Abstract
Purpose:
Breast cancer survivorship has improved in recent decades, but few studies have assessed patterns of employment status following diagnosis and the impact of job loss on long-term well-being in ethnically diverse breast cancer survivors. We hypothesized that post-treatment employment status is an important determinant of survivor well-being and varies by race and age.
Methods:
In the Carolina Breast Cancer Study, 1,646 employed women with primary breast cancer were longitudinally evaluated for post-diagnosis job loss and overall well-being. Work status was classified as: ‘sustained work’, ‘returned to work’, ‘job loss’, or ‘persistent non-employment’. Well-being was assessed by the Functional Assessment of Cancer Therapy (FACT-G) instrument. Analysis of covariance was used to evaluate the association between work status and well-being (physical, functional, social, and emotional).
Results:
At 25-months post-diagnosis, 882 (53.6%) reported ‘sustained work’, 330 (20.1%) ‘returned to work’, 162 (9.8%) ‘job loss’, and 272 (16.5%) ‘persistent non-employment’. Nearly half of the study sample (46.4%) experienced interruptions in work during two-years post-diagnosis. Relative to baseline (5-month FACT-G), women who sustained work or returned to work had higher increases in all well-being domains than women with job loss and persistent non-employment. Job loss was more common among Black than White women (adjusted odds ratio=3.44; 95% confidence interval 2.37–4.99) and was associated with service/laborer job types, lower education and income, later stage at diagnosis, longer treatment duration, and non-private health insurance. However, independent of clinical factors, job loss was associated with lower well-being in multiple domains.
Conclusions:
Work status is commonly disrupted in breast cancer survivors, but sustained work is associated with well-being. Interventions to support women’s continued employment after diagnosis are an important dimension of breast cancer survivorship.
Keywords: Breast Cancer, Employment, Return to Work, Job Loss, Racial Disparities, Health-Related Quality of Life, Well-being
Introduction
Work status is a component of financial well-being and affects many dimensions of social, emotional, and functional status [1–6] [7]. Breast cancer in working-age women leads to 10–29% greater job loss and reduced job productivity compared to working-age women without breast cancer [8–12]. While studies on employment and breast cancer survivorship indicate the importance of returning to work [1–3], several evidence gaps exist. Many studies assessed work within a year of diagnosis, which may indicate treatment-related factors rather than long-term recovery [13]. Other studies used secondary data sources, such as the Medical Expenditure Panel Survey, that lack clinical information including the cancer site and stage [14–16]. Further, the intersection of race, employment, and cancer has received limited study. Previous studies of breast cancer survivors have emphasized white-majority populations (81–92%) [8–11, 17].
To address these gaps, we assessed risk factors for job loss and relationships between work and health-related quality of life, specifically multidimensional well-being (using subdomains [physical, functional, social, and emotional] of the validated patient-reported outcome measure: Functional Assessment of Cancer Therapy – General [FACT-G]) 25 months after treatment. We conducted this research in a racially diverse, population-based, longitudinal cohort of 1,646 working-aged women diagnosed with breast cancer in the Carolina Breast Cancer Study phase 3 (CBCS3) that oversampled younger women and Black women to better understand their survivorship experiences. We hypothesized that employment among breast cancer survivors is associated with improved multidimensional well-being, after adjustment for clinical variables and that younger and Black women had greater burden of both employment loss and employment-associated decreases in quality of life.
Methods
Study Population and Data Collection
CBCS3 is the third phase of a population-based cohort involving women diagnosed with breast cancer in North Carolina. Overall, the population is diverse with respect to race, work status, and socioeconomic status. Relative to Behavioral Risk Factor Surveillance System (BRFSS) respondents in NC, the CBCS3 population had a slightly higher income and health insurance, reflecting CBCS3 sampling of more populous counties [18]. CBCS3 oversampled young (<50 age) and self-identifying Black women using randomized recruitment, with approximately half of the participants in each of these categories. All women were recruited through rapid case ascertainment via the North Carolina Central Cancer Registry. This study was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill, and all participants provided written informed consent.
CBCS3 included women age 20–74 years at the time of diagnosis within 44 counties, who were diagnosed with primary invasive breast cancer for the first time between May 1, 2008, and October 21, 2013. In total, 2,561 women completed the follow-up questionnaire at 25-months post-diagnosis (85% response proportion). Analysis was restricted to women who 1) completed treatment by 20-months; 2) reported ever being employed prior to diagnosis; 3) completed the baseline (approximately 5-months post-diagnosis) and 25-month post-diagnosis surveys, and 4) were of working age at the time of diagnosis (age ≤60) [19]. Ninety-four patients who were <65 years old and receiving Medicare due to disability were excluded. The final analytical cohort included 1,649 women (Supplemental Figure 1).
Information on sociodemographic, occupational history, and well-being was collected in a nurse-administered baseline in-person interview approximately 5-months post-diagnosis. Information on stage, treatment type/date, and estrogen receptor status was obtained from medical record abstraction and pathology reports. Approximately 25-months post-diagnosis, women completed a follow-up mailed survey that included health-related quality of life, work and work function.
Work Measures
Employment categories were based on working status and job loss due to breast cancer at baseline (approximately 5-months post-diagnosis) and 25 months post-diagnosis. At baseline and 25 months post-diagnosis, participants were asked, “Have you been working since your diagnosis?” and “Did you lose your job due to your diagnosis of breast cancer?” Responses to these questions were then used to categorize participants into four working status groups as: ‘sustained work’ (working and no job loss at baseline or 25-months), ‘return to work’ (not working at baseline, working at 25-months), ‘job loss’ (working at baseline, job loss at baseline or 25-month, and not working at 25-months), and ‘persistent non-employment’ (not working at either baseline or 25 months post-diagnosis and no job loss reported as a consequence of breast cancer at baseline and 25-months).
Well-being Measures
At baseline and 25-months post-diagnosis, the Functional Assessment of Cancer Therapy – General (FACT-G) validated 27-item questionnaire was administered to measure health-related quality of life, computed from the assessment of 4 domains including Physical, Functional, Social/Family (each 7 items, range 0–28), and Emotional Well-Being (7 items, range 0–24) [20]. Higher scores denote higher well-being. A minimally important difference per domain is defined as 2 points, and to 4-point change (i.e., minimally worse: −2, −3, −4; minimally better: +2, +3, +4) [21]; meaningful and clinically significant score changes indicate sizably worse (−5, −6, −7) and sizably better (+5, +6, +7) outcomes [22].
Socioeconomic, Clinical and Treatment Measures
Baseline information was collected on self-reported race (Black or White), age (≤50, >50 years; 20–39, 40–49, 50–60 years), highest level of education (<high school, high school/some college, or college/technical/professional degree), household income (<$15,000, $15000- $29999, $30000-$49999, or $50000+), number dependent on income (1–2 or 3+ people), marital status (married, never married, or separated/divorced/windowed), presence of comorbidities (one or more, or none), and rural residency (yes/no). Comorbidities, including diabetes, heart disease, and chronic obstructive pulmonary disease, and clinical factors, including stage at diagnosis (I/II or III/IV) and estrogen receptor status (positive/borderline or negative), were abstracted from medical records. Treatment was classified as: surgery only, surgery and radiation, surgery, and chemotherapy, or all 3 modalities. Treatment initiation time was calculated from diagnosis to first treatment; and treatment delay was defined as >60 days [23]. Treatment duration (days between the first and last treatment) was defined based on quartiles of women within each treatment modality and was categorized as timely or prolonged, where timely indicated a duration of treatment <75th percentile of a given modality.
Statistical Analysis
To identify socioeconomic and clinical factors associated with work status, polytomous logistic regression models were used to estimate odds ratios and 95% confidence intervals (CIs) for the categorical work status variable, overall and adjusted for race, age, or treatment duration.
We evaluated the association between work status and post-diagnosis well-being score domains. Analysis of covariance (ANCOVA) models were used to examine the overall change in 25-month well-being domain scores (per well-being domain) by working status, adjusting for race, age at diagnosis, education, household income, marital status, insurance type, comorbidities, stage at diagnosis, treatment modality, and treatment duration. ANCOVA models were then stratified by race and adjusted for age at diagnosis, education, household income, marital status, insurance type, comorbidities, stage at diagnosis, treatment modality, and treatment duration. Statistical analyses were performed using SAS software, version 9.4 (SAS Institute, Cary, NC). A significance level of 5% was used for all analyses, and all tests were two-sided.
Results
Demographic and clinical characteristics associated with work status
Among 1,646 eligible participants who were working prior to diagnosis, nearly half (47.9%) were Black and about two-thirds (66.8%) were <50 years old, with an overall age range of 23 to 60 years. Figure 1 shows the work status distribution. At 25-months post-diagnosis, 882 (53.6%) reported ‘sustained work’, 330 (20.1%) ‘returned to work’, 162 (9.8%) ‘job loss’, and 272 (16.5%) ‘persistent non-employment’. Thus, a large fraction of the study population experienced some interruptions in work during the two-year period following diagnosis (Table 1). Black women were more likely to experience job loss (aOR = 3.44, 95% CI 2.37 to 4.99) than white women. Younger women were more likely to remain employed with adjusted odds ratios <1 for all groups (compared to sustained work) with one exception: compared to women aged 50–60 years, women younger than 40 years were more likely to experience a change in employment and then return to work (adjusted odds ratio [aOR]: 1.63, 95% CI 1.13 to 2.34) and less likely to experience persistent non-employment (aOR: 0.55, 95% CI 0.36 to 0.86). Women who reported job loss were more likely to be divorced or separated (aOR=1.93, 95% CI 1.31 to 2.86) and to be enrolled in Medicaid or uninsured (aOR=6.75, 95% CI 4.11 to 11.1; 10.2, 95% CI 5.27 to 19.7, respectively). Compared to the other work status categories, women who reported sustained work had higher rates of professional/administrative-type jobs (63.4%), higher education (61.1% in the college/professional degree category), annual household income >$50,000 (66.1%), being married (57.0%), and private insurance (63.6%). Conversely, lower education, lower household income, service-related work, and non-private insurance/uninsured status were associated with job loss and persistent non-employment.
Figure 1.
Flow diagram of participants’ working status from baseline (5-months after diagnosis) and 25-months after diagnosis. Green node (n=882) indicates “sustained work”; blue node (n=330) indicates “return to work”; purple node (n=162) indicates “job loss”; and orange node (n=272) indicates “persistent non-employment”. The primary difference between the ‘job loss’ and ‘return to work’ categories is that at the 25-month interview, the ‘job loss’ category still had not reported resumption of employment.
Table 1.
Baseline sample characteristics by 25-month post-diagnosis work status† (n=1646)
| Sustained work (n=882) (Referent) | Return to work (n=330) | Job loss (n=162) | Persistent non-employment (n=272) | ||||||||||||
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| n | (%) | n | (%) | aOR | (95% CI) | n | (%) | aOR | (95% CI) | n | (%) | aOR | (95% CI) | P ‡ | |
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| Race | <.001 | ||||||||||||||
| White (n=858) | 519 | (60.5) | 172 | (20.1) | 1.00 | (ref) | 45 | (5.2) | 1.00 | (ref) | 122 | (14.2) | 1.00 | (ref) | |
| Black (n=788) | 363 | (46.1) | 158 | (20.0) | 1.25 | (0.96 to 1.61) | 117 | (14.9) | 3.44 | (2.37 to 4.99) | 150 | (19.0) | 1.66 | (1.25 to 2.19) | |
| Age at diagnosis, y | <.001 | ||||||||||||||
| 20–39 (n=275) | 136 | (49.5) | 81 | (29.4) | 1.63 | (1.13 to 2.34) | 25 | (9.1) | 0.86 | (0.50 to 1.46) | 33 | (12.0) | 0.55 | (0.36 to 0.86) | |
| 40–49 (n=824) | 474 | (57.5) | 151 | (18.3) | 0.87 | (0.65 to 1.18) | 80 | (9.7) | 0.83 | (0.57 to 1.22) | 119 | (14.4) | 0.56 | (0.42 to 0.76) | |
| 50–60 (n=547) | 272 | (49.7) | 98 | (17.9) | 1.00 | (ref) | 57 | (10.4) | 1.00 | (ref) | 120 | (21.9) | 1.00 | (ref) | |
| Type of work | <.001 | ||||||||||||||
| Professional, administrative (n=857) | 543 | (63.4) | 161 | (18.8) | 1.00 | (ref) | 58 | (6.8) | 1.00 | (ref) | 95 | (11.1) | 1.00 | (ref) | |
| Clerical, sales, technician (n=344) | 181 | (52.6) | 74 | (21.5) | 1.37 | (1.01 to 1.91) | 30 | (8.7) | 1.38 | (0.85 to 2.24) | 59 | (17.2) | 1.71 | (1.17 to 2.48) | |
| Crafts/factory work, mechanic (n=127) | 53 | (41.7) | 25 | (19.7) | 1.60 | (0.95 to 2.69) | 20 | (15.8) | 2.51 | (1.37 to 4.58) | 29 | (22.8) | 2.57 | (1.53 to 4.32) | |
| Service work, laborer, farmer (n=301) | 93 | (30.9) | 67 | (22.3) | 2.28 | (1.57 to 3.31) | 53 | (17.6) | 3.84 | (2.45 to 6.03) | 88 | (29.2) | 4.50 | (3.08 to 6.58) | |
| Education | <.001 | ||||||||||||||
| College/professional degree (n=908) | 555 | (61.1) | 180 | (19.8) | 1.00 | (ref) | 58 | (6.4) | 1.00 | (ref) | 115 | (12.7) | 1.00 | (ref) | |
| High school/some college (n=659) | 317 | (48.1) | 135 | (20.5) | 1.30 | (1.00 to 1.70) | 82 | (12.4) | 2.03 | (1.40 to 2.95) | 125 | (19.0) | 1.77 | (1.32 to 2.39) | |
| Less than high school (n=79) | 10 | (12.7) | 15 | (19.0) | 3.80 | (1.64 to 8.77) | 22 | (27.8) | 13.2 | (5.84 to 29.9) | 32 | (40.5) | 11.2 | (5.25 to 23.8) | |
| Amount of work | 0.17 | ||||||||||||||
| Full-time (n=1514) | 820 | (54.2) | 300 | (19.8) | 1.00 | (ref) | 150 | (9.9) | 1.00 | (ref) | 244 | (16.1) | 1.00 | (ref) | |
| Part-time (n=126) | 60 | (47.6) | 29 | (23.0) | 1.30 | (0.82 to 2.09) | 11 | (8.7) | 1.21 | (0.61 to 2.40) | 26 | (20.6) | 1.55 | (0.94 to 2.55) | |
| Household income | <.001 | ||||||||||||||
| $50K+ (n=844) | 558 | (66.1) | 157 | (18.6) | 1.00 | (ref) | 41 | (4.9) | 1.00 | (ref) | 88 | (10.4) | 1.00 | (ref) | |
| $30–50K (n=304) | 168 | (55.3) | 65 | (21.4) | 1.36 | (0.96 to 1.93) | 35 | (11.5) | 2.30 | (1.40 to 3.80) | 36 | (11.8) | 1.25 | (0.80 to 1.94) | |
| $15–30K (n=261) | 108 | (41.4) | 63 | (24.1) | 1.96 | (1.34 to 2.86) | 46 | (17.6) | 4.41 | (2.69 to 7.23) | 44 | (16.9) | 2.45 | (1.58 to 3.81) | |
| <$15K (n=180) | 29 | (16.1) | 34 | (18.9) | 3.96 | (2.29 to 6.83) | 33 | (18.3) | 10.6 | (5.69 to 19.8) | 84 | (46.7) | 16.8 | (10.1 to 27.9) | |
| Number dependent on income | 0.02 | ||||||||||||||
| 1–2 (n=782) | 407 | (52.1) | 143 | (18.3) | 1.00 | (ref) | 84 | (10.7) | 1.00 | (ref) | 148 | (18.9) | 1.00 | (ref) | |
| 3+ (n=862) | 473 | (54.9) | 187 | (21.7) | 1.09 | (0.83 to 1.42) | 78 | (9.1) | 0.92 | (0.65 to 1.32) | 124 | (14.4) | 0.86 | (0.64 to 1.15) | |
| Marital status | <.001 | ||||||||||||||
| Married/living as married (n=1014) | 578 | (57.0) | 203 | (20.0) | 1.00 | (ref) | 72 | (7.1) | 1.00 | (ref) | 161 | (15.9) | 1.00 | (ref) | |
| Never married (n=209) | 102 | (48.8) | 46 | (22.0) | 1.08 | (0.72 to 1.62) | 25 | (12.0) | 1.26 | (0.74 to 2.15) | 36 | (17.2) | 1.13 | (0.73 to 1.77) | |
| Separated/divorced/widowed (n=423) | 202 | (47.8) | 81 | (19.2) | 1.10 | (0.80 to 1.51) | 65 | (15.4) | 1.93 | (1.31 to 2.86) | 75 | (17.7) | 1.10 | (0.78 to 1.53) | |
| Insurance status | <.001 | ||||||||||||||
| Private (n=1277) | 812 | (63.6) | 242 | (18.9) | 1.00 | (ref) | 87 | (6.8) | 1.00 | (ref) | 136 | (10.7) | 1.00 | (ref) | |
| Medicaid (n=230) | 45 | (19.6) | 53 | (23.0) | 3.46 | (2.22 to 5.39) | 46 | (20.0) | 6.75 | (4.11 to 11.1) | 86 | (37.4) | 11.2 | (7.26 to 17.3) | |
| Uninsured (n=115) | 18 | (15.7) | 26 | (22.6) | 4.65 | (2.49 to 8.67) | 26 | (22.6) | 10.2 | (5.27 to 19.7) | 45 | (39.1) | 13.9 | (7.70 to 25.1) | |
| Comorbidities | <.001 | ||||||||||||||
| 0 (n=1459) | 805 | (55.2) | 303 | (20.8) | 1.00 | (ref) | 137 | (9.4) | 1.00 | (ref) | 214 | (14.7) | 1.00 | (ref) | |
| 1+ (n=187) | 77 | (41.2) | 27 | (14.4) | 0.95 | (0.59 to 1.53) | 25 | (13.4) | 1.36 | (0.81 to 2.27) | 58 | (31.0) | 2.16 | (1.45 to 3.22) | |
| Rural residence at diagnosis | 0.12 | ||||||||||||||
| Non-rural (n=1380) | 753 | (54.6) | 272 | (19.7) | 1.00 | (ref) | 134 | (9.7) | 1.00 | (ref) | 221 | (16.0) | 1.00 | (ref) | |
| Rural (n=259) | 126 | (48.7) | 57 | (20.1) | 0.74 | (0.52 to 1.04) | 28 | (10.8) | 0.69 | (0.43 to 1.10) | 48 | (18.5) | 0.75 | (0.52 to 1.10) | |
Among a cohort of previously employed workers. Percentage values represent the percentage in work status groups relative to the total for each variable. Polytomous logistic regression models adjusted for duration of treatment and race or age (race model adjusted for age, age model adjusted for race). Duration of treatment (time from first treatment to last treatment in days) was defined based on quartiles of patients with the same treatment modality (surgery only, surgery + radiation, surgery + chemotherapy, and all 3 modalities)
P values calculated using a two-sided χ2 test of differences of each variable across the work status categories
Abbreviations: aOR, adjusted odds ratio; CI, confidence interval
More aggressive treatment may induce side effects that conflict with work duties, and therefore we assessed whether tumor clinical factors were associated with job loss. Several clinical factors were associated with employment status (Table 2). Participants with sustained work exhibited less aggressive tumors and timely treatment duration compared to those with job loss (aOR=1.85, 95% CI 1.17 to 2.92; 1.69, 95% CI 1.16 to 2.48, respectively) and persistent non-employment (aOR=2.30, 95% CI 1.59 to 3.32; 2.10, 95% CI 1.54 to 2.85, respectively). The clinical and demographic factors in Tables 1 and 2 that showed relationships with employment status are also possible predictors of quality of life changes, and therefore we considered these as covariates in multivariable models.
Table 2.
Baseline clinical characteristics by 25-months post-diagnosis work status† (n=1646)
| Sustained work (n=882) (Referent) | Return to work (330) | Job loss (162) | Persistent non-employment (272) | ||||||||||||
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| n | (%) | n | (%) | aOR | (95% CI) | n | (%) | aOR | (95% CI) | n | (%) | aOR | (95% CI) | P ‡ | |
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| Stage | <.001 | ||||||||||||||
| I and II (n=1390) | 779 | (56.0) | 287 | (20.7) | 1.00 | (ref) | 123 | (8.9) | 1.00 | (ref) | 201 | (14.5) | 1.00 | (ref) | |
| III and IV (n=236) | 94 | (39.8) | 42 | (17.8) | 1.07 | (0.72 to 1.59) | 34 | (14.4) | 1.85 | (1.17 to 2.92) | 66 | (28.0) | 2.30 | (1.59 to 3.32) | |
| ER status | 0.03 | ||||||||||||||
| Positive (n=1170) | 650 | (55.6) | 226 | (19.3) | 1.00 | (ref) | 102 | (8.7) | 1.00 | (ref) | 192 | (16.4) | 1.00 | (ref) | |
| Negative (n=442) | 216 | (48.9) | 101 | (22.8) | 1.25 | (0.94 to 1.67) | 54 | (12.2) | 1.28 | (0.88 to 1.87) | 71 | (16.1) | 1.00 | (0.73 to 1.39) | |
| First course treatment | <.001 | ||||||||||||||
| Surgery (n=1305) | 735 | (56.2) | 245 | (18.8) | 1.00 | (ref) | 116 | (8.9) | 1.00 | (ref) | 209 | (16.0) | 1.00 | (ref) | |
| Chemotherapy (n=338) | 146 | (43.2) | 84 | (24.9) | 1.43 | (1.04 to 1.96) | 46 | (13.6) | 1.49 | (0.99 to 2.26) | 62 | (18.3) | 1.24 | (0.87 to 1.77) | |
| Patient treatment group | <.001 | ||||||||||||||
| Surgery only (n=168) | 96 | (57.1) | 33 | (19.6) | 1.00 | (ref) | 11 | (6.6) | 1.00 | (ref) | 28 | (16.7) | 1.00 | (ref) | |
| Surgery + radiation (n=307) | 212 | (69.1) | 41 | (13.4) | 0.59 | (0.35 to 0.99) | 19 | (6.2) | 0.75 | (0.34 to 1.65) | 35 | (11.4) | 0.50 | (0.28 to 0.88) | |
| Surgery + chemotherapy (n=266) | 134 | (50.4) | 52 | (19.6) | 1.12 | (0.67 to 1.87) | 32 | (12.0) | 2.16 | (1.02 to 4.56) | 48 | (18.1) | 1.31 | (0.76 to 2.25) | |
| All (n=894) | 438 | (49.0) | 202 | (22.6) | 1.31 | (0.85 to 2.02) | 98 | (11.0) | 1.68 | (0.85 to 3.29) | 156 | (17.5) | 1.17 | (0.73 to 1.88) | |
| Treatment delay, days | 0.56 | ||||||||||||||
| No (≤60) (n=1458) | 778 | (53.4) | 297 | (20.4) | 1.00 | (ref) | 140 | (9.6) | 1.00 | (ref) | 243 | (16.8) | 1.00 | (ref) | |
| Yes (>60) (n=185) | 103 | (55.7) | 32 | (17.3) | 0.75 | (0.49 to 1.15) | 22 | (11.9) | 0.86 | (0.51 to 1.45) | 28 | (15.1) | 0.72 | (0.45 to 1.13) | |
| Prolonged treatment duration § | <.001 | ||||||||||||||
| No (Quartile 1–3) (n=1238) | 714 | (57.7) | 236 | (19.1) | 1.00 | (ref) | 111 | (9.0) | 1.00 | (ref) | 177 | (14.3) | 1.00 | (ref) | |
| Yes (Quartile 4) (n=397) | 166 | (41.8) | 92 | (23.2) | 1.63 | (1.21 to 2.19) | 49 | (12.3) | 1.69 | (1.16 to 2.48) | 90 | (22.7) | 2.10 | (1.54 to 2.85) | |
Among a cohort of previously employed workers. Polytomous logistic regression models adjusted for race, age, and duration of treatment. Duration of treatment (time from first treatment to last treatment in days) was defined based on quartiles of patients with the same treatment modality (surgery only, surgery + radiation, surgery + chemotherapy, and all 3 modalities)
P values calculated using a two-sided χ2 test of differences of each variable across the work status categories
Quartiles were defined separately by treatment modality; Q4 was equal to 59, 104, 157, and 258 days for surgery only, surgery + radiation, surgery + chemotherapy, and all 3 modalities, respectively
Abbreviations: aOR, adjusted odds ratio; CI, confidence interval
Multidimensional well-being and work status
To assess relationships between work status and well-being we evaluated FACT-G domains by employment categories (Table 3) in multivariable models adjusted for demographic and clinical factors. We estimated these associations separately for Black and white participants. Both White and Black women who experienced job loss (vs. sustained work) had significantly lower scores across all domains of well-being, although the decrements were greater among Black women (all change scores p <0.05). Black women who experienced persistent non-employment reported a clinically meaningful decrease in functional well-being domains at 25 months than did White women (change scores = −5.10 vs. −2.30 in whites). Among Black women who returned to work, physical and functional well-being at 25 months were lower (change scores = −1.33 and −2.43, respectively, p <0.05) compared to Black women who sustained work, whereas among White women who returned to work, well-being at 25 months did not statistically significantly change.
Table 3.
Well-being at baseline and 25-months post-diagnosis by work status (n=1646)
| Baseline well-being scores |
25-month well-being scores |
25-month well-being change scores according to work status‡ |
ANCOVA models by well-being domains§ |
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| Sustained work (n=882) | Return to work (n=330) | Job loss (n=162) | Persistent Non-employment (n=272) | Sustained work (n=882) | Return to work (n=330) | Job loss (n=162) | Persistent Non-employment (n=272) | Sustained work | Return to work | Job loss | Persistent Non-employment | Sustained work | Return to work | Job loss | Persistent Non-employment | |||||||||
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| Well-being† | Mean | (SD) | Mean | (SD) | Mean | (SD) | Mean | (SD) | Mean | (SD) | Mean | (SD) | Mean | (SD) | Mean | (SD) | ||||||||
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| Physical | 21.3 | (5.7) | 18.4 | (6.7) | 15.4 | (6.7) | 17.1 | (7.2) | 23.7 | (4.5) | 22.0 | (5.3) | 16.6 | (7.3) | 18.9 | (7.2) | (ref) | −1.7 | −7.1 | −4.8 | (ref) | −0.78* | −5.31** | −2.92** |
| Functional | 21.3 | (5.2) | 17.5 | (6.4) | 15.4 | (6.3) | 16.6 | (6.3) | 22.9 | (4.8) | 21.2 | (5.9) | 14.8 | (7.1) | 17.4 | (6.9) | (ref) | −1.7 | −8.1 | −5.5 | (ref) | −0.82* | −6.38** | −3.55** |
| Social | 23.9 | (4.3) | 22.8 | (5.0) | 21.4 | (5.4) | 22.8 | (4.9) | 23.0 | (5.2) | 22.2 | (5.7) | 18.2 | (6.7) | 21.6 | (5.6) | (ref) | −0.8 | −4.8 | −1.4 | (ref) | −0.46 | −3.38** | −0.72 |
| Emotional | 20.0 | (3.5) | 19.0 | (3.9) | 18.0 | (4.6) | 17.9 | (4.8) | 20.1 | (3.7) | 19.6 | (4.1) | 16.6 | (5.5) | 18.1 | (5.0) | (ref) | −0.5 | −3.5 | −2.0 | (ref) | −0.18 | −3.27** | −1.43** |
| White | (n=519) | (n=172) | (n=45) | (n=122) | (n=519) | (n=172) | (n=45) | (n=122) | ||||||||||||||||
| Physical | 21.7 | (5.5) | 20.2 | (6.6) | 18.6 | (5.9) | 18.5 | (6.8) | 24.2 | (4.1) | 23.2 | (4.8) | 18.6 | (7.2) | 20.4 | (7.2) | (ref) | −1.0 | −5.6 | −3.8 | (ref) | −0.41 | −4.52** | −2.47** |
| Functional | 21.6 | (5.0) | 18.9 | (6.4) | 18.1 | (5.2) | 17.9 | (6.2) | 23.1 | (4.5) | 22.7 | (5.0) | 15.7 | (7.0) | 19.7 | (6.1) | (ref) | −0.4 | −7.4 | −3.4 | (ref) | 0.33 | −6.25** | −2.30** |
| Social | 24.0 | (4.1) | 23.5 | (4.6) | 22.4 | (5.0) | 23.6 | (4.4) | 23.0 | (5.1) | 22.6 | (5.2) | 20.0 | (6.6) | 22.2 | (5.7) | (ref) | −0.4 | −3.0 | −0.8 | (ref) | 0.00 | −2.48* | −0.98 |
| Emotional | 19.7 | (3.5) | 19.0 | (3.8) | 18.9 | (4.0) | 18.6 | (4.0) | 19.7 | (3.6) | 19.4 | (3.6) | 16.4 | (6.2) | 18.5 | (4.8) | (ref) | −0.3 | −3.3 | −1.2 | (ref) | 0.05 | −2.89** | −0.92* |
| Black | (n=363) | (n=158) | (n=117) | (n=150) | (n=363) | (n=158) | (n=117) | (n=150) | ||||||||||||||||
| Physical | 20.6 | (5.9) | 16.4 | (6.3) | 14.2 | (6.5) | 15.9 | (7.3) | 22.9 | (4.9) | 20.7 | (5.5) | 15.9 | (7.2) | 17.7 | (7.0) | (ref) | −2.2 | −7.0 | −5.2 | (ref) | −1.33* | −5.99** | −3.52** |
| Functional | 20.8 | (5.5) | 15.9 | (6.1) | 14.4 | (6.4) | 15.6 | (6.3) | 22.6 | (5.1) | 19.6 | (6.5) | 14.4 | (7.1) | 15.5 | (6.9) | (ref) | −3.0 | −8.2 | −7.1 | (ref) | −2.43** | −7.07** | −5.10** |
| Social | 23.6 | (4.6) | 22.2 | (5.4) | 21.0 | (5.5) | 22.2 | (5.1) | 23.0 | (5.3) | 21.7 | (6.2) | 18.5 | (6.7) | 21.2 | (5.5) | (ref) | −1.3 | −4.5 | −1.8 | (ref) | −1.08 | −3.80** | −0.69 |
| Emotional | 20.4 | (3.5) | 19.0 | (4.0) | 17.7 | (4.7) | 17.3 | (5.3) | 20.7 | (3.7) | 19.8 | (4.5) | 16.7 | (5.2) | 17.8 | (5.2) | (ref) | −0.9 | −4.0 | −2.9 | (ref) | −0.51 | −3.64** | −2.05** |
2-point change per domain is minimally important difference that patients perceive as important and represents a clinically meaningful score change.
Change in score indicates the 25-month score difference in return to work, work loss and persistent non-employment compared to sustained work. A positive score change indicate a beneficial change, whereas a negative score change indicate a harmful change.
Analysis of covariance (ANCOVA) models by well-being domains (per well-being domain): (model 1) adjusted for race, age at diagnosis, education, household income, marital status, insurance type, comorbidities, stage at diagnosis, treatment modality, and treatment duration and (model 2) stratified by race, adjusted for age at diagnosis, education, household income, marital status, insurance type, comorbidities, stage at diagnosis, treatment modality, and treatment duration
p < 0.05
p < 0.001
Job loss and persistent non-employment (vs. sustained work) were associated with clinically meaningful and statistically lower well-being at 25 months in all four domains, whereas the return-to-work group had relatively high well-being across all domains and was not clinically different from the sustained work group. Additionally, women who returned to work had increases in physical and functional well-being at 25 months compared to baseline (mean= +3.6 and +3.7, respectively). Women who experienced job loss reported the lowest physical, functional, social, and emotion well-being (change scores= −7.1, −8.1, −4.8 and −3.5, respectively) (Table 3, change scores). Across all models, multivariable adjustment was associated with attenuation of effects, but the differences remained statistically significant, with the job loss group reporting the lowest physical, functional, social, and emotion well-being (change scores= −5.31, −6.38, −3.38, and −3.27 respectively, p’s <0.001) (Table 3; Supplemental table 1).
Discussion
This study evaluated the relationship between work status and well-being among racially diverse working-age breast cancer survivors (ages ≤60 years). Work status was associated with a wide range of demographic and clinical factors, but even after adjusting for these baseline associations, well-being at 25 months varied according to employment status. Well-being was similar between women who reported sustained work and return to work, whereas job loss and persistent non-employment were strong indicators of lower physical, functional, social, and emotional well-being. Our study also raises concerns about racial equity, given that Black women were more likely to experience job loss and greater decrements in well-being with job loss. Women in non-professional/administrative jobs, those with less education and household income, non-private insurance, later stage, and longer treatment duration are also at risk for job loss and long term well-being decrements. Overall, our findings suggest that employment status is a functional indicator of multidimensional well-being and that employment status should be considered in survivorship trajectories. Interventions that help women return to work may be important in addressing longstanding differences in well-being by race [3, 24, 25].
Our findings support those from previous studies reporting that health, socioeconomic, and employment characteristics are associated with work status among breast cancer survivors [3, 26]. Consistent with our findings, work status has been associated with multimodal treatment, upper limb impairment, fatigue, public insurance/uninsured status at 24 months, chemotherapy/radiotherapy, younger age, and less education [19, 27–29]. However, many of these studies have focused on a very short interval around diagnosis, usually less than a year. Our study adds new insights by extending the study timeframe to employment status at 25 months. We also found that Black women were more likely than White women to experience job loss after breast cancer diagnosis. Two previous studies reported that Black women were less likely to return to work at 18-months post-diagnosis [30] and experience job loss 24-months after surgery [19], but these studies had few Black women.
This study documented that job loss was associated with lower well-being across all four FACT-G domains. Previous international studies reported worse work-related physical and mental health among cancer survivors, generally [31] [32], but self-reported measures of multidimensional well-being are not widely available in studies of breast cancer survivors. One systematic review found that return to work was associated with better social but not emotional functioning in breast cancer survivors [33], while another U.S. study of breast cancer survivors found better functioning, as measured by the FACT total score (not domain-specific), among women who continued to work through treatment [13]. Our study highlighted the role of race and/or racism in these differences; Black women were more likely than White women to experience a decrease in physical and function well-being. This suggests that disruption in work may be compounded by other social- and health-related factors.
While this analysis had strengths, including a large, diverse cohort of Black and younger women with follow up for employment and well-being, the study also had limitations. First, women in this cohort had high rates of private insurance (78.7%) and therefore, may not be representative of all working-aged women, particularly those who are uninsured. Given the higher rates of job-loss among part-time workers in our study, it is likely that our population may underestimate rates of job-loss and/or unemployment-related well-being decrements. Second, we did not collect detailed information regarding reasons for non-employment. Third, 2% (n=38) of our sample identified as Hispanic, which we did not include as a separate racial/ethnic group in our analysis. However, sensitivity analyses excluding Hispanic patients did not affect our estimates (data not shown). Finally, for participants reporting change in employment at the 25-month timepoint, the causal relationship between employment and well-being is unclear. Nonetheless, the associations we observed between job loss and well-being persisted after adjusting for other clinical and demographic factors.
Despite these limitations, the study suggests important disparities between Black and White women, which could arise from factors such as job discrimination and less accommodating employers. A recent study suggested stigma may play a role in employment after cancer [34]. Regardless of the causes of job loss, it is evident that breast cancer-related changes in employment are compounded by other well-being changes and employment status may be an important point for intervention in survivorship disparities.
Conclusion
As the number of women with breast cancer in the U.S. continues to grow [35], so does the need for enhancing the quality of life and care across the survivorship trajectory [36–41]. Cancer survivors must manage treatment-related and long-term sequelae, including impaired social and physical functioning and psychological distress, [42, 43] that can persist for years after diagnosis [44–46]. Return to work and job loss are strongly associated with physical, functional, social, and emotional well-being. Therefore, work continuation or return to work may be a useful measure for an array of well-being concerns, particularly among Black survivors who experience greater job loss, work function difficulties, and worse well-being. Patients who have not returned to work may be more likely to suffer from a variety of well-being issues, some of which may identified by providers who then may advocate for key physical, psychological, social, and economic interventions.
Supplementary Material
Funding
This work was supported by grants from the UNC Lineberger Comprehensive Cancer Center funded by the University Cancer Research Fund (LCCC2017T204), the National Cancer Institute of the National Institutes of Health (P50-CA58223, U01-CA179715, T32-CA057726, P30 CA046934), and the National Institute of Environmental Health Sciences of the National Institutes of Health (P30-ES010126).
Role of the funder:
The study sponsors had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript, and the decision to submit the manuscript for publication.
Footnotes
Competing Interests: The authors have no relevant financial or non-financial interests to disclose
Disclosures: The authors declare that they have no conflict of interest.
Ethics Approval: This study was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill,
Consent to Participate: All participants provided written informed consent.
Consent to Publish: No individual person’s data are presented
Data Availability:
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions
REFERENCES
- 1.Sun Y, Shigaki CL, Armer JM. Return to work among breast cancer survivors: A literature review. Support Care Cancer. 2017;25(3):709–18. Epub 2016/11/23. doi: 10.1007/s00520-016-3446-1. [DOI] [PubMed] [Google Scholar]
- 2.Cocchiara RA, Sciarra I, D’Egidio V, Sestili C, Mancino M, Backhaus I, et al. Returning to work after breast cancer: A systematic review of reviews. Work (Reading, Mass). 2018;61(3):463–76. Epub 2018/11/08. doi: 10.3233/wor-182810. [DOI] [PubMed] [Google Scholar]
- 3.Islam T, Dahlui M, Majid HA, Nahar AM, Mohd Taib NA, Su TT. Factors associated with return to work of breast cancer survivors: a systematic review. BMC public health. 2014;14 Suppl 3:S8. Epub 2014/12/02. doi: 10.1186/1471-2458-14-s3-s8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Stanton AL, Bower JE. Psychological Adjustment in Breast Cancer Survivors. Advances in experimental medicine and biology. 2015;862:231–42. Epub 2015/06/11. doi: 10.1007/978-3-319-16366-6_15. [DOI] [PubMed] [Google Scholar]
- 5.Stafford L, Judd F, Gibson P, Komiti A, Mann GB, Quinn M. Screening for depression and anxiety in women with breast and gynaecologic cancer: course and prevalence of morbidity over 12 months. Psycho-oncology. 2013;22(9):2071–8. Epub 2013/02/13. doi: 10.1002/pon.3253. [DOI] [PubMed] [Google Scholar]
- 6.Avis NE, Levine B, Naughton MJ, Case LD, Naftalis E, Van Zee KJ. Age-related longitudinal changes in depressive symptoms following breast cancer diagnosis and treatment. Breast Cancer Res Treat. 2013;139(1):199–206. Epub 2013/04/17. doi: 10.1007/s10549-013-2513-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Michael YL, Kawachi I, Berkman LF, Holmes MD, Colditz GA. The persistent impact of breast carcinoma on functional health status: prospective evidence from the Nurses’ Health Study. Cancer. 2000;89(11):2176–86. Epub 2001/01/09. doi: 10.1002/1097-0142(20001201)89:11<2176::aid-cncr5>3.0.co;2-6. [DOI] [PubMed] [Google Scholar]
- 8.Maunsell E, Drolet M, Brisson J, Brisson C, Masse B, Deschenes L. Work situation after breast cancer: results from a population-based study. J Natl Cancer Inst. 2004;96(24):1813–22. Epub 2004/12/17. doi: 10.1093/jnci/djh335. [DOI] [PubMed] [Google Scholar]
- 9.Bradley CJ, Bednarek HL, Neumark D. Breast cancer and women’s labor supply. Health services research. 2002;37(5):1309–28. Epub 2002/12/14. doi: 10.1111/1475-6773.01041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Yabroff KR, Lawrence WF, Clauser S, Davis WW, Brown ML. Burden of illness in cancer survivors: findings from a population-based national sample. J Natl Cancer Inst. 2004;96(17):1322–30. Epub 2004/09/02. doi: 10.1093/jnci/djh255. [DOI] [PubMed] [Google Scholar]
- 11.Moran JR, Short PF, Hollenbeak CS. Long-term employment effects of surviving cancer. J Health Econ. 2011;30(3):505–14. Epub 2011/03/25. doi: 10.1016/j.jhealeco.2011.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bradley CJ, Neumark D, Luo Z, Schenk M. Employment and cancer: findings from a longitudinal study of breast and prostate cancer survivors. Cancer investigation. 2007;25(1):47–54. Epub 2007/03/17. doi: 10.1080/07357900601130664. [DOI] [PubMed] [Google Scholar]
- 13.Mahar KK, BrintzenhofeSzoc K, Shields JJ. The impact of changes in employment status on psychosocial well-being: a study of breast cancer survivors. Journal of psychosocial oncology. 2008;26(3):1–17. Epub 2008/12/02. doi: 10.1080/07347330802115400. [DOI] [PubMed] [Google Scholar]
- 14.Finkelstein EA, Tangka FK, Trogdon JG, Sabatino SA, Richardson LC. The personal financial burden of cancer for the working-aged population. The American journal of managed care. 2009;15(11):801–6. [PubMed] [Google Scholar]
- 15.Guy GP Jr., Yabroff KR, Ekwueme DU, Smith AW, Dowling EC, Rechis R, et al. Estimating the health and economic burden of cancer among those diagnosed as adolescents and young adults. Health affairs (Project Hope). 2014;33(6):1024–31. Epub 2014/06/04. doi: 10.1377/hlthaff.2013.1425. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Yabroff KR, Dowling EC, Guy GP Jr., Banegas MP, Davidoff A, Han X, et al. Financial Hardship Associated With Cancer in the United States: Findings From a Population-Based Sample of Adult Cancer Survivors. J Clin Oncol. 2016;34(3):259–67. Epub 2015/12/09. doi: 10.1200/jco.2015.62.0468. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Short PF, Vasey JJ, Tunceli K. Employment pathways in a large cohort of adult cancer survivors. Cancer. 2005;103(6):1292–301. Epub 2005/02/09. doi: 10.1002/cncr.20912. [DOI] [PubMed] [Google Scholar]
- 18.Emerson MA, Golightly YM, Tan X, Aiello AE, Reeder-Hayes KE, Olshan AF, et al. Integrating access to care and tumor patterns by race and age in the Carolina Breast Cancer Study, 2008–2013. Cancer Causes Control. 2020. Epub 2020/01/18. doi: 10.1007/s10552-019-01265-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ekenga CC, Perez M, Margenthaler JA, Jeffe DB. Early-stage breast cancer and employment participation after 2 years of follow-up: A comparison with age-matched controls. Cancer. 2018;124(9):2026–35. Epub 2018/02/14. doi: 10.1002/cncr.31270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Brady MJ, Cella DF, Mo F, Bonomi AE, Tulsky DS, Lloyd SR, et al. Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument. J Clin Oncol. 1997;15(3):974–86. Epub 1997/03/01. doi: 10.1200/jco.1997.15.3.974. [DOI] [PubMed] [Google Scholar]
- 21.Guyatt GH, Osoba D, Wu AW, Wyrwich KW, Norman GR. Methods to explain the clinical significance of health status measures. Mayo Clinic proceedings. 2002;77(4):371–83. Epub 2002/04/09. doi: 10.4065/77.4.371. [DOI] [PubMed] [Google Scholar]
- 22.Cella D, Hahn EA, Dineen K. Meaningful change in cancer-specific quality of life scores: differences between improvement and worsening. Qual Life Res. 2002;11(3):207–21. Epub 2002/06/21. doi: 10.1023/a:1015276414526. [DOI] [PubMed] [Google Scholar]
- 23.Bleicher RJ, Ruth K, Sigurdson ER, Beck JR, Ross E, Wong YN, et al. Time to Surgery and Breast Cancer Survival in the United States. JAMA oncology. 2016;2(3):330–9. Epub 2015/12/15. doi: 10.1001/jamaoncol.2015.4508. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Blinder V, Eberle C, Patil S, Gany FM, Bradley CJ. Women With Breast Cancer Who Work For Accommodating Employers More Likely To Retain Jobs After Treatment. Health affairs (Project Hope). 2017;36(2):274–81. Epub 2017/02/09. doi: 10.1377/hlthaff.2016.1196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bradley CJ, Neumark D, Luo Z, Bednarek HL. Employment-contingent health insurance, illness, and labor supply of women: evidence from married women with breast cancer. Health Econ. 2007;16(7):719–37. Epub 2006/12/21. doi: 10.1002/hec.1191. [DOI] [PubMed] [Google Scholar]
- 26.Employment Mehnert A. and work-related issues in cancer survivors. Critical reviews in oncology/hematology. 2011;77(2):109–30. Epub 2010/02/02. doi: 10.1016/j.critrevonc.2010.01.004. [DOI] [PubMed] [Google Scholar]
- 27.Balak F, Roelen CA, Koopmans PC, Ten Berge EE, Groothoff JW. Return to work after early-stage breast cancer: a cohort study into the effects of treatment and cancer-related symptoms. Journal of occupational rehabilitation. 2008;18(3):267–72. Epub 2008/08/02. doi: 10.1007/s10926-008-9146-z. [DOI] [PubMed] [Google Scholar]
- 28.Blinder VS, Patil S, Thind A, Diamant A, Hudis CA, Basch E, et al. Return to work in low-income Latina and non-Latina white breast cancer survivors: a 3-year longitudinal study. Cancer. 2012;118(6):1664–74. Epub 2011/10/20. doi: 10.1002/cncr.26478. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Fantoni SQ, Peugniez C, Duhamel A, Skrzypczak J, Frimat P, Leroyer A. Factors related to return to work by women with breast cancer in northern France. Journal of occupational rehabilitation. 2010;20(1):49–58. Epub 2009/11/11. doi: 10.1007/s10926-009-9215-y. [DOI] [PubMed] [Google Scholar]
- 30.Bouknight RR, Bradley CJ, Luo Z. Correlates of return to work for breast cancer survivors. J Clin Oncol. 2006;24(3):345–53. Epub 2006/01/20. doi: 10.1200/jco.2004.00.4929. [DOI] [PubMed] [Google Scholar]
- 31.Gudbergsson SB, Fossa SD, Dahl AA. A study of work changes due to cancer in tumor-free primary-treated cancer patients. A NOCWO study. Support Care Cancer. 2008;16(10):1163–71. Epub 2008/02/08. doi: 10.1007/s00520-008-0407-3. [DOI] [PubMed] [Google Scholar]
- 32.Taskila T, Martikainen R, Hietanen P, Lindbohm ML. Comparative study of work ability between cancer survivors and their referents. European journal of cancer (Oxford, England : 1990). 2007;43(5):914–20. Epub 2007/02/23. doi: 10.1016/j.ejca.2007.01.012. [DOI] [PubMed] [Google Scholar]
- 33.Bijker R, Duijts SFA, Smith SN, de Wildt-Liesveld R, Anema JR, Regeer BJ. Functional Impairments and Work-Related Outcomes in Breast Cancer Survivors: A Systematic Review. Journal of occupational rehabilitation. 2018;28(3):429–51. Epub 2017/11/01. doi: 10.1007/s10926-017-9736-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Shim S, Kang D, Bae KR, Lee WY, Nam SJ, Sohn TS, et al. Association between cancer stigma and job loss among cancer survivors. Psycho-oncology. 2021;30(8):1347–55. Epub 2021/04/06. doi: 10.1002/pon.5690. [DOI] [PubMed] [Google Scholar]
- 35.DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, et al. Breast cancer statistics, 2019. CA: a cancer journal for clinicians. 2019;69(6):438–51. Epub 2019/10/03. doi: 10.3322/caac.21583. [DOI] [PubMed] [Google Scholar]
- 36.Rowland JH, Gallicchio L, Mollica M, Saiontz N, Falisi AL, Tesauro G. Survivorship Science at the NIH: Lessons Learned From Grants Funded in Fiscal Year 2016. J Natl Cancer Inst. 2019;111(2):109–17. Epub 2019/01/19. doi: 10.1093/jnci/djy208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Pinto AC, de Azambuja E. Improving quality of life after breast cancer: dealing with symptoms. Maturitas. 2011;70(4):343–8. doi: 10.1016/j.maturitas.2011.09.008. [DOI] [PubMed] [Google Scholar]
- 38.Stan D, Loprinzi CL, Ruddy KJ. Breast cancer survivorship issues. Hematology/oncology clinics of North America. 2013;27(4):805–27, ix. Epub 2013/08/07. doi: 10.1016/j.hoc.2013.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.de Moor JS, Mariotto AB, Parry C, Alfano CM, Padgett L, Kent EE, et al. Cancer survivors in the United States: prevalence across the survivorship trajectory and implications for care. Cancer Epidemiol Biomarkers Prev. 2013;22(4):561–70. Epub 2013/03/29. doi: 10.1158/1055-9965.epi-12-1356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Forsythe LP, Kent EE, Weaver KE, Buchanan N, Hawkins NA, Rodriguez JL, et al. Receipt of psychosocial care among cancer survivors in the United States. J Clin Oncol. 2013;31(16):1961–9. Epub 2013/04/24. doi: 10.1200/jco.2012.46.2101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Kroenke CH, Rosner B, Chen WY, Kawachi I, Colditz GA, Holmes MD. Functional impact of breast cancer by age at diagnosis. J Clin Oncol. 2004;22(10):1849–56. Epub 2004/05/15. doi: 10.1200/jco.2004.04.173. [DOI] [PubMed] [Google Scholar]
- 42.Carreira H, Williams R, Muller M, Harewood R, Stanway S, Bhaskaran K. Associations Between Breast Cancer Survivorship and Adverse Mental Health Outcomes: A Systematic Review. J Natl Cancer Inst. 2018;110(12):1311–27. Epub 2018/11/08. doi: 10.1093/jnci/djy177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Payne DK, Sullivan MD, Massie MJ. Women’s psychological reactions to breast cancer. Seminars in oncology. 1996;23(1 Suppl 2):89–97. [PubMed] [Google Scholar]
- 44.Avis NE, Levine BJ, Case LD, Naftalis EZ, Van Zee KJ. Trajectories of depressive symptoms following breast cancer diagnosis. Cancer Epidemiol Biomarkers Prev. 2015;24(11):1789–95. Epub 2015/09/18. doi: 10.1158/1055-9965.epi-15-0327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Burg MA, Adorno G, Lopez ED, Loerzel V, Stein K, Wallace C, et al. Current unmet needs of cancer survivors: analysis of open-ended responses to the American Cancer Society Study of Cancer Survivors II. Cancer. 2015;121(4):623–30. Epub 2015/01/13. doi: 10.1002/cncr.28951. [DOI] [PubMed] [Google Scholar]
- 46.Foster C, Wright D, Hill H, Hopkinson J, Roffe L. Psychosocial implications of living 5 years or more following a cancer diagnosis: a systematic review of the research evidence. European journal of cancer care. 2009;18(3):223–47. Epub 2009/05/13. doi: 10.1111/j.1365-2354.2008.01001.x. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions