High SES reduced CVD risk by 48% (OR 0.52) and middle SES by 25% (OR 0.75) versus low SES; the nomogram predicted CVD risk with AUC ~0.84.
Is higher socioeconomic status associated with a reduced risk of cardiovascular disease in American adults?
Higher socioeconomic status is independently associated with a lower risk of cardiovascular disease, and a nomogram incorporating SES provides good predictive performance for individualized CVD risk assessment.
Objective Socioeconomic status (SES) is considered a key social determinant influencing the development of cardiovascular disorders (CVDs). This study aims to assess the association between SES and CVD risk and to develop and validate a nomogram prediction model incorporating SES. Methods Data were obtained from American adults enrolled in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Individuals aged 18 and older with complete data on the poverty income ratio (PIR) and CVD outcomes were included. SES was measured using the PIR. Multivariable logistic regression was employed to evaluate the correlation between SES and CVD risk, while the least absolute shrinkage and selection operator (LASSO) regression was used to identify key predictors and construct the nomogram model. Data were randomly split into training and validation sets in a 7:3 ratio. Model performance and clinical utility were assessed using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA). Results A total of 11,180 participants were included, with an overall CVD prevalence of 11.15%. Middle SES was associated with a moderately reduced risk of CVD (OR = 0.75, 95% CI: 0.62–0.91, p = 0.003), while high SES was significantly associated with a lower risk of CVD (OR = 0.52, 95% CI: 0.41–0.66, p < 0.0001), compared to the low SES reference group. The nomogram model incorporating SES and other risk factors achieved an area under the curve (AUC) of 0.846 in the training set and 0.834 in the validation set, demonstrating good discrimination and calibration. DCA further confirmed the potential clinical benefits of the model in predicting CVD risk. Conclusion SES is an important factor influencing CVD among American adults. The nomogram prediction model based on SES and other variables provides a scientific basis for individualized CVD risk assessment and optimal allocation of health resources. Further validation in diverse populations and prospective studies is warranted to confirm the model’s generalizability.
Li et al. (2026) studied this question. High SES reduced CVD risk by 48% (OR 0.52) and middle SES by 25% (OR 0.75) versus low SES; the nomogram predicted CVD risk with AUC ~0.84.