Key Features.
The Work Environment and Health in Denmark (WEHD) study was established to monitor employees’ work environment and health and to analyse the association between working conditions and health. The nationwide scope and comprehensive linkage to national registries are unique, allowing long-term follow-up with minimal attrition.
-
Survey data of employees in Denmark were collected biennially from 2012 to 2018 with continuous follow-up in national registries, yielding three cohorts, with responders 18–64 years old and 53% women:
i) N = 88 076 responding to at least one survey and followed up in registries;
ii) N = 15 852 responding in 2012 and followed up in subsequent waves (2014, 2016, 2018, responders to all four waves: N=5933, 37.4%) and registries;
iii) N = 30 070 responding in either 2012 or 2016 and invited to survey follow-up 2 years later (2014 and 2018, respectively, responders: N = 22 445, 74.7%) and followed up in registries.
The surveys collected data on physical, chemical, biological, and psychosocial working conditions, and self-reported health. The registries provided sociodemographic, labour market, and health data for responders and responders’ cohabiting partners, children, and parents.
Data are stored at Statistics Denmark. For collaboration, please contact the National Research Centre for the Working Environment, Copenhagen, Denmark (nfa@nfa.dk, Dr Jeppe Karl Sørensen).
Why was the cohort set up?
Conditions at the workplace, including physical, chemical, biological, and psychosocial exposures, are important determinants of population health [1–3]. Considering the ethical and practical constraints of conducting randomized controlled trials on potentially hazardous work conditions, and the limitations of cross-sectional and case-control studies regarding causal interpretations, cohort studies are essential for elucidating the relationships between working conditions and health outcomes.
Examples for occupational cohort studies with specific job groups and industries are the British Whitehall II study (civil servants) [4], the French GAZEL study (employees with civil servant-like status in an utility firm) [5], the Finnish Public Sector Study (public employees, predominately hospital workers) [6], and the US-American Nurses’ Health Study [7]. Examples of occupational cohort studies with a focus on a broader range of occupations are the Danish Work Environment Cohort Study (DWECS) [8], the Swedish Longitudinal Occupational Survey of Health (SLOSH) [9], and the French CONSTANCES Cohort [10].
The Work Environment and Health in Denmark (WEHD, Danish: Arbejdsmiljø og helbred i Danmark, AH) study was established in 2012. WEHD has a unique nationwide scope with both random and workplace-stratified samples of employees and a comprehensive linkage to national registries. Its main objectives are:
To monitor work environment and health of employees in Denmark over time.
To investigate the short- and long-term effects of physical, chemical, biological, and psychosocial working conditions on health and labour market participation.
Data collection of WEHD was carried out by the Danish National Research Centre for the Working Environment (Danish: Det Nationale Forskningscenter for Arbejdsmiljø). Data are stored on a secured research server of Statistics Denmark and are managed by the Danish Working Environment Authority (Danish: Arbejdstilsynet). Data collection, basic data management, and the monitoring of working conditions were funded by a grant from the Danish government. Analyses on the association between working conditions and health, and analyses for scientific publications have been funded by different grants, e.g. from the Danish Working Environment Research Fund on risk factors for mental disorders (AMFF-10–2019-03) and the influence of age on the association between work environment and health (AMFF-21–2019-09).
Who is in the cohort?
Cohort design
WEHD combines self-administered questionnaire data concerning working conditions and health, with national register data concerning sociodemographic characteristics, labour market participation, and health status. Survey data and register data are merged using the participants’ unique civil registration number that is assigned to all Danish residents. WEHD was registered and approved by the Danish Data Protection Agency (Datatilsynet, journal number 2015–57-0074), and informed consent was collected from participants. According to Danish law, studies based on questionnaire and register data only do not need ethical approval.
The study design includes both cross-sectional and longitudinal components. We collected survey data in cross-sectional samples in 2012, 2014, 2016, and 2018. Responders from the 2012 survey were invited to the surveys in 2014, 2016, and 2018. Responders from the 2016 survey were invited to the 2018 survey. All responders are followed up continuously in Danish national population, labour market, and health registries. WEHD consists therefore of three cohorts:
Cohort I: Individuals who responded at least once to any WEHD survey (N=88 076).
Cohort II: Individuals who responded to the WEHD survey in 2012 and were invited to the waves in 2014, 2016, and 2018 (N=15 852). Of these, N=14 085, N=10 961, and N=5933 responded in at least two, three, or all four waves, respectively.
Cohort III: Individuals who responded to the WEHD survey for the first time in either 2012 or 2016 (N=30 070) and were invited to another survey 2 years later (2014 and 2018, respectively). Of these, N=22 445 responded.
Recruitment of participants
In spring 2012, NFA received from Statistics Denmark home addresses of Danish residents that fulfilled the following criteria: (i) aged 18 to 64 years; (ii) subject to taxation in Denmark; (iii) had been employed for at least 35 hours between 30 November 2011 and 31 January 2012; (iv) monthly income of at least 3000 Danish kroners (€404, £337, $523 as of 1 January 2012).
This yielded a source population of 2 267 485 individuals [11], which was divided into two parts:
The ‘stratified workplace population’, consisting of 198 789 employees from 1060 small (10–99 full-time employees, FTEs), medium-size (100–499 FTEs), and large workplaces (≥500 FTEs). Home addresses were available for 169 762 individuals. From those, a random population sample of 15 811 individuals were drawn, of which 15 767 were alive and residing in Denmark in September 2012, constituting the final stratified workplace sample.
The ‘random population’ consisting of the remaining 2 086 696 employees from the source population. Addresses were available for 1 750 266 individuals. From those, a random sample of 35 111 individuals were drawn, of which 35 039 were alive and residing in Denmark in September 2012, constituting the final random population sample.
NFA sent a link to an online questionnaire to the potential participants by postal mail in April 2012. Non-responders were contacted again by letter, then text message, then received a printed questionnaire with a stamped return envelope.
In 2014, 2016, and 2018, we recruited new random population samples, following the same procedure as in 2012. In 2016, we further recruited a new stratified workplace sample. In addition, eligible responders from the 2012 random population sample were invited to participate in the 2014, 2016, and 2018 waves. Eligible responders from the 2016 random population sample were invited to participate in the 2018 wave. Table 1 presents the number of invited individuals, responders, and response rates across the four waves and sampling methods. In 2012 and 2016, 50 806 and 49 968 individuals, respectively, were invited, from the workplace sample and the random population sample. In 2014 and 2018, 37 737 and 37 331 individuals, respectively, were invited from the random population sample only. Response rates ranged from 48.2% in 2016 to 56.3% in 2018.
Table 1.
Number of initially invited individuals, responders, and response rate for the 2012, 2014, 2016, and 2018 Work Environment and Health in Denmark (WEHD) waves
| WEHD wave | Sample method | Initially invited individuals, n | Responders, n (response rate %) |
|---|---|---|---|
| 2012 | Total | 50 806 | 26 180 (51.5%) |
| − Workplace | 15 767 | 8518 (54.0%) | |
| − Random population | 35 039 | 17 662 (50.4%) | |
| 2014 | Total (random population only) | 34 737 | 17 486 (50.3%) |
| 2016 | Total | 49 968 | 24 071 (48.2%) |
| − Workplace | 15 407 | 7667 (49.8%) | |
| − Random population | 34 561 | 16 404 (47.5%) | |
| 2018 | Total (random population only) | 37 331 | 21 009 (56.3%) |
Table 2 compares the sociodemographic characteristics of responders (n = 88 076) and non-responders (n = 81 846) combined for the four WEHD waves. If individuals were by chance invited to more than one wave, we only considered the first response.
Table 2.
Sociodemographic characteristics of unique responders and non-responders in Work Environment and Health in Denmark (WEHD) (Cohort I)
| 2012–2018 Responders (n = 88 076) | 2012–2018 Non-responders (n = 81 846) | |
|---|---|---|
| Sex | ||
| Men, n (%) | 41 475 (47.1%) | 47 078 (57.5%) |
| Women, n (%) | 46 601 (52.9%) | 34 768 (42.5%) |
| Age | ||
| 18–24, n (%) | 5760 (6.5%) | 11 628 (14.2%) |
| 25–34, n (%) | 12 523 (14.2%) | 18 601 (22.7%) |
| 35–44, n (%) | 20 284 (23.0%) | 20 977 (25.6%) |
| 45–54, n (%) | 26 839 (30.5%) | 19 578 (23.9%) |
| 55–69, n (%) | 22 670 (25.7%) | 11 062 (13.5%) |
| Mean age, years (SD) | 45.1 (11.8) | 39.6 (12.2) |
| Education | ||
| Low level of education (< 10 years), n (%) | 12 019 (13.7%) | 15 723 (19.5%) |
| Medium level of education (10–12 years), n (%) | 38 336 (43.8%) | 38 931 (48.3%) |
| Medium-high level education (13–15 years), n (%) | 25 780 (29.5%) | 17 729 (22.0%) |
| High level of education (> 15 year), n (%) | 11 332 (13.0%) | 8295 (10.3%) |
| Unknown education, n (%) | 609 (0.7%) | 1168 (1.4%) |
Responders and non-responders combined from the WEHD cross-sectional waves in 2012, 2014, 2016, and 2018. If an individual was by chance invited to more than one wave, only the data from the first wave are included. Data on education were retrieved from a registry in 2011, i.e. the year preceding the WEHD 2012 wave.
Responders were more often women, of higher age, and had a higher education, compared to non-responders. A more detailed non-response analysis of the 2012 wave is published elsewhere [11].
Figure 1 presents the intersection prevalence for the different possible combinations of WEHD responders across the four waves.
Figure 1.
Responders of possible combination of the four Work Environment and Health in Denmark (WEHD) waves.
How often have they been followed up?
In Cohort II, of the initial 17 662 responders from the 2012 random population sample, 1810 were not eligible for follow-up because of self-reported non-employment, migration, dead, or address-protection, yielding a sample of 15 852 responders that were followed up in surveys in 2014, 2016, and 2018. Number of responders were 11 471 in 2014, 10 307 in 2016, and 9201 in 2018, yielding attrition rates of 27.6%, 35.0%, and 42.0%, respectively. There were 14 085, 10 961, and 5933 individuals who responded to at least two, three, or all four waves, respectively, thus attrition rate considering all four waves was 62.6%.
In Cohort III, of the initial 34 066 responders from the random population samples in 2012 and 2016 (N = 17 662 and N = 16 404, respectively), 30 070 were eligible for follow-up and re-invited in 2014 (N = 15 852) and 2018 (N = 14 218), respectively. Of these, 22 455 responded (N = 11 781 and N = 10 674 in 2014 and 2018, respectively), yielding an attrition rate of 25.3%.
Table 3 shows sociodemographic characteristics for Cohort II and Cohort III at baseline and follow-up. In Cohort II, those who participated in all four waves were older, more educated, more likely to report excellent or very good self-rated health, and less likely to report a probable depressive disorder at baseline. In Cohort III, those who responded to the follow-up (2014 or 2018) were older, slightly more educated, reported slightly better self-rated health, and were less likely to report a probable depressive disorder at baseline.
Table 3.
Sociodemographic characteristics, self-rated health, and depressive symptoms Cohort II and Cohort III at baseline and at follow-up
| Cohort II | Cohort III | |||
|---|---|---|---|---|
| Responders in 2012, eligible for follow-up in 2014, 2016, 2018 (n = 15 852) | Responders in all three follow-up waves (2014, 2016, 2018) (n = 5933) | Responders in 2012 and responders in 2016 that were eligible for follow-up in 2014 and 2018, respectively (n = 30 070) | Responders at follow-up in 2014 and 2018, respectively (n = 22 445) | |
| Sex | ||||
| Men n (%) | 7337 (46.3%) | 2739 (46.2%) | 13 985 (46.5%) | 10 269 (45.8%) |
| Women, n (%) | 8515 (53.7%) | 3194 (53.8%) | 16 085 (56.9%) | 12 176 (54.3%) |
| Mean age years (SD) | 45.1 (11.3) | 46.1 (9.1) | 44.9 (11.3) | 46.3 (10.8) |
| Education | ||||
| Low n (%) | 2224 (14.0%) | 629 (10.6%) | 4050 (13.5%) | 2723 (12.1%) |
| Medium-low (%) | 6949 (43.8%) | 2459 (41.4%) | 12 942 (43.0%) | 9418 (42.0%) |
| Medium-high n (%) | 4661 (29.4%) | 1958 (33.0%) | 9001 (29.9%) | 7105 (31.7%) |
| High n (%) | 1912 (12.1%) | 859 (14.5%) | 3881 (12.9%) | 3070 (13.7%) |
| Unknown n (%) | 106 (0.7%) | 28 (0.5%) | 196 (0.7%) | 129 (0.3%) |
| Self-rated health ‘Very good’ or ‘Excellent’ | ||||
| Excellent/Very good n (%) | 7245 (45.7%) | 3311 (55.8%) | 15 043 (50.0%) | 11 352 (50.6%) |
| Good/Fair/Poor n (%) | 8339 (52.6%) | 2568 (43.3%) | 13 433 (44.7%) | 10 130 (45.1%) |
| Unknown n (%) | 268 (1.7%) | 54 (0.9%) | 1594 (5.3%) | 963 (4.3%) |
| Probable depressive disorder | ||||
| Yes n (%) | 1121 (7.1%) | 359 (6.1%) | 2178 (7.2%) | 1562 (7.0%) |
| No n (%) | 14 429 (91.0%) | 5516 (93.0%) | 26 230 (87.2%) | 19 871 (88.5%) |
| Unknown n (%) | 302 (1.9%) | 58 (1.0%) | 1662 (5.5%) | 1012 (4.5%) |
Data on education were retrieved from a registry the year preceding the first Work Environment and Health in Denmark (WEHD) wave. Probable depressive disorder defined by scoring ≥21 points on the Major Depression Inventory in the first WEHD wave.
What has been measured?
Table 4 shows the variables measured in 2012, 2014, 2016, and 2018. The four surveys were, with a few exceptions, identical. The exceptions included, for example, that from 2014 onwards, items on job satisfaction, sickness absence, and working from home were added and that the wording of three items were slightly modified. See the Supplementary Material for a detailed documentation.
Table 4.
Measurements in the Work Environment and Health in Denmark (WEHD) cohorts in the surveys and in Danish national population and health registries
| General construct | Specific measures |
|---|---|
| A) Data from the surveys | |
| A.1) Sociodemographic characteristics: | Occupational position; Height; Weight. |
| A.2) General working conditions: | Working hours; Working from home (from 2014 onwards); Seniority; Shift work; Attitudes towards preventing and improving work environment issues; Workplace health promotion activities |
| A.3) Physical, chemical, and biological working conditions: | Lifting; Noise; Vibrations; Wet or moist hands at work; Skin contact with chemicals; Physically straining work; Work postures; Dust (organic, mineral, and metal, in 2018); Gases and vapours (in 2018) |
| A.4) Psychosocial working conditions: | Quantitative demands; Emotional demands; Influence; Leadership behaviours; Justice; Role clarity; Appreciation by colleagues; Collaboration and support from colleagues; Job insecurity; Work-private life-conflict; Job satisfaction (from 2024 onwards); Conflicts and quarrels; Bullying; Witnessing of bullying; Physical Violence; Threats of violence; Sexual harassment; In addition the following composite measures can be built by combining two or more of the above-listed variables: Job strain; Effort-reward imbalance; Workplace social capital |
| A.5) Accidents at work: | Frequency of accidents; Safety climate |
| A.6) Health-related behaviours: | Smoking; Alcohol consumption; Leisure time physical activity |
| A.7) Personal attitudes: | Work engagement; Desire for early retirement |
| A.8) Health: | General health; Doctor-diagnosed diseases; Work-related diseases; Skin disorders; Mental health; Exhaustion; Sleep disturbances; Depressive disorder and depressive symptoms; Anxiety disorders and anxiety symptoms; Workability (general, physical, and mental); Pain (general and in specific body parts); Reduced functioning because of pain; Feelings of being stressed; Feelings of loss of control at work; Sickness absence (from 2014 onwards) |
| B) Data from the registries (inception year of the registry) | |
| B.1) Sociodemographic and occupational data: | Sex (1976); Age (1976); Education (1980); Individual-level income (1987); Household income (1987); Marital status (1985); Cohabitation status (1985); Number and age of children (1985); Job group (DISCO codes) (1976); Industry (1976); Occupational grade (1976); Labour market status (In work; On leave; Unemployed; Retired) (1991) |
| B.2) Health: | Date and cause of death (ICD-8 or ICD-10 codes, ICD-9 was never used in Denmark) (1977); Date and cause of hospital contact due (both somatic illness and mental disorder, ICD-8 or ICD-10 codes) (1977); Purchase of prescription medicine (ATC codes) (1995); Short- and long-term sickness absence (2008/1991); Disability retirement (1991) |
| B.3) Data from other individuals related to the participants | Selected sociodemographic (1976), occupational (1976), and health data from participants’ spouses, cohabiting partners, children, and parents (1977) |
DISCO codes: Danish version of the ‘International Standard Classification of Occupations’; ICD codes: ‘International Statistical Classification of Diseases and Related Health Problems’; ATC codes: ‘Anatomical Therapeutic Chemical Classification System’.
In the surveys, we measured general working conditions, physical, chemical, biological, and psychosocial working conditions, accidents at work, health-related behaviours, personal attitudes, and physical and mental health. A detailed overview of all items is provided in the Supplementary Material.
Table 4 also shows the registries that were linked to the survey and that allow retrieving information for example on sociodemographic characteristics (sex, age, cohabitation, education, job title, occupational grade, industry), hospital admission for somatic and psychiatric disorders, purchase of prescription medicine, mortality and cause of mortality, sickness absence, disability pension, and labour market participation (in work, on leave, unemployed, retired). The registries cover the time period at baseline (when the survey was filled in), the time period before baseline (in principal back until inception of the registry), and the time period after baseline (in principal forward until the registry ceases to exist). The registries include but are not limited to the Danish Civil Registration System [12], the Danish Register of Causes of Death [13], The Danish Labour Market Account [14], The Danish National Patient Register [15], The Danish Psychiatric Central Research Register [16], The Danish National Prescription Registry [17], and the Danish Register for Evaluation of Marginalisation [18].
By linking data from participants’ cohabiting partners, children, and parents, we further included measures on participants’ childhood adversity (e.g. death of a parent, family disruption, childhood poverty), and on adverse life events both before baseline and during follow-up (e.g. diagnosed mental disorder or severe somatic illness of a partner, child, or parent, divorce/separation from a partner). WEHD does not provide such information for all participants due to limitations in the registries. For example, participants born before the establishment of the registry or those for grow up outside of Denmark. Previous publications have assessed childhood adversities for approximately 40% of the participants. For details on the operationalization and availability of childhood adversity and adverse life events, see Sørensen et al. [19].
What has it found?
Until today, around 40 peer-reviewed research articles have been published with WEHD data. See the Supplementary Material for a bibliography. Psychosocial work environment factors (e.g. workplace bullying, emotional demands, effort-reward imbalance) were the most examined exposures, followed by physical and ergonomic exposures. The most frequently examined outcome was labour market participation (e.g. sickness absence, working life expectancy, labour market exit), followed by mental health outcomes (e.g. depressive and anxiety symptoms and disorders), and various physical disorders (e.g. musculoskeletal pain, diabetes, cardiovascular disease). Examples are studies that examined associations between effort-reward imbalance at work and risk of type 2 diabetes [20], between occupational lifting and risk of long-term sickness absence [21], and between self-reported work-related stress and labour market costs [14].
WEHD data have been included in multi-cohort studies with cohorts from other Nordic countries. These studies reported among other things that exposure to workplace violence was associated with an increased risk of suicide [22] and that higher levels of psychosocial resources at work were associated with a decreased risk of cardiovascular disease [23]. Furthermore, studies used WEHD data to develop job exposure matrices for psychosocial and physical working conditions and applied these matrices in population register-based studies [24–27].
Supplementary Table S1 gives an example of two studies that examined the association between psychosocial work environment and mental health in two different WEHD cohorts.
The study by Sørensen et al. linked survey data on exposure to positive leadership behaviours to register data on treatment for a mental disorder (purchase of antidepressants or anxiolytics or hospital diagnosis of depressive or anxiety disorder) for individuals from Cohort I that fulfilled additional eligibility criteria (e.g. had a leader, no missing values on key covariates, no history of treatment for depressive or anxiety disorders, N = 59 743). The hazard ratio for onset of mental disorders was 1.36 (95% CI: 1.03 to 1.81) among workers who reported a low level of positive leadership behaviours compared to workers who reported a high level [19]. Estimates were adjusted for a wide range of covariates, including childhood adversities and adverse life events (see Supplementary Table S1 for details).
The study by Rugulies et al. analysed the association between onset of sexual harassment in WEHD 2014 (among individuals who had been free of sexual harassment in WEHD 2012) and changes in depressive symptoms (measured by the MDI-scale) from 2012 to 2016 (Cohort II, N = 6647, 2018 data were not available, when the analysis was conducted) [28]. Responders who reported onset of sexual harassment from workplace personnel in 2014 had a higher mean depressive symptoms in 2016 than responders who did not report onset of sexual harassment in 2014. Estimates were adjusted for several covariates, including depressive symptoms in 2012 (see Supplementary Table S1 for details).
What are the main strengths and weaknesses?
The main strengths of the WEHD cohort are the large nationwide data that comprised all types of job groups and was based on both random and company-stratified sample of employees in Denmark, and the linkage of comprehensive survey data, including detailed assessments of working conditions, with numerous population, labour market, and health registries. The repeated measurements of working conditions allow analysing the association between onset and changes in exposure to certain working conditions and subsequent incidence of diseases and disorders. The linkage of data from participants’ cohabiting partners, children, and parents allows estimating participants’ childhood adversity and adulthood life events, which are important potential confounders when analysing the association between working conditions and health [19].
A weaknesses of the WEHD cohort is possible selection bias. Baseline response rate was about 50% and analyses indicate that responders were more often women, of higher age, and had higher education than responders. Whereas the extent to which selection bias is a concern in exposure-outcome analyses is debated [29], selection is considered a concern when reporting the prevalence of specific working conditions, diseases and disorders. WEHD therefore includes statistical weights to adjust prevalence rates to the source population.
Reporting bias is another concern. Working conditions are assessed by self-report and unmeasured confounders (e.g. personal dispositions, mood, subclinical health conditions) may have influenced both the reporting of working conditions and the likelihood of subsequent onset of diseases and disorders. One mitigation strategy for this bias is to use onset of exposure in individuals who reported absence of exposure in an earlier wave [28]. Another strategy is to use WEHD data to build job exposure matrices and apply these matrices in national registries [24]. However, job exposure matrices have their own limitations, e.g. exposure misclassification, and they are applicable only to certain working conditions [30].
Can I get hold of the data? Where can I find out more?
All data are stored at a secured research server located at Statistics Denmark and can be accessed by authorized researchers with an affiliation to a Danish research institution. Researchers interested in collaboration should contact the National Research Centre for the Working Environment, Copenhagen, Denmark (email: nfa@nfa.dk, contact persons: Dr Jeppe Karl Sørensen and Prof. Reiner Rugulies).
Ethics approval
WEHD was registered and approved by the Danish Data Protection Agency (Datatilsynet, journal number 2015–57-0074). According to Danish law, studies that are based on questionnaire and register data only do not need approval from the Danish National Committee on Health Research Ethics. The study complies with the ethical standards of the Helsinki Declaration.
Supplementary Material
Acknowledgements
We thank all individuals who participated in the survey and all research personnel who helped with collecting and managing the data.
Contributor Information
Jeppe K Sørensen, National Research Centre for the Working Environment, Copenhagen, Denmark.
Louise Dalsager, National Research Centre for the Working Environment, Copenhagen, Denmark.
Lars L Andersen, National Research Centre for the Working Environment, Copenhagen, Denmark; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Hans Bay, The Danish Working Environment Authority, Copenhagen, Denmark; Department of Economics, Copenhagen University, Copenhagen, Denmark.
Anne H Garde, National Research Centre for the Working Environment, Copenhagen, Denmark; Section of Social Medicine, Department of Public Health, Copenhagen University, Copenhagen, Denmark.
Nina F Johnsen, Research Ethical Committees of the Capital Region of Denmark, Copenhagen, Denmark.
Nanna P Larsson, National Research Centre for the Working Environment, Copenhagen, Denmark.
Ida E H Madsen, National Research Centre for the Working Environment, Copenhagen, Denmark; National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark.
Jacob Pedersen, National Research Centre for the Working Environment, Copenhagen, Denmark.
Vivi Schlünssen, Unit for Work, Environment and Health, Danish Ramazzini Centre, Department of Public Health, Aarhus University, Aarhus, Denmark.
Kathrine Sørensen, National Research Centre for the Working Environment, Copenhagen, Denmark; Department of Psychology, University of Copenhagen, Copenhagen, Denmark.
Reiner Rugulies, National Research Centre for the Working Environment, Copenhagen, Denmark; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Author contributions
J.K.S., L.D., and R.R. conceived the design of the cohort profile. L.L.A., A.H.G., N.F.J., I.E.H.M., V.S., and R.R. contributed to the development of WEHD. J.K.S. and L.D. conducted the data analysis for the cohort profile with support by N.P.L., I.E.H.M., and K.S. All authors contributed to the interpretation of the data. J.K.S., L.D., and R.R. wrote the first draft, and all other authors critically revised the draft. All authors approved the final version for publication, and all authors agreed to be accountable for all aspects of the work. J.K.S. is the guarantor for the article.
Supplementary data
Supplementary data is available at IJE online.
Conflict of interest: L.D. contributed to this paper while being employed at the Danish National Research Centre for the Working Environment (NFA) and she remains affiliated to NFA as a guest researcher. L.D. is now employed at Signum Life Science, a company that, among other things, analyses data on pharmaceuticals. Signum Life Science had no involvement in the data analyses and in the writing of the article and in the decision to submit the article. All other authors declare no conflicts of interest.
Funding
This work was supported by a grant from the Danish Government to the Danish National Research Centre for the Working Environment and the Danish Working Environment Authority for Data Collection and Basic Data Management (no grant number). Data analysis for this cohort profile paper and the writing of this paper was supported by a grant from the Danish Working Environment Research Fund (grant number: AMFF-10–2019-03).
Data availability
See ‘Can I get hold of the data?’ Section above.
Use of artificial intelligence (AI) tools
AI tools were not used in collecting or analysing data, producing images or writing the paper.
References
- 1. Gochfeld M. Chronologic history of occupational medicine. J Occup Environ Med 2005;47:96–114. [DOI] [PubMed] [Google Scholar]
- 2. Stayner LT, Collins JJ, Guo YL et al. Challenges and opportunities for occupational epidemiology in the twenty-first century. Curr Environ Health Rep 2017;4:319–24. [DOI] [PubMed] [Google Scholar]
- 3. Frank J, Mustard C, Smith P et al. Work as a social determinant of health in high-income countries: past, present, and future. Lancet 2023;402:1357–67. [DOI] [PubMed] [Google Scholar]
- 4. Marmot M, Brunner E. Cohort profile: the Whitehall II study. Int J Epidemiol 2005;34:251–6. [DOI] [PubMed] [Google Scholar]
- 5. Goldberg M, Leclerc A, Bonenfant S et al. Cohort profile: the GAZEL cohort study. Int J Epidemiol 2007;36:32–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Virtanen P, Pentti J, Vahtera J, Kivimäki M, Virtanen M. Self-rated health of the temporary employees in a Nordic welfare state: findings from the Finnish public sector study. J Occup Environ Med 2018;60:e106–11. [DOI] [PubMed] [Google Scholar]
- 7. Bao Y, Bertoia ML, Lenart EB et al. Origin, methods, and evolution of the three nurses’ health studies. Am J Public Health 2016;106:1573–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Burr H, Bjorner JB, Kristensen TS, Tüchsen F, Bach E. Trends in the Danish work environment in 1990-2000 and their associations with labor-force changes. Scand J Work Environ Health 2003;29:270–9. [DOI] [PubMed] [Google Scholar]
- 9. Magnusson Hanson LL, Leineweber C, Persson V, Hyde M, Theorell T, Westerlund H. Cohort profile: the Swedish Longitudinal Occupational Survey of Health (SLOSH). Int J Epidemiol 2018;47:691–2i. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Goldberg M, Carton M, Descatha A et al. ; for the Constances team. CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile. Occup Environ Med 2017;74:66–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Johnsen NF, Thomsen BL, Hansen JV, Christensen BS, Rugulies R, Schlünssen V. Job type and other socio-demographic factors associated with participation in a national, cross-sectional study of Danish employees. BMJ Open 2019;9:e027056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Pedersen CB. The Danish civil registration system. Scand J Public Health 2011;39:22–5. [DOI] [PubMed] [Google Scholar]
- 13. Helweg-Larsen K. The Danish register of causes of death. Scand J Public Health 2011;39:26–9. [DOI] [PubMed] [Google Scholar]
- 14. Pedersen J, Graversen BK, Hansen KS, Madsen IEH. The labor market costs of work-related stress: a longitudinal study of 52 763 Danish employees using multi-state modeling. Scand J Work Environ Health 2024;50:61–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Lynge E, Sandegaard JL, Rebolj M. The Danish national patient register. Scand J Public Health 2011;39:30–3. [DOI] [PubMed] [Google Scholar]
- 16. Mors O, Perto GP, Mortensen PB. The Danish psychiatric central research register. Scand J Public Health 2011;39:54–7. [DOI] [PubMed] [Google Scholar]
- 17. Kildemoes HW, Sørensen HT, Hallas J. The Danish national prescription registry. Scand J Public Health 2011;39:38–41. [DOI] [PubMed] [Google Scholar]
- 18. Burr H, Pedersen J, Hansen JV. Work environment as predictor of long-term sickness absence: linkage of self-reported DWECS data with the DREAM register. Scand J Public Health 2011;39:147–52. [DOI] [PubMed] [Google Scholar]
- 19. Sørensen K, Dalsager L, Conway PM, Li J, Rugulies R. Leadership at work and risk of treatment for depressive and anxiety disorders in Denmark. A nationwide prospective study with register-based follow up. Psychiatry Res 2024;337:115870. [DOI] [PubMed] [Google Scholar]
- 20. Nordentoft M, Rod NH, Bonde JP et al. Effort-reward imbalance at work and risk of type 2 diabetes in a national sample of 50,552 workers in Denmark: a prospective study linking survey and register data. J Psychosom Res 2020;128:109867. [DOI] [PubMed] [Google Scholar]
- 21. Bláfoss R, Skovlund SV, Skals S et al. Duration and intensity of occupational lifting and risk of long-term sickness absence: prospective cohort study with register follow-up among 45 000 workers. Scand J Work Environ Health 2023;49:283–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Magnusson Hanson LL, Pentti J, Nordentoft M et al. Association of workplace violence and bullying with later suicide risk: a multicohort study and meta-analysis of published data. Lancet Public Health 2023;8:e494–503. [DOI] [PubMed] [Google Scholar]
- 23. Xu T, Rugulies R, Vahtera J et al. Workplace psychosocial resources and risk of cardiovascular disease among employees: a multi-cohort study of 135 669 participants. Scand J Work Environ Health 2022;48:621–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Madsen IEH, Gupta N, Budtz-Jørgensen E et al. Physical work demands and psychosocial working conditions as predictors of musculoskeletal pain: a cohort study comparing self-reported and job exposure matrix measurements. Occup Environ Med 2018;75:752–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Flachs EM, Petersen SEB, Kolstad HA et al. Cohort profile: DOCX: a nationwide Danish occupational cohort with eXposure data—an open research resource. Int J Epidemiol 2019;48:1413–1413k. [DOI] [PubMed] [Google Scholar]
- 26. Bonde JPE, Flachs EM, Madsen IEH et al. Acute myocardial infarction in relation to physical activities at work: a nationwide follow-up study based on job-exposure matrices. Scand J Work Environ Health 2020;46:268–77. [DOI] [PubMed] [Google Scholar]
- 27. Sørensen JK, Pedersen J, Burr H et al. Psychosocial working conditions and sickness absence among younger employees in Denmark: a register-based cohort study using job exposure matrices. Scand J Work Environ Health 2023;49:249–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Rugulies R, Sørensen K, Aldrich PT et al. Onset of workplace sexual harassment and subsequent depressive symptoms and incident depressive disorder in the Danish workforce. J Affect Disord 2020;277:21–9. [DOI] [PubMed] [Google Scholar]
- 29. Munafò MR, Tilling K, Taylor AE, Evans DM, Davey Smith G. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol 2018;47:226–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Peters S. Although a valuable method in occupational epidemiology, job-exposure -matrices are no magic fix. Scand J Work Environ Health 2020;46:231–4. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
See ‘Can I get hold of the data?’ Section above.