• Michael Korvink, Laura H Gunn, German Molina, Dani Hackner, and John Martin.
• ITS Data Science, Premier, Inc., Charlotte, North Carolina. Electronic address: michael_korvink@premierinc.com.
• Am J Prev Med. 2023 Oct 1; 65 (4): 727734727-734.

IntroductionA variety of industry composite indices are employed within health research in risk-adjusted outcome measures and to assess health-related social needs. During the COVID-19 pandemic, the relationships among risk adjustment, clinical outcomes, and composite indices of social risk have become relevant topics for research and healthcare operations. Despite the widespread use of these indices, composite indices are often comprised of correlated variables and therefore may be affected by information duplicity of their underlying risk factors.MethodsA novel approach is proposed to assign outcome- and disease group-driven weights to social risk variables to form disease and outcome-specific social risk indices and apply the approach to the county-level Centers for Disease Control and Prevention social vulnerability factors for demonstration. The method uses a subset of principal components reweighed through Poisson rate regressions while controlling for county-level patient mix. The analyses use 6,135,302 unique patient encounters from 2021 across seven disease strata.ResultsThe reweighed index shows reduced root mean squared error in explaining county-level mortality in five of the seven disease strata and equivalent performance in the remaining strata compared with the reduced root mean squared error using the current Centers for Disease Control and Prevention Social Vulnerability Index as a benchmark.ConclusionsA robust method is provided, designed to overcome challenges with current social risk indices, by accounting for redundancy and assigning more meaningful disease and outcome-specific variable weights.Copyright © 2023 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

14 keywords...

hide…