title: Explaining the social gradient in coronary heart disease: comparing relative and absolute risk approaches.
abstract: OBJECTIVE
  There are contradictory perspectives on the importance of conventional coronary heart disease (CHD) risk factors in explaining population levels and social gradients in CHD. This study examined the contribution of conventional CHD risk factors (smoking, hypertension, dyslipidaemia, and diabetes) to explaining population levels and to absolute and relative social inequalities in CHD. This was investigated in an entire population and by creating a low risk sub-population with no smoking, dyslipidaemia, diabetes, and hypertension to simulate what would happen to relative and social inequalities in CHD if conventional risk factors were removed.
  
  
  METHODS
  Population based study of 2682 eastern Finnish men aged 42, 48, 54, 60 at baseline with 10.5 years average follow up of fatal (ICD9 codes 410-414) and non-fatal (MONICA criteria) CHD events.
  
  
  RESULTS
  In the whole population, 94.6% of events occurred among men exposed to at least one conventional risk factor, with a PAR of 68%. Adjustment for conventional risk factors reduced relative social inequality by 24%. However, in a low risk population free from conventional risk factors, absolute social inequality reduced by 72%.
  
  
  CONCLUSIONS
  Conventional risk factors explain the majority of absolute social inequality in CHD because conventional risk factors explain the vast majority of CHD cases in the population. However, the role of conventional risk factors in explaining relative social inequality was modest. This apparent paradox may arise in populations where inequalities in conventional risk factors between social groups are low, relative to the high levels of conventional risk factors within every social group. If the concern is to reduce the overall population health burden of CHD and the disproportionate population health burden associated with the social inequalities in CHD, then reducing conventional risk factors will do the job.
authors.affiliation: Department Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Canada QC H3A 1A2. John.Lynch@McGill.ca
authors.forename: John
authors.initials: J
authors.lastname: Lynch
authors.forename: George
authors.initials: G
authors.lastname: Davey Smith
authors.forename: Sam
authors.initials: S
authors.lastname: Harper
authors.forename: Kathleen
authors.initials: K
authors.lastname: Bainbridge
mesh_terms: D000328:Adult; D003327:Coronary Disease / Q000453:epidemiology*; D003920:Diabetes Mellitus / Q000453:epidemiology; D050171:Dyslipidemias / Q000453:epidemiology; D005387:Finland / Q000453:epidemiology; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology; D012923:Social Class