Patients
The data source is a national multicentre cross-sectional follow-up study where all survivors treated with HDT-ASCT for lymphoma in Norway between 1995 and 2008, aged ≥18 years at HDT-ASCT, alive at survey, residing in Norway and currently not undergoing systemic therapy for active malignancy were eligible and invited to participate (n=355, figure 1). The survey, performed in 2012-2013, consisted of a detailed self-reported questionnaire [3, 6], a comprehensive out-patient clinical examination [7], as well as data retrieved from patients’ charts and the clinical quality register for lymphoma at Oslo University Hospital (OUH). For the present study, we include only survivors treated with BEAM (carmustine, etoposide, cytarabine and melphalan), which has been the standard high-dose regimen in Norway since 1995, excluding six survivors treated with total body irradiation (TBI). In Norway, all citizens are entitled to old-age pension from 67 years of age. Respondents who reported receiving old-age pension (n=40) or being students (n=3) at survey are excluded in the present study since the focus here is on work patterns before and after treatment, and these two groups are not considered part of the formal labour force. In general, the survey had a low percentage of missing data.
Treatment
Survivors are categorized according to primary lymphoma entity: Hodgkin lymphoma (HL), indolent non-Hodgkin lymphoma (NHL) and aggressive NHL, number of regimens prior to HDT-ASCT, and whether they experienced relapse after HDT-ASCT or not [3]. Body Mass Index (BMI) is from the clinical examination (kg/m2). We replaced 36 missing values using statistical measures based on self-reported BMI.
Main outcomes
Patients were asked to retrospectively report their employment status when first diagnosed and before HDT-ASCT, as well as their current work situation (at survey), according to eleven categories. We use this information to construct three categories for employment status; fulltime workers (having a fulltime job, being self-employed, or on sick leave), part-time workers (part-time job), or not employed (unemployment insurance, disability insurance, temporary disability insurance, or homemaker).
Patients rated their work ability on a scale of 1 to 10 (where 10 is best), both as they perceived it when answering the survey, and how they remembered it before onset of illness (i.e. when first diagnosed). They were also asked to report the number of weekly work hours at survey and when diagnosed. Their current employment status, current work hours and current work ability are the main outcomes in our analyses.
Based on their current work situation (at the time of survey), we construct a three-part categorical variable for employment; full-time workers, part-time workers and not employed (table 1), and a binary variable for being employed at follow-up (not distinguishing between full and part time) or not.
Withdrawal from work life
We use the three questions that pin their employment status to three points in time (when diagnosed, before HDT-ASCT, and at survey) to create the tree shown in figure 1 to describe withdrawal from work life. We postulate that once you move from a blue box (inclusion) to a red box (exclusion) it is harder to return to a blue one. We refer to this as “withdrawal” from work life, conditioned on inclusion at the time of diagnosis, thus only defined for the left-hand side of the tree. Non-withdrawals are the respondents on the left branch of the left-hand side of the tree (three positives, n=125).
Fig. 1: Flowchart and tree showing who works when
Flowchart of the study and tree showing who works when diagnosed, before treatment and at survey (three time points). Withdrawal is defined as moving from a blue box (yes = inclusion) to a red box (no = exclusion). Deviances in sums due to missing values (8 total).
Pseudo panels for withdrawals and non-withdrawals
We exploit the variation in time from when a patient received HDT-ASCT, until he or she completed the questionnaire. We construct three intervals: 3-7 years, 8-12 years and 13 years or more, of relatively equal size. We thus simulate panel data, creating ‘pseudo panels’ for current work hours and work ability, supplemented with work hours and work ability from when they were first diagnosed.
Table 1: Descriptives
Variable description
|
Total
|
Full-time workers
|
Part-time workers
|
Not employed
|
N
|
|
225
|
113
|
40
|
68
|
Current work situation
(from questionnaire)
|
|
|
|
|
|
Background variables
|
|
Per cent
|
Per cent
|
Female
|
|
39
|
21
|
63
|
54
|
Married (missing n=1)
|
|
74
|
72
|
73
|
75
|
Higher education (missing n=1)
|
|
47
|
51
|
54
|
37
|
|
|
Mean (sd)
|
Mean (sd)
|
Age at diagnosis
|
|
40 (12.9)
|
39 (12.7)
|
40 (13.3)
|
42 (12.3)
|
Age at treatment
|
|
43 (12.9)
|
42 (12.3)
|
43 (14.0)
|
45 (12.6)
|
Age at questionnaire
|
|
52 (11.6)
|
51 (10.7)
|
52 (13.5)
|
54 (11.6)
|
Body Mass Index
|
|
26 (4.9)
|
26 (4.0)
|
26 (6.0)
|
26 (5.6)
|
Time: Diagnose to HDT-ASCT
|
|
2.9 (3.8)
|
2.9 (4.32)
|
3.1 (3.7)
|
2.7 (2.8)
|
Time: HDT-ASCT to survey
|
|
8.7 (3.7)
|
8.7 (3.8)
|
9.1 (3.7)
|
8.5 (3.6)
|
|
|
|
|
|
|
Labour market characteristics
|
|
Per cent
|
Per cent
|
Employed at diagnosis
|
|
85
|
89
|
90
|
75
|
Employed before HDT-ASCT (missing n=4)
|
|
77
|
91
|
79
|
53
|
Employed at survey (missing n=4)
|
|
69
|
100
|
100
|
0
|
|
|
Mean (sd)
|
Mean (sd)
|
Work hours at diagnosis (missing n=32)
|
|
34 (13.3)
|
36 (12.2)
|
34 (11.0)
|
31 (14.6)
|
Work hours if employed now
|
|
21 (18.6)
|
34 (12.6)
|
21 (12.8)
|
0 (0)
|
Work ability at diagnosis (missing n=20)
|
|
8.5 (2.8)
|
9.3 (1.5)
|
8.9 (2.7)
|
6.8 (3.9)
|
Work ability at survey(missing n=26)
|
|
6.1 (3.2)
|
7.9 (2.1)
|
5.8 (2.0)
|
2.8 (2.9)
|
|
|
|
|
|
|
Health-related characteristics
|
|
Per cent
|
Per cent
|
Heart disease (missing n=1)
|
|
9
|
8
|
8
|
12
|
Second cancer
|
|
11
|
7
|
10
|
19
|
Relapse after HDT-ASCT
|
|
21
|
18
|
28
|
24
|
Chronic fatigue (missing n=1)
|
|
33
|
21
|
43
|
49
|
Anxiety
|
|
21
|
12
|
18
|
37
|
Lymphoma diagnose
|
|
|
|
|
|
|
|
27
|
28
|
25
|
26
|
|
|
65
|
65
|
70
|
62
|
|
|
8
|
7
|
5
|
12
|
Treatment lines before HDT-ASCT
|
|
|
|
|
|
|
|
28
|
33
|
23
|
21
|
|
|
59
|
54
|
63
|
65
|
|
|
14
|
13
|
15
|
15
|
Sample descriptives with sociodemographic and health variables and work-related characteristics. Deviancies due to missing values (of the 225 included in the final analyses, four had missing values for current work situation and could therefore not be categorised as full time, part time or not working). HDT-ASCT: High-dose chemotherapy with autologous stem-cell transplantation, sd: Standard deviation. Fulltime workers: Fulltime job (94) Self-employed (18) Sickleave (1). Part-time workers: (40). Not employed: unemployment insurance (2), disability insurance (50), temporary disability insurance (15), homemaker (1).
The term ‘pseudo panel’ refers to the fact that we do not have the opportunity to follow the same patients over time. Instead, we group respondents according to how long it has been from the time they received treatment (HDT-ASCT) until they completed the questionnaire.
Ratios for work hours and work ability
We calculate ratios for work hours as work hours in the withdrawal group divided by work hours amongst those who stayed employed from diagnosis to survey, repeating the calculation for work ability.
Economic loss
We estimate an economic loss from the difference in work hours between the withdrawal group and the non-withdrawals using wage statistics from Statistics Norway (https://www.ssb.no/en/statbank/table/08057). We estimate an expected mean wage for the withdrawal group using monthly earnings (NOK) for 2012, covering all employees, matching them by gender and level of education.
Explanatory variables
We define marriage as being in a paired relationship, and higher education as more than 12 years of education. We construct two binary variables for somatic illnesses; one for having had one or more of three heart diseases (myocardial infarction, angina pectoris or heart failure), and another for second cancers (new cancer diagnosis, other than lymphoma). Chronic fatigue is assessed according to the Fatigue Questionnaire [8], containing 11 items concerning physical (7 items) and mental (4 items) fatigue during the last month. Two additional items cover duration and extent of fatigue. Responses are dichotomised (0 and 1 scored as 0, and 2 and 3 scored as 1), with CF defined as sum score of ≥4 of the dichotomised responses with duration of ≥6 months. Anxiety is derived from the Hospital Anxiety and Depression Scale (HADS), consisting of an anxiety and a depression subscale with seven items each [9]. Each item is scored from 0 (not present) to 3 (highly present), and anxiety caseness is defined as a sum score of ≥8 on the anxiety subscale.
Statistical analyses
We run a multinomial logistic regression, allowing for comparison between more than two groups (table 2). The dependent variable was the categorical three-part variable divided into not working (base outcome), part time (work) and full time (work) at the time of survey. We include six covariates, relaxing the rule of thumb of 10 events per variable in logistic regression [10]. Employment before treatment is our main variable of interest. Gender and age at survey are necessary individual characteristics. We choose second cancers, chronic fatigue, and anxiety based on their significance in the regression models (table 2) and their clinical relevance. Other covariates were tested, but they had limited explanatory power, and were not included in the proceeding analyses.
We run five regression models with the binary variable for employment (i.e. employed or not) at follow-up as the dependent variable, conditioned on being employed at diagnosis (table 3). In model 1, employment before treatment is the sole covariate. We expand the model stepwise, adding new covariates in each step. In model 2, we include the sociodemographic variables gender, relationship status, education and age at survey. In model 3, we add the somatic health variables BMI, heart disease, second cancer and relapse of lymphoma. Next, we add the mental health variables chronic fatigue and anxiety (model 4). In model 5, we add the number of treatment lines before HDT-ASCT and lymphoma type.
We use a standard t-test to test whether the difference in means between withdrawals’ and non-withdrawals’ work hours and work ability at more than 13 years is different from the difference in means at onset. We restrict the test to the patients observed at diagnosis and 13 years or more after treatment. We test the null hypothesis, that there is no difference in means.
We exclude respondents with missing observations from the analyses when the relevant variable enters the equation. Despite a somewhat higher incidence of missing values for work hours and work ability, we choose not to use statistical measures to replace them, considering the risk of manipulating the results too great. We do not have additional information (as we did for BMI), which could be used to estimate missing values, and would therefore have to rely on imputations based on other respondents’ values.
Ethics
The South-East Regional Committee for Medical and Health Research Ethics (REC South East) approved the study, and all participants gave written informed consent.