Beskæftigelsesudvalget 2019-20
BEU Alm.del Bilag 255
Offentligt
2197731_0001.png
Workplace
Original research
High physical work demands and working life
expectancy in Denmark
Jacob Pedersen ,
1
Bastian Bygvraa schultz,
1
ida e h Madsen
svetlana solovieva,
2
lars l andersen
1
►
additional material is
published online only. To view
please visit the journal online
(http://dx.doi.org/10.1136/
oemed-2019-106359).
,
1
ABSTRACT
Objective
in most european countries, political
reforms gradually increase the statutory retirement age
to counter the economic costs of a growing elderly
population. however, working to a high age may be
1
national research centre
difficult for people with hard physical labour. We aim
for the Working environment,
to study the impact of high physical work demands on
copenhagen, Denmark
2
working life expectancy (Wle).
Finnish institute of
Methods
We combined physical work demands
Occupational health, helsinki,
Finland
assessed by job exposure matrix (JeM) and longitudinal
high-quality national registers (outcome) in 1.6 million
Correspondence to
Danish workers to estimate Wle and years of sickness
Dr Jacob Pedersen, national
absence, unemployment and disability pension. The JeM
research centre for the Working
value for physical work demand is a summarised score of
environment, copenhagen,
eight ergonomic exposures for 317 occupations groups,
Denmark; [email protected]
sex and age. The Wle was estimated using a multistate
received 10 December 2019
proportional hazards model in a 4-year follow-up period.
revised 11 March 2020
Results
individuals with high physical work demands
accepted 14 March 2020
had a significantly lower Wle, than those with low
physical work demands, with largest differences seen
among women. at age 30 years, women with high
physical work demands can expect 3.1 years less
working, 11 months more of sickness absence and
16 months more of unemployment than low-exposed
women. For 30-year-old men, the corresponding results
were 2.0 years, 12 months and 8 months, respectively.
Conclusion
Our findings show that high physical
work demands are a marked risk factor for a shortened
working life and increased years of sickness absence and
unemployment. The results are important when selecting
high-risk occupations, and expand the knowledge
base for informed political decision making concerning
statutory retirement age.
Key messages
What is already known about this subject?
►
Physical work demands are strongly associated
with long-term sickness absence and disability
retirement.
►
Job exposure matrices provide mean
occupational exposure and therefore have a
high utility for large register studies.
►
Working life expectancy (WLE) is a useful
measure to inform preventive policies and
practices.
What are the new findings?
►
Applying a multistate framework with a life
course perspective, we showed that high
physical work demands markedly reduces the
WLE.
►
Male and female employees in occupations
with high physical demands spend substantially
more time in sickness absence and
unemployment compared with employees in
occupations with low physical work demands.
How might this impact on policy or clinical
practice in the foreseeable future?
►
Considering the expected increase in statutory
retirement age in many European countries, the
findings emphasise the urgency of addressing
problems in high-risk occupations.
INTRODUCTION
The increasing life span and declining birth rates
are transforming the age distribution in Europe
towards a growing elderly population above the
statutory retirement age.
1
This has led to increases in
retirement age based on the assumption that longer
life span equals proportionally better health and
workability. In Denmark, the statutory retirement
age is set to increase from 65�½ years in 2019 to 72
years in 2050.
2
The intention of these regulations
is that an average person should have 14�½ living
years after retirement. However, such assump-
tions are not without challenges. First, healthy
life expectancy is not increasing at the same rate
as life expectancy, in part due to modern medical
treatments, leading to increased survival of elderly
individuals with life-threatening disease.
3
Second,
© 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:
Pedersen J,
schultz BB, Madsen ieh,
et al.
Occup Environ Med
epub
ahead of print: [please include
Day Month Year]. doi:10.1136/
oemed-2019-106359
due to socioeconomic inequalities in health, not
all groups in society will live to the same age nor
have the same number of healthy life years after
retirement.
4–6
Third—and the primary focus of the
present study—these reforms may not account for
the ageing process in occupations with hard phys-
ical labour. For example, from the age of 40 years
muscle strength declines 1%–2% per year, making
physical labour increasingly more difficult as age
progresses.
7
Indeed, numerous prospective studies
have documented the negative impact of physically
heavy work on health, workability, and risk of sick-
ness absence and early retirement.
8–15
Most of previous studies on health, workability
and labour market affiliation, however, use a single
end point as outcome—for example, transitioning
from employment to disability retirement—without
considering the many possible transitions occurring
during working life, for example, from employment
to unemployment, from employment to sickness
1
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
AUTHOR PROOF
 BEU, Alm.del - 2019-20 - Bilag 255: Orientering om resultater fra et studie, gennemført af Det Nationale Forskningsinstitut for Arbejdsmiljø (NFA), om sammenhæng mellem høje fysiske krav i arbejdet og den forventede arbejdslivslængde, fra beskæftigelsesministeren
2197731_0002.png
Workplace
absence, and back to work. Consequently, little is known about
the impact of physically heavy work on workability during
working life course.
Working life expectancy (WLE) is one summary measure of
health and labour market affiliation in the working popula-
tion.
16–26
WLE, as analogue of life expectancy, expresses the
number of years a person at a given age is expected to be at work
until retirement from the labour market. In the present study we
additionally estimate the number of years a person is expected
to be in unemployment, sickness absence and early retirement
due to disability pension. The WLE measure is easily converted
into to working years lost (WYL), a useful indicator in evaluating
long-term consequences of changes in retirement legislations.
Likewise, WYL may show interesting changes in the expected
duration of unemployment, sickness absence, disability pension
and the risk of early death.
The aim of the present study is to explore the impact of high
physical work demands on WLE in Denmark. The analysis of
WLE combines information on labour market transitions from
high-quality national registers and information on physical work
demands based on a job exposure matrix (JEM).
AUTHOR PROOF
Figure 1
The multistate model with the six states: work, sickness
absence, unemployment, disability retirement pension (disability),
temporary exclusion from the labour market (temporary out), and death.
The transitions are represented as arrows (adapted from Pedersen and
Bjorner [19]).
METHOD
Jurisdictional context: the Danish labour market
from eight specific physical workload exposures, scored from
1 (never) to 6 (almost all the time). Thus, an increasing score
indicates increasing physical work demands. A detailed descrip-
tion of the specific physical workload exposures is in the online
supplementary material.
To increase the exposure contrast we divided the sample
into three groups according to the exposure values: lower
than 16 (low physical work demands), 16 or between 16 and
28 (medium physical demands), and 28 or higher (high phys-
ical demands). Because the exposure contains specific exposures
that are opposing—for example, an item on standing and one
on siting—it is unfeasible to gain a score of 4 and above consis-
tently on each item. Therefore, the limit for high physical work
demands was set to 28.
The online supplementary tables 1–3 show the top 10 male
and female most frequent occupations, for the high-exposed,
medium-exposed and low-exposed groups, respectively. The
male occupations in the high exposure group include construc-
tion and general manual labour such as carpentry, masonry,
painting and plumbing. The female occupations with the high
physical work demands are related to cleaning labour and manu-
facturing industries.
The Danish labour market is characterised by a flexicurity
system with high labour market participation rates (75% for
the first quarter of 2019),
27
low formal employment protection,
generous and accessible social benefits, and a high turnover of
the workforce.
28
The Danish system contains both insurance and
non-insurance unemployment benefits and a sickness absence
benefit that compensate the employer from the 30th day of sick
listing. Additionally, the Danish system includes early retirement
schemes of which the disability retirement pension is the only
one accessible for all. For a start, a disabled individual can be
approved for either the full or the gradual disability retirement
pension. The official retirement age in Denmark concerning the
follow-up period of the study is 65 years of age.
Study design
The source population of this longitudinal study was provided by
Statistics Denmark, and includes all Danes between the ages of 18
years and 65 years with a primary occupation ultimo November
2013 (n=2 162 390, 49% women). The sample included infor-
mation on occupation, sex and date of birth. The occupations
were coded in the Danish Classification of Occupations format,
which corresponds to the International Classification of Occu-
pations. The sample was linked with the Danish Register for
Evaluation of Marginalisation (DREAM), which contains weekly
registrations of all major social benefits payments in the period
from 1 January 2014 until 31 December 2017.
29
Information
on date of death was taken from the Danish death register, if
applicable. All data were provided on an individual level with an
encrypted person’s identification number.
Labour market affiliation
The labour market affiliation was measured according to the
multistate model shown in
figure 1,
with boxes illustrating the
labour market states and arrows showing the possible transi-
tions. The model contains four recurrent labour market states:
(1) Work—when not receiving social payments. (2) Unem-
ployment—when receiving unemployment benefit and being
available for immediate labour. (3) Sickness absence—when
receiving sickness absence benefits. (4) Temporarily out of the
labour market—when on a leave for example, maternity leave,
receiving education benefits or emigrated. The model addition-
ally contains two absorbing states: (1) Disability Retirement
Pension—when on disability retirement benefits due to limited
or no workability. (2) Death. We based all six states on the
records from the DREAM register and the Danish death register.
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
Exposure
The individual exposure values of physical work demand were
provided by linking a JEM to the study population by their
age, sex and occupation code. The JEM was made using the
Danish Work Environment and Health study 2012 and has been
described by Madsen
et al.
30
The JEM values on the physical work demand, ranging
between 8 and 48, were estimated by regression analysis. The
exposure values correspond to a summary index, constructed
2
 BEU, Alm.del - 2019-20 - Bilag 255: Orientering om resultater fra et studie, gennemført af Det Nationale Forskningsinstitut for Arbejdsmiljø (NFA), om sammenhæng mellem høje fysiske krav i arbejdet og den forventede arbejdslivslængde, fra beskæftigelsesministeren
2197731_0003.png
Workplace
Statistical analyses
We used a multistate model (figure
1)
when analysing the transi-
tion probabilities between the six labour market states using age
as the underlying time axis (30–65 years) in the follow-up period
from 1 January 2014 to 31 December 2017. Individuals enter
the model by left truncation either at the age at the start of the
follow-up period or during the follow-up period from the date
of turning 30 years. A person exits the model by right censoring
when the person turns 65 years, or at the end of the follow-up
period, whichever comes first.
We followed the procedure introduced by Pedersen and
Bjorner to estimate the WLE, using long formatted data and age
as time axis.
19
We estimated an instantaneous transition matrix
for each age by the hundredths. We estimated the matrices by sex
and for the low exposed. We used the Chapman-Kolmogorov
equation to gain transition-specific baseline hazards and the
state occupation probabilities. To gain the instantaneous tran-
sition matrix of the highly exposed we adjusted the matrices of
the low exposed with estimates from a Cox proportional hazard
regression, using the low exposed as the reference group. During
Cox regression we collapsed the transitions to the temporary out
state to ensure a sufficient number of transitions to the state at
all ages. This was also done for the disability pension and death
states.
We estimated the WLE as the expected duration of time
in the work state given by the combined area under the state
probability and the incoming transition probabilities curves. We
estimated the expected time in the unemployment, the sickness
absence, and the disability states using the same approach.
31
We
additionally restricted our results to individuals who, during
the follow-up period,were 30 years and older. This is because
the results for individuals younger than 30 years are in general
more uncertain, due to more frequent periods of education and
maternity leave. We calculated 95% CIs using the Greenwood
variance. All analyses were made in SAS V
.9.4 by custom-made
code and the PHREG procedure.
more time in unemployment, receiving sickness absence benefit
or disability pension than the low exposed (figure
2).
In both
sexes and at all ages, the absolute numbers of
figure 2
show that
WLE was decreasing with increasing physical work demands
(table
2).
In contrast, expected time in other states was linearly
associated with physical work demands. At the age of 30 years,
the expected time at work was 31.9 years among high-exposed
men and 33.9 years among low-exposed men. For women, the
corresponding numbers were 29.6 years and 32.7 years, respec-
tively. Furthermore, a 30-year-old man with high exposure was
expected to have 1.5 years (18 months) of sickness absence and
1.1 years (13 months) of unemployment, while a low-exposed
man was expected to have 0.4 years of sickness absence and 0.4
years (5 months) of unemployment. The corresponding results
for women were 1.9 years of sickness absence and 1.9 years (23
months) of unemployment for the high exposed, while 1 year of
sickness absence and 0.6 years (7 months) of unemployment for
the low exposed.
The reduction in WLE (WYL) for women with high physical
work demands was statistically significantly larger at the age of
30 years and tended to be larger at other ages than the compa-
rable loss for men (table
3
and online supplementary figure 1).
The first column of
table 3
shows that a 30-year-old woman
exposed to high physical work demands is expected to be 3.1
years less at work than a woman exposed to low physical work
demands. The comparable difference for a 30-year-old man was
2.0 years. In addition, a 30-year-old woman with high exposure
was expected to spend 0.9 years (11 months) more on sickness
absence and 1.3 years (16 months) more in unemployment,
compared with a similarly aged and low-exposed woman. For
a 30-year-old man, the difference between high-exposed and
low-exposed groups in expected number of years was 1.0 more
year for sickness absence and 0.7 additional year (8 months) for
unemployment.
In addition,
table 3
shows that a 30-year-old high-exposed
woman was expected to have 0.6 year (7 month) more of
disability pension than a 30-year-old low-exposed woman, while
the difference for a 30-year-old man was 0.1 year (approximately
1 month). The differences for the temporary out state and death
state were non-significant based on the 95% CIs.
RESULTS
To limit the number and size of the tables, we show only the
results for women and men aged 30 years, 40 years and 50 years.
Additionally, we focus on the results comparing the high exposed
to the low exposed.
More men than women were classified as having high phys-
ical work demands during the follow-up period (table
1).
The
men with high exposure were on average 2.8 years younger than
the low-exposed men, while the high-exposed women were 0.8
years older than the low-exposed women.
In both sexes, individuals with high physical work demands
were expected to spend significantly less time working and
DISCUSSION
We used a nationwide register-based data set and a recently
developed JEM to quantify the impact of high physical work
demands on WLE and loss of working years in the Danish
working population. We found that in both sexes from the age
of 30–65 years, the WLE is inversely associated with physical
work demands. In contrast, the expected sickness absence time
and unemployment time are positively associated with physical
Table 1
Age distribution of the study population by the physical work demands group among men and women
Sex
Men
Physical
demands
Low
Mid
High
Total
Women
Low
Mid
High
Total
N (%)
305 527 (37.4)
475 102 (58.2)
35 920 (4.4)
816 549
310 301 (38.0)
460 010 (57.8)
25 842 (3.2)
796 153
Mean age (STD)
45.1 (9.2)
44.8 (9.4)
42.3 (9.6)
44.8 (9.3)
44.3 (9.1)
44.6 (9.3)
45.1 (8.5)
44.3 (9.1)
Aged 30 years N (%)
11 969 (26.8)
27 826 (62.2)
4 935 (11.0)
44 730
18 150 (35.0)
32 072 (61.8)
1 637 (3.2)
51 859
Aged 40 years N (%)
52 079 (41.1)
71 891 (56.4)
3 193 (2.5)
127 506
52 079 (41.8)
70 814 (56.9)
1 640 (1.3)
124 533
Aged 50 years N (%)
52 022 (37.2)
80 964 (57.9)
6 933 (5.0)
139 919
54 167 (39.3)
78 411 (56.8)
5 422 (3.9)
138 000
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
AUTHOR PROOF
3
 BEU, Alm.del - 2019-20 - Bilag 255: Orientering om resultater fra et studie, gennemført af Det Nationale Forskningsinstitut for Arbejdsmiljø (NFA), om sammenhæng mellem høje fysiske krav i arbejdet og den forventede arbejdslivslængde, fra beskæftigelsesministeren
2197731_0004.png
Workplace
AUTHOR PROOF
Figure 2
The working life expectancy (Wle) and expected time of unemployment and sickness absence for 30–60 years old among individuals with low,
medium, and high physical work demands, stratified by sex. First row: number of years expected to be working, second row: number of years expected to be
on sickness absence, and third row: number of years expected to be unemployed.
work demands. As compared with people with low physical
work demands, WLE at the age of 50 years, 40 years and 30
years was significantly reduced among men and women with
high physical work demands by 1.2–3.1 years and 1.0–2.0 years,
respectively. In both sexes, high-exposed persons were expected
to have significantly more time on sickness absence, unem-
ployment and disability pension than the low-exposed. Among
men, the largest reduction in WLE was due to sickness absence,
4
followed by unemployment. While among women, the expected
time of unemployment was higher than the expected time of
sickness absence. Overall, women were expected to have more
time of unemployment than men and the difference between the
high-exposed and low-exposed groups was larger for women
than men. Although the difference in the expected years of sick-
ness absence between the high-exposed and low-exposed groups
was similar in both sexes, irrespective of physical work demands,
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
 BEU, Alm.del - 2019-20 - Bilag 255: Orientering om resultater fra et studie, gennemført af Det Nationale Forskningsinstitut for Arbejdsmiljø (NFA), om sammenhæng mellem høje fysiske krav i arbejdet og den forventede arbejdslivslængde, fra beskæftigelsesministeren
2197731_0005.png
Workplace
Table 2
The expected time in different states by sex, age and physical work demands group
Status
Women
30 years
Low
Mid
High
40 years
Low
Mid
High
50 years
Low
Mid
High
Men
30 years
Low
Mid
High
40 years
Low
Mid
High
50 years
Low
Mid
High
33.89 (33.82 to 33.97)
32.34 (32.20 to 32.47)
31.87 (31.71 to 32.04)
24.22 (24.18 to 24.27)
23.10 (23.00 to 23.19)
22.72 (22.61 to 22.84)
14.48 (14.45 to 14.51)
13.73 (13.67 to 13.79)
13.46 (13.38 to 13.54)
0.44 (0.40 to 0.49)
0.95 (0.86 to 1.03)
1.48 (1.36 to 1.60)
0.36 (0.32 to 0.39)
0.76 (0.70 to 0.82)
1.19 (1.10 to 1.28)
0.25 (0.23 to 0.28)
0.54 (0.50 to 0.58)
0.84 (0.78 to 0.90)
0.36 (0.32 to 0.41)
0.95 (0.86 to 1.04)
1.05 (0.95 to 1.15)
0.24 (0.21 to 0.27)
0.63 (0.57 to 0.69)
0.70 (0.63 to 0.77)
0.16 (0.14 to 0.18)
0.42 (0.38 to 0.46)
0.47 (0.42 to 0.51)
0.11 (0.10 to 0.13)
0.15 (0.13 to 0.17)
0.08 (0.07 to 0.10)
0.03 (0.02 to 0.04)
0.04 (0.03 to 0.05)
0.02 (0.01 to 0.03)
0.01 (0.00 to 0.01)
0.01 (0.01 to 0.02)
0.01 (0.00 to 0.01)
0.09 (0.07 to 0.11)
0.40 (0.35 to 0.45)
0.21 (0.17 to 0.26)
0.07 (0.06 to 0.09)
0.32 (0.28 to 0.35)
0.17 (0.13 to 0.20)
0.04 (0.03 to 0.05)
0.20 (0.17 to 0.22)
0.11 (0.08 to 0.13)
0.09 (0.07 to 0.11)
0.15 (0.13 to 0.18)
0.16 (0.14 to 0.19)
0.08 (0.06 to 0.09)
0.13 (0.11 to 0.15)
0.14 (0.12 to 0.16)
0.05 (0.04 to 0.06)
0.09 (0.08 to 0.10)
0.09 (0.08 to 0.11)
32.67 (32.56 to 32.78)
31.07 (30.91 to 31.23)
29.62 (29.41 to 29.84)
23.64 (23.57 to 23.71)
22.58 (22.48 to 22.68)
21.62 (21.49 to 21.76)
14.20 (14.15 to 14.24)
13.58 (13.52 to 13.65)
12.98 (12.89 to 13.07)
0.99 (0.91 to 1.07)
1.53 (1.42 to 1.64)
1.90 (1.77 to 2.03)
0.73 (0.68 to 0.78)
1.13 (1.06 to 1.20)
1.41 (1.33 to 1.50)
0.45 (0.42 to 0.49)
0.70 (0.66 to 0.75)
0.88 (0.83 to 0.94)
0.56 (0.50 to 0.62)
0.94 (0.86 to 1.03)
1.90 (1.75 to 2.06)
0.35 (0.31 to 0.39)
0.59 (0.54 to 0.65)
1.20 (1.11 to 1.30)
0.23 (0.20 to 0.25)
0.38 (0.34 to 0.42)
0.77 (0.71 to 0.84)
0.46 (0.43 to 0.49)
0.53 (0.49 to 0.56)
0.54 (0.51 to 0.58)
0.06 (0.05 to 0.07)
0.07 (0.06 to 0.08)
0.07 (0.06 to 0.09)
0.01 (0.00 to 0.01)
0.01 (0.00 to 0.01)
0.01 (0.00 to 0.02)
0.29 (0.25 to 0.33)
0.88 (0.80 to 0.95)
0.93 (0.84 to 1.02)
0.19 (0.16 to 0.21)
0.56 (0.52 to 0.61)
0.60 (0.55 to 0.66)
0.09 (0.08 to 0.11)
0.28 (0.25 to 0.31)
0.30 (0.27 to 0.33)
0.04 (0.03 to 0.05)
0.06 (0.04 to 0.08)
0.07 (0.06 to 0.09)
0.03 (0.02 to 0.04)
0.06 (0.05 to 0.08)
0.02 (0.02 to 0.03)
0.03 (0.03 to 0.04)
0.04 (0.03 to 0.05)
0.05 (0.04 to 0.06)
Physical work
demands
Work
Years (CI)
Sick
Years (CI)
Unemployed
Years (CI)
Temporary out
Years (CI)
Disability pension
Years (CI)
Death
Years (CI)
the expected years of sickness absence were higher for women
than men. Though the study focuses on the contrast between the
high-exposed and low-exposed, the study found no significant
difference between the high-exposed and mid-exposed men with
regard to the expected time in unemployment. This could be due
to the high number of men with an unknown occupation in the
mid-exposed group, some of whom may be misclassified by the
JEM—though, still exposed because of their sex and age.
28
Comparison with previous studies
Previous studies investigating the effect of physical work
demands on labour market affiliation have typically quantified
its impact on sickness absence and unemployment separately,
without using a multistate framework or a life course perspec-
tive. Common estimates like ORs, relative risks or HRs can be
difficult to interpret in a set-up containing multiple outcomes
and recurrent transitions. In addition, relative estimates are
typically difficult to convert to absolute numbers which show a
direct impact.
19
To our knowledge, this is the first study to explore the asso-
ciation between physical work demands and WLE. Therefore,
the results of this study are not directly comparable with the
findings of published studies. A recent Dutch study found that
compared with highly educated workers, the WLE at age 30
years of low-educated men and women was reduced by 7.3
years and 9.9 years, respectively.
25
Low-educated persons often
lack vocational education. As compared with highly educated
persons, they usually enter the workforce earlier and more
frequently are occupied in jobs with physically demanding tasks.
Compared with the Dutch study, our study may underestimate
WLE at age 30 years between persons with high and low phys-
ical work demands. However, the two studies cannot be directly
compared, due to different labour market and social systems,
and because our study included only employed individuals while
the Dutch study contains all individuals aged 30–66 years. Addi-
tionally, the Danish registers contain more accurate information
on social payments by weekly updates, compared with the Dutch
data which are based on monthly summaries.
Our results are in concordance with numerous previous
studies on associations between physical workload and work
disability (sickness absence and disability retirement),
8 10 11 14
as well with those reporting excess risk of work disability in
Table 3
Difference in working life expectancy and expected time in different states between high and low physical demands groups by sex and
age
Work
Years (CI)
Women
30 years
40 years
50 years
Men
30 years
40 years
50 years
−2.02 (−2.38 to −1.66)
−1.50 (−1.74 to −1.26)
−1.02 (−1.19 to −0.85)
1.04 (0.79 to 1.29)
0.83 (0.64 to 1.02)
0.59 (0.46 to 0.72)
0.69 (0.48 to 0.90)
0.46 (0.31 to 0.61)
0.31 (0.21 to 0.41)
−0.03 (−0.07 to 0.01)
−0.01 (−0.04 to 0.02)
0.00 (−0.01 to 0.01)
0.12 (0.02 to 0.22)
0.10 (0.03 to 0.17)
0.07 (0.02 to 0.12)
0.07 (0.01 to 0.13)
0.06 (0.01 to 0.11)
0.04 (0.00 to 0.08)
−3.05 (−3.52 to −2.58)
−2.02 (−2.32 to −1.72)
−1.22 (−1.42 to −1.02)
0.91 (0.61 to 1.21)
0.68 (0.49 to 0.87)
0.43 (0.30 to 0.56)
1.34 (1.01 to 1.67)
0.85 (0.65 to 1.05)
0.54 (0.40 to 0.68)
0.08 (−0.01 to 0.17)
0.01 (−0.03 to 0.05)
0.00 (−0.02 to 0.02)
0.64 (0.45 to 0.83)
0.41 (0.29 to 0.53)
0.21 (0.14 to 0.28)
Sick
Years (CI)
Unemployed
Years (CI)
Temporary out
Years (CI)
Disability pension
Years (CI)
Death
Years (CI)
0.03 (−0.01 to 0.07)
0.03 (−0.01 to 0.07)
0.02 (−0.00 to 0.04)
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
AUTHOR PROOF
5
 BEU, Alm.del - 2019-20 - Bilag 255: Orientering om resultater fra et studie, gennemført af Det Nationale Forskningsinstitut for Arbejdsmiljø (NFA), om sammenhæng mellem høje fysiske krav i arbejdet og den forventede arbejdslivslængde, fra beskæftigelsesministeren
2197731_0006.png
Workplace
occupations involving physically demanding tasks.
12 32–37
The
excess risk of preterm exit from paid employment via disability
retirement in manual workers has been attributed to a range
of factors including educational level and exposure to physical
workload factors.
8 33–36
Although in our study, both men and women were negatively
affected by high physical work demands, we observed a clear
difference between the sexes, with women being more nega-
tively affected than men. There may be several explanations
for this finding. First, women have a lower physical capacity by
muscle strength than men, which increases the relative physical
work demands in situations of similar absolute work demands
(eg, lifting an object of 15 kg). Considering the inherent loss of
physical capacity with age in both men and women, women may
reach a critical threshold at a younger age than the men, where
the physical work is simply too demanding and withdrawal from
the labour market is more likely.
The loss of working years for the high-exposed men and
women was primary due to additional years of sickness absence
and unemployment, but not by an equivalent increase of disability
pension years. We suspect this finding is due to the disability
pension reform of 1 January 2013, after which it became more
difficult to be awarded a disability pension.
38
Thus, as a conse-
quence of this reform, an almost complete loss of workability is
required to obtain disability pension. Furthermore, the process
is usually longer than the 4-year follow-up period of the present
study.
other chronic diseases, like for example, diabetes or degenera-
tive joint diseases, increases. As chronic diseases increase the risk
of sickness absence, and so on,
39
it is very likely that the presence
of chronic diseases is affecting the WLE results, for example, by
increasing the bias of those with high physical work demands
who remain employees at age 60 years. For this purpose, an
alternative model with information on diagnosis-specific sick-
ness absence states could have been informative, but due to
Danish law, the reason for sickness absence is not registered,
and individual diagnosis-specific information is very difficult to
access for such a large sample.
Fifth, lifestyle factors like obesity and smoking are likely to
affect the labour market affiliation,
40
but the nature of their
role as confounders and/or mediators of high physical work
demands is unclear, and difficult to incorporate into a life course
approach. For example, one might assume that high physical
work demands lead to poor lifestyle and chronic disease, but
poor lifestyles may also influence what job type a person has.
26
The study uses a large register-based sample with no informa-
tion available on individual lifestyle factors. Sixth, the study
does not include other physical exposures like chemical and
psychosocial exposures that are likely to influence the WLE
results, for example, cause additional time of sickness absence,
for certain occupations. Seventh, the WLE estimation is prog-
nostic in nature, and is based on the theoretical assumption that
by cumulating the behaviour of employees of different ages one
can create a profile-specific behavioural pattern that represents
employees of all ages. Such an assumption only holds for the
purpose of predictions as long as the underlying conditions like
the economic situation are comparable and relative stable. Like-
wise, our results are probably restricted to countries with social
systems similar to Denmark. Finally, using a JEM-based expo-
sure estimate may underestimate the influence of physical work
demands on WLE, as it does not take into account the individual
variability in exposure within the job groups and may cause non-
differential misclassification of exposure.
AUTHOR PROOF
Strengths and limitations
The strengths of our study include the nationally representative
register data with rich and complete information on social bene-
fits, including sickness absence, disability retirement and unem-
ployment from the DREAM register and assessment of physical
work demands using sex-specific and age-specific JEM. The data
made it possible to identify transitions between the different
labour market states for each participant during the entire 4-year
follow-up and assign time-varying exposure status. As the data
on social benefits were register based and exposure was assessed
by the validated JEM, there was neither selection bias nor recall
bias. The large study sample and the detailed longitudinal data
allowed to provide reliable estimates of WLE with very narrow
CIs.
The study has limitations. First, the work state was defined
as not receiving social benefits. Such periods could also suggest
periods when living off, for example, savings, private pension
schemes or the income of others. This definition of the work
state is prone to misclassification bias and may cause an over-
estimation of the WLE. Second, the results of the study corre-
spond to the high contrast division of the high-exposed and the
low-exposed, and the study did not include a sensitivity analysis
by a second classification. Third, the reduced workability for
employees with high physical work demands is likely to be driven
by additional causes not included in the study. For example, may
low education levels and reduced job opportunities increase
the time of unemployment, when businesses—characterised by
high physical work demand—invest in automated production.
Though some individuals may be able to change occupation, for
example, through education, it might be difficult for others, for
example, due to age, social, and economic reasons.
Fourth, among employees with long-term exposure to phys-
ically heavy work, there is evidence of a higher risk of chronic
musculoskeletal disease than among employees with low physical
work demands. Overall, as people grow older, the risk of having
CONCLUSION
This study showed that high physical work demands are a marked
risk factor for a shortened expected working life and increased
years of sickness absence and unemployment. The effect for a
30-year-old woman is 3.1 years less of working, 11 more months
of sickness absence and 16 more months of unemployment. For
a 30-year-old man, the corresponding numbers are 2.0 years,
12 months and 8 months, respectively. The findings highlight
the urgency of addressing problems related to physical work
demands with regard to, for example, an increasing statutory
retirement age, and it identifies groups for which it is advisable
to place efforts, for example, young women with high physical
work demands.
Twitter
lars l andersen @larslandersen
Contributors
JP wrote the original manuscript draft and designed the study,
BBs conducted the analysis and contributed to writing the results section. iehM
constructed the job exposure matrices, contributed to writing the manuscript and the
interpretation of the results. ss and lla oversaw the study design and interpretation
of the results and contributed to writing the final manuscript. The corresponding
author had full access to all data and had final responsibility to submit for
publication.
Funding
The study was supported by nordForsk (grant number 76659) (JP, ss); and
the nordic council of Ministers (grant number 101250) (JP, ss).The funders of the
study had no role in study design, data collection, data analysis, data interpretation
or writing of the report.
Competing interests
none declared.
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
6
 BEU, Alm.del - 2019-20 - Bilag 255: Orientering om resultater fra et studie, gennemført af Det Nationale Forskningsinstitut for Arbejdsmiljø (NFA), om sammenhæng mellem høje fysiske krav i arbejdet og den forventede arbejdslivslængde, fra beskæftigelsesministeren
2197731_0007.png
Workplace
Patient consent for publication
not required.
Ethics approval
according to Danish law, research studies that use solely register
data do not need approval from the national committee on health research ethics
(Den nationale Videnskabetiske Komité).
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. Data is available on the researcher access at statistics
Denmark, see www.dst.dk/en/Tilsalg/Forskningsservice.
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
Jacob Pedersen
http://orcid.org/0000-0003-4429-3485
ida e h Madsen
http://orcid.org/0000-0003-3635-3900
lars l andersen
http://orcid.org/0000-0003-2777-8085
16 Willekens FJ. Multistate analysis: tables of working life.
Environ Plan A
1980;12:563–88.
17 smith sJ.
Tables of working life: the increment-decrement model: bulletin 2135.
Washington, Dc: Us Department of labor, Bureau of labor statistics, 1982. www.
fraser.stlouisfed.org/files/docs/publications/bls/bls_2135_1982.pdf
18 nurminen M, nurminen T. Multistate worklife expectancies.
Scand J Work Environ
Health
2005;31:169–78.
19 Pedersen J, Bjorner JB. Worklife expectancy in a cohort of Danish employees aged
55-65 years - comparing a multi-state cox proportional hazard approach with
conventional multi-state life tables.
BMC Public Health
2017;17:879.
20 Dudel c, lópez gómez Ma, Benavides Fg,
et al.
The length of working life in
spain: levels, recent trends, and the impact of the financial crisis.
Eur J Popul
2018;34:769–91.
21 loichinger e, Weber D. Trends in working life expectancy in europe.
J Aging Health
2016;28:1194–213.
22 Pedersen J, Thorsen sV, schultz BB. Working life expectancy in Denmark [Den
forventede arbejdslivslængde i Danmark. et studie af arbejdsmiljøet og fastholdelse
på arbejdsmarkedet]. national research centre for the Working environment.
copenhagen. available: http://nfa.dk/da/Forskning/Udgivelse?journalid=1f2d0f09-
e273-4c03-9691-bb1b0a016fd1 [accessed 31 Dec 2018].
23 de Wind a, van der noordt M, Deeg DJh,
et al.
Working life expectancy in good and
poor self-perceived health among Dutch workers aged 55-65 years with a chronic
disease over the period 1992-2016.
Occup Environ Med
2018;75:792–7.
24 sirén M, Viikari-Juntura e, arokoski J,
et al.
Work participation and working life
expectancy after a disabling shoulder lesion.
Occup Environ Med
2019;76:363–9.
25 robroek sJ, nieboer D, Järvholm B,
et al.
educational differences in duration of
working life and loss of paid employment: working life expectancy in the netherlands.
Scand J Work Environ Health
2020;46:77–84.
26 Pedersen J, Thorsen sV, andersen MF,
et al.
impact of depressive symptoms on
worklife expectancy: a longitudinal study on Danish employees.
Occup Environ Med
2019;76:838–44.
27 Danmarks statistiks. arbejdsstyrken øget med 35.000 personer. available: www.dst.
dk/da/statistik/nyt/nythtml?cid=27897 [accessed 10 aug 2019].
28 Madsen PK.
How can it possibly fly?: the paradox of a dynamic labor market in
a Scandinavian welfare state. CARMA research papers; 2.
38. carMa: aalborg
Universitet, 2005. www.citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.498.
5526&rep=rep1&type=pdf
29 hjollund nh, larsen FB, andersen Jh. register-based follow-up of social benefits
and other transfer payments: accuracy and degree of completeness in a Danish
interdepartmental administrative database compared with a population-based survey.
Scand J Public Health
2007;35:497–502.
30 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.
31 Beyersmann J, Putter h. a brief note on computing average state occupation times.
Demogr Res
2014;30:1681–96.
32 sundstrup e, hansen Åse Marie, Mortensen el,
et al.
cumulative occupational
mechanical exposures during working life and risk of sickness absence and disability
pension: prospective cohort study.
Scand J Work Environ Health
2017;43:415–25.
33 haukenes i, Mykletun a, Knudsen aK,
et al.
Disability pension by occupational class-
-the impact of work-related factors: the hordaland health study cohort.
BMC Public
Health
2011;11:406.
34 Polvinen a, laaksonen M, gould r,
et al.
socioeconomic differences in cause-specific
disability retirement in Finland, 1988 to 2009.
J Occup Environ Med
2016;58:840–5.
35 leinonen T, Pietiläinen O, laaksonen M,
et al.
Occupational social class and disability
retirement among municipal employees--the contribution of health behaviors and
working conditions.
Scand J Work Environ Health
2011;37:464–72.
36 solovieva s, Kontio T, Viikari-Juntura e. Occupation, physical workload factors,
and disability retirement as a result of hip osteoarthritis in Finland, 2005-2013.
J
Rheumatol
2018;45:555–62.
37 Kadefors r, nilsson K, Östergren P-O,
et al.
social inequality in working life
expectancy in sweden.
Z Gerontol Geriatr
2019;52:52–61.
38 evaluation: reform of early retirement and flex jobs well underway but still challenges.
available: https://bm.dk/nyheder-presse/pressemeddelelser/2018/03/evaluering-
reform-af-foertidspension-og-fleksjob-godt-paa-vej-men-fortsat-udfordringer/
[accessed 13 nov 2019].
39 van den Berg T, schuring M, avendano M,
et al.
The impact of ill health on exit
from paid employment in europe among older workers.
Occup Environ Med
2010;67:845–52.
40 Virtanen M, ervasti J, head J,
et al.
lifestyle factors and risk of sickness absence from
work: a multicohort study.
Lancet Public Health
2018;3:e545–54.
REFERENCES
1 ilmarinen J. The ageing workforce--challenges for occupational health.
Occup Med
2006;56:362–4.
2 The Danish agency for labour Market and recruitment (sTar). Folkepensionsalderen
nu og fremover. available: https://star.dk/ydelser/pension-og-efterloen/folkepension-
og-foertidspension/folkepension/folkepensionsalderen-nu-og-fremover/ [accessed 30
May 2019].
3 lunenfeld B, stratton P. The clinical consequences of an ageing world and preventive
strategies.
Best Pract Res Clin Obstet Gynaecol
2013;27:643–59.
4 World health Organization. european health report 2018, 2019. available: http://
www.euro.who.int/en/data-and-evidence/european-health-report/european-health-
report-2018 [accessed 30 May 2019].
5 schuring M, robroek sJW, Otten FWJ,
et al.
The effect of ill health and socioeconomic
status on labor force exit and re-employment: a prospective study with ten years
follow-up in the netherlands.
Scand J Work Environ Health
2013;39:134–43.
6 etman a, Burdorf a, Van der cammen TJM,
et al.
socio-demographic determinants
of worsening in frailty among community-dwelling older people in 11 european
countries.
J Epidemiol Community Health
2012;66:1116–21.
7 newton rU, hakkinen K, hakkinen a,
et al.
Mixed-methods resistance training
increases power and strength of young and older men.
Med Sci Sports Exerc
2002;34:1367–75.
8 robroek sJW, Järvholm B, van der Beek aJ,
et al.
influence of obesity and physical
workload on disability benefits among construction workers followed up for 37 years.
Occup Environ Med
2017;74:621–7.
9 Veiersted B, Knardahl s, Wærsted M. Mekaniske eksponeringer i arbeid som årsak
til muskel- og skjelettplager. sTaMi, 2017. available: https://stami.no/ny-rapport-
mekaniske-eksponeringer-i-arbeid-som-arsak-til-muskel-og-skjelettplager/ [accessed
20 Feb 2018].
10 andersen ll, Fallentin n, Thorsen sV,
et al.
Physical workload and risk of long-
term sickness absence in the general working population and among blue-collar
workers: prospective cohort study with register follow-up.
Occup Environ Med
2016;73:246–53.
11 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.
12 andersen ll, Villadsen e, clausen T. influence of physical and psychosocial working
conditions for the risk of disability pension among healthy female eldercare workers:
prospective cohort.
Scand J Public Health:140349481983182
(epub ahead of print
February 28, 2019).
13 van den Berg TiJ, elders laM, de Zwart Bch,
et al.
The effects of work-related and
individual factors on the work ability index: a systematic review.
Occup Environ Med
2009;66:211–20.
14 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.
15 Fridriksson JF, Tómasson K, Midtsundstad T,
et al.
Working environment and work
retention. nordisk Ministerråd, 2017. available: http://urn.kb.se/resolve?urn=urn:nbn:
se:norden:org:diva-5061 [accessed 25 apr 2019].
Pedersen J,
et al. Occup Environ Med
2020;0:1–7. doi:10.1136/oemed-2019-106359
AUTHOR PROOF
7