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Work-related violence and depressive disorder among 955,573
employees followed for 6.99 million person-years. The Danish Work
Life Course Cohort study
Ida E.H. Madsen PhD , Annemette Coop Svane-Petersen PhD ,
Anders Holm PhD , Hermann Burr PhD , Elisabeth Framke PhD ,
Maria Melchior PhD , Naja Hulvej Rod DMSc ,
Børge Sivertsen PhD , Stephen Stansfeld PhD ,
Jeppe Karl Sørensen MSc , Marianna Virtanen PhD ,
Reiner Rugulies PhD
PII:
DOI:
Reference:
To appear in:
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Accepted date:
S0165-0327(21)00291-3
https://doi.org/10.1016/j.jad.2021.03.065
JAD 13193
Journal of Affective Disorders
29 September 2020
29 January 2021
19 March 2021
Please cite this article as: Ida E.H. Madsen PhD , Annemette Coop Svane-Petersen PhD ,
Anders Holm PhD ,
Hermann Burr PhD ,
Elisabeth Framke PhD ,
Maria Melchior PhD ,
Naja Hulvej Rod DMSc , Børge Sivertsen PhD , Stephen Stansfeld PhD , Jeppe Karl Sørensen MSc ,
Marianna Virtanen PhD , Reiner Rugulies PhD , Work-related violence and depressive disorder
among 955,573 employees followed for 6.99 million person-years. The Danish Work Life Course
Cohort study,
Journal of Affective Disorders
(2021), doi:
https://doi.org/10.1016/j.jad.2021.03.065
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2021 Published by Elsevier B.V.
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Work-related violence and depressive disorder among 955,573 employees
followed for 6.99 million person-years. The Danish Work Life Course Cohort study
RUNNING TITLE: Work-related violence and depression
Ida E. H. Madsen, PhD , Annemette Coop Svane-Petersen, PhD , Anders Holm, PhD , Hermann Burr, PhD , Elisabeth
Framke, PhD , Maria Melchior, PhD , Naja Hulvej Rod, DMSc, Børge Sivertsen, PhD,
Karl Sørensen, MSc, Marianna Virtanen, PhD,
1
1
1
2
3
1
4
5
6,7,8
Stephen Stansfeld, PhD, Jeppe
9
1
10,11
and Reiner Rugulies, PhD
1,5,12
National Research Centre for the Working Environment, Copenhagen, Denmark
Department of Sociology, University of Western Ontario, London, Canada
3
Unit Mental Health and Cognitive Capacity, Federal Institute for Occupational Safety and Health, Berlin, Germany
4
Social Epidemiology Research Group, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), INSERM UMR_S 1136,
Paris, France.
5
Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark
6
Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway
7
Department of Research and Innovation, Helse Fonna HF, Haugesund, Norway
8
Department of Mental Health, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
9
Centre for Psychiatry, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
10
School of Educational Sciences and Psychology, University of Eastern Finland, Joensuu, Finland
11
Division of Insurance Medicine, Karolinska Institute, Sweden
12
Department of Psychology, University of Copenhagen, Denmark
2
Word count abstract: 247
Word count manuscript: 3,929
Tables: 4
Figures: 1
Online Supplementary Material: 1
HIGHLIGHTS
A high probability of violence at work predicted risk of depressive disorder
Among women, the association was robust across industries
Amon men, the association was limited to certain industries
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ABSTRACT
Background:
We examined the association between probability of work-related violence and first diagnosis
of depressive disorder whilst accounting for the potential selection of individuals vulnerable to depression
into occupations with high probability of work-related violence.
Methods:
Based on a pre-published study protocol, we analysed nationwide register data from the Danish
Work Life Course Cohort study, encompassing 955,573 individuals followed from their entry into the
workforce, and free from depressive disorder before work-force entry. Depressive disorder was measured
from psychiatric in- and outpatient admissions. We measured work-related violence throughout the
worklife by the annual average occupational risk of violence exposure. Using Cox proportional hazards
regression, we examined the longitudinal association between work-related violence (both past year and
cumulative life-long exposure) and first depressive disorder diagnosis, whilst adjusting for numerous
confounders including parental psychiatric and somatic diagnoses, childhood socioeconomic position, and
health services use before workforce entry.
Results:
The risk of depressive disorder was higher in individuals with high probability of past year work-
related violence (hazard ratio: 1.11, 95% CI: 1.06-1.16) compared to employees with low probability of
exposure, after adjustment for confounders. Among women, associations were robust across industries,
whereas among men, associations were limited to certain industries.
Limitations:
Violence was measured on the job group and not the individual level, likely resulting in some
misclassification of the exposure.
Conclusions:
Work-related violence may increase the risk of depressive disorder, independent of pre-
existing risk factors for depressive disorder. These findings underline the importance of preventing work-
related violence.
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Keywords:
Occupational exposure; Workplace violence; Offending Behaviors; Depressive disorder;
Occupational stress
INTRODUCTION
Depressive disorders are a leading cause of disability (Hay et al., 2017) with a complex etiology involving
biologic, psychological and social risk factors (Kendler and Gardner, 2016; Kendler et al., 2002, 2006; Köhler
et al., 2018; Pettersson et al., 2018). Work-related risk factors for depression may provide potential targets
for intervention and primary prevention. Several psychosocial working conditions, including high demands
and low control at work, low social support, and bullying have been linked with the risk of depressive
symptoms or clinical depression (Madsen et al., 2017; Theorell et al., 2015). One work-related risk factor,
work-related violence, has been found to predict depressive symptoms (Butterworth et al., 2016),
antidepressant treatment (Dement et al., 2014; Madsen et al., 2011), and affective disorders (Wieclaw et
al., 2006), but studies examining risk of depressive disorders using a clinical endpoint are rare. A recent
systematic review on the topic found four such studies, including two examining the risk of antidepressant
treatment, and reported an association between work-related violence and depressive disorder with a
hazard ratio of 1.42 (95% CI: 1.31-1.54) (Rudkjoebing et al., 2020). However, as pointed out by the
systematic review, the existing evidence is limited by several methodological concerns, including a lack of
studies assessing exposure non-self-reportedly and repeatedly (Rudkjoebing et al., 2020). Furthermore,
previous studies have not accounted for bias by the potential selection of individuals with higher
depression vulnerability into occupations with high risk of violence, which has been raised as a concern
(Madsen et al., 2011). Such selection is plausible, because work-related violence occurs most frequently in
person-related work, such as police- prison, or caring work (Madsen et al., 2010), and studies suggest that
individuals from socially disadvantaged backgrounds or with parental illness tend to be selected into such
occupations
Fussell a d Bo
ey,
; Nik e i et al.,
. Supporting this notion, a previous study
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found that caring professionals had higher antidepressant treatment rates years before entering care work
(Madsen et al., 2012).
Aims of the study
In the present study, we examine the longitudinal associations between work-related violence and
depressive disorder in the Danish Work Life Course Cohort (DaWCo), an open cohort of employees in
Denmark followed from the beginning of their working lives. The design and analyses were pre-defined in a
pre-published protocol (Madsen et al., 2018b). We estimated exposure annually, as the average
occupational risk of work-related violence and measured incident depressive disorder from hospital
registers containing psychiatric inpatient and outpatient diagnoses. To account for selection of vulnerable
individuals into high risk occupations, we adjusted for risk factors from before workforce entry, including
parental psychiatric and somatic diagnoses, childhood socioeconomic position, and health services use. To
distinguish between short-term and accumulating effects, we included measures of both past year
exposure and exposure cumulated throughout the work life.
MATERIAL AND METHODS
DaWCo is an open inception cohort study of all individuals who first entered the Danish workforce during
the years 1995-2009 and were 15 to 30 years old at entry (Svane-Petersen et al., 2019). DaWCo was
constructed using nationwide Danish registers on employment, health, demographic- and socioeconomic
factors to examine effects of working conditions on health. Working conditions are measured repeatedly
throughout the work life and health endpoints are based on clinical diagnosis. Workforce entry was defined
as the first year with employment as the main source of income (n=979,257). We excluded individuals with
missing data on gender and ethnicity (n=5,176), and individuals who died (n=71), emigrated (n=13,087), or
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received disability pension (n=361) in their year of entry, leaving 960,562 individuals in the cohort. To study
incident depressive disorder, we further excluded individuals with diagnosed depressive disorder before or
in the year of workforce entry (n=4,989), yielding a final cohort of 955,573 individuals followed for
6,991,811 person years (mean follow up: 7.3 years).
Measurement of work-related violence
Work-related violence was measured using a job exposure matrix (JEM) estimating the average gender- and
age-specific probability of work-related violence in occupations classified according to the Danish version of
the International Standard Classification of Occupations (International Labour Organization, 2004). The JEM
was constructed using survey data from the Danish Work Environment Cohort Study (Burr et al., 2003). For
details see the study protocol (Madsen et al., 2018b). The JEM showed good validity when compared to
self-reported information using ROC-curve analysis. Within the Danish Working Environment Cohort study,
the JEM showed an Area under the Curve (AUC) of 0.89, and a similar JEM showed AUCs of 0.84 and 0.86
for men and women respectively, when tested in an independent sample (Madsen et al., 2018a). The
occupations with highest and lowest exposure are presented in
Supplementary Material, Table S1.
Each individual was assigned an annual probability of work-related violence exposure, with a probability
of 0 in years of non-employment. We measured cumulated exposure by summing these annual
probabilities. After applying logarithmic transformation (log2), the measures for past year and cumulated
work-related violence exposure were included as continuous variables in the main analysis to estimate the
risk of depressive disorder associated with a doubling of exposure. To avoid logarithmic transformation of 0
we added a small constant before log-transformation.
For comparability to previous research, we also constructed a categorical measure for past year risk of
work-related violence defined as low (0 to <1% probability of work-related violence), medium (1 to 2%
probability), and high (>2% probability). The categories were constructed using the quartiles of exposure
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probability but collapsing the first two quartiles. We further constructed a categorical measure for the
proportion of previous annual measures with high risk of work-related violence categorized into 0 to 5%,
6% to 25%, 26% to 75%, and 76% or more. The
Pearso ’s
correlation between past year and cumulated
work-related violence risk was 0.74 and the relation between past year and cumulated high probability of
violence is illustrated in
Supplementary Material, Table S1.
Measurement of depressive disorder
Information on depressive disorder was obtained from The Psychiatric Central Research Register (Mors et
al., 2011) during 1969-1994 and The National Patient Register during 1995-2010 (Lynge et al., 2011). The
registers encompass all inpatient psychiatric admissions in Denmark since 1969, and from 1995 onwards
also outpatient admissions (Mors et al., 2011). We defined depressive disorder as a main diagnosis of F32
or F33 from ICD-10 (for years 1994 to 2010), and 296.0, 296.2, 298.0, 300.4 from ICD-8 (for years 1969 to
1993). ICD-9 was never used in Denmark. To exclude individuals with depressive disorder prior to workforce
entry, we also used the codes F92.0 (ICD-10) and 308.02 (ICD-8) for depressive disorder in childhood or
adolescence.
Measurement of covariates
As covariates, we included calendar year, gender, age, cohabitation, ethnicity, number of children,
employment status, education, years of non-employment, years of work experience, income, number of
health services used in the year before workforce entry, childhood socioeconomic position, and maternal
and paternal psychiatric and somatic diagnoses before the cohort member entered the workforce. All
covariates were included from National Danish Registers (Andersen et al., 2011; Baadsgaard and Quitzau,
2011; Jensen and Rasmussen, 2011; Lynge et al., 2011; Mors et al., 2011; Pedersen, 2011). Childhood
socioeconomic position was measured by maternal and paternal educational level and labour market status
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when the cohort member was 15 years. Linkage to parental data was available from 1980 onwards and only
for individuals with parents residing in Denmark and 11.3% of individuals had missing maternal data and
12.6% had missing paternal data. To ensure cohort completeness, we retained these individuals in the
analysis but adjusted for missing data. Data on number of health services used was included from the
Danish National Health services register, encompassing mainly primary health care services (Andersen et
al., 2011). Age, cohabitation, number of children, education, income, employment status, years of non-
employment, and years of work experience were included as annual time-varying variables, while
remaining covariates were considered time-invariant.
Statistical modeling
We analyzed data using Cox regression with time-to-first diagnosis of depressive disorder as the outcome,
modelling past year and cumulated exposures concurrently. We used calendar time as the time-axis to
account for period effects on psychiatric treatment (Mors et al., 2011) and analyzed data longitudinally
with a one year time-lag, relating exposure during year t to events during year t+1. Individuals were
followed from workforce entry until first depressive disorder diagnosis, death, emigration, receipt of
disability pension, or end of follow-up 31 December 2010, whichever came first. We limited follow-up to
2010 because exposures could not be updated after 2009 due to changes in the occupational classification.
The main analysis (model 1) adjusted for gender, age, cohabitation, ethnicity, number of children,
employment status (employed vs. non-employed with studying and self-employment included as
dummies), years of non-employment, years of work experience, income, childhood socioeconomic position,
and maternal and paternal psychiatric and somatic diagnoses. In model 2, we further adjusted for the
number of health services used in the year before workforce entry to account for non-specific health-
related selection into high risk occupations. Fulfillment of the proportional hazards assumption were
assessed by visual inspection of the log-log hazard plots.
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Sensitivity analyses
As pre-planned (Madsen et al., 2018b), we conducted sensitivity analyses stratifying by 1) gender, 2)
ethnicity, 3) educational level, 4) industry. We conducted sensitivity analyses 1, 3, and 4 to assess if
associations were similar in men and women and across educational levels and industries. We conducted
sensitivity analysis 2 because no data were available for migrants prior to their migration to Denmark, and
thus these individuals could have worked or been diagnosed with depressive disorder in another country
before cohort entry.
Post hoc supplementary analyses
As post hoc supplementary analyses we included analyses 1) without adjustment for parental data to gauge
the extent of confounding in previous studies without parental data, 2) adjusting only for gender, age and
employment status (minimally adjusted), 3) stepwise adding covariates from model 1 to the minimally
adjusted model, 4) excluding individuals with any psychiatric diagnoses before workforce entry, 5) adjusting
for occupational level of job control, as low job control may be a risk factor for depressive disorder , 6)
examining two alternate cut-off points for the categorical definition of work-related violence, based on a
previous study on work-related violence and affective disorder (Wieclaw et al., 2006) (a) low: <1%,
ediu : ≥ % to < %, a d high: ≥ %;
low: <1%, medium:
≥ % to < 4%, a d high: ≥ 4% ,
) stratifying by
both industry and gender to further examine gender-specific patterns, 8) estimating the extent of bias
caused by non-differential misclassification of exposure by the JEM for work-related violence. This analysis
was conducted using the methods for quantitative bias analysis proposed by Lash, Fox, & Fink (2009)
applying a sensitivity of 0.85 and a specificity of 0.80 based on data from a previous JEM validation
(Madsen et al., 2018a).
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RESULTS
Cohort characteristics
Table 1
shows the characteristics of the study population in their year of cohort entry. There were 49.3
percent women and the mean age was 20.2 years. About seven percent of the population had a mother
with a psychiatric diagnosis.
*** Table 1 ***
We identified 16,148 cases of incident hospital-treated depressive disorder during 6.99 million person-
years of follow-up (23 cases per 10,000 person years). Individuals were censored due to migration
(n=103,762, 10.9%), death (n=2,734, 0.3%), and disability pensioning (n=8,132, 0.9%).
Table 2
shows the
association between population characteristics and the risk of depressive disorder. As expected, risk of first
diagnosis with depressive disorder was higher in women than men, for individuals with lower education,
and individuals with a parent with a psychiatric diagnosis, especially for maternal psychiatric diagnosis.
*** Table 2 ***
Work-related violence and depressive disorder
Table 3
shows the association between work-related violence and risk of depressive disorder. Higher past
year probability of work-related violence was statistically significantly associated with a small elevated risk
of depressive disorder with a hazard ratio of 1.01 (95%CI: 1.00-1.01) per doubling of exposure probability of
work-related violence. As an example of interpretation, this estimate shows that individuals with 4%
probability of work-related violence had a hazard ratio of 1.01 compared to individuals with a 2%
probability of work-related violence. When quantifying the association in terms of exposure categories, the
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hazard ratio for high vs. low probability of work-related violence was 1.11 (95% CI: 1.06-1.17). There was no
additional association with accumulated exposure to work-related violence. Results were similar without
(model 1) and with (model 2) adjustment for health care services use prior to workforce entry.
**** Table 3 ****
Preplanned sensitivity analyses
Table 4
shows the results of the gender and ethnicity-stratified sensitivity analyses. The association
between probability of work-related violence and depressive disorder was seen in women (HR= 1.14,
95%CI: 1.08-1.21 for high vs. low exposure) but not in men (HR= 0.99, 95%CI: 0.90-1.08 for high vs. low
exposure).
**** Table 4 ****
The results for high compared to low work-related violence probability within educational strata are shown
in
Supplementary Material, Table S3.
High work-related violence probability was associated with
depressive disorders among individuals
i the strata of Primary
and lower secondary education
, Upper
se o dary edu atio
y le tertiary edu atio
a d Ba helor edu atio
hereas the asso iatio
as a se t i the strata of
Short
a d Masters’ le el edu atio .
When stratifying analyses by industry, the association between high work-related violence probability and
risk of depressive disorders was present in four industries, including industries with high risk of work-
related viole
e su h as Pu li
-
a d perso al ser i es ,
with point estimates ranging from 1.11 to 1.18.
The association was absent in three industries, where statistically non-significant tendencies of increased
risk were seen for employees in occupations with medium risk of violence (Table
S4).
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Supplementary post hoc analyses
Figure 1
summarizes the results from analyses stratified by industry and gender (for detailed results see
Supplementary Material, Table S5).
We found associations or statistically non-significant tendencies
towards associations between work-related violence probability and depressive disorder for women in all
industries, and for men in certain industries (
Retail
trade; hotels and restaurants
,
Financial
i ter ediatio , usi ess et . ,
and
Pu li a d perso al ser i es
).
****Figure 1 ****
Supplementary Material, Table S6
shows results of the post hoc supplementary analyses. Results were
similar after omitting adjustment for parental data. The minimally adjusted model showed an association
which was only slightly stronger than the main analyses, with a hazard ratio of 1.21 (95% CI: 1.15-1.27) for
high probability of work-related violence and 1.15 (95% CI: 1.09-1.21) for medium probability of work-
related violence. When applying stepwise adjustment, the largest reduction in the hazard ratios was related
to further adjustment for income, which reduced the hazard to 1.15 (95% CI: 1.10-1.21) for high probability
of work-related violence and 1.09 (95% CI: 1.04-1.15) for medium probability of work-related violence.
Results remained similar when excluding individuals with any psychiatric diagnosis prior to work-force entry
(n=48,638). When adjusting for occupational job control, the estimate for work-related violence increased.
When changing the cut-off points defining high, medium and low probability of work-related violence, the
association in women became more pronounced, but still no association was seen in men. We found some
indication of accumulation when analyzing the proportion of annual measures with high probability of
work-related violence, with an additionally higher risk of depressive disorder for individuals with 76% or
more of measurements with high exposure probability.
Our quantitative bias analysis, accounting for measurement error in terms of exposure misclassification
by the JEM, showed that the hazard ratio of 1.11 for high vs. low probability of work-related violence
increased to 1.22 when accounting for exposure misclassification.
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DISCUSSION
Our study, based on an inception cohort of the entire employed population of Denmark between 1995 and
2009, found that employees in occupations risking work-related violence had a higher risk of first-time
hospital diagnosed depressive disorder. This association was not explained by risk factors for depressive
disorder pre-existing work-force entry, including parental psychiatric or somatic diagnoses, childhood
socioeconomic position, and primary care health services use. The association between work-related
violence and depressive disorder was gender- and industry-specific. While the association in women was
largely robust, only men employed in certain industries had a higher risk of depressive disorder in relation
to work-related violence. The association was modest in strength, but may be a conservative estimate of
the underlying association, as also indicated by the quantitative bias analysis.
This study is the first to examine the association between work-related violence and depressive disorder
whilst accounting for risk factors for depressive disorder that pre-exist workforce entry. Our findings
suggest a causal effect of work-related violence on the development of depressive disorder. Such effect is
plausible given the evidence concerning effects of domestic violence on risk of depressive disorder
(Campbell, 2002; Weich et al., 2009). Mechanisms could include feelings of helplessness and hopelessness,
which have been related to the etiology of depressive disorder (Beck and Alford, 2009). Our findings extend
previous studies associating work-related violence with indicators of depressive disorder (Butterworth et
al., 2016; Madsen et al., 2011; Wieclaw et al., 2006). When we omitted adjustment for potential
confounders pre-existing workforce entry, results were similar to those of our main analyses, suggesting
that selection of individuals vulnerable to depressive disorder into occupations with high risk of violence
was probably not a major bias in previous studies on the topic.
The gender- and industry-specific patterns of the association between work-related violence and
depressive disorder may be related to differences in the type of violence encountered by women and men
and in different industries. Although there may be gender-difference in the etiology of depressive disorder
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(Kendler and Gardner, 2014), the association between work-related violence probability and depressive
disorder was seen also for men, if they worked in industries involving person-related work, such as
Retail
trade; hotels a
d restaura ts a d Pu li a d perso al ser i es.
Employees in these industries are at risk
of work-related violence (Madsen et al., 2010), predominantly from their clients (Wieclaw et al., 2006). The
gender-difference contrasts a previous study on Danish registers, which found higher risk of affective
disorders in both men and women in occupations with risk of violence (Wieclaw et al., 2006). The previous
study used a nested case-control design, and it is possible that this is why the industry-specific patterns
were not revealed to the same extent as in our study. Another difference is that we followed individuals
from the beginning of their work lives and included only individuals aged 15-30 years at cohort entry,
whereas the previously studied population included individuals aged 18-65.
Our results concerning accumulation of effects of work-related violence on depressive disorder remain
inconclusive. No accumulation was seen in the main analysis, but supplementary analyses showed some
indication, i.e. there was a higher risk of depressive disorder for employees with 76% or more
measurements with high probability of work-related violence after accounting for past year exposure.
There is a paucity of studies examining the accumulation of risks related to work-related violence, as most
previous studies have measured exposure only at single time points. One study examined the cumulative
effects of trauma, including violence and abuse outside the workplace, in a sample of female nurses and
nursing personnel (Cavanaugh et al., 2014). The results suggested that different types of trauma
accumulated, and nurses who had experienced three or more types of trauma had a two to six-fold higher
risk of depression and post-traumatic stress, compared to non-exposed nurses. Further research is needed
to conclude firmly regarding the potential accumulation of effects of work-related violence on depressive
disorder.
Our results suggest that the association between work-related violence and depressive disorder may be
causal. This finding has important implications for policy and practice as it underlines the importance of
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preventive efforts targeting work-related violence. Preventive interventions may include staff training in
de-escalation techniques or aggression management, and implementing a systematic approach to risk
management (O'Rourke et al., 2018; Price et al., 2015; Wassell, 2009). From a clinical perspective,
occupational information regarding work-related violence may be considered a risk factor in the
assessment of patients possibly presenting with depressive disorder.
Strengths and limitations
The strengths of this study include the register-based design allowing us to follow an exhaustive cohort of
employees with annual measures of work-related violence from the beginning of their work lives, without
selective participation or drop out. Further strengths include the outcome measure based on clinical
diagnosis, and the inclusion of potential confounders from before workforce entry. Including individuals in
the study from workforce entry also means that potential underestimation of effects due to healthy worker
selection was diminished.
While the register-based design entails important strengths, it also carries certain limitations. First,
exposure to work-related violence was not measured at the individual level but using a JEM estimating the
probability of work-related violence in relation to occupation, gender and age. Consequently, the findings
should be carefully interpreted in light of previous studies showing associations at the individual level.
Inherent in the JEM-measurement is a misclassification of exposure, as most persons classified as high-
probability of work-related violence had not actually experienced work-related violence. This
misclassification likely results in an underestimation of the effects of work-related violence on depressive
disorder risk, as also suggested by the quantitative bias analysis. To further strengthen the evidence base
concerning the effect of work-related violence on depression, future studies should apply individual level
exposure measurement. In light of the relatively low prevalence of work-related violence in the general
working population, one suggestion might be to analyse data from pooled cohort studies, such as has been
conducted concerning the association between work-related violence and diabetes (Xu et al., 2018).
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However, considering the already existing and mounting evidence on the adverse effects of work-related
violence on (mental) health (Butterworth et al., 2016; Madsen et al., 2011; Rudkjoebing et al., 2020), the
development and implementation of methods to prevent and manage violence in workplaces may be
timely.
We employed a one-year time lag between exposure and outcome to avoid reverse causation. This
longitudinal approach may be considered a study strength, but simultaneously it is a limitation if effects of
work-related violence are acute and triggering, in which case it may have caused an underestimation.
Furthermore, we could not include data on several known vulnerability factors such as life events (Kendler
and Gardner, 2016) or genetics (Pettersson et al., 2018). We would argue that it is unlikely that the
association found between work-related violence and depressive disorder is attributable to confounding by
life events, as there is a social gradient in life events, with higher prevalence in adolescents with lower
parental education and income (Glasscock et al., 2013; Masters Pedersen et al., 2015). Consequently, the
association should be attenuated with adjustment for indicators of childhood socioeconomic position, if it
were attributable to confounding by life events, and this was not the case in the present analysis.
Furthermore, the information on parental psychiatric diagnoses could be considered a marker of biologic
risk. Nevertheless, the lack of data on life events and genetics remains a limitation of our study. Finally, it
should be noted that we included only cases of depressive disorder that were hospital diagnosed and
associations may differ from associations with symptom-based measures (Thielen et al., 2009) as many
cases of depressive disorder are untreated or treated exclusively in primary care (Alonso et al., 2004).
To conclude, we found that employees in occupations with high probability of work-related violence had
a higher risk of depressive disorder, and that this association was not explained by known risk factors for
depressive disorder pre-existing workforce entry, including parental psychiatric diagnoses and childhood
socioeconomic position. The association was gender- and industry-specific, possibly related to industry-
specific patterns in the type of work-related violence encountered. Our findings suggest a casual role of
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work-related violence in the development of depressive disorder and underline the importance of
preventive efforts to reduce work-related violence.
AUTHOR CONTRIBUTIONS
IEHM conceived the study and designed and conducted the analyses. All authors interpreted the
data and discussed the results. IEHM wrote the first draft of the manuscript and all authors revised
the draft critically for important intellectual content. All authors read and approved the final
version for submission.
Acknowledgements:
None.
Funding statement:
The Danish Worklife Course Cohort is funded by a grant from the Danish Working
Environment Research Fund (grant number 17-2014-03). The analyses for this article were further
supported by grants from the Danish Working Environment Research Fund (grant number 43-2014-03) and
NordForsk (grant number 75021).
Conflicts of interest:
None.
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TABLES
Table 1. Characteristics of the study population in their year of entry into the workforce
Total sample
Gender
Men
Women
Age
15-17
18-19
20-25
25-30
Cohabitation
Yes
N
955,573
484,422
471,151
%
100
50.7
49.3
Mean
20.2
103,410
379,211
378,299
94,653
613,502
10.8
39.7
39.6
9.9
64.2
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No
Unknown
Ethnicity
Danish
Non-Danish
Children in the household
0
≥1
Education
Primary or lower secondary
Upper secondary
Short cycle tertiary
Bachelor or equivalent
Master or equivalent
Doctoral or equivalent
Not classified/unknown
Maternal psychiatric diagnosis
Yes
No
Paternal psychiatric diagnosis
Yes
No
Maternal somatic diagnosis
Yes
No
Missing
Paternal somatic diagnosis
Yes
No
Missing
Maternal education
Primary or lower secondary
Upper secondary
Short cycle tertiary
Bachelor or equivalent
Master or doctoral
Not classified/unknown
Paternal education
Primary or lower secondary
Upper secondary
Short cycle tertiary
Bachelor or equivalent
Master or doctoral
Not classified/unknown
298,507
43,564
811,622
143,951
286,040
669,533
686,791
172,169
4,319
13,319
5,389
13
73,573
62,543
893,030
57,733
897,840
40,617
821,176
93,780
31.2
4.6
84.9
15.1
29.9
70.1
71.9
18.0
0.5
1.4
0.6
<0.1
7.7
6.5
93.5
6.0
94.0
4.3
85.9
9.8
55,373
794,235
105,965
207,277
238,304
16,331
111,198
22,624
359,839
123,473
258,260
22,091
54,981
45,357
451,411
5.8
83.1
11.1
21.7
24.9
1.7
11.6
2.4
37.7
12.9
27.0
2.3
5.8
4.8
47.2
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Maternal labour market status
Employed
Non-employed
Unknown
Paternal labour market status
Employed
Non-employed
Unknown
Missing maternal data
Yes
No
Missing paternal data
Yes
No
Depression status
Yes
No
653,987
192,581
109,005
705,361
122,213
127,999
93,780
861,793
105,965
849,608
16,148
7,038,803
68.4
20.2
11.4
73.8
12.8
13.4
9.8
90.2
11.1
88.9
0.2
99.8
Table 2. Characteristics of the study population throughout the study period and first depression diagnosis
Incident depressive
disorder
per 10 000 PY
15
31
N person years
(PY)
Gender
3,557,929
Men
3,433,882
Women
Age
15-17
18-19
20-25
25 ≤
Cohabitation
Yes
No
Unknown
4,125,032
2,827,071
39,709
129,766
713,866
2,950,696
3,197,484
%
50.9
49.1
1.9
10.2
42.2
45.7
14
20
22
25
59.0
40.4
0.6
19
29
12
25
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Ethnicity
Danish
Non-Danish
Children in the household
0
≥1
Income
Decile 1 (lowest)
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 7
Decile 8
Decile 9
Decile 10 (highest)
Education
Primary or lower secondary
Upper secondary
Short cycle tertiary
Bachelor or equivalent
Master or equivalent
Doctoral or equivalent
Not classified/unknown
Maternal psychiatric diagnosis
Yes
No
Missing
Paternal psychiatric diagnosis
408,335
6,130,292
543,184
5.8
87.7
6.5
46
22
21
2,532,115
3,234,617
194,981
610,136
260,469
8,512
150,981
36.2
46.3
2.8
8.7
3.7
0.1
2.2
30
20
14
17
10
11
18
695,042
696,497
697,306
696,029
695,626
698,686
701,324
703,373
704,381
703,546
3,971,528
3,020,283
56.8
43.2
25
21
6,227,382
764,430
89.1
10.9
23
21
9.9
10.0
10.0
10.0
9.9
10.0
10.0
10.1
10.1
10.1
28
27
24
27
31
27
23
17
14
10
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฀฀฀฀฀฀฀฀฀
Yes
No
Missing
Maternal somatic diagnosis
Yes
No
Missing
Paternal somatic diagnosis
Yes
No
Missing
Maternal education
Primary or lower secondary
Upper secondary
Short cycle tertiary
Bachelor or equivalent
Master or doctoral
Not classified/unknown
Paternal education
Primary or lower secondary
Upper secondary
Short cycle tertiary
Bachelor or equivalent
Master or doctoral
Not classified/unknown
376,882
6,083,487
531,442
291,007
6,247,620
453,184
394,292
6,066,078
531,442
1,355,122
1,568,099
107,161
781,177
146,172
3,034,080
5.4
87.0
7.6
43
22
22
4.2
89.4
6.5
28
23
21
5.6
86.8
7.6
28
23
22
19.4
22.4
1.5
11.2
2.1
43.4
28
23
19
22
21
21
691,532
1,707,880
153,423
395,212
312,296
3,731,469
9.9
24.4
2.2
5.7
4.5
53.4
30
23
21
19
21
23
27
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Maternal labour market status
Employed
Non-employed
Unknown
Paternal labour market status
Employed
Non-employed
Unknown
5,352,716
895,407
743,689
76.6
12.8
10.6
21
32
24
4,984,017
1,410,346
597,448
71.3
20.2
8.5
21
30
22
Table 3. Association between work-related violence and first depression diagnosis
Model 1
HR
95% CI
Model 2
HR
95% CI
Person-years
at risk (PY)
Past year work-
related violence,
per doubling
Past year work-
related violence
Low
Medium
High
Cumulated work-
related violence,
per doubling
Depression
cases per 10
000 PY
23
P-value
P-value
6,991,811
1.01
1.00-1.01
<.001
1.01
1.00-1.01
<.001
<.001
4,091,891
1,457,375
1,442,546
6,991,811
23
21
25
Ref.
1.06
1.11
-
1.01-1.11
1.06-1.17
Ref.
1.06
1.11
-
1.01- 1.11
1.06- 1.16
<.001
23
1.00
1.00-1.00
0.486
1.00
1.00-1.00
0.797
PY: Person years, HR: Hazard Ratio, CI: Confidence Interval.
Associations for past year work-related violence and cumulated work-related violence are included in the same models (mutually adjusted).
Model 1 is adjusted for gender, age, cohabitation, ethnicity, number of children, employment status, years of non-employment, years of work experience,
income, maternal and paternal psychiatric and somatic diagnoses, maternal and paternal education and labour market status.
Model 2 is adjusted for health services use in the year before workforce entry in addition to the covariates of model 1.
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Table 4. Association between work-related violence and first depression diagnosis stratified by gender and
ethnicity
Person-years
at risk (PY)
Men
Past year work-related violence
Low
Medium
High
Cumulated work-related violence,
per doubling
Women
Past year work-related violence
Low
Medium
High
Cumulated work-related violence,
per doubling
Danish ethnicity
Past year work-related violence
Low
Medium
High
Cumulated work-related violence,
per doubling
Non-Danish ethnicity
Past year work-related violence
Low
Medium
High
Cumulated work-related violence,
per doubling
Depression cases
per 10 000 PY
HR
95% CI
P-value
0.760
2,277,292
806,952
473,685
16
13
14
Ref.
1.02
0.99
1.00
-
0.94-1.11
0.90-1.08
1.00-1.01
0.452
<.001
1,814,599
650,422
968,860
32
30
31
Ref.
1.07
1.14
1.00
-
1.01-1.14
1.08-1.21
1.00-1.01
0.399
<.001
3,637,503
1,288,121
1,301,758
23
21
26
Ref.
1.06
1.12
1.00
-
1.01-1.12
1.06-1.18
1.00-1.0
0.285
0.398
454,388
169,254
140,788
23
16
22
Ref.
0.98
1.09
1.00
-
0.83-1.12
0.93-1.18
0.99-1.00
0.565
PY: Person years, HR: Hazard Ratio, CI: Confidence Interval.
Associations for past year work-related violence and cumulated work-related violence are included in the same models (mutually adjusted). Estimates
are adjusted for gender (except in the gender-stratified analysis), age, cohabitation, ethnicity (except in the ethnicity-stratified analysis), number of
children, employment status, years of non-employment, years of work experience, income, maternal and paternal psychiatric and somatic diagnoses,
maternal and paternal education and labour market status..
Figure Legend
Figure 1. Incident depressive disorder for employees with high risk of work-related violence compared to low
within industries for men and women separately
29