Kevin Fiscella1, Daniel Tancredi, Peter Franks. 1. Department of Family Medicine, University of Rochester, Rochester, NY 14620, USA. kevin_fiscella@urmc.rochester.edu
Abstract
BACKGROUND: The purpose of the study was to examine the potential of adding socioeconomic status (SES) to Framingham Risk Scoring (FRS) to improve coronary heart disease (CHD) prediction by SES. METHODS: We assessed the effect of measures of SES (<12 years of education or low income) on model discrimination and calibration when added to FRS in a prospective cohort, Atherosclerosis Risk in Communities. We validated use of this model in a second cohort, the National Health and Nutritional Examination Survey linked to the National Death Index. RESULTS: Based on FRS alone, persons of higher and lower SES had a predicted CHD risk of 3.7% and 3.9%, respectively, compared with observed risks of 3.2% and 5.6%. Adding SES to a model with FRS improved calibration with predicted risk estimates of 3.1% and 5.2% for those with higher and lower SES, mitigating the discrepancy between predicted and observed CHD events for low-SES persons. Model discrimination (area under the receiver operator curve) was not significantly affected, and consistent findings were observed in the validation sample. Inclusion of SES in the model resulted in upgrading of risk classification for 15.1% of low-SES participants (95% CI 13.9-29.4%). CONCLUSIONS: Standard FRS underestimates CHD risk for those at low SES; treatment decisions ignoring SES may exacerbate SES disparities. Adding SES to CHD risk assessment reduces this bias.
BACKGROUND: The purpose of the study was to examine the potential of adding socioeconomic status (SES) to Framingham Risk Scoring (FRS) to improve coronary heart disease (CHD) prediction by SES. METHODS: We assessed the effect of measures of SES (<12 years of education or low income) on model discrimination and calibration when added to FRS in a prospective cohort, Atherosclerosis Risk in Communities. We validated use of this model in a second cohort, the National Health and Nutritional Examination Survey linked to the National Death Index. RESULTS: Based on FRS alone, persons of higher and lower SES had a predicted CHD risk of 3.7% and 3.9%, respectively, compared with observed risks of 3.2% and 5.6%. Adding SES to a model with FRS improved calibration with predicted risk estimates of 3.1% and 5.2% for those with higher and lower SES, mitigating the discrepancy between predicted and observed CHD events for low-SES persons. Model discrimination (area under the receiver operator curve) was not significantly affected, and consistent findings were observed in the validation sample. Inclusion of SES in the model resulted in upgrading of risk classification for 15.1% of low-SES participants (95% CI 13.9-29.4%). CONCLUSIONS: Standard FRS underestimates CHD risk for those at low SES; treatment decisions ignoring SES may exacerbate SES disparities. Adding SES to CHD risk assessment reduces this bias.
Authors: Matthew P Banegas; John F Dickerson; Nicole L Friedman; David Mosen; Althea X Ender; T Ruth Chang; Tracy A Runge; Mark C Hornbrook Journal: Perm J Date: 2019
Authors: Paul J Christine; Rebekah Young; Sara D Adar; Alain G Bertoni; Michele Heisler; Mercedes R Carnethon; Rodney A Hayward; Ana V Diez Roux Journal: Am J Prev Med Date: 2017-08 Impact factor: 5.043
Authors: Michael D Miedema; Andrew B Petrone; Donna K Arnett; John A Dodson; J Jeffrey Carr; James S Pankow; Steven C Hunt; Michael A Province; Aldi Kraja; J Michael Gaziano; Luc Djousse Journal: Circ Cardiovasc Imaging Date: 2013-12-11 Impact factor: 7.792