A Comparison of Area-Level Socioeconomic Status Indices in Colorectal Cancer Care.

J Surg Res

Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Published: December 2022

Introduction: There are multiple measures of area socioeconomic status (SES) and there is little evidence on the comparative performance of these measures. We hypothesized adding area SES measures improves model ability to predict guideline concordant care and overall survival compared to models with standard clinical and demographic data alone.

Materials And Methods: We included patients with colorectal cancer from 2006 to 2015 from the North Carolina Cancer Registry merged with insurance claims data. The primary area SES study variables were the Social Deprivation Index, Distressed Communities Index, Area Deprivation Index, and Social Vulnerability Index. We used multivariable logistic modeling and Cox proportional hazards modeling to assess the adjusted association of each indicator, with guideline concordant care and overall survival, respectively. Model performance of the SES measures was compared to a base model using likelihood ratio testing and area under the curve (AUC) assessments to compare SES indicator models with each other.

Results: We found that the Area Deprivation Index, Social Vulnerability Index and Social Deprivation Index, but not Distressed Communities Index, were significantly associated with receiving guideline concordant care and significantly improved model fit over the base model on likelihood ratio testing. All models had similar AUCs. With respect to overall survival, we found that all indices were independently and significantly associated with survival and had significantly improved model fit over the base model on likelihood ratio testing. AUC analysis again showed all area SES measures had comparable performance for overall survival at 5 y.

Conclusions: This analysis demonstrates the importance of including these measures in risk adjustment models. However, of the commonly available measures, no one measure stood out as superior to others.

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Source
http://dx.doi.org/10.1016/j.jss.2022.07.036DOI Listing

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