Adding social determinants of health to the equation: Development of a cardiometabolic disease staging model using clinical and social determinants of health to predict type 2 diabetes
Authors' conclusion
Including SDoH in T2D risk prediction and stratification at the population level may aid in better classifying T2D risk among vulnerable populations, which has important implications for screening strategies.
Study context
- Population
- REasons for Geographic And Racial Differences in Stroke (REGARDS) participants without T2D at baseline who completed a follow-up visit 10 years later; mean age 63 years (SD 8.5), 54% female, 33% non-Hispanic Black
- Participants
- 9,907 participants
- Comparison
- Clinical plus social determinants of health (SDoH) model vs clinical-only cardiometabolic disease staging model
- Outcome
- Prediction of incident T2D (model performance)
- Follow-up
- 10 years
Methodological detailsBayesian predictive model, external validation in CARDIA
- Bayesian predictive model
- External validation in CARDIA
Performance
As reported in the abstract
Model performance (AUC)
- Adding SDoH to the clinical model modestly improved performance
- Clinical + SDoH vs Clinical: P = 0.01
Further findings
Incident T2D · 10 years
- Developed T2D
- 1268 (12.8%)
Calibration in SDoH subgroups
- Clinical model underpredicted risk in disadvantaged SDoH subgroups; clinical + SDoH model improved prediction accuracy in subgroups
Reclassification
- Clinical + SDoH model accurately reclassified individuals categorized as borderline risk by the clinical-only model
Original publication
Adding social determinants of health to the equation: Development of a cardiometabolic disease staging model using clinical and social determinants of health to predict type 2 diabetes. Diabetes Obes Metab. Published 10 February 2025. doi:10.
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