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P62 Educational inequalities in statin treatment: cross-sectional analysis of UK biobank
  1. Alice Carter1,2,
  2. Dipender Gill3,4,5,6,7,
  3. Richard Morris2,8,
  4. George Davey Smith1,2,9,
  5. Amy Taylor2,9,
  6. Neil Davies1,2,1,
  7. Laura Howe1,2
  1. 1MRC Integrative Epidemiology Unit,, University of Bristol, Bristol, UK
  2. 2Population Health Sciences, University of Bristol, Bristol, UK
  3. 3Department of Epidemiology and Biostatistics, Imperial College London, London, UK
  4. 4Centre for Pharmacology and Therapeutics, Imperial College London, London, UK
  5. 5Clinical Pharmacology and Therapeutics Section, St George’s, University of London, London, UK
  6. 6Clinical Pharmacology Group, St George’s, University of London, London, UK
  7. 7Centre for Academic Primary Care, University of Bristol, Bristol, UK
  8. 8NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
  9. 9K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway


Background Despite reductions in the rates of cardiovascular disease in high income countries, individuals who are the most socioeconomically deprived remain at the highest risk of disease. Although intermediate lifestyle and behavioural risk factors explain some of this, much of the effect remains unexplained. It is not known whether differences in risk adjusted use of statins between educational groups may contribute to these inequalities.

Methods Using data from a large prospective cohort study, UK Biobank, we calculated a QRISK3 cardiovascular risk score for 472 097 eligible participants with complete data on self-reported educational attainment and statin use (55% female; mean age, 56). We used logistic regression to explore the association between i) QRISK3 score and ii) educational attainment on self-report statin use. We then stratified the association between QRISK3 score, and statin use by educational attainment to test for interactions. We then replicated analyses using QRISK or QRISK2 scores recorded in primary care data and statin prescriptions recorded in primary care prescription records.

Results There was evidence of an interaction between QRISK3 scores and education. For an equivalent QRISK3 score, more educated individuals were more likely to report taking statins. In women with 7 years of schooling, a one unit increase in QRISK3 score was associated with a 7% higher odds of statin use (odds ratio (OR) 1.07, 95% CI 1.07, 1.07). In women with 20 years of schooling, a one unit increase in QRISK3 score was associated with an 14% higher odds of statin use (OR 1.14, 95% CI 1.14, 1.15). Comparable ORs in men were 1.04 (95% CI 1.04, 1.05) for 7 years of schooling and 1.08 (95% CI 1.08, 1.08) for 20 years of schooling. These inequalities were also present in analyses using primary care data.

Conclusion For the same level of cardiovascular risk, individuals with lower educational attainment are less likely to receive statins, likely contributing to cardiovascular inequalities. The mechanisms leading to these differences are unknown, but both health seeking behaviours and clinical factors may contribute.

  • cardiovascular disease
  • statins
  • education

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