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Using SAS/STAT® Software to Validate a Health Literacy Prediction Model in a Primary Care Setting

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Abstract

Existing health literacy assessment tools developed for research purposes have constraints that limit their feasibility for use in clinical settings. The measurement of health literacy in clinical practice can be impractical due to the staff burden and time requirements of existing assessment tools. Single Item Literacy Screener (SILS) items, which are self-administered brief screening questions, have been developed to address this constraint. We developed a model to predict limited health literacy that consists of two SILS and demographic information (age, gender, race, and education) using a sample of patients in a St. Louis emergency department. In this paper, we validate this prediction model in a separate sample of patients visiting a safety net primary care clinic in St. Louis. Using the prediction model developed in the previous study, we use SAS/STAT® software to validate this model based on two goodness of fit criteria: rescaled R-squared and AIC. The Rapid Assessment of Health Literacy in Medicine – Revised (REALM-R) is used as the gold standard health literacy measure. We evaluate the prediction model by examining the concordance, area under the ROC curve, sensitivity, specificity, and kappa statistics. Using the Youden Index, we choose a cutpoint of 0.54 to define health literacy status. Results show 73.0% concordance when comparing the model estimation to the REALM-R. Development of a validated prediction model for inadequate health literacy based on self-reported data would provide a feasible way to assess health literacy in fast-paced clinical settings. This would allow for the identification of patients with limited health literacy and the implementation of personalized care and communication interventions (e.g., teach-back) that meet their information needs.

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