ABSTRACT
Objective
The objective of this paper is to provide empirical guidance by comparing the performance of six different area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit.
Methods
We compared the performance of six area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit using random forest classification algorithm. Data came from 209,605 patient encounters at a federally qualified health center. Models with each area-based measurement approach were compared against the patient-level data only model using area under the curve, sensitivity, specificity, and precision.
Results
Addition of area-level features to patient-level data improved the overall performance of models predicting need for a social worker referral. Entering area-level measures as individual features resulted in highest model performance.
Conclusion
Researchers seeking to include area-level SDoH measures in risk prediction may be able to forego more complex measurement approaches.
Acknowledgments
The authors thank Eskenazi Health and the Regenstrief Data Core for their assistance.
Declaration of interest statement
The authors have no conflicts to disclose.