Abstract
OBJECTIVES: The association between cardiovascular disease (CVD) risk and socio-economic status (SES) remains poorly studied. The purpose of this study was to investigate the relationship between SES and estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk among the general UK Biobank population.
STUDY DESIGN: This was a population-based study.
METHODS: Among 311,928 volunteers (47.7% men) of the UK Biobank population, SES was assessed by a questionnaire, and ASCVD risk was calculated using pooled cohort equation models. Associations between SES and ASCVD risk were estimated using multiple gender-specific regressions.
RESULTS: The findings from this study showed that men had higher estimated 10-year ASCVD risk than women (8.6% vs 2.7%; P < 0.001), higher education level (38.3% vs 36.2%; P < 0.001), higher income level (31.0% vs 25.1%; P < 0.001), higher levels of employment (65.4% vs 60.5%; P < 0.001) and higher scores of Townsend deprivation (P < 0.001). Using the multiple logistic regression model, a decreased 10-year ASCVD risk in men was associated with high income level (odds ratio [OR] = 0.64 [95% confidence interval {CI} 0.61-0.68]; P < 0.001), high educational level (OR = 0.71 [95% CI 0.68-0.74]; P < 0.001), higher Townsend deprivation quintile (OR = 0.81 [95% CI 0.78-0.85]; P < 0.001) and employed status (OR = 0.74 [95% CI 0.69-0.80]; P < 0.001). The same results were observed in women, with high income level (OR = 0.68 [95% CI 0.55-0.68]; P < 0.001), high educational level (OR = 0.87 [95% CI 0.82-0.93]; P < 0.001), higher Townsend deprivation quintile (OR = 0.74 [95% CI 0.69-0.80]; P < 0.001) and employed status (OR = 0.53 [95% CI 0.45-0.63]; P < 0.001) being associated with a lower 10-year ASCVD risk. When considering the false discovery rate logworth analysis, SES factors presented a similar contribution to CVD risk as lifestyle factors.
CONCLUSIONS: Health policies should consider the SES factors identified in this study, in addition to traditional risk factors, when designing prevention campaigns for CVD. Further research is required to improve the ASCVD risk prediction models among different SES variables.