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 (N = 18,435 adults). Cardiovascular disease prevalence was defined using self-reported diagnoses and International Classification of Diseases, 10th Revision (ICD-10), codes. Candidate predictors included variables from the seven Vital Conditions domains, age, and sex. Models included least absolute shrinkage and selection operator (LASSO) regression and extreme gradient boosting (XGBoost). Internal validation used stratified k-fold cross-validation and 2022 MEPS data. Discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC), Brier score, calibration slope, calibration-in-the-large (CITL), and observed-to-expected (O:E) ratio. Subgroup performance was evaluated by age, sex, and race or ethnicity.

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.