Abstract
Background
Studies have shown an association between socioeconomic status and breast cancer treatment. We examined the relation between socioeconomic status and the treatment of breast cancer (surgical, systemic and radiation) in a universal health care system.
Method
Data from a single urban Canadian centre were collected for consecutive patients who received a diagnosis of breast cancer from January 2010 to December 2011. Variables included patient and disease factors, surgery type, systemic and radiation treatment, and breast reconstruction. Socioeconomic variables were obtained from 2006 Canadian census data. We used multivariable logistic regression to identify predictors of breast cancer treatment.
Results
A total of 721 patients were treated for breast cancer during the study period. Socioeconomic variables were not related to type of breast surgery for breast cancer. Age less than 50 years, having a first-degree relative with breast cancer and income status were predictors of breast reconstruction. Employment status was a consistent predictor of systemic and radiation treatment.
Conclusion
Employment consistently predicted systemic and radiation treatment, and age and income were predictors of breast reconstruction in a universal health care system. Further research is required to determine precisely how socioeconomic factors affect care and to minimize possible disparities in delivery of health care services.
Abstract
Contexte
Des études ont montré un lien entre la situation socio-économique et le traitement du cancer du sein. Nous avons analysé ce lien entre la situation socio-économique et le traitement (chirurgie, chimiothérapie, radiothérapie) du cancer du sein dans un système de santé universel.
Méthodes
Les données d’un seul centre urbain canadien ont été compilées pour les patientes consécutives ayant reçu un diagnostic de cancer du sein entre janvier 2010 et décembre 2011. Les variables incluaient des facteurs propres aux patientes et à la maladie, le type de chirurgie, la chimiothérapie, la radiothérapie et la reconstruction mammaire. Les variables socio-économiques proviennent des données du recensement canadien de 2006. Nous avons utilisé la régression logistique multivariée pour identifier les prédicteurs du traitement du cancer du sein.
Résultats
En tout, 721 patientes ont été traitées pour un cancer du sein durant la période de l’étude. Les variables socio-économiques n’ont pas influé sur le type de chirurgie mammaire pour cancer du sein. L’âge inférieur à 50 ans, un cancer du sein chez une parente au premier degré et le revenu ont été des prédicteurs de la reconstruction mammaire. La situation professionnelle a été un prédicteur fiable du traitement systémique et de la radiothérapie.
Conclusion
L’emploi a été un prédicteur fiable du traitement systémique et de la radiothérapie, et l’âge et le revenu ont été des prédicteurs de la reconstruction mammaire, dans un système de santé universel. Il faudra approfondir la recherche pour déterminer plus précisément l’influence des facteurs socio-économiques sur les soins et pour réduire les possibles disparités dans leur prestation.
Breast cancer is the most commonly diagnosed cancer and the second-leading cause of cancer mortality in North American women.1 Breast cancer is treated with a multidisciplinary approach involving surgery, radiation, chemotherapy and antihormonal treatments. It is a highly curable type of cancer if diagnosed early.
There is evidence that nonbiological factors may affect the presentation, diagnosis, treatment and outcome of breast cancer in women. Many studies have shown a positive relation between higher socioeconomic status and improved breast cancer survival in the United States,2–5 the United Kingdom,6 the Netherlands,7 Australia8 and Canada. 9–12 In addition, compared to patients of higher socioeconomic status, those of lower socioeconomic status were found to be less often in screening programs,13–15 to receive adjuvant chemoradiation less often16–18 and to receive the diagnosis of breast cancer at a later stage.19 Conversely, Gorey and colleagues20,21 compared 2 areas in Canada and 2 areas in the US and found a positive association between socioeconomic status and breast cancer survival in Hawaii and Iowa but not in Manitoba and Ontario. They implicated universal health coverage in Canada in their discussion to explain this result. Regionally, socioeconomic status was found to have an impact on overall health and longevity in a study in Hamilton, Ontario.22 The incidence of breast cancer in the inner city and urban core was lower than in the more affluent suburban areas, where socioeconomic status was higher, but rates of breast cancer mortality were higher in the inner city.23
Breast reconstruction is available for patients undergoing mastectomy and is offered in an immediate or delayed fashion. Several studies showed that reconstruction was more likely to be chosen by women who were younger and white and who resided in an urban setting where there was better access to plastic surgeons.24–26 Higher socioeconomic status has also been reported to be associated with a higher rate of breast reconstruction following mastectomy. 27 However, a report from Nova Scotia suggested that this might not be the case in Canada.28
The current study aimed to assess the relation between socioeconomic status, surgical and nonsurgical treatment of breast cancer, and the rate of breast reconstruction in an urban Canadian centre where care is publicly funded. The city of Hamilton is an amalgamated city with an older postindustrial core and more affluent suburbs. The potential value of such research is to identify any discrepancy in care that is associated with factors related to socioeconomic status. Universal health care is designed to be equal and accessible to everyone; however, there remains underlying inequality in access to health care services that is not fully understood. The findings from this study should aid in increasing awareness of determinants of health and consumption of health care services, and potentially foster changes in public policy, resource allocation and education.
Methods
Study cohort
Consecutive patients with a diagnosis of breast cancer (surgical and nonsurgical cases, including invasive cancer and ductal carcinoma in situ) living in Hamilton from January 2010 to December 2011 were included in this retrospective chart review. Cases were identified through health record queries of hospital databases and the Regional Cancer Centre database in Hamilton. Male patients, patients with a diagnosis of benign breast disease and patients who resided outside of Hamilton or had their primary treatment for breast cancer outside Hamilton were excluded. Local research ethics board approval was obtained for the study.
Data collection
Trained data abstractors reviewed hospital and cancer centre charts and extracted the following information: patient demographic characteristics (age at diagnosis, marital status, obesity [body mass index > 30]), employment status, smoking status, comorbidities, presence of first-degree relative with breast cancer, previous breast cancer, method of tumour identification (asymptomatic screening v. symptomatic investigation), location of primary imaging (Ontario Breast Screening Program, hospital or nonhospital clinic), disease stage at diagnosis, type of breast and nodal surgery (breast-conserving surgery or mastectomy, sentinel lymph node biopsy, axillary lymph node dissection or status on breast reconstruction), pathological features (tumour size, nodal status, receptor status), receipt of neoadjuvant or adjuvant therapy (chemotherapy including trastuzumab, radiation to breast, chest wall and nodes, and hormonal therapy) and, for nonsurgical cases, type of palliative treatment. Employment status was coded as employed versus not employed (i.e., unemployed, receiving disability support, homemaker or retired).
Census data
We linked patients’ postal codes to 2006 Canadian census data to extract the following data: education level (proportion of census tract population who completed college or university), immigration status (proportion of census tract population who immigrated in the previous 5 yr) and average income of the census tract of patient residence. We divided income into quintiles.
Statistical analysis
For data analysis, we performed descriptive statistics. Categorical variables were reported as counts and percentages and were compared with the use of the χ2 test or the Fisher exact test. Continuous variables were reported as mean and standard deviation and were compared with the use of a t test for independent samples. We performed multivariable logistic regression analysis to identify patient, pathological and socioeconomic status variables that had an impact on surgical and nonsurgical treatment of breast cancer, and rate of breast reconstruction. Odds ratios (ORs) with 95% confidence intervals (CIs) and Hosmer–Lemeshow goodness-of-fit values are reported. We checked multicolinearity using correlation analysis. To maximize power, we performed univariable analysis and entered variables with a value less than 0.1 into the multivariable regression. Statistical significance was set at p < 0.05. Data analyses were performed with SPSS Statistical Software version 23.0 (IBM Corp.).
Results
A total of 721 cases met the inclusion criteria and were reviewed. Table 1 outlines the demographic and clinical characteristics of the study cohort. Breast-conserving surgery was performed in 393 patients (54.5%), nodal surgery in 545 (75.6%) and reconstruction after mastectomy in 67/276 (24.3%). Most patients presented with early-stage disease, but 176 (24.4%) presented with stage 3 or 4 disease. Almost half (337 [46.7%]) presented with screen-detected cancers. Income quintile, completion of college or university, and immigration status showed relatively similar distributions across the various categories.
Table 1.
Demographic characteristics, disease factors and type of treatment among women with breast cancer
| Variable | No. (%) of patients n = 721 |
|---|---|
| Age at diagnosis, yr | |
| ≤ 50 | 149 (20.7) |
| 51–70 | 364 (50.5) |
| > 70 | 208 (28.8) |
| Obese (body mass index > 30) | |
| Yes | 255 (35.4) |
| No | 435 (60.3) |
| NOS | 31 (4.3) |
| First-degree relative with breast cancer | |
| Yes | 159 (22.0) |
| No | 519 (72.0) |
| NOS | 43 (6.0) |
| Smoking status | |
| Smoker/previous smoker | 289 (40.1) |
| Never smoked | 426 (59.1) |
| NOS | 6 (0.8) |
| No. of comorbidities | |
| 0 | 183 (25.4) |
| 1–2 | 296 (41.0) |
| ≥ 3 | 242 (33.6) |
| Previous breast cancer | 87 (12.1) |
| Location of primary imaging | |
| Ontario Breast Screening Program | 258 (35.8) |
| Hospital | 314 (43.6) |
| Nonhospital clinic | 140 (19.4) |
| NOS | 9 (1.2) |
| Method of tumour identification | |
| Asymptomatic screening | 337 (46.7) |
| Symptomatic investigation | 383 (53.1) |
| NOS | 1 (0.1) |
| Imaging in previous 2 yr | |
| Yes | 317 (44.0) |
| No | 313 (43.4) |
| NOS | 91 (12.6) |
| Marital status | |
| Married | 447 (62.0) |
| Not married | 272 (37.7) |
| NOS | 2 (0.3) |
| Employment status | |
| Employed | 277 (38.4) |
| Not employed | 430 (59.6) |
| NOS | 14 (1.9) |
| Income quintile | |
| 1 (lowest) | 147 (20.4) |
| 2 | 141 (19.6) |
| 3 | 148 (20.5) |
| 4 | 140 (19.4) |
| 5 (highest) | 139 (19.3) |
| NOS | 6 (0.8) |
| % of census tract population who completed college or university | |
| > 40 | 304 (42.2) |
| 31–40 | 237 (32.9) |
| < 31 | 177 (24.5) |
| NOS | 3 (0.4) |
| % of census tract population who immigrated in previous 5 yr | |
| > 30 | 199 (27.6) |
| 21–30 | 224 (31.1) |
| < 21 | 295 (40.9) |
| NOS | 3 (0.4) |
| Type of breast surgery | |
| Breast-conserving | 393 (54.5) |
| Mastectomy | 276 (38.3) |
| No surgery | 52 (7.2) |
| Type of nodal surgery | |
| Sentinel lymph node biopsy | 317 (44.0) |
| Axillary lymph node dissection* | 228 (31.6) |
| No nodal surgery | 176 (24.4) |
| Breast reconstruction after mastectomy (n = 276) | 67 (24.3) |
| Tumour stage | |
| Tis | 74 (10.3) |
| T1 | 291 (40.4) |
| T2 | 223 (30.9) |
| T3–T4 | 133 (18.4) |
| Nodal stage | |
| N0 | 365 (50.6) |
| N1–N3 | 228 (31.6) |
| NX | 127 (17.6) |
| NOS | 1 (0.1) |
| Disease stage (TNM) | |
| 0–1 | 323 (44.8) |
| 2 | 222 (30.8) |
| 3–4 | 176 (24.4) |
| Estrogen receptor/progesterone receptor status† (n = 637) | |
| Positive | 536 (84.1) |
| Negative | 101 (15.8) |
| Tumour grade | |
| I | 145 (20.1) |
| II | 362 (50.2) |
| III | 175 (24.3) |
| NOS | 39 (5.4) |
| Histologic type† (n = 647) | |
| Ductal | 529 (81.8) |
| Lobular‡ | 78 (12.0) |
| Other (e.g., mucinous, tubular) | 32 (4.9) |
| NOS | 8 (1.2) |
| Chemotherapy§ | |
| Yes | 256 (35.5) |
| No | 461 (63.9) |
| NOS | 4 (0.6) |
| Hormonal therapy§ | |
| Yes | 395 (54.8) |
| No | 322 (44.7) |
| NOS | 4 (0.6) |
| Radiation therapy | 460 (63.8) |
| Recurrent breast cancer | 50 (6.9) |
| No treatment | 5 (0.7) |
| Neoadjuvant treatment | 79 (11.0) |
NOS = not otherwise specified.
Includes cases with sentinel lymph node biopsy and axillary lymph node dissection, and axillary lymph node dissection alone
For invasive cases only.
Lobular or mixed ductal/lobular.
Includes neoadjuvant and adjuvant.
Socioeconomic status versus surgical treatment of breast cancer
Table 2 outlines patient, disease and socioeconomic status factors for type of breast surgery received. Univariate analysis showed that mastectomy rates were significantly higher among women less than 51 years of age, those with stage 3 or 4 disease and those with a previous history of breast cancer. Mastectomy rates did not vary with other factors, such as higher education or income quintile. Of the 276 patients who underwent mastectomy, 67 (24.3%) had breast reconstruction (immediately in 40 cases [60%] and delayed in 27 [40%]). Breast reconstruction was done significantly more frequently in women who were younger, employed, healthier (fewer comorbidities) and more educated, had earlier-stage disease and lived in higher-income census tracts. Multivariable analysis showed that only early disease stage (stage 0–1: OR 22.75, 95% CI 12.8–40.5; stage 2: OR 9.44, 95% CI 5.3–16.6) and a previous history of breast cancer (OR 5.25, 95% CI 2.9–9.4) were independent predictors of breast-conserving surgery versus mastectomy (Table 3). Socioeconomic status factors were not significant predictors. After baseline differences were adjusted for, multivariable logistic regression analysis showed that patients younger than 50 years were 30 times more likely to have reconstruction, and those with a first-degree relative with breast cancer were twice as likely to have reconstruction. Furthermore, with each increase in income quintile, the odds of having breast reconstruction doubled for women aged 51–70 years (Table 3).
Table 2.
Univariate analysis of demographic, clinical and socioeconomic factors versus surgical treatment of breast cancer and reconstruction following mastectomy
| Variable | No. (%) of patients* | p value | No. (%) of patients* | p value | ||
|---|---|---|---|---|---|---|
|
|
|
|||||
| Breast-conserving surgery n = 393 |
Mastectomy n = 276 |
Breast reconstruction n = 67 |
No breast reconstruction n = 209 |
|||
| Age at diagnosis, yr | ||||||
|
| ||||||
| < 51 | 68 (17.3) | 76 (27.5) | 0.004 | 37 (55.2) | 39 (18.7) | < 0.001 |
|
| ||||||
| 51–70 | 219 (55.7) | 126 (45.6) | 29 (43.3) | 97 (46.4) | ||
|
| ||||||
| > 70 | 106 (27.0) | 74 (26.8) | 1 (1.5) | 73 (34.9) | ||
|
| ||||||
| Obese | ||||||
|
| ||||||
| Yes | 146 (38.6) | 95 (35.7) | 0.4 | 21 (31.8) | 74 (37.0) | 0.4 |
|
| ||||||
| No | 232 (61.4) | 171 (64.3) | 45 (68.2) | 126 (63.0) | ||
|
| ||||||
| Employment | ||||||
|
| ||||||
| Employed | 152 (39.6) | 118 (43.1) | 0.4 | 49 (75.4) | 69 (33.0) | < 0.001 |
|
| ||||||
| Not employed | 232 (60.4) | 156 (56.9) | 16 (24.6) | 140 (67.0) | ||
|
| ||||||
| Marital status | ||||||
|
| ||||||
| Married | 256 (65.1) | 172 (62.8) | 0.5 | 44 (66.7) | 128 (61.8) | 0.6 |
|
| ||||||
| Not married | 137 (34.9) | 102 (37.2) | 22 (33.3) | 79 (38.2) | ||
|
| ||||||
| First-degree relative with breast cancer | ||||||
|
| ||||||
| Yes | 92 (24.5) | 54 (21.0) | 0.3 | 18 (40.9) | 36 (18.6) | 0.1 |
|
| ||||||
| No | 283 (75.5) | 203 (79.0) | 26 (59.1) | 157 (81.3) | ||
|
| ||||||
| Smoking status | ||||||
|
| ||||||
| Smoker/previous smoker | 152 (38.8) | 117 (42.8) | 0.3 | 31 (47.0) | 86 (41.5) | 0.4 |
|
| ||||||
| Never smoked | 240 (61.2) | 156 (57.1) | 35 (53.0) | 121 (58.4) | ||
|
| ||||||
| No. of comorbidities† | ||||||
|
| ||||||
| 0 | 105 (26.7) | 71 (25.7) | 0.8 | 30 (44.8) | 41 (19.6) | < 0.001 |
|
| ||||||
| ≥ 1 | 288 (73.3) | 205 (74.3) | 37 (55.2) | 168 (80.4) | ||
|
| ||||||
| Income quintile | ||||||
|
| ||||||
| 1 | 78 (20.1) | 60 (21.8) | 0.2 | 9 (13.4) | 51 (24.5) | 0.01 |
|
| ||||||
| 2 | 66 (17.0) | 63 (22.9) | 9 (13.4) | 54 (26.0) | ||
|
| ||||||
| 3 | 84 (21.6) | 55 (20.0) | 15 (22.4) | 40 (19.2) | ||
|
| ||||||
| 4 | 77 (19.8) | 55 (20.0) | 19 (28.4) | 36 (17.3) | ||
|
| ||||||
| 5 | 83 (21.4) | 42 (15.3) | 15 (22.4) | 27 (13.0) | ||
|
| ||||||
| % of census tract population who completed college or university | ||||||
|
| ||||||
| > 40 | 159 (40.7) | 121 (44.0) | 0.07 | 39 (58.2) | 82 (39.4) | 0.02 |
|
| ||||||
| 31–40 | 143 (36.6) | 78 (28.4) | 16 (23.9) | 62 (29.8) | ||
|
| ||||||
| < 31 | 89 (22.8) | 76 (27.6) | 12 (17.9) | 64 (30.8) | ||
|
| ||||||
| % of census tract population who immigrated in previous 5 yr | > 0.99 | |||||
|
| ||||||
| > 30 | 107 (27.4) | 75 (27.3) | 14 (20.9) | 61 (29.3) | 0.1 | |
|
| ||||||
| 21–30 | 124 (31.7) | 88 (32.0) | 19 (28.4) | 69 (33.2) | ||
|
| ||||||
| < 21 | 160 (40.9) | 112 (40.7) | 34 (50.7) | 78 (37.5) | ||
|
| ||||||
| Previous breast cancer | ||||||
|
| ||||||
| Yes | 29 (7.4) | 51 (18.5) | < 0.001 | 9 (13.4) | 42 (20.1) | 0.2 |
|
| ||||||
| No | 364 (92.6) | 225 (81.5) | 58 (86.6) | 167 (79.9) | ||
|
| ||||||
| Disease stage (TNM) | ||||||
|
| ||||||
| 0–1 | 241 (61.3) | 80 (29.0) | < 0.001 | 30 (44.8) | 50 (23.9) | 0.004 |
|
| ||||||
| 2 | 130 (33.1) | 83 (30.1) | 17 (25.4) | 66 (31.6) | ||
|
| ||||||
| 3–4 | 22 (5.6) | 113 (40.9) | 20 (29.8) | 93 (44.5) | ||
Numbers do not sum to total in all cases because of missing data.
Includes chronic obstructive pulmonary disease, rheumatic disease, coronary artery disease, myocardial infarction, coronary heart failure, diabetes, kidney disease, major psychiatric illness, morbid obesity (body mass index > 40), other cancer diagnosis, osteoporosis, hypertension, hypercholesterolemia and neurodegenerative diseases (e.g., Parkinson’s disease).
Table 3.
Multivariable analyses exploring predictors of breast cancer treatment
| Variable | OR (95% CI) |
|---|---|
| Predictors of breast-conserving surgery v. mastectomy (n = 591) | |
| No previous breast cancer | 5.25 (2.9–9.4) |
| Stage 0–1 disease | 22.75 (12.8–40.5) |
| Stage 2 disease | 9.44 (5.3–16.6) |
| Stage 3–4 disease | Reference |
| Hosmer–Lemeshow p value = 0.7 | |
| Predictors of breast reconstruction (n = 276) | |
| Age < 50 yr at diagnosis | 30.3 (9.2–99.2) |
| Age 51–70 yr by income quintile | 2.0 (1.5–2.8) |
| First-degree relative with breast cancer | 2.7 (1.1–6.5) |
| Hosmer–Lemeshow p value = 0.6 | |
| Predictors of no nodal surgery v. nodal surgery (n = 627) | |
| Age > 70 yr | 1.54 (0.9–2.5) |
| Not employed | 1.85 (1.1–3.1) |
| Nonsmoker | 1.97 (1.3–3.1) |
| Previous breast cancer | 3.66 (2.2–6.2) |
| Hosmer–Lemeshow p value = 0.9 | |
| Predictors of SLNB v. ALND (n = 493) | |
| No previous breast cancer | 3.00 (1.3–7.0) |
| Stage 0–1 disease | 166.26 (64.8–426.6) |
| Stage 2 disease | 26.58 (11.1–63.9) |
| Stage 3–4 disease | Reference |
| Hosmer–Lemeshow p value = 0.9 | |
| Predictors of chemotherapy (n = 623) | |
| Age < 51 yr | 5.25 (2.9–9.4) |
| Stage 0–1 disease | Reference |
| Stage 2 disease | 7.50 (4.40–13.1) |
| Stage 3–4 disease | 36.64 (19.5–68.6) |
| Employed | 4.84 (2.9–7.9) |
| Married | 1.90 (1.2–3.0) |
| Hosmer–Lemeshow p value = 0.7 | |
| Predictors of radiation therapy for breast-conserving surgery cases (n = 347) | |
| Employed | 3.06 (1.1–8.3) |
| No comorbidities | 9.34 (1.2–71.2) |
| Hosmer–Lemeshow p value = 0.6 | |
| Predictors of hormonal therapy for ER/PR-positive cases (n = 468) | |
| Employed | 3.55 (2.1–60.5) |
| Stage 0–1 disease | Reference |
| Stage 2 disease | 7.45–(2.1–5.8) |
| Stage 3–4 disease | 14.00 (5.8–33.8) |
| Hosmer–Lemeshow p value = 0.2 | |
ALND = axillary lymph node dissection; CI = confidence interval; ER/PR = estrogen/progesterone receptor; OR = odds ratio; SLNB = sentinel lymph node biopsy.
Table 4 outlines patient, disease and socioeconomic status factors compared with the type of nodal surgery. Of the 721 patients, 545 (75.6%) had nodal surgery, of whom 317 (58.2%) had sentinel lymph node biopsy and 228 (41.8%) had axillary lymph node dissection. Univariate analysis showed that age, having a first-degree relative with breast cancer and disease stage predicted sentinel lymph node biopsy; socioeconomic status factors (e.g., income quintile, higher education) were not significant. On multivariable analysis, predictors of not having nodal surgery were age more than 70 (OR 1.54, 95% CI 0.9–2.5), not employed (OR 1.85, 95% CI 1.1–3.1), nonsmoker (OR 1.97, 95% CI 1.3–3.1) and previous breast cancer (OR 3.66, 95% CI 2.2–6.2) (Table 3). Independent predictors of sentinel lymph node biopsy versus axillary lymph node dissection included no previous breast cancer (OR 3.00, 95% CI 1.3–7.0) and early stage disease (stage 0–1: OR 166.26, 95% CI 64.8–426.6; stage 2: OR 26.58, 95% CI 11.1–63.9).
Table 4.
Univariate analysis of patient demographic and socioeconomic factors versus nodal surgery
| Variable | No. (%) of patients* | p value | No. (%) of patients* | p value | ||
|---|---|---|---|---|---|---|
|
|
|
|||||
| Nodal surgery n = 545 |
No nodal surgery n = 176 |
Sentinel lymph node biopsy n = 317 |
Axillary lymph node dissection† n = 228 |
|||
| Age at diagnosis, yr | ||||||
|
| ||||||
| < 51 | 129 (23.7) | 20 (11.4) | < 0.001 | 62 (19.6) | 67 (29.4) | 0.008 |
|
| ||||||
| 51–70 | 283 (51.9) | 81 (46.0) | 181 (57.1) | 102 (44.7) | ||
|
| ||||||
| > 70 | 133 (24.4) | 75 (42.6) | 74 (23.3) | 59 (25.9) | ||
|
| ||||||
| Obese | ||||||
|
| ||||||
| Yes | 206 (39.0) | 49 (30.2) | 0.04 | 117 (38.2) | 89 (40.1) | 0.7 |
|
| ||||||
| No | 322 (61.0) | 113 (69.8) | 189 (61.8) | 133 (59.9) | ||
|
| ||||||
| Employment | ||||||
|
| ||||||
| Employed | 233 (43.5) | 44 (25.7) | < 0.001 | 133 (42.8) | 100 (44.4) | 0.7 |
|
| ||||||
| Not employed | 303 (56.5) | 127 (74.3) | 178 (57.2) | 125 (55.6) | ||
|
| ||||||
| Marital status | ||||||
|
| ||||||
| Married | 354 (65.2) | 93 (52.8) | 0.003 | 210 (66.7) | 144 (63.2) | 0.4 |
|
| ||||||
| Not married | 189 (34.8) | 83 (47.2) | 105 (33.3) | 84 (36.8) | ||
|
| ||||||
| First-degree relative with breast cancer | ||||||
|
| ||||||
| Yes | 120 (22.9) | 39 (25.3) | 0.5 | 80 (26.2) | 40 (18.3) | 0.03 |
|
| ||||||
| No | 404 (77.1) | 115 (74.7) | 225 (73.8) | 179 (81.7) | ||
|
| ||||||
| Smoking status | ||||||
|
| ||||||
| Smoker/previous smoker | 240 (44.3) | 49 (28.3) | < 0.001 | 131 (41.6) | 109 (48.0) | 0.1 |
|
| ||||||
| Never smoked | 302 (55.7) | 124 (71.7) | 184 (58.4) | 118 (52.0) | ||
|
| ||||||
| No. of comorbidities | ||||||
|
| ||||||
| 0 | 149 (27.3) | 34 (19.3) | 0.03 | 83 (26.2) | 66 (28.9) | 0.48 |
|
| ||||||
| ≥ 1 | 396 (72.7) | 142 (80.7) | 234 (73.8) | 162 (71.0) | ||
|
| ||||||
| Income quintile | ||||||
|
| ||||||
| 1 | 112 (20.8) | 35 (19.9) | 0.9 | 54 (17.2) | 58 (25.7) | 0.2 |
|
| ||||||
| 2 | 110 (20.4) | 31 (17.6) | 65 (20.8) | 45 (19.9) | ||
|
| ||||||
| 3 | 108 (20.0) | 40 (22.7) | 66 (21.1) | 42 (18.6) | ||
|
| ||||||
| 4 | 106 (19.7) | 34 (19.3) | 65 (20.8) | 41 (18.1) | ||
|
| ||||||
| 5 | 103 (19.1) | 36 (20.4) | 63 (20.1) | 40 (17.7) | ||
|
| ||||||
| % of census tract population who completed college or university | ||||||
|
| ||||||
| > 40 | 230 (42.4) | 74 (42.0) | > 0.99 | 135 (42.8) | 95 (41.8) | 0.2 |
|
| ||||||
| 31–40 | 179 (33.0) | 58 (33.0) | 111 (35.2) | 68 (30.0) | ||
|
| ||||||
| < 31 | 133 (24.5) | 44 (25.0) | 69 (21.9) | 64 (28.2) | ||
|
| ||||||
| % of census tract population who immigrated in previous 5 yr | ||||||
|
| ||||||
| > 30 | 151 (27.8) | 48 (27.3) | 0.3 | 84 (26.7) | 67 (29.5) | 0.8 |
|
| ||||||
| 21–30 | 161 (29.7) | 63 (35.8) | 96 (30.5) | 65 (28.6) | ||
|
| ||||||
| < 21 | 230 (42.4) | 65 (36.9) | 135 (42.8) | 95 (41.8) | ||
|
| ||||||
| Previous breast cancer | ||||||
|
| ||||||
| Yes | 47 (8.6) | 40 (22.7) | < 0.001 | 27 (8.5) | 20 (8.8) | 0.9 |
|
| ||||||
| No | 498 (91.4) | 136 (77.3) | 290 (91.5) | 208 (91.2) | ||
|
| ||||||
| Disease stage (TNM) | ||||||
|
| ||||||
| 0–1 | 233 (42.8) | 90 (51.1) | 0.005 | 205 (64.7) | 28 (12.3) | < 0.001 |
|
| ||||||
| 2 | 185 (33.9) | 37 (21.0) | 105 (33.1) | 80 (35.1) | ||
|
| ||||||
| 3–4 | 127 (23.3) | 49 (27.8) | 7 (2.2) | 120 (52.6) | ||
Numbers do not sum to total in all cases because of missing data.
Includes cases with sentinel lymph node biopsy and axillary lymph node dissection, or axillary lymph node dissection alone.
Socioeconomic status versus systemic and radiation treatment for breast cancer
Table 5 outlines the demographic and socioeconomic status factors compared to systemic and radiation therapies. A total of 256 patients (35.5%) received chemotherapy, either in a neoadjuvant or an adjuvant setting, and 342 patients (87.0%) who had breast-conserving surgery received adjuvant radiation to the breast. Hormonal therapy was taken by 390 patients (72.8%) with positive receptor status. On multivariable analyses, predictors for chemotherapy included age less than 51 (OR 5.25, 95% CI 2.9–9.4), later-stage disease (stage 2: OR 7.50, 95% CI 4.40–13.1; stage 3–4: OR 36.64, 95% CI 19.5–68.6), being employed (OR 4.84, 95% CI 2.9–7.9) and being married (OR 1.90, 95% CI 1.2–3.0) (Table 3). Predictors of radiation included being employed (OR 3.06, 95% CI 1.1–8.3) and having no comorbidities (OR 9.34, 95% CI 1.2–71.2). Finally, predictors for hormonal therapy included being employed (OR 3.55, 95% CI 2.1–60.5) and later-stage disease (stage 2: OR 7.45, 95% CI 2.1–5.8; stage 3–4: OR 14.00, 95% CI 5.8–33.8). No relation was identified between socioeconomic status factors and these adjuvant therapies.
Table 5.
Univariate analysis of patient demographic and socioeconomic factors versus neoadjuvant and adjuvant therapies
| Variable | No. (%) of patients* | p value | No. (%) of patients* | p value | No. (%) of patients | p value | |||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|||||||
| Chemotherapy n = 256 |
No chemotherapy n = 461 |
Radiation therapy† n = 342 |
No radiation therapy n = 51 |
Hormonal therapy‡ n = 390 |
No hormonal therapy n = 146 |
||||
| Age at diagnosis, yr | |||||||||
|
| |||||||||
| < 51 | 99 (38.7) | 50 (10.8) | < 0.001 | 63 (18.) | 5 (9.8) | < 0.001 | 89 (22.8) | 16 (11.0) | < 0.001 |
|
| |||||||||
| 51–70 | 135 (52.7) | 228 (49.4) | 199 (58.) | 20 (39.2) | 210 (53.8) | 52 (35.6) | |||
|
| |||||||||
| > 70 | 22 (8.6) | 183 (39.7) | 80 (23.) | 26 (51.0) | 91 (23.3) | 78 (53.4) | |||
|
| |||||||||
| Obese | |||||||||
|
| |||||||||
| Yes | 91 (36.2) | 161 (37.0) | 0.8 | 130 (39.) | 16 (34.0) | 0.49 | 140 (37.0) | 51 (37.2) | > 0.99 |
|
| |||||||||
| No | 160 (63.7) | 274 (63.0) | 201 (61.) | 31 (66.0) | 238 (63.0) | 86 (62.8) | |||
|
| |||||||||
| Employment | |||||||||
|
| |||||||||
| Employed | 156 (61.7) | 120 (26.7) | < 0.001 | 143 (43.) | 9 (18.0) | 0.001 | 169 (43.9) | 27 (19.1) | < 0.001 |
|
| |||||||||
| Not employed | 97 (38.3) | 330 (73.3) | 191 (57.) | 41 (82.0) | 216 (56.1) | 114 (80.8) | |||
|
| |||||||||
| Marital status | |||||||||
|
| |||||||||
| Married | 185 (72.3) | 259 (56.4) | < 0.001 | 228 (67.) | 28 (4.9.) | 0.1 | 242 (62.2) | 83 (57.2) | 0.3 |
|
| |||||||||
| Not married | 71 (27.7) | 200 (43.6) | 114 (33.) | 23 (45.1) | 147 (37.8) | 62 (42.8) | |||
|
| |||||||||
| First-degree relative with breast cancer | |||||||||
|
| |||||||||
| Yes | 47 (18.8) | 111 (26.2) | 0.03 | 79 (24.) | 13 (31.7) | 0.2 | 85 (22.8) | 36 (27.5) | 0.3 |
|
| |||||||||
| No | 203 (81.2) | 313 (73.8) | 255 (76.) | 28 (68.3) | 287 (77.2) | 95 (72.5) | |||
|
| |||||||||
| Smoking status | |||||||||
|
| |||||||||
| Smoker/previous smoker | 118 (46.6) | 169 (36.9) | < 0.001 | 137 (40.) | 15 (30.0) | 0.2 | 178 (45.8) | 50 (34.7) | 0.02 |
|
| |||||||||
| Never smoked | 135 (53.4) | 289 (63.1) | 205 (60.) | 35 (70.0) | 211 (54.2) | 94 (65.3) | |||
|
| |||||||||
| No. of comorbidities | |||||||||
|
| |||||||||
| 0 | 93 (36.3) | 90 (19.5) | < 0.001 | 100 (29.) | 5 (9.8) | 0.003 | 97 (24.9) | 28 (19.2) | 0.2 |
|
| |||||||||
| ≥ 1 | 163 (63.7) | 371 (80.5) | 242 (71.) | 46 (91.2) | 293 (75.1) | 118 (80.8) | |||
|
| |||||||||
| Income quintile | |||||||||
|
| |||||||||
| 1 | 49 (19.2) | 98 (21.5) | 0.2 | 67 (20.) | 11 (21.6) | 0.9 | 76 (19.6) | 32 (22.4) | 0.2 |
|
| |||||||||
| 2 | 46 (18.0) | 94 (20.6) | 57 (17.) | 9 (17.6) | 69 (17.8) | 36 (25.2) | |||
|
| |||||||||
| 3 | 45 (17.6) | 102 (22.4) | 71 (21.) | 13 (25.5) | 85 (21.9) | 29 (20.3) | |||
|
| |||||||||
| 4 | 56 (22.0) | 83 (18.2) | 68 (20.) | 9 (17.6) | 80 (20.6) | 23 (16.1) | |||
|
| |||||||||
| 5 | 59 (23.1) | 79 (17.3) | 74 (22.) | 9 (17.6) | 78 (20.1) | 23 (16.1) | |||
|
| |||||||||
| % of census tract population who completed college or university | |||||||||
|
| |||||||||
| > 40 | 116 (45.5) | 186 (40.5) | 0.4 | 146 (43.) | 13 (25.5) | 0.05 | 101 (26.0) | 49 (34.0) | 0.01 |
|
| |||||||||
| 31–40 | 82 (32.2) | 154 (33.6) | 118 (35.) | 25 (49.0) | 123 (31.6) | 59 (41.0) | |||
|
| |||||||||
| < 31 | 57 (22.4) | 119 (25.9) | 76 (22.) | 13 (25.5) | 165 (42.4) | 36 (25.0) | |||
|
| |||||||||
| % of census tract population who immigrated in previous 5 yr | |||||||||
|
| |||||||||
| > 30 | 65 (25.5) | 133 (29.0) | 0.6 | 89 (26.) | 18 (35.3) | 0.1 | 101 (26.0) | 43 (29.9) | 0.6 |
|
| |||||||||
| 21–30 | 83 (32.5) | 140 (30.5) | 105 (31.) | 19 (37.2) | 123 (31.6) | 46 (31.9) | |||
|
| |||||||||
| < 21 | 107 (42.0) | 186 (40.5) | 146 (43.) | 14 (27.4) | 165 (42.4) | 55 (38.2) | |||
|
| |||||||||
| Previous breast cancer | |||||||||
|
| |||||||||
| Yes | 22 (8.6) | 64 (13.9) | 0.04 | 26 (8.) | 3 (5.9) | 0.7 | 36 (9.2) | 24 (16.4) | 0.02 |
|
| |||||||||
| No | 234 (91.4) | 397 (86.1) | 316 (92.) | 48 (94.1) | 354 (90.8) | 122 (83.6) | |||
|
| |||||||||
| Disease stage (TNM) | |||||||||
|
| |||||||||
| 0–1 | 41 (16.0) | 280 (60.7) | < 0.001 | 213 (62.) | 28 (54.9) | 0.3 | 121 (31.0) | 96 (65.8) | < 0.001 |
|
| |||||||||
| 2 | 89 (34.8) | 131 (28.4) | 112 (33.) | 18 (35.3) | 145 (37.2) | 40 (27.4) | |||
|
| |||||||||
| 3–4 | 126 (49.2) | 50 (10.8) | 17 (5.) | 5 (9.8) | 124 (31.8) | 10 (6.8) | |||
Numbers do not sum to total in all cases because of missing data.
To breast and/or nodes in patients who had breast-conserving surgery.
In patients who were positive for estrogen/progesterone receptor.
Discussion
There are numerous treatment options in the management of breast cancer. Recommendations are based on many potential factors: patient factors, tumour factors, local experience and guidelines. Many factors also can influence decisions that patients make. In our urban Canadian centre, we examined the relation between socioeconomic status and breast cancer treatment and found that socioeconomic status was related only to reconstruction rates after mastectomy and not to other surgical interventions. In addition, patients more than 70 years of age were less likely than younger patients to undergo axillary surgery, including sentinel node biopsy and axillary dissection. This might be explained by the findings from a randomized controlled trial that axillary surgery can be avoided in older women receiving hormonal therapy, with no adverse oncologic outcomes.29
A US study showed that patients in lower socioeconomic classes were less likely than those in higher socioeconomic classes to undergo breast-conserving surgery owing to larger tumour size at the time of diagnosis.30 Such results were not observed in our study. This may be partially attributable to Canada’s universal health coverage but also to an ongoing effort to make screening more accessible, especially for women in lower socioeconomic status neighbourhoods.25,26 In the Hamilton region, a “Screen for Life” bus was initiated in 2012, and more than 600 patients were screened in its first year of operation. The program targeted neighbourhoods with low socioeconomic status and poor access to screening centres, and cultural groups less inclined to accept cancer screening.31,32 Such programs have also been used in northern Ontario, where access to health care is limited, and in urban neighbourhoods with lower socioeconomic status.
International studies have shown that patients with lower socioeconomic status are less likely than those with higher socioeconomic status to receive adjuvant chemoradiation. 11,12,33 Most of the studies were conducted in 2-tier health care systems; however, a recent study from Ontario showed that socioeconomic status affected breast cancer treatment and outcomes in a universal health care system. 12 We also found that patients who were not employed (including homemakers and those who were unemployed, receiving disability support or retired) were less likely than employed patients to receive systemic and radiation treatment in a publicly funded health care system. The explanation for this is unclear, as systemic and radiation therapies are generally covered by public health insurance; there may be limitations in coverage of certain drugs, and there may be other, ancillary costs (such as transportation and costs related to side effects of treatment) associated with these therapies that discourages treatment. It also unknown whether these therapies are recommended but not accepted by patients, or whether different recommendations are made by oncologists based on employment status, as these details were not included in our data collection. A more detailed study focused on this issue would elucidate whether patients with low socioeconomic status are receiving fewer systemic and radiation therapies owing to access versus patient choice versus recommendation by the medical or radiation oncologist.
Interestingly, married patients were 1.9 times more likely to receive chemotherapy than unmarried patients, presumably owing to increased social support and social networks. A retrospective cohort study using the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database34 showed that, compared to married women, unmarried women were more likely to receive their breast cancer diagnosis at a later stage, were less likely to receive therapy and had higher mortality, which reaffirms the importance of social support for patients to complete diagnostic and therapeutic care.
Socioeconomic status has been shown to influence the frequency of breast reconstruction in the US, Australia, England and Denmark.18–21,35 In 2008, the rate of immediate reconstruction was 11.7% in Ontario.30 Hamilton is considered to have good access to plastic surgery, and the rate of immediate reconstruction in our cohort was 15%. We found a strong association between socioeconomic status and rate of breast reconstruction in our study cohort: the odds of breast reconstruction doubled with each increase in income quintile among women aged 51–70. Past studies have suggested that patients with lower socioeconomic status have more comorbidities, 36 which makes them less ideal candidates for tissue-based reconstruction. One study showed that the rate of complications following breast reconstruction was also higher in patients in the lowest income quintile.37 Implant-based reconstruction can be a less morbid procedure than autologous reconstruction, but it requires multiple visits to the clinic and repeat surgery to exchange the expander to a permanent implant once the ideal size is reached. This may entail a financial burden in terms of prolonged time off work and travel costs, making it less attractive for women with lower socioeconomic status. Reconstruction after mastectomy has been associated with better quality of life.38 Increased public awareness and policy change are needed to make this a more available option for patients of lower socioeconomic status.
Strengths and limitations
Our demographic and clinical data were obtained directly from chart review, which allowed for more accurate and detailed information, with minimal missing data (about 10%, less than that in socioeconomic status studies using amalgamated data39,40). Because of the regional nature of breast cancer management in our centre, the chart review allowed us to retrieve extensive and complete data. However, it was a retrospective review and does not have the potential advantages of a prospective, amalgamated database. We were unable to obtain individual patient information for income, education and immigration status but, rather, used the neighbourhood average from the census tract. Furthermore, we used 2006 census data, as the 2011 census was a short form that did not contain variables of interest for this study. However, it is unlikely that there was marked change in economic status within the few years from the census to the data collection period (2010–2011). If there were absolute changes in income, it is even less likely that these changes would have affected quintile distributions.
Conclusion
Despite universal health care funding, socioeconomic status appeared to affect breast cancer care in our Hamilton cohort in terms of neoadjuvant and adjuvant therapies, and breast reconstruction. Future directions include evaluation of socioeconomic status with the rate of screening and stage at diagnosis to see whether socioeconomic status has an impact before initiation of breast cancer treatment. It would also be interesting to evaluate whether there is any correlation between socioeconomic status, accessibility to a primary care physician, primary care practice models, and early breast cancer detection and diagnosis. Further research is required to more precisely determine how socioeconomic factors affect care, to identify such disparities and make all forms of breast cancer treatment more accessible to those with lower socioeconomic status.
Footnotes
Results from this project were presented as a poster presentation at the Canadian Surgery Forum conference, Toronto, Ont., Sept. 10, 2016.
Competing interests: None declared.
Contributors: J. Li, S. Cornacchi, F. Farrokhyar, N. Johnston, S. Forbes, S. Reid and P. Lovrics designed the study. J. Li, S. Cornacchi, N. Johnston, N. Hodgson, S. Lovrics and K. Lucibello acquired the data, which J. Li, S. Cornacchi, F. Farrokhyar, S. Reid and P. Lovrics analyzed. J. Li, S. Cornacchi and P. Lovrics wrote the article, which all authors reviewed and approved for publication.
Funding: Funding was provided by a grant from McMaster Surgical Associates. The funding source played no role in the design, conduct or reporting of this study.
References
- 1.Taghian A, El-Ghamry MN, Merajver SD. Overview of the treatment of newly diagnosed, non-metastatic breast cancer. UpToDate. 2016. [accessed 2016 Mar 20]. Available: https://www.uptodate.com/contents/overview-of-the-treatment-of-newly-diagnosed-non-metastatic-breast-cancer.
- 2.Woods LM, Rachet B, Coleman MP. Origins of socio-economic inequalities in cancer survival: a review. Ann Oncol. 2006;17:5–19. doi: 10.1093/annonc/mdj007. [DOI] [PubMed] [Google Scholar]
- 3.Sprague BL, Trentham-Dietz A, Gagnon RE, et al. Socioeconomic status and survival after an invasive breast cancer diagnosis. Cancer. 2011;117:1542–51. doi: 10.1002/cncr.25589. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Vona-Davis L, Rose DP. The influence of socioeconomic disparities on breast cancer tumor biology and prognosis: a review. J Womens Health (Larchmt) 2009;18:883–93. doi: 10.1089/jwh.2008.1127. [DOI] [PubMed] [Google Scholar]
- 5.Klassen AC, Smith KC. The enduring and evolving relationship between social class and breast cancer burden: a review of the literature. Cancer Epidemiol. 2011;35:217–34. doi: 10.1016/j.canep.2011.02.009. [DOI] [PubMed] [Google Scholar]
- 6.Potosky AL, Merrill RM, Riley GF. Breast cancer survival and treatment in health maintenance organization and fee-for-service settings. J Natl Cancer Inst. 1997;89:1683–91. doi: 10.1093/jnci/89.22.1683. [DOI] [PubMed] [Google Scholar]
- 7.Aarts MJ, Hamelinck VC, Bastiaannet E, et al. Small but significant socioeconomic inequalities in axillary staging and treatment of breast cancer in the Netherlands. Br J Cancer. 2012;107:12–7. doi: 10.1038/bjc.2012.205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Dasgupta P, Baade PD, Aitken JF, et al. Multilevel determinants of breast cancer survival: association with geographic remoteness and area-level socioeconomic disadvantage. Breast Cancer Res Treat. 2012;132:701–10. doi: 10.1007/s10549-011-1899-y. [DOI] [PubMed] [Google Scholar]
- 9.Mackillop WJ, Zhang-Salomons J, Groome PA, et al. Socioeconomic status and cancer survival in Ontario. J Clin Oncol. 1997;15:1680–9. doi: 10.1200/JCO.1997.15.4.1680. [DOI] [PubMed] [Google Scholar]
- 10.Zhang-Salomons J, Quian H, Holowaty E, et al. Association between socioeconomic status and cancer survival: choice of SES indicator may affect results. Ann Epidemiol. 2006;16:521–8. doi: 10.1016/j.annepidem.2005.10.002. [DOI] [PubMed] [Google Scholar]
- 11.Booth CM, Li G, Zhang-Salomons J, et al. The impact of socioeconomic status on stage of cancer diagnosis and survival: a population-based study in Ontario, Canada. Cancer. 2010;116:4160–7. doi: 10.1002/cncr.25427. [DOI] [PubMed] [Google Scholar]
- 12.Kumachev A, Trudeau ME, Chan KK, et al. Associations among socioeconomic status, patterns of care and outcomes in breast cancer patients in a universal health care system: Ontario’s experience. Cancer. 2016;122:893–8. doi: 10.1002/cncr.29838. [DOI] [PubMed] [Google Scholar]
- 13.Borugian MJ, Spinelli JJ, Abanto Z, et al. Breast cancer incidence and neighbourhood income. Health Rep. 2011;22:1–7. [PubMed] [Google Scholar]
- 14.Katz SJ, Zemencuk JK, Hofer TP. Breast cancer screening in the United States and Canada, 1994: socioeconomic gradients persist. Am J Public Health. 2000;90:799–803. doi: 10.2105/ajph.90.5.799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Maddison AR, Asada Y, Urquhart R. Inequity in access to cancer care: a review of the Canadian literature. Cancer Causes Control. 2011;22:359–66. doi: 10.1007/s10552-010-9722-3. [DOI] [PubMed] [Google Scholar]
- 16.Coburn N, Fulton J, Pearlman DN, et al. Treatment variation by insurance status for breast cancer patients. Breast J. 2008;14:128–34. doi: 10.1111/j.1524-4741.2007.00542.x. [DOI] [PubMed] [Google Scholar]
- 17.Paszat LF, Mackillop WJ, Groome PA, et al. Radiotherapy for breast cancer in Ontario: rate variation associated with region, age and income. Clin Invest Med. 1998;21:125–34. [PubMed] [Google Scholar]
- 18.Gorey KM, Luginaah IN, Holowaty EJ, et al. Wait times for surgical and adjuvant radiation treatment of breast cancer in Canada and the United States: greater socioeconomic inequity in America. Clin Invest Med. 2009;32:E239–49. doi: 10.25011/cim.v32i3.6113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Clegg LX, Reichman ME, Miller BA, et al. Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the Surveillance, Epidemiology, and End Results: National Longitudinal Mortality Study. Cancer Causes Control. 2009;20:417–35. doi: 10.1007/s10552-008-9256-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Gorey KM, Kliewer E, Holowaty EJ, et al. An international comparison of breast cancer survival: Winnipeg, Manitoba and De Moines, Iowa metropolitan areas. Ann Epidemiol. 2003;13:32–41. doi: 10.1016/s1047-2797(02)00259-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gorey KM, Holowaty EJ, Fehringer F, et al. An international comparison of cancer survival: metropolitan Toronto, Ontario and Honolulu, Hawaii. Am J Public Health. 2000;90:1866–72. doi: 10.2105/ajph.90.12.1866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.DeLuca PF, Buist S, Johnston N. The code red project: engaging communities in health system change in Hamilton, Canada. Soc Indic Res. 2012;108:317–27. [Google Scholar]
- 23.Johnston N, Buist S, DeLuca PF. Code Red. Day 2: spotlight on breast cancer. Hamilton Spectator. [accessed 2014 Jan 22]. Available: http://thespec-codered.com/?p=866.
- 24.Christian CK, Niland J, Edge SB, et al. A multi-institutional analysis of the socioeconomic determinants of breast reconstruction: a study of the National Comprehensive Cancer Network. Ann Surg. 2006;243:241–9. doi: 10.1097/01.sla.0000197738.63512.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhong T, Fernandes KA, Saskin R, et al. Barriers to immediate breast reconstruction in the Canadian universal health care system. J Clin Oncol. 2014;32:2133–41. doi: 10.1200/JCO.2013.53.0774. [DOI] [PubMed] [Google Scholar]
- 26.Forte T, Porter G, Rahal R, et al. Geographic disparities in surgery for breast and rectal cancer in Canada. Curr Oncol. 2014;21:97–9. doi: 10.3747/co.21.1936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Azzopardi J, Walsh D, Chong C, et al. Surgical treatment for women with breast cancer in relation to socioeconomic and insurance status. Breast J. 2014;20:3–8. doi: 10.1111/tbj.12203. [DOI] [PubMed] [Google Scholar]
- 28.Barnsley GP, Sigurdson L, Kirkland S. Barriers to breast reconstruction after mastectomy in Nova Scotia. Can J Surg. 2008;51:447–52. [PMC free article] [PubMed] [Google Scholar]
- 29.Rudenstam CM, Zahrieh D, Forbes JF, et al. Randomized trial comparing axillary clearance vs. no axillary clearance in older patients with breast cancer: first results of International Breast Cancer Study Group Trial 10–93. J Clin Oncol. 2006;24:337–44. doi: 10.1200/JCO.2005.01.5784. [DOI] [PubMed] [Google Scholar]
- 30.Nguyen BC, Alawadi ZM, Roife D, et al. Do socioeconomic factors and race determine the likelihood of breast conserving surgery? Clin Breast Cancer. 2016;16:e93–7. doi: 10.1016/j.clbc.2016.05.008. [DOI] [PubMed] [Google Scholar]
- 31.Chapman T. Cancer screening bus rolls onto Hamilton streets. CBC News. 2013. Jun 26, [accessed 2016 Mar 20]. Available: www.cbc.ca/news/canada/hamilton/news/cancer-screening-bus-rolls-onto-hamilton-streets-1.1362640.
- 32.Craggs S. Hamilton cancer-screening bus “saved my life.”. CBC News. 2014. Aug 3, [accessed 2016 Mar 20]. Available: www.cbc.ca/news/canada/hamilton/news/hamilton-cancer-screening-bus-saved-my-life-1.2724044.
- 33.Hershman DL, Tsui J, Wright JD, et al. Household net worth, racial disparities and hormonal therapy adherence among women with early-stage breast cancer. J Clin Oncol. 2015;33:1053–9. doi: 10.1200/JCO.2014.58.3062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Osborne C, Ostir GV, Du X, et al. The influence of marital status on the stage at diagnosis, treatment, and survival of older women with breast cancer. Breast Cancer Res Treat. 2005;93:41–7. doi: 10.1007/s10549-005-3702-4. [DOI] [PubMed] [Google Scholar]
- 35.Platt J, Zhong T, Moineddin R, et al. Geographic variation immediate and delayed breast reconstruction utilization in Ontario, Canada and plastic surgeon availability: a population-based observational study. World J Surg. 2015;39:1909–21. doi: 10.1007/s00268-015-3060-2. [DOI] [PubMed] [Google Scholar]
- 36.Alder NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff (Millwood) 2002;21:60–76. doi: 10.1377/hlthaff.21.2.60. [DOI] [PubMed] [Google Scholar]
- 37.Jeevan R, Browne JP, Pereira J, et al. Socioeconomic deprivation and inpatient complication rates following mastectomy and breast reconstruction surgery. Br J Surg. 2015;102:1064–70. doi: 10.1002/bjs.9847. [DOI] [PubMed] [Google Scholar]
- 38.Dean C, Chetty U, Forrest AP. Effects of immediate breast reconstruction on psychosocial morbidity after mastectomy. Lancet. 1983;1:459–62. doi: 10.1016/s0140-6736(83)91452-6. [DOI] [PubMed] [Google Scholar]
- 39.Kim HM, Goodman M, Kim BI, et al. Frequency and determinants of missing data in clinical and prognostic variables recently added to SEER. J Registry Manag. 2011;38:120–31. [PubMed] [Google Scholar]
- 40.Du XL, Key CR, Dickie L, et al. Information on chemotherapy and hormone therapy from tumor registry had moderate agreement with chart reviews. J Clin Epidemiol. 2006;59:53–60. doi: 10.1016/j.jclinepi.2005.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]