# Practical Considerations for using Social Determinants of Health for Disease Prediction in All of Us

## TL;DR

This paper explores how social factors like income and education can be used to predict chronic diseases, finding that combining personal and community-level data improves disease risk models.

## Contribution

The study introduces disease-specific polysocial risk scores that integrate individual and area-level social determinants of health for better disease prediction.

## Key findings

- Diseases have distinct 'social architectures' with varying associations between individual and area-level SDoH measures.
- Income and education often capture most of the disease-related signal from complex individual-level data.
- Combining individual- and area-level SDoH measures often improves polysocial risk scores for disease prediction.

## Abstract

Growing recognition that social determinants of health (SDoH) strongly influence health outcomes has expanded their inclusion in biomedical research, underscoring the need to evaluate how best to incorporate them into disease prediction models. To this end, we applied the Healthy People 2030 framework to transform rich individual-level SDoH survey data from the All of Us Research Program into theory-driven composite scores. We then compared these composite scores with area-level indices, and evaluated their associations with nine common chronic conditions. We found that diseases have distinct “social architectures,” differing in the strength and direction of associations across individual- and area-level measures. We then developed disease-specific polysocial risk scores (PsRS). Income and education generally captured the majority of disease-related signal from more complex individual-level data. Many PsRS improved when both individual- and area-level SDoH were included. Our findings underscore the value and complexity of utilizing diverse SDoH measures in disease risk modelling.