• Melody S Goodman, Richard T Griffey, Christopher R Carpenter, Melvin Blanchard, and Kimberly A Kaphingst.
• From the Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO (MSG); the Division of Emergency Medicine, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO (RTG, CRC); the Department of Medicine, Washington University School of Medicine, St. Louis, MO (MB); and the Department of Communication and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT (KAK). goodmanm@wudosis.wustl.edu.
• J Am Board Fam Med. 2015 Sep 1; 28 (5): 584594584-94.

BackgroundExisting health literacy assessments developed for research purposes have constraints that limit their utility for clinical practice, including time requirements and administration protocols. The Brief Health Literacy Screen (BHLS) consists of 3 self-administered Single-Item Literacy Screener (SILS) questions and obviates these clinical barriers. We assessed whether the addition of SILS items or the BHLS to patient demographics readily available in ambulatory clinical settings reaching underserved patients improves the ability to identify limited health literacy.MethodsWe analyzed data from 2 cross-sectional convenience samples of patients from an urban academic emergency department (n = 425) and a primary care clinic (n = 486) in St. Louis, Missouri. Across samples, health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine-Revised (REALM-R), Newest Vital Sign (NVS), and the BHLS. Our analytic sample consisted of 911 adult patients, who were primarily female (62%), black (66%), and had at least a high school education (82%); 456 were randomly assigned to the estimation sample and 455 to the validation sample.ResultsThe analysis showed that the best REALM-R estimation model contained age, sex, education, race, and 1 SILS item (difficulty understanding written information). In validation analysis this model had a sensitivity of 62%, specificity of 81%, a positive likelihood ratio (LR(+)) of 3.26, and a negative likelihood ratio (LR(-)) of 0.47; there was a 28% misclassification rate. The best NVS estimation model contained the BHLS, age, sex, education and race; this model had a sensitivity of 77%, specificity of 72%, LR(+) of 2.75, LR(-) of 0.32, and a misclassification rate of 25%.ConclusionsFindings suggest that the BHLS and SILS items improve the ability to identify patients with limited health literacy compared with demographic predictors alone. However, despite being easier to administer in clinical settings, subjective estimates of health literacy have misclassification rates >20% and do not replace objective measures; universal precautions should be used with all patients.© Copyright 2015 by the American Board of Family Medicine.

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