Integrating Social Determinants of Health and Established Risk Factors to Predict Cardiovascular Disease Risk Among...

Recent evidence underscores the significant impact of social determinants of health (SDoH) on cardiovascular disease (CVD). However, available CVD risk assessment tools often neglect SDoH. This study aimed to integrate SDoH with traditional risk factors to predict CVD risk.

The data was sourced from the ASPirin in Reducing Events in the Elderly (ASPREE) longitudinal study, and its sub-study, the ASPREE Longitudinal Study of Older Persons (ALSOP). The study included 12,896 people (5884 men and 7012 women) aged 70 or older who were initially free of CVD, dementia, and independence-limiting physical disability. The participants were followed for a median of eight years. CVD risk was predicted using state-of-the-art machine learning (ML) and deep learning (DL) models: Random Survival Forest (RSF), Deepsurv, and Neural Multi-Task Logistic Regression (NMTLR), incorporating both SDoH and traditional CVD risk factors as candidate predictors. The permutation-based feature importance method was further utilized to assess the predictive potential of the candidate predictors.

Among men, the RSF model achieved relatively good performance (C-index = 0.732, integrated brier score (IBS) = 0.071, 5-year and 10-year AUC = 0.657 and 0.676 respectively). For women, DeepSurv was the best-performing model (C-index = 0.670, IBS = 0.042, 5-year and 10-year AUC = 0.676 and 0.677 respectively). Regarding the contribution of the candidate predictors, for men, age, urine albumin-to-creatinine ratio, and smoking, along with SDoH variables, were identified as the most significant predictors of CVD. For women, SDoH variables, such as social network, living arrangement, and education, predicted CVD risk better than the traditional risk factors, with age being the exception.

SDoH can improve the accuracy of CVD risk prediction and emerge among the main predictors for CVD. The influence of SDoH was greater for women than for men, reflecting gender-specific impacts of SDoH.