Prediction of Prevalent Cardiovascular Disease Using Social, Environmental, and Behavioral Factors: A Vital Conditions Framework Approach.

Background: Cardiovascular disease (CVD) is the leading cause of death in the United States. We examined whether a model that classifies prevalent CVD using clinical risk factor precursors (social, environmental, and behavioral factors), age, and sex, organized according to the Vital Conditions for Health framework, demonstrates good discrimination and calibration. Methods: In this cross-sectional study, we analyzed the 2021 Medical Expenditure Panel Survey (MEPS) Social Determinants of Health (SDOH) dataset ( Results: Both LASSO and XGBoost performed well. LASSO achieved an AUC of 0.822 (95% confidence interval [CI], 0.815-0.831), with a calibration slope of 1.022 and an O:E ratio of 1.001. XGBoost achieved an AUC of 0.822 (95% CI, 0.814-0.831), with a calibration slope of 1.026 and an O:E ratio of 1.002. Discrimination was stable across sex and race or ethnicity subgroups, although some calibration drift occurred among Hispanic participants. Performance declined among adults aged ≥ 65 years (XGBoost AUC, 0.67) compared with those aged < 65 years (AUC, 0.812). Key predictors included age, sex, exercise, social isolation, healthcare access, food insecurity, financial strain, adverse childhood experiences (ACEs), smoking, and transportation barriers. Conclusions: Models using upstream social determinants of health, age, and sex demonstrated good discrimination when classifying prevalent CVD. External and prospective validation is needed before these models can be considered for clinical or population-level screening.