520354 research-article2014
SJP0010.1177/1403494813520354K. Carlsen et al.Unemployment and breast cancer
Scandinavian Journal of Public Health, 2014; 42: 319–328
Original Article
Unemployment among breast cancer survivors
Kathrine Carlsen1, Marianne Ewertz2, Susanne Oksbjerg Dalton3, Jens Henrik Badsberg1 & Merete Osler1 1Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark, 2Department of Oncology, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Denmark, and 3Survivorship, Danish Cancer Society Research Center, Copenhagen, Denmark
Abstract Aim: Though about 20% of working age breast cancer survivors do not return to work after treatment, few studies have addressed risk factors for unemployment. The majority of studies on occupational consequences of breast cancer focus on non-employment, which is a mixture of sickness absence, unemployment, retirement pensions and other reasons for not working. Unemployment in combination with breast cancer may represent a particular challenge for these women. The aim of the present study is therefore to analyze the risk for unemployment in the years following diagnosis and treatment for breast cancer. Method: This study included 14,750 women diagnosed with breast cancer in Denmark 2001–2009 identified through a population-based clinical database and linked with information from Danish administrative population based registers for information on labour market affiliation, socio-demography and co-morbid conditions. Multivariable analyses were performed by Cox’s proportional hazard models. Results: Two years after treatment, 81% of patients were still part of the work force, 10% of which were unemployed. Increasing duration of unemployment before breast cancer was associated with an adjusted HR = 4.37 (95% CI: 3.90–4.90) for unemployment after breast cancer. Other risk factors for unemployment included low socioeconomic status and demography, while adjuvant therapy did not increase the risk of unemployment. Conclusions: Duration of unemployment before breast cancer was the most important determinant of unemployment after breast cancer treatment. This allows identification of a particularly vulnerable group of patients in need of rehabilitation. Key Words: Breast cancer, return to work, socioeconomic status, treatment, unemployment
Introduction Over the past 30 years, survival after breast cancer has improved substantially [1] leading to an increasing number of breast cancer survivors returning to work after completion of treatment. Surgery and adjuvant treatment such as chemotherapy, radiotherapy and endocrine therapy have all contributed to the improved survival. These treatments can, however, alone or in combination have a negative impact on the daily life and wellbeing of the breast cancer survivors [2,3]. Despite the fact that up to 80% of younger breast cancer survivors usually can continue to work after treatment, late effects such as fatigue, lymph
oedema, arm and shoulder problems, depression and cognitive problems have all been reported by breast cancer survivors in work [4–6]. Occupational concerns have been shown to continue in the years following diagnosis, as maintaining an affiliation to the labour market has been shown to be a challenge for cancer survivors many years after treatment [7–9]. In the majority of studies about cancer and labour market affiliation after diagnosis and treatment, the outcome has been dichotomized to working / not working. This is, however, a simplification of the occupational consequences of a cancer disease as not
Correspondence: Kathrine Carlsen, Research Centre for Prevention and Health, Glostrup University Hospital, Nordre Ringvej, Glostrup 2600, Denmark. E-mail: [email protected] (Accepted 17 December 2013) © 2014 the Nordic Societies of Public Health DOI: 10.1177/1403494813520354
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320 K. Carlsen et al. working can be split into different more or less voluntary schemes. Of those 20% who do not return to work after end of treatment, some have left the labour force due to a pension scheme while others are still part of the labour force but sick-listed or unemployed. In order to identify the mechanisms behind returning to work and labour market participation, it is essential to go into more detail with the different pathways from diagnosis and treatment to labour market participation. One of the pathways, which has not been studied extensively, is unemployment in the years following breast cancer and treatment. Unemployment in itself can be a strain but in combination with cancer it has been shown to have a profound impact on the physical and psychological well-being of the individual [10]. Unemployment after cancer as an explicit outcome has been the focus of very few studies. A Danish register-based cohort study demonstrated that cancer survivors in general had a small but significantly increased risk for unemployment in the years following diagnosis [9]. However, in the same study, women with breast cancer had a non-significantly elevated risk for unemployment (RR: 1.22; 95% CI: 0.92–1.62) compared to an age matched and cancer free control group. In Denmark, the labour market is characterized by a very high turnover rate, with around 1/3 of all employed persons changing job annually and close to 1/5 of the work force being struck by unemployment during a year [11]. The risk for unemployment among both cancer survivors and controls is therefore high over a lifetime and a comparison may lead to an oversimplification of the effect of cancer on unemployment. Risk factors for not working after breast cancer have been found to be associated with health related as well as socioeconomic factors [5,9,12,13] but to our knowledge, health-related and socioeconomic risk factors for unemployment among breast cancer survivors remain to be analyzed jointly in a prospective cohort study. The aim of this Danish prospective cohort study is therefore to analyze the risk for unemployment in the years following diagnosis and treatment for breast cancer and identify health and socioeconomic risk factors.
Danish Breast Cancer Cooperative Group (DBCG) Since 1977, DBCG has performed clinical trials and issued national guidelines for treatment. Data have been collected prospectively on diagnosis, histopathology, surgery, and adjuvant treatment for all newly-diagnosed early-stage breast cancer patients in Denmark [14]. The registry holds information on 95% of all Danish breast cancer patients aged less than 75 years. The database is updated continuously with information on vital status, date of and localisation of first recurrence up to 10 years after diagnosis. The analysis included information on tumour size and number of positive lymph nodes as well as type of surgery (mastectomy, lumpectomy and biopsy). Adjuvant treatment was grouped into no medical treatment, chemotherapy alone, endocrine therapy alone and combinations of chemotherapy and endocrine therapy. Persons not allocated to standard treatment were grouped as other. Adjuvant radiotherapy was dichotomised into yes or no. Statistics Denmark Information on demographic and socioeconomic characteristics derived from the population-based Integrated Database for Labour Market Research (IDA), which has been administrated by Statistics Denmark since 1980 [15]. From IDA we obtained information about country of origin, marital status, education (primary school: < 10 years, vocational and short education: 10–14 years, medium and long education: ≥ 14 years) and job type (manual work, non-manual [office and sale], management and knowledge work). In order to obtain information on disposable income for the family, we also identified partners and their income. Disposable income was calculated as the average of the family income 3 years prior to the year of diagnosis and was deflated according to the 2000 value of the Danish kroner. The National Patient Registry (NPR) This register holds information on all hospitalizations since 1978 and outpatient visits since 1995 in Denmark. In this study, we used information of date of admission and discharge and diagnosis coded according to the International Classifications of Diseases (ICD-10) [16].
Materials and methods This study is based on linking information from five Danish population based registers using the unique personal identification number assigned to all Danish residents.
The Register of Medical Product Statistics (RMPS) Since 1994, every medical product sold on prescription by Danish pharmacies has been registered. From
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Unemployment among breast cancer survivors 321 this register we had information on date for redemption of the prescription and substance classified according to the anatomical-therapeutic-chemical (ATC) system [17]. Comorbidity 5 years preceding the year of diagnosis was obtained from NPR and RMPS. The following chronic co-morbid conditions were included and grouped into somatic comorbidity, cardiovascular disease, chronic obstructive pulmonary disease, diabetes and diseases of the liver, kidney or connective tissue. Depression and schizophrenia were grouped as psychiatric comorbidity. Register-based Evaluation of Marginalization (DREAM) The Danish labour market is characterized by a system with a high degree of economic compensation in case of unemployment or reduced work ability, but also with a high turnover rate. Unemployed persons are warranted economic compensation if they are actively seeking work. During the study period, it was possible to receive a maximum of 4 years of unemployment benefit. After the end of these 4 years, or if a person was not qualified for unemployment benefit (that is, not member of a union) it was possible to receive social income. If a person was unable to work due to illness or disability, it was possible to receive sickness benefit for a maximum of 52 weeks during a period of 2 years or to apply for early retirement if the work-ability was reduced to a level where it was not possible to hold a job. This holds for all Danish citizens independent of job type and insurance status. During the study period, the retirement age was 64 years. The outcome of the study was receipt of unemployment benefit (both full-time and part-time) or social income in the years following diagnosis and treatment. Information about social transfer payments was obtained from the Danish populationbased administrative register DREAM. DREAM covers all residents in Denmark who have received social transfer payments from the state [18] in any given week since 1991 until 2011. Being in work was defined as not receiving any social transfer payments for 6 consecutive weeks. Transfer income was divided into sickness benefit, unemployment benefit (including social income), and permanent withdrawal from the workforce due to disablement or voluntarily between the age of 60 and 64 years. Study population Between 2001 and 2009, 35,625 breast cancer patients were identified from the DBCG. As the outcome under study was unemployment, we restricted the
Figure 1. Flowchart showing the selection of persons from the total database to the final study population.
analysis to women in their working age (18–63 years) at time of diagnosis (N = 19,974) and part of the work force (N = 15,495). Furthermore, we excluded women with missing data on core variables (N = 745) ending up with a study population of 14,750 women diagnosed with breast cancer (Figure 1). Women were followed from time of diagnosis until unemployment, death, early retirement, emigration, age 64 years or end of follow-up (last week of March 2011), whatever came first. This led to a follow-up time of up to 568 weeks and a total of 52,861 person years. Statistical analysis Descriptive analyses by use of chi2 and t tests were conducted in order to examine the characteristics of the sample. Date of diagnosis was defined as date of surgery. The main outcome of the study was receipt of unemployment benefits by persons less than 64 years of age. We created three models for the analysis: The first with mutual adjustments within each group (that is, among the socioeconomic-, demographic-, clinicaland other health-related factors), the second with mutual adjustment for all included factors, and in the third, we stratified this full model for unemployment before diagnosis. Hazard ratios (HRs) for unemployment with 95% confidence intervals (CIs) were estimated on the assumption that any events were generated according to Cox’s proportional hazard model in SAS (The PHREG procedure, SAS version 9.2). The proportional hazard assumption was tested by Schoenfeld residuals and possible interactions were tested by including the interaction term in the fully adjusted regression models. The time scale used was duration from date of diagnosis to unemployment or censoring. This implies
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322 K. Carlsen et al. that the included persons only added person years to the model as long as they were part of the workforce and therefore under risk for the outcome. The socioeconomic, demographic, clinical and other health variables were included as time constant covariates. We also repeated the analyses in strata for unemployment before diagnosis in order to evaluate the impact the included factors had on the future risk for unemployment. Results Table I shows that 21% of the study population had experienced at least one week of unemployment in the two years preceding breast cancer. The median duration of unemployment in this group was 43 weeks. Compared with patients without unemployment before diagnosis, those with unemployment had significantly lower socioeconomic position (education, income, job type), were more often born outside Denmark, younger (and more often premenopausal), more often single, and had more psychological comorbidity. Though significant, only minor differences without a systematic pattern were observed in the clinical characteristics of patients with and without prior unemployment. At time of diagnosis, 20% of the patients were working while 76% were on sick leave (Table II). Two years after treatment, 81% were still part of the work force. Among these, 72% were in work, 10% were unemployed and 13% on sick leave. The remaining 5 % were students, in labour market arrangements, or on other kind of leave. The majority of patients who had left the labour force were under the age of 63 years and thus receiving early retirement pension or anticipatory pension. Among previously unemployed patients, 66% experienced at least 1 week of unemployment after breast cancer while 15% of patients without prior unemployment became unemployed during followup (Table III). Having adjusted for the effects of all factors, the multivariable analysis showed that the major determinants of risk of unemployment after breast cancer were unemployment before diagnosis, low education, low income and manual work. Among patients with unemployment before breast cancer, there was a significant trend (p < 0.0001) of increasing risk of unemployment after breast cancer with increasing duration of unemployment before breast cancer, with a HR = 4.37 (95% CI: 3.90–4.90) for unemployment of more than 79 weeks compared with less than 26 weeks. After adjustment for the socioeconomic factors, the demographic factors (country of birth, age, marital status) and mental comorbidity (HR = 1.27; 95% CI: 1.15–1.40)
remained significant. Most of the clinical characteristics were not significantly associated with the risk of unemployment, but risks estimates below one were observed for adjuvant chemotherapy. Women who were working 6 weeks prior to diagnosis but have experienced unemployment in the preceding 2 years had a 60% (HR = 0.37; 95% CI: 0.33–0.42) reduced risk for unemployment after breast cancer compared to women who were unemployed at least 1 of 6 weeks prior to breast cancer diagnosis (data not shown). The risk factors for unemployment during follow up did, however, not differ between these two subgroups. Discussion This nationwide, prospective cohort study, including close to 15,000 working-age women diagnosed with breast cancer in Denmark 2001–2009, demonstrated that the risk of unemployment after breast cancer was associated mainly with socioeconomic and demographic factors. At least one episode of unemployment during the 2 years prior to diagnosis was the most important risk factor for future unemployment. Considering the substantial beneficial effects of adjuvant chemotherapy and radiotherapy [19,20], it was particularly reassuring that breast cancer treatment did not increase the risk of future unemployment. Different pathways Older age, low socioeconomic position, high demand job, non-supportive work environment, co-morbidities and receiving chemotherapy have been reported as barriers for returning to work among breast cancer survivors [5]. The risk factors for not returning to work may depend on the reason for not working. Like sickness benefit, early retirement pension in Denmark is primarily assigned to people based on an evaluation of their health status whereas unemployment is affected by other factors related to work and society. In the majority of studies conducted so far, these outcomes have been analyzed jointly as nonworking which may mix up the risk factors. In order to elucidate the mechanisms behind the different pathways from sickness absence back to daily life, it is important to shed more light on the risk factors for unemployment after cancer It is, however, important to have in mind that these pathways are mutually exclusive and a decreased risk for unemployment not necessarily means an increased risk for being in work. Exemplified by the fact, that women who are treated with chemotherapy either with or without endocrine treatment were at a
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Unemployment among breast cancer survivors 323 Table I. Characteristics of the study population (N = 14,750) without (N = 11,644) or with (N = 3106) episodes of unemployment in the 2 years prior to breast cancer in Denmark, 2001–2009. Total population N (%) Group 1: Socioeconomic factors Unemployment 2 years before diagnosis 0 weeks 11,644 (79) 1–26 weeks 1363 (9) 27–52 weeks 553 (4) 53–78 weeks 440 (3) 79–104 weeks 750 (5) Highest attained education Primary school 4077 (28) Vocational and short education 6241 (42) Medium and long education 4432 (30) Disposable income (DK) 3683 (25) Lowest quartile 3752 (25) Low–middle quartile High–middle quartile 3665 (25) 3650 (25) Highest quartile Job type Management and knowledge work 2452 (17) Office and sale (non-manual) 7870 (53) 4428 (30) Manual Group 2: Demographic factors Age at diagnosis 18–46 years 3935 (27) 47–52 years 3791 (26) 53–57 years 3743 (25) > 57 years 3281 (22) Country of birth 13,980 (95) Denmark 770 (5) Other Marital status Married / cohabiting 10,367 (70) 4383 (30) Single Year of diagnosis 1551 (11) 2001 1650 (11) 2002 1578 (11) 2003 1636 (11) 2004 1550 (11) 2005 2006 1558 (11) 1560 (11) 2007 1745 (12) 2008 1922 (13) 2009 Group 3: Clinical factors Menopausal status 7289 (49) Premenopausal 7461 (51) Postmenopausal Tumour size 0–10 mm 2616 (18) 11–15 mm 3439 (23) 16–20 mm 2717 (18) 21–30 mm 3326 (23) 31–50 mm 1551 (11) >50 mm 656 (4) 445 (3) Unknown / not relevant No. of positive lymph nodes 0 1–3
6973 (47) 4735 (32)
No episodes of unemployment
At least one episode of unemployment
11,644 (100 %)
– 1363 (44) 553 (18) 440 (14) 750 (24)
P
– < 0.0001 < 0.0001 < 0.0001
2935 (25) 4891 (42) 3818 (33)
1142 (37) 1350 (44) 614 (20)
2374 (20) 2863 (25) 3106 (27) 3301 (28)
1309 (42) 889 (29) 559 (18) 349 (11)
2223 (19) 6616 (57) 2805 (24)
229 (7) 1254 (40) 1623 (52)
2755 (24) 3067 (26) 3035 (26) 2787 (24)
1180 (38) 724 (23) 708 (23) 494 (16)
11,187 (96) 457 (4)
2793 (90) 313 (10)
8443 (73) 3201 (27)
1924 (62) 1182 (38)
1221 (10) 1260 (11) 1232 (11) 1279 (11) 1198 (10) 1219 (10) 1270 (11) 1388 (12) 1577 (14)
330 (11) 390 (13) 346 (11) 357 (11) 352 (11) 339 (11) 290 (9) 357 (11) 345 (11)
5523 (47) 6121 (53)
1766 (57) 1340 (43)
2105 (18) 2721 (23) 2164 (19) 2610 (22) 1182 (10) 508 (4) 354 (3)
511 (16) 718 (23) 553 (18) 716 (23) 369 (12) 148 (5) 91 (3)
< 0.0001 0.04
5503 (47) 3761 (32)
1470 (47) 974 (32)
0.53
< 0.0001 < 0.0001 < 0.0001 0.0004
(Continued)
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324 K. Carlsen et al. Table I. (Continued) Total population N (%) 4–9 >10 Unknown / not relevant Type of surgery Mastectomy Lumpectomy Biopsy only Adjuvant treatment No treatment Only chemotherapy Only endocrine treatment Both chemotherapy and endocrine treatment Unknown (incl. not allocated to protocol) Radiotherapy No Yes Group 4: Other health-related factors Comorbidity (mental) No Yes Comorbidity (physical) No Yes Recurrence No Yes
No episodes of unemployment
P
At least one episode of unemployment
1568 (11) 951 (6) 523 (4)
1235 (11) 731 (6) 414 (4)
333 (11) 220 (7) 109 (4)
6316 (43) 8091 (55) 343 (2)
4915 (42) 6461 (55) 268 (2)
1401 (45) 1630 (52) 75 (2)
2477 (17) 5504 (37) 2819 (19) 2698 (18)
1998 (17) 4268 (37) 2324 (20) 2088 (18)
479 (15) 1236 (40) 495 (16) 610 (20)
1252 (8)
966 (8)
286 (9)
10,738 (73) 4012 (27)
8505 (73) 3139 (27)
2233 (72) 873 (28)
0.20
13,403 (91) 1347 (9)
10,732 (92) 912 (8)
2671 (86) 435 (14)
11,019 (75) 3731 (25)
8686 (75) 2958 (25)
2333 (75) 773 (25)
13 492 (91) 1258 (9)
10 691 (92) 953 (8)
2801 (90) 305 (10)
0.01 < 0.0001
< 0.0001 0.56 0.004
Table II. Labour force participation after breast cancer in Denmark 2001–2009 among women aged 18–63 years (N = 14,750).
Part of the work force In work Unemployed Sick leave Other Not part of the work force Pension < 63 years of age Pension ≥ 63 years of age Dead
1 week after diagnosis N (%)
0.5 year after diagnosis N (%)
1 year after diagnosis N (%)
1.5 year after diagnosis N (%)
2 years after diagnosis N (%)*
14,678 (100) 2872 (20) 487 (3) 11 121 (76) 198 (1) 67 (0)
14,215 (96) 4762 (33) 648 (5) 8565 (60) 240 (2) 461 (3)
13,511 (92) 8015 (59) 930 (7) 4213 (31) 353 (3) 1048 (7)
12,727 (86) 8728 (69) 1161 (9) 2345 (18) 493 (4) 1665 (11)
11,745 (81) 8398 (72) 1180 (10) 1530 (13) 637 (5) 2234 (15)
350 (76) 111 (24) 74 (1)
791 (75) 257 (25) 191 (1)
1263 (76) 402 (24) 358 (2)
1642 (74) 592 (26) 532 (4)
61 (91) 6 (9) 5 (0)
*Does not sum to total due to end of follow-up (N = 239).
lower risk for unemployment compared to women without treatment. As the treatment to a high degree reflects the severity of the disease these women are probably not in work but are assigned to a prolonged sickness benefit or early retirement. Unemployment Unemployment after cancer has been evaluated in few studies. In studies comparing cancer survivors
with a background population the risk for unemployment after breast cancer has been shown to be at the same level in the two groups [7,9]. This is in contrast to a meta-analysis of de Boer et al. [12] who reported that non-employment among breast cancer survivors was 28% higher compared with a control population. In this meta-analysis, all 10 studies concerning breast cancer analyzed the risk for non-employment, which covers a broad spectrum for not working as sickness absence, unemployment, homemakers and being on
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Unemployment among breast cancer survivors 325 Table III. Risk (Hazard Ratio, HR) for unemployment among 14,750 breast cancer patients without (N = 11,644) and with (N = 3106) unemployment before diagnosis aged 18–63 years at diagnosis, Denmark 2001–2009. Number of events: 3769
Group adjusted HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Not unemployed before diagnosis Number of events: 1727
Unemployed 1–104 weeks before diagnosis Number of events: 2042
26 %
15 %
66 %
0.26 (0.24–0.29) 1 1.74 (1.53–1.98) 2.10 (1.84–2.40) 4.37 (3.90–4.90)
–
– 1 1.69 (1.49–1.92) 1.97 (1.72–2.25) 3.74 (3.32–4.21)
1 0.97 (0.90–1.05)
1 0.92 (0.82–1.03)
1 1.02 (0.92–1.13)
0.81 (0.73–0.90)
0.73 (0.64–0.85)
0.90 (0.78–1.04)
1 0.86 (0.79–0.93) 0.79 (0.72–0.87) 0.60 (0.53–0.67)
1 0.86 (0.75–0.97) 0.74 (0.65–0.86) 0.51 (0.43–0.60)
1 0.87 (0.78–0.97) 0.82 (0.71–0.95) 0.78 (0.66–0.93)
0.47 (0.41–0.54)
0.39 (0.32–0.47)
0.68 (0.55–0.83)
0.61 (0.57–0.66)
0.57 (0.51–0.63)
0.64 (0.58–0.71)
1
1
1
1 0.88 (0.80–0.96) 0.92 (0.80–1.04) 0.75 (0.64–0.87)
1 0.84 (0.74–0.95) 0.83 (0.69–0.98) 0.47 (0.37–0.60)
1 0.89 (0.78–1.00) 0.96 (0.79–1.16) 1.05 (0.85–1.31)
1 1.30 (1.15–1.46)
1 1.25 (1.00–1.53)
1 1.37 (1.19–1.58)
1 1.19 (1.11–1.27) 1.00 (0.98–1.02)
1 1.28 (1.16–1.42) 1.03 (1.01–1.05)
1 1.14 (1.04–1.25) 0.98 (0.96–1.00)
1 1.07 (0.95–1.21)
1 1.05 (0.89–1.24)
1 1.09 (0.91–1.30)
1 0.95 (0.86–1.06) 1.05 (0.94–1.17) 1.03 (0.92–1.15) 1.01 (0.88–1.15) 1.09 (0.90–1.31) 0.87 (0.50–1.55)
1 0.84 (0.72–0.99) 1.04 (0.89–1.22) 0.97 (0.83–1.15) 1.05 (0.86–1.28) 0.99 (0.74–1.29) 0.49 (0.22–1.10)
1 1.03 (0.90–1.18) 1.04 (0.89–1.21) 1.05 (0.90–1.22) 1.02 (0.84–1.22) 1.18 (0.91–1.52) 1.31 (0.60–3.08)
% of unemployment events during follow-up
26 %
Group 1: Socioeconomic factors Unemployed before diagnosis (weeks) 0 0.25 (0.23–0.28) 1 1–26 1.76 (1.55–2.00) 27–52 2.13 (1.87–2.43) 53–78 4.38 (3.92–4.90) 79–104 Highest attained education Primary school 1 Vocational and short 0.98 (0.92–1.06) education Medium and long 0.85 (0.76–0.93) education Disposable income (DK) 1 Lowest quartile 0.86 (0.79–0.93) Low–middle quartile High–middle quartile 0.79 (0.72–0.86) Highest quartile 0.58 (0.52–0.65) Job type 0.46 (0.40–0.53) Management and knowledge work Office and sale (non0.61 (0.56–0.65) manual) 1 Manual Group 2: Demographic factors Age 18–46 years 1 47–52 years 0.76 (0.70–0.83) 53–57 years 0.83 (0.77–0.90) > 58 years 0.63 (0.56–0.70) Country of birth 1 Denmark 2.03 (1.81–2.27) Other Marital status Married / cohabiting 1 1.47 (1.37–1.57) Single Year of diagnosis (per 0.97 (0.96–0.98) year) Group 3: Clinical factors Menopausal status 1 Premenopausal 0.89 (0.82–0.96) Postmenopausal Tumour size 0–10 mm 1 11–15 mm 0.98 (0.88–1.09) 16–20 mm 1.10 (0.98–1.22) 21–30 mm 1.11 (0.99–1.24) 31–50 mm 1.22 (1.06–1.39) >50 mm 1.13 (0.94–1.36) 0.80 (0.46–1.44) Not relevant / Unknown
(Continued)
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326 K. Carlsen et al. Table III. (Continued) Number of events: 3769
Group adjusted HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Not unemployed before diagnosis Number of events: 1727
Unemployed 1–104 weeks before diagnosis Number of events: 2042
26 %
15 %
66 %
1 0.93 (0.84–1.02) 0.99 (0.87–1.13) 0.75 (0.63–0.88) 1.16 (0.73–1.75)
1 1.06 (0.92–1.22) 1.16 (0.95–1.40) 1.06 (0.83–1.35) 1.54 (0.82–2.64)
1 0.85 (0.74–0.97) 0.88 (0.73–1.05) 0.61 (0.48–0.76) 0.86 (0.41–1.60)
1 0.98 (0.89–1.08) 0.71 (0.44–1.15)
1 0.90 (0.78–1.04) 0.90 (0.44–1.84)
1 1.06 (0.93–1.20) 0.69 (0.37–1.35)
1 0.97 (0.87–1.09) 0.99 (0.86–1.13)
1 1.14 (0.96–1.35) 0.96 (0.78–1.18)
1 0.82 (0.70–0.96) 1.01 (0.84–1.21)
0.87 (0.77–0.98)
1.12 (0.93–1.34)
0.70 (0.59–0.83)
0.94 (0.80–1.11)
0.99 (0.77–1.27)
0.86 (0.68–1.07)
1 0.98 (0.87–1.12)
1 0.88 (0.73–1.07)
1 1.04 (0.87–1.24)
1 1.27 (1.15–1.40)
1 1.54 (1.31–1.79)
1 1.14 (1.00–1.29)
1 0.96 (0.89–1.04)
1 0.97 (0.86–1.09)
1 0.92 (0.83–1.03)
1 0.89 (0.79–1.00)
1 0.81 (0.66–0.97)
1 0.94 (0.80–1.09)
% of unemployment events during follow-up
26 %
No. of positive lymph nodes 1 0 0.93 (0.85–1.03) 1–3 4–9 0.96 (0.84–1.10) >10 0.88 (0.74–1.03) Not relevant / Unknown 1.13 (0.71–1.71) Type of surgery Mastectomy 1 0.94 (0.86–1.04) Lumpectomy 0.93 (0.58–1.52) Biopsy only Adjuvant treatment No treatment 1 Only Chemotherapy 1.05 (0.94–1.18) 1.05 (0.91–1.19) Only endocrine treatment 1.04 (0.92–1.18) Both chemotherapy and endocrine treatment 1.07 (0.91–1.25) Other / Unknown Radiotherapy 1 No 0.95 (0.84–1.08) Yes Group 4: Other health-related factors Comorbidity (mental) 1 No 1.72 (1.56–1.89) Yes Comorbidity (physical) No 1 0.96 (0.89–1.04) Yes Recurrence 1 No 1.00 (0.90–1.13) Yes Numbers in bold: p
SJP0010.1177/1403494813520354K. Carlsen et al.Unemployment and breast cancer
Scandinavian Journal of Public Health, 2014; 42: 319–328
Original Article
Unemployment among breast cancer survivors
Kathrine Carlsen1, Marianne Ewertz2, Susanne Oksbjerg Dalton3, Jens Henrik Badsberg1 & Merete Osler1 1Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark, 2Department of Oncology, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Denmark, and 3Survivorship, Danish Cancer Society Research Center, Copenhagen, Denmark
Abstract Aim: Though about 20% of working age breast cancer survivors do not return to work after treatment, few studies have addressed risk factors for unemployment. The majority of studies on occupational consequences of breast cancer focus on non-employment, which is a mixture of sickness absence, unemployment, retirement pensions and other reasons for not working. Unemployment in combination with breast cancer may represent a particular challenge for these women. The aim of the present study is therefore to analyze the risk for unemployment in the years following diagnosis and treatment for breast cancer. Method: This study included 14,750 women diagnosed with breast cancer in Denmark 2001–2009 identified through a population-based clinical database and linked with information from Danish administrative population based registers for information on labour market affiliation, socio-demography and co-morbid conditions. Multivariable analyses were performed by Cox’s proportional hazard models. Results: Two years after treatment, 81% of patients were still part of the work force, 10% of which were unemployed. Increasing duration of unemployment before breast cancer was associated with an adjusted HR = 4.37 (95% CI: 3.90–4.90) for unemployment after breast cancer. Other risk factors for unemployment included low socioeconomic status and demography, while adjuvant therapy did not increase the risk of unemployment. Conclusions: Duration of unemployment before breast cancer was the most important determinant of unemployment after breast cancer treatment. This allows identification of a particularly vulnerable group of patients in need of rehabilitation. Key Words: Breast cancer, return to work, socioeconomic status, treatment, unemployment
Introduction Over the past 30 years, survival after breast cancer has improved substantially [1] leading to an increasing number of breast cancer survivors returning to work after completion of treatment. Surgery and adjuvant treatment such as chemotherapy, radiotherapy and endocrine therapy have all contributed to the improved survival. These treatments can, however, alone or in combination have a negative impact on the daily life and wellbeing of the breast cancer survivors [2,3]. Despite the fact that up to 80% of younger breast cancer survivors usually can continue to work after treatment, late effects such as fatigue, lymph
oedema, arm and shoulder problems, depression and cognitive problems have all been reported by breast cancer survivors in work [4–6]. Occupational concerns have been shown to continue in the years following diagnosis, as maintaining an affiliation to the labour market has been shown to be a challenge for cancer survivors many years after treatment [7–9]. In the majority of studies about cancer and labour market affiliation after diagnosis and treatment, the outcome has been dichotomized to working / not working. This is, however, a simplification of the occupational consequences of a cancer disease as not
Correspondence: Kathrine Carlsen, Research Centre for Prevention and Health, Glostrup University Hospital, Nordre Ringvej, Glostrup 2600, Denmark. E-mail: [email protected] (Accepted 17 December 2013) © 2014 the Nordic Societies of Public Health DOI: 10.1177/1403494813520354
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320 K. Carlsen et al. working can be split into different more or less voluntary schemes. Of those 20% who do not return to work after end of treatment, some have left the labour force due to a pension scheme while others are still part of the labour force but sick-listed or unemployed. In order to identify the mechanisms behind returning to work and labour market participation, it is essential to go into more detail with the different pathways from diagnosis and treatment to labour market participation. One of the pathways, which has not been studied extensively, is unemployment in the years following breast cancer and treatment. Unemployment in itself can be a strain but in combination with cancer it has been shown to have a profound impact on the physical and psychological well-being of the individual [10]. Unemployment after cancer as an explicit outcome has been the focus of very few studies. A Danish register-based cohort study demonstrated that cancer survivors in general had a small but significantly increased risk for unemployment in the years following diagnosis [9]. However, in the same study, women with breast cancer had a non-significantly elevated risk for unemployment (RR: 1.22; 95% CI: 0.92–1.62) compared to an age matched and cancer free control group. In Denmark, the labour market is characterized by a very high turnover rate, with around 1/3 of all employed persons changing job annually and close to 1/5 of the work force being struck by unemployment during a year [11]. The risk for unemployment among both cancer survivors and controls is therefore high over a lifetime and a comparison may lead to an oversimplification of the effect of cancer on unemployment. Risk factors for not working after breast cancer have been found to be associated with health related as well as socioeconomic factors [5,9,12,13] but to our knowledge, health-related and socioeconomic risk factors for unemployment among breast cancer survivors remain to be analyzed jointly in a prospective cohort study. The aim of this Danish prospective cohort study is therefore to analyze the risk for unemployment in the years following diagnosis and treatment for breast cancer and identify health and socioeconomic risk factors.
Danish Breast Cancer Cooperative Group (DBCG) Since 1977, DBCG has performed clinical trials and issued national guidelines for treatment. Data have been collected prospectively on diagnosis, histopathology, surgery, and adjuvant treatment for all newly-diagnosed early-stage breast cancer patients in Denmark [14]. The registry holds information on 95% of all Danish breast cancer patients aged less than 75 years. The database is updated continuously with information on vital status, date of and localisation of first recurrence up to 10 years after diagnosis. The analysis included information on tumour size and number of positive lymph nodes as well as type of surgery (mastectomy, lumpectomy and biopsy). Adjuvant treatment was grouped into no medical treatment, chemotherapy alone, endocrine therapy alone and combinations of chemotherapy and endocrine therapy. Persons not allocated to standard treatment were grouped as other. Adjuvant radiotherapy was dichotomised into yes or no. Statistics Denmark Information on demographic and socioeconomic characteristics derived from the population-based Integrated Database for Labour Market Research (IDA), which has been administrated by Statistics Denmark since 1980 [15]. From IDA we obtained information about country of origin, marital status, education (primary school: < 10 years, vocational and short education: 10–14 years, medium and long education: ≥ 14 years) and job type (manual work, non-manual [office and sale], management and knowledge work). In order to obtain information on disposable income for the family, we also identified partners and their income. Disposable income was calculated as the average of the family income 3 years prior to the year of diagnosis and was deflated according to the 2000 value of the Danish kroner. The National Patient Registry (NPR) This register holds information on all hospitalizations since 1978 and outpatient visits since 1995 in Denmark. In this study, we used information of date of admission and discharge and diagnosis coded according to the International Classifications of Diseases (ICD-10) [16].
Materials and methods This study is based on linking information from five Danish population based registers using the unique personal identification number assigned to all Danish residents.
The Register of Medical Product Statistics (RMPS) Since 1994, every medical product sold on prescription by Danish pharmacies has been registered. From
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Unemployment among breast cancer survivors 321 this register we had information on date for redemption of the prescription and substance classified according to the anatomical-therapeutic-chemical (ATC) system [17]. Comorbidity 5 years preceding the year of diagnosis was obtained from NPR and RMPS. The following chronic co-morbid conditions were included and grouped into somatic comorbidity, cardiovascular disease, chronic obstructive pulmonary disease, diabetes and diseases of the liver, kidney or connective tissue. Depression and schizophrenia were grouped as psychiatric comorbidity. Register-based Evaluation of Marginalization (DREAM) The Danish labour market is characterized by a system with a high degree of economic compensation in case of unemployment or reduced work ability, but also with a high turnover rate. Unemployed persons are warranted economic compensation if they are actively seeking work. During the study period, it was possible to receive a maximum of 4 years of unemployment benefit. After the end of these 4 years, or if a person was not qualified for unemployment benefit (that is, not member of a union) it was possible to receive social income. If a person was unable to work due to illness or disability, it was possible to receive sickness benefit for a maximum of 52 weeks during a period of 2 years or to apply for early retirement if the work-ability was reduced to a level where it was not possible to hold a job. This holds for all Danish citizens independent of job type and insurance status. During the study period, the retirement age was 64 years. The outcome of the study was receipt of unemployment benefit (both full-time and part-time) or social income in the years following diagnosis and treatment. Information about social transfer payments was obtained from the Danish populationbased administrative register DREAM. DREAM covers all residents in Denmark who have received social transfer payments from the state [18] in any given week since 1991 until 2011. Being in work was defined as not receiving any social transfer payments for 6 consecutive weeks. Transfer income was divided into sickness benefit, unemployment benefit (including social income), and permanent withdrawal from the workforce due to disablement or voluntarily between the age of 60 and 64 years. Study population Between 2001 and 2009, 35,625 breast cancer patients were identified from the DBCG. As the outcome under study was unemployment, we restricted the
Figure 1. Flowchart showing the selection of persons from the total database to the final study population.
analysis to women in their working age (18–63 years) at time of diagnosis (N = 19,974) and part of the work force (N = 15,495). Furthermore, we excluded women with missing data on core variables (N = 745) ending up with a study population of 14,750 women diagnosed with breast cancer (Figure 1). Women were followed from time of diagnosis until unemployment, death, early retirement, emigration, age 64 years or end of follow-up (last week of March 2011), whatever came first. This led to a follow-up time of up to 568 weeks and a total of 52,861 person years. Statistical analysis Descriptive analyses by use of chi2 and t tests were conducted in order to examine the characteristics of the sample. Date of diagnosis was defined as date of surgery. The main outcome of the study was receipt of unemployment benefits by persons less than 64 years of age. We created three models for the analysis: The first with mutual adjustments within each group (that is, among the socioeconomic-, demographic-, clinicaland other health-related factors), the second with mutual adjustment for all included factors, and in the third, we stratified this full model for unemployment before diagnosis. Hazard ratios (HRs) for unemployment with 95% confidence intervals (CIs) were estimated on the assumption that any events were generated according to Cox’s proportional hazard model in SAS (The PHREG procedure, SAS version 9.2). The proportional hazard assumption was tested by Schoenfeld residuals and possible interactions were tested by including the interaction term in the fully adjusted regression models. The time scale used was duration from date of diagnosis to unemployment or censoring. This implies
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322 K. Carlsen et al. that the included persons only added person years to the model as long as they were part of the workforce and therefore under risk for the outcome. The socioeconomic, demographic, clinical and other health variables were included as time constant covariates. We also repeated the analyses in strata for unemployment before diagnosis in order to evaluate the impact the included factors had on the future risk for unemployment. Results Table I shows that 21% of the study population had experienced at least one week of unemployment in the two years preceding breast cancer. The median duration of unemployment in this group was 43 weeks. Compared with patients without unemployment before diagnosis, those with unemployment had significantly lower socioeconomic position (education, income, job type), were more often born outside Denmark, younger (and more often premenopausal), more often single, and had more psychological comorbidity. Though significant, only minor differences without a systematic pattern were observed in the clinical characteristics of patients with and without prior unemployment. At time of diagnosis, 20% of the patients were working while 76% were on sick leave (Table II). Two years after treatment, 81% were still part of the work force. Among these, 72% were in work, 10% were unemployed and 13% on sick leave. The remaining 5 % were students, in labour market arrangements, or on other kind of leave. The majority of patients who had left the labour force were under the age of 63 years and thus receiving early retirement pension or anticipatory pension. Among previously unemployed patients, 66% experienced at least 1 week of unemployment after breast cancer while 15% of patients without prior unemployment became unemployed during followup (Table III). Having adjusted for the effects of all factors, the multivariable analysis showed that the major determinants of risk of unemployment after breast cancer were unemployment before diagnosis, low education, low income and manual work. Among patients with unemployment before breast cancer, there was a significant trend (p < 0.0001) of increasing risk of unemployment after breast cancer with increasing duration of unemployment before breast cancer, with a HR = 4.37 (95% CI: 3.90–4.90) for unemployment of more than 79 weeks compared with less than 26 weeks. After adjustment for the socioeconomic factors, the demographic factors (country of birth, age, marital status) and mental comorbidity (HR = 1.27; 95% CI: 1.15–1.40)
remained significant. Most of the clinical characteristics were not significantly associated with the risk of unemployment, but risks estimates below one were observed for adjuvant chemotherapy. Women who were working 6 weeks prior to diagnosis but have experienced unemployment in the preceding 2 years had a 60% (HR = 0.37; 95% CI: 0.33–0.42) reduced risk for unemployment after breast cancer compared to women who were unemployed at least 1 of 6 weeks prior to breast cancer diagnosis (data not shown). The risk factors for unemployment during follow up did, however, not differ between these two subgroups. Discussion This nationwide, prospective cohort study, including close to 15,000 working-age women diagnosed with breast cancer in Denmark 2001–2009, demonstrated that the risk of unemployment after breast cancer was associated mainly with socioeconomic and demographic factors. At least one episode of unemployment during the 2 years prior to diagnosis was the most important risk factor for future unemployment. Considering the substantial beneficial effects of adjuvant chemotherapy and radiotherapy [19,20], it was particularly reassuring that breast cancer treatment did not increase the risk of future unemployment. Different pathways Older age, low socioeconomic position, high demand job, non-supportive work environment, co-morbidities and receiving chemotherapy have been reported as barriers for returning to work among breast cancer survivors [5]. The risk factors for not returning to work may depend on the reason for not working. Like sickness benefit, early retirement pension in Denmark is primarily assigned to people based on an evaluation of their health status whereas unemployment is affected by other factors related to work and society. In the majority of studies conducted so far, these outcomes have been analyzed jointly as nonworking which may mix up the risk factors. In order to elucidate the mechanisms behind the different pathways from sickness absence back to daily life, it is important to shed more light on the risk factors for unemployment after cancer It is, however, important to have in mind that these pathways are mutually exclusive and a decreased risk for unemployment not necessarily means an increased risk for being in work. Exemplified by the fact, that women who are treated with chemotherapy either with or without endocrine treatment were at a
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Unemployment among breast cancer survivors 323 Table I. Characteristics of the study population (N = 14,750) without (N = 11,644) or with (N = 3106) episodes of unemployment in the 2 years prior to breast cancer in Denmark, 2001–2009. Total population N (%) Group 1: Socioeconomic factors Unemployment 2 years before diagnosis 0 weeks 11,644 (79) 1–26 weeks 1363 (9) 27–52 weeks 553 (4) 53–78 weeks 440 (3) 79–104 weeks 750 (5) Highest attained education Primary school 4077 (28) Vocational and short education 6241 (42) Medium and long education 4432 (30) Disposable income (DK) 3683 (25) Lowest quartile 3752 (25) Low–middle quartile High–middle quartile 3665 (25) 3650 (25) Highest quartile Job type Management and knowledge work 2452 (17) Office and sale (non-manual) 7870 (53) 4428 (30) Manual Group 2: Demographic factors Age at diagnosis 18–46 years 3935 (27) 47–52 years 3791 (26) 53–57 years 3743 (25) > 57 years 3281 (22) Country of birth 13,980 (95) Denmark 770 (5) Other Marital status Married / cohabiting 10,367 (70) 4383 (30) Single Year of diagnosis 1551 (11) 2001 1650 (11) 2002 1578 (11) 2003 1636 (11) 2004 1550 (11) 2005 2006 1558 (11) 1560 (11) 2007 1745 (12) 2008 1922 (13) 2009 Group 3: Clinical factors Menopausal status 7289 (49) Premenopausal 7461 (51) Postmenopausal Tumour size 0–10 mm 2616 (18) 11–15 mm 3439 (23) 16–20 mm 2717 (18) 21–30 mm 3326 (23) 31–50 mm 1551 (11) >50 mm 656 (4) 445 (3) Unknown / not relevant No. of positive lymph nodes 0 1–3
6973 (47) 4735 (32)
No episodes of unemployment
At least one episode of unemployment
11,644 (100 %)
– 1363 (44) 553 (18) 440 (14) 750 (24)
P
– < 0.0001 < 0.0001 < 0.0001
2935 (25) 4891 (42) 3818 (33)
1142 (37) 1350 (44) 614 (20)
2374 (20) 2863 (25) 3106 (27) 3301 (28)
1309 (42) 889 (29) 559 (18) 349 (11)
2223 (19) 6616 (57) 2805 (24)
229 (7) 1254 (40) 1623 (52)
2755 (24) 3067 (26) 3035 (26) 2787 (24)
1180 (38) 724 (23) 708 (23) 494 (16)
11,187 (96) 457 (4)
2793 (90) 313 (10)
8443 (73) 3201 (27)
1924 (62) 1182 (38)
1221 (10) 1260 (11) 1232 (11) 1279 (11) 1198 (10) 1219 (10) 1270 (11) 1388 (12) 1577 (14)
330 (11) 390 (13) 346 (11) 357 (11) 352 (11) 339 (11) 290 (9) 357 (11) 345 (11)
5523 (47) 6121 (53)
1766 (57) 1340 (43)
2105 (18) 2721 (23) 2164 (19) 2610 (22) 1182 (10) 508 (4) 354 (3)
511 (16) 718 (23) 553 (18) 716 (23) 369 (12) 148 (5) 91 (3)
< 0.0001 0.04
5503 (47) 3761 (32)
1470 (47) 974 (32)
0.53
< 0.0001 < 0.0001 < 0.0001 0.0004
(Continued)
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324 K. Carlsen et al. Table I. (Continued) Total population N (%) 4–9 >10 Unknown / not relevant Type of surgery Mastectomy Lumpectomy Biopsy only Adjuvant treatment No treatment Only chemotherapy Only endocrine treatment Both chemotherapy and endocrine treatment Unknown (incl. not allocated to protocol) Radiotherapy No Yes Group 4: Other health-related factors Comorbidity (mental) No Yes Comorbidity (physical) No Yes Recurrence No Yes
No episodes of unemployment
P
At least one episode of unemployment
1568 (11) 951 (6) 523 (4)
1235 (11) 731 (6) 414 (4)
333 (11) 220 (7) 109 (4)
6316 (43) 8091 (55) 343 (2)
4915 (42) 6461 (55) 268 (2)
1401 (45) 1630 (52) 75 (2)
2477 (17) 5504 (37) 2819 (19) 2698 (18)
1998 (17) 4268 (37) 2324 (20) 2088 (18)
479 (15) 1236 (40) 495 (16) 610 (20)
1252 (8)
966 (8)
286 (9)
10,738 (73) 4012 (27)
8505 (73) 3139 (27)
2233 (72) 873 (28)
0.20
13,403 (91) 1347 (9)
10,732 (92) 912 (8)
2671 (86) 435 (14)
11,019 (75) 3731 (25)
8686 (75) 2958 (25)
2333 (75) 773 (25)
13 492 (91) 1258 (9)
10 691 (92) 953 (8)
2801 (90) 305 (10)
0.01 < 0.0001
< 0.0001 0.56 0.004
Table II. Labour force participation after breast cancer in Denmark 2001–2009 among women aged 18–63 years (N = 14,750).
Part of the work force In work Unemployed Sick leave Other Not part of the work force Pension < 63 years of age Pension ≥ 63 years of age Dead
1 week after diagnosis N (%)
0.5 year after diagnosis N (%)
1 year after diagnosis N (%)
1.5 year after diagnosis N (%)
2 years after diagnosis N (%)*
14,678 (100) 2872 (20) 487 (3) 11 121 (76) 198 (1) 67 (0)
14,215 (96) 4762 (33) 648 (5) 8565 (60) 240 (2) 461 (3)
13,511 (92) 8015 (59) 930 (7) 4213 (31) 353 (3) 1048 (7)
12,727 (86) 8728 (69) 1161 (9) 2345 (18) 493 (4) 1665 (11)
11,745 (81) 8398 (72) 1180 (10) 1530 (13) 637 (5) 2234 (15)
350 (76) 111 (24) 74 (1)
791 (75) 257 (25) 191 (1)
1263 (76) 402 (24) 358 (2)
1642 (74) 592 (26) 532 (4)
61 (91) 6 (9) 5 (0)
*Does not sum to total due to end of follow-up (N = 239).
lower risk for unemployment compared to women without treatment. As the treatment to a high degree reflects the severity of the disease these women are probably not in work but are assigned to a prolonged sickness benefit or early retirement. Unemployment Unemployment after cancer has been evaluated in few studies. In studies comparing cancer survivors
with a background population the risk for unemployment after breast cancer has been shown to be at the same level in the two groups [7,9]. This is in contrast to a meta-analysis of de Boer et al. [12] who reported that non-employment among breast cancer survivors was 28% higher compared with a control population. In this meta-analysis, all 10 studies concerning breast cancer analyzed the risk for non-employment, which covers a broad spectrum for not working as sickness absence, unemployment, homemakers and being on
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Unemployment among breast cancer survivors 325 Table III. Risk (Hazard Ratio, HR) for unemployment among 14,750 breast cancer patients without (N = 11,644) and with (N = 3106) unemployment before diagnosis aged 18–63 years at diagnosis, Denmark 2001–2009. Number of events: 3769
Group adjusted HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Not unemployed before diagnosis Number of events: 1727
Unemployed 1–104 weeks before diagnosis Number of events: 2042
26 %
15 %
66 %
0.26 (0.24–0.29) 1 1.74 (1.53–1.98) 2.10 (1.84–2.40) 4.37 (3.90–4.90)
–
– 1 1.69 (1.49–1.92) 1.97 (1.72–2.25) 3.74 (3.32–4.21)
1 0.97 (0.90–1.05)
1 0.92 (0.82–1.03)
1 1.02 (0.92–1.13)
0.81 (0.73–0.90)
0.73 (0.64–0.85)
0.90 (0.78–1.04)
1 0.86 (0.79–0.93) 0.79 (0.72–0.87) 0.60 (0.53–0.67)
1 0.86 (0.75–0.97) 0.74 (0.65–0.86) 0.51 (0.43–0.60)
1 0.87 (0.78–0.97) 0.82 (0.71–0.95) 0.78 (0.66–0.93)
0.47 (0.41–0.54)
0.39 (0.32–0.47)
0.68 (0.55–0.83)
0.61 (0.57–0.66)
0.57 (0.51–0.63)
0.64 (0.58–0.71)
1
1
1
1 0.88 (0.80–0.96) 0.92 (0.80–1.04) 0.75 (0.64–0.87)
1 0.84 (0.74–0.95) 0.83 (0.69–0.98) 0.47 (0.37–0.60)
1 0.89 (0.78–1.00) 0.96 (0.79–1.16) 1.05 (0.85–1.31)
1 1.30 (1.15–1.46)
1 1.25 (1.00–1.53)
1 1.37 (1.19–1.58)
1 1.19 (1.11–1.27) 1.00 (0.98–1.02)
1 1.28 (1.16–1.42) 1.03 (1.01–1.05)
1 1.14 (1.04–1.25) 0.98 (0.96–1.00)
1 1.07 (0.95–1.21)
1 1.05 (0.89–1.24)
1 1.09 (0.91–1.30)
1 0.95 (0.86–1.06) 1.05 (0.94–1.17) 1.03 (0.92–1.15) 1.01 (0.88–1.15) 1.09 (0.90–1.31) 0.87 (0.50–1.55)
1 0.84 (0.72–0.99) 1.04 (0.89–1.22) 0.97 (0.83–1.15) 1.05 (0.86–1.28) 0.99 (0.74–1.29) 0.49 (0.22–1.10)
1 1.03 (0.90–1.18) 1.04 (0.89–1.21) 1.05 (0.90–1.22) 1.02 (0.84–1.22) 1.18 (0.91–1.52) 1.31 (0.60–3.08)
% of unemployment events during follow-up
26 %
Group 1: Socioeconomic factors Unemployed before diagnosis (weeks) 0 0.25 (0.23–0.28) 1 1–26 1.76 (1.55–2.00) 27–52 2.13 (1.87–2.43) 53–78 4.38 (3.92–4.90) 79–104 Highest attained education Primary school 1 Vocational and short 0.98 (0.92–1.06) education Medium and long 0.85 (0.76–0.93) education Disposable income (DK) 1 Lowest quartile 0.86 (0.79–0.93) Low–middle quartile High–middle quartile 0.79 (0.72–0.86) Highest quartile 0.58 (0.52–0.65) Job type 0.46 (0.40–0.53) Management and knowledge work Office and sale (non0.61 (0.56–0.65) manual) 1 Manual Group 2: Demographic factors Age 18–46 years 1 47–52 years 0.76 (0.70–0.83) 53–57 years 0.83 (0.77–0.90) > 58 years 0.63 (0.56–0.70) Country of birth 1 Denmark 2.03 (1.81–2.27) Other Marital status Married / cohabiting 1 1.47 (1.37–1.57) Single Year of diagnosis (per 0.97 (0.96–0.98) year) Group 3: Clinical factors Menopausal status 1 Premenopausal 0.89 (0.82–0.96) Postmenopausal Tumour size 0–10 mm 1 11–15 mm 0.98 (0.88–1.09) 16–20 mm 1.10 (0.98–1.22) 21–30 mm 1.11 (0.99–1.24) 31–50 mm 1.22 (1.06–1.39) >50 mm 1.13 (0.94–1.36) 0.80 (0.46–1.44) Not relevant / Unknown
(Continued)
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326 K. Carlsen et al. Table III. (Continued) Number of events: 3769
Group adjusted HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Adjusted for all four groups HR (95% CI)
Not unemployed before diagnosis Number of events: 1727
Unemployed 1–104 weeks before diagnosis Number of events: 2042
26 %
15 %
66 %
1 0.93 (0.84–1.02) 0.99 (0.87–1.13) 0.75 (0.63–0.88) 1.16 (0.73–1.75)
1 1.06 (0.92–1.22) 1.16 (0.95–1.40) 1.06 (0.83–1.35) 1.54 (0.82–2.64)
1 0.85 (0.74–0.97) 0.88 (0.73–1.05) 0.61 (0.48–0.76) 0.86 (0.41–1.60)
1 0.98 (0.89–1.08) 0.71 (0.44–1.15)
1 0.90 (0.78–1.04) 0.90 (0.44–1.84)
1 1.06 (0.93–1.20) 0.69 (0.37–1.35)
1 0.97 (0.87–1.09) 0.99 (0.86–1.13)
1 1.14 (0.96–1.35) 0.96 (0.78–1.18)
1 0.82 (0.70–0.96) 1.01 (0.84–1.21)
0.87 (0.77–0.98)
1.12 (0.93–1.34)
0.70 (0.59–0.83)
0.94 (0.80–1.11)
0.99 (0.77–1.27)
0.86 (0.68–1.07)
1 0.98 (0.87–1.12)
1 0.88 (0.73–1.07)
1 1.04 (0.87–1.24)
1 1.27 (1.15–1.40)
1 1.54 (1.31–1.79)
1 1.14 (1.00–1.29)
1 0.96 (0.89–1.04)
1 0.97 (0.86–1.09)
1 0.92 (0.83–1.03)
1 0.89 (0.79–1.00)
1 0.81 (0.66–0.97)
1 0.94 (0.80–1.09)
% of unemployment events during follow-up
26 %
No. of positive lymph nodes 1 0 0.93 (0.85–1.03) 1–3 4–9 0.96 (0.84–1.10) >10 0.88 (0.74–1.03) Not relevant / Unknown 1.13 (0.71–1.71) Type of surgery Mastectomy 1 0.94 (0.86–1.04) Lumpectomy 0.93 (0.58–1.52) Biopsy only Adjuvant treatment No treatment 1 Only Chemotherapy 1.05 (0.94–1.18) 1.05 (0.91–1.19) Only endocrine treatment 1.04 (0.92–1.18) Both chemotherapy and endocrine treatment 1.07 (0.91–1.25) Other / Unknown Radiotherapy 1 No 0.95 (0.84–1.08) Yes Group 4: Other health-related factors Comorbidity (mental) 1 No 1.72 (1.56–1.89) Yes Comorbidity (physical) No 1 0.96 (0.89–1.04) Yes Recurrence 1 No 1.00 (0.90–1.13) Yes Numbers in bold: p
