Long-term associations of psychosocial working conditions with depressive symptoms and work-related emotional exhaustion: comparing effects in a 5-year prospective study of 1949 workers in Germany.

OBJECTIVE: To test the hypothesis that psychosocial working conditions are more strongly associated with subsequent work-related emotional exhaustion (core component of burnout) than with depressive symptoms at follow-up. METHODS: A 5-year cohort study (2011/2012-2017), based on a random sample of persons in employment subject to payment of social contributions aged 31-60 years (Study on Mental Health at Work; S-MGA; N = 1949), included self-reported measures of organisational demands (organisational layoffs and restructuring), task-level demands (work pace and amount of work) and job resources (influence at work, possibilities for development, control over working time, role clarity), all taken from the COPSOQ, except the organisational demands that were single-item measures. Work-related emotional exhaustion and depressive symptoms were measured with the Oldenburg Burnout Inventory and the Patient Health Questionnaire-9, respectively. RESULTS: Cochrane Q tests revealed stronger associations between psychosocial working conditions and work-related emotional exhaustion only for the amount of work (p = 0.013) and control over working time (p = 0.027). No differences were observed for the Demands and Resources Indexes, capturing overall exposure to psychosocial working conditions. The same differences were observed in a subsample including only participants who remained at the same employer from baseline to follow-up, although more psychosocial working conditions were associated with work-related emotional exhaustion than with depressive symptoms. Supplementary analyses employing dichotomous measures of work-related emotional exhaustion and depressive symptoms confirmed these results. CONCLUSIONS: Overall, the findings provide limited evidence supporting the hypothesis that psychosocial working conditions are more strongly associated with work-related emotional exhaustion than with depressive symptoms.

Physical and psychosocial working conditions as predictors of 5-year changes in work ability among 2078 employees in Germany.

OBJECTIVE: To examine 5-year prospective associations between working conditions and work ability among employees in Germany. METHODS: A cohort study (2011/2012-2017), based on a random sample of employees in employments subject to payment of social contributions aged 31-60 years (Study on Mental Health at Work; S-MGA; N = 2,078), included data on physical and quantitative demands, control (influence, possibilities for development, control over working time), relations (role clarity and leadership quality) and work ability (Work Ability Index, WAI; subscale 'subjective work ability and resources'). Data were analysed using linear regression. RESULTS: Physical demands and control were associated with small 5-year changes in work ability (ΔR2 = 1%). Among the subgroup of employees with ≥ 25 sickness days, possibilities for development, control and quality of leadership were associated with changes in work ability (ΔR2 = 8%). CONCLUSIONS: The impact of working conditions on long term changes in work ability seems to be negligible. However, in vulnerable subpopulations experiencing poor health, working conditions may be associated to a larger extent to work ability over this time span.

Factorial Validity of the Work Ability Index Among Employees in Germany.

Purpose The Work Ability Index (WAI) is a routinely applied instrument for the assessment of work ability. It is a single score index, based on the implicit assumption of a single factor underlying the construct of work ability. The few studies with a focus on the WAI's factor structure are mainly based on non-representative samples. The objective of this study was to examine the factor structure of the WAI within a representative sample of employees working in Germany, applying analysis procedures that consider the metric of the variables. Methods Analyses are based on a nationwide representative sample of employees aged 31-60 years from the "Study on Mental Health at Work" (German: S-MGA). Responses from n = 3968 participants were used in confirmatory factor analyses comparing competing models of the structure underlying the WAI. Results The results of the analyses suggest that the intercorrelations between the indicators of the WAI are explained better by a model with two correlated factors than by a simple one-factor structure. A model solely allowing a single loading for each indicator fits the data well and allows for an easy interpretation of the two underlying factors. Conclusions There are two correlated factors underlying the WAI: one refers to "subjective work ability and resources", the other one can be considered a "health related factor".

Physical working conditions as covered in European monitoring questionnaires.

BACKGROUND: The prevalence of workers with demanding physical working conditions in the European work force remains high, and occupational physical exposures are considered important risk factors for musculoskeletal disorders (MSD), a major burden for both workers and society. Exposures to physical workloads are therefore part of the European nationwide surveys to monitor working conditions and health. An interesting question is to what extent the same domains, dimensions and items referring to the physical workloads are covered in the surveys. The purpose of this paper is to determine 1) which domains and dimensions of the physical workloads are monitored in surveys at the national level and the EU level and 2) the degree of European consensus among these surveys regarding coverage of individual domains and dimensions. METHOD: Items on physical workloads used in one European wide/Spanish and five other European nationwide work environment surveys were classified into the domains and dimensions they cover, using a taxonomy agreed upon among all participating partners. RESULTS: The taxonomy reveals that there is a modest overlap between the domains covered in the surveys, but when considering dimensions, the results indicate a lower agreement. The phrasing of items and answering categories differs between the surveys. Among the domains, the three domains covered by all surveys are "lifting, holding & carrying of loads/pushing & pulling of loads", "awkward body postures" and "vibrations". The three domains covered less well, that is only by three surveys or less, are "physical work effort", "working sitting", and "mixed exposure". CONCLUSIONS: This is the first thorough overview to evaluate the coverage of domains and dimensions of self-reported physical workloads in a selection of European nationwide surveys. We hope the overview will provide input to the revisions and updates of the individual countries' surveys in order to enhance coverage of relevant domains and dimensions in all surveys and to increase the informational value of the surveys.

Dimensional comparability of psychosocial working conditions as covered in European monitoring questionnaires.

BACKGROUND: In most countries in the EU, national surveys are used to monitor working conditions and health. Since the development processes behind the various surveys are not necessarily theoretical, but certainly practical and political, the extent of similarity among the dimensions covered in these surveys has been unclear. Another interesting question is whether prominent models from scientific research on work and health are present in the surveys--bearing in mind that the primary focus of these surveys is on monitoring status and trends, not on mapping scientific models. Moreover, it is relevant to know which other scales and concepts not stemming from these models have been included in the surveys. The purpose of this paper is to determine (1) the similarity of dimensions covered in the surveys included and (2) the congruence of dimensions of scientific research and of dimensions present in the monitoring systems. METHOD: Items from surveys representing six European countries and one European wide survey were classified into the dimensions they cover, using a taxonomy agreed upon among all involved partners from the six countries. RESULTS: The classification reveals that there is a large overlap of dimensions, albeit not in the formulation of items, covered in the seven surveys. Among the available items, the two prominent work-stress-models--job-demand-control-support-model (DCS) and effort-reward-imbalance-model (ERI)--are covered in most surveys even though this has not been the primary aim in the compilation of these surveys. In addition, a large variety of items included in the surveillance systems are not part of these models and are--at least partly--used in nearly all surveys. These additional items reflect concepts such as "restructuring", "meaning of work", "emotional demands" and "offensive behaviour/violence & harassment". CONCLUSIONS: The overlap of the dimensions being covered in the various questionnaires indicates that the interests of the parties deciding on the questionnaires in the different countries overlap. The large number of dimensions measured in the questionnaires and not being part of the DCS and ERI models is striking. These "new" dimensions could inspire the research community to further investigate their possible health and labour market effects.

Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany.

OBJECTIVE: The aim of this study was to identify the occupational risk for a SARS-CoV-2 infection in a nationwide sample of German workers during the first wave of the COVID-19 pandemic (1 February-31 August 2020). METHODS: We used the data of 108 960 workers who participated in a COVID follow-up survey of the German National Cohort (NAKO). Occupational characteristics were derived from the German Classification of Occupations 2010 (Klassifikation der Berufe 2010). PCR-confirmed SARS-CoV-2 infections were assessed from self-reports. Incidence rates (IR) and incidence rate ratios (IRR) were estimated using robust Poisson regression, adjusted for person-time at risk, age, sex, migration background, study center, working hours, and employment relationship. RESULTS: The IR was 3.7 infections per 1000 workers [95% confidence interval (CI) 3.3-4.1]. IR differed by occupational sector, with the highest rates observed in personal (IR 4.8, 95% CI 4.0-5.6) and business administration (IR 3.4, 95% CI 2.8-3.9) services and the lowest rates in occupations related to the production of goods (IR 2.0, 95% CI 1.5-2.6). Infections were more frequent among essential workers compared with workers in non-essential occupations (IRR 1.95, 95% CI 1.59-2.40) and among highly skilled compared with skilled professions (IRR 1.36, 95% CI 1.07-1.72). CONCLUSIONS: The results emphasize higher infection risks in essential occupations and personal-related services, especially in the healthcare sector. Additionally, we found evidence that infections were more common in higher occupational status positions at the beginning of the pandemic.

Authors' response: Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias.

We thank van Tongeren et al for responding to our study on occupational disparities in SARS-CoV-2 infection risks during the first pandemic wave in Germany (1). The authors address the potential for bias resulting from differential testing between occupational groups and propose an alternative analytical strategy for dealing with selective testing. In the following, we want to discuss two aspects of this issue, namely (i) the extent and reasons of differential testing in our cohort and (ii) the advantages and disadvantages of different analytical approaches to study risk factors for SARS-CoV-2 infection. Our study relied on nationwide prospective cohort data including more than 100 000 workers in order to compare the incidence of infections between different occupations and occupational status positions. We found elevated infection risks in personal services and business administration, in essential occupations (including health care) and among people in higher occupational status positions (ie, managers and highly skilled workers) during the first pandemic wave in Germany (2). Van Tongeren's et al main concern is that the correlations found could be affected by a systematic bias because people in healthcare professions get tested more often than employees in other professions. A second argument is that better-off people could be more likely to use testing as they are less affected by direct costs (prices for testing) and the economic hardship associated with a positive test result (eg, loss of earnings in the event of sick leave). We share the authors' view that differential testing must be considered when analysing and interpreting the data. Thus, in our study, we examined the proportion of tests conducted in each occupational group as part of the sensitivity analyses (see supplementary figure S1, accessible at www.sjweh.fi/article/4037). As expected, testing proportions were exceptionally high in medical occupations (due to employer requirements). However, we did not observe systematic differences among non-medical occupations or when categorising by skill-level or managerial responsibility. This might be explained by several reasons. First, SARS-CoV-2 testing was free of charge during the first pandemic wave in Germany, but reporting a risk contact or having symptoms was a necessary condition for testing ( https://www.bundesgesundheitsministerium.de/coronavirus/chronik-coronavirus.html (accessed 5 September 2022). The newspaper article cited by van Tongeren et al is misleading as it refers to a calendar date after our study period. Second, different motivation for testing due to economic hardship in case of a positive test result is an unlikely explanation, because Germany has a universal healthcare system, including paid sick leave and sickness benefits for all workers (3). Self-employed people carry greater financial risks in case of sickness. We therefore included self-employment in the multivariable analyses to address this potential source of bias. While the observed inverse social gradient may be surprising, it actually matches with findings of ecological studies from Germany (4, 5), the United States (6, 7) as well as Spain, Portugal, Sweden, The Netherlands, Israel, and Hong Kong (8), all of which observed higher infection rates in wealthier neighbourhoods during the initial outbreak phase of the pandemic. One possible explanation is the higher mobility of managers and better educated workers, who are more likely to participate in meetings and engage in business travel and holiday trips like skiing. Given the increasing number of studies providing evidence for this hypothesis, we conclude that the inverse social gradient in our study likely reflects different exposure probabilities and is not a result of systematic bias. This also holds true for the elevated infection risks in essential workers, which is actually corroborated by a large body of research (9-11). Regarding differential likelihood of testing, van Tongeren et al state that "[i]t is relatively simple to address this problem by using a test-negative design" (1). As van Tongeren et al describe, this is a case-control approach only including individuals who were tested (without considering those who were not tested). However, the proposed analytical strategy can lead to another (more serious) selection bias if testing proportions and/or testing criteria differ between groups (12). This can be easily illustrated when comparing the results based on a time-incidence design with those obtained by a test-negative design as shown in table 1 (see PDF). Both approaches show similar results in terms of vertical occupational differences. Infection was more common if individuals had a high skill level or had a managerial position, but associations were stronger in the time-incidence design and did not reach statistical significance in the test-negative design (as indicated by the confidence intervals overlapping "1"). Unfortunately, the test-negative approach relies on a strongly reduced sample size and thus results in greater statistical uncertainty and loss of statistical power (13). In contrast, the test-negative design yields a different picture when estimating the association between essential occupation and infection risk: In this analysis, essential workers did not differ from non-essential workers in their chance of being infected with SARS-CoV-2 (the test-negative design even exhibits a lower chance for essential workers). This is rather counter-intuitive and is not in accordance with what we know about the occupational hazards of healthcare workers during the pandemic (14). The main problem is that proportions of positive tests are highly unreliable when testing proportions and/or testing criteria differ between groups. As essential workers were tested more often without being symptomatic (due to employer requirements), a lower proportion of positive tests in this group does not necessarily correspond to a lower risk of infection. Consequently, we are not convinced that the test-negative design should be the 'gold standard' for studying risk factors for SARS-CoV-2 infections (15). Especially problematic is the loss of statistical power (increasing the probability of a type II error) and the low validity of the test-positivity when test criteria and/or test proportions differ between groups. References 1. van Tongeren M, Rhodes S, Pearce N. Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias. Scand J Work Environ Health 2022;48(7):586-587. https://doi.org/10.5271/sjweh.4052. 2. Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell S, et al. Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany. Scand J Work Environ Health 2022;48:446-56. https://doi.org/10.5271/sjweh.4037. 3. Busse R, Blümel M, Knieps F, Bärnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet 2017;390:882-97. https://doi.org/10.1016/S0140-6736(17)31280-1. 4. Wachtler B, Michalski N, Nowossadeck E, Diercke M, Wahrendorf M, Santos-Hövener C, et al. Socioeconomic inequalities in the risk of SARS-CoV-2 infection - First results from an analysis of surveillance data from Germany. J Heal Monit 2020;5:18-29. https://doi.org/10.25646/7057. 5. Plümper T, Neumayer E. The pandemic predominantly hits poor neighbourhoods? SARS-CoV-2 infections and COVID-19 fatalities in German districts. Eur J Public Health 2020;30:1176-80. https://doi.org/10.1093/eurpub/ckaa168. 6. Abedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, et al. Racial, Economic, and Health Inequality and COVID-19 Infection in the United States. J Racial Ethn Heal Disparities 2021;8:732-42. https://doi.org/10.1007/s40615-020-00833-4. 7. Mukherji N. The Social and Economic Factors Underlying the Incidence of COVID-19 Cases and Deaths in US Counties During the Initial Outbreak Phase. Rev Reg Stud 2022;52. https://doi.org/10.52324/001c.35255. 8. Beese F, Waldhauer J, Wollgast L, Pförtner T, Wahrendorf M, Haller S, et al. Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic-A Scoping Review. Int J Public Health 2022;67:1-14. https://doi.org/10.3389/ijph.2022.1605128. 9. Nguyen LH, Drew DA, Graham MS, Joshi AD, Guo C-G, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Heal 2020;5:e475-83. https://doi.org/10.1016/S2468-2667(20)30164-X. 10. Chou R, Dana T, Buckley DI, Selph S, Fu R, Totten AM. Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers. Ann Intern Med 2020;173:120-36. https://doi.org/10.7326/M20-1632. 11. Stringhini S, Zaballa M-E, Pullen N, de Mestral C, Perez-Saez J, Dumont R, et al. Large variation in anti-SARS-CoV-2 antibody prevalence among essential workers in Geneva, Switzerland. Nat Commun 2021;12:3455. https://doi.org/10.1038/s41467-021-23796-4. 12. Accorsi EK, Qiu X, Rumpler E, Kennedy-Shaffer L, Kahn R, Joshi K, et al. How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19. Eur J Epidemiol 2021;36:179-96. https://doi.org/10.1007/s10654-021-00727-7. 13. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Editio. New York: Routledge; 2013. https://doi.org/10.4324/9780203771587. 14. The Lancet. The plight of essential workers during the COVID-19 pandemic. Lancet 2020;395:1587. https://doi.org/10.1016/S0140-6736(20)31200-9. 15. Vandenbroucke JP, Brickley EB, Pearce N, Vandenbroucke-Grauls CMJE. The Evolving Usefulness of the Test-negative Design in Studying Risk Factors for COVID-19. Epidemiology 2022;33:e7-8. https://doi.org/10.1097/EDE.0000000000001438.

The Demand-Control Model as a Predictor of Depressive Symptoms-Interaction and Differential Subscale Effects: Prospective Analyses of 2212 German Employees.

Testing assumptions of the widely used demand-control (DC) model in occupational psychosocial epidemiology, we investigated (a) interaction, i.e., whether the combined effect of low job control and high psychological demands on depressive symptoms was stronger than the sum of their single effects (i.e., superadditivity) and (b) whether subscales of psychological demands and job control had similar associations with depressive symptoms. Logistic longitudinal regression analyses of the 5-year cohort of the German Study of Mental Health at Work (S-MGA) 2011/12-2017 of 2212 employees were conducted. The observed combined effect of low job control and high psychological demands on depressive symptoms did not indicate interaction (RERI = -0.26, 95% CI = -0.91; 0.40). When dichotomizing subscales at the median, differential effects of subscales were not found. When dividing subscales into categories based on value ranges, differential effects for job control subscales (namely, decision authority and skill discretion) were found (p = 0.04). This study does not support all assumptions of the DC model: (1) it corroborates previous studies not finding an interaction of psychological demands and job control; and (2) signs of differential subscale effects were found regarding job control. Too few prospective studies have been carried out regarding differential subscale effects.

Methodological and conceptual issues regarding occupational psychosocial coronary heart disease epidemiology.

Overview Psychosocial occupational epidemiology has mainly focused on the demand-control and, to a much lesser extent, the effort-reward-imbalance (ERI) models. These models and the strong focus on them raise some conceptual and methodological issues we will address in the following letter. The conceptual issues include the empirical confirmation of the assumptions of these models, the extent to which the focus on the demand-control and ERI models is warranted, and whether the sub-dimensions of the scales in these models have common health effects. We argue that there is a lack of empirical approval of (i) the assumptions behind both models and (ii) the focus on these models. The methodological issues include how exposure to job strain is categorized, how ERI previously has been measured, and the validity of self-reports of job strain. We argue that (i) a population independent definition of job strain is lacking, (ii) the older measurements of ERI mix exposure and effect, and (iii) we know little regarding the validity of the measurement of the psychosocial working environment. Finally, we suggest that analyses of monitoring data with a broader focus on the psychosocial working environment can be used to shed light to some of the issues raised above. Introduction In the last three decades (1, 2), psychosocial occupational epidemiology related to coronary heart disease (CHD) has mainly focused on the job-strain model, also referred to as the demand-control model (3, 4). In this model, two aspects of work are deemed relevant: demands and control. Negative consequences to health are to be expected when high demands are simultaneously present with low control. This combination has been termed job strain (3, 4). Recently, there has also been increased interest in the ERI model (5, 6) which considers the level of effort relative to rewards at work: an imbalance is present when the efforts outweigh the rewards (5, 6). In longitudinal studies of CHD, there has been only a limited focus on investigating occupational psychosocial factors outside of these two models (1, 2). In this letter, we would like to raise some conceptual and methodological issues which are inherent to these two stress models but also which arise from the heavy emphasis placed on them. Conceptual issues The conceptual issues we discuss below are empirical confirmation of the assumptions of these models and to what extent the focus on the demand-control and ERI models is warranted. Investigating the assumptions of the models Both the demand-control and the ERI models are based on assumptions which have only been tested empirically to a limited extent (1, 2). We pose three specific questions: (i) Does the interaction of demands and control constitute a risk factor for CHD? (ii) Does the imbalance between effort and reward explain more variance in CHD risk than high effort and low reward alone? (iii) Do the sub-dimensions of the scales in these models have common health effects? Regarding the interaction of demands and control. The concept of the demand-control model is useful when the health risk of being exposed to job strain (simultaneous high demands and low control) differs greatly from the sum of individual health risks of being exposed solely to high demands and low control. If this interaction were not present, it would be warranted to look separately at high demands and low control. This would for instance counteract overlooking those persons exposed to low control but not high demands (known as "passive work"; 3, 4). It should be emphasized that the interaction of demands and control has only been tested in very few - underpowered - cases (1, 2). Initial support for an interaction within the demand-control model can be tentatively derived from the work of the IPD-Work Consortium (7): In a reanalysis of an earlier study (8), it was shown that while neither demands nor job control alone (appendix to 8) predicted CHD, job strain did when controlling for sex, age and socioeconomic status (SES) (9). This indicates that an interaction takes place. Controlling for SES is of high relevance - otherwise, the results point in a different direction (10). However, a formal test of interaction was not performed on the IPD-Work Consortium data. Even the IPD study itself might not have sufficient statistical power to analyze a possible interaction directly: this requires many more observations than simply looking at the main effects (2). If one is interested in investigating an interaction, more incident outcomes are often required (11). Regarding effort-reward imbalance. Similarly to the combined effect of demands and control described above, focusing on the ERI model makes sense only if the imbalance of effort and reward explains the risk of CHD over and above the effect of high efforts and low rewards. To our knowledge, this has not been verified in any longitudinal study of CHD (1, 2). Regarding the effect of sub-dimensions. Finally, using the scales of the two models (demands and control or efforts and rewards) is meaningful only if the sub-dimensions of the scales all have about equal effect sizes and signs. For example, the scale psychological demands covers the sub-dimensions work pace, role conflict and work amount while control covers both influence (decision authority) and opportunities for development (skill discretion). Do these dimensions predict the risk of CHD to equal amounts within their respective scales? For now, this has not been tested elaborately to our knowledge (1, 2, 12). Consequently, it is possible that certain risk factors in the psychosocial work environment may be overlooked due to different risk factors being merged into one scale. Is the focus on the demand-control and ERI models warranted? In the past, longitudinal epidemiological research on psychosocial work characteristics and their association with the risk of CHD has mainly focused on the demand-control and - to a much lesser extent - ERI models (1). For example, in a recent review (2) covering 44 papers and including 170 analyses, 70% percent of those dealt with these models or sub-dimensions thereof. Interestingly, the demand-control model alone accounted for 66% of the analyses and ERI only 4%. A further 11% of the analyses dealt with working hours, 9% with social support, 5% with job insecurity, 3% with leadership and the remaining 3% covered conflicts, justice or predictability. Maintaining the currently high degree of focus on the DC and ERI models requires evidence that job strain and ERI are by far the most important risk factors for CHD. The review by Pejtersen et al (2) has additionally pointed out that of the 44 studies mentioned above, only two - an IPD-Work Consortium study (8) and a Swedish case-control study (13) - contained analyses with sufficient statistical power to detect an elevated CHD risk of 20%. These two sufficiently powered studies available as of April 2013 have led to the following conclusions: (i) job strain was found to be predictive of CHD in the IPD-Work Consortium study (8); and (ii) both low control and low social support predicted CHD in the Swedish study (13). Recently, a well-powered study on working hours (14) indicated that long working hours constitute a risk factor for CHD. Additionally, a recently published large study on job insecurity (15) is worth mentioning. While there was not sufficient power to detect a 20% increased risk due the relatively low prevalence of job insecurity, the study did have sufficient power to find a risk of 1.32 - which is the value actually found empirically (15). Summarizing the small number of well-powered studies available at this time indicates that both model dimensions (job strain) as well as non-model dimensions (social support and working hours) predict CHD (8, 13-15). In this context, one should bear in mind that the variety of possible dimensions that can be considered as constituting "psychosocial work environment" is large. The latter is exemplified by a recent analysis of the psychosocial content of seven European work environment monitoring questionnaires which showed that there are 34 distinct dimensions of the psychosocial work environment (16). Around half of these dimensions are not found in either the demand-control or ERI models (16). These include for instance emotional demands, demands on hiding emotions, sensorial demands, meaning of work, commitment to the workplace, organizational influence, trust, social community at work, quality of leadership, predictability, role clarity, restructuring, safety culture, work life balance, and negative acts (eg, violence, bullying). Little is currently known on the health effects of these "non-model" dimensions. Research on their possible effects might show that they are small - and that the DC and ERI dimensions are indeed the main psychosocial risk factors for CHD. However, results may also point to the importance of the non-model dimensions. To date, this remains to be investigated. Methodological issues In addition to the conceptual issues discussed above, we would like to highlight some methodological issues related to one or both of these models. The three main points address: (i) how exposure to job strain is categorized; (ii) how ERI has been measured up to now; and (iii) the validity of self-reports of job strain. Practical definition of job strain Job strain is usually operationalized as a median split of the two dimensions demands and control in the population investigated (3, 17). Hence, whether a certain worker experiences job strain or not depends on which other workers are part of the sample (18). This poses a problem when the distributions of demands and control differ between populations. Comparisons between Denmark and Spain and across Europe suggest that such differences exist (19, 20), rendering it at the least a challenge to combine populations in meta-analyses. (ABSTRACT TRUNCATED)