Choice of measurement approach for area-level social determinants of health and risk prediction model performance

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.

KEYWORDS:

• Census
• social determinants of health
• epidemiologic measurements
• machine learning
• data processing

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.

Additional information

Funding

This work was supported by the Robert Wood Johnson Foundation under Grant ID 75549.