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Does consideration of either psychological or material disadvantage improve coronary risk prediction? Prospective observational study of Scottish men
  1. John Macleod1,
  2. Chris Metcalfe2,
  3. George Davey Smith2,
  4. Carole Hart3
  1. 1Department of Primary Care and General Practice, University of Birmingham, Birmingham, UK
  2. 2Department of Social Medicine, University of Bristol, Briatol, UK
  3. 3Public Health and Health Policy, University of Glasgow, Glasgow, UK
  1. Correspondence to:
 Dr J Macleod
 Department of Primary Care and General Practice, Primary Care Clinical Sciences and Learning Centre Building, University of Birmingham, Edgbaston, Birmingham, UK, B15 2TT; j.a.macleod{at}


Objective: To assess the value of psychosocial risk factors in discriminating between individuals at higher and lower risk of coronary heart disease, using risk prediction equations.

Design: Prospective observational study.

Setting: Scotland.

Participants: 5191 employed men aged 35 to 64 years and free of coronary heart disease at study enrolment

Main outcome measures: Area under receiver operating characteristic (ROC) curves for risk prediction equations including different risk factors for coronary heart disease.

Results: During the first 10 years of follow up, 203 men died of coronary heart disease and a further 200 were admitted to hospital with this diagnosis. Area under the ROC curve for the standard Framingham coronary risk factors was 74.5%. Addition of “vital exhaustion” and psychological stress led to areas under the ROC curve of 74.5% and 74.6%, respectively. Addition of current social class and lifetime social class to the standard Framingham equation gave areas under the ROC curve of 74.6% and 74.9%, respectively. In no case was there strong evidence for improved discrimination of the model containing the novel risk factor over the standard model.

Conclusions: Consideration of psychosocial risk factors, including those that are strong independent predictors of heart disease, does not substantially influence the ability of risk prediction tools to discriminate between individuals at higher and lower risk of coronary heart disease.

  • ECG-LVH, ECG determined definite left ventricular hypertrophy
  • ROC, receiver operating characteristic
  • RSI, Reeder stress inventory
  • cardiovascular disease
  • risk assessment
  • Framingham risk score
  • primary prevention
  • psychosocial factors

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  • Funding: JM is supported by a career scientist fellowship from the Department of Health.

    All views expressed are those of the authors and not necessarily of the Department of Health.

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