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Scand J Work Environ Health

Online-first -article

Published online: 05 Jul 2022

doi:10.5271/sjweh.4050

Physical work demands and expected labor market affiliation

(ELMA): Prospective cohort with register-follow-up among 46

169 employees

Pedersen J, Bjorner JB, Andersen LL

Using the Expected Labor Market Affiliation (ELMA) method during

two-year follow-up, we show that: • Middle-aged and older workers

with high physical work demands have markedly reduced active labor

market affiliation. • ‘moderate’ to ‘very high’ levels of physical work

demands is associated with up to 35 days reduced active

working-time - mainly due to increased sickness absence.

Affiliation:

National Research Centre for the Working Environment

Lersø Parkallé 105. DK-2100 Copenhagen Ø, Denmark. [email protected]

Refers to the following texts of the Journal:

2020;46(1):77-84

2021;47(1):5-14

Key terms:

ELMA; expected labor market affiliation; labor market

affiliation; longitudinal; multi-state; physical work demand;

prospective cohort; register-follow-up; sickness absence;

unemployment; work

This article in PubMed:

www.ncbi.nlm.nih.gov/pubmed/35789276

Additional material

Please note that there is additional material available belonging to

this article on the

Scandinavian Journal of Work, Environment & Health

-website.

This work is licensed under a

Creative Commons Attribution 4.0 International License.

Print ISSN: 0355-3140 Electronic ISSN: 1795-990X

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riginal article

Scand J Work Environ Health – online first: 5 July 2022. doi:10.5271/sjweh.4050

This work is licensed under a Creative Commons Attribution

4.0 International License.

Physical work demands and expected labor market affiliation (ELMA): Prospective

cohort with register-follow-up among 46 169 employees

By Jacob Pedersen, PhD,

Jakob Bue Bjorner, PhD,

1–3

Lars L Andersen, PhD

Pedersen J, Bjorner JB, Lars L Andersen LL. Physical work demands and expected labor market affiliation (ELMA): Prospective

cohort with register-follow-up among 46 169 employees.

Scand J Work Environ Health

– online first. doi:10.5271/sjweh.4050

Objective

This study aimed to estimate the impact of high physical work demands on expected labor market

affiliation (ELMA) among men and women of different ages in the general working population.

Methods

After participating in the Danish Work Environment and Health study (2012, 2014, and/or 2016), 46

169 employees were followed for two years in national registers. Using multi-state modeling, taking all day-

to-day transition probabilities of labor market affiliation into account (work, unemployment, sickness absence,

temporary out, and permanently out), and performing multilevel adjustment, we estimated the prospective asso-

ciation between physical work demands (ergonomic index including 7 factors) and ELMA.

Results

During 104 896 person-years of follow-up, we identified of 439 045 transitions. Using low physical

work demands as reference, higher physical work demands were associated with fewer days of active work (2–35

days) during 730 days of follow-up, and more days of sickness absence (4–26 days) and unemployment (ranging

1-9 days) among men and women of aged 40–49 and 50–64 years. Among men and women aged 18–39 years,

high physical work demands only had minor and inconsistent impact on ELMA.

Conclusions

Analyzing multiple and highly detailed patterns of transition probabilities concerning labor market

affiliation, we showed that reducing physical work demands is likely to increase the active working time and pre-

vent high societal cost of sickness absence and unemployment, especially among middle-aged and older workers.

Key words

multi-state; longitudinal; sickness absence; unemployment; work.

Several job groups are characterized by high physi-

cal work demands, eg, painters, bricklayers, masons,

carpenters, cleaners, industrial labor, manufacturing

labor, and service work (1). Even with technological

advances, many job groups will likely continue having

high physical work demands in the future. Additionally,

the 2021 National Health Profile in Denmark shows that

the prevalence of disc herniation or other back diseases

increases from 6.6% for men and 7.4% for women aged

25–34 years to 21.5% for men and 20.2% for women

age 55–64 years, respectively. The prevalence of osteo-

arthritis is even higher, especially among women, where

it increases from 23.2% at 45–54 years to 41.0% at

55–64 years (2).

Previous studies have shown that high physical

work demands increase the risk of sickness absence

(3–6). These studies typically rely on risk assessment

of a single outcome – like the probability of a transition

from work to sickness absence – while leaving infor-

mation about other labor market outcomes unattended.

However, in several European countries, including the

Scandinavian countries, the labor market is quite flex-

ible meaning that individuals are likely to have multiple

periods of sickness absence without being fired, and to

have recurrent events of unemployment.

Multi-state analysis is an effective way of analyzing

the impact on the labor market affiliation when the sys-

tem is highly flexible and contain multiple states. This

study uses the expected labor market affiliation (ELMA)

method developed by Pedersen et al (7) for analyzing the

impact on labor market affiliation of Danish employees

having different levels of physical work demands. The

1 National Research Centre for the Working Environment, Copenhagen, Denmark

2 Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

3 QualityMetric, Lincoln, RI, USA.

Correspondence to: Jacob Pedersen, National Research Centre for the Working Environment Lersø Parkallé 105. DK-2100 Copenhagen Ø,

Denmark. [[email protected]]

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Physical work demands and expected labor market affiliation

ELMA method relies on multi-state modeling of the

labor market system for analyzing multiple transitions

and summarizing the effect into expected durations

of each state (8–10). In addition, the ELMA method

provides the possibility to include variables that may

change during follow-up, eg, the individual level of

education or civil status, adjustment for multiple vari-

ables simultaneously, and weights for making the results

representative.

The aim of the present study is to estimate the impact

of high physical work demands on ELMA among men

and women of different ages in the general Danish work-

ing population. The analyses rely on multi-state model-

ing of the labor market transitions and focus on time in

work, sickness absence, and unemployment.

not include small private companies with <10 employ-

ees and these are therefore not included in the present

study. Small companies represent a large part of private

companies (approximately 260 000 small private com-

panies exist in Denmark) (15). RoWA contains weights

for making the private sample representative to all

private employees in companies with ≥10 employees.

The Education Register contains records of the highest

education level completion for all Danes. The Emigra-

tion and Immigration Register contains dates on all

emigrations and immigrations in Denmark. The Death

The linked data set contains individual and date-

based information on labor market affiliation and indi-

vidual characteristics retained from the surveys.

Study sample and data preparation

Methods

Study design and source population

This longitudinal study uses a linkage of registers and

survey data on physical work demands from three suc-

cessive waves of the Work Environment and Health in

Denmark (WEHD) survey conducted in 2012, 2014,

and 2016 (11, 12). The survey data was linked to other

registers through an encrypted version of the central per-

son register number (13). All WEHD responders, aged

18–64 years, were included and followed in registers for

two years from the day they answered the questionnaire.

The WEHD surveys were linked with the following

registers, provided by Statistics Denmark: (i) the Dan-

ish Labor Market Accountant Register (LMAR), (ii)

(v) the Death Register. LMAR contains information on

all major social benefits payments, including unemploy-

ment, sickness absence, disability pension, pension, and

all salary payments reported to the tax authorities from

2008 onwards.

RoWA is a linkage of the Absence and Employment

(FRPE), both from Statistics Denmark. FRPE includes

date-based information about sickness absence spells

from the first day of absence, and FRAN includes date-

based employment information of employees with and

without sickness absence spells (11). RoWA contains

records of both public and private employees. The date-

based records of sickness absence spells are complete

for all public employees and private companies with

>250 employees. RoWA contains a yearly weighted

sample from companies with 10–250 employees (14).

This means that RoWA covers approximately 37% of

all private employees in Denmark (11). RoWA does

The WEHD data included 67 053 individuals of which

63 912 (95.3%) were eligible for the current study.

Receivers of disability pension or retires at the start of

the follow-up period (N=2945), individuals aged >64

years at the start of the follow-up (N=195, 28% women),

or not found in LMAR (N=1) were excluded.

In RoWA, (i) all records for public employment have

the weight one and (ii) all records for private employ-

ment have a specialized weight that is constructed

based on the sampling probability. RoWA only includes

records of individuals in employment, but in this study,

the weights were carried forward in LMAR to include

periods of unemployment etc, but only until a new

employment period.

Records from LMAR that could not be linked to a

private or public employment in RoWA were excluded

(~7%. 0.6 million records). Similarly, records of pri-

vate employments without a weight (9%), and public

employment period with a specialized weight (0.1%)

were excluded.

The final sample was divided into six subsamples

according to gender and age-range at the start of the

follow-up period (18–39, 40–49, and 50–64 years),

prioritizing clearly defined age-intervals over an even

number of individuals in each age category. Of the N=46

169 individuals, 78% answered one of the three waves

of questionnaires, 8% two questionnaires, and 14%

answered all three waves of questionnaires – totalling

62 677 follow-up periods.

Physical work demand

Physical work demands were measured through an

ergonomic index, which was constructed by seven ques-

tions (please see supplementary material,

www.sjweh.

fi/article/4050,

A). For individuals answering all seven

questions, an average score was calculated ranging from

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Pedersen et al

0–100. All other individuals were registered by a ‘miss-

ing’ category. In accordance with Andersen et al (1), the

individual average score was categorized into four cat-

egories: low (0–10), moderate (>10–20), high (>20–30),

and very high (>30) physical work demands. The ergo-

nomic index has shown to predict the risk of long-term

sickness absence using standard Cox-regression (1).

Covariates and weights

The analysis includes nine covariates previously used

in studies about physical occupational exposures and

physical health in relation to long-term sickness absence

(16–18) and work disability (19, 20). The covariates

are associated with adverse health outcomes, possible

through selection, eg selection into part time work, or

through causation, eg, smoking and sickness absence.

Six variables were included from WEHD: (i) work-

ing time arrangement (Part-time: <37 hours/week or

full time: ≥37 hours/week); (ii) body mass index (BMI)

(<18.0, 18.5–<25.0, 25.0–<29.9, and ≥29.9 kg/m

); (iii)

smoking (yes: daily and sometimes; no: prior smoker

and never); (iv) physical activity “How much time on

average do you use on each of the following physical

activities in the last year?” as “exercise, heavy gardening

or fast walking / cycling where you sweat and getting

short of breath?” with answers dichotomized as (yes:

<2, 2–4 and >4 hours/week; no: “Does not practice this

activity” and missing); (v) disease treatment – in terms

of a dichotomy variable indicating if the individual has

had treatment for one of the following diseases (no/

yes): depression, asthma, diabetes, atherosclerosis or

blood clot in the heart, blood clot in the brain (cerebral

hemorrhage), cancer, back disease, migraine, or other

long-term disease; (vi) symptoms of depression, defined

by the individual Major Depression Index (MDI) score

(depressive symptoms: ≥21; no depressive symptoms:

<21) (21); (vii) employment sector (private/public)

variable was obtained from FRAN; (viii) highest accom-

plished education (low/middle/high) variable obtained

from the Education Registers; and (ix) “number of sur-

vey waves” was constructed to account for the number

of WEHD survey waves the individual had attended –1

of 3, 2 of 3, and 3 of 3. Variable (vii–viii) was allowed to

change during the follow-up period, while the variables

obtained from the surveys (i–vi) could only change if the

individual participated in a new survey wave.

Labor market affiliation

The labor market affiliation was modeled by seven mutu-

ally exclusive labor market states based on the longitu-

dinal registrations of LMAR and RoWA. Of the seven

states, four are categorized as recurrent states, meaning

that multiple individual periods of the same state are

possible: (i)

work

reflecting the periods of receiving

salary payments and not simultaneously registered as

sick-listed; (ii)

sickness absence

for periods when the

individual is registered as sick-listed by the employer or

receiving sickness absence benefit; (iii)

unemployment

for periods when a person receives social benefit related

to unemployment, given the condition that the person

is immediately available for work if such opportunity

arises; (iv)

temporary out

for periods when an individual

is not in the work, sickness absence, or unemployment

states but with the possibility of returning to those states.

This state contains the time of for example maternity

leave, emigration, periods of education, and periods with

no registration. The three absorbing states suggests that

no further transitions are possible after the first entry;

(v)

disability pension

when receiving full or gradually

disability retirement pension due to personal disability;

(vi)

retirement

due to receiving age retirement pension

or the voluntary retirement pension; and (vii)

death

(supplementary material B contains a short introduction

to the Danish labor market and social system).

Individuals start the follow-up in any of the four

recurrent states.

Statistical analysis

The study uses the Expected Labor Market Affiliation

(ELMA) method developed by Pedersen et al (7), which

relies on estimated transition probabilities between the

possible states of the multi-state model. The ELMA

incorporates both time-dependent variables and time-

dependent weights in terms of eg, inverse probability

weights. The ELMA uses a non-parametric approach

except for the confidence estimation of the expected

state duration results.

For each subsample of gender and age groups, we

estimated the time-dependent baseline probability for

every transition of the multi-state model according to the

reference value of the covariates. The transitions prob-

abilities for the non-reference values were estimated by

adjusting the corresponding baseline probabilities with

estimates derived from Cox proportional hazard regres-

sion. The Cox regressions were conducted on the entire

multi-state model with the data arranged in a long format

(22). Based on the transition probabilities we estimated

the state probabilities – expressing the probability of

being in one of the seven states from day one and until

day 730 (two years).

We summarized the area under each transition prob-

ability and state probability curve for each combination

of covariates.

Assuming normally distributed area estimates, we

produced 500 random resamples and conducted a vari-

ance regression model. This was done in order to pro-

duce the final estimates of state duration including

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Physical work demands and expected labor market affiliation

95% confidence intervals (CI). All variables, except the

ergonomic index variable, were incorporated into the

model as inverse probability weights and multiplied by

the weights from the employment register.

For light comparison with and control of the ELMA

results, a crude estimate of the time spent in each state

was made. This was done by summing the time spent in

each state during the follow-up period and then dividing

by the number of individual follow-up periods.

Results

Table 1 shows that despite a slight predominance of

women (59% women) in the sample, the proportion of

individuals in each age group are comparable between

the genders (mean age by gender and age group – men:

31.6, 45.3, 56.6 years; women: 31.3, 45.2, 56.2 years).

Similar comparability is seen for levels of the ergonomic

index.

Figure 1 shows that during the follow-up period, the

transitions between the work and the sickness absence

states were the most frequent, with the highest transi-

tion incidence for women. A high transition incidence

for women is moreover seen for the transitions between

work and unemployment, though not as frequent as

between work and sickness absence, and between work

and temporary out, when compared to the respective

transitions incidences of the men.

Table 2 shows that for men, the risk of a transition to

sickness absence from work increases with an increasing

level of physical work demands – except for young men

with moderate and high physical work demands. The

Table 1.

Descriptive baseline characteristics at start of the first follow-up period

Men

18–39 years

N (%)

Total

Physical work demand

Low

Moderate

High

Very high

Not available

Working time

Full-time

Part-time

Not available

Body mass index

Underweight

Normal

Overweight

Obesity

Not available

Smoking

Non-smoker

Smoker

Not available

Physical activity

Yes

Depression symptoms

Yes

Not available

Disease treatment

Yes

Employment sector

Private

Public

Highest educational level

Short

Medium

Long

Not available

Number of survey waves

1 of 3

2 of 3

3 of 3

5701 (30)

1838 (32)

1334 (23)

686 (12)

1195 (21)

648 (11)

4589 (80)

801 (14)

311 (5)

32 (1)

2667 (47)

1751 (31)

572 (10)

679 (12)

3930 (69)

1168 (20)

603 (11)

3266 (57)

2435 (43)

3793 (67)

1300 (23)

608 (11)

4623 (81)

1078 (19)

3369 (59)

2332 (41)

648 (11)

2368 (42)

2665 (47)

20 (0)

4651 (82)

491 (9)

559 (10)

40–49 years

N (%)

5248 (28)

1929 (37)

1444 (28)

704 (13)

894 (17)

277 (5)

4773 (91)

327 (6)

148 (3)

9 (0)

1942 (37)

2244 (43)

795 (15)

258 (5)

4027 (77)

979 (19)

242 (5)

2887 (55)

2361 (45)

3899 (74)

1109 (21)

240 (5)

3951 (75)

1297 (25)

3468 (66)

1780 (34)

603 (11)

2223 (42)

2392 (46)

30 (1)

4076 (78)

436 (8)

736 (14)

50–64 years

N (%)

7945 (42)

2651 (33)

2379 (30)

1189 (15)

1303 (16)

423 (5)

7129 (90)

533 (7)

283 (4)

18 (0)

2696 (34)

3622 (46)

1246 (16)

363 (5)

6026 (76)

1606 (20)

313 (4)

4603 (58)

3342 (42)

6210 (78)

1414 (18)

321 (4)

5360 (67)

2585 (33)

4601 (58)

3344 (42)

1291 (16)

3590 (45)

3004 (38)

60 (1)

6080 (77)

536 (7)

1329 (17)

18–39 years

N (%)

8749 (32)

2443 (28)

2028 (23)

1335 (15)

1771 (20)

1172 (13)

4939 (56)

2949 (34)

861 (10)

232 (3)

4773 (55)

1590 (18)

830 (9)

1324 (15)

6186 (71)

1462 (17)

1101 (13)

5398 (62)

3351 (38)

5116 (58)

2527 (29)

1106 (13)

6442 (74)

2307 (26)

2560 (29)

6189 (71)

626 (7)

3147 (36)

4951 (57)

25 (0)

7083 (81)

814 (9)

852 (10)

Women

40–49 years

N (%)

7828 (29)

2786 (36)

2159 (28)

1142 (15)

1277 (16)

464 (6)

5078 (65)

2470 (32)

280 (4)

105 (1)

4111 (53)

2090 (27)

1068 (14)

454 (6)

6094 (78)

1365 (17)

369 (5)

4687 (60)

3141 (40)

5461 (70)

1997 (26)

370 (5)

5266 (67)

2562 (33)

2308 (29)

5520 (71)

552 (7)

3053 (39)

4208 (54)

15 (0)

6010 (77)

683 (9)

1135 (14)

50–64 years

N (%)

10 698 (39)

3389 (32)

3190 (30)

1759 (16)

1763 (16)

597 (6)

6576 (61)

3683 (34)

439 (4)

160 (1)

5501 (51)

3032 (28)

1424 (13)

581 (5)

8215 (77)

2028 (19)

455 (4)

6474 (61)

4224 (39)

7705 (72)

2544 (24)

449 (4)

6911 (65)

3787 (35)

2506 (23)

8192 (77)

1508 (14)

4198 (39)

4957 (46)

35 (0)

8174 (76)

722 (7)

1802 (17)

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Pedersen et al

Figure 1.

The Multi-state model with boxes as states

and arrow as transitions including the number of

transitions per 1000 person-years during follow-up

for men (M) and women (W).

Table 2.

Hazard ratios (HR) and 95% confidence intervals (CI) for the transitions between the states of work, sickness absence, and unemployment

state.

Physical work demand

18-39 years

HR (95% CI)

Men

40-49 years

HR (95% CI)

50-64 years

HR (95% CI)

18-39 years

HR (95% CI)

Women

40-49 years

HR (95% CI)

50-64 years

HR (95% CI)

1.00 (-)

1.04 (0.96-1.13)

1.03 (0.95-1.12)

1.05 (0.96-1.15)

1.00 (-)

1.32 (0.88-1.97)

2.24 (1.45-3.45)

3.16 (1.83-5.45)

1.00 (-)

0.74 (0.60-0.91)

0.70 (0.57-0.85)

0.60 (0.50-0.71)

1.00 (-)

2.20 (0.92-5.27)

0.86 (0.44-1.71)

1.32 (0.73-2.36)

1.00 (-)

1.04 (0.65-1.66)

1.41 (0.86-2.31)

2.10 (1.32-3.35)

1.00 (-)

2.00 (1.09-3.68)

0.75 (0.38-1.48)

1.23 (0.73-2.07)

Work to sickness absence

Low

1.00 (-)

Moderate

0.95 (0.83-1.10)

1.14 (1.00-1.30)

1.11 (1.00-1.24)

High

0.94 (0.76-1.16)

1.37 (1.14-1.65)

1.28 (1.13-1.46)

Very high

1.25 (1.08-1.45)

1.48 (1.30-1.70)

1.36 (1.19-1.55)

Work to Unemployment

Low

1.00 (-)

Moderate

1.92 (1.02-3.63)

1.91 (0.87-4.20)

1.76 (0.82-3.77)

High

1.77 (0.94-3.31) 7.41 (2.30-23.83)

2.63 (1.44-4.80)

Very high

3.56 (1.97-6.43)

3.45 (1.54-7.74)

5.37 (2.22-12.96)

Sickness absence to Work

Low

1.00 (-)

Moderate

1.17 (0.84-1.63)

0.75 (0.54-1.03)

1.01 (0.77-1.34)

High

0.89 (0.62-1.30)

0.53 (0.35-0.81)

0.65 (0.45-0.93)

Very high

0.73 (0.49-1.08)

0.49 (0.38-0.64)

0.84 (0.63-1.13)

Sickness absence to unemployment

Low

1.00 (-)

Moderate

1.06 (0.38-3.00)

1.66 (0.36-7.63)

0.87 (0.36-2.12)

High

2.57 (0.88-7.47)

0.00 (0.00-0.02)

0.85 (0.26-2.73)

Very high

2.84 (0.94-8.57)

0.66 (0.16-2.75)

3.79 (1.45-9.90)

Unemployment to Work

Low

1.00 (-)

Moderate

1.46 (0.80-2.67)

2.34 (1.02-5.37)

1.84 (1.08-3.16)

High

1.15 (0.56-2.37)

2.34 (0.90-6.10)

1.12 (0.57-2.21)

Very high

1.47 (0.97-2.23)

2.55 (1.09-5.94)

1.49 (0.85-2.61)

Unemployment to Sickness absence

Low

1.00 (-)

Moderate

1.32 (0.47-3.73)

2.80 (0.53-14.82)

0.77 (0.30-2.00)

High

1.35 (0.37-4.87)

2.43 (0.37-16.08)

1.29 (0.29-5.70)

Very high

0.86 (0.33-2.29)

1.33 (0.22-8.11)

2.48 (1.04-5.87)

1.00 (-)

0.96 (0.87-1.05)

1.08 (0.99-1.16)

1.10 (1.00-1.22)

1.18 (1.06-1.32)

0.96 (0.81-1.13)

1.10 (0.99-1.22)

1.00 (-)

1.54 (1.04-2.28)

1.63 (1.00-2.68)

1.83 (1.14-2.95)

1.97 (1.14-3.41)

2.72 (1.53-4.84)

12.23 (2.64-56.62)

1.00 (-)

0.98 (0.80-1.22)

0.78 (0.66-0.93)

0.94 (0.75-1.18)

0.74 (0.60-0.91)

0.98 (0.80-1.19)

0.56 (0.41-0.76)

1.00 (-)

2.12 (0.95-4.71)

0.92 (0.53-1.60)

1.33 (0.63-2.83)

0.65 (0.34-1.26)

2.32 (1.01-5.29)

0.96 (0.51-1.82)

1.00 (-)

1.36 (0.92-1.99)

1.35 (0.86-2.14)

1.46 (0.91-2.35)

1.81 (1.13-2.91)

1.69 (1.09-2.61)

2.97 (1.78-4.97)

1.00 (-)

1.86 (0.95-3.62)

1.05 (0.58-1.90)

1.53 (0.79-2.99)

0.92 (0.46-1.83)

1.53 (0.75-3.13)

0.72 (0.23-2.28)

1% significant.

5% significant

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Physical work demands and expected labor market affiliation

Figure 2.

The

expected labor market affiliation (

ELMA)

results by the expected duration (+/-) of working time,

sickness absence, unemployment, and temporarily out

(of 730 days) when compared to the absolute duration

time of individuals with low physical work demands.

Grouped by gender and age.

highest risk is seen for men aged 40–49 years having

very high physical work demands (48%). A similar pat-

tern is not seen for the women, but both men and women

aged ≥40 years have lover likelihood of returning to

work from sickness absence if they experienced high

and very high physical work demands. The risk of being

unemployed is highly associated with high or very high

physical work demands – only a moderately equivalent

likelihood is seen for a transition back to work.

Supplementary material table C1 shows – in supple-

ment to figure 1 and table 2 – the raw number of transi-

tions (events) occurring between work, sickness absence,

and unemployment along with the unadjusted transition

incidences by the number of events per 1000 person-years.

Figure 2 shows that the additional expected time in

work, sickness absence, and unemployment for young

men and women is mostly unaffected by physical

work demands. However, a steep increased in sickness

absence time is seen for the middle-aged men (6, 19, and

23 days respectively) and women (8, 11, and 26 days

respectively) by increasing physical work demands with

a parallel decline in working time (table 3 presents the

precise estimates).

For the oldest age group, the effect of physical

demand level is more complex. Women with moderate

and high physical work demands experience an almost

identical increase of sickness absence time (12 and 13

days, respectively), while women with very high physi-

cal work demands experience additionally 23 sickness

absence days – compared to 28 for women with low

physical work demands.

Moderate physical work demands have almost no

effect on the men aged 50–64 years. However, a high

level inflicts additionally 20 days of sickness absence,

while a very high level inflicts 12 additional days of

sickness absence and 9 additional days of unemploy-

ment. The time in the 'temporary out' state is highly

uncertain for men with high and very high physical

work demands.

For men and women aged 50–64 years, one can cal-

culate expected time spend in retirement (supplementary

table 1D). However, for those having moderate, high and

very high physical work demands, the results show a

small decline in retirement time – most for men having

a high level (10 days) and next for women with very

high physical work demands (8 days).

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Pedersen et al

Table 3.

The expected labor market affiliation (ELMA) and crude mean results of the expected change (+/-) in duration of working time, sickness

absence, unemployment, and temporarily out (per 730 days) when compared to the absolute duration time of individuals with low level of physi-

cal work demands (reference group). Grouped by gender and age. Supplementary tables (1B), shows the additional results of the three absorbing

states. [CI=confidence interval.]

Physical work demand

ELMA

Men

18-39 years

Low (reference)

Moderate

High

Very high

40-49 years

Low (reference)

Moderate

High

Very high

50–64 years

Low (reference)

Moderate

High

Very high

Women

18-39 years

Low (reference)

Moderate

High

Very high

40-49 years

Low (reference)

Moderate

High

Very high

50–64 years

Low (reference)

Moderate

High

Very high

Work

Crude

days

days (95% CI)

Sickness absence

ELMA

days (95% CI)

Crude

days

Unemployment

ELMA

days (95% CI)

5.4 (3.4:7.4)

- 0.2 (-3.0:2.6)

+ 2.3 (-0.5:5.1)

+ 5.2 (2.4:8.0)

2.8 (1.6:4.1)

+ 1.3 (-0.5:3.1)

+ 6.9 (5.1:8.7)

+ 3.8 (2.0:5.6)

3.6 (2.3:4.9)

+ 0.7 (-1.1:2.5)

+ 5.2 (3.4:7.1)

+ 9.4 (7.5:11.2)

9.2 (7.5:10.9)

- 0.0 (-2.4:2.4)

+ 0.3 (-2.1:2.7)

+ 4.3 (1.9:6.7)

6.0 (4.6:7.4)

+ 0.8 (-1.2:2.8)

+ 4.0 (2.0:6.0)

5.0 (3.7:6.3)

+ 3.7 (1.9:5.5)

+ 5.3 (3.5:7.1)

Crude

days

5.4

+ 2.3

+ 3.3

+ 7.8

1.8

+ 2.1

+ 4.1

+ 3.9

3.5

+ 0.6

+ 4.5

+ 8.9

7.9

+ 1.9

+ 3.3

+ 6.2

5.3

+ 1.3

+ 2.6

+ 7.4

5.2

+ 2.3

+ 3.7

+ 5.5

Temporary Out

ELMA

days (95% CI)

31.2 (28.3:34.1)

- 2.1 (-6.3:2.0)

- 13.4 (-17.5:-9.3)

- 9.9 (-14.1:-5.8)

9.9 (8.7:11.1)

- 7.3 (-9.0:-5.6)

- 6.8 (-8.5:-5.1)

+ 1.7 (0.1:3.4)

4.8 (3.5:6.0)

- 0.0 (-0.2:0.2)

+ 0.7 (0.5:0.9)

+ 0.5 (0.3:0.7)

61.5 (57.0:66.1)

- 6.4 (-12.8:0.0)

+ 6.8 (0.4:13.2)

- 2.6 (-9.0:3.8)

6.3 (5.2:7.5)

+ 1.5 (-0.2:3.1)

+ 5.7 (4.0:7.3)

+ 0.2 (-1.4:1.9)

5.6 (4.5:6.6)

- 1.3 (-2.8:0.1)

- 2.8 (-4.3:-1.3)

- 0.6 (-2.1:0.9)

Crude

days

23.6

+ 5.4

+ 4.3

- 0.7

6.0

- 2.7

+ 0.8

5.3

+ 1.5

- 0.2

- 0.8

61.0

+ 3.2

+ 5.4

- 0.8

6.9

+ 0.2

+ 2.1

+ 0.1

5.4

- 0.9

- 2.6

+ 0.1

677.1 (672.2:682.0)

+ 8.6 (1.7:15.6)b

+ 7.5 (0.6:14.5)b

- 8.3 (-15.3:-1.4)b

704.8 (700.9:708.6)

- 3.5 (-9.0:2.0)

- 20.7 (-26.1:-15.2)

- 26.0 (-31.5:-20.6)

671.0 (666.9:675.0)

- 1.6 (-7.3:4.2)

- 23.2 (-29.0:-17.5)

- 21.7 (-27.4:-15.9)

616.5 (611.1:622.0)

+ 8.7 (1.0:16.4)

- 4.1 (-11.8:3.5)

+ 7.7 (0.0:15.4)

688.7 (684.7:692.7)

- 15.7 (-21.4:-10.0)

- 23.0 (-28.7:-17.3)

- 34.5 (-40.1:-28.8)

654.1 (650.0:658.3)

- 2.8 (-8.6:3.0)

- 10.1 (-16.0:-4.3)

- 21.1 (-26.9:-15.2)

582.9

+ 14.0

- 33.9

- 80.7

602.0

+ 13.8

- 28.0

- 79.0

587.5

- 1.3

- 30.6

- 58.9

568.6

+ 21.3

- 3.1

- 24.8

628.4

+ 17.1

+ 12.8

- 18.3

609.3

+ 10.8

+ 7.7

- 25.2

14.6 (12.0:17.3)

- 3.0 (-6.7:0.7)

+ 0.8 (-2.9:4.5)

+ 8.6 (4.9:12.3)

9.3

+ 2.2

+ 5.5

+ 9.0

9.6 (6.6:12.7)

9.2

+ 5.8 (1.5:10.1)

+ 5.2

+ 19.2 (14.9:23.5)

+ 9.5

+ 23.3 (19.0:27.6)

+ 13.3

18.5 (15.9:21.0)

15.1

+ 3.7 (0.1:7.3)

+ 2.5

+ 20.2 (16.6:23.8)

+ 10.6

+ 12.1 (8.5:15.7)

+ 13.1

32.8 (29.6:36.0)

- 0.7 (-5.2:3.8)

+ 0.2 (-4.3:4.7)

- 3.8 (-8.3:0.7)

23.7

+ 4.0

+ 8.8

+ 9.3

27.2 (24.1:30.3)

23.8

+ 8.2 (3.8:12.6)

+ 10.9

+ 11.3 (6.9:15.8)

+ 15.5

+ 26.3 (21.8:30.7)

+ 22.3

27.8 (24.9:30.7)

24.7

+ 13.4 (9.4:17.5)

+ 7.9

+ 12.4 (8.3:16.5)

+ 12.1

+ 23.1 (19.1:27.2)

+ 23.2

1% significant.

5% significant

Generally, we found fair agreement between the

ELMA and the crude estimates. However, across work-

ing time outcomes the ELMA method found higher

numbers in the reference group than the crude estimates.

Discussion

In this prospective longitudinal study, we showed that

physically demanding work is associated with poorer

labor market affiliation of Danish employees. Physical

work demands were measured using a combined ergo-

nomic index and categorized into four levels. The study

used several highly detailed registers with date-based

information and included all lengths of sickness absence

of both public and private employees.

By using the ELMA method, we showed that, mod-

erate-to-very high physical work demands were associ-

ated with increased sickness absence time and decreased

time working, but only for employees ≥40 years. For

the younger employees, physical work demands did

not affect labor market affiliation within the two-year

follow-up period. These findings agree with previous

findings showing increased risk of long-term sickness

absence in older – but not younger – workers from

high physical work demands (1). There may be several

reasons for these findings. First, as muscle strength

declines with increasing age (23), younger workers are

better physically fit for the job than older workers. Thus,

at any given absolute workload, the relative workload

is lower among younger workers. This may also have

consequences for muscle recovery after work. Second,

the accumulated hazardous effect of high physical work

demands is more likely to affect older workers because

they have been exposed for more years (ie, a higher

accumulated exposure time) (24).

The decrease in time working and increased sick-

ness absence time are significant from moderate physi-

cal work demands, but most pronounced for men and

women with high and very high physical work demands.

However, the effect for men with a moderate level was

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Physical work demands and expected labor market affiliation

very low. The higher level of muscle strength among

men compared with women (23) may explain that mod-

erate levels of physical work demands only affected men

to a minor extent. Another possible explanation is the

somewhat gender-segregated labor market in Denmark,

ie, different types of physical work is performed by men

and women, for example, cleaning, hospital and elderly

care occupations are more frequent among women,

while more men are occupied as bricklayers, carpenters,

or in similar jobs (25).

Comparison with previous studies

Concerning the risk of a transition from work to sickness

absence, our results are in line with a previous analysis

using traditional Cox-regression analyses and the same

ergonomic index (1). We found comparably increased

risks of sickness absence for employees aged ≥40 years

with increasing physical work demands. However, our

results show that this almost linear increasing effect is

most pronounced among men, whereas this is not seen

among women. For the likelihood of returning to work

from sickness absence, we found similar results for both

genders as the risk reduced with increasing physical

work demands. These results may be due to the use of

all length sickness absence instead of solely long-term

sickness absence (11) and that we included all employ-

ees regardless of prior sickness absence.

Only a sparse number of previous studies use multi-

state modeling for investigating labor market affiliation,

and even fewer focus on physical work demands as

exposure (10, 26, 27), which limits the comparison with

previous results. Pedersen et al (9) used a life course

perspective and found comparable decreased working-

time and increased sickness absence and unemployment

time for employees aged >40 years with high physical

work demands.

The results suggest that special attention should

be paid to middle-age and older employees in occupa-

tions characterized by high or very high physical work

demands. For this age group, our results suggest a

potential for decreasing the time in sickness absence

and increasing the working-time if the level of physi-

cal demand is lowered. A potential gain in effective

working-time is additionally present for women with a

moderate level of physically demanding work.

Strengths and limitations

The study strengths include a substantial sample of Dan-

ish employees from three survey waves. The study analy-

ses the individual labor market affiliation on a day-to-day

basis, by a linkage with detailed register data. The study

incorporates a multi-level and -state setting controlling

for recurrent and competing events. Moreover, the study

includes sickness absence periods down to a duration

of one day. The data and information retained from the

surveys and registers all contained a high level of con-

sistency during the entire follow-up period (2012–2018).

The flexibility of the ELMA method makes it pos-

sible to examine different aspects of the labor market

affiliation and to include time-dependent variables and

weights. Compared to a crude mean of state-specific

duration time, this adds important new knowledge on

transitions changes in labor market affiliation on indi-

viduals having physically demanding jobs. The ELMA

method is also effective in highlighting trends in labor

marked outcomes not easily identified from the HR

estimated in the multi-state models. For example, for

women in the 50–64 year age group, a clear and statisti-

cally highly significant increase in sick days is seen with

increased physical work demands (table 3). This trend is

not immediately clear from the HR in table 2 that do not

show a significant increased risk of going from work to

sickness absence for 50–64-year-old women with high

physical work demands. The increase in sick days seen

in table 3 is due to several factors but mainly to a much

lower chance of getting back to work from sickness

absence for middle-aged women with high physical

demands (table 2). This example illustrated the ability

of the ELMA method to summarize complicated results.

Moreover, comparing ELMA with the crude results

reflects the fact that the ELMA method presents the

expected labor market outcomes. This means handling

individuals that are censored and time when not at risk

better than the crude estimates, which are highly affected

by censoring and time when not at risk, implying gener-

ally lower risk estimates.

The use of multiple survey waves increases the

total sample size and adds the possibility of incorporat-

ing time-dependent adjustment of the exposure to the

analysis. This is possible as the baselines of the two-year

follow-up are set individually thoughout 2012 and 2016,

and set repeatedly for employees attending multiple

survey waves. A large sample strengthens the multi-

state analysis as multiple transitions between the states

are likely to occur and increase the group sizes, eg the

number of employees having a very high level of physi-

cal work exposure. However, the use of the relatively

short two-year follow-up period between the survey

waves implies limitations concerning the long-term

perspective of the individual labor market affiliation. For

example, is the number of employees experiencing long-

term effects of physical work demands underestimated

eg, employees experiencing disability pension? This is

because individuals cannot attend the survey if they are

no longer employed, but instead are on, for example,

long-term sickness absence or unemployment benefit

while awaiting disability pension.

The study includes additional limitations. First, the

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Pedersen et al

sample represents a wide variety of Danish employees

and the study is likely to be generalizable to the Dan-

ish workforce, however, some caution should be taken

due to lack of response from men and young employees

(WEHD) (11, 12) and the limits of RoWA concerning

small private companies (14, 15). Second, though only

a few individuals entered the disability pension and pen-

sion state, there is a small possibility of overestimating

the time spend there as the model did not include the

possibility of a transition from disability pension to pen-

sion, from disability pension to death, and from pension

to death. Third, the study included both part-time and

full-time benefits and, if multiple benefits were paid

simultaneously or along with salary payments, a priori-

tization between payments was made. This is likely to

slightly underestimate the duration of the working time

and overestimate the duration of the other states. Fourth,

the study relates to the Danish labor market system,

which makes comparison with other countries difficult.

However, the results should make room for some general

consideration on employees experiencing physically

demanding jobs. Finally, the study does not include all

aspects of the physical workload, it is for example likely

that chronic disease and other health-related conditions

will influence the working-time and time with sickness

absence.

Concluding remarks

Moderate-to-very high levels of physical work demands

are associated with markedly reduced active labor mar-

ket affiliation among middle-aged and older Danish

employees but not among young workers. The changes

of the expected labor market affiliation mainly con-

cern increased time in sickness absence on the cost

of reduced active working-time. Preventive initiatives

focusing on gender and age of the employees are likely

to decrease the negative impact of physically demanding

work in occupations with high physical work exposures.

Ethics approval

According to Danish law, research studies that use solely

survey and register data do not need approval from the

National Committee on Health Research Ethics (Den

Nationale Videnskabetiske Komité).

Data sharing statement

The SAS code can be shared upon reasonable request by

authorized researchers after application to the NRCWE.

Data is available on the Researcher access at Statistics

Denmark, see

www.dst.dk/en/TilSalg/Forskningsser-

vice.

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Received for publication: 19 April 2022

Scand J Work Environ Health – online first