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

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. We compared the performance of six area-level SDoH me...

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Bibliographic Details
Published in:	Informatics for health & social care Vol. 47; no. 1; pp. 80 - 91
Main Authors:	Vest, J.R., Kasthurirathne, S.N., Ge, W., Gutta, J., Ben-Assuli, O., Halverson, P.K.
Format:	Journal Article
Language:	English
Published:	England Taylor & Francis 02-01-2022
Subjects:	Census data processing epidemiologic measurements Humans machine learning Social Determinants of Health Social Factors Census social determinants of health epidemiologic measurements machine learning data processing
Online Access:	Get full text
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Summary:	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. 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. 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. Researchers seeking to include area-level SDoH measures in risk prediction may be able to forego more complex measurement approaches.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1753-8157 1753-8165
DOI:	10.1080/17538157.2021.1929999