Abstracts

Introduction

Breast cancer is the most common malignant neoplasm among women in Brazil, with an expected incidence of 57,120 new cases for the year 2014 ¹1. Instituto Nacional de Câncer José Alencar Gomes da Silva. INCA e Ministério da Saúde apresentam estimativas de câncer para 2014. http://www2.inca.gov.br/wps/wcm/connect/agencianoticias/site/home/noticias/2013/inca_ministerio_saude_apresentam_estimativas_cancer_2014 (accessed on 20/Jul/2014).
http://www2.inca.gov.br/wps/wcm/connect/... . Age is the strongest risk factor for the disease ²2. Turner N, Zafarana E, Becheri D, Mottino G, Biganzoli L. Breast cancer in the elderly: which lessons have we learned? Future Oncol 2013; 9:1871-81. and thus, breast cancer incidence is increasing with population aging in Brazil³3. Instituto Nacional de Câncer José Alencar Gomes da Silva. Atlas de mortalidade por câncer. http://mortalidade.inca.gov.br/Mortalidade/ (accessed on 24/Jun/2012).
http://mortalidade.inca.gov.br/Mortalida... ,⁴4. Instituto Brasileiro de Geografia e Estatística. Atlas do Censo Demográfico 2010. http://www.ibge.gov.br/home/geociencias/geografia/atlas.shtm?c=5 (accessed on 15/Sep/2014).
http://www.ibge.gov.br/home/geociencias/... .

Many studies have reported that older women (≥ 70 years of age) have less aggressive breast cancer, including a higher frequency of lower grade tumors and positivity for hormone receptors⁵5. Gennari R, Curigliano G, Rotmensz N, Robertson C, Colleoni M, Zurrida S, et al. Breast carcinoma in elderly women: features of disease presentation, choice of local and systemic treatments compared with younger postmenopasual patients. Cancer 2004; 101:1302-10.,⁶6. Schonberg MA, Marcantonio ER, Ngo L, Li D, Silliman RA, McCarthy EP. Causes of death and rl.ive survival of older women after a breast cancer diagnosis. J Clin Oncol 2011; 29:1570-7.,⁷7. Dutra MC, Rezende MA, de Andrade VP, Soares FA, Ribeiro M V, de Paula EC, et al. Imunofenótipo e evolução de câncer de mama: comparação entre mulheres muito jovens e mulheres na pós-menopausa. Rev Bras Ginecol Obstet 2009; 31:54-60.. However, they may receive less than standard treatment, due to the presence of comorbidities or to a belief in a less aggressive disease in this age subgroup⁶6. Schonberg MA, Marcantonio ER, Ngo L, Li D, Silliman RA, McCarthy EP. Causes of death and rl.ive survival of older women after a breast cancer diagnosis. J Clin Oncol 2011; 29:1570-7.,⁸8. Schonberg MA, Marcantonio ER, Li D, Silliman RA, Ngo L, McCarthy EP. Breast cancer among the oldest old: tumor characteristics, treatment choices, and survival. J Clin Oncol 2010; 28:2038-45.,⁹9. Yood MU, Owusu C, Buist DSM, Geiger AM, Field TS, Thwin SS, et al. Mortality impact of less-than-standard therapy in older breast cancer patients. J Am Coll Surg 2008; 206:66-75.. Compared to the elderly, young women (≤ 35 years of age) have more frequently higher grade breast cancer and negativity for hormone receptors ⁷7. Dutra MC, Rezende MA, de Andrade VP, Soares FA, Ribeiro M V, de Paula EC, et al. Imunofenótipo e evolução de câncer de mama: comparação entre mulheres muito jovens e mulheres na pós-menopausa. Rev Bras Ginecol Obstet 2009; 31:54-60.,¹⁰10. Thomas GA, Leonard RC. How age affects the biology of breast cancer. Clin Oncol (R Coll Radiol) 2009; 21:81-5.. Yet, it remains unclear whether age is an independent prognostic factor for the lower survival among younger and older patients ¹⁰10. Thomas GA, Leonard RC. How age affects the biology of breast cancer. Clin Oncol (R Coll Radiol) 2009; 21:81-5.,¹¹11. Colzani E, Liljegren A, Johansson ALV, Adolfsson J, Hellborg H, Hall PFL, et al. Prognosis of patients with breast cancer: causes of death and effects of time since diagnosis, age, and tumor characteristics. J Clin Oncol 2011; 29:4014-21. or if the increase in mortality risk is associated with different tumor features in these groups.

In a previous study ¹²12. Balabram D, Turra CM, Gobbi H. Survival of patients with operable breast cancer (Stages I-III) at a Brazilian public hospital – a closer look into cause-specific mortality. BMC Cancer 2013; 13:434., we found that women 70 years of age and older have a higher risk of dying from breast cancer, independent of tumor related factors, in comparison with patients 36 to 69 years of age. Other studies have also demonstrated a higher chance of dying from breast cancer for both older and younger age groups²2. Turner N, Zafarana E, Becheri D, Mottino G, Biganzoli L. Breast cancer in the elderly: which lessons have we learned? Future Oncol 2013; 9:1871-81.,⁸8. Schonberg MA, Marcantonio ER, Li D, Silliman RA, Ngo L, McCarthy EP. Breast cancer among the oldest old: tumor characteristics, treatment choices, and survival. J Clin Oncol 2010; 28:2038-45.,¹⁰10. Thomas GA, Leonard RC. How age affects the biology of breast cancer. Clin Oncol (R Coll Radiol) 2009; 21:81-5.,¹³13. Beadle BM, Woodward WA, Buchholz TA. The impact of age on outcome in early-stage breast cancer. Semin Radiat Oncol 2011; 21:26-34.,¹⁴14. Jatoi I, Anderson WF, Rosenberg PS. Qualitative age-interactions in breast cancer: a tale of two diseases? Am J Clin Oncol 2008; 31:504-6.. Furthermore, older age is related to a higher prevalence of comorbidities, which may reduce overall and disease-specific survival¹⁵15. Berglund A, Wigertz A, Adolfsson J, Ahlgren J, Fornander T, Wärnberg F, et al. Impact of comorbidity on management and mortality in women diagnosed with breast cancer. Breast Cancer Res Treat 2012; 135:281-9.,¹⁶16. Jørgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer 2012; 106:1353-60.. Here, we examine in more detail the relationship between age and mortality from breast cancer and other causes of death, by looking at the role played by several intervening variables, including skin color, comorbidities, tumor factors and use of systemic treatments. We use data from patients treated at a public Brazilian hospital between the years 2001 and 2008.

Methods

Study design and population

We obtained data from a retrospective cohort study of patients with breast cancer, stages I-III, who underwent surgery for breast cancer treatment at the Hospital das Clínicas of the Minas Gerais Federal University, Belo Horizonte, Minas Gerais State, Brazil (HC-UFMG), from the years 2001 to 2008. The UFMG Ethics Research Committee approved the study's protocol on March 7, 2012 (project CAAE number 0660.0.203.000-11).

Among the 1,004 patients who underwent surgery for treatment of invasive breast cancer at the HC-UFMG between 2001 and 2008, we excluded 75 women for whom treatment was not paid for by the public health system. Of the remaining 929 individuals, we excluded cases without medical records (n = 76), patients who underwent surgery only for palliative purposes (stage IV disease, n = 7), individuals with recurrent breast cancer (n = 14), patients who had incomplete information on tumor stage (n = 30), and 35 cases with missing data on the independent variables. After excluding these cases, our cohort analysis contained 767 patients.

Variables

We examined four sets of determinants of mortality: patient demographic characteristics, health status, tumor characteristics, and systemic treatments (hormone and chemotherapy).

Besides age, measured in three categories (up to 35, 36 to 69, and 70 years and older), we included tumor size and lymph node status according to theAmerican Joint Committee on Cancer Staging Manual¹⁷17. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A. AJCC cancer staging manual. 7th Ed. New York: Springer; 2009.,¹⁸18. Lakhani SR, Ellis IO, Schnitt SJ, Tan PH, van de Vijver MJ. WHO classification of tumours of the breast. 4th Ed. Lyon: IARC Press; 2012., as well as tumor type and grade ¹⁸18. Lakhani SR, Ellis IO, Schnitt SJ, Tan PH, van de Vijver MJ. WHO classification of tumours of the breast. 4th Ed. Lyon: IARC Press; 2012.. From the patients' medical records we obtained information on skin color and comorbidities. Skin color was assigned by the attending physician as white, black and brown skin. We dichotomized the patients into white and non-white (black or brown skin). As for the comorbidities, we used the Charlson comorbidity index, which combines mortality risk levels associated with different chronic conditions, such as diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, and dementia¹⁹19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40:373-83.. Each condition receives a different score (1, 2, 3 or 6), depending on the risk of death associated with it. The scores are then added up to provide a total score for each individual¹⁹19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40:373-83.. In our study cohort, all patients had at least a score of 2 because of the presence of tumor without metastasis. Most of them (625 patients, 81.5%) had no other major comorbidities and thus, maintained a score of 2. 114 patients (14.9%) had a comorbidity that resulted in one extra point (final score of 3). The remaining patients (28, 3.7%) had scores of 4, 5 and 6. In order to reduce the small variability of counts in the index, we constructed a dichotomous variable, which is equal to one for patients who had at least a score of 3, and zero for those with a score that is equal to 2.

We added both the use of hormone and chemotherapy in our analysis. Some of the patients in our study underwent systemic therapies in other hospitals and therefore, have missing values for chemotherapy (36 patients, 4.7%) and hormone therapy (41 patients, 5.5%). We used information on estrogen receptor status to impute the missing values of hormone therapy. We assumed that patients with estrogen receptor (ER) positive tumors (21 cases) received treatment, whereas patients with ER negative tumors (20 cases) did not. One may notice that we chose not to add ER as an independent variable in our models, since there were too many missing values for this measure (55 cases, 7.2%). For imputing missing data for chemotherapy we applied a relatively more complex algorithm, based on four variables: disease stage, age, use of hormone therapy, and presence of comorbidities. These variables are frequently used to predict the benefit of prescribing chemotherapy for patients with breast cancer²⁰20. Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012; 379:432-44.,²¹21. Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, et al. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 2011; 378:1707-16.. We assumed that patients with stage I disease did not undergo chemotherapy. Also, we considered that stage II patients received chemotherapy, only if they were younger than 70 years of age, had no major comorbidities, and had not received endocrine therapy. We further assumed that stage II patients who were older than 70 years of age received chemotherapy if they did not receive hormone therapy and had no comorbidities. We considered all stage III patients who were younger than 70 years of age to have received chemotherapy. Stage III patients older than 70 years only received chemotherapy if they did not receive hormone therapy and had no major comorbidities. After the imputation procedure, we determined that 20 patients received chemotherapy, while the other 16 did not. Since the number of missing cases is somewhat small, alternative imputation procedures for both the hormone and chemotherapies proved to have only minor effects on our results.

We retrieved data on cause and date of death as checked in the Mortality Information System (SIM) of the Brazilian Ministry of Health ³3. Instituto Nacional de Câncer José Alencar Gomes da Silva. Atlas de mortalidade por câncer. http://mortalidade.inca.gov.br/Mortalidade/ (accessed on 24/Jun/2012).
http://mortalidade.inca.gov.br/Mortalida... , from 2001 to 2011. When the cause of death was unknown or the patient died without assistance (7 cases, 2.8% of total of deaths), breast cancer was considered to be the cause ²²22. Dignam JJ, Huang L, Ries L, Reichman M, Mariotto A, Feuer E. Estimating breast cancer-specific and other-cause mortality in clinical trial and population-based cancer registry cohorts. Cancer 2009; 115:5272-83.,²³23. Howlader N, Ries LAG, Mariotto AB, Reichman ME, Ruhl J, Cronin KA. Improved estimates of cancer-specific survival rates from population-based data. J Natl Cancer Inst 2010; 102:1584-98.. We used a probabilistic record linkage strategy to identify patients in our database who had died up to December 31^st, 2011²⁴24. Camargo Jr. KR, Coeli CM. Reclink: aplicativo para o relacionamento de bases de dados, implementando o métodoprobabilistic record linkage. Cad Saúde Pública 2000; 16:439-47.. The program used was the RecLink version 3.0 ²⁴24. Camargo Jr. KR, Coeli CM. Reclink: aplicativo para o relacionamento de bases de dados, implementando o métodoprobabilistic record linkage. Cad Saúde Pública 2000; 16:439-47.. Survival time was counted from the first day of treatment (surgery or chemotherapy) until the date of death or the end of the study period. The patient's first and last name, their mother's name and their date of birth were retrieved from both databases (the study's and the SIM database), and different linkage strategies were used to find patients who had died ¹²12. Balabram D, Turra CM, Gobbi H. Survival of patients with operable breast cancer (Stages I-III) at a Brazilian public hospital – a closer look into cause-specific mortality. BMC Cancer 2013; 13:434.. Patients not found in the MIS database were considered to be alive at the end of the observation period. We classified the causes of death according to the International Classification of Diseases, 10^th revision (ICD-10)²⁵25. World Health Organization. International Classification of Diseases (ICD). http://www.who.int/classifications/icd/en/ (accessed on 03/Sep/2011).
http://www.who.int/classifications/icd/e... . The linkage procedure was described in detail in an earlier study ¹²12. Balabram D, Turra CM, Gobbi H. Survival of patients with operable breast cancer (Stages I-III) at a Brazilian public hospital – a closer look into cause-specific mortality. BMC Cancer 2013; 13:434..

Statistical analysis

We used the exact two-sided linear-by-linear association statistic to compare the distribution of patient and tumor characteristics across age groups. The significance level was defined as 0.05. Mean and median survival times were calculated. We use Cox proportional hazards model for survival analysis. The hazard ratios (HR) and 95% confidence intervals (95%CI) for each independent variable were calculated.

We tested interaction terms between age and each one of the covariates in our final model, but none of them was significant. Further, adding interaction terms did not improve our model as indicated by the log-likelihood statistic. We confirmed the proportional hazard assumption for all variables in each of the models by verifying Schoenfeld residuals against survival time. All statistical analyses were performed with the IBM SPSS software, version 21.0 (IBM Corp., Armonk, USA).

In the first set of regression models, we examined cause-specific survival, by censoring patients who died from causes not related to breast cancer. We specified four Cox models to explore the effects of age on mortality. Model 1 includes only age. Model 2 adds other patient's characteristics (skin color and the Charlson comorbidity index). The tumor characteristics were added in Model 3, and in Model 4 the use of chemo and hormone therapy were included as well. In the second set of regression models, we followed the same sequence of Cox proportional hazard models as in the first set, but considered survival only from causes not related to the disease.

Results

The observation period ranged from 1 to 131 months, with a median of 62 months. Overall, there were 251 deaths (33.7%), 205 of them due to breast cancer (82% of the total of deaths, Table 1). Among deaths due to other causes, the most frequent causes were: cardiovascular diseases (13 cases, 5.2% of the total of deaths), respiratory diseases (9 deaths, 3.6%) and other cancers (9 deaths, 3.6%). There were no deaths related to external causes. Not surprisingly, deaths due to breast cancer were relatively more frequent in the youngest age group (94.1% of the total of deaths at this age), followed by women aged 36-69 years (89% of deaths), and the oldest age group (61.4% of deaths). The prevalence of comorbidities increased with age (39.4% of patients 70 years and older had at least one comorbidity).

Table 1 shows the distribution of the explanatory variables by age. Younger patients were more frequently non-white (78% non-white versus 22% white) than the oldest age group (52.8% non-white versus 47.2% white). The distributions of tumor type, tumor size, and lymph node status were not different across age groups. However, when lymph node status was dichotomized in negative and positive, younger patients were more likely to have positive axillary lymph nodes than the older patients: 28 patients younger than 35 years of age (68.3%) had at least one positive lymph node compared to 74 patients (52.1%) at the age of 70 years old and older. High-grade tumors were also more frequent among the youngest patients. Regarding the use of systemic therapies, chemotherapy was more frequently used by younger women (35 women in the youngest age group, 85.4%, versus 68 women, 47.9%, in the oldest cohort). The use of hormone therapy did not change across age categories. Table 2 shows crude hazard ratios for each of the covariates, in models of disease-specific and mortality associated with other causes.

Table 3 shows the results for cause-specific Cox regression models. According to model 1, both patients aged 35 years old and younger (HR = 1.81; 95%CI: 1.08-3.03), and 70 years old and older (HR = 1.42; 95%CI: 1.01-2.00), had lower disease-specific survival than women 36 to 69 years old. The coefficients for age changed little in Model 2, indicating that most of the effect of age is not captured by the presence of at least one comorbidity and patients' skin color. Model 2 also shows that non-white women have a higher risk of dying than white women (HR = 0.71; 95%CI: 0.53-0.96). Having at least one comorbidity was not associated with lower disease-specific survival (HR = 1.06; 95%CI: 0.73-1.54).

Model 3 (Table 3) shows that multiple tumor characteristics are significantly associated with the risk of dying during the observation period. Higher tumor grade, larger tumor size, and higher number of positive nodes were all associated with higher mortality risks. The effect of the youngest age group, however, became no longer statistically significant (HR = 1.64; 95%CI: 0.97-2.76). On the other hand, the coefficient for the oldest age group remained virtually unchanged and statistically significant in Model 3 (HR = 1.44; 95%CI: 1.01-2.06). The patient's skin color (HR = 0.79; 95%CI: 0.58-1.07), and the presence of at least one comorbidity (HR = 1.19; 95%CI: 0.82-1.72), were not statistically associated with death due to breast cancer.

When adding chemo and hormone therapies (Model 4, Table 3), the youngest age group became again associated with a higher risk of dying due to breast cancer (HR = 1.78; 95%CI: 1.05-3.01), while the effect for the oldest age group became slightly greater (HR = 1.51; 95%CI: 1.04-2.18). Tumor related factors remained significantly associated with survival. Chemotherapy was not statistically associated with longer survival among the patients, although the use of hormone therapy seemed to have a statistically significant protective effect (HR = 0.59; 95%CI: 0.44-0.80).

In Table 4, we compare the regression models for mortality risk due to causes other than breast cancer, excluding patients up to 35 years of age, since there was only one death not associated with breast cancer in this age group. Thus, we had a total of 726 cases in this analysis. The oldest age category was significantly associated with a higher risk of dying in every model than the age group 36 to 69 years old. Also, the mortality risk from other causes of death was significantly increased for white women and for those who had at least one comorbidity. The variables associated with tumor characteristics and systemic treatments were not predictors of mortality from other causes of death in any of the models.

Discussion

In the current study, we showed that the relationship between age and breast cancer survival remained statistically significant after including all the control variables available for our analysis. When age was considered alone, we found that the oldest age group (70 years old and older, HR = 1.42; 95%CI: 1.01-2.00) and the youngest one (up to 35 years old, HR = 1.81; 95%CI: 1.08-3.03) had higher risk of dying due to breast cancer than patients 36 to 69 years old. When tumor and patient characteristics, as well as use of systemic therapies were added to the model, the survival disadvantage of the youngest (up to 35 years old, HR = 1.78; 95%CI: 1.05-3.01) and the oldest groups (70 years old and older, HR = 1.51; 95%CI: 1.04-2.18) remained statistically significant.

Nevertheless, our results also revealed that the magnitude and significance of some of the age effects varied depending on the different sets of variables included in the models. We found that tumor characteristics explain to some extent the survival disadvantage among the youngest patients. These results are in accordance with previous research that has shown that younger patients have more aggressive tumors, whereas older patients have lower grade diseases, but a higher frequency of comorbidities, and more advanced stages at diagnosis²2. Turner N, Zafarana E, Becheri D, Mottino G, Biganzoli L. Breast cancer in the elderly: which lessons have we learned? Future Oncol 2013; 9:1871-81.,⁷7. Dutra MC, Rezende MA, de Andrade VP, Soares FA, Ribeiro M V, de Paula EC, et al. Imunofenótipo e evolução de câncer de mama: comparação entre mulheres muito jovens e mulheres na pós-menopausa. Rev Bras Ginecol Obstet 2009; 31:54-60.,¹⁰10. Thomas GA, Leonard RC. How age affects the biology of breast cancer. Clin Oncol (R Coll Radiol) 2009; 21:81-5.,¹³13. Beadle BM, Woodward WA, Buchholz TA. The impact of age on outcome in early-stage breast cancer. Semin Radiat Oncol 2011; 21:26-34.,¹⁴14. Jatoi I, Anderson WF, Rosenberg PS. Qualitative age-interactions in breast cancer: a tale of two diseases? Am J Clin Oncol 2008; 31:504-6.,²⁶26. Biganzoli L, Wildiers H, Oakman C, Marotti L, Loibl S, Kunkler I, et al. Management of elderly patients with breast cancer: updated recommendations of the International Society of Geriatric Oncology (SIOG) and European Society of Breast Cancer Specialists (EUSOMA). Lancet Oncol 2012; 13:e148-60.,²⁷27. Ring A, Reed M, Leonard R, Kunkler I, Muss H, Wildiers H, et al. The treatment of early breast cancer in women over the age of 70. Br J Cancer 2011; 105:189-93.. In our study, the presence of comorbidities was not associated with a higher hazard of dying from breast cancer, as shown in the study by Berglund et al.¹⁵15. Berglund A, Wigertz A, Adolfsson J, Ahlgren J, Fornander T, Wärnberg F, et al. Impact of comorbidity on management and mortality in women diagnosed with breast cancer. Breast Cancer Res Treat 2012; 135:281-9..

Some of the crude hazard ratios for tumor related factors (tumor size and grade, and lymph node status) in the univariate analysis (Table 2) were substantially larger than the adjusted effects in the multivariate models shown in Table 3. One possible explanation for this pattern is the existence of high correlation among the predictor variables. Yet, in tests that we performed using the variance inflation factor, we found multicollinearity to be within the limits of tolerance. Keeping the predictor variables together in our final model is also in accordance with the literature that has shown they are independent prognostic factors in cancer survival.

The appropriate treatment for elderly women with breast cancer remains a matter of debate. Since older women are usually not included in treatment trials, the benefits of therapy for them are more difficult to evaluate²2. Turner N, Zafarana E, Becheri D, Mottino G, Biganzoli L. Breast cancer in the elderly: which lessons have we learned? Future Oncol 2013; 9:1871-81.,²⁶26. Biganzoli L, Wildiers H, Oakman C, Marotti L, Loibl S, Kunkler I, et al. Management of elderly patients with breast cancer: updated recommendations of the International Society of Geriatric Oncology (SIOG) and European Society of Breast Cancer Specialists (EUSOMA). Lancet Oncol 2012; 13:e148-60.. Also, the incidence of toxicity after adjuvant treatments²2. Turner N, Zafarana E, Becheri D, Mottino G, Biganzoli L. Breast cancer in the elderly: which lessons have we learned? Future Oncol 2013; 9:1871-81. and the presence of comorbidities ¹⁵15. Berglund A, Wigertz A, Adolfsson J, Ahlgren J, Fornander T, Wärnberg F, et al. Impact of comorbidity on management and mortality in women diagnosed with breast cancer. Breast Cancer Res Treat 2012; 135:281-9.,¹⁶16. Jørgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer 2012; 106:1353-60. is higher among the elderly, thus reducing the use of these types of therapies. On the other hand, some studies have shown that less than standard treatment can be harmful for older patients⁶6. Schonberg MA, Marcantonio ER, Ngo L, Li D, Silliman RA, McCarthy EP. Causes of death and rl.ive survival of older women after a breast cancer diagnosis. J Clin Oncol 2011; 29:1570-7.,⁸8. Schonberg MA, Marcantonio ER, Li D, Silliman RA, Ngo L, McCarthy EP. Breast cancer among the oldest old: tumor characteristics, treatment choices, and survival. J Clin Oncol 2010; 28:2038-45.,⁹9. Yood MU, Owusu C, Buist DSM, Geiger AM, Field TS, Thwin SS, et al. Mortality impact of less-than-standard therapy in older breast cancer patients. J Am Coll Surg 2008; 206:66-75., and thus, the individualization of treatment strategies is recommended. Chemotherapy was less frequently used among older patients (p-value < 0.001) in our cohort and it did not seem to offer mortality protection among patients of any age group. The benefits of chemotherapy are known to be more important among hormone receptor-negative patients²⁰20. Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012; 379:432-44.,²¹21. Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, et al. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 2011; 378:1707-16., and since around 70% of our study comprises patients with hormone receptor-positive tumors, the cohort size may be too small to show the protective effects of this type of treatment. Other treatment modalities, like type of surgical treatment and use of radiation therapy, were omitted from our analysis, since they have a weaker association with overall survival than the other measures²⁰20. Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012; 379:432-44.,²¹21. Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, et al. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 2011; 378:1707-16.,²⁶26. Biganzoli L, Wildiers H, Oakman C, Marotti L, Loibl S, Kunkler I, et al. Management of elderly patients with breast cancer: updated recommendations of the International Society of Geriatric Oncology (SIOG) and European Society of Breast Cancer Specialists (EUSOMA). Lancet Oncol 2012; 13:e148-60..

A similar debate exists surrounding the ideal age to interrupt breast cancer screening. In Brazil, the recommended age span for screening by the Public Health System is from 50 to 69 years of age²⁸28. Instituto Nacional de Câncer José Alencar Gomes da Silva. SISMAMA – informação para o avanço das ações de controle do câncer de mama no Brasil. http://bvsms.saude.gov.br/bvs/publicacoes/inca/Sismama.pdf (accessed on 31/Mar/2013).
http://bvsms.saude.gov.br/bvs/publicacoe... . According to the International Society of Geriatric Oncology (SIOG), the decision to maintain screening over the age of 70 should be “based on risks and benefits, patient preference, physiological age, and life expectancy”²⁶26. Biganzoli L, Wildiers H, Oakman C, Marotti L, Loibl S, Kunkler I, et al. Management of elderly patients with breast cancer: updated recommendations of the International Society of Geriatric Oncology (SIOG) and European Society of Breast Cancer Specialists (EUSOMA). Lancet Oncol 2012; 13:e148-60.(p. e152). Unfortunately we cannot test directly for delayed diagnosis, although in our sample the distribution of patients by tumor size and lymph node status, compared to countries where screening is available, suggests that there are more advanced cases in Brazil, particularly among the elderly²⁹29. National Cancer Institute. SEER Stat Fact Sheets: breast. http://seer.cancer.gov/statfacts/html/breast.html (accessed on 01/Apr/2014).
http://seer.cancer.gov/statfacts/html/br... ,³⁰30. Liedke PER, Finkelstein DM, Szymonifka J, Barrios CH, Chavarri-Guerra Y, Bines J, et al. Outcomes of breast cancer in Brazil related to health care coverage: a retrospective cohort study. Cancer Epidemiol Biomarkers Prev 2014; 23:126-33..

One of the limitations of our study is that we drew our data from pathology records, which means we have selected a sample of patients which were at least fit enough to undergo surgical treatment. Patients with a lower health status who could not have undergone surgery were excluded from our cohort study from the start, which precludes us from generalizing our conclusions to all breast cancer patients. In addition, we obtained data on comorbidities and adjuvant therapies from medical records. Therefore, we were not able to identify which chemo and hormone therapy regimens were applied to each patient, although we recognize that they vary depending on disease characteristics and comorbidities.

One interesting finding from our study is the association between skin color and mortality. In Brazil, classifying patients by race is not trivial due to the high miscegenation rate³¹31. Alves C, Silva MSD, Pinto LM, Toralles MBP, Tavares-Neto J. Definition and use of the variable “race” by medical students in Salvador, Brazil. São Paulo Med J 2010; 128:206-10.,³²32. Pena SDJ, Di Pietro G, Fuchshuber-Moraes M, Genro JP, Hutz MH, Kehdy FSG, et al. The genomic ancestry of individuals from different geographical regions of Brazil is more uniform than expected. PLoS One 2011; 6:e17063.,³³33. Pena SDJ. Razões para banir o conceito de raça da medicina brasileira. Hist Ciênc Saúde-Manguinhos 2005; 12:321-46., and thus, we used information on patient's skin color collected by the attending physicians. The prevalence of non-white women was higher in the youngest age group (78%) compared with older individuals (52.8% of women 70 years old and older were non-white). Also, non-white women had shorter breast-cancer specific survival, when controlling for age and the presence of comorbidities. The mortality disadvantage, however, became insignificant when the variables associated with tumor characteristics were accounted for in our regression models. These results suggest that non-white patients may have more aggressive tumors ³⁴34. Smith EC, Ziogas A, Anton-Culver H. Delay in surgical treatment and survival after breast cancer diagnosis in young women by race/ethnicity. JAMA Surg 2013; 148:516-23., more difficult access to health care³⁵35. Chor D. Health inequalities in Brazil: race matters. Cad Saúde Pública 2013; 29:1272-5.,³⁶36. Chor D, Lima CRA. Aspectos epidemiológicos das desigualdades raciais em saúde no Brasil. Cad Saúde Pública 2005; 21:1586-94., or both. In the analysis of mortality due to other causes of death, we found an inverted relation between skin color and mortality: white patients had a higher risk of dying than non-white patients. To interpret these results, one should note that when implementing the cause-specific Cox regression models we treated deaths from cause of interest as events and the other group of deaths as right censored observations. In the presence of right censored event times, the regression coefficients give us only the effect of skin color on the instantaneous hazard of dying. Therefore, we cannot directly link our results to the cumulative incidence function ³⁷37. Haller B, Schmidt G, Ulm K. Applying competing risks regression models: an overview. Lifetime Data Anal 2013; 19:33-58. and conclude, based on the lower instantaneous hazard from other causes of deaths among non-whites, that this group truly experiences lower incidence of this types of deaths.

Our finding that tumor related characteristics were not significantly associated with mortality due to other causes of death is not surprising and indicates the quality of our data, particularly the accuracy of the classification of causes of death. One should note that the risk of dying from diseases other than breast cancer increases with time since diagnosis³⁸38. Riihimäki M, Thomsen H, Brandt A, Sundquist J, Hemminki K. Death causes in breast cancer patients. Ann Oncol 2012; 23:604-10.,³⁹39. Du XL, Fox EE, Lai D. Competing causes of death for women with breast cancer and change over time from 1975 to 2003. Am J Clin Oncol 2008; 31:105-16., especially after ten years. Since the median follow-up time in our study was much shorter, we already expected a larger proportion of deaths (82% of total of deaths) due to breast cancer compared to other studies. However, in at least one study of American women, which followed patients for a period of time (2000 to 2007) shorter than the observation period in our study, the proportion of breast cancer deaths was relatively lower (only 56%)⁴⁰40. Cho H, Mariotto AB, Mann BS, Klabunde CN, Feuer EJ. Assessing non-cancer-related health status of US cancer patients: other-cause survival and comorbidity prevalence. Am J Epidemiol 2013; 178:339-49..

Multiple factors are involved in mortality from breast cancer and there is still much to be learned about the biological, medical and socioeconomic mechanisms responsible for improving survival. The current study has extended previous research for Brazil in showing that age is an independent predictor of cause-specific mortality, at least in the presence of the numerous control variables available for our analysis. Of course, survival differentials by age may depend on a variety of other key factors not included in our models such as the socioeconomic status of patients³⁰30. Liedke PER, Finkelstein DM, Szymonifka J, Barrios CH, Chavarri-Guerra Y, Bines J, et al. Outcomes of breast cancer in Brazil related to health care coverage: a retrospective cohort study. Cancer Epidemiol Biomarkers Prev 2014; 23:126-33., access to health care³¹31. Alves C, Silva MSD, Pinto LM, Toralles MBP, Tavares-Neto J. Definition and use of the variable “race” by medical students in Salvador, Brazil. São Paulo Med J 2010; 128:206-10.,³²32. Pena SDJ, Di Pietro G, Fuchshuber-Moraes M, Genro JP, Hutz MH, Kehdy FSG, et al. The genomic ancestry of individuals from different geographical regions of Brazil is more uniform than expected. PLoS One 2011; 6:e17063., and more detailed data on types of treatment employed ¹²12. Balabram D, Turra CM, Gobbi H. Survival of patients with operable breast cancer (Stages I-III) at a Brazilian public hospital – a closer look into cause-specific mortality. BMC Cancer 2013; 13:434.,¹⁷17. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A. AJCC cancer staging manual. 7th Ed. New York: Springer; 2009.,⁴¹41. Oliveira EXG, Melo ECP, Pinheiro RS, Noronha CP, Carvalho MS. Acesso à assistência oncológica: mapeamento dos fluxos origem-destino das internações e dos atendimentos ambulatoriais. O caso do câncer de mama. Cad Saúde Pública 2011; 27:317-26.,⁴²42. Silber JH, Rosenbaum PR, Clark AS, Giantonio BJ, Ross RN, Teng Y, et al. Characteristics associated with differences in survival among black and white women with breast cancer. JAMA 2013; 310:389-97., which reinforces the need for further analysis. Understanding the pathways linking age to mortality due to breast cancer should help doctors, epidemiologists and policy makers to propose specific measures to improve the chances of survival for women of different age groups, particularly in a context of profound changes in the population age structure.

Acknowledgments

This study was supported by grants from the CNPq, the FAPEMIG and the Capes. We are grateful to Elisa Balabram for revising the English manuscript.

References

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