Journal of Psychosocial Oncology, 33:263–277, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0734-7332 print / 1540-7586 online DOI: 10.1080/07347332.2015.1019661
Racial Differences in Depressive Symptoms and Self-Rated Health Among Breast Cancer Survivors on Aromatase Inhibitor Therapy CARLA CALHOUN, MSW The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, Baltimore, MD, USA
KATHY J. HELZLSOUER, MD, MHS The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, Baltimore, MD, USA; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
LISA GALLICCHIO, PhD The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; and Department of Epidemiology and Public Health, University of Maryland, Baltimore, Baltimore, MD, USA
The goal of this survey-based study was to examine whether aromatase inhibitor (AI) therapy was associated with depressive symptoms and self-rated health among Black and White breast cancer survivors (N = 761). Results showed that among Black, but not White, breast cancer survivors current AI therapy was associated with a significant increase in the odds of both depressive symptoms (OR 3.59; 95% CI 1.01, 13.00) and poorer self-rated health (OR 3.16; 95% CI 1.06, 9.46). Presence of pain was significantly associated with increased odds of both outcomes among both groups. The findings underscore the importance of addressing not only physical but mental health among breast cancer survivors on AIs, especially those of Black race. KEYWORDS aromatase inhibitors, breast cancer, estrogen, depression, self-rated health, endocrine therapy, estrogen receptor positive Address correspondence to Lisa Gallicchio, PhD, The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, 227 St. Paul Place, Baltimore, MD 21202. E-mail: [email protected] 263
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C. Calhoun et al.
INTRODUCTION Aromatase inhibitors (AIs) are currently the hormonal therapy of choice among postmenopausal women diagnosed with estrogen receptor-positive breast cancer. AIs work by binding to the aromatase enzyme, thereby preventing the conversion of peripheral androgens to estrogens. (Conte & Frassoldati, 2007; Ghandi & Verma, 2006; Litton, Arun, Brown, & Hortobagyi, 2012; Rugo, 2008; Schilder et al., 2009; Sehdev et al., 2009; Wasan et al., 2005). The result of this profound decrease in estrogen is a significant reduction in the risk of recurrence and breast cancer-related mortality (Baum, 2001; Baum et al., 2002; Litton, 2012; Rugo, 2008). Although the safety profile of AIs is superior to that of the alternative breast cancer hormonal therapy, tamoxifen, AIs have been consistently reported to be associated with certain side effects, including musculoskeletal symptoms, hot flashes, hair loss, weight gain, sleep disturbances, and poor sexual functioning (Bower, 2008; Cella & Fallowfield, 2008; Eversley et al., 2005; Fann et al. 2008; Fontaine et al., 2008; Gallicchio et al., 2012; Helzlsouer et al., 2013; Gupta et al., 2006; Irwin et al., 2013; Jones et al., 2007; Morales et al., 2004; Reyes-Gibby et al., 2012). Most of these reported side effects are physical in nature. Less frequently studied are the psychosocial effects of AIs, including depression. Some data suggest that there is a correlation between the reduction of estrogen and the onset of depression (Bower, 2008; Halbreich, 1997; Massie, 2004); therefore, it is possible that AI therapy may increase the risk of depression among cancer survivors. Several studies conducted among Japanese breast cancer survivors showed no difference in depressive symptoms, as measured using the Center for Epidemiological Studies-Depression (CES-D) scale, among women treated with exemestane, tamoxifen, or anastrozole during the first year of treatment, and no difference in depressive symptoms among breast cancer survivors who switched from tamoxifen to anastrozole compared to those who remained on tamoxifen (Ohsumi et al., 2011; Takei, 2012). In general, these studies are limited by the fact that they have been conducted within clinical trials, where patients are healthier than the general breast cancer population, and the comparison group is on other hormonal therapies, which may be associated with psychosocial side effects themselves. Because depression can lead to increased mortality and morbidity, poor medical adherence, reduced cognitive functioning, caregiver burden, family problems, and increased health costs (Bower, 2008; Ell et al., 2005; Fann et al., 2008; Onitilo, Nietert, & Egede, 2006; Polsky, 2005; Reyes-Gibby et al., 2012), it is important to know the psychological impact of AI therapy. In addition to depression, AIs may also affect the breast cancer patient’s perception of her self-rated health. Self-rated health is the most widely measure used to assess an individual’s health status, and has been consistently shown to be associated with morbidity and mortality in the general
Racial Differences in Aromatase Inhibitor Therapy
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population (Benyamini, Leventhal & Leventhal, 2003; Idler & Benyamini, 1997; Jylha, 2009; Knauper & Turner, 2003). Self-rated health assessments can be viewed as an individual’s perceived view of their health status based on their social, cultural, and historical environment as well as their medical history. It is a combination of their actual medical state, their mental representation of their existing health, and their personal definition of “health” (Bailis, Segall, & Chipperfield, 2003; Jylha, 2009, Miilunpalo et al., 1997). The purpose of this study was to examine the associations between AI use and both depressive symptoms and self-rated health among breast cancer survivors. Data were analyzed from 761 Black and White breast cancer survivors diagnosed at a single institution between 1996 and 2007 who responded to a mailed survey. AI treatment information was collected using survey responses and medical chart review. We hypothesized that AI use would be associated with both an increased risk of depressive symptoms and poorer self-rated health.
METHODS Study Sample Data were analyzed from a survey-based study of cancer survivors at Mercy Medical Center (MMC) in Baltimore, Maryland. Detailed methods of data collection are published elsewhere (Forsythe, 2012). Briefly, in June 2008, a self-administered questionnaire was sent to cancer survivors listed in the MMC cancer registry as being diagnosed and/or treated at the hospital between January 1996 and July 2007. In November 2009, a second mailing was made to nonrespondents from the first mailing. A returned questionnaire implied consent. The Institutional Review Board at MMC approved the study. The questionnaire was sent to a total of 2,513 breast cancer survivors, which constituted 57.1% of all cancer survivors in the registry dataset. Approximately 35% of the breast cancer survivors (n = 868) returned the questionnaire. Respondents were more likely to be of White race compared to nonrespondents; however, the two groups did not differ in terms of time since diagnosis or tumor behavior (malignant vs. in situ). Inclusion criteria for the present analysis were: White or Black race; postmenopausal and, thus, eligible to receive AI therapy; and data on at least one of the two outcome measures (depressive symptoms and self-rated health). The final analytic dataset for this study was comprised of 107 Black and 654 White postmenopausal breast cancer survivors.
Measures Depressive symptoms were assessed on the survey using the 20-question CES-D (Radloff, 1977). Individuals with a score ≥16 were identified as having depressive symptoms (Lewinsohn, 1997). Self-rated health was examined us-
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ing the participant’s response to the question: “In general would you say your health is:” with possible choices of excellent, very good, good, fair, or poor. For the regression analyses, the self-rated health outcome was dichotomized into the categories of “excellent/very good/good” versus “fair/poor.” Age at the time of the survey was calculated from self-reported date of birth and the date on which the survey was completed. Race was selfclassified as White, Black, or other. Highest level of education was classified as grade school/high school, college/technical school, or graduate school. Marital status was classified as single, married/living with partner, or widowed. Employment status was categorized as “yes” and “no”; those who were classified as employed included those who reported being employed full- or part-time. Menopause status was determined based on the question “Have your menstrual periods stopped permanently?” Women who responded “yes” were categorized as postmenopausal. Body mass index (BMI) was calculated based on respondents’ self-reported weight and height. Smoking status was classified as “never,” “former,” or “current.” Alcohol use data were collected using the question: “During the past year, on average how many days per month did you have at least one alcohol drink?” Participants who responded that they drank any alcohol in the past year were classified as current drinkers. Physical functioning was measured by calculating a summary score (range: 0–32) for the 12-item Limitation of Functional Activities component of the Adult Health Status and Limitation of Activity section from the National Health Interview Survey (Gallicchio, 2013). The summary score was then categorized into “no limitation” and three tertilebased categories (low, intermediate, and high limitation scores). Pain was categorized as “yes” and “no” based on participant report of headache, joint, bone, or muscle pain during the past 4 weeks. The respondents were also asked whether they had “ever been told by a doctor or other health professional” that they had any of 49 medical conditions. These conditions included, but were not limited to, diabetes, high cholesterol, high blood pressure, depression, and osteoporosis. They also had the option to list “other” medical conditions. Conditions were then grouped into the following 12 overarching categories: diabetes, heart, bone, thyroid, lung, autoimmune, gastrointestinal, kidney/bladder, neurological, gynecological, eye, and emotional. A variable was created that reflected the total number of co-morbid condition types. Cancer treatment information was self-reported on the survey. For approximately 95% of the patients who responded, treatment information was verified through their medical records. Some medical records were not available because the patient was treated at another institution and the records were not transferred to MMC. Of those records that were reviewed, the survey data were 100% accurate for whether or not they had been treated with chemotherapy and/or radiation therapy. There was 98% agreement between the survey and medical records for those treated with hormone therapy
Racial Differences in Aromatase Inhibitor Therapy
267
(tamoxifen and AIs). A medical record review was also performed for those women indicating AI treatment to determine duration of use (if available) and whether the AI was being taken at the time of or prior to the survey. All cancer diagnoses, including their breast cancer, were self-reported by respondents on the survey and verified by the registry. Their self-reported information included the age at which each cancer was diagnosed and was supplemented with data from the cancer registry. Time since diagnosis of first breast cancer was calculated. The median time since breast cancer diagnosis of the breast cancer survivors who responded to the survey was 7 years (range 1–43 years).
STATISTICAL ANALYSIS Chi-square and Student t tests were conducted to examine differences in participant characteristics, cancer treatments, and symptoms by race, AI use, and categories of each outcome variable. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (95% CI) for the associations between AI use and (1) depressive symptoms (CES-D ≥16) and (2) poorer (fair/poor) self-rated health. Race was identified as a potential effect modifier a priori and, thus, all multivariable models were conducted for Black and White participants separately. To identify potential confounders, bivariate analyses were performed to assess the association between a potential confounder and AI use (ever vs. never) as well as the association between a potential confounder and each of the outcomes. A covariate was considered as a potential confounder of the relationship between AI use and the outcome if it was associated (p < 0.05) with both AI use and the outcome. The potential confounder was then tested in a multivariable logistic regression model. If inclusion of the covariate changed the regression coefficient for AI use by more than 10%, the covariate was considered as a confounder with respect to the association between AI use and that outcome. The following variables were tested as potential confounders: marital status, BMI, education, employment status, smoking status, physical activity, alcohol intake, prior cancer treatments (chemotherapy, radiation, or tamoxifen), number of chronic conditions, pain, and physical functioning. Collinearity between identified confounders in each of the built logistic regression models was also assessed; in the cases of both outcomes, number of chronic conditions and pain were identified as confounders and were also were found to be strongly correlated. The pain variable was associated with a greater change in the OR for the association between AI use and each outcome with its inclusion into the model and, thus, pain, and not number of chronic conditions, was included as a variable in the final logistic regression model for both outcomes. Age was adjusted for in all adjusted
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models although it was not identified as a potential confounder for any of the associations examined. All analyses were performed using SAS statistical software v. 9.3. A twotailed p < 0.05 was considered statistically significant.
RESULTS Characteristics of the analytic study sample overall and by race are shown in Table 1. The mean age of participants at the time of the survey was 63.6 years (standard deviation 10.5). Approximately 86% of the respondents were White. The majority of the study sample was not employed, married, and had at least some college education. One third were categorized as obese (BMI ≥ 30 kg/m2), 67% were current drinkers, and 7.1% reported being current smokers. About half had been treated with chemotherapy, and approximately 40% were being treated or had been treated with an AI. Most respondents rated their health as very good or good, 15.5% had depressive symptoms, and the majority had two or more chronic conditions or reported pain. Over 80% had a least some physical functioning limitations. Overall, the Black breast cancer survivors were significantly less likely than their White counterparts to be employed, to be married, and to be current alcohol drinkers. The Black breast cancer survivors were more likely to be obese, to report currently smoking cigarettes, to report that their health was fair or poor, and to have severe physical functioning limitations than the White breast cancer survivors. There were no statistically significant differences with regards to age, education, prior chemotherapy, AI treatment, number of chronic conditions, presence of pain, and years since breast cancer diagnosis. The association between AI use and depressive symptoms differed by race (Table 2). After adjustment for participant age and presence of pain, Black breast cancer survivors who were being treated with an AI at the time of the survey were approximately three and a half times more likely to report depressive symptoms than Black breast cancer survivors who were never treated with an AI (OR 3.59; 95% CI 1.01, 13.00). Prior AI use was associated with an increased risk of depressive symptoms among Black women, but this increase was not statistically significant. Among White women, neither current nor prior AI use was associated with depressive symptoms. Presence of pain was significantly associated with a fivefold increase in odds of depressive symptoms among Black women (95% CI 1.43, 17.76) and a 2.67-fold increase in odds among White women (95% CI 1.64, 4.35). The association between AI use and poorer self-rated health also differed by race (Table 3). After adjustment for participant age and presence of pain, Black breast cancer survivors who were being treated with an AI at the time of the survey were approximately three times more
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Racial Differences in Aromatase Inhibitor Therapy TABLE 1 Characteristics of Study Sample (N = 761) All Participants Characteristic Age, years, mean (SD) Age, years, categorized
Racial Differences in Depressive Symptoms and Self-Rated Health Among Breast Cancer Survivors on Aromatase Inhibitor Therapy CARLA CALHOUN, MSW The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, Baltimore, MD, USA
KATHY J. HELZLSOUER, MD, MHS The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, Baltimore, MD, USA; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
LISA GALLICCHIO, PhD The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; and Department of Epidemiology and Public Health, University of Maryland, Baltimore, Baltimore, MD, USA
The goal of this survey-based study was to examine whether aromatase inhibitor (AI) therapy was associated with depressive symptoms and self-rated health among Black and White breast cancer survivors (N = 761). Results showed that among Black, but not White, breast cancer survivors current AI therapy was associated with a significant increase in the odds of both depressive symptoms (OR 3.59; 95% CI 1.01, 13.00) and poorer self-rated health (OR 3.16; 95% CI 1.06, 9.46). Presence of pain was significantly associated with increased odds of both outcomes among both groups. The findings underscore the importance of addressing not only physical but mental health among breast cancer survivors on AIs, especially those of Black race. KEYWORDS aromatase inhibitors, breast cancer, estrogen, depression, self-rated health, endocrine therapy, estrogen receptor positive Address correspondence to Lisa Gallicchio, PhD, The Prevention and Research Center, The Weinberg Center for Women’s Health and Medicine, Mercy Medical Center, 227 St. Paul Place, Baltimore, MD 21202. E-mail: [email protected] 263
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C. Calhoun et al.
INTRODUCTION Aromatase inhibitors (AIs) are currently the hormonal therapy of choice among postmenopausal women diagnosed with estrogen receptor-positive breast cancer. AIs work by binding to the aromatase enzyme, thereby preventing the conversion of peripheral androgens to estrogens. (Conte & Frassoldati, 2007; Ghandi & Verma, 2006; Litton, Arun, Brown, & Hortobagyi, 2012; Rugo, 2008; Schilder et al., 2009; Sehdev et al., 2009; Wasan et al., 2005). The result of this profound decrease in estrogen is a significant reduction in the risk of recurrence and breast cancer-related mortality (Baum, 2001; Baum et al., 2002; Litton, 2012; Rugo, 2008). Although the safety profile of AIs is superior to that of the alternative breast cancer hormonal therapy, tamoxifen, AIs have been consistently reported to be associated with certain side effects, including musculoskeletal symptoms, hot flashes, hair loss, weight gain, sleep disturbances, and poor sexual functioning (Bower, 2008; Cella & Fallowfield, 2008; Eversley et al., 2005; Fann et al. 2008; Fontaine et al., 2008; Gallicchio et al., 2012; Helzlsouer et al., 2013; Gupta et al., 2006; Irwin et al., 2013; Jones et al., 2007; Morales et al., 2004; Reyes-Gibby et al., 2012). Most of these reported side effects are physical in nature. Less frequently studied are the psychosocial effects of AIs, including depression. Some data suggest that there is a correlation between the reduction of estrogen and the onset of depression (Bower, 2008; Halbreich, 1997; Massie, 2004); therefore, it is possible that AI therapy may increase the risk of depression among cancer survivors. Several studies conducted among Japanese breast cancer survivors showed no difference in depressive symptoms, as measured using the Center for Epidemiological Studies-Depression (CES-D) scale, among women treated with exemestane, tamoxifen, or anastrozole during the first year of treatment, and no difference in depressive symptoms among breast cancer survivors who switched from tamoxifen to anastrozole compared to those who remained on tamoxifen (Ohsumi et al., 2011; Takei, 2012). In general, these studies are limited by the fact that they have been conducted within clinical trials, where patients are healthier than the general breast cancer population, and the comparison group is on other hormonal therapies, which may be associated with psychosocial side effects themselves. Because depression can lead to increased mortality and morbidity, poor medical adherence, reduced cognitive functioning, caregiver burden, family problems, and increased health costs (Bower, 2008; Ell et al., 2005; Fann et al., 2008; Onitilo, Nietert, & Egede, 2006; Polsky, 2005; Reyes-Gibby et al., 2012), it is important to know the psychological impact of AI therapy. In addition to depression, AIs may also affect the breast cancer patient’s perception of her self-rated health. Self-rated health is the most widely measure used to assess an individual’s health status, and has been consistently shown to be associated with morbidity and mortality in the general
Racial Differences in Aromatase Inhibitor Therapy
265
population (Benyamini, Leventhal & Leventhal, 2003; Idler & Benyamini, 1997; Jylha, 2009; Knauper & Turner, 2003). Self-rated health assessments can be viewed as an individual’s perceived view of their health status based on their social, cultural, and historical environment as well as their medical history. It is a combination of their actual medical state, their mental representation of their existing health, and their personal definition of “health” (Bailis, Segall, & Chipperfield, 2003; Jylha, 2009, Miilunpalo et al., 1997). The purpose of this study was to examine the associations between AI use and both depressive symptoms and self-rated health among breast cancer survivors. Data were analyzed from 761 Black and White breast cancer survivors diagnosed at a single institution between 1996 and 2007 who responded to a mailed survey. AI treatment information was collected using survey responses and medical chart review. We hypothesized that AI use would be associated with both an increased risk of depressive symptoms and poorer self-rated health.
METHODS Study Sample Data were analyzed from a survey-based study of cancer survivors at Mercy Medical Center (MMC) in Baltimore, Maryland. Detailed methods of data collection are published elsewhere (Forsythe, 2012). Briefly, in June 2008, a self-administered questionnaire was sent to cancer survivors listed in the MMC cancer registry as being diagnosed and/or treated at the hospital between January 1996 and July 2007. In November 2009, a second mailing was made to nonrespondents from the first mailing. A returned questionnaire implied consent. The Institutional Review Board at MMC approved the study. The questionnaire was sent to a total of 2,513 breast cancer survivors, which constituted 57.1% of all cancer survivors in the registry dataset. Approximately 35% of the breast cancer survivors (n = 868) returned the questionnaire. Respondents were more likely to be of White race compared to nonrespondents; however, the two groups did not differ in terms of time since diagnosis or tumor behavior (malignant vs. in situ). Inclusion criteria for the present analysis were: White or Black race; postmenopausal and, thus, eligible to receive AI therapy; and data on at least one of the two outcome measures (depressive symptoms and self-rated health). The final analytic dataset for this study was comprised of 107 Black and 654 White postmenopausal breast cancer survivors.
Measures Depressive symptoms were assessed on the survey using the 20-question CES-D (Radloff, 1977). Individuals with a score ≥16 were identified as having depressive symptoms (Lewinsohn, 1997). Self-rated health was examined us-
266
C. Calhoun et al.
ing the participant’s response to the question: “In general would you say your health is:” with possible choices of excellent, very good, good, fair, or poor. For the regression analyses, the self-rated health outcome was dichotomized into the categories of “excellent/very good/good” versus “fair/poor.” Age at the time of the survey was calculated from self-reported date of birth and the date on which the survey was completed. Race was selfclassified as White, Black, or other. Highest level of education was classified as grade school/high school, college/technical school, or graduate school. Marital status was classified as single, married/living with partner, or widowed. Employment status was categorized as “yes” and “no”; those who were classified as employed included those who reported being employed full- or part-time. Menopause status was determined based on the question “Have your menstrual periods stopped permanently?” Women who responded “yes” were categorized as postmenopausal. Body mass index (BMI) was calculated based on respondents’ self-reported weight and height. Smoking status was classified as “never,” “former,” or “current.” Alcohol use data were collected using the question: “During the past year, on average how many days per month did you have at least one alcohol drink?” Participants who responded that they drank any alcohol in the past year were classified as current drinkers. Physical functioning was measured by calculating a summary score (range: 0–32) for the 12-item Limitation of Functional Activities component of the Adult Health Status and Limitation of Activity section from the National Health Interview Survey (Gallicchio, 2013). The summary score was then categorized into “no limitation” and three tertilebased categories (low, intermediate, and high limitation scores). Pain was categorized as “yes” and “no” based on participant report of headache, joint, bone, or muscle pain during the past 4 weeks. The respondents were also asked whether they had “ever been told by a doctor or other health professional” that they had any of 49 medical conditions. These conditions included, but were not limited to, diabetes, high cholesterol, high blood pressure, depression, and osteoporosis. They also had the option to list “other” medical conditions. Conditions were then grouped into the following 12 overarching categories: diabetes, heart, bone, thyroid, lung, autoimmune, gastrointestinal, kidney/bladder, neurological, gynecological, eye, and emotional. A variable was created that reflected the total number of co-morbid condition types. Cancer treatment information was self-reported on the survey. For approximately 95% of the patients who responded, treatment information was verified through their medical records. Some medical records were not available because the patient was treated at another institution and the records were not transferred to MMC. Of those records that were reviewed, the survey data were 100% accurate for whether or not they had been treated with chemotherapy and/or radiation therapy. There was 98% agreement between the survey and medical records for those treated with hormone therapy
Racial Differences in Aromatase Inhibitor Therapy
267
(tamoxifen and AIs). A medical record review was also performed for those women indicating AI treatment to determine duration of use (if available) and whether the AI was being taken at the time of or prior to the survey. All cancer diagnoses, including their breast cancer, were self-reported by respondents on the survey and verified by the registry. Their self-reported information included the age at which each cancer was diagnosed and was supplemented with data from the cancer registry. Time since diagnosis of first breast cancer was calculated. The median time since breast cancer diagnosis of the breast cancer survivors who responded to the survey was 7 years (range 1–43 years).
STATISTICAL ANALYSIS Chi-square and Student t tests were conducted to examine differences in participant characteristics, cancer treatments, and symptoms by race, AI use, and categories of each outcome variable. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (95% CI) for the associations between AI use and (1) depressive symptoms (CES-D ≥16) and (2) poorer (fair/poor) self-rated health. Race was identified as a potential effect modifier a priori and, thus, all multivariable models were conducted for Black and White participants separately. To identify potential confounders, bivariate analyses were performed to assess the association between a potential confounder and AI use (ever vs. never) as well as the association between a potential confounder and each of the outcomes. A covariate was considered as a potential confounder of the relationship between AI use and the outcome if it was associated (p < 0.05) with both AI use and the outcome. The potential confounder was then tested in a multivariable logistic regression model. If inclusion of the covariate changed the regression coefficient for AI use by more than 10%, the covariate was considered as a confounder with respect to the association between AI use and that outcome. The following variables were tested as potential confounders: marital status, BMI, education, employment status, smoking status, physical activity, alcohol intake, prior cancer treatments (chemotherapy, radiation, or tamoxifen), number of chronic conditions, pain, and physical functioning. Collinearity between identified confounders in each of the built logistic regression models was also assessed; in the cases of both outcomes, number of chronic conditions and pain were identified as confounders and were also were found to be strongly correlated. The pain variable was associated with a greater change in the OR for the association between AI use and each outcome with its inclusion into the model and, thus, pain, and not number of chronic conditions, was included as a variable in the final logistic regression model for both outcomes. Age was adjusted for in all adjusted
268
C. Calhoun et al.
models although it was not identified as a potential confounder for any of the associations examined. All analyses were performed using SAS statistical software v. 9.3. A twotailed p < 0.05 was considered statistically significant.
RESULTS Characteristics of the analytic study sample overall and by race are shown in Table 1. The mean age of participants at the time of the survey was 63.6 years (standard deviation 10.5). Approximately 86% of the respondents were White. The majority of the study sample was not employed, married, and had at least some college education. One third were categorized as obese (BMI ≥ 30 kg/m2), 67% were current drinkers, and 7.1% reported being current smokers. About half had been treated with chemotherapy, and approximately 40% were being treated or had been treated with an AI. Most respondents rated their health as very good or good, 15.5% had depressive symptoms, and the majority had two or more chronic conditions or reported pain. Over 80% had a least some physical functioning limitations. Overall, the Black breast cancer survivors were significantly less likely than their White counterparts to be employed, to be married, and to be current alcohol drinkers. The Black breast cancer survivors were more likely to be obese, to report currently smoking cigarettes, to report that their health was fair or poor, and to have severe physical functioning limitations than the White breast cancer survivors. There were no statistically significant differences with regards to age, education, prior chemotherapy, AI treatment, number of chronic conditions, presence of pain, and years since breast cancer diagnosis. The association between AI use and depressive symptoms differed by race (Table 2). After adjustment for participant age and presence of pain, Black breast cancer survivors who were being treated with an AI at the time of the survey were approximately three and a half times more likely to report depressive symptoms than Black breast cancer survivors who were never treated with an AI (OR 3.59; 95% CI 1.01, 13.00). Prior AI use was associated with an increased risk of depressive symptoms among Black women, but this increase was not statistically significant. Among White women, neither current nor prior AI use was associated with depressive symptoms. Presence of pain was significantly associated with a fivefold increase in odds of depressive symptoms among Black women (95% CI 1.43, 17.76) and a 2.67-fold increase in odds among White women (95% CI 1.64, 4.35). The association between AI use and poorer self-rated health also differed by race (Table 3). After adjustment for participant age and presence of pain, Black breast cancer survivors who were being treated with an AI at the time of the survey were approximately three times more
269
Racial Differences in Aromatase Inhibitor Therapy TABLE 1 Characteristics of Study Sample (N = 761) All Participants Characteristic Age, years, mean (SD) Age, years, categorized
