals (OR =0.53, 95% CI =0.39–0.72, P<0.001) had significantly lower rates of CPM. Finally, those patients with an SEER graded tumor stage of regional, lymph nodes only, had almost a 25% lower rate of CPM as compared to patients with localized disease (OR =0.78, 95% CI =0.62–0.98, P=0.03).
Table 3

Multivariate logistic regression models for the primary binary outcome of undergoing bilateral mastectomy from the FCDS and AHCA datasets (1996–2009)

VariableCategoryModel 1
Model 2*
OR (95% CI)P-valueOR (95% CI)P-value
RaceWhite1.00 (reference)1.00 (ref)
Black0.55 (0.39, 0.78)<0.0010.59 (0.42, 0.83)0.002
Other1.27 (0.83, 1.93)0.2701.31 (0.87, 1.99)0.200
HispanicNon-Hispanic1.00 (ref)1.00 (ref)
Hispanic1 (0.78, 1.27)0.9720.99 (0.79, 1.25)0.965
SESLowest1.00 (ref)1.00 (ref)
Middle-low1.08 (0.83, 1.41)0.5511.09 (0.83, 1.41)0.545
Middle-high1.15 (0.85, 1.56)0.3521.16 (0.86, 1.58)0.329
Highest1.37 (1.06, 1.76)0.0161.38 (1.07, 1.8)0.014
Age at diagnosisYears0.95 (0.94, 0.96)<0.0010.95 (0.94, 0.96)<0.001
Marital statusNever married1.00 (ref)1.00 (ref)
Married1.07 (0.84, 1.37)0.5731.08 (0.85, 1.38)0.519
Divorced/separated/widowed0.99 (0.76, 1.27)0.9080.99 (0.76, 1.28)0.931
Primary payer at diagnosisPrivate insurance1.00 (ref)1.00 (ref)
Uninsured0.58 (0.36, 0.95)0.0290.60 (0.36, 0.98)0.043
Medicaid0.83 (0.59, 1.17)0.2920.81 (0.57, 1.15)0.230
Medicare0.86 (0.67, 1.11)0.2480.86 (0.67, 1.11)0.249
Defense/military/veteran0.81 (0.46, 1.45)0.4820.80 (0.45, 1.42)0.440
Indian/public0.72 (0.24, 2.11)0.5470.66 (0.22, 1.97)0.461
Insurance, NOS0.70 (0.55, 0.89)0.0040.7 (0.55, 0.89)0.003
Urban/rural livingUrban1.00 (ref)1.00 (ref)
Rural0.83 (0.55, 1.25)0.3710.82 (0.54, 1.25)0.355
Teaching hospitalTeaching hospital1.00 (ref)1.00 (ref)
Non-teaching hospital0.69 (0.49, 0.99)0.0420.73 (0.51, 1.05)0.091
Hospital volumeHigh1.00 (ref)1.00 (ref)
Low0.53 (0.39, 0.72)<0.0010.53 (0.4, 0.72)<.001
Surveillance, Epidemiology, and End Results stageLocalized1.00 (ref)1.00 (ref)
Regional, direct extension ± lymph nodes0.93 (0.67, 1.28)0.6510.99 (0.7, 1.4)0.969
Regional, lymph nodes only0.78 (0.62, 0.98)0.0300.85 (0.64, 1.14)0.276
Distant0.83 (0.45, 1.55)0.5680.94 (0.46, 1.91)0.862

Notes:

Model 2 also includes comorbidities. OR (95% CI): Odds ratio and 95% confidence interval. SES = neighborhood SES was based on percent of individuals living below the federal poverty line from US census tract-level information: lowest (≥20%), middle-low (≥10% and <20%), middle-high (≥5% and <10%), or highest (<5%).

Abbreviations: FCDS, Florida Cancer Data System; AHCA, Florida’s Agency for Health Care Administration; SES, socioeconomic status; NOS, not otherwise specified.

When co-morbidities were included in the multivariate analysis (Table 3; Model 2), the only results that changed in a substantative way, ie, went from significant to non-significant, were that patients at non-teaching hospitals no longer had significantly lower CPM rates (P=0.091) and patients with a SEER stage of regional, lymph nodes only, no longer had lower rates than those with localized disease, indicating that once comorbidities were controlled for, patients with a worse tumor stage were no longer more likely to receive CPM. We also were able to investigate whether any comorbidities were associated with receipt of CPM. We found that two comorbidities were associated with a higher likelihood of CPM: fluid and electrolyte disorder (OR =1.23, 95% CI =1.01–1.49, P=0.038) and depression (OR =1.52, 95% CI =1.24–1.85, P<0.001); and two were associated with a lower rate of CPM: deficiency anemia (OR =0.64, 95% CI =0.43–0.95, P=0.028) and neurological disorders (OR =0.66, 95% CI =0.45–0.99, P=0.44) (results not shown in table). Throughout the period of data collection, the rates of CPM are about 2% from 1996 to 2000 but increase up to 8% in 2008 (Table 4).
Table 4

Rates of CPM (1996–2009)

NoYes

n%n%
CPM
All21,60820,77196.18373.9
Year of diagnosis
 19961,6401,60497.8362.2
 19971,0541,03297.9222.1
 19981,0691,04697.8232.2
 19991,3141,28797.9272.1
 20001,2211,20098.3211.7
 20011,2101,18197.6292.4
 20021,3011,26797.4342.6
 20031,4781,43797.2412.8
 20041,4821,43196.6513.4
 20051,9721,90796.7653.3
 20061,9201,82895.2924.8
 20071,9021,80194.71015.3
 20082,0111,87993.41326.6
 20092,0341,87192.01638.0

Abbreviation: CPM, contralateral prophylactic mastectomy.

Discussion

In this study using large, comprehensive databases, we found a number of associations between demographics characteristics and the likelihood of undergoing CPM. For example, we found that Black women were significantly less likely than Whites to undergo CPM. This may be due to physician differential advice on risks and benefits of this procedure or system issues such as access. Previous studies of clinicians’ prescription and treatment behavior have revealed racial differences in the treatment of breast carcinoma, as well as other conditions for Black patients, including neoadjuvant therapy for esophageal and gastric cancer, chronic cardiac conditions, and lower extremity amputation for peripheral vascular disease.17,18,20–35 We found that age, SES, and insurance status were associated with which patients were more likely to receive a CPM. For SES in the fully adjusted model, we found that patients living in highest SES neighborhoods had a 38% greater likelihood of undergoing CPM compared with the lowest SES. Whether this is a reflection of education of that group or access to health care cannot be concluded from analyses of our large dataset. Older patients were less likely to undergo CPM, but it is unclear if this is related to higher anxiety in younger women, different self-image in older women, or some other factor. Finally, uninsured patients in our study were less likely to have CPM compared with those patients who are privately insured; whether this is related to access to care or patient preference is unclear. Hospital characteristics were also found to be significant predictors of CPM in our study. In the model adjusted for all covariates other than comorbidities, those treated in non-teaching hospitals were less likely to have CPM than those in teaching hospitals. This became non-significant when co-morbidities were added to the model. However, those in low vs high volume hospitals remained less likely to undergo CPM in the fully adjusted model controlling for comorbidities. This could be an admission rate bias or a lower threshold in high volume centers where more complex procedures are embarked upon more frequently. A novel finding for us was that a few comorbidities were associated with a statistically significant incidence of CPM; the strongest association was with major depression where patients with depression were over 50% more likely to undergo CPM (OR =1.52; 95% CI =1.24–1.85, P<0.001). The limited research that has been done on the psychosocial implications of the preventive surgery suggests that prophylactic mastectomy may be effective in reducing distress levels in high-risk women.36–39 Therefore, it may be that a higher likelihood of undergoing CPM is associated with a larger fear of CBC and higher levels of distress and depression after a diagnosis of unilateral breast cancer. Conversely, those with deficiency anemia (OR =0.64, 95% CI =0.43–0.95, P=0.028) or neurological disorders (OR =0.66, 95% CI =0.45–0.99, P=0.44), were approximately 35% less likely to undergo CPM; this may stem from a clinical recognition of these patients being less suitable for the more extensive surgery. Limitations of this study include a relatively small sample size for appropriate subgroup analysis and the lack of data about the presence of BRCA deleterious mutations. Also, the FCDS and AHCA databases do not contain accurate or sufficient information on hormone receptor-negative status of tumors, and therefore, we were unable to explore the association of estrogen/progesterone receptors or triple negative patients with receipt of CPM. Finally, detailed information on additional risk factors such as number and degree of affected relatives or opportunities for nonsurgical risk reduction are not available in the databases. Nonetheless, this study is based on a large statewide cancer registry database and allows us to examine a wide variety of associations of demographic, clinical and comorbid characteristics, with choice of CPM. Thus, it paves the way for subsequent studies that will look more in depth into factors affecting whether patients undergo CPM, in order to ensure treatment equality for all patients. In conclusion, CPM rates reveal significant differences among patients of different race, socioeconomic class, and insurance coverage. The reasons for these differences as well as the rising rates of CPM in the face of lack of evidence of survival benefits remain unexplained. It seems that personal and psychosocial factors are driving the choices about this treatment. It may be that more public health education is needed to better inform patients in their decision making process. Health care delivery systems optimization for more equitable and accessible delivery of breast cancer care, at the same standard, to different socioeconomic classes of patients, at different geographical locations, and regardless of insurance status, should be planned and attempted. More in-depth study of the causes of these disparities is critically needed so as to guide future planning for health care delivery in this area.
  39 in total

1.  Racial differences in the presentation and surgical management of breast cancer.

Authors:  V Velanovich; M U Yood; U Bawle; S D Nathanson; V F Strand; G B Talpos; W Szymanski; F R Lewis
Journal:  Surgery       Date:  1999-04       Impact factor: 3.982

2.  Prognosis among African-American women and white women with lymph node negative breast carcinoma: findings from two randomized clinical trials of the National Surgical Adjuvant Breast and Bowel Project (NSABP).

Authors:  J J Dignam; C K Redmond; B Fisher; J P Costantino; B K Edwards
Journal:  Cancer       Date:  1997-07-01       Impact factor: 6.860

3.  Clinical management factors contribute to the decision for contralateral prophylactic mastectomy.

Authors:  Tari A King; Rita Sakr; Sujata Patil; Inga Gurevich; Michelle Stempel; Michelle Sampson; Monica Morrow
Journal:  J Clin Oncol       Date:  2011-04-04       Impact factor: 44.544

4.  Disaggregating the effects of race on breast cancer survival.

Authors:  D L Howard; R Penchansky; M B Brown
Journal:  Fam Med       Date:  1998-03       Impact factor: 1.756

Review 5.  Prophylactic bilateral mastectomy for breast cancer prevention.

Authors:  Kelly A Metcalfe
Journal:  J Womens Health (Larchmt)       Date:  2004-09       Impact factor: 2.681

6.  Contralateral breast cancer in BRCA1 and BRCA2 mutation carriers.

Authors:  Kelly Metcalfe; Henry T Lynch; Parviz Ghadirian; Nadine Tung; Ivo Olivotto; Ellen Warner; Olufunmilayo I Olopade; Andrea Eisen; Barbara Weber; Jane McLennan; Ping Sun; William D Foulkes; Steven A Narod
Journal:  J Clin Oncol       Date:  2004-06-15       Impact factor: 44.544

7.  Widening disparity in survival between white and African-American patients with breast carcinoma treated in the U. S. Department of Defense Healthcare system.

Authors:  Ismail Jatoi; Heiko Becher; Charles R Leake
Journal:  Cancer       Date:  2003-09-01       Impact factor: 6.860

8.  Increasing rates of contralateral prophylactic mastectomy among patients with ductal carcinoma in situ.

Authors:  Todd M Tuttle; Stephanie Jarosek; Elizabeth B Habermann; Amanda Arrington; Anasooya Abraham; Todd J Morris; Beth A Virnig
Journal:  J Clin Oncol       Date:  2009-02-17       Impact factor: 44.544

9.  Nonclinical factors associated with surgery received for treatment of early-stage breast cancer.

Authors:  E R Satariano; G M Swanson; P P Moll
Journal:  Am J Public Health       Date:  1992-02       Impact factor: 9.308

10.  Effect of anastrozole and tamoxifen as adjuvant treatment for early-stage breast cancer: 100-month analysis of the ATAC trial.

Authors:  John F Forbes; Jack Cuzick; Aman Buzdar; Anthony Howell; Jeffrey S Tobias; Michael Baum
Journal:  Lancet Oncol       Date:  2008-01       Impact factor: 41.316

View more
  6 in total

1.  State Variation in the Receipt of a Contralateral Prophylactic Mastectomy Among Women Who Received a Diagnosis of Invasive Unilateral Early-Stage Breast Cancer in the United States, 2004-2012.

Authors:  Rebecca Nash; Michael Goodman; Chun Chieh Lin; Rachel A Freedman; Laura S Dominici; Kevin Ward; Ahmedin Jemal
Journal:  JAMA Surg       Date:  2017-07-01       Impact factor: 14.766

2.  Motivations for contralateral prophylactic mastectomy as a function of socioeconomic status.

Authors:  Dadrie F Baptiste; Erina L MacGeorge; Maria K Venetis; Ashton Mouton; L Brooke Friley; Rebekah Pastor; Kristen Hatten; Janaka Lagoo; Susan E Clare; Monet W Bowling
Journal:  BMC Womens Health       Date:  2017-02-01       Impact factor: 2.809

3.  Disparities in contralateral prophylactic mastectomy use among women with early-stage breast cancer.

Authors:  Younji Kim; Anne Marie McCarthy; Mirar Bristol; Katrina Armstrong
Journal:  NPJ Breast Cancer       Date:  2017-01-27

4.  Higher Stage of Disease Is Associated With Bilateral Mastectomy Among Patients With Breast Cancer: A Population-Based Survey.

Authors:  Rachel A Freedman; Elena M Kouri; Dee W West; Shoshana Rosenberg; Ann H Partridge; Joyce Lii; Nancy L Keating
Journal:  Clin Breast Cancer       Date:  2015-08-28       Impact factor: 3.078

5.  Social Network, Surgeon, and Media Influence on the Decision to Undergo Contralateral Prophylactic Mastectomy.

Authors:  Maria K Venetis; Erina L MacGeorge; Dadrie F Baptiste; Ashton Mouton; Lorin B Friley; Rebekah Pastor; Kristen Hatten; Janaka Lagoo; Monet W Bowling; Susan E Clare
Journal:  Am J Clin Oncol       Date:  2018-06       Impact factor: 2.339

6.  Trends in contralateral prophylactic mastectomy rate according to clinicopathologic and socioeconomic status.

Authors:  Ho Jong Jeon; Hyung Seok Park; Ji Soo Park; Eun Ji Nam; Seung-Tae Lee; Jeongwoo Han
Journal:  Ann Surg Treat Res       Date:  2019-08-29       Impact factor: 1.859

  6 in total

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