Long working hours as a risk factor for atrial fibrillation: A multi-cohort study

Aims Studies suggest that people who work long hours are at increased risk of stroke, but the association of long working hours with atrial fibrillation, the most common cardiac arrhythmia and a risk factor for stroke, is unknown. We examined the risk of atrial fibrillation in individuals working long hours (>55 per week) and those working standard 35-40 hours per week. Methods In this prospective multi-cohort study from the Individual-Participant-Data Meta-analysis in and results Working Populations (IPD-Work) Consortium, the study population was 85,494 working men and women (mean age 43.4 years) with no recorded atrial fibrillation. Working hours were assessed at study baseline (1991-2004). Mean follow-up for incident atrial fibrillation was 10 years and cases were defined using data on electrocardiograms, hospital records, drug reimbursement registers, and death certificates. We identified 1061 new cases of atrial fibrillation (10-year cumulative incidence 12.4 per 1000). After adjustment for age, sex and socioeconomic status, individuals working long hours had a 1.4-fold increased risk of atrial fibrillation compared to those working standard hours (hazard ratio=1.42, 95%CI=1.13-1.80, P=0.003). There was no significant heterogeneity between the cohortspecific effect estimates (I2=0%, P=0.66) and the finding remained after excluding participants with coronary heart disease or stroke at baseline or during the follow-up (N=2006, hazard ratio=1.36, 95%CI=1.05-1.76, P=0. 0180). Adjustment for potential confounding factors, such as obesity, risky alcohol use and high blood pressure, had little impact on this association. Conclusion Individuals who worked long hours were more likely to develop atrial fibrillation than those working standard hours

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published cohort studies from a systematic literature search in PubMed and PsycNET and obtained 14 cohort studies with unpublished individuallevel data from the Individual-Participant-Data Meta-analysis in Working Populations (IPD-Work) consortium.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

Sample description (N = 13695).

<p>Sample description (N = 13695).</p

Policy indicators.

<p>Note. ^a For the analyses, we followed previous procedures [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121573#pone.0121573.ref049" target="_blank">49</a>] and weighted measures (% of GDP) according to existing unemployment rates. This prevents the possibility that country's higher expenditures were simply related to higher levels of unemployment [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121573#pone.0121573.ref050" target="_blank">50</a>].</p><p>Policy indicators.</p

Association between policy indicators and work stress (model 1) and interactions between education and policy indicators (model 2): Results of random intercept linear multilevel regressions. Unstandardized regression coefficients (p-values).

<p>Note. All models are adjusted for sex, age groups, employment status and work time. Expenditures into active (ALMP) and passive labour market policies (PLMP) are weighted by unemployment rate.</p><p>Association between policy indicators and work stress (model 1) and interactions between education and policy indicators (model 2): Results of random intercept linear multilevel regressions. Unstandardized regression coefficients (p-values).</p

Predicted levels of work stress by education at different levels of policy indicators.

<p>Note. Expenditures into active (ALMP) and passive labour market policies (PLMP) are weighted by unemployment rate. Results are based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121573#pone.0121573.t004" target="_blank">Table 4</a>, model 2.</p

Educational differences in work stress (low vs. high education) and ALMP (expenditure into active labour market programmes).

<p>Note. Mean differences are adjusted for age, sex, self-employment and work time (based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121573#pone.0121573.t002" target="_blank">Table 2</a>). Expenditures into active labour market policies (ALMP) are based on % of GDP (weighted by unemployment rate).</p

Associations between education and work stress scores by country: Results of linear regression models (unstandardized regression coefficients and p-values).

<p>Note. All models are adjusted for sex, age groups, employment status and work time.</p><p>Associations between education and work stress scores by country: Results of linear regression models (unstandardized regression coefficients and p-values).</p

Additional file 1 of The role of sociodemographic, psychosocial, and behavioral factors in the use of preventive healthcare services in children and adolescents: results of the KiGGS Wave 2 study

Supplementary Material

Low socio-economic position is associated with poor social networks and social support: results from the Heinz Nixdorf Recall Study-1

<p><b>Copyright information:</b></p><p>Taken from "Low socio-economic position is associated with poor social networks and social support: results from the Heinz Nixdorf Recall Study"</p><p>http://www.equityhealthj.com/content/7/1/13</p><p>International Journal for Equity in Health 2008;7():13-13.</p><p>Published online 5 May 2008</p><p>PMCID:PMC2424055.</p><p></p