RESUMEN
OBJECTIVE: Examine whether the relationship between the pooled cohort equations (PCE) predicted 10-year risk for atherosclerotic cardiovascular disease (ASCVD) and absolute risk for ASCVD is modified by socioeconomic status (SES). DESIGN: Population-based longitudinal cohort study-Atherosclerosis Risk in Communities (ARIC)-investigating the development of cardiovascular disease across demographic subgroups. SETTING: Four communities in the USA-Forsyth County, North Carolina, Jackson, Mississippi, suburbs of Minneapolis, Minnesota and Washington County, Maryland. PARTICIPANTS: We identified 9782 ARIC men and women aged 54-73 without ASCVD at study visit 4 (1996-1998). PRIMARY OUTCOME MEASURES: Risk ratio (RR) differences in 10-year incident hospitalisations or death for ASCVD by SES and PCE predicted 10-year ASCVD risk categories to assess for risk modification. SES measures included educational attainment and census-tract neighbourhood deprivation using the Area Deprivation Index. PCE risk categories were 0%-5%, >5%-10%, >10%-15% and >15%. SES as a prognostic factor to estimate ASCVD absolute risk categories was further investigated as an interaction term with the PCE. RESULTS: ASCVD RRs for participants without a high school education (referent college educated) increased at higher PCE estimated risk categories and was consistently >1. Results indicate education is both a risk modifier and delineates populations at higher ASCVD risk independent of PCE. Neighbourhood deprivation did modify association but was less consistent in direction of effect. However, for participants residing in the most deprived neighbourhoods (referent least deprived neighbourhoods) with a PCE estimated risk >10%-15%, risk was significantly elevated (RR 1.65, 95% CI 1.05 to 2.59). Education and neighbourhood deprivation inclusion as an interaction term on the PCE risk score was statistically significant (likelihood ratio p≤0.0001). CONCLUSIONS: SES modifies the association between PCE estimated risk and absolute risk of ASCVD. SES added into ASCVD risk prediction models as an interaction term may improve our ability to predict absolute ASCVD risk among socially disadvantaged populations.