Beskæftigelsesudvalget 2020-21
BEU Alm.del Bilag 34
Offentligt
2274439_0001.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
ORIGINAL RESEARCH
Associations between physical and psychosocial work
environment factors and sickness absence incidence
depend on the lengths of the sickness absence
episodes: a prospective study of 27 678
Danish employees
Sannie Vester Thorsen ,
1
Mari-Ann Flyvholm ,
1
Jacob Pedersen
Ute Bültmann ,
1,2
Lars L Andersen ,
3
Jakob Bue Bjorner
1,4,5
►
Additional material is
published online only. To view
please visit the journal online
(http://dx.doi.org/10.1136/
oemed-2020-106554).
,
1
ABSTRACT
Objectives
This study examined if the association
between work environment factors and sickness absence
(SA) depended on the inclusion or exclusion of short-
term SA episodes.
1
Analysis and Data, National
Methods
We linked the ’Work Environment and Health
Research Centre for the Working
in Denmark’ survey with the ’Danish Register of Work
Environment, Copenhagen,
Absences’ (n=27 678). Using covariate adjusted Cox
Denmark
2
regression, we examined the associations between work
Health Sciences, Community
and Occupational Medicine,
environment factors and SA by changing the cut-off
University of Groningen,
points for the length of the SA episodes, for example,
Groningen, Netherlands
episodes ≥1 day, ≥6 days and ≥21 days. We examined
3
Musculoskeletal disorders and
three physical work environment factors: ’Back bend
physical workloads, National
Research Centre for the Working
or twisted’, ’Lifting or carrying’, ’Wet hands’ and three
Environment, Copenhagen,
psychosocial work environment factors: ’Poor influence’,
Denmark
’Role conflicts’ and ’Bullying’.
4
QualityMetric, Johnston, Rhode
Results
’Back bend or twisted’ and ’Lifting or carrying’
Island, USA
5
had small significant HRs for SA episodes ≥1 day and
Department of Public Health,
University of Copenhagen,
large and highly significant HRs for SA episodes ≥6 days
Copenhagen, Denmark
and ≥21 days. ’Wet hands’ had small significant HRs
for SA episodes ≥1 day for both sexes and large and
Correspondence to
highly significant HR for ≥6 days for women. HRs of all
Dr Sannie Vester Thorsen,
Analysis and Data, The National
three psychosocial factors were highly significant for SA
Research Centre of the Working
episodes ≥1 day and ≥6 days for both sexes, and ’Poor
Environment, Copenhagen DK-
influence’ and ’Role conflicts’ were significant for SA
2100, Denmark; [email protected]
episodes ≥21 days for women.
Conclusions
The physical work factors had higher
Received 18 March 2020
associations with SA when SA episodes of 1–5 days
Revised 16 July 2020
Accepted 25 July 2020
were excluded and focus was on SA episodes ≥6 days.
The psychosocial work factors were strongly associated
with SA both with and without SA episodes of 1–5 days
included in the analyses.
Key messages
What is already known about this subject?
►
Poor physical and poor psychosocial work
environment factors are associated with long-
term sickness absence from work.
►
Short-term sickness absence (1–5 days)
constitutes a considerable part of the total
sickness absence from work.
What are the new findings?
►
Physical work environment factors ‘Back bend
or twisted’ and ‘Carrying and lifting’ were
strongly associated with sickness absence
of ≥6 days for both men and women, but
the inclusion of short-term sickness absence
episodes (1–5 days) deflated the association.
►
The psychosocial work environment factors
‘Role conflict’ and ‘Bullying’ were strongly
associated with sickness absence of ≥6 days
for both men and women. Including short-term
sickness absence episodes of 1–5 days only
slightly deflated the association.
How might this impact on policy or clinical
practice in the foreseeable future?
►
Work environment interventions that reduce
strenuous physical work may reduce sickness
absence episodes of ≥6 days. Work environment
interventions that improve different aspects
of the psychosocial work environment may be
important in the prevention of sickness absence
of all lengths.
such as low influence,
4 5
low decision authority,
6 7
role conflicts
3 5 8
and exposure to bullying
9
have
been associated with long-term SA in several
studies. Physical work environment factors such
as excessive ergonomic exposures (bending and
twisting of neck or back, lifting and carrying, squat-
ting and kneeling, etc) and heavy physical workload
have consistently been associated with long-term
SA among men and women.
10–12
Exposure of the
hands to wet work has been associated with long-
term SA in women.
13
1
© Author(s) (or their
employer(s)) 2020. Re-use
permitted under CC BY-NC. No
commercial re-use. See rights
and permissions. Published
by BMJ.
To cite:
Thorsen SV,
Flyvholm M-A,
Pedersen J,
et al.
Occup Environ Med
Epub
ahead of print: [please include
Day Month Year]. doi:10.1136/
oemed-2020-106554
BACKGROUND
In Denmark, 3.6% of all work hours are lost due
to sickness absence (SA).
1
The expense of SA bene-
fits alone exceeds 1.3 billion euro per year
2
not
counting the additional cost of lost productivity and
healthcare expenses. Knowledge about the associa-
tion between work environment factors and SA is
a prerequisite for reducing SA through preventive
efforts.
Poor work environment is associated with long-
term SA.
3
Psychosocial work environment factors
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0002.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
However, there is no consensus regarding the definition of
long-term SA. Earlier, long-term SA has been defined as episodes
exceeding 7 days,
14
14 days,
15
20 days
4
, 30 days
16
or even
56 days
3
. In Denmark, it is estimated that episodes of 1–7 days
account for 44% of all SA days.
1
While this percentage may be
high, compared with other Nordic countries,
17 18
studies that
exclude short-term SA from analyses ignore a large part of the
total SA. If short-term SA is unrelated to the work environment,
it would make sense to exclude it, since it would only add noise
to a study. Most studies have excluded short-term SA, perhaps
because data of short-term SA were lacking or perhaps because
short-term SA episodes were considered to be caused by diseases
that are not or to a lesser extent influenced by the work environ-
ment, for example, a influenza or a cold. However, given data
on all SA are available, the question arises: should short-term
SA be excluded? And if it should, how much of the short-term
SA should be excluded? Episodes of 1 day? Episodes less than 7
days? Episodes less than 21 days?
Theoretically, several mechanisms may explain the associ-
ations between work environment factors and SA. Work may
cause disease or worsen an existing disease. The disease may
cause the employee to call in sick because of reduced work
ability, required time for treatment or (for infectious diseases)
the risk for coworkers or clients/customers. Moreover, for a
given level of work ability, the work environment may affect the
individual’s decision to go to work or to call in sick. SA is a non-
specific outcome, potentially influenced by many factors.
Using the Danish Register of Work Absences (RoWA),
19
which
include SA of all lengths, the present study systematically exam-
ined the associations between work environment factors and SA
where SA was defined respectively as SA episodes ≥1, ≥2, ≥4,
≥6, ≥8, ≥12, ≥16, ≥21 and ≥31 days. If short-term SA episodes
are primarily related to infectious diseases and not influenced
by the work environment, we expect inclusion of short-term
episodes to add noise to the analyses and deflate the associations
between work environment factors and SA. However, if short-
term SA episodes are influenced by work environment factors,
we expect strong associations between these work environment
factors and SA when short-term SA episodes are included in the
analyses.
The present study aims to answer two questions: (1) Do the
associations between physical and psychosocial work environ-
ment factors and incidence of SA depend on the inclusion/exclu-
sion of short-term SA? and (2) What is the optimal threshold for
length of SA to be included in analyses for the highest associa-
tions? We examined the associations between work environment
factors and SA for both men and women.
the Danish population using a stratified probability sample. The
employees received a letter with an invitation to participate in
a web-based survey. Non-respondents received a reminder by
phone and later a reminder by letter with a paper-questionnaire.
Danish Register of Work Absences
Statistic Denmark have since 2007 registered SA data, irrespec-
tively of episode length, in RoWA. RoWA is a combination of
Statistics Denmark’s ‘Absence and Employment’-register (FRAN)
and ‘Periods of Absence’-register (FRPE). The RoWA contains
start and end dates of the absence periods due to ‘own sick-
ness’, ‘child sickness’, ‘occupational injury’ and ‘maternity and
adoption leave’ from (1) all public institutions, (2) all private
companies with more than 250 employees, and (3) a probability
sample of private companies with 10–250 employees (a new
sample drawn every year). Private companies with less than 10
employees are not included in RoWA.
21
Study population
A total of 104 329 employees were invited to participate in
the WEHD survey (a new sample each survey round, 2012:
n=35 034, 2014: n=34 736, 2016: n=34 559), of which 51 552
(49%) responded to the questionnaire (respondents 2012: n=17
662 (50%), 2014: n=17 486 (50%), 2016: n=16 404 (47%)).
As RoWA covers 100% of all public employees and about 37%
of all private employees, 32 191 WEHD respondents could be
linked to the RoWA. We excluded 2525 (8%) employees that
had received SA benefit (due to long-term SA (≥31 days)) in 2
years preceding response date, and 1988 (7%) employees, with
missing answers to main questions and main covariates, leaving
n=27 678 employees (women n=16 356, men n=11 322). Of
these, n=22 919 employees (women n=13 577, men n=9342)
had complete questionnaire data on the secondary covariates
chronic illness, smoking and exercise.
Physical and psychosocial work environment factors
This study used three physical and three psychosocial work
environment factors from the WEHD survey: (1) ‘Back twisted
or bend’, (2) ‘Lifting or carrying burdens’, (3) ‘Wet hands’, (4)
‘Influence’, (5) ‘Role conflicts’, and (6) ‘Bullying’. All factors
were measured with one question, except ‘Influence’ that was
measured as the average of two questions. The questions,
response categories, scoring and the answers’ distribution are
in online supplementary material. The questions have shown
predictive validity in previous research, that is, ‘Back twisted
or bend’, ‘Lifting or carrying burdens’, ‘Role conflicts’ (‘Role
conflicts’ is formulated slightly different) have predicted long-
term SA,
3
‘Wet hands’ has predicted hand eczema
22
and bullying
has predicted onset of depression.
23
We scored ‘Back bend or
twisted’, ‘Lifting or carrying’, ‘Wet hands’ and ‘Bullying’ as yes/
no variables, and ‘Influence’ and ‘Role conflicts’ with increasing
values for each response-category, following scoring-methods
from previous research.
10 24 25
METHODS
Study design
We linked work environment data from the
Work Environment
and
Health
in
Denmark
(WEHD) survey
20
with SA data from
the Danish RoWA.
21
We followed the respondents for up to 18
months in RoWA.
SA outcome
WEHD survey
The Danish National Research Centre for the Working Envi-
ronment conducted the WEHD survey biannually from 2012 to
2016 as part of an occupational health and safety surveillance.
Eligible employees had to fulfil the following criteria: age 18–64
years, monthly income minimum Kr3000/€400 (average last 3
months), and minimum 8 weekly work hours (average last 3
months). Each survey year, eligible employees were drawn from
2
Outcome was ‘own sickness’ (all-cause SA) from RoWA. In
different analyses, we used SA episodes of ≥1, ≥2, ≥4, ≥6, ≥8,
≥11, ≥16, ≥21, ≥26, and ≥31 days.
Covariates
We used the following covariates: age (in years), educa-
tion (0=primary school or no record (n=137) of education,
1=upper secondary school, 2=apprentice/trainee, 3=short
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0003.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
higher education, 4=long higher education), sector (private or
public, of which public is further divided into state, region and
municipality), survey rounds (2012, 2014 and 2016), previous
SA (any SA in the last 2 months up to baseline (yes/no)), smoking
(0=never, 1=quit smoking, 2=smoke sometimes, 3=smoke
daily), exercise (1=no exercise or at most 2 hours of light exer-
cise per week, 2=more than 2 hours light exercise or/and at most
4 hours of medium exercise per week, 3=more than 4 hours a
week of medium exercise or/and at most 2 hours hard exercise
per week, 4=more than 2 hours per week of hard exercise), and
receiving treatment in the last 12 months for chronic illnesses:
depression (yes/no), back disease (yes/no), eczema (yes/no), other
long standing illness (yes/no).
Sector and survey round were categorical variables; all other
covariates were continuous variables. Sex, age, sector and
previous SA were derived from RoWA, education from Statistics
Denmark’s 'education program' register (UDDF, the register has
information about highest completed education); all other vari-
ables were from the WEHD questionnaires.
Table 1
Sample characteristics of n=27 678 Danish employees in the
study
Women
N
Age (years)
Follow-up time
(months)
Sector
Private sector
Public sector
Education
Primary school 1588
Upper
secondary
school
Apprentice/
trainee
Short higher
education
Long higher
education
Chronic illnesses
Depression
Back disease
Eczema
Other illness
Smoking
Never
Quit
Sometimes
Daily
Exercise
Light exercise
Light-medium
exercise
Medium-hard
exercise
Hard exercise
Employees with
recurrent SA
episodes
No episodes
1 episode
2 or more
episodes
3810
2984
9562
23.3
18.3
58.5
4438
2347
4537
39.2
20.7
40.1
1021
5141
7270
447
7.4
37
52.4
3.2
703
2489
5582
742
7.4
26.2
58.7
7.8
8527
4725
849
2159
52.4
29.1
5.2
13.3
5763
3237
643
1618
51.2
28.7
5.7
14.4
771
1423
1489
3563
4.8
8.9
9.3
22.3
361
1024
821
1923
3.2
9.2
7.4
17.3
1088
9.7
6.7
1477
798
13.0
7.0
4752
11 604
29.1
70.9
6982
4340
61.7
38.4
16 356
16 356
Per cent
Mean
46.4
14.2
Men
N
11 322
11 322
Per cent
Mean
46.9
13.7
4858
6500
2322
29.7
39.7
14.2
3804
3135
2108
33.6
27.7
18.6
Statistical analysis
We used a Cox-regression model with recurrent events. Chris-
tensen
et al
26
recommended the Cox model with recurrent
events over Poisson regression for SA analyses, because Poisson
regression had, for example, less statistical power. We used the
statistical program SAS V
.9.4 and the procedure Phreg.
Cox regression uses ‘time to event’, and ‘event (yes/no)’ in the
analyses. We followed employees from the day they answered
the questionnaire up until a SA-event happened. The Cox
regression with recurrent events allows an employee, who has
returned to work after a SA-event, to re-enter the model with
a new entry date, ‘time to event’ and ‘event (yes/no)’. To adjust
for an employee may enter the model several times, the model
uses the ‘robust sandwich estimator’ that takes into account
the within subject correlation.
27
This adjustment will result in
wider CI than if all ‘time to event’ and ‘event (yes/no)’ data had
been from independent employees. We censored employees
during periods of maternity leave or absences due to occupa-
tional injury. Employees were also censored, if they lost their
job or if their workplace no longer were included in the register
(n=4884). Average follow-up time was 14 months. The propor-
tional hazard assumption of the Cox regression model was tested
by visual inspection of cumulative hazard plots and Schoenfeld
residuals.
27 28
All analyses were stratified for sex and adjusted in two
steps. In model 1, we adjusted for the following covariates:
age, education, sector and survey rounds. In model 2, we
additionally adjusted for: previous SA, smoking, exercise,
chronic illnesses, and we adjusted physical work environment
factors for psychosocial work environment, and psychosocial
work environment factors for physical work environment. To
avoid multicollinearity from closely related variables, we did
not mutually adjust the three psychosocial work environment
factors or mutually adjust the three physical work environment
factors.
29
To test if HRs were significantly different for men and women,
we included an interaction term between sex and the particular
work environment factor in analysis including data from both
sexes. If the interaction term was significant, the HRs were
significantly different.
We performed separate analyses with the above models where
we systematically changed the definition of an SA event, including
SA episodes of ≥1, ≥2, ≥4, ≥6, ≥8, ≥11, ≥16, ≥21, ≥26 and
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
SA, sickness absence.
≥31 days, respectively, to examine if the HRs depended on the
cut-off point for inclusion/exclusion of short-term SA.
RESULTS
Our final sample included a wide variety of Danish employees
(see
table 1),
for example, both private and public sector, and
both employees with short and long education. Women had
48 261 SA episodes and men had 21 150 SA episodes (table
2).
A small subset of these (6038 episodes for women and 2293
episodes for men) lasted 6 days or longer, and only 2055
episodes for women and 622 episodes for men lasted 21 days or
longer. Thus, short-term SA episodes were more frequent than
long-term SA episodes.
3
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0004.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
Table 2
Number of sickness absence (SA) episodes for different cut-
off points for the length of the SA episodes
Women
SA episodes≥1 day
SA episodes≥2 days
SA episodes≥4 days
SA episodes≥6 days
SA episodes≥8 days
SA episodes≥12 days
SA episodes≥16 days
SA episodes≥21 days
SA episodes≥26 days
SA episodes≥31 days
48 261
27 447
10 692
6038
4501
3125
2489
2055
1731
1465
Per cent
100
56.9
22.2
12.5
9.3
6.5
5.2
4.3
3.6
3.0
Men
21 150
12 052
4541
2293
1677
1086
806
622
518
434
Per cent
100
57
21.5
10.8
7.9
5.1
3.8
2.9
2.4
2.1
‘Bullying’, the HRs were considerably higher for ≥6 days than
for ≥1 day, with almost no overlap of the CIs.
For men, the psychosocial work environment factors were
significantly associated with SA episodes ≥1 day and ≥6
days, but not with ≥21 days in the fully adjusted model, for
example, for ‘Poor influence’ the HRs were, respectively, 1.72
(1.59 to 1.85), 1.92 (1.53 to 2.42) and 1.32 (0.84 to 2.08).
The HRs of the psychosocial work factors were higher for SA
episodes ≥6 days than for ≥1 day; however, the CIs had large
overlaps.
Men had significantly higher HRs for ‘Poor influence’ for
≥1 day and ≥6 days compared with women, that is, p values
for interaction were <0.0001 and 0.013 (not shown in tables).
All other HRs were higher, but not significantly higher, for
women compared with men.
Associations between work environment factors and SA
episodes ≥1 day, episodes ≥6 days and episodes ≥21 days
HRs represent the increased risk for an SA episode at any
given time, for example, an HR at 1.30 represents a 30%
increased risk.
Table 3
shows the HRs for SA episodes ≥1 day
(lowest possible cut-off point), ≥6 days (cut-off point repre-
senting the strongest association for many predictors) and
≥21 days (highest cut-off point for which HR could be esti-
mated for most scales). All CIs were smaller for analyses with
SA episodes ≥1 day compared with analyses with SA episodes
≥6 days that again were smaller compared with analyses with
SA episodes ≥21 days; hence, results were more precise the
more SA episodes that were included in the analyses.
Figures 1
and 2
show HRs for all cut-off points for the fully adjusted
models. The proportional hazards assumption was fulfilled for
all analyses with SA episodes ≥1 day and ≥6 days, but it could
not be shown to be fulfilled for ‘Bullying’ at ≥21 days. Tables
and figures only show analyses that fulfilled the proportional
hazard assumption.
Sensitivity analyses
We performed sensitivity analyses, where (1) we weighted
data with provided weights for representative population and
(2) we split data up in public and private sector. The main
result, that is, the HRs of the physical work environment were
higher for SA episodes ≥6 days than ≥1 day, were repeated in
all sensitivity analyses. See online supplementary material for
details.
DISCUSSION
Our aims were to examine if the associations between physical
and psychosocial work environment factors and SA depended
on the inclusion/exclusion on short-term SA episodes, and
to find the optimal cut-off point for which SA episodes to
include/exclude in the analyses.
The physical work environment factors’ association with SA
was considerably higher if we focused on SA episodes≥6 days
compared with analyses with SA episodes≥1 day. For ‘Back
bend or twisted’ and ‘Lifting or carrying’, the magnitude of
the associations was stable when we restricted analyses to even
longer SA lengths (eg, ≥21 days), but the CIs became larger,
that is, the precision of the estimates decreased. The associ-
ation of ‘Wet hands’ with SA episode ≥21 days were non-
significant for both men and women.
The psychosocial work environment factors, ‘Role conflict’
and ‘Bullying’ had higher associations with SA if we focused
on SA episodes ≥6 days, but associations were also highly
significant when 1–5 days SA episodes were included in the
analyses, that is, analyses of SA episodes≥1 day. The associa-
tions between ‘Poor Influence’ and SA were equally strong for
SA episodes≥1 day and ≥6 days.
Men had in general higher associations between physical
work environment factors and SA than women. Women had
in general higher associations between psychosocial work
environment factors and SA than men, except ‘Poor influence’
where the associations with SA episodes ≥1 day and ≥6 days
were significantly higher for men.
The deflation of the association between physical work
environment factors and SA, when short-term SA episodes of
1–5 days were included in the analyses, could be explained if
short-term SA is primarily associated with other factors. That
is, if short-term SA is influenced by factors unrelated to the
physical work environment, for example, a cold or a influenza.
It is possible that poor physical work environment factors are
mainly associated with severe illnesses, for example, chronic
pain,
30
from which it is difficult to recover from in a few days,
and therefore, the associations of physical work factors and
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
Physical work environment factors
In the fully adjusted model, ‘Back bend or twisted’, ‘Lifting
or carrying’ and ‘Wet hands’ had small associations with SA
episodes ≥1 day and large associations with SA episodes ≥6
days (see
table 3);
for example, for ‘Back bend and twisted’
for women, the HR at ≥1 day was 1.02 (1.00 to 1.04) and the
HR at ≥6 days was 1.24 (1.17 to 1.32). The HRs at ≥6 days
were considerably higher than the HRs at ≥1 day, that is, most
95% CIs did not overlap. For SA episodes of ≥21 days, the
HRs of ‘Back bend or twisted’ and ‘Lifting or carrying’ were
significant, HRs of ‘Wet hands’ were not significant.
The HRs of the physical work environment factors were
higher for men than for women in most analyses. The HRs
were significantly higher for ‘Back bend or twisted’ for SA
episodes≥1 day, ‘Wet hands’ for SA episodes ≥1 day, ‘Lifting
and carrying’ for SA episodes≥6 days and ‘Lifting and carrying’
for SA episodes ≥21 days. The p values for interaction were,
respectively, p=0.003, p=0.024, p=0.001, p=0.011 (not
shown in tables).
Psychosocial work environment factors
For women, the psychosocial work environment factors ‘Poor
influence’, ‘Role conflicts’ and ‘Bullying’ were significantly
associated with SA episodes ≥1 day, ≥6 days and ≥21 days
in the fully adjusted model, for example, for ‘Poor influence’
the HRs were, respectively, 1.48 (1.41 to 1.57), 1.41 (1.21
to 1.63) and 1.49 (1.15 to 1.92). For ‘Role conflicts’ and
4
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0005.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
Table 3
HRs for physical and psychosocial work environment factors’ association with sickness absence (SA) episodes ≥1 day, ≥6 days and
episodes ≥21 days
SA episodes ≥1 day
Work environment
Physical work environment
Model 1 partly adjusted*
Women n=16 356
Back bend or twisted
Lifting or carrying
Wet hands
Men n=11 322
Back bend or twisted
Lifting or carrying
Wet hands
Model 2 fully adjusted†
Women n=13 577
Back bend or twisted
Lifting or carrying
Wet hands
Men n=9342
Back bend or twisted
Lifting or carrying
Wet hands
Psychosocial work environment
Model 1 partly adjusted*
Women n=16 357
Poor influence
Role conflicts
Bullying‡
Men n=11 322
Poor influence
Role conflicts
Bullying‡
Model 2 fully adjusted†
Women n=13 577
Poor influence
Role conflicts
Bullying‡
Men n=9342
Poor influence
Role conflicts
Bullying‡
1.72
1.09
1.17
(1.59 to 1.85)
(1.03 to 1.16)
(1.11 to 1.22)
<0.0001
0.005
<0.0001
1.92
1.27
1.29
(1.53 to 2.42)
(1.05 to 1.53)
(1.13 to 1.47)
<0.0001
0.013
<0.001
1.32
1.32
–
(0.84 to 2.08)
(0.92 to 1.89)
0.229
0.138
1.48
1.17
1.18
(1.41 to 1.57)
(1.12 to 1.22)
(1.15 to 1.22)
<0.0001
<0.0001
<0.0001
1.41
1.38
1.31
(1.21 to 1.63)
(1.22 to 1.55)
(1.21 to 1.42)
<0.0001
<0.0001
<0.0001
1.49
1.67
–
(1.15 to 1.92)
(1.35 to 2.05)
0.002
<0.0001
2.12
1.16
1.36
(1.98 to 2.26)
(1.10 to 1.23)
(1.30 to 1.41)
<0.0001
<0.0001
<0.0001
2.47
1.30
1.63
(2.03 to 3.01)
(1.10 to 1.53)
(1.46 to 1.82)
<0.0001
0.002
<0.0001
1.63
1.14
–
(1.10 to 2.42)
(0.83 to 1.57)
0.015
0.417
1.70
1.29
1.33
(1.63 to 1.79)
(1.24 to 1.34)
(1.30 to 1.37)
<0.0001
<0.0001
<0.0001
1.96
1.66
1.56
(1.72 to 2.23)
(1.49 to 1.85)
(1.46 to 1.66)
<0.0001
<0.0001
<0.0001
1.99
1.96
–
(1.59 to 2.50)
(1.63 to 2.36)
<0.0001
<0.0001
1.06
1.05
1.05
(1.02 to 1.11)
(1.01 to 1.09)
(1.00 to 1.10)
0.002
0.016
0.037
1.36
1.47
1.13
(1.22 to 1.52)
(1.32 to 1.64)
(0.99 to 1.28)
<0.0001
<0.0001
0.066
1.32
1.60
1.04
(1.06 to 1.63)
(1.30 to 1.96)
(0.81 to 1.35)
0.012
<0.0001
0.744
1.02
1.03
1.01
(1.00 to 1.04)
(1.01 to 1.06)
(0.99 to 1.04)
0.095
0.008
0.26
1.24
1.24
1.26
(1.17 to 1.32)
(1.16 to 1.33)
(1.19 to 1.35)
<0.0001
<0.0001
<0.0001
1.16
1.29
1.08
(1.04 to 1.30)
(1.15 to 1.44)
(0.96 to 1.21)
0.008
<0.0001
0.189
1.22
1.18
1.22
(1.18 to 1.27)
(1.14 to 1.22)
(1.17 to 1.27)
<0.0001
<0.0001
<0.0001
1.67
1.67
1.48
(1.52 to 1.83)
(1.52 to 1.83)
(1.33 to 1.65)
<0.0001
<0.0001
<0.0001
1.73
1.75
1.30
(1.45 to 2.07)
(1.47 to 2.09)
(1.05 to 1.61)
<0.0001
<0.0001
0.018
1.11
1.07
1.08
(1.08 to 1.13)
(1.05 to 1.09)
(1.06 to 1.10)
<0.0001
<0.0001
<0.0001
1.4
1.31
1.38
(1.33 to 1.48)
(1.24 to 1.39)
(1.31 to 1.47)
<0.0001
<0.0001
<0.0001
1.31
1.37
1.23
(1.19 to 1.45)
(1.24 to 1.51)
(1.11 to 1.36)
<0.0001
<0.0001
<0.0001
HR
CI
P value
SA episodes ≥6 days
HR
CI
P value
SA episodes ≥21 days
HR
CI
P value
*Adjusted for age, education, private/public sector, and survey round.
†Adjusted as previous model plus additionally adjusted for depression, eczema, back disease, other chronic illness, SA in 2 months up to baseline, smoking, exercise, plus physical
work environment factors are adjusted for psychosocial work environment, and psychosocial work environment factors are adjusted for physical work environment.
‡‘Bullying’ did not fulfil the proportional hazard assumption in analyses with SA episodes ≥21 days and is therefore not shown.
SA are highest if we focus on SA episodes≥6 days. A recent
cohort study found that a reduction in physical workload was
non-significantly associated with short-term SA episodes of
1–3 days, but significantly associated with SA of more than
14 days.
31
The psychosocial work environment factors ‘Role conflicts’
and ‘Bullying’ had a trend towards higher HRs with SA
episodes ≥6 days compared with SA episodes ≥1 day, but
the trends were less pronounced than for the physical work
environment factors. ‘Poor influence’ had similar HRs for SA
episodes ≥1 day and ≥6 days. This suggest that the factors
determining short-term SA may at least to some extent be influ-
enced by psychosocial work environment. The psychosocial
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
work environment factors may be associated with short-term
SA episodes due to psychological distress responses
32
and with
long-term SA episodes, due to for example, depression.
33
The HRs of the physical work environment factors were
in general higher for men than for women. Some previous
studies
34 35
has shown higher associations for men, in line with
our study, but other studies have shown mixed results,
3 13
or
higher associations for women.
36
The HRs of the psychosocial work environment factors
were in general higher for women, but some were signifi-
cantly higher for men. Previous studies have also shown mixed
results.
8 34 37
If there is a sex difference, it may be related to
a gender-segregated labour market
38
with different physical
5
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0006.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
hand eczema.
22
Fourth, while the RoWA include SA of any
length, it is possible that companies register SA exceeding 30
days more carefully, since companies can get compensation for
salary expense to sick-listed employees, when the SA episodes
exceed 30 days. However, a study comparing RoWA with self-
reported SA found high correlations,
19
supporting the validity
of RoWA. Fifth, our study is based on data from Denmark and
may not be directly transferable to other countries. However,
previous comparisons have found that the association between
work environment and SA is similar in European countries.
41
The results of the present study add to the understanding
of the association between work environment factors and SA
and it may guide researchers when designing SA studies. For
example, according to our results, analyses of physical work
environment factors should primarily focus on SA episodes
≥6 days. The psychosocial work environment factors ‘Role
conflict’ and ‘Bullying’ may also have higher associations with
SA when focus is on SA episodes≥6 days; however, for all
Figure 1
The HRs for different cut-off points for sickness absence (SA)
episode length. Analyses of physical work environment adjusted for age,
education, private/public sector, survey round, depression, eczema, back
disease or other chronic illness, SA in 2 months up to baseline, smoking,
exercise and psychosocial work environment. Analyses that did not fulfil the
proportional hazard assumption are not shown in the figure.
and psychosocial exposures in male-dominated and female-
dominated jobs such as the construction industry compared
with cleaning, nursing and childcare. The differences could
also be related to different home demands and expectations
for men and women.
While our study has notable strengths, e.g. the large
sample size and linkage with a national SA register, several
limitations must be mentioned. First, the work environment
measures were self-reported and the measures may be biased,
for example, self-reported physical demands and self-reported
wet work are imprecise compared to objective measure-
ments.
24 39 40
Second, the response rate was 50% and only
63% of those could be linked to the register, that is, our data
are not representative for the entire Danish workforce. Third,
our study is an observational study and not a randomised
controlled study; hence, it is difficult to show causality. We
adjusted our analyses for important covariates, but we may
both have adjusted too little or too much. For example, it may
be an overadjustment to adjust analyses of ‘Wet hands’ for
eczema, as wet hands may cause or exacerbate occupational
6
Figure 2
The HRs for different cut-off points for sickness absence (SA)
episode length. Analyses for psychosocial work environment adjusted for
age, education, private/public sector, survey round, depression, eczema,
back disease or other chronic illness, SA in 2 months up to baseline,
smoking, exercise and physical work environment. Analyses that did not
fulfil the pro
portional hazard assumption are not shown in the figure.
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0007.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
three psychosocial work factors, the associations were highly
significant when all SA episodes were included, that is, anal-
yses of SA episodes ≥1 day.
The practical implications of our study are that reduction of
strenuous physical work will probably not reduce short-term
SA episodes of less than 5 days, but may reduce longer SA
episodes. By contrast, improving certain aspects of the psycho-
social work environment may be important in the prevention
of SA of all lengths. Thus, improving different aspects of the
work environment seem to be important to deal with SA in
general.
4 Clausen T, Burr H, Borg V. Do psychosocial job demands and job resources predict
long-term sickness absence? an analysis of register-based outcomes using pooled
data on 39,408 individuals in four occupational groups.
Int Arch Occup Environ
Health
2014;87:909–17.
5 Rugulies R, Aust B, Pejtersen JH. Do psychosocial work environment factors measured
with scales from the Copenhagen psychosocial questionnaire predict register-
based sickness absence of 3 weeks or more in Denmark?
Scand J Public Health
2010;38:42–50.
6 Andrea H, Beurskens AJHM, Metsemakers JFM,
et al.
Health problems and
psychosocial work environment as predictors of long term sickness absence in
employees who visited the occupational physician and/or general practitioner in
relation to work: a prospective study.
Occup Environ Med
2003;60:295–300.
7 Munir F, Burr H, Hansen JV,
et al.
Do positive psychosocial work factors protect
against 2-year incidence of long-term sickness absence among employees with
and those without depressive symptoms? A prospective study.
J Psychosom Res
2011;70:3–9.
8 Lund T, Labriola M, Christensen KB,
et al.
Psychosocial work environment exposures
as risk factors for long-term sickness absence among Danish employees: results from
DWECS/DREAM.
J Occup Environ Med
2005;47:1141–7.
9 Nielsen MB, Indregard A-MR, Øverland S. Workplace bullying and sickness absence: a
systematic review and meta-analysis of the research literature.
Scand J Work Environ
Health
2016;42:359–70.
10 Andersen LL, Thorsen SV, Flyvholm M-A,
et al.
Long-Term sickness absence from
combined factors related to physical work demands: prospective cohort study.
Eur J
Public Health
2018;28:824–9.
11 Lund T, Labriola M, Christensen KB,
et al.
Physical work environment risk factors for
long term sickness absence: prospective findings among a cohort of 5357 employees
in Denmark.
BMJ
2006;332:449–52.
12 Sterud T. Work-Related mechanical risk factors for long-term sick leave: a
prospective study of the general working population in Norway.
Eur J Public Health
2014;24:111–6.
13 Alfonso JH, Tynes T, Thyssen JP,
et al.
Self-Reported occupational skin exposure and
risk of Physician-certified long-term sick leave: a prospective study of the general
working population of Norway.
Acta Derm Venereol
2016;96:336–40.
14 Head J, Kivimäki M, Siegrist J,
et al.
Effort-reward imbalance and relational
injustice at work predict sickness absence: the Whitehall II study.
J Psychosom Res
2007;63:433–40.
15 Holtermann A, Hansen JV, Burr H,
et al.
Prognostic factors for long-term sickness
absence among employees with neck-shoulder and low-back pain.
Scand J Work
Environ Health
2010;36:34–41.
16 Johansen V. Risk factors of long-term sickness absence in Norway and Sweden.
Nord J
Soc Res
2013:71–82.
17 Krane L, Fleten N, Stapelfeldt CM,
et al.
Comparison of sick leave patterns between
Norway and Denmark in the health and care sector: a register study.
Scand J Public
Health
2013;41:684–91.
18 Thorsen SV, Friborg C, Lundstøm B,
et al.
Sickness absence in the Nordic countries,
Copenhagen 2015.
19 Thorsen SV, Flyvholm M-A, Bültmann U. Self-Reported or register-based? A
comparison of sickness absence data among 8110 public and private employees in
Denmark.
Scand J Work Environ Health
2018;44:631–8.
20 Jensen CT, Johnsen NF, Linde P,
et al.
Danskernes arbejdsmiljø 2016
Danskernes
Arbejdsmiljø,
København: Det Nationale Forskningcenter for Arbejdsmiljø (NFA)
2018:400.
21 Denmark S.
Documentation of statistics for absence 2017.
21. Copenhagen: Statistics
Denmark, 2019.
22 Lund T, Petersen SB, Flachs EM,
et al.
Risk of work-related hand eczema in relation to
wet work exposure.
Scand J Work Environ Health
2020;46:437–45.
23 Rugulies R, Madsen IEH, Hjarsbech PU,
et al.
Bullying at work and onset of a major
depressive episode among Danish female eldercare workers.
Scand J Work Environ
Health
2012;38:218–27.
24 Flyvholm M-A, Lindberg M, OEESC-2005 organizing committee. OEESC-
2005--summing up on the theme irritants and wet work.
Contact Dermatitis
2006;55:317–21.
25 Madsen IEH, Gupta N, Budtz-Jørgensen E,
et al.
Physical work demands and
psychosocial working conditions as predictors of musculoskeletal pain: a cohort study
comparing self-reported and job exposure matrix measurements.
Occup Environ Med
2018;75:752–8.
26 Christensen KB, Andersen PK, Smith-Hansen L,
et al.
Analyzing sickness absence with
statistical models for survival data.
Scand J Work Environ Health
2007;33:233–9.
27 Sun RJ. Paper 255-2010: analysis of survival data with recurrent events using SAS.
SAS Global Forum Proceedings
2010.
28 Kukhareva P. Paper 428-2013: COX proportional hazard model evaluation in one shot.
SAS Global Forum Proceedings
2013.
29 Tibshirani G.
An introduction to statistical learning.
New York: Springer
Science+Business Media, 2013.
30 Coenen P, Gouttebarge V, van der Burght ASAM,
et al.
The effect of lifting during
work on low back pain: a health impact assessment based on a meta-analysis.
Occup
Environ Med
2014;71:871–7.
CONCLUSION
For both men and women, the physical work environment
factors ‘Back bend or twisted’ and ‘Carrying and lifting’ had
larger and more significant associations with SA if analyses
focused on SA episodes≥6 days. The psychosocial work envi-
ronment factors had highly significant associations with SA
both when short-term SA was included and excluded, though
‘Role conflict’ and ‘Bullying’ may have slightly larger associa-
tions with SA if analyses focus on SA episodes≥6 days.
Twitter
Lars L Andersen @larslandersen
Acknowledgements
The authors are grateful to the Surveillance group at
the Danish National Research Centre for the Working Environment for valuable
assistance with access to and quality assurance of data.
Contributors
All authors were involved in the design of the study. S.V.Thorsen
performed the analyses and drafted the paper. M-A.Flyvholm, J.Pedersen,
U.Bültmann, L.L.Andersen and J.B. Bjorner made critical revisions to the draft
and helped with the interpretation of the results. All authors approved the final
manuscript.
Funding
The present study was funded by a special grant the Danish Research
Center of the Working Environment receives from the state. The grant is given in
accordance with the Danish Finance Act §17.21.02.30.
Competing interests
None declared.
Patient consent for publication
Not required.
Ethics approval
The Danish Data Protection Agency has approved the WEHD
survey (journal number 2012-54-0017). According to Danish law, questionnaire-
based and register-based studies do not need approval by committees of ethics, nor
do they need informed consent.
42 43
Provenance and peer review
Not commissioned; externally peer reviewed.
Data availability statement
Data may be obtained from a third party and are
not publicly available. Access to sickness absence data can be bought from Statistics
Denmark. Access to work environment data can be granted by the Danish National
Research Centre for the Working Environment.
Open access
This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
ORCID iDs
Sannie Vester Thorsen
http://orcid.org/0000-0002-8778-9053
Mari-Ann Flyvholm
http://orcid.org/0000-0002-8942-754X
Jacob Pedersen
http://orcid.org/0000-0003-4429-3485
Ute Bültmann
http://orcid.org/0000-0001-9589-9220
Lars L Andersen
http://orcid.org/0000-0003-2777-8085
Jakob Bue Bjorner
http://orcid.org/0000-0001-7033-8224
REFERENCES
1 Flyvholm M, Thorsen SV, Bültmann U. Fraværsrapport 2019. Deskriptiv analyse af
lønmodtagernes sygefravær i Danmark - belyst ud fra register- og spørgeskemadata
Fraværsrapport,
København: Det Nationale Forskningscenter for Arbejdsmiljø 2019.
2 Beskæftigelsesministeriet. NOTAT - Udviklingen i udgifterne i sygedagpengesystemet,
København Beskæftigelsesministeriet 2018;3.
3 Bang Christensen K, Lund T, Labriola M,
et al.
The fraction of long-term sickness
absence attributable to work environmental factors: prospective results from the
Danish work environment cohort study.
Occup Environ Med
2007;64:487–9.
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554
7
 BEU, Alm.del - 2020-21 - Bilag 34: Orientering om NFA-artikel om sammenhængen mellem arbejdsmiljø og sygefravær, fra beskæftigelsesministeren
2274439_0008.png
Workplace
Occup Environ Med: first published as 10.1136/oemed-2020-106554 on 9 September 2020. Downloaded from
http://oem.bmj.com/
on September 10, 2020 at National Institute of
Occupational Health - DNLA. Protected by copyright.
31 Shiri R, Hiilamo A, Pietilainen O,
et al.
Favourable changes in physical working
conditions and the risk of all-cause sickness absence: a pseudo-experiment.
Eur J
Public Health
2019.
32 Volmer J, Fritsche A. Daily negative work events and employees’ physiological and
psychological reactions.
Front Psychol
2016;7:7.
33 Rugulies R, Bültmann U, Aust B,
et al.
Psychosocial work environment and incidence
of severe depressive symptoms: prospective findings from a 5-year follow-up of the
Danish work environment cohort study.
Am J Epidemiol
2006;163:877–87.
34 Foss L, Gravseth HM, Kristensen P,
et al.
The impact of workplace risk factors on long-
term musculoskeletal sickness absence: a registry-based 5-year follow-up from the
Oslo health study.
J Occup Environ Med
2011;53:1478–82.
35 Lund T, Labriola M, Christensen KB,
et al.
Return to work among sickness-absent
Danish employees: prospective results from the Danish work environment
cohort Study/National register on social transfer payments.
Int J Rehabil Res
2006;29:229–35.
36 Corbett K, Gran JM, Kristensen P,
et al.
Adult social position and sick leave: the
mediating effect of physical workload.
Scand J Work Environ Health
2015;41:542–53.
37 Nielsen ML, Rugulies R, Christensen KB,
et al.
Psychosocial work environment
predictors of short and long spells of registered sickness absence during a 2-year
follow up.
J Occup Environ Med
2006;48:591–8.
38 Gonäs L, Wikman A, Vaez M,
et al.
Changes in the gender segregation of occupations
in Sweden between 2003 and 2011.
Scand J Public Health
2019;47:344–7.
39 Gupta N, Heiden M, Mathiassen SE,
et al.
Prediction of objectively measured physical
activity and sedentariness among blue-collar workers using survey questionnaires.
Scand J Work Environ Health
2016;42:237–45.
40 Gupta N, Heiden M, Mathiassen SE,
et al.
Is self-reported time spent sedentary and in
physical activity differentially biased by age, gender, body mass index, and low-back
pain?
Scand J Work Environ Health
2018;44:163–70.
41 Slany C, Schütte S, Chastang J-F,
et al.
Psychosocial work factors and long sickness
absence in Europe.
Int J Occup Environ Health
2014;20:16–25.
42 Ethics TCoBR. Committee system on biomedical research ethics. guidelines about
notification, 2011. Available: http://www. dnvk. dk
43 Agency TDDP. Private research and statistics projects, 2010. Available: https://www.
datatilsynet. dk
8
Thorsen SV,
et al. Occup Environ Med
2020;0:1–8. doi:10.1136/oemed-2020-106554