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Validity of Area-Based Social Risk Indices Used at Higher-Level Geographies and Clinic Locations
Nathaniel Hendrix1, Nicole Gladish2, Neil S Kamdar2,3,4
1Center for Professionalism and Value in Health Care, American Board of Family Medicine, Washington, DC.
JAMA Network Open
|June 30, 2026
Summary
Area-based social risk indices using higher-level geographies and clinic addresses may misrepresent patient risk. Granular data are crucial for accurate social risk assessment in healthcare research.
Area of Science:
- Epidemiology
- Health Services Research
- Social Determinants of Health
Background:
- Area-based social risk indices are vital for analyzing social exposures when individual-level data are unavailable.
- Higher-level geographies and clinic addresses are often used due to missing small area location data.
Purpose of the Study:
- To evaluate the accuracy of social risk indices calculated using broader geographic levels and clinic addresses.
- To compare these indices against those derived from granular patient residential data.
Main Methods:
- A cohort study analyzed primary care patient records (2019-2021) linked to social risk indices.
- Indices were calculated using residential and clinic addresses across various geographic levels (Census block group to 3-digit ZCTA).
- Associations with chronic kidney disease, hypertension, and diabetes were compared across different geographic resolutions.
Main Results:
- Indices at higher geographic levels (county, 3-digit ZCTA) showed moderate correlation with granular data (0.34-0.48).
- Indices calculated using clinic addresses were uncorrelated with patient residential data.
- Associations with diseases were generally weaker when using higher-level geographies or clinic addresses compared to block group data.
Conclusions:
- Area-based social risk indices using county and 3-digit ZCTA data can misrepresent patient risk at more granular levels.
- Clinic addresses are not a reliable proxy for measuring patients' social risks.
- Accurate social risk assessment requires granular geographic data, such as Census block groups or tracts.
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