Abstract
Background
Change in health behaviors can occur among women newly diagnosed with DCIS. We sought to understand whether partnership status and socioeconomic status (SES) impacted behavioral changes in body weight, physical activity, alcohol consumption, and smoking.
Methods
The Wisconsin In Situ Cohort (WISC) study comprises 1,382 women diagnosed with DCIS with information on demographics, SES factors, and pre- and post-DCIS diagnosis health-related behaviors. Logistic regression models were used to determine the association between partnership status, education, and income with change in behavior variables.
Results
Higher educational attainment was associated with lower likelihood of stopping physical activity (OR 0.45, 95% CI: 0.32-0.63, college vs high school degree), or starting to drink alcohol (OR 0.34, 95% CI: 0.15-0.80). Results suggested that higher family income was associated with lower likelihood of gaining >5% body mass index (P=0.07) or stopping physical activity (P=0.09). Living with a partner was not strongly associated with behavior changes.
Conclusion
Higher educational attainment and higher income, but not living with a partner, were associated with positive health behaviors after a DCIS diagnosis.
Impact
The associations between higher educational attainment and, to a lesser extent, higher income with positive health behaviors underscore the importance of considering SES when identifying those at risk for negative behavioral change after DCIS diagnosis.
Keywords: Ductal Carcinoma In Situ, breast neoplasms, partnership status, socioeconomic status, health behavior
INTRODUCTION
Ductal carcinoma in situ (DCIS), a non-invasive stage-0 breast cancer consists of the clonal proliferation of malignant-appearing cells that are bounded by the basement membrane of the breast ducts (1). DCIS is a non-obligate precursor lesion that has the potential to transform into an invasive cancer over time which can range from a few years to decades (2). Breast cancer remains the most common cancer among U.S. women; about 231,840 new cases of invasive breast cancer and 50,000 new cases of DCIS are estimated in 2015 (3). Women with DCIS are four times more likely to develop invasive breast cancer than the general population (4). However, given current surveillance strategies and treatment, 96% of DCIS patients will survive breast cancer and ultimately die of competing causes (5).
Like the general population, the most frequent cause of death among DCIS patients is cardiovascular disease (CVD) (6). Risk of breast cancer death and CVD death are both influenced by health-related behaviors. Many DCIS patients have poor health-related behaviors at the time of diagnosis, and these can be exacerbated during and after the diagnosis of DCIS and its treatment. Sprague et al. found that women after a DCIS diagnosis were more likely to gain weight and use anti-depressants, but on average had little or no change in alcohol intake and a substantial fraction either quit or substantially decrease smoking (7). Ligibel et al. assessed physical activity behavior in 487 women with newly diagnosed DCIS, and found women were physically inactive at diagnosis and remained so over a 18-month period (8). Given the competing risk of CVD in this population, it is important to understand what drives health behaviors among women newly diagnosed with DCIS and identify those most at risk of adverse changes.
Prior studies have shown a strong correlation between key indicators of SES, such as education and income, and health behavior (9). Furthermore, partnership status is known to impact health behaviors such as physical activity, diet, smoking, and alcohol intake (10). In contrast, two studies reported that, among women diagnosed with invasive breast cancer, marital status was not related to change in recreational physical activity (11), and that partnership status was not associated with change in tobacco use, alcohol consumption, or body mass index (12). However, the extent to which partnership status and SES are associated with change in health behavior among DCIS patients has not been previously studied. To address this question, we utilized the Wisconsin In Situ Cohort (WISC) study comprised of 1,925 women with demographic information, SES factors, and health behaviors that were assessed pre- and post-DCIS diagnosis. In this study we hypothesized that living with a partner, having a higher educational attainment, and higher income would have a positive impact on health behavior after a DCIS diagnosis.
MATERIALS AND METHODS
Study Population
The WISC study has been described in previously (7, 13, 14). Briefly, the WISC study recruited 1,925 women aged 20-74 with an incident first primary DCIS diagnosis between 1995-2006 from the mandatory state-wide Wisconsin Cancer Reporting System (14). Informed verbal consent was obtained from all participants and the study has been approved by both the University of Wisconsin Health Science Institutional Review Board and the University of Vermont Medical Center Institutional Review Board.
Data Collection
The current study analyzed data collected from 1996 through 2010. Participants completed an initial telephone interview when enrolled in the study. Baseline interviews were conducted an average of 1.3 years following diagnosis. Enrollment eligibility and participation is shown in Figure 1. Patients were eligible to be re-contacted if a minimum of two years had passed since their last telephone interview. Since women initially enrolled in the study at different periods of time and the eligibility for re-contact remained at least two years, re-contact periods overlapped. Consequently, not all patients in the study were eligible for a specific cycle of re-contact interviews. Among patients eligible for the first re-contact interview, the participation rate was 79%. For the second re-contact interview 85% of eligible women participated. 73% participated in the third re-contact interview. Women who completed only the baseline interview but did not complete any follow-up surveys were similar to women who continued to participate in the study according to racial descent, comorbidity level, body mass index, physical activity, and alcohol intake; women who did not participate in subsequent waves of data collection tended to have less education, lower income, and were less like to report living with a partner (Supplementary Table S1).
Figure 1.
Wisconsin In Situ Cohort (WISC) study eligibility and participation according to year of enrollment, 1997-2010.
The baseline telephone interview elicited patient demographic information, health behaviors, medication use, comorbidities, and socioeconomic status (SES) factors (7, 13). Subjects were asked to recall body weight, physical activity participation, alcohol consumption, and smoking habits 1 year prior to diagnosis. Body mass index (BMI) was calculated as weight (kg) divided by squared height (m2). The duration and frequency per week of each type of physical activity for each participant was summed to obtain hours/week of physical activity. For alcohol use, participants were asked to recall the number of bottles or cans of beer, glasses of wine, and drinks of hard liquor consumed per day, week, or month. Smoking status was obtained at baseline by asking whether participants had smoked more than 100 cigarettes in their lifetime and if they were current smokers. At the re-contact questionnaires, subjects were asked to update their current body weight, physical activity, alcohol consumption, and smoking habits.
Baseline data in the WISC cohort included patients’ “partnership status” – whether or not they are living with a spouse or partner, as well as SES factors (education and income). Participants’ education levels were defined as less than high school degree, high school degree, some college, and having a college degree. Annual household income was categorized into four levels: ≤$30,000, $30,001-50,000, $50,001-100,000, and >$100,000. Diagnosis with 21 comorbidities based on the Charlson Comorbidity index (15) were self-reported and summarized for statistical analysis.
We excluded 162 women who reported a second breast cancer event during follow-up, since treatment may affect behavior change. A total of 381 women provided no behavior information beyond baseline and were thus excluded, leaving a study population of 1,382 women.
Statistical analysis
The overall analytic plan was based on analysis of several exposure variables including partnership status and SES factors (household income and education) in relation to the outcomes of change in lifestyle behavior (BMI, physical activity, alcohol intake, and smoking status) after diagnosis. Change in behavior was defined as the differences between recalled pre-diagnosis levels one year prior to diagnosis and the first re-contact levels post-diagnosis. Also, change in behavior is referred to as ”positive health behavior” when any potential or actual impact has positive outcomes. Conversely, negative health behavior is associated with negative health outcomes.
Missing values for all covariates and behaviors at each data collection period were estimated using multiple imputation with ten imputations (16). The imputation model included all covariates listed in Table 1 in addition to menopausal status, family history of breast cancer, use of post-treatment endocrine therapy (tamoxifen, raloxifene, aromatase inhibitors), surgical treatment type, mammographic screening history, and use of postmenopausal hormone therapy, all assessed at the baseline interview. Regression analyses used the combined results of the ten imputations to create valid statistical inferences that account for the uncertainty created by the missing values (17, 18). Where variables with imputed values were categorized for presentation, classification of subjects into categories was based on the mode of the ten estimated values for each subject created under the imputation model.
Table 1.
Selected characteristics of the study population at baseline (N=1,382): WISC 1996-2010
| Characteristic | N (%)a |
|---|---|
| Age, years | |
| 20-44 | 157 (11.4) |
| 45-54 | 508 (36.8) |
| 55-64 | 449 (32.5) |
| 65-74 | 268 (19.4) |
| Descent | |
| European-American | 1,327 (96.0) |
| African-American | 28 (2.1) |
| Other or multicultural | 27 (1.9) |
| Number of comorbidities | |
| None | 987 (71.4) |
| 1 | 261 (18.9) |
| 2 | 106 (7.7) |
| ≥3 | 28 (2.0) |
| Partnership status | |
| Not living with a partner | 226 (16.3) |
| Living with a partner | 1,156 (83.7) |
| Education | |
| No high school degree | 57 (4.1) |
| High school degree | 521 (37.7) |
| Some college | 380 (27.5) |
| College degree | 424 (30.7) |
| Income | |
| ≤ $30,000 | 269 (19.5) |
| $30,001 - 50,000 | 458 (33.1) |
| $50,001 - 100,000 | 500 (36.2) |
| ≥$100,001 | 155 (11.2) |
| Body mass index, kg/m2 | |
| Underweight & normal: <25 | 650 (47.0) |
| Overweight: 25 - 29.9 | 450 (32.6) |
| Obese: ≥30 | 282 (20.4) |
| Physical activity, hours/week | |
| No activity | 371 (26.8) |
| 0.1 - 2 | 701 (50.7) |
| 2.1 - 5 | 226 (16.4) |
| >5 | 219 (15.9) |
| Alcohol intake, drinks/week | |
| Non-drinker | 236 (17.1) |
| 0.1 - 1.9 | 701 (50.7) |
| 2 - 6.9 | 226 (16.4) |
| ≥7 | 219 (15.8) |
| Smoking status | |
| Non-smoker or former smoker | 1,187 (85.9) |
| Current smoker | 195 (14.1) |
Values were imputed for women missing information on race (N=3, 0.2%), number of comorbidities (N=603, 44%), income (N=429, 31%), BMI (N=69, 5%), and smoking status (N=5, 0.4%). Information was complete for all other variables.
We used multivariate logistic regression to determine the effect of partnership status and SES factors on change in behavior after a DCIS diagnosis. Change in BMI (gain or loss >5%) was categorized based on the difference in BMI since pre-diagnosis. Physical activity (sedentary/any), alcohol intake (none/any) and current smoking (no/yes) were categorized at the two time points, pre- and first post-diagnosis wave of data collection. Women who initiated a behavior during the study period were compared to those who consistently did not participate in the behavior. Women who stopped a behavior during the study period were compared to those who consistently participated in that behavior. Separate models were constructed for each SES factor and change in behavior outcome. To reduce the potential influence of multiple comparisons on statistical analysis, the overall statistical significance of each SES factor in the regression model was tested by comparing the model with and without the addition of that SES factor; odds ratio estimates are presented for all factors but examined closely only for those with overall p-values <0.05.
Each model was adjusted for the following confounding factors: age at diagnosis, time between interviews, calendar year of diagnosis, and the number of reported comorbidities at baseline. Comorbidity information was not collected at baseline for women enrolled from 1997-2000. Therefore, the number of comorbidities was imputed for these women (43.6% of study population).
All statistical analysis was performed using SAS statistical software Version 9.3 (SAS Institute Inc.). Effects were considered significant at P<0.05 level.
RESULTS
The mean age at enrollment in the WISC study was 55.5 years. Approximately 16% of women were living alone at baseline, 30% had a college degree, and 11% had a household income over $100,000 yearly. Over 50% of the cohort was overweight or obese (Table 1). Twenty-seven percent did not participate in any physical activity. Alcohol use was common among participants, though a majority of women consumed fewer than 2 drinks/week. Current smoking was prevalent in 14% of the cohort at baseline.
Table 2 demonstrates associations between partnership status, education, household income, and change in body mass index from one year pre-diagnosis to the first post-diagnosis interview. Neither partnership status nor either of the SES variables were significantly associated with gaining or losing >5% BMI. However, results suggested that women with greater income were less likely to gain >5% BMI since diagnosis (P=0.07).
Table 2.
Multivariable association between partnership status, income, and education at baseline and change in body mass index from one year pre-diagnosis to the earliest recontact (N=1,382): WISC 1996-2010
| >5% loss “Cases” | Stable ±5% “Controls” | Overall | >5% gain “Cases” | Stable ±5% “Controls” | Overall | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| N (%) | N (%) | ORa | 95% CI | P value | N (%) | N (%) | ORa | 95% CI | P value | |
| Partnership status | ||||||||||
| Not living with a partner | 33 (16.4) | 84 (15.4) | 1 | Ref | 0.88 | 110 (17.2) | 84 (15.4) | 1 | Ref | 0.25 |
| Living with a partner | 168 (83.6) | 462 (84.6) | 0.96 | 0.60, 1.54 | 531 (82.8) | 462 (84.6) | 0.83 | 0.60, 1.15 | ||
| Education | ||||||||||
| No high school degree | 9 (4.5) | 20 (3.7) | 1.24 | 0.50, 3.07 | 0.39 | 28 (4.4) | 20 (3.7) | 1.15 | 0.61, 2.16 | 0.82 |
| High school degree | 69 (34.3) | 207 (37.9) | 1 | Ref | 248 (38.7) | 207 (37.9) | 1 | Ref | ||
| Some college | 63 (31.3) | 138 (25.3) | 1.43 | 0.92, 2.22 | 181 (28.2) | 138 (25.3) | 1.01 | 0.75, 1.36 | ||
| College degree | 60 (29.9) | 181 (33.1) | 1.01 | 0.64, 1.57 | 184 (28.7) | 181 (33.1) | 0.78 | 0.58, 1.04 | ||
| Income | ||||||||||
| ≤ $30,000 | 43 (21.4) | 101 (18.5) | 1 | Ref | 0.36 | 125 (19.5) | 101 (18.5) | 1 | Ref | 0.07 |
| $30,001 - 50,000 | 80 (39.8) | 175 (32.1) | 0.93 | 0.55, 1.57 | 208 (32.5) | 175 (32.1) | 0.82 | 0.55, 1.21 | ||
| $50,001 - 100,000 | 60 (29.8) | 200 (36.6) | 0.77 | 0.42, 1.40 | 241 (37.6) | 200 (36.6) | 1.31 | 0.53, 1.10 | ||
| ≥ $100,001 | 18 (9.0) | 70 (12.8) | 0.69 | 0.35, 1.37 | 67 (10.4) | 70 (12.8) | 0.63 | 0.40, 0.99 |
Adjusted for age at diagnosis, number of comorbidities, time between interviews, and year of diagnosis
Among women who were physically active at one year pre-diagnosis, those with at least some college education were less likely to stop activity by the earliest re-contact than those with no education beyond high school (OR 0.69, 95% CI: 0.50-0.96 for some college; OR 0.45, 95% CI: 0.32-0.63 for college degree) (Table 3). While change in physical activity was not significantly associated with partnership status or income, there was a suggestion that greater income was associated with reduced risk of stopping physical activity after diagnosis (P=0.09).
Table 3.
Multivariable association between partnership status, income, and education at baseline and change in physical activity from one year pre-diagnosis to the earliest recontact (N=1,382): WISC 1996-2010
| Stopped activity “Cases” |
Consistently active “Controls” |
Overall | Started activity “Cases” |
Consistently sedentary “Controls” |
Overall | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| N (%) | N (%) | ORa | 95% CI | P value | N (%) | N (%) | ORa | 95% CI | P value | |
| Partnership status | ||||||||||
| Not living with a partner | 63 (17.0) | 90 (14.1) | 1 | Ref | 0.60 | 19 (17.9) | 54 (20.4) | 1 | Ref | 0.93 |
| Living with a partner | 308 (83.0) | 550 (85.9) | 0.90 | 0.62, 1.31 | 87 (82.1) | 211 (79.6) | 1.03 | 0.55, 1.90 | ||
| Education | ||||||||||
| No high school degree | 16 (4.3) | 13 (2.1) | 1.29 | 0.57, 2.92 | 0.01 | 4 (3.8) | 24 (9.1) | 0.39 | 0.12, 1.25 | 0.28 |
| High school degree | 159 (42.9) | 189 (29.5) | 1 | Ref | 50 (47.2) | 123 (46.4) | 1 | Ref | ||
| Some college | 105 (28.3) | 187 (29.2) | 0.69 | 0.50, 0.96 | 27 (25.5) | 61 (23.0) | 1.01 | 0.55, 1.84 | ||
| College degree | 91 (24.5) | 251 (39.2) | 0.45 | 0.32, 0.63 | 25 (23.6) | 57 (21.5) | 0.99 | 0.54, 1.80 | ||
| Income | ||||||||||
| ≤ $30,000 | 92 (14.4) | 79 (21.3) | 1 | Ref | 0.09 | 25 (23.6) | 73 (27.6) | 1 | Ref | 0.63 |
| $30,001 - 50,000 | 186 (29.1) | 132 (35.6) | 0.87 | 0.57, 1.33 | 43 (40.6) | 97 (36.6) | 1.21 | 0.58, 1.48 | ||
| $50,001 - 100,000 | 274 (42.8) | 118 (31.8) | 0.62 | 0.39, 0.97 | 29 (27.4) | 79 (29.8) | 0.90 | 0.42, 1.91 | ||
| ≥ $100,001 | 88 (13.7) | 42 (11.3) | 0.66 | 0.39, 1.11 | 9 (8.5) | 16 (6.0) | 1.48 | 0.55, 4.01 |
Adjusted for age at diagnosis, number of comorbidities, time between interviews, and year of diagnosis
Among women who reported not consuming alcohol at one year pre-diagnosis, those with more education were less likely to start drinking than women with a high school degree (OR 0.38, 95% CI: 0.17-0.85 for some college; OR 0.34, 95% CI: 0.15-0.80 for college degree) (Table 4). Income and partnership status were not significantly associated with changing alcohol consumption behavior after diagnosis.
Table 4.
Multivariable association between partnership status, income, and education at baseline and change in alcohol intake from one year pre-diagnosis to the earliest recontact (N=1,382): WISC 1996-2010
| Stopped drinking “Cases” |
Consistent drinker “Controls” |
Overall | Started drinking “Cases” |
Consistent non-drinker “Controls” |
Overall | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| N (%) | N (%) | ORa | 95% CI | P value | N (%) | N (%) | ORa | 95% CI | P value | |
| Partnership status | ||||||||||
| Not living with a partner | 19 (22.4) | 159 (15.0) | 1 | Ref | 0.26 | 8 (12.7) | 40 (23.1) | 1 | Ref | 0.10 |
| Living with a partner | 66 (77.6) | 902 (85.0) | 0.73 | 0.42, 1.27 | 55 (87.3) | 133 (76.9) | 2.07 | 0.88, 1.59 | ||
| Education | ||||||||||
| No high school degree | 7 (8.2) | 31 (2.9) | 2.70 | 1.06, 6.91 | 0.09 | 2 (3.2) | 17 (9.8) | 0.26 | 0.05, 1.24 | < 0.01 |
| High school degree | 28 (32.9) | 391 (36.9) | 1 | Ref | 37 (28.7) | 65 (37.6) | 1 | Ref | ||
| Some college | 23 (27.1) | 296 (27.9) | 1.15 | 0.64, 2.07 | 13 (20.6) | 48 (27.7) | 0.38 | 0.17, 0.85 | ||
| College degree | 27 (31.8) | 343 (32.3) | 1.19 | 0.68, 2.09 | 11 (17.5) | 43 (24.9) | 0.34 | 0.15, 0.80 | ||
| Income | ||||||||||
| ≤ $30,000 | 21 (24.7) | 175 (16.5) | 1 | Ref | 0.26 | 15 (23.8) | 58 (33.5) | 1 | Ref | 0.66 |
| $30,001 - 50,000 | 31 (36.5) | 345 (32.5) | 0.95 | 0.46, 1.95 | 27 (42.9) | 55 (31.8) | 1.88 | 0.78, 4.55 | ||
| $50,001 - 100,000 | 29 (34.1) | 405 (38.2) | 0.77 | 0.38, 1.56 | 19 (30.1) | 47 (27.2) | 1.59 | 0.62, 4.03 | ||
| ≥ $100,001 | 4 (4.7) | 136 (12.8) | 0.42 | 0.13, 1.33 | 2 (3.2) | 13 (7.5) | 0.64 | 0.11, 3.76 |
Adjusted for age at diagnosis, number of comorbidities, time between interviews, and year of diagnosis
No significant associations between partnership status and SES with the likelihood of smoking cessation or starting smoking were observed (Table 5). Few women started smoking after diagnosis.
Table 5.
Multivariable association between partnership status, income, and education at baseline and change in smoking status from one year pre-diagnosis to the earliest recontact (N=1,382): WISC 1996-2010
| Stopped smoking “Cases” |
Consistent smoker “Controls” |
Overall | Started smoking “Cases” |
Consistent non-smoker “Controls” |
Overall | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| N (%) | N (%) | ORa | 95% CI | P value | N (%) | N (%) | ORa | 95% CI | P value | |
| Partnership status | ||||||||||
| Not living with a partner | 20 (23.8) | 29 (26.1) | 1 | Ref | 0.28 | 3 (33.3) | 174 (14.8) | 1 | Ref | 0.14 |
| Living with a partner | 64 (76.2) | 82 (73.9) | 1.50 | 0.72, 1.14 | 6 (66.7) | 1004 (85.2) | 0.33 | 0.08, 1.42 | ||
| Education | ||||||||||
| No high school degree | 4 (4.8) | 8 (7.2) | 0.55 | 0.13, 2.34 | 0.94 | 1 (11.1) | 44 (3.7) | 2.14 | 0.22, 21.0 | 0.34 |
| High school degree | 37 (44.0) | 49 (44.1) | 1 | Ref | 5 (55.6) | 430 (36.5) | 1 | Ref | ||
| Some college | 22 (26.2) | 40 (36.0) | 0.83 | 0.40, 1.69 | 1 (11.1) | 317 (26.9) | 0.20 | 0.02, 1.73 | ||
| College degree | 21 (25.0) | 14 (12.6) | 2.03 | 0.86, 4.77 | 2 (22.2) | 387 (32.9) | 0.30 | 0.05, 1.72 | ||
| Income | ||||||||||
| ≤ $30,000 | 24 (28.6) | 27 (24.3) | 1 | Ref | 0.64 | 3 (33.3) | 215 (18.2) | 1 | Ref | 0.95 |
| $30,001 - 50,000 | 28 (33.3) | 47 (42.3) | 0.83 | 0.33, 2.05 | 4 (44.4) | 379 (32.2) | 1.74 | 0.38, 8.08 | ||
| $50,001 - 100,000 | 28 (33.3) | 31 (27.9) | 1.70 | 0.61, 4.78 | 2 (22.2) | 439 (37.3) | 0.52 | 0.08, 3.37 | ||
| ≥ $100,001 | 4 (4.8) | 6 (5.4) | 1.38 | 0.32, 6.03 | 0 | 145 (12.3) | -b | -b |
Adjusted for age at diagnosis, time between interviews, and year of diagnosis
Odds ratio not estimated
DISCUSSION
In the current study we found that having higher educational attainment and high income, but not living with a partner, were associated with a positive change in health behaviors after a DCIS diagnosis. Women with higher SES characteristics (household income, education) were less likely to gain significant weight or stop physical activity after DCIS diagnosis. Women with higher SES were less likely to start drinking after DCIS diagnosis. These results speak to the importance of considering SES when determining which women after DCIS diagnosis are at risk for a change in health behavior.
Research on partnership status and change in health behavior after a diagnosis of in situ or invasive breast cancer is sparse. Recent studies have established that a state of heightened distress including anxiety, depression, and psychosocial issues exist even years after an initial DCIS diagnosis (19-22). Liu et al observed that being married or partnered provided social support and intimate relationships that could allay a patient's concerns and minimize the perceived risk of recurrence (23). The LACE study concluded that support within the family strongly influenced prolonging survival of breast cancer patients (24). Also, Dawood et al showed that being married at the time of diagnosis of breast cancer improves survival regardless of patient and tumor characteristics (25). Though these studies have suggested that social networks and marital status enhance survival rates, the influence of partnership status on specific health behaviors after a breast cancer diagnosis has yet to be demonstrated. Our study did not support that living with a partner had a positive impact on change in health behaviors after a DCIS diagnosis, but these findings do not rule out beneficial impacts of partners for treatment adherence and maintaining healthy lifestyles.
Sprague et al. previously demonstrated a decline in invasive breast cancer incidence in women with high SES (26). A prior study suggested that women with low income are more likely to be depressed or anxious compared to higher income groups (27). Financial hardships can be exacerbated by the stress of a major event, which can potentially worsen depression and negatively influence health behaviors (28, 29). Further, DCIS women with higher SES may have a better opportunity and flexibility to modulate lifestyle behaviors in accordance with their health needs, unlike women with lower income or education. Finally, financial discomfort and less education have been associated with heightened perceived risks, which have the potential to negatively influence behavior change (30). In aggregate, our results support the need to intervene and mitigate negative changes in behavior, and focus these efforts on lower SES DCIS patients who are most susceptible.
Associations in the present study were adjusted for comorbidity burden. It is possible that individuals with comorbidities present to health care providers more often and are more likely to hear a message about behavior change regardless of their SES or partnership status. Moreover, it has been previously demonstrated that patients with lower socioeconomic status tend to develop co-morbidities at younger ages along with a higher prevalence of mental health problems, compounding challenges to adopt positive health behaviors (31). Research on effective interventions to improve outcomes in patients with multiple comorbid conditions is limited, and the extent to which socioeconomic status influences intervention effectiveness is unknown (32). Co-morbidities may also influence a person's physical functioning and attitudes towards their health behavior. Future research is recommended that focuses on subgroups defined by comorbidity burden.
This study has certain limitations and strengths. First, the WISC cohort predominately includes non-Hispanic women of European descent, whereby the study could not analyze differences by race/ethnicity. Second, both telephone interviews and follow-up questionnaires were dependent upon participants’ self-reporting, and pre-diagnosis behaviors were recalled at baseline, which occurred at an average of 1.3 years after diagnosis. While self-reported behaviors tend to be recalled with adequate reliability (33), prior studies suggest that the accuracy of recalled factors including body weight and smoking may vary based on SES (34, 35). Changes in SES during follow-up, although uncommon, were also not accounted for in the analysis. Any resulting non-differential misclassification would therefore be expected to attenuate our observed associations and result in underestimation of the associations between SES and change in health behaviors. Small samples sizes especially in some behavior-SES subgroups limited ability to detect modest associations. Finally, though major comorbidities like CVD, heart failure, stroke, and COPD etc. were taken into account and the list of comorbidities based on the Charlson index is well-established, the list is not necessarily exhaustive. Importantly, a major strength of the study is that the cohort was designed to assess DCIS patient behavior and outcomes so that data were collected uniformly multiple times.
In the current study, SES but not partnership status was associated with change in health behaviors among women diagnosed with DCIS. These key behaviors included sedentary lifestyle, decrease in physical activity, weight gain, alcohol consumption, and smoking. Future research should be undertaken to further enhance our understanding of these relationships for DCIS patients and assess the effectiveness of interventions targeted to higher risk subgroups.
Supplementary Material
Acknowledgments
The authors would like to express their gratitude to Julie McGregor, Kathy Peck, and Laura Stephenson for assistance with data collection and project management, and to Drs. Andreas Friedl, Kathleen Egan, Linda Titus, Hazel Nichols, and Shaneda Warren Andersen for advice and support for this project.
Funding Source: This work was supported by the Vermont Center on Behavior and Health (P20 GM103644, S.T. Higgins PI) and grants from the National Cancer Institute (PROSPR, U54 CA163303 to B.L. Sprague; R01 CA067264 to A. Trentham-Dietz and P. Newcomb; and P30 CA014520 to A. Trentham-Dietz).
Footnotes
Disclosure Statement: The authors have nothing to disclose.
References
- 1.Burstein HJ, Polyak K, Wong JS, Lester SC, Kaelin CM. Ductal carcinoma in situ of the breast. N Engl J Med. 2004;350:1430–41. doi: 10.1056/NEJMra031301. [DOI] [PubMed] [Google Scholar]
- 2.Barnes NLP, Ooi JL, Yarnold JR, Bundred NJ. Ductal carcinoma in situ of the breast. BMJ. 2012;314:e797. doi: 10.1136/bmj.e797. [DOI] [PubMed] [Google Scholar]
- 3.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65:5–29. doi: 10.3322/caac.21254. [DOI] [PubMed] [Google Scholar]
- 4.Warnberg F, Yuen J, Holmberg L. Risk of subsequent invasive breast cancer after breast carcinoma in situ. Lancet. 2000;35:724–25. doi: 10.1016/S0140-6736(99)03703-4. [DOI] [PubMed] [Google Scholar]
- 5.Berkman A, Cole B, Ades PA, Dickey S, Higgins ST, Trentham-Dietz A, et al. Racial differences in breast cancer, cardiovascular disease, and all-cause mortality among women with ductal carcinoma in situ of the breast. Breast Cancer Res Treat. 2014;148:407–13. doi: 10.1007/s10549-014-3168-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Darby SC, Ewertz M, McGale P, Bennet AM, Blom-Goldman U, Brønnum D, et al. Risk of ischemic heart disease in women after radiotherapy for breast cancer. N Engl J Med. 2013;368:987–98. doi: 10.1056/NEJMoa1209825. [DOI] [PubMed] [Google Scholar]
- 7.Sprague BL, Trentham-Dietz A, Nichols HB, Hampton JM, Newcomb PA. Change in lifestyle behaviors and medication use after a diagnosis of ductal carcinoma in situ. Breast Cancer Res Treat. 2010;124:487–95. doi: 10.1007/s10549-010-0869-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ligibel JA, Partridge A, Giobbie-Hurder A, Golshan M, Emmons K, Winer EP. Physical activity behaviors in women with newly diagnosed ductal carcinoma-In-Situ. Am Surg Oncol. 2009;16:106–12. doi: 10.1245/s10434-008-0174-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Pampel FC, Krueger PM, Denny JT. Socioeconomic Disparities in Health Behaviors. Annu Rev Sociol. 2010;36:349–70. doi: 10.1146/annurev.soc.012809.102529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Umberson D, Crosnoe R, Reczek C. Social Relationships and Health Behaviors Across Life Course. Annu Rev Sociol. 2010;36:139–57. doi: 10.1146/annurev-soc-070308-120011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Devoogdt N, Van Kampen M, Geraerts I, Coremans T, Fieuws S, Lefevre J, et al. Physical activity levels after treatment for breast cancer: one year follow-up. Breast Cancer Res Treat. 2010;123:417–25. doi: 10.1007/s10549-010-0997-6. [DOI] [PubMed] [Google Scholar]
- 12.Bidstrup PE, Dalton SO, Christensen J, Tjonneland A, Larsen SB, Karlsen R, et al. Changes in body mass index and alcohol and tobacco consumption among breast cancer survivors and cancer-free women: A prospective study in the Danish Diet, Cancer and Health Cohort. Acta Oncol. 2013;52:327–35. doi: 10.3109/0284186X.2012.746466. [DOI] [PubMed] [Google Scholar]
- 13.McLaughlin V, Trentham-Dietz A, Hampton JM, Newcomb PA, Sprague BL. Lifestyle factors and the risk of a second breast cancer after ductal carcinoma in situ. Cancer Epidemiol Biomarkers Prev. 2014;23:450–60. doi: 10.1158/1055-9965.EPI-13-0899. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Sprague BL, McLaughlin V, Hampton JM, Newcomb PA, Trentham-Dietz A. Disease-free survival by treatment after a DCIS diagnosis in a population-based cohort study. Breast Cancer Res Treat. 2013;141:145–54. doi: 10.1007/s10549-013-2670-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47:1245–51. doi: 10.1016/0895-4356(94)90129-5. [DOI] [PubMed] [Google Scholar]
- 16.Schafer J. Analysis of incomplete multivariate data. Chapman and Hall; London: 1997. [Google Scholar]
- 17.Rubin DB. Multiple imputation for nonresponse in surveys. John Wiley & Sons; New York: 1987. [Google Scholar]
- 18.Rubin DB. Inference and missing data. Biometrika. 1976;63:581–92. [Google Scholar]
- 19.Partridge A, Adloff K, Blood E, Dees EC, Kaelin C, Golshan M, et al. Risk perceptions and psychosocial outcomes of women with ductal carcinoma in situ: longitudinal results from a cohort study. J Natl Cancer Inst. 2008;100:243–51. doi: 10.1093/jnci/djn010. [DOI] [PubMed] [Google Scholar]
- 20.Lauzier S, Maunsell E, Levesque P, Mondor M, Robert J, Robidoux A, et al. Psychological distress and physical health in the year after diagnosis of DCIS or invasive breast cancer. Breast Cancer Res Treat. 2010;120:685–91. doi: 10.1007/s10549-009-0477-z. [DOI] [PubMed] [Google Scholar]
- 21.Phillips K, McGinty HL, Gonzalez BD, Jim HS, small BJ, Minton S, et al. Factors associated with breast cancer worry three years after completion of adjuvant treatment. Psychooncology. 2013;22:936–39. doi: 10.1002/pon.3066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kennedy F, Harcourt D, Rumsey N, White P. The psychosocial impact of ductal carcinoma in situ (DCIS): a longitudinal prospective study. Breast. 2010;19:382–87. doi: 10.1016/j.breast.2010.03.024. [DOI] [PubMed] [Google Scholar]
- 23.Liu Y, Perez M, Schootman M, Aft RL, Gillanders WE, Ellis MJ, et al. A longitudinal study of factors associated with perceived risk of recurrence in women with ductal carcinoma in situ and early-stage invasive breast cancer. Breast Cancer Res Treat. 2010;124:835–44. doi: 10.1007/s10549-010-0912-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kroenke CH, Quesenberry C, Kwan ML, Sweeney C, Castillo A, Caan BJ. Social networks, social support, and burden in relationships, and mortality after breast cancer diagnosis in the Life After Breast Cancer Epidemiology (LACE) Study. Breast Cancer Res Treat. 2013;137:261–71. doi: 10.1007/s10549-012-2253-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Dawood SS, Lei X, Dent R, Mainwaring PN, Gupta S, Cortes J, et al. Impact of marital status on prognostic outcome of women with breast cancer. J Clin Oncol. 2014;32:5s. abstr 594. [Google Scholar]
- 26.Sprague BL, Trentham-Dietz A, Burnside ES. Socioeconomic disparities in the decline in invasive breast cancer incidence. Breast Cancer Res Treat. 2010;122:873–78. doi: 10.1007/s10549-010-0737-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.De Moor JS, Partridge AH, Winer EP, Ligibel J, Emmons KM. The role of socioeconomic status in adjustment after ductal carcinoma in situ. Cancer. 2010:1218–23. doi: 10.1002/cncr.24832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Burgess C, Cornelius V, Love S, Graham J, Richards M, Ramirez A. Depression and anxiety in women with early breast cancer: five year observational cohort study. BMJ. 2005;330:702. doi: 10.1136/bmj.38343.670868.D3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Rakovitch E, Franssen E, Kim J, Ackerman I, Pignol JP, Paszat L, et al. A comparison of risk perception and psychological morbidity in women with ductal carcinoma in situ and early invasive breast cancer. Breast Cancer Res Treat. 2003;77:285–93. doi: 10.1023/a:1021853302033. [DOI] [PubMed] [Google Scholar]
- 30.Ruddy KL, Meyer ME, Giobbie-Hurder A, Emmons KM, Weeks JC, Winer EP, et al. Long-term risk perceptions of women with ductal carcinoma in situ. Oncologist. 2013;18:362–68. doi: 10.1634/theoncologist.2012-0376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380:37–43. doi: 10.1016/S0140-6736(12)60240-2. [DOI] [PubMed] [Google Scholar]
- 32.Smith SM, Soubhi H, Fortin M, Hudon C, O'Dowd T. Managing patients with multimorbidity: systematic review of interventions in primary care and community settings. BMJ. 2012;345:e5205. doi: 10.1136/bmj.e5205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Trentham-Dietz A, Newcomb PA, Storer BE, Remington PL. Risk factors for carcinoma in situ of the breast. Cancer Epidemiol Biomarkers Prev. 2000;9:69–703. [PubMed] [Google Scholar]
- 34.Dekkers JC, van Wier MF, Hendriksen IJM, Twisk JWR, van Mechelen W. Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Med Res Method. 2008;8:69. doi: 10.1186/1471-2288-8-69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Palta M, Prineas RJ, Berman R, Hannan P. Comparison of self-reported and measured height and weight. Am J Epidemiol. 1982;115:223–30. doi: 10.1093/oxfordjournals.aje.a113294. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.