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Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data

a , b , c , * , c , a , d , e , f , g , c , e , h , i , j , k , l , l , m , a , n , o , p , q , a , o , o , c , b , k , r , s , c , t , a , h , a , i , u , v , v , h , w , v , x , m ,   a , q , y , z , c , v , y , aa , c , ab , g , p , q , a , g , for the IPD-Work Consortium

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      Summary

      Background

      Published work assessing psychosocial stress (job strain) as a risk factor for coronary heart disease is inconsistent and subject to publication bias and reverse causation bias. We analysed the relation between job strain and coronary heart disease with a meta-analysis of published and unpublished studies.

      Methods

      We used individual records from 13 European cohort studies (1985–2006) of men and women without coronary heart disease who were employed at time of baseline assessment. We measured job strain with questions from validated job-content and demand-control questionnaires. We extracted data in two stages such that acquisition and harmonisation of job strain measure and covariables occurred before linkage to records for coronary heart disease. We defined incident coronary heart disease as the first non-fatal myocardial infarction or coronary death.

      Findings

      30 214 (15%) of 197 473 participants reported job strain. In 1·49 million person-years at risk (mean follow-up 7·5 years [SD 1·7]), we recorded 2358 events of incident coronary heart disease. After adjustment for sex and age, the hazard ratio for job strain versus no job strain was 1·23 (95% CI 1·10–1·37). This effect estimate was higher in published (1·43, 1·15–1·77) than unpublished (1·16, 1·02–1·32) studies. Hazard ratios were likewise raised in analyses addressing reverse causality by exclusion of events of coronary heart disease that occurred in the first 3 years (1·31, 1·15–1·48) and 5 years (1·30, 1·13–1·50) of follow-up. We noted an association between job strain and coronary heart disease for sex, age groups, socioeconomic strata, and region, and after adjustments for socioeconomic status, and lifestyle and conventional risk factors. The population attributable risk for job strain was 3·4%.

      Interpretation

      Our findings suggest that prevention of workplace stress might decrease disease incidence; however, this strategy would have a much smaller effect than would tackling of standard risk factors, such as smoking.

      Funding

      Finnish Work Environment Fund, the Academy of Finland, the Swedish Research Council for Working Life and Social Research, the German Social Accident Insurance, the Danish National Research Centre for the Working Environment, the BUPA Foundation, the Ministry of Social Affairs and Employment, the Medical Research Council, the Wellcome Trust, and the US National Institutes of Health.

      Related collections

      Most cited references 53

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      Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.

      Although more than 80% of the global burden of cardiovascular disease occurs in low-income and middle-income countries, knowledge of the importance of risk factors is largely derived from developed countries. Therefore, the effect of such factors on risk of coronary heart disease in most regions of the world is unknown. We established a standardised case-control study of acute myocardial infarction in 52 countries, representing every inhabited continent. 15152 cases and 14820 controls were enrolled. The relation of smoking, history of hypertension or diabetes, waist/hip ratio, dietary patterns, physical activity, consumption of alcohol, blood apolipoproteins (Apo), and psychosocial factors to myocardial infarction are reported here. Odds ratios and their 99% CIs for the association of risk factors to myocardial infarction and their population attributable risks (PAR) were calculated. Smoking (odds ratio 2.87 for current vs never, PAR 35.7% for current and former vs never), raised ApoB/ApoA1 ratio (3.25 for top vs lowest quintile, PAR 49.2% for top four quintiles vs lowest quintile), history of hypertension (1.91, PAR 17.9%), diabetes (2.37, PAR 9.9%), abdominal obesity (1.12 for top vs lowest tertile and 1.62 for middle vs lowest tertile, PAR 20.1% for top two tertiles vs lowest tertile), psychosocial factors (2.67, PAR 32.5%), daily consumption of fruits and vegetables (0.70, PAR 13.7% for lack of daily consumption), regular alcohol consumption (0.91, PAR 6.7%), and regular physical activity (0.86, PAR 12.2%), were all significantly related to acute myocardial infarction (p<0.0001 for all risk factors and p=0.03 for alcohol). These associations were noted in men and women, old and young, and in all regions of the world. Collectively, these nine risk factors accounted for 90% of the PAR in men and 94% in women. Abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits, vegetables, and alcohol, and regular physical activity account for most of the risk of myocardial infarction worldwide in both sexes and at all ages in all regions. This finding suggests that approaches to prevention can be based on similar principles worldwide and have the potential to prevent most premature cases of myocardial infarction.
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        General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

        Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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          Protective and damaging effects of stress mediators.

           B McEwen (1998)
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            Author and article information

            Affiliations
            [a ]Department of Epidemiology and Public Health, University College London, London, UK
            [b ]Institute of Behavioral Sciences, University of Helsinki, Helsinki, Finland
            [c ]Finnish Institute of Occupational Health, Helsinki, Finland
            [d ]Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
            [e ]Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
            [f ]School of Health Sciences, Jönköping University, Jönköping, Sweden
            [g ]Stress Research Institute, Stockholm University, Stockholm, Sweden
            [h ]National Research Centre for the Working Environment, Copenhagen, Denmark
            [i ]Department of Occupational and Environmental Medicine, Bispebjerg University Hospital, Copenhagen, Denmark
            [j ]Federal Institute for Occupational Safety and Health, Berlin, Germany
            [k ]School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
            [l ]Department of Public Health, Ghent University, Ghent, Belgium
            [m ]Department of Medical Sociology, University of Düsseldorf, Düsseldorf, Germany
            [n ]School of Community and Social Medicine, University of Bristol, Bristol, UK
            [o ]TNO, Hoofddorp, Netherlands
            [p ]Versailles-Saint Quentin University, Versailles, France
            [q ]Inserm U1018, Centre for Research in Epidemiology and Population Health, Villejuif, France
            [r ]Department of Health Sciences, Mid Sweden University, Sundsvall, Sweden
            [s ]Department of Public Health, University of Helsinki, Helsinki, Finland
            [t ]School of Sociology, Social Policy and Social Work, Queen's University Belfast, Belfast, UK
            [u ]Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine, Umeå University, Umeå, Sweden
            [v ]Finnish Institute of Occupational Health, Turku, Finland
            [w ]Department of Public Health and Department of Psychology, University of Copenhagen, Copenhagen, Denmark
            [x ]Department of Psychology, University of Turku, Turku, Finland
            [y ]Department of Public Health, University of Turku, Turku, Finland
            [z ]Folkhälsan Research Centre, Helsinki, Finland
            [aa ]Turku University Hospital, Turku, Finland
            [ab ]Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden
            Author notes
            [* ]Correspondence to: Prof Mika Kivimäki, Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK m.kivimaki@ 123456ucl.ac.uk
            Contributors
            Journal
            Lancet
            Lancet
            Lancet
            Lancet Publishing Group
            0140-6736
            1474-547X
            October 2012
            October 2012
            : 380
            : 9852
            : 1491-1497
            22981903 3486012 LANCET60994 10.1016/S0140-6736(12)60994-5
            © 2012 Elsevier Ltd. All rights reserved.

            This document may be redistributed and reused, subject to certain conditions.

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