Job strain as a risk factor for clinical depression: systematic review and meta-analysis with additional individual participant data

Background Adverse psychosocial working environments characterized by job strain (the combination of high demands and low control at work) are associated with an increased risk of depressive symptoms among employees, but evidence on clinically diagnosed depression is scarce. We examined job strain as a risk factor for clinical depression. Methods We identified published cohort studies from a systematic literature search in PubMed and PsycNET and obtained 14 cohort studies with unpublished individuallevel data from the Individual-Participant-Data Meta-analysis in Working Populations (IPD-Work) consortium. Summary estimates of the association were obtained using random effects models. Individual-level data analyses were based on a pre-published study protocol (F1000Res 2013;2:233). Results We included 6 published studies with a total of 27 461 individuals and 914 incident cases of clinical depression. From unpublished datasets we included 120 221 individuals and 982 first episodes of hospital-treated clinical depression. Job strain was associated with an increased risk of clinical depression in both published (Relative Risk [RR]= 1.77, 95% confidence interval [CI] 1.47-2.13) and unpublished datasets (RR=1.27, 95% CI 1.04-1.55). Further individual participant analyses showed a similar association across sociodemographic subgroups and after excluding individuals with baseline somatic disease. The association was unchanged when excluding individuals with baseline depressive symptoms (RR=1.25, 95% CI: 0.94-1.65), but attenuated on adjustment for a continuous depressive symptoms score (RR=1.03, 95% CI: 0.81- 1.32). Conclusion Job strain may precipitate clinical depression among employees. Future intervention studies

Odds ratios (OR) and 95% confidence interval (95% CI) of above-average salary increase between 2008 and 2010 for predictors measured in 2008.

<p>Values in bold type indicate significant results (p<0.05).</p><p>Adjusted for labour market sector and staff category at baseline.</p><p>Data are derived from the SLOSH study containing 3670 female and 2773 male participants.</p

Additional file 2: of Interactional justice at work is related to sickness absence: a study using repeated measures in the Swedish working population

Standard and autoregressive GEE models. (DOCX 25 kb

Descriptive statistics in numbers (percentages) according to included study variables among participants (in paid work who answered the SLOSH questionnaires in 2008 and 2010).

<p><i>p</i> level of significance in chi-square tests of differences between women and men</p><p><i>n</i>.<i>s</i>. non-significant</p><p>Descriptive statistics in numbers (percentages) according to included study variables among participants (in paid work who answered the SLOSH questionnaires in 2008 and 2010).</p

Odds ratios (OR) and 95% confidence interval (95% CI) of promotion measured in 2010 for predictors measured in 2008.

<p>Values in bold type indicate significant results (p<0.05).</p><p>Adjusted for labour market sector and staff category at baseline.</p><p>Data are derived from the SLOSH study containing 3670 female and 2773 male participants.</p

Additional file 4: of Interactional justice at work is related to sickness absence: a study using repeated measures in the Swedish working population

Results of standard generalized estimating equations (GEE) analyses of the association between covariates and long and frequent sickness absence, respectively, presented as risk ratios (RR) with 95% CIs. RRs represent the uncontrolled risk ratios. (DOCX 26 kb

Additional file 3: of Interactional justice at work is related to sickness absence: a study using repeated measures in the Swedish working population

Attrition analysis comparing those who had full information in all waves compared to those with missing information on at least one variable at least at one wave. (DOCX 27 kb

Work time control, sleep & accident risk: A prospective cohort study

<p>We examined whether the beneficial impact of work time control (WTC) on sleep leads to lower accident risk, using data from a nationally representative survey conducted in Sweden. Logistic regressions examined WTC in 2010 and 2012 as predictors of accidents occurring in the subsequent 2 years (<i>N</i> = 4840 and 4337, respectively). Sleep disturbance and frequency of short sleeps in 2012 were examined as potential mediators of the associations between WTC in 2010 and subsequent accidents as reported in 2014 (<i>N</i> = 3636). All analyses adjusted for age, sex, education, occupational category, weekly work hours, shift work status, job control and perceived accident risk at work. In both waves, overall WTC was inversely associated with accidents (<i>p</i> = 0.048 and <i>p</i> = 0.038, respectively). Analyses of the sub-dimensions of WTC indicated that Control over Daily Hours (influence over start and finish times, and over length of shift) did not predict accidents in either wave, while Control over Time-off (CoT; influence over taking breaks, running private errands during work and taking paid leave) predicted fewer accidents in both waves (<i>p</i> = 0.013 and <i>p</i> = 0.010). Sleep disturbance in 2012 mediated associations between WTC/CoT in 2010 and accidents in 2014, although effects’ sizes were small (<i>effect_WTC</i> = −0.006, 95% confidence interval [CI] = −0.018 to −0.001; <i>effect_CoT</i> = −0.009, 95%CI = −0.022 to −0.001; unstandardized coefficients), with the indirect effects of sleep disturbance accounting for less than 5% of the total direct and indirect effects. Frequency of short sleeps was not a significant mediator. WTC reduces the risk of subsequently being involved in an accident, although sleep may not be a strong component of the mechanism underlying this association.</p

Descriptive statistics of outcomes, nurse-level & department-level variables.

<p><i>Note:</i> SD  =  Standard deviation; Min  =  Minimum; Max  =  Maximum.</p

Two-level random intercept models of the dimensions of personal accomplishment.

<p><i>Note:</i> OR  =  Odds ratios; CI  =  Confidence interval; *<i>p</i><0.05; **<i>p</i><0.001.</p