title: Does adding information on job strain improve risk prediction for coronary heart disease beyond the standard Framingham risk score? The Whitehall II study.
abstract: BACKGROUND
  Guidelines for coronary heart disease (CHD) prevention recommend using multifactorial risk prediction algorithms, particularly the Framingham risk score. We sought to examine whether adding information on job strain to the Framingham model improves its predictive power in a low-risk working population.
  
  
  METHODS
  Our analyses are based on data from the prospective Whitehall II cohort study, UK. Job strain among 5533 adults (mean age 48.9 years, 1666 women) was ascertained in Phases 1 (1985-88), 2 (1989-90) and 3 (1991-93). Variables comprising the Framingham score (blood lipids, blood pressure, diabetes and smoking) were measured at Phase 3. In men and women who were CHD free at baseline, CHD mortality and non-fatal myocardial infarction (MI) were ascertained from 5-yearly screenings and linkage to mortality and hospital records until Phase 7 (2002-04).
  
  
  RESULTS
  A total of 160 coronary deaths and non-fatal MIs occurred during the mean follow-up period of 11.3 years. The addition of indicators of job strain to the Framingham score increased the C-statistics from 0.725 [95% confidence intervals (95% CIs): 0.575-0.854] to only 0.726 (0.577-0.855), corresponding to a net reclassification improvement of 0.7% (95% CIs: -4.2 to 5.6%). The findings were similar after inclusion of definite angina in the CHD outcome (352 total cases) and when using alternative operational definitions for job strain.
  
  
  CONCLUSIONS
  In this middle-aged low-risk working population, job strain was associated with an increased risk of CHD. However, when compared with the Framingham algorithm, adding job strain did not improve the model's predictive performance.
authors.affiliation: Department of Epidemiology and Public Health, University College London, London, UK. m.kivimaki@ucl.ac.uk
authors.forename: Mika
authors.initials: M
authors.lastname: Kivimäki
authors.forename: Solja T
authors.initials: ST
authors.lastname: Nyberg
authors.forename: G David
authors.initials: GD
authors.lastname: Batty
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Shipley
authors.forename: Jane E
authors.initials: JE
authors.lastname: Ferrie
authors.forename: Marianna
authors.initials: M
authors.lastname: Virtanen
authors.forename: Michael G
authors.initials: MG
authors.lastname: Marmot
authors.forename: Jussi
authors.initials: J
authors.lastname: Vahtera
authors.forename: Archana
authors.initials: A
authors.lastname: Singh-Manoux
authors.forename: Mark
authors.initials: M
authors.lastname: Hamer
mesh_terms: D000465:Algorithms; D001794:Blood Pressure; D015331:Cohort Studies; D003327:Coronary Disease / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control / Q000523:psychology; D048909:Diabetes Complications; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006305:Health Status Indicators; D006801:Humans; D008055:Lipids / Q000097:blood; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000209:etiology / Q000401:mortality; D016272:Occupational Health; D010353:Patient Education as Topic / Q000706:statistics & numerical data; D018570:Risk Assessment; D012907:Smoking / Q000009:adverse effects; D013315:Stress, Psychological / Q000150:complications / Q000453:epidemiology* / Q000523:psychology; D017132:Workplace / Q000523:psychology*
chemical_list: D008055:Lipids