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ORIGINAL ARTICLE
Burnout as a predictor of self-reported sickness absence
among human service workers: prospective findings from
three year follow up of the PUMA study
M Borritz, R Rugulies, K B Christensen, E Villadsen, T S Kristensen
...............................................................................................................................
See end of article for
authors’ affiliations
.......................
Correspondence to:
Dr M Borritz, National
Institute of Occupational
Health, Denmark, Lersoe
Parkalle´ 105, DK-2100
Copenhagen O, Denmark;
mb@ami.dk
Accepted 29 July 2005
.......................
Occup Environ Med 2006;63:98–106. doi: 10.1136/oem.2004.019364
Aim: To investigate whether burnout predicts sickness absence days and sickness absence spells in human
service workers.
Method: A total of 824 participants from an ongoing prospective study in different human service sector
organisations were eligible for the three year follow up analysis. Burnout was measured with the work
related burnout scale of the Copenhagen Burnout Inventory. Sickness absence was measured with self-
reported number of days and spells during the last 12 months before the baseline and the follow up
survey. A Poisson regression model with a scale parameter was used to account for over dispersion. A
linear regression model was used for analysing changes in burnout and absence between baseline and
follow up.
Results: Burnout was prospectively associated with both sickness absence days and sickness absence spells
per year. Differences in sickness absence days varied from a mean of 5.4 days per year in the lowest
quartile of the work related burnout scale to a mean of 13.6 in the highest quartile. An increase of one
standard deviation on the work related burnout scale predicted an increase of 21% in sickness absence
days per year (rate ratio 1.21, 95% CI 1.11 to 1.32) after adjusting for gender, age, organisation,
socioeconomic status, lifestyle factors, family status, having children under 7 years of age, and prevalence
of diseases. Regarding sickness absence spells, an increase of one standard deviation on the work related
burnout scale predicted an increase of 9% per year (rate ratio 1.09, 95% CI 1.02 to 1.17). Changes in
burnout level from baseline to follow up were positively associated with changes in sickness absence days
(estimate 1.94 days/year, SE 0.63) and sickness absence spell (estimate 0.34 spells/year, SE 0.08).
Conclusion: The findings indicate that burnout predicts sickness absence. Reducing burnout is likely to
reduce sickness absence.
S
ickness absence is a major problem for the individual,
the workplace, and society. For the individual, sickness
absence can be the beginning of social decline with
periods of longer sickness absence, job dismissal, and even
permanent exclusion from the labour market. For work-
places, sickness absence denotes loss of manpower, payments
for temporary workers, reduced productivity, and increased
job turnover. For society, sickness absence means payments
to sickness benefits and reduced productivity.
In the literature, it is assumed that sickness absence is a
complex phenomenon that can be caused by individual, work
related, organisational, and societal factors.
1–5
Sickness
absence has been discussed as a consequence of ill health,
46
a coping mechanism,
2
a behaviour of social equity,
7
a reaction
to organisational injustice,
8
or a consequence of exposure to
adverse work–environment factors.
2 3 9–12
Burnout is described as a negative consequence of human
service work, characterised by emotional exhaustion, loss of
energy, and withdrawal from work;
13–16
more than 5500
studies on burnout have been published since the beginning
of the 1980s.
13 15
Despite this wealth of research, we found
only 16 studies on burnout and absence. Of those, six were
prospective studies,
9 17–21
one was a population study,
13
and
nine were cross-sectional in design.
11 22–29
The prospective
studies in general have found positive associations between
burnout and sickness absence; however, the methodological
heterogeneity of these studies, for example differences in
population size, in follow up time, or occupational group
makes interpretation difficult. Moreover, because of the
assumed multi-factorial causation of sickness absence,
studies on burnout and sickness absence must control for a
wide range of potential confounders, a challenge that no
studies have met so far.
The aim of this paper is to investigate the impact of
burnout on sickness absence in a large cohort while
controlling for numerous potential confounders. We consider
burnout as a special category of occupational stress. We
assume that burnout can make the individual more
susceptible to illnesses, for example to infections such as
the common cold.
30
Burnout might also cause sickness
absence without the presence of illnesses, if sickness absence
is chosen as a strategy to save energy and recover from
exhaustion.
31–33
We analysed the associations between burn-
out and absence, both in a cross-sectional sample and in a
prospective cohort, allowing us to compare the magnitude of
effect sizes in these two different types of study designs.
We hypothesised that: (1) burnout at baseline is associated
with sickness absence at baseline; (2) burnout at baseline
predicts sickness absence at follow up; and (3) changes in
burnout level from baseline to follow up predict changes in
sickness absence from baseline to follow up.
METHODS
Study design and population
PUMA (Danish acronym for Study on Burnout, Motivation
and Job satisfaction) is an ongoing five year prospective
intervention study in seven different organisations in the
human service sector, including social security offices in an
urban area, a psychiatric prison, county institutions for
98
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severely disabled people, four somatic wards from two county
hospitals, one psychiatric ward from a psychiatric hospital,
one homecare service in a rural area, and one homecare
service in an urban area. All occupational groups in each
organisation were eligible for the study. To date, data have
been collected at baseline (1999–2000) and at the first follow
up (2002–03), resulting in a mean follow up time of 3 years
(2K to 4 years). Survey questionnaires were sent to the home
address of all employees in both rounds. The Danish Data
Protection Agency (Datatilsynet) and Scientific Ethical
Committees (Videnskabsetisk Komite´er) in the respective
counties have given approval for the PUMA study. A more
detailed description on the background, design, study
population, and measurements of PUMA can be found
elsewhere.
34
At baseline, 1914 of 2391 eligible employees participated in
the survey (response rate 80.1%). At follow up, 1759 of 2335
responded (response rate 75.3%). Of the 1914 responders
from the baseline survey, 1463 were still employed in the
same organisation at follow up. Of these 1463 employees,
1024 responded to the follow up questionnaire (response rate
70.0%).
For this article, we conducted analyses of two samples:
cross-sectional analyses of the 1914 employees who partici-
pated in the baseline survey (cross-sectional sample), and
prospective analyses of the 1024 employees who participated
both at baseline and at follow up (prospective cohort). Due to
missing values on one or more variables, 353 employees of
the cross-sectional sample and 200 employees of the
prospective cohort had to be excluded, resulting in final
samples of 1561 (cross-sectional sample) and 824 (prospec-
tive cohort) respectively.
Measurements
Burnout
Burnout was measured with the Copenhagen Burnout
Inventory (CBI), an instrument specifically developed for
PUMA.
34
In contrast to the widely used Maslach Burnout
Inventory
35
that includes three components of burnout
(emotional exhaustion, depersonalisation, and reduced per-
sonal accomplishment), the CBI has its focus on exhaustion
and its attribution by the person. The CBI has scales on
personal burnout (six items on general exhaustion without a
specific attribution), work related burnout (seven items on
exhaustion attributed to work in general), and client related
burnout (six items on exhaustion attributed to work with
clients).
34
All items have five response categories, ranging
either from ‘‘to a very low degree’’ to ‘‘to a very high degree’’
or from ‘‘never’’ to ‘‘always’’. Each scale ranges from 0 to 100
points, with high scores indicating high levels of burnout.
Missing values were low, ranging from 0.6% to 1.3%.
Cronbach’s alphas for the scales were 0.87 for both personal
and work related burnout, and 0.85 for client related
burnout. The correlation coefficients between the scales were
0.73 for personal and work related burnout, 0.46 for personal
and client burnout, and 0.61 for work and client burnout,
indicating some overlap but also differences between the
scales. Co-occurrence of the burnout domains differed
substantially between occupational groups, with some
occupational groups showing high scores on two scales,
whereas other groups scored high on one scale and low on
the other.
34
In this paper, our main focus will be on the work related
burnout. This area offers the best potential for prevention,
because the work environment is amenable to change. Hence,
if we find an association between work related burnout and
sickness absence, this would be an indication that improving
the work environment could reduce both burnout and
sickness absence. Selected findings on the personal and
client related burnout scales will also be presented.
Sickness absence
We measured both sickness absence days and spells, because
we wanted to analyse if burnout was related to both
frequency and length of absence. We asked the participants
in both the baseline and the follow up questionnaire to report
their sickness absence during the last 12 months. The two
questions were ‘‘How many days of sickness absence did you
have in the last 12 months?’’ and ‘‘How many spells of
sickness absence did you have in the last 12 months?’’. This
means that, at baseline, we assessed sickness absence for the
12 months period before the baseline survey. At the three
year follow up, we assessed sickness absence for the
12 month period prior to the follow up survey.
Covariates
As covariates, we included age, gender, organisation the
participants worked for, socioeconomic status, family status,
smoking, alcohol consumption, and body mass index. These
variables have been found to be associated with sickness
absence in other studies,
36
and we wanted to analyse if
burnout had an effect that was independent of them. We
further adjusted for prevalence of disease, because prevalent
disease might cause both burnout at baseline and sickness
absence at follow up.
Sociodemographic and work related factors
The covariate ‘‘family status’’ was made by combining
cohabiting status and having children at home to a new
variable with four groups: (1) cohabiting with children at
home; (2) cohabiting without children at home; (3) being
single with children at home; and (4) being single without
children.
Socioeconomic status (SES) was based on job function and
education using three groups: 1 = participants with super-
visory function for more than 50 subordinates and/or with
advanced education (academics); 2 = participants with
supervisory function for less than 50 subordinates and/or
with middle range education; and 3 = participants who were
subordinates and/or had a short term education.
Health related lifestyle and body mass index
Smoking habits were categorised in four groups: non-
smoker, former smoker, light smoker (less than 15 cigarettes
per day), and heavy smoker (15 cigarettes or more per day).
Weekly physical activity was assessed in four groups: (1)
no exercise (no or light exercise for less than two hours); (2)
light exercise for 2–4 hours; (3) moderate exercise (light
exercise for more than 4 hours); and (4) strenuous exercise
for more than 4 hours per week.
Alcohol consumption was categorised in three groups:
non-drinker, moderate drinker (14 or less drinks per week for
women and 21 or less drinks for men), and heavy drinker
(more than 14 drinks for women and more than 21 drinks for
men). Participants were further asked to state their height
and weight; we calculated the resulting body mass index
(BMI).
Prevalence of diseases
In the follow up survey, participants were given a list with
several diseases and were asked to check if they had these
diseases now or have had them in the past. We defined an
index of four disease categories: chronic diseases (diabetes,
raised blood pressure, chronic bronchitis, asthma); severe
diseases (coronary thrombosis or cardiovascular spasm,
cerebral haemorrhage or cerebral thrombosis, cancer, gastric
ulcer); ‘‘women’s diseases’’ (illnesses of the internal female
Burnout and absence 99
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Table 1 Characteristics of the cross-sectional (baseline) sample and the prospective cohort
Baseline Follow up
n
Work related burnout at
baseline
Absence days in the
12 months before
baseline
n
Work related burnout at
baseline
Work related burnout at
follow up
Absence days in the
12 months before
baseline
Absence days in the
12 months before
follow up
Mean (95% CI) Mean (95% CI) Mean (95% CI) Mean (95% CI) Mean (95% CI) Mean (95% CI)
Women 1274 33.0 (32.0 to 33.9) 9.9 (8.9 to 10.8) 676 31.6 (30.3 to 32.9) 36.6 (35.2 to 38.0) 8.4 (7.5 to 9.4) 10.4 (9.0 to 11.7)
Men 287 30.9 (29.1 to 32.7) 6.8 (5.7 to 8.0) 148 32.1 (29.5 to 34.6) 38.2 (35.1 to 41.3) 6.9 (5.2 to 8.5) 8.4 (6.3 to 10.6)
Age ,35 391 33.8 (32.0 to 35.5) 9.2 (7.9 to 10.5) 129 30.5 (27.9 to 33.1) 38.8 (35.5 to 42.1) 8.5 (6.8 to 10.2) 12.5 (8.6 to 16.4)
Age 35–44 495 32.7 (31.1 to 34.2) 9.9 (8.4 to 11.4) 273 32.1 (30.1 to 34.0) 36.0 (33.8 to 38.2) 8.9 (7.2 to 10.6) 9.8 (7.8 to 11.8)
Age .45 675 31.8 (30.5 to 33.2) 8.9 (7.6 to 10.2) 422 31.8 (30.1 to 33.5) 36.8 (35.0 to 38.7) 7.5 (6.4 to 8.7) 9.4 (7.9 to 10.9)
SES 1 (high) 83 30.1 (26.5 to 33.8) 4.3 (3.3 to 5.4) 38 30.1 (24.9 to 35.3) 32.6 (26.5 to 38.8) 3.9 (2.3 to 5.6) 3.8 (1.7 to 6.0)
SES 2 625 34.1 (32.7 to 35.5) 7.6 (6.5 to 8.6) 356 33.2 (31.4 to 34.9) 35.5 (33.6 to 37.5) 7.3 (6.0 to 8.6) 8.5 (6.8 to 10.2)
SES 3 (low) 853 31.7 (30.5 to 32.9) 11.1 (9.8 to 12.3) 430 30.6 (29.1 to 32.2) 38.4 (36.6 to 40.2) 9.2 (8.0 to 10.5) 11.8 (10.0 to 13.6)
Non-smoker 567 30.9 (29.6 to 32.3) 7.7 (6.5 to 9.0) 312 30.4 (28.6 to 32.1) 35.5 (33.4 to 37.5) 6.9 (5.5 to 8.2) 8.8 (6.9 to 10.8)
Ex-smoker 398 33.4 (31.6 to 35.1) 9.3 (7.5 to 11.2) 217 32.6 (30.3 to 34.9) 36.6 (34.3 to 39.0) 8.2 (6.3 to 10.1) 8.5 (6.9 to 10.0)
Light smoker 226 31.4 (29.2 to 33.6) 8.0 (6.6 to 9.4) 106 30.7 (27.6 to 33.8) 35.3 (31.3 to 39.3) 7.5 (5.8 to 9.1) 9.2 (6.8 to 11.6)
Heavy smoker 370 34.9 (33.1 to 36.8) 12.5 (10.7 to 14.3) 189 33.3 (30.8 to 35.9) 40.4 (37.5 to 43.3) 10.7 (8.9 to 12.5) 14.2 (10.9 to 17.5)
No alcohol consumption 254 33.7 (31.4 to 36.1) 12.9 (10.2 to 15.6) 120 32.5 (29.1 to 35.9) 37.9 (34.4 to 41.5) 10.4 (7.7 to 13.2) 13.1 (8.6 to 17.7)
Moderate alcohol consumption 1205 32.1 (31.1 to 33.1) 8.4 (7.6 to 9.2) 643 31.2 (29.9 to 32.4) 36.8 (35.3 to 38.3) 7.3 (6.5 to 8.2) 9.3 (8.1 to 10.5)
Heavy alcohol consumption 102 35.4 (31.7 to 39.1) 11.1 (7.8 to 14.5) 61 35.7 (30.9 to 40.4) 35.5 (30.8 to 40.2) 12.1 (6.9 to 17.4) 11.5 (7.0 to 15.9)
No exercise 104 38.4 (34.8 to 42.1) 11.5 (7.9 to 15.2) 44 35.5 (30.9 to 40.2) 39.2 (33.4 to 45.1) 9.0 (5.8 to 12.3) 12.1 (6.2 to 17.9)
Light exercise 861 32.7 (31.5 to 33.8) 9.4 (8.4 to 10.4) 456 31.7 (30.2 to 33.2) 37.5 (35.7 to 39.3) 8.3 (7.2 to 9.3) 11.0 (9.2 to 12.8)
Moderate exercise 516 31.9 (30.4 to 33.4) 8.6 (7.2 to 10.0) 289 31.5 (29.5 to 33.5) 35.4 (33.3 to 37.5) 7.9 (6.3 to 9.5) 8.6 (7.1 to 10.1)
Strenuous exercise 80 28.4 (24.7 to 32.1) 10.1 (6.0 to 14.2) 35 28.7 (23.6 to 33.8) 38.2 (31.8 to 44.5) 8.1 (3.5 to 12.6) 6.8 (4.2 to 9.4)
Cohabiting with children 767 32.1 (30.9 to 33.3) 8.9 (7.8 to 10.0) 433 31.0 (29.5 to 32.5) 36.8 (35.0 to 38.6) 8.6 (7.0 to 9.6) 10.0 (8.3 to 11.8)
Cohabiting without children 486 32.2 (30.7 to 33.8) 9.3 (7.9 to 10.7) 261 31.6 (29.4 to 33.7) 36.3 (33.9 to 38.7) 8.1 (6.8 to 9.4) 9.7 (7.7 to 11.8)
Single with children 137 37.0 (33.9 to 40.2) 10.9 (7.8 to 14.0) 65 37.9 (32.9 to 43.0) 40.0 (34.8 to 45.2) 7.1 (5.4 to 8.8) 11.3 (8.4 to 14.1)
Single without children 171 32.0 (29.3 to 34.6) 9.9 (6.9 to 12.8) 65 30.7 (26.6 to 34.8) 36.7 (32.5 to 40.9) 8.7 (5.5 to 11.9) 9.7 (6.2 to 13.1)
Children ,7 years at home 369 33.2 (31.4 to 35.0) 10.4 (8.4 to 12.4) 158 30.7 (28.2 to 33.2) 37.6 (34.5 to 40.7) 9.0 (6.6 to 11.4) 10.3 (7.8 to 12.8)
No children or older at home 1192 32.4 (31.4 to 33.4) 9.0 (8.1 to 9.8) 666 31.9 (30.6 to 33.2) 36.7 (35.3 to 38.2) 8.0 (7.1 to 8.8) 9.9 (8.6 to 11.3)
Total 1561 32.6 (31.7 to 33.4) 9.3 (8.5 to 10.1) 824 31.7 (30.6 to 32.8) 36.9 (35.6 to 38.2) 8.2 (7.3 to 9.0) 10.0 (8.8 to 11.2)
100 Borritz, Rugulies, Christensen, et al
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sexual organs, cystitis, menstruation related pain); and other
diseases (mental disorder, allergy, skin diseases, backache).
Data analysis
Significance of changes in sickness absence days and spells
between baseline and follow up were analysed using paired t
tests. With regard to the impact of burnout on sickness
absence, we analysed: (1) cross-sectional associations
between burnout and sickness absence at baseline; (2)
prospective associations between burnout at baseline and
sickness absence at follow up; and (3) prospective associa-
tions between changes in burnout and changes in absence
from baseline to follow up. For the cross-sectional analyses
and the analyses on the impact of burnout at baseline on
absence at follow up, we calculated rate ratios (RR) and 95%
confidence intervals (CI) using Poisson regression models
with a scale parameter to account for over dispersion. Poisson
distributions are a natural choice for modelling count data
and are widely used in the sickness absence literature. For
the analyses on associations between changes in burnout and
changes in sickness absence from baseline to follow up, we
calculated estimates and standard errors (SE) with linear
regression models, because changes in absence are not count
data and therefore cannot be analysed with a Poisson model.
The analyses were adjusted successively for different covari-
ates: Model I for age and gender; Model II additionally for
type of organisation and SES; Model III additionally for BMI,
smoking, alcohol consumption, and leisure time physical
activity; Model IV additionally for family status and having
children below the age of 7; and Model V additionally for
prevalence of disease. Because prevalence of disease was only
measured at follow up, the cross-sectional analyses included
Models I to IV only, whereas the prospective analyses
included all five models.
To illustrate the impact of changes in burnout on changes
in absence, we further dichotomised burnout by the midpoint
of the scale to define low (,50 points) and high burnout
(>50 points). By choosing the midpoint of the scale, rather
than the mean or median of the scores, we ensured that the
definition of low and high burnout was independent from
the actual distribution of burnout in the study sample. The
dichotomisation resulted in four groups regarding burnout
level at baseline and follow up: (1) low–low; (2) low–high;
(3) high–high; and (4) high–low. For each of these groups we
created figures, showing means and 95% confidence intervals
of absence days at baseline and follow up. Significance of
changes between baseline and follow up was analysed with
paired t tests in each of the four groups.
All statistical analyses were performed using SAS 8.2.
RESULTS
Characteristics of the study population
In the prospective cohort, the mean number of sickness
absence days in the study population increased from 8.2 days
(95% CI 7.3 to 9.0) at baseline to 10.0 days (95% CI 8.8 to
11.2) at follow up (p , 0.01, table 1). Mean number of
sickness absence spells was 1.7 (95% CI 1.6 to 1.8) at baseline
and 1.8 (95% CI 1.6 to 1.9) at follow up (p = 0.77). For the
cross-sectional sample the annual number of spells was 1.8
(95% CI 1.7 to 1.9) at baseline.
The lowest educated socioeconomic status group (SES 3)
had more sickness absence days than the higher educated
groups; and heavy smokers had a higher number of annual
sickness absence days compared with non-, ex-, and light-
smokers. In general, women had more sickness absence days
and spells than men.
We also investigated burnout levels and absence data of
the persons who participated at baseline only (that is, those
who had left the workplace or were non-responders at follow
up). The burnout level in this group (33.8 points, 95% CI 32.6
to 35.0) was comparable to the burnout level of persons who
participated in both rounds (32.3 points, 95% CI 31.3 to 33.4).
With regard to sickness absence, no substantial differences
could be found in sickness absence spells (1.9 spells per year
(95% CI 1.8 to 2.0) and 1.8 spells per year (95% CI 1.7 to 1.9)
for persons who participated at baseline only, and partici-
pants in both rounds, respectively). However, non-partici-
pants at follow up had more sickness absence days (10.5 days
per year, 95% CI 9.2 to 11.8) than persons who participated in
both rounds (8.3 days per year, 95% CI 7.5 to 9.1).
Cross-sectional associations between work related
burnout and sickness absence
Cross-sectionally, work-related burnout was positively asso-
ciated with absence days and spells in all models (table 2).
An increment of one standard deviation ( = 17.7 points) on
the work related burnout scale was associated with 37% (95%
CI 30% to 45%) more absence days and with 22% (95% CI
16% to 28%) more absence spells in the full model.
Being a woman was associated with both higher sickness
absence days and spells, and higher age was associated with
more sickness absence days but fewer sickness absence spells.
The cross-sectional associations of the client related and
the personal burnout scale showed patterns similar to the
scale for work related burnout showed in table 1.
Prospective impact of work related burnout on
sickness absence
Participants with higher work related burnout at baseline
had a higher number of sickness absence days at follow up
than participants with lower work related burnout (table 3).
The rate ratios became attenuated with further adjustments,
but remained significant in the full model. An increment of
one standard deviation (17.7 points) on the work related
burnout scale at baseline predicted 21% (95% CI 11% to 32%)
more sickness absence days and 9% (95% CI 2% to 17%) more
spells at follow up in the full model.
Participants who ranked in the lowest quartile of the work
related burnout scale had 5.4 days of sickness absence per
year, while participants in the following quartiles had 6.3,
9.0, and 13.6 absence days, respectively.
Being a woman increased sickness absence days by 39%
(95% CI 4% to 87%) in the full model but not sickness
absence spells. Higher age predicted a lower number of
sickness absence spells, but was not associated with sickness
absence days. The lowest SES group had more annual
absence days than the two others, but showed no difference
regarding spells. Heavy smokers had more absence days than
ex-smokers, and more absence spells than non- and light-
smokers. Participants doing light weekly exercise had more
absence days than the heavily exercising groups and the
passive group, but showed no difference regarding spells
(data on SES, smoking, and exercise not shown in table).
The personal and the client related burnout scales showed
similar associations with absence as work related burnout. In
the full models, an increase of one standard deviation on the
personal burnout and the client related burnout scales,
predicted 21% (95% CI 11% to 31%) and 14% (95% CI 5% to
25%) more sickness absence days, respectively.
Prospective impact of change in work related burnout
on change in absence
A change of one standard deviation on the work related
burnout scale from baseline to follow up predicted a change
of 1.94 sickness absence day per year (SE 0.63, p = 0.002) in
the full model, independent of where on the scale the starting
point was sited (table 4). This means that an increase in work
related burnout from baseline to follow up predicted an
Burnout and absence 101
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increase in sickness absence, and a decrease in work related
burnout from baseline to follow up predicted a decrease in
sickness absence days.
For client related burnout, a difference of one standard
deviation from baseline to follow up led to a difference of
2.39 sickness absence days (SE 0.70, p , 0.001) over time in
the full model. For personal burnout, a difference of one
standard deviation from baseline to follow up resulted in a
difference of 2.03 sickness absence days (SE 0.62, p = 0.001)
over time in the full model.
Absence days at baseline and follow up related to
work related burnout levels
Figure 1 shows how sickness absence days changed over
time, stratified by groups with high or low work related
burnout at baseline and follow up. Participants with low
Table 2 Cross-sectional associations of work related burnout with sickness absence days and spells at baseline (cross-sectional
sample, n = 1561)
Model I Model II Model III Model IV
Rate ratio p value Rate ratio p value Rate ratio p value Rate ratio p value
Sickness absence days
Work related burnout*
RR 1.39 ,0.001 1.39 ,0.001 1.37 ,0.001 1.37 ,0.001
95% CI 1.32 to 1.47 1.31 to 1.47 1.29 to 1.45 1.30 to 1.45
Being a woman
RR 1.36 ,0.001 1.38 0.001 1.42 ,0.001 1.45 ,0.001
95% CI 1.36 to 1.63 1.14 to 1.67 1.17 to 1.72 1.19 to 1.76
Age
RR 1.00 NS 1.04 NS 1.04 NS 1.10 0.007
95% CI 0.94 to 1.06 0.98 to 1.10 0.98 to 1.10 1.03 to 1.18
Sickness absence spells
Work related burnout*
RR 1.25 ,0.001 1.23 ,0.001 1.22 ,0.001 1.22 ,0.001
95% CI 1.20 to 1.31 1.18 to 1.29 1.16 to 1.28 1.16 to 1.28
Being a woman
RR 1.11 NS 1.19 0.02 1.21 0.01 1.21 0.01
95% CI 0.98 to 1.26 1.03 to 1.37 1.05 to 1.40 1.05 to 1.40
Age
RR 0.84 ,0.001 0.85 ,0.001 0.84 ,0.001 0.84 ,0.001
95% CI 0.84 to 0.88 0.81 to 0.89 0.80 to 0.88 0.80 to 0.89
*Increases of 1 standard deviation on the work related burnout scale. Increases of 10 years.
Model I: Adjusted for age, gender.
Model II: Model I plus adjustment for organisation and SES.
Model III: Model II plus adjustment for BMI, smoking, alcohol consumption, and leisure time physical activity.
Model IV: Model III plus adjustment for family status and having children below the age of 7.
Table 3 Prospective impact of work related burnout at baseline on sickness absence days and sickness absence spells at three
year follow up (prospective cohort, n = 824)
Model I Model II Model III Model IV Model V
Rate ratio p value Rate ratio p value Rate ratio p value Rate ratio p value Rate ratio p value
Sickness absence days
Work related burnout*
RR 1.31 ,0.001 1.29 ,0.001 1.26 ,0.001 1.26 ,0.001 1.21 ,0.001
95% CI 1.20 to 1.42 1.18 to 1.40 1.16 to 1.37 1.16 to 1.37 1.11 to 1.32
Being a woman
RR 1.20 NS 1.40 0.02 1.50 0.004 1.50 0.004 1.39 0.03
95% CI 0.93 to 1.53 1.06 to 1.85 1.14 to 1.97 1.14 to 1.98 1.04 to 1.87
Age
RR 0.91 NS 0.96 NS 0.94 NS 0.95 NS 0.96 NS
95% CI 0.82 to 1.01 0.86 to 1.06 0.85 to 1.04 0.84 to 1.06 0.86 to 1.08
Sickness absence spells
Work related burnout*
RR 1.19 ,0.001 1.15 ,0.001 1.13 ,0.001 1.13 ,0.001 1.09 0.01
95% CI 1.11 to 1.27 1.07 to 1.24 1.05 to 1.21 1.05 to 1.21 1.02 to 1.17
Being a woman
RR 1.00 NS 1.09 NS 1.13 NS 1.12 NS 1.05 NS
95% CI 0.82 to 1.21 0.88 to 1.35 0.91 to 1.39 0.90 to 1.39 1.05 to 0.83
Age
RR 0.74 ,0.001 0.77 ,0.001 0.77 ,0.001 0.76 ,0.001 0.75 ,0.001
95% CI 0.68 to 0.80 0.71 to 0.83 0.71 to 0.83 0.69 to 0.83 0.69 to 0.83
*Increases of 1 standard deviation on the work related burnout scale. Increases of 10 years.
Model I: Adjusted for age, gender.
Model II: Model I plus adjustment for organisation and SES.
Model III: Model II plus adjustment for BMI, smoking, alcohol consumption, and leisure time physical activity.
Model IV: Model III plus adjustment for family status and having children below the age of 7.
Model V: Model IV plus adjustment for prevalence of disease.
102 Borritz, Rugulies, Christensen, et al
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burnout both at baseline and at follow up (low–low group)
had a mean increase of 1.3 absence days from baseline to
follow up (p = 0.06). Participants with increasing burnout
(low–high) showed an increase of 4.5 days (p = 0.01), and
participants with constant high burnout levels (high–high)
had 3.8 more days of sickness absence days from baseline to
follow up (p = 0.04). Participants with decreasing burnout
(high–low), though, showed a decrease of 2.8 absence days
(p = 0.34).
DISCUSSION
This study has two main findings. First, we found that
burnout was associated with sickness absence days and
sickness absence spells in the cross-sectional sample and
predicted sickness absence days and spells in the prospective
cohort. The length of follow up time of three years combined
with adjustments for demographic, work, and health related
confounders is to our knowledge unique and provides
important insights into the impact of burnout on sickness
absence.
Second, we found that changes in burnout levels predict
changes in sickness absence, meaning that increase in
burnout predicts increase in sickness absence, and decrease
in burnout predicts decrease in sickness absence. Although,
there was a general increase in sickness absence days in the
whole study population (1.9 days more), this increase was
much more pronounced among those who changed from low
burnout at baseline to high burnout at follow up (4.5 days
more) or who had constant high burnout (3.8 days) than in
persons with constant low burnout (1.3 days more). The only
group that showed a decrease in sickness absence days were
the individuals who had changed from high burnout at
baseline to low burnout at follow up (2.8 days less). This
finding confirms a study from Harvey and Burns, where
similar associations were found, but in a smaller population
(n = 18) and with a follow up of six months.
19
The results are
also in line with an intervention study among 300 health
professionals which found in the experimental group
(n = 36) that a reduction of burnout was followed by a
decrease in sickness absence 12 months later, whereas in the
control group sickness absence increased.
17
Burnout predicted both absence days and spells in the
prospective analyses. However, the higher rate ratios for
absence days suggest that burnout had a stronger influence
on the length than on the frequency of absence periods. This
could indicate that burnout is not only a risk factor for
sickness absence but also for delayed return to work. For the
future, we are planning specific analyses on burnout and
return to work by merging the PUMA dataset with social
transfer payment registries.
Strengths
This is a prospective study that controlled for a wide range of
potential confounders. Among observational studies, pro-
spective designs are the gold standard for studying assumed
causal associations between predictors and endpoints. The
results in our study were robust and remained significant
after adjustment for a large number of potential confounders.
However, we acknowledge that even in prospective designs,
causal inference has to be drawn with caution, because, in
contrast to randomised experimental studies, observational
studies can only control for known and measured confoun-
ders. Therefore, we cannot rule out that we have missed other
variables that might have had an impact on the statistical
association between burnout and absence.
While causal inference needs to be drawn with caution, a
prospective design is clearly superior to cross-sectional
designs, which have been used in most previous research
on burnout and sickness absence. Direction of causation
cannot be established in cross-sectional studies and effect
estimates might be inflated by the simultaneous assessment
of predictor and outcome. It is of interest to note that while
we found statistically significant effects in both the cross-
sectional and the prospective analyses, rate ratios were
attenuated in the prospective analyses both for sickness
absence days (1.37 v 1.26) and spells (1.22 v 1.13). This
indicates that cross-sectional analyses are prone to a
Table 4 Changes in work related burnout predicting changes in sickness absence days and sickness absence spells
(prospective cohort, n = 824)
Model I Model II Model III Model IV Model V
Estimate p value Estimate p value Estimate p value Estimate p value Estimate p value
Sickness absence days
Work related burnout*
Estimate 1.97 0.001 2.04 0.001 2.00 0.001 2.02 0.001 1.94 0.002
SE 0.61 0.63 0.63 0.63 0.63
Being a woman
Estimate 0.51 NS 20.16 NS 0.03 NS 20.05 NS 20.48 NS
SE 1.60 1.84 1.87 1.88 2.08
Age
Estimate 20.01 NS 0.01 NS 20.09 NS 20.40 NS 20.03 NS
SE 0.72 0.73 0.75 0.86 0.88
Sickness absence spells
Work related burnout*
Estimate 0.31 ,0.001 0.33 ,0.001 0.33 ,0.001 0.33 ,0.001 0.34 ,0.001
SE 0.07 0.08 0.08 0.08 0.08
Being a woman
Estimate 20.16 NS 20.24 NS 20.24 NS 20.27 NS 20.07 NS
SE 0.19 0.22 0.22 0.23 0.25
Age
Estimate 20.17 0.05 20.19 0.03 20.17 NS 20.22 0.03 20.26 0.02
SE 0.09 0.09 0.09 0.10 0.11
*Changes of 1 standard deviation on the work related burnout scale. Increases of 10 years.
Model I: Adjusted for age, gender.
Model II: Model I plus adjustment for organisation and SES.
Model III: Model II plus adjustment for BMI, smoking, alcohol consumption, and leisure time physical activity.
Model IV: Model III plus adjustment for family status and having children below the age of 7.
Model V: Model IV plus adjustment for prevalence of disease.
Burnout and absence 103
www.occenvmed.com
moderate overestimation of the associations between burn-
out and sickness absence.
The study was conducted among employees who were
working with clients, the part of the workforce believed to be
most likely to develop burnout.
16
However, our study is not a
single occupation study, but a study that included a wide
range of different organisations and occupational groups
within the human service work sector. The human service
work sector is a large part of the labour market in Denmark
and there is growing need to understand work and health in
this area better, including burnout and sickness absence.
The fact that decrease in burnout over time was accom-
panied with decrease in sickness absence points to the
possibility that interventions aimed at reducing burnout
could also reduce sickness absence. In PUMA, we have
documented if and which ad hoc interventions have been
conducted by the workplaces during the follow up. In future
analyses we will investigate if these interventions have
impacted burnout and sickness absence.
Limitations
The relative long follow up period of three years has not only
advantages (for example, sufficient time for exposure to
create effects on the outcome variable), but also disadvan-
tages. During this period, burnout levels of some individuals
might have changed several times. Hence, the long follow up
period might have caused some non-differential misclassifi-
cation of the exposure variable, which would bias our results
towards an underestimation of the effects.
With regard to selection bias, it has to be pointed out that
890 employees participated at baseline but not at follow up,
resulting in a 46.5% reduction. Job turnover constituted the
major part of the reduction as 39.2% (range 29.0–51.8%) of
the employees had quit their job between baseline and follow
up. While we found no difference in burnout between the
group that participated at baseline only and the group that
participated in both rounds, there was a slightly higher level
of sickness absence days among those who did not participate
in both rounds.
Another limitation is the use of self-reported data for both
predictor and endpoint. This inherits the danger for common
method variance, which could bias effect estimates towards
an overestimation. We would have preferred to use absence
registries from the participating organisations, not only to
avoid the possibility of common method variance, but also
because this is the most objective measure of sickness
absence.
37
However, not all organisations and employees
were able or willing to provide individual data from absence
registries. In addition, the available registries were not
comparable between the different organisations (for exam-
ple, some collected pregnancy related sickness absence
together with ordinary sickness, whereas others recorded
this separately). While we would have had preferred data
from absence registries, we are nevertheless confident that
our absence data are reliable. We compared some of the self-
reported absence data with information on absence rates we
obtained from the organisation project committees and found
high correlations. Moreover, other studies have shown that
self-reported absence is highly correlated with company
based measurement, but with a tendency to under-report the
factual absence days and spells.
38 39
Ferrie et al recently
compared self-reported sickness absence with company
recorded sickness absence in the Whitehall II study and
found that in 63% of the women and 67% of the men, the
total number of self-reported annual absence days was
within 2 days of the recorded number of days.
40
Finally, there are two limitations with regard to the
measurement of prevalent diseases. First, this variable was
based on self-report, and therefore might have resulted in
incomplete data. Second, the variable was assessed at follow
up, but not at baseline. This is problematic, because we
included this variable to control for the possibility that
20
15
10
5
Low-low WB (n = 542)
Absence days/year
T
2
8.1
T
1
6.8
20
15
10
5
Low-high WB (n = 142)
T
2
12.9
T
1
8.4
20
15
10
5
High-high WB (n = 88)
Absence days/year
T
2
17.4
T
1
13.6
20
15
10
5
High-low WB (n = 52)
T
2
9.9
T
1
12.7
Figure 1 Mean number of absence days per year (95% CI) at baseline (T
1
) and follow up (T
2
) for 824 participants in the prospective cohort with
constant low burnout (low–low), constant high burnout (high–high), increasing burnout (low–high), or decreasing burnout (high–low). Low burnout was
defined by scoring ,50 points, high burnout by scoring >50 points on the work related burnout scale.
104 Borritz, Rugulies, Christensen, et al
www.occenvmed.com
prevalent diseases at baseline might have caused both
burnout at baseline and sickness absence at follow up and
therefore might have been a confounder. By measuring this
variable at follow up only, it is possible that a few diseases
listed by the participants had been developed after the
baseline assessment. Controlling for these diseases would be
over-adjustment, because they might have been in the causal
pathway from burnout to sickness absence. We were aware of
this, but still decided to include the variable, because we
valued adjustment for a potential confounder (diseases at
baseline) higher than the potential danger of over-adjust-
ment (diseases that have been developed after baseline).
Empirically, it turned out that adjusting for prevalent
diseases reduced the effect estimates only marginally, as
can be seen by comparing Model IV with Model V.
Reflections
This study explores two complex phenomena: burnout and
absence. We consider burnout as a special category of
occupational stress, mostly to be found in human service
work, and with emotional exhaustion as the core symptom.
With regard to medical aspects, burnout might make the
individual more susceptible to infections such as the common
cold.
30
With regard to behavioural aspects, the theory on
conservation of resources (COR) might explain why burnout
could increase sickness absence, even without the presence of
a disease.
31 32
The COR theory proposes that individuals strive
to retain and protect their values—that is, resources as
objects, conditions, personal characteristics, and energies.
According to the COR theory, psychological stress occurs
when the individual is threatened with or has actually
experienced the loss of resources, or when the individual fails
to gain new resources after investing existing resources.
Hobfoll considers burnout as a special form of psychological
stress, emerging when a significant investment of time and
energy does not lead to the gain of new resources.
32
In this
perspective, taking sickness absence might be viewed as a
strategy of a burned out individual to save energy and to
recover from exhaustion.
33
It has also been proposed to view
sickness absence as a coping behaviour often used by persons
who do not have a diagnosed medical disease, but never-
theless feel limited in their health.
2
Burnout might well fall in
this grey area between good health and medical disease.
When sickness absence under 14 days can be taken without a
medical certification, as is the case for most Danish employ-
ees, limited health without diagnosed medical disease might
be an important determinant for sickness absence days and
spells.
Conclusion
The findings in this study indicate that burnout is a
contributor to sickness absence. The length of follow up
time of three years combined with adjustments for demo-
graphic, work, and health related confounders is to our
knowledge unique. The fact that changes in burnout level
over time predicted changes in sickness absence in the same
direction points to the possibility that preventing burnout can
reduce sickness absence.
Authors’ affiliations
.....................
M Borritz, R Rugulies, K B Christensen, E Villadsen, T S Kristensen,
National Institute of Occupational Health, Copenhagen, Denmark
Funding: The PUMA study has been funded by grants from The Work
Environment Fund (Arbejdsmiljøfondet), The Danish Work Environment
Service (Arbejdstilsynet), The Work Environment Council
(Arbejdsmiljøra˚dets Servicecenter via Arbejdsministeriets sundhedsfrem-
mepulje), and The Health Insurance Foundation (Sygekassernes
Helsefond).
Competing interests: none declared
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ECHO...................................................................................................................
Hydrolysable tannins in plant dusts cause airway obstruction
Please visit the
Occupational
and
Environmental
Medicine
website [www.
occenvmed.
com] for a link
to the full text
of this article.
I
n vitro research has suggested that airway obstruction linked to occupational or
environmental exposure to plant dusts is caused by the hydrolysable tannins they
contain.
A series of tests in organ baths on standard lengths of trachea and bronchi dissected from
guinea pigs previously unexposed to the dusts established that hydrolysable tannins inhibit
synthesis of protective nitric oxide in the airway epithelium, resulting in spasm. Tannic acid
provoked a rapid concentration dependent contraction of the bronchotracheal rings for
30–60 minutes with a mean EC
50
of 0.19 mmol/l and maximum contraction of 85%; the
threshold concentration causing significant contraction was 0.7 nmol/l (equivalent to
1.2 mg/m
3
).
The reaction was shown to be due to non-competitive inhibition of the constitutive
endothelial isoform of nitric oxide synthase in airway epithelium by using an array of
inhibitors and pretreatments in combination with hydrolysable tannic acid. It was also
specific to hydrolysable tannins.
Total hydrolysable tannins in barley flour, oak wood, and green tea were determined
spectrophotometrically as 8.7, 11.2, and 5.9 mg/g, respectively. The researchers predict that
an acute response in people encountering the dusts for the first time would be elicited by a
direct effect of hydrolysable tannic acids at dust concentrations over 100 mg/m
3
. Chronic
obstructive respiratory symptoms resulting from occupational exposure to dusts at average
inhalable concentrations of 5–20 mg/m
3
would be accounted for by tannin accumulation in
the airway epithelium.
Exposure to plant dusts is known to increase risk of obstructive lung diseases but until
now the triggers and underlying mechanism were not.
m Taubert D, et al. Thorax 2005;60:789–791.
106 Borritz, Rugulies, Christensen, et al
www.occenvmed.com
