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. Author manuscript; available in PMC: 2014 Jun 1.
Published in final edited form as: Psychooncology. 2012 Aug 22;22(6):1411–1420. doi: 10.1002/pon.3157

Employment Status and Quality of Life in Recently Diagnosed Breast Cancer Survivors

Allegra W Timperi 1, Isaac Joshua Ergas 1, David H Rehkopf 2, Janise M Roh 1, Marilyn L Kwan 1, Lawrence H Kushi 1
PMCID: PMC3519968  NIHMSID: NIHMS399457  PMID: 22912069

Abstract

Objective

Breast cancer survivors are less likely to be employed than similar healthy women, yet effects of employment on the well-being of survivors are largely unknown. In a prospective cohort study of 2,013 women diagnosed from 2006–2011 with invasive breast cancer in Kaiser Permanente Northern California, we describe associations between hours worked per week and change in employment with quality of life (QOL) from diagnosis through active treatment.

Methods

Participants completed information on employment status and QOL approximately 2-months and 8-months post-diagnosis. QOL was assessed by the Functional Assessment of Cancer Therapy-Breast Cancer (FACT-B). Multivariable linear regression models adjusted for potential confounders including demographic, diagnostic, and medical care factors to examine associations between employment and QOL.

Results

At baseline, overall well-being was higher for women who worked at least some hours per week compared to women who were not working. Women working 1–19 hours per week at baseline also had higher functional well-being compared to women who were not working. There was a significant, positive association between hours worked per week and physical and social well-being. At the six-month follow-up, women working at least 20 hours per week had higher physical and functional well-being than those not working. Lower scores for physical and functional well-being were observed among women who stopped working during the six-month follow-up period.

Conclusions

Continuing to work after a breast cancer diagnosis may be beneficial to multiple areas of QOL. Strategies to help women continue working through treatment should be explored.

Keywords: cancer, oncology, breast cancer, employment, quality of life, FACT-B

INTRODUCTION

Nearly 2.5 million women are living after a diagnosis of breast cancer in the US today, and this number is expected to increase to 3.4 million by 2015 [1, 2]. A growing proportion of these women are living their lives similar to if they had not had breast cancer. Even so, identification of factors that can improve quality of life, especially in the active treatment phase when coping with the disease requires numerous decisions, can result in improvement of the life experience for women with breast cancer. Employment status is one factor that has attracted some interest, but the current literature on its effect on well-being is sparse.

There is evidence among breast cancer survivors that unemployment is higher and work ability is lower than among similar healthy women [3–10], and that employment status can affect quality of life (QOL) [11, 12] and mortality [13] in breast cancer survivors. These observations are particularly important in light of evidence that stress and depression may affect recurrence and mortality for breast cancer survivors [14–16]. While the relation between employment and stress or depression can be complex, employment may have beneficial effects as it typically requires human interaction. This hypothesis is consistent with existing studies having found benefits of employment for other health outcomes including: overall health, physical functioning, and mental health in people without disease and in women in particular [17–22]. However, none of these prior studies have examined if these benefits exist for breast cancer survivors. If so, unemployment and reduced work ability among survivors may have consequences beyond the direct economic costs.

Given the evidence for benefits of employment in the larger population, and that the risk of unemployment is higher among breast cancer survivors than among other comparable women, we examined whether employment confers similar benefits to breast cancer survivors. We examined the relation between employment status (measured as both yes/no and the number of hours worked per week) and multiple domains of QOL around the time of diagnosis and six months later in a prospective cohort study of women with breast cancer. We also examined the relation of QOL with change in employment status during the six-month period following diagnosis. Results from this study may provide guidance on support and special needs of recently diagnosed breast cancer survivors in the workforce, especially those who continue to work while undergoing treatment.

METHODS

The Pathways Study is an ongoing, prospective cohort study recruiting women recently diagnosed with invasive breast cancer from the population membership of Kaiser Permanente Northern California (KPNC) [23]. KPNC is one of the largest integrated health care delivery systems in the US, with 3.2 million members and approximately 2,500 incident cases of invasive breast cancer annually [24]. As of July 1, 2012, 4,221 patients have been enrolled since recruitment began in January 2006. Briefly, cases are ascertained rapidly on a daily basis by automatic scanning of electronic pathology reports with subsequent verification of cancer diagnosis and patient notification by a medical record analyst. Eligibility criteria include: current KPNC membership; at least 21 years of age at diagnosis; diagnosis of first primary invasive breast cancer (all stages); no prior history of cancer other than non-melanoma skin cancer; ability to speak English, Spanish, Cantonese, or Mandarin; and residence within a 65-mile radius of a field interviewer. Passive consent is obtained from the patient’s physician of record, followed by written informed consent from all participants before they are enrolled in the study. The study was approved by the Institutional Review Boards of KPNC and all collaborating institutions.

Data collection

The baseline interview is conducted in-person by a trained interviewer approximately two months after diagnosis. During the baseline interview, information is collected on demographic factors such as age at diagnosis, race/ethnicity, educational attainment, annual household income, marital status, and clinical factors including height, weight, and menopausal status. Detailed employment information is collected as described below. A 6-month follow-up questionnaire (approximately eight months post-diagnosis) is mailed to participants asking for updates on the same information obtained at baseline.

Employment

Details on employment are collected at the baseline and 6-month follow-up interviews as part of a physical activity questionnaire based on the Arizona Activity Frequency Questionnaire (AAFQ) [25, 26]. The questionnaire asks if the participant was employed or engaged in weekly volunteer activity in the past six months. If the participant answers yes to either question, they are asked how many days per week they did paid and/or volunteer activities and how many hours per day.

Health-related QOL

The Functional Assessment of Cancer Therapy-Breast Cancer (FACT-B), Version 3, is administered during the baseline interview and at the six month follow-up to assess health-related QOL. The FACT-B consists of five subscales: physical well-being (PWB), functional well-being (FWB), emotional well-being (EWB), social/family well-being (SWB), and breast cancer-specific concerns (BCS). An overall well-being score is calculated by summing the individual subscale scores. The instrument has a total of 41 statements asking respondents to rate how true each statement is for the preceding seven days. Response scales range from 0 (not at all) to 4 (very much). The instrument has been well-validated elsewhere [27, 28], and the internal validity in our study population is high (Cronbach’s alpha = 0.90 for baseline and 0.91 for follow-up).

Clinical characteristics

Diagnostic characteristics are obtained from the KPNC Cancer Registry (KPNCCR) [24]. These include data on stage of disease and tumor characteristics such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status. Supplemental results from additional testing for equivocal HER2 expression are obtained directly from the KPNC regional cytogenetics laboratory. Information on breast surgery, chemotherapy, radiation therapy, and hormonal therapy are obtained from the KPNCCR and KPNC electronic data sources.

Data analysis

The present analysis was limited to 2,013 women enrolled in the cohort who completed baseline and 6-month follow-up employment and QOL information as of July 21, 2011. The mean (standard deviation [SD]) time from diagnosis to the baseline interview was 1.92 (0.65) months and from baseline interview to follow-up data collection was 6.11 (1.55) months.

The number of hours worked per week was categorized as follows. Participants who answered “No” to both “Were you employed in the past six months?” and “Did you do any weekly volunteer activity in the past six months?” on the AAFQ were placed in the “none” category for hours worked per week. For participants who answered “Yes” to at least one of those questions, summaries of hours worked per week were created by using the mid-point of each available category (1–3 days/week, 4–5 days/week, >5 days/week; 0–4 hours/day, 5–8 hours/day, >8 hours/day) and multiplying days worked per week by hours worked per day. They were then grouped into one of three categories of hours worked per week: 1–19 hours, 20–34 hours, and ≥35 hours. Thirty-five (35) hours per week is considered full-time by the United States Bureau of Labor Statistics [29].

Women who answered “Yes” to at least one of the above questions at both baseline and the 6-month follow-up were considered to have worked continuously through treatment, whereas women who answered “Yes” to doing paid work or regular volunteer activity at the baseline interview but answered “No” to both at the 6-month follow-up were considered to have stopped working during the active treatment period.

Analyses began by examining distributions of potential covariates according to QOL and employment variables. Associations of hours worked per week, change in employment, and QOL were calculated using multivariable linear regression [30]. Initial analyses adjusted for demographic and clinical characteristics, including age at diagnosis, race, menopausal status, body mass index (BMI), clinical characteristics, and treatment type. Subsequent analyses accounted for additional sociodemographic and employment characteristics that may influence the employment-QOL association, including educational attainment, partner status, annual household income, number of people supported by household income, occupation category, difficulty taking time off from work, and job-related stress level. In order to address potential reverse causality of physical status influencing the ability to obtain and/or maintain employment, the fully-adjusted models were also adjusted for baseline FACT-B PWB scores, except where baseline PWB was the outcome of interest. Finally, for models examining follow-up FACT-B scores, baseline scores were also adjusted for, to examine effects of employment conditional on baseline QOL. We repeated analyses for the subset of women aged 65 y or younger (n=1,262), as these are women are most likely to be employed at the time of breast cancer diagnosis. Results were largely similar, and so we present findings for the larger analytic population of 2,013 women with breast cancer.

RESULTS

The characteristics of the study population overall, by hours worked per week at baseline and the 6-month follow-up, and by employment change from baseline to the 6-month follow-up are provided in Tables 1 and 2. Over half (54%) of participants were 60 years of age or older at breast cancer diagnosis. The majority of women were diagnosed with early stage breast cancer (52% and 33% Stage I and II, respectively). Sixty-two percent of women had breast-conserving surgery only, while 37% had a mastectomy (Table 1). Forty-four percent received chemotherapy, 43% received radiation therapy, and 69% received hormonal therapy (data not shown). Participants were primarily white (70%) and highly educated, with 85% having at least some college education. More than half (60%) of the participants were married at the time of the baseline interview, and 45% reported that two people were supported by their household income (Table 1).

Table 1.

Demographic, employment factors and clinical characteristics of Pathways participants by hours worked per week at baseline and 6-month follow-up

Baseline Hours Worked per Week
All (n=2013) 0 Hours (n=601) 1–19 Hours (n=488) 20–34 Hours (n=489) ≥35 Hours (n=411) P -value
n % n % n % n % n %
Demographic factors
Age at diagnosis <.0001
 <50 384 19.08 39 6.49 52 10.66 147 30.06 141 34.31
 50–59 545 27.07 76 12.65 76 15.57 205 41.92 181 44.04
 60–69 637 31.64 238 39.60 187 38.32 126 25.77 79 19.22
 ≥70 447 22.21 248 41.26 173 35.45 11 2.25 10 2.43
Race/ethnicity <.0001
 White 1418 70.44 436 72.55 390 79.92 299 61.15 277 67.40
 Black 116 5.76 38 6.32 21 4.30 35 7.16 21 5.11
 Hispanic 210 10.43 57 9.48 41 8.40 64 13.09 45 10.95
 Asian 215 10.68 48 7.99 24 4.92 83 16.97 56 13.63
 Other 54 2.68 22 3.66 12 2.46 8 1.64 12 2.92
Education <.0001
 High school or less 294 14.16 143 23.79 54 11.07 68 13.91 28 6.81
 Some college 696 34.58 250 41.60 153 31.35 164 33.54 122 29.68
 College graduate 546 27.12 117 19.47 144 29.51 148 30.27 126 30.66
 Post-graduate 476 23.65 91 15.14 136 27.87 109 22.29 135 32.85
Annual household income <.0001
 <$25,000 189 9.39 99 16.47 54 11.07 24 4.91 10 2.43
 $25,000–49,999 390 19.37 123 20.47 110 22.54 99 20.25 53 12.90
 $50,000–89,999 605 30.05 161 26.79 152 31.15 155 31.70 128 31.14
 ≥90,000 630 31.30 110 18.30 133 27.25 174 35.58 205 49.88
 Unknown 199 9.89 108 17.97 39 7.99 37 7.57 15 3.65
Number of people supported by household income <.0001
 1 564 28.02 187 31.11 158 32.38 120 24.54 93 22.63
 2 899 44.66 299 49.75 238 48.77 195 39.88 157 38.20
 ≥3 511 25.38 94 15.64 83 17.01 167 34.15 159 38.69
Partner Status <.0001
 Married 1198 59.51 361 60.07 306 62.70 287 58.69 229 55.72
 Living as married 59 2.93 7 1.16 6 1.23 23 4.70 22 5.35
 Widowed 228 11.33 114 18.97 65 13.32 26 5.32 23 5.60
 Separated/Divorced 365 18.13 97 16.14 78 15.98 92 18.81 91 22.14
 Single 160 7.95 22 3.66 32 6.56 60 12.27 45 10.95
Employment Factors
Baseline occupation category <.0001
 Managerial/Professional 1003 49.83 241 40.10 258 52.87 229 46.83 263 63.99
 Technical/Sales/Administrative 491 24.39 148 24.63 112 22.95 153 31.29 72 17.52
 Service 205 10.18 56 9.32 41 8.40 69 14.11 37 9.00
 Operators/Laborers 41 2.04 16 2.66 7 1.43 7 1.43 10 2.43
 Homemakers 98 4.87 65 10.82 28 5.74 3 0.61 0 0.00
Difficulty taking time off from work when sick <.0001
 Not difficult 1315 65.33 428 71.21 324 66.39 320 65.44 228 55.47
 Difficult 524 26.03 66 10.98 107 21.93 164 33.54 180 43.80
 Unknown 174 8.64 107 17.80 57 11.68 5 1.02 3 0.73
Stress from job <.0001
 Not stressful 1376 68.36 543 90.35 379 77.66 243 49.69 196 47.69
 Stressful 596 29.61 36 5.99 96 19.67 243 49.69 213 51.82
 Unknown 41 2.04 22 3.66 13 2.66 3 0.61 2 0.49
Clinical Characteristics
AJCC Stage 0.0004
 Stage I 1038 51.56 337 56.07 278 56.97 217 44.38 193 46.96
 Stage II 665 33.04 184 30.62 133 27.25 188 38.45 153 37.23
 Stage III 196 9.74 46 7.56 48 9.84 54 11.04 45 10.95
 Stage IV 24 1.19 6 1.00 8 1.64 4 0.82 6 1.46
 Unknown 90 4.47 28 4.66 21 4.30 26 5.32 14 3.41
Surgery 0.19
 Lumpectomy 1243 61.75 373 62.06 325 66.60 283 57.87 249 60.58
 Mastectomy 751 37.31 222 36.94 158 32.38 201 41.10 159 38.69
 None 19 0.94 6 1.00 5 1.02 5 1.02 3 0.73
Hormone therapy 0.39
 Yes 1379 68.50 400 66.56 339 69.47 342 69.94 284 69.10
 No 609 30.25 199 33.11 143 29.30 140 28.63 118 28.71
 Unknown 25 1.24 2 0.33 6 1.23 7 1.43 9 2.19
Follow-Up Hours Worked per Week (only characteristics where pattern differed from baseline shown)
All (n=2013) 0 Hours (n=969) 1–19 Hours (n=402) 20–34 Hours (n=360) ≥35 Hours (n=219) P -value
n % n % n % n % n %
Employment Factors
Baseline occupation category <.0001
 Managerial/Professional 1003 49.83 410 42.31 210 52.24 208 57.78 142 64.84
 Technical/Sales/Administrative 491 24.39 242 24.97 97 24.13 96 26.67 41 18.72
 Service 205 10.18 107 11.04 37 9.20 37 10.28 19 8.68
 Operators/Laborers 41 2.04 27 2.79 7 1.74 2 0.56 3 1.37
 Homemakers 98 4.87 82 8.46 15 3.73 1 0.28 0 0.00
Clinical Characteristics
AJCC Stage 0.01
 Stage I 1038 51.56 461 47.57 226 56.22 190 52.78 130 59.36
 Stage II 665 33.04 347 35.81 110 27.36 117 32.50 70 31.96
 Stage III 196 9.74 107 11.04 40 9.95 30 8.33 12 5.48
 Stage IV 24 1.19 12 1.24 6 1.49 5 1.39 1 0.46
 Unknown 90 4.47 42 4.33 20 4.98 18 5.00 6 2.47
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Table 2.

Demographic, employment factors and clinical characteristics of Pathways participants by employment change from baseline to 6-month follow-up

All (n=2013) Not working at baseline or follow-up (n=843) Began working between baseline and follow-up (n=32) Stopped working between baseline and follow-up (n=347) Working at baseline and follow-up (n=789) P -value
n % n % n % n % n %
Demographic factors
Age at diagnosis <.0001
 <50 384 19.08 49 5.81 7 21.88 97 27.95 231 29.28
 50–59 545 27.07 95 11.27 3 9.38 139 40.06 307 38.91
 60–69 637 31.64 321 38.08 15 46.88 88 25.36 213 27.00
 ≥70 447 22.21 378 44.84 7 21.88 23 6.63 38 4.82
Race/ethnicity <.0001
 White 1418 70.44 640 75.92 24 75.00 198 57.06 554 70.22
 Black 116 5.76 52 6.17 0 0.00 27 7.78 37 4.69
 Hispanic 210 10.43 72 8.54 3 9.38 50 14.41 85 10.77
 Asian 215 10.68 52 6.17 4 12.50 65 18.73 94 11.91
 Other 54 2.68 27 3.20 1 3.13 7 2.02 19 2.41
Education <.0001
 High school or less 294 14.61 168 19.93 7 21.88 48 13.83 71 9.00
 Some college 696 34.58 327 38.79 11 34.38 113 32.56 245 31.05
 College graduate 546 27.12 194 23.01 6 18.75 109 31.41 236 29.91
 Post-graduate 476 23.65 153 18.15 8 25.00 77 22.19 237 30.04
Annual household income <.0001
 <$25,000 189 9.39 132 15.66 3 9.38 22 6.34 32 4.06
 $25,000–49,999 390 19.37 185 21.95 5 15.63 58 16.71 141 17.87
 $50,000–89,999 605 30.05 235 27.88 15 46.88 103 29.68 251 31.81
 ≥90,000 630 31.30 165 19.57 6 18.75 135 38.90 324 41.06
 Unknown 199 9.89 126 14.95 3 9.38 29 8.36 41 5.20
Number of people supported by household income <.0001
 1 564 28.02 271 32.15 10 31.25 65 18.73 217 27.50
 2 899 44.66 428 50.77 10 31.25 147 42.36 313 39.67
 ≥3 511 25.38 120 14.23 10 31.25 129 37.18 252 31.94
Partner Status <.0001
 Married 1198 59.51 509 60.38 19 59.38 221 63.69 448 56.78
 Living as married 59 2.93 9 1.07 1 3.13 14 4.03 35 4.44
 Widowed 228 11.33 161 19.10 6 18.75 14 4.03 47 5.96
 Separated/Divorced 365 18.13 129 15.30 6 18.75 67 19.31 162 20.53
 Single 160 7.95 34 4.03 0 0.00 31 8.93 95 12.04
Employment Factors
Baseline occupation category <.0001
 Managerial/Professional 1003 49.83 360 42.70 18 56.25 157 45.24 466 59.06
 Technical/Sales/Administrative 491 24.39 211 25.03 4 12.50 91 26.22 185 23.45
 Service 205 10.18 66 7.83 4 12.50 55 15.85 80 10.14
 Operators/Laborers 41 2.04 18 2.14 1 3.13 13 3.75 9 1.14
 Homemakers 98 4.87 92 10.97 0 0.00 4 1.15 2 0.25
Difficulty taking time off from work when sick <.0001
 Not difficult 1315 65.33 587 69.63 24 75.00 224 64.55 478 60.58
 Difficult 524 26.03 105 12.46 6 18.75 109 31.41 304 38.53
 Unknown 174 8.64 151 17.91 2 6.25 14 4.03 7 0.89
Stress from job <.0001
 Not stressful 1376 68.36 766 90.87 27 84.38 215 61.96 366 46.39
 Stressful 596 29.61 43 5.10 5 15.63 128 36.89 420 53.23
 Unknown 41 2.04 34 4.03 0 0.00 4 1.15 3 0.38
Clinical Characteristics
AJCC Stage <.0001
 Stage I 1038 51.56 484 57.41 15 46.88 111 31.99 427 54.12
 Stage II 665 33.04 254 29.06 13 40.63 157 45.24 249 31.56
 Stage III 196 9.74 64 7.59 2 6.25 60 17.29 70 8.87
 Stage IV 24 1.19 10 1.19 0 0.00 4 1.15 10 1.27
 Unknown 90 4.47 40 4.74 2 6.25 15 4.32 33 4.18
Surgery 0.001
 Lumpectomy 1243 61.75 555 65.84 20 62.50 178 51.30 488 61.85
 Mastectomy 751 37.31 280 33.21 12 37.50 164 47.26 295 37.39
 None 19 0.94 8 0.95 0 0.00 5 1.44 6 0.76
Hormone therapy 0.06
 Yes 1379 68.50 564 66.90 22 68.75 227 65.42 564 71.48
 No 609 30.25 274 32.50 10 31.25 115 33.14 210 26.62
 Unknown 25 1.24 5 0.59 0 0.00 5 1.44 15 1.90
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A substantial majority of study participants (69%) were engaged in either paid work or regular volunteer activities at the time of the baseline interview, whereas by the 6-month follow-up the proportion of participants so engaged had decreased to 49%. At baseline, 65% percent of participants reported that it was not difficult to take time off of work when they were sick or needed medical treatments, and 68% reported that they had not experienced stress from their job in the past seven days (Table 1). Forty-two percent of participants reported they were not working at either time point, 39% were working continuously, and 17% quit working (Table 2). The mean FACT-B scores for each domain overall and by number of hours worked per week at baseline and the 6-month follow-up are given in Table 3. A small increase in overall domain scores and the overall well-being score was observed for all FACT-B domains other than BCS between baseline and follow-up.

Table 3.

Unadjusted linear regression models showing FACT-B domain scores by hours worked per week at baseline and follow-up (n=2013)

Baseline FACT-B Scores
Physical well-being Social well-being Emotional well-being
Mean (SD) P -value Mean (SD) P -value Mean (SD) P -value
Overall 22.22 (5.63) <.0001 24.55 (4.01) 0.36 19.20 (4.03) <.0001
Baseline hours worked per week
None 23.07 (5.24) - 24.29 (4.30) - 19.71 (3.97) -
1–19 23.02 (5.19) 0.88 24.82 (3.88) 0.03 19.61 (3.82) 0.68
20–34 21.24 (5.95) <.0001 24.47 (3.90) 0.47 18.60 (4.05) <.0001
≥35 21.22 (5.94) <.0001 24.67 (3.90) 0.15 18.72 (4.19) 0.0001
Functional well-being Breast cancer specific concerns Overall FACT-B
Mean (SD) P -value Mean (SD) P -value Mean (SD) P -value
Overall 20.73 (5.49) 0.003 25.78 (5.94) <.0001 112.22 (18.70) <.0001
Baseline hours worked per week
None 20.66 (5.75) - 26.59 (5.49) - 114.19 (18.07) -
1–19 21.76 (5.03) 0.001 27.30 (5.49) 0.05 116.37 (17.44) 0.06
20–34 20.20 (5.25) 0.17 24.49 (5.98) <.0001 108.79 (18.53) <.0001
≥35 20.26 (5.73) 0.25 24.47 (6.38) <.0001 108.89 (19.84) <.0001
Follow-Up FACT-B Scores
Physical well-being Social well-being Emotional well-being
Mean (SD) P -value Mean (SD) P -value Mean (SD) P -value
Overall 22.69 (5.35) 0.002 25.56 (4.66) 0.89 20.23 (3.50) 0.27
Follow-up hours worked per week
None 22.09 (5.61) - 23.54 (4.66) - 20.15 (3.58) -
1–19 23.54 (5.03) <.0001 23.88 (4.15) 0.22 20.75 (3.21) 0.004
20–34 23.18 (4.72) 0.001 23.43 (4.75) 0.69 20.21 (3.40) 0.77
≥35 23.23 (5.15) 0.004 23.73 (4.82) 0.59 19.91 (3.68) 0.36
Functional well-being Breast cancer specific concerns Overall FACT-B
Mean (SD) P -value Mean (SD) P -value Mean (SD) P -value
Overall 21.54 (5.28) <.0001 25.73 (6.10) 0.10 113.68 (18.77) 0.04
Follow-up hours worked per week
None 20.64 (5.68) - 25.47 (6.03) - 111.76 (19.29) -
1–19 22.44 (4.76) <.0001 27.04 (5.85) <.0001 117.37 (17.43) <.0001
20–34 22.24 (4.74) <.0001 25.45 (6.07) 0.95 114.48 (17.58) 0.02
≥35 22.67 (4.52) <.0001 25.09 (6.40) 0.40 115.20 (18.39) 0.02
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In initial cross-sectional analyses at baseline that included demographic and clinical characteristics, women working 1–19 hours per week had significantly higher FWB compared to women who were not working (p=0.03). Examining hours per week as a continuous variable, hours per week was positively associated with SWB (p=0.05), but negatively associated with BCS (p=0.05). In cross-sectional analyses at the 6-month follow-up, hours worked per week was related to PWB, FWB, BCS, and overall well-being. Compared with women who were not working, PWB was higher for those who reported working, with the highest scores among women working 20–34 hours per week (p=<0.0001). Compared with women who were not working, FWB and overall well-being increased with increasing category of hours worked per week (p=<0.0001 for both). Women who were working 1–34 hours per week had higher scores for BCS compared to women who were not working, with women working 1–19 hours having the highest scores (p=0.02). When examining hours per week as a continuous variable, hours per week was positively associated with physical (p=<0.0001), functional (p=<0.0001), and overall well-being (p=0.0003) (Table 4).

Table 4.

Minimally-adjusted* linear regression models showing employment status predicting FACT-B scores

Baseline Employment Status (n=2013)
Physical well-being Social well-being Emotional well-being
Baseline hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - 0.90 Ref - 0.12 Ref - 0.76
1–19 0.08 −0.61, 0.77 0.48 −0.04, 0.99 0.08 −0.43, 0.59
20–34 0.30 −0.47, 1.08 0.45 −0.11, 1.02 0.23 −0.34, 0.80
≥35 0.14 −0.67, 0.96 0.66 0.06, 1.27 0.31 −0.29, 0.92
Hours per week (continuous) 0.004 −.01, 0.02 0.64 0.013 −0.00, 0.03 0.05 0.004 −0.01, 0.02 0.50
Functional well-being Breast cancer specific concerns Overall FACT-B
Baseline hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - 0.03 Ref - 0.11 Ref - 0.29
1–19 1.01 0.32, 1.70 0.56 −0.15, 1.27 2.28 −0.05, 4.62
20–34 0.70 −0.07, 1.46 −0.27 −1.07, 0.52 1.35 −1.27, 3.96
≥35 0.62 −0.20, 1.43 −0.42 −1.26, 0.42 1.12 −1.64, 3.88
Hours per week (continuous) 0.008 −0.01, 0.03 0.33 −0.018 −0.04, −0.00 0.05 0.004 −0.05, 0.06 0.88
Follow-Up Employment Status (n=2013)
Physical well-being Social well-being Emotional well-being
Follow-up hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - <.0001 Ref - 0.70 Ref - 0.12
1–19 0.90 0.27, 1.52 0.21 −0.37, 0.79 0.48 0.05, 0.92
20–34 1.61 0.92, 2.29 −0.14 −0.78, 0.50 0.38 −0.10, 0.86
≥35 1.38 0.57, 2.2 0.26 −0.50, 1.02 0.14 −0.43, 0.71
Hours per week (continuous) 0.036 0.02, 0.05 <.0001 0.001 −0.02, 0.02 0.93 0.004 −0.01, 0.02 0.47
Functional well-being Breast cancer specific concerns Overall FACT-B
Follow-up hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - <.0001 Ref - 0.02 Ref - <.0001
1–19 1.51 0.87, 2.15 0.99 0.28, 1.70 3.89 1.62, 6.17
20–34 1.86 1.15, 2.57 0.85 0.07, 1.63 4.63 2.15, 7.12
≥35 2.15 1.31, 2.99 0.26 −0.67, 1.18 4.88 1.94, 7.82
Hours per week (continuous) 0.048 0.03, 0.07 <.0001 0.008 −0.01, 0.03 0.38 0.108 0.05, 0.17 0.0003
Employment Change from Baseline to Follow-up (n=1136)
Physical well-being Social well-being Emotional well-being
Baseline to follow-up employment pattern β 95% CI P -value β 95% CI P -value β 95% CI P -value
Worked continuously Ref - 0.004 Ref - 0.37 Ref - 0.61
Quit Working −1.12 −1.87, −0.36 −0.31 −0.99, 0.37 −0.13 −0.65, 0.38
Functional well-being Breast cancer specific concerns Overall FACT-B
Baseline to follow-up employment pattern β 95% CI P -value β 95% CI P -value β 95% CI P -value
Worked continuously Ref - <.0001 Ref - 0.04 Ref - 0.002
Quit Working −1.45 −2.15, −0.75 −0.89 −1.75, −0.03 −4.22 −6.88, −1.56
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*

Adjusted for age, race, menopausal status, AJCC stage, hormone receptor status, Her2 status, surgery type, treatment type (chemotherapy, radiation, and hormone therapy), and BMI.

After accounting for additional covariates, hours worked per week at baseline continued to be associated with SWB and FWB, and additionally associated with PWB and overall well-being. Compared with women who were not working at baseline, SWB increased with each increasing category of hours worked per week (p=0.001). Women who were working 1–19 hours per week had higher FWB compared to women who were not working (p=0.005). Overall well-being was higher for women in all categories of hour per week compared to women who were not working, and highest for women working 1–19 hours per week (p=0.002). Examining hours per week as a continuous variable, there was a positive association between hours worked per week and both PWB and SWB (p=0.02 and p=0.001 respectively). At the 6-month follow-up, hours worked per week remained related to PWB and FWB, but not to other domains. For both PWB and FWB, women who worked at least 20 hours per week had higher scores than those who were not working (p=0.002 and p=0.005 respectively). When examining hours per week as a continuous variable, hours worked per week was positively associated with PWB and FWB (p=0.001 for both) (Table 5).

Table 5.

Fully-adjusted* linear regression models showing employment status predicting FACT-B scores

Baseline Employment Status (n=2013)
Physical well-being Social well-being** Emotional well-being**
Baseline hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - 0.17 Ref - 0.001 Ref - 0.20
1–19 0.69 −0.20, 1.57 1.03 0.38, 1.67 0.38 −0.24, 0.99
20–34 0.94 −0.05, 1.92 1.08 0.36, 1.79 0.68 −0.01, 1.37
≥35 1.17 0.11, 2.22 1.47 0.70, 2.23 0.75 0.01, 1.48
Hours per week (continuous) 0.025 0.004, 0.05 0.02 0.026 0.01, 0.04 0.001 0.009 −0.01, 0.02 0.22
Functional well-being** Breast cancer specific concerns** Overall FACT-B**
Baseline hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - 0.005 Ref - 0.11 Ref - 0.002
1–19 1.38 0.63, 2.13 0.95 0.14, 1.77 3.81 1.81, 5.80
20–34 0.76 −0.08, 1.60 0.61 −0.30, 1.53 3.33 1.10, 5.56
≥35 0.75 −0.14, 1.64 0.32 −0.65, 1.29 3.35 0.98, 5.72
Hours per week (continuous) 0.004 −0.01, 0.02 0.63 −0.007 −0.0, 0.01 0.47 0.033 −0.02, 0.08 0.18
Follow-Up Employment Status (n=2013)
Physical well-being** Social well-being**‡ Emotional well-being**‡
Follow-up hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - 0.002 Ref - 0.24 Ref - 0.25
1–19 0.61 −0.12, 1.32 −0.30 −0.89, 0.29 0.40 −0.02, 0.82
20–34 1.40 0.64, 2.15 −0.63 −1.26, −0.01 0.00 −0.45, 0.45
≥35 1.31 0.42, 2.20 −0.21 −0.94, 0.53 0.08 −0.45, 0.60
Hours per week (continuous) 0.031 0.01, 0.05 0.001 −0.010 −0.03, 0.01 0.20 −0.002 −0.01, 0.01 0.73
Functional well-being**‡ Breast cancer specific concerns**‡ Overall FACT-B**‡
Follow-up hours worked per week β 95% CI P -value β 95% CI P -value β 95% CI P -value
None Ref - 0.005 Ref - 0.55 Ref - 0.42
1–19 0.47 −0.09, 1.04 0.39 −0.27, 1.06 0.84 −0.79, 2.47
20–34 0.85 0.24, 1.45 0.23 −0.48, 0.95 −0.10 −1.82, 1.61
≥35 1.21 0.49, 1.92 −0.13 −0.97, 0.71 1.28 −0.74, 3.30
Hours per week (continuous) 0.025 0.01, 0.04 0.001 −0.004 −0.02, 0.01 0.67 0.010 −0.03, 0.05 0.65
Employment Change from Baseline to Follow-up (n=1136)
Physical well-being** Social well-being**‡ Emotional well-being**‡
Baseline to follow-up employment pattern β 95% CI P -value β 95% CI P -value β 95% CI P -value
Worked continuously Ref - 0.02 Ref - 0.35 Ref - 0.75
Quit Working −1.00 −1.81, −0.18 0.30 −0.33, 0.93 0.08 −0.39, 0.54
Functional well-being**‡ Breast cancer specific concerns**‡ Overall FACT-B**‡
Baseline to follow-up employment pattern β 95% CI P -value β 95% CI P -value β 95% CI P -value
Worked continuously Ref - 0.01 Ref - 0.54 Ref - 0.52
Quit Working −0.85 −1.45, −0.25 −0.23 −0.96, 0.50 −0.57 −2.29, 1.16
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*

Also adjusted for education level, partner status, annual household income, number of people supported by income, occupation category, difficulty taking time off from work, and job related stress level.

**

Adjusted for baseline physical well-being.

‡

Adjusted for baseline score.

Additional models restricted to 1,136 women examined differences in QOL measures between women who quit working between the baseline interview and the 6-month follow-up (n=347) and those who worked continuously between these two time points (n=789). In the models adjusting for demographic and clinical factors, women who quit working between baseline and the 6-month follow-up had lower scores for PWB (p=0.004), FWB (p=<0.0001), and overall well-being (p=0.002), as well as for BCS (p=0.04), compared to women who worked continuously (Table 4). In the fully-adjusted models, similar but attenuated differences were seen.

For all models, we also stratified by race to examine if the association between employment and quality of life differed by racial/ethnic groups. Results were virtually the same among white women (n=1,415) compared with the entire cohort. Results were also similar, although attenuated, among Hispanic women (n=210). Among Asian women (n=215), results were attenuated in several domains, and an association was no longer seen between physical and social well-being and employment at baseline, nor between breast cancer specific concerns and employment. Among African American women (n=116), the association between functional well-being and employment at follow-up was no longer observed and the association between physical well-being and employment at follow-up became attenuated. Furthermore, African American women who quit working had significantly higher social well-being compared to women who continued working after diagnosis, a pattern that was not seen in the overall cohort (data not shown).

Finally, we examined whether the association between employment and quality of life differed between women of working age and women who continue working past the normal age of retirement by excluding women age 65 y and older from all models. Results remained fundamentally unchanged after adjusting for covariates (data not shown).

DISCUSSION

Several studies have documented changes in work patterns after a breast cancer diagnosis [3, 4, 6–8, 31–33]. In our study, we were able to further examine the potential impact of the amount of time worked and changes in work status on quality of life among women with breast cancer. In this prospective cohort study of 2,013 breast cancer survivors, we found that hours worked per week was related to specific quality of life measures at both the baseline interview and the 6-month follow-up. A primary finding was that women who worked at least some hours tended to fare better in overall well-being as measured by the FACT-B and in several of its subscales compared to women who did not work. These findings remained when analyses were limited to the 1,262 women younger than age 65 y at the time of breast cancer diagnosis.

The finding that physical well-being was positively associated with hours worked per week was not surprising; however, the relation between working and other quality of life domains may help better understand the role of employment at the time of a breast cancer diagnosis. For example, social well-being may be higher in women who are working at the time of their breast cancer diagnosis due to enhanced social support available from colleagues and friends in the workplace. While research in this area is limited, in a study of returning to work after a cancer diagnosis, Kennedy, et al. reported that all participants (n=29) told their employer about their diagnosis and 69% said they received support from coworkers [34]. Another study of 378 breast cancer patients reported that approximately half of the women disclosed their diagnosis to coworkers or their employer [35]. Functional and overall well-being may be higher in women working at the time of diagnosis because their ability to work may signify a sense of normalcy despite the cancer diagnosis. Indeed, Kennedy, et al. reported that 34% of women said that working was a distraction from their illness and helped them return to normal life [34].

Interestingly, we found that women working 1–19 hours per week had significantly higher functional well-being than women who were not working at the time of their diagnosis, while working more than 20 hours per week was not associated with higher functional well-being. It is possible that working less than 20 hours per week provided women with a higher sense of self-efficacy while coping with their diagnosis, but that working more than 20 hours per week became overwhelming when also trying to manage their personal life and cancer treatments. Overall well-being was higher for women in all categories of hours per week compared to women not working at the baseline interview; however, scores were highest in women working 1–19 hours per week. Again, being engaged in some work around the time of diagnosis may be beneficial, while working too many hours may negatively impact quality of life shortly after diagnosis.

Hours worked per week at the 6-month follow-up was related to higher physical and functional well-being at follow-up. Furthermore, women who continued working between the baseline and follow-up interview had higher physical and functional well-being compared to women who quit working during this time. Similar to baseline, we observed a positive association between hours per week and physical well-being at follow-up, and higher functional well-being in women working at least twenty hours per week compared to those who were not working. However, unlike at baseline, functional well-being was not significantly higher for women working 1–19 hours per week at follow-up compared to women who were not working. We suggest that as women with breast cancer progress further into the treatment period, the greater the therapeutic effect of working and the sense of continuing normalcy in life compared to when a women is first confronted with a breast cancer diagnosis.

Higher social well-being was observed among African American women who quit working after diagnosis compared to those who continued to work. This finding was not seen in any other racial group, nor in the overall cohort and may reflect differences between cultures in where women seek social and emotional support. Because the number of African Americans was small in this analysis, the findings are not as stable as for the overall cohort and require confirmation in other minority populations.

Our results support previous findings that continuing work after a breast cancer diagnosis is beneficial in multiple quality of life domains. In a study of 185 breast cancer survivors Bloom, et al. found greater increases in physical well-being among women working at least part time during the five years after diagnosis [12]. Another study of 369 women found lower levels of psychosocial distress and higher levels of physical and mental functioning and quality of life in women who continued working through breast cancer treatment [11]. Finally, in a study of 100 cancer survivors, greater physical and psychological symptoms and fears were reported in women who reduced work by more than four hours per week [36].

Although our findings highlight the potential importance of continuing employment on quality of life for breast cancer survivors, we did not collect reasons for change in employment status, and therefore, we do not know why some women stopped working between the baseline and follow-up interviews while others did not. For example, it is possible that women who stopped working were on sick leave and were planning to return to work. Because at study enrollment, all participants were members of Kaiser Permanente Northern California, an integrated health care delivery system, the sample is representative primarily of breast cancer patients with uniform access to health insurance at the time of their diagnosis. Although we adjusted for physical well-being and baseline quality of life scores in order to reduce the potential for reverse causality of physical well-being affecting the ability to work, we cannot definitively rule out that declines in health and functioning may have driven changes in working status. However, in our study population we were able to examine effects of type of treatment on employment, and this did not appear to explain these findings. For example, hours worked per week did not differ substantially by the type of surgery (lumpectomy/mastectomy). Furthermore, the proportion of women undergoing radiation therapy was higher in all categories of hours worked per week at follow-up than in women not working. If undergoing therapy would be expected to result in decreased employment, this is the opposite of what would be expected (data not shown). To further understand the potential role of reverse causation due to participants with low physical well-being not being able to work, we conducted a sensitivity analysis of our primary results excluding participants in the bottom 10% of physical well-being scores at baseline (n=174). Associations with follow-up FACT-B scores remained essentially unchanged (data not shown), consistent with the fact that reverse by physical functioning is not the primary driver of our findings.

Despite these potential limitations, to our knowledge, this is the largest prospective study to date examining employment and quality of life in breast cancer survivors. Furthermore, since we collected information on a number of clinical and demographic characteristics, we were able to adjust for a comprehensive set of possible confounders, such as stage of disease, treatment type, difficulty taking time off from work, etc. In addition, as data become available from further follow-up intervals, future analyses with this cohort may further examine these questions with more variation from baseline measures, allowing more detailed analysis of change in quality of life. Also of potential interest is exploring the association between employment and prognosis in the cohort, as data on recurrence and survival are being actively collected. Indeed, decreased survival time for women who stopped working after their diagnosis compared with those who continued to work was reported by Waxler-Morrison, et al. in a prospective study of 168 women with breast cancer [13]. Additionally, future analyses examining job characteristics such as the ability to take time off of work for treatment and job-related stress levels may elucidate the contributions of work characteristics versus hours worked per week to quality of life.

In summary, we found in this prospective study of women with breast cancer that working around the time of cancer diagnosis and through the active treatment phase was positively associated with multiple areas of quality of life. For breast cancer survivors who want to continue working through treatment, strategies to help them do so, such as better management of treatment related side-effects and office place support, should be explored. Indeed, one study of 1,490 employed cancer survivors found that low workplace support was associated with lower work ability [9]. A number of government, disability, and cancer groups provide resources about employment and cancer, such as entitlements to cancer patients under the Americans with Disabilities Act [37], managing cancer related issues at work (e.g. who to tell about their diagnosis and what to share) [38], information on potential accommodations for common problems faced by cancer patients in the workplace [39], and how to access legal help for workplace discrimination [40]. Ensuring breast cancer patients are aware of and have access to this information may help them understand all of their rights and options as a working cancer survivor.

Acknowledgments

This work was supported by the National Cancer Institute (grant number R01 CA105274). The authors thank study staff for data collection, processing, and preparation. We thank all Pathways Study participants for their numerous contributions to this study. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies.

Footnotes

CONFLICTS OF INTEREST: none

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