Identifying elderly at greatest risk of inadequate health literacy: A predictive model for population-health decision makers

BackgroundDespite concern that inadequate functional health literacy (FHL) is a widely prevalent problem and is associated with a variety of adverse health consequences, there is an absence of tools that population-health decision makers can use to efficiently identify those at risk of inadequate FHL.ObjectivesThe objective of this study was to develop and validate a predictive model for estimating FHL in the elderly, generate a national estimate of FHL, and assess the construct validity of the national estimate.MethodsUsing data from the largest study of FHL in the elderly, a multiple regression model to estimate FHL was developed and validated using common demographic predictors. Subsequently, the model was used to estimate FHL in the 65-year or older subgroup of the 1992 National Adult Literacy Survey (NALS). Construct validity of the FHL estimate was assessed by evaluating the direction, magnitude, and significance of association with reported general functional literacy (GFL) proficiency in the 1992 NALS.ResultsA 20-variable model was derived (R2 = 0.365). The model correctly classified 73.2% of the sample into the appropriate FHL category. National prevalence of inadequate and marginal FHL was estimated to be 39.2% and 5.2%, respectively. FHL was significantly correlated with prose, document, and quantitative dimensions of GFL at r = 0.58 or higher (P < .0001).ConclusionsThis study was the first to quantitatively model and substantiate the high national prevalence of inadequate FHL in the elderly. The proposed quantitative model can be used in subsequent research to efficiently risk-stratify individuals by FHL level in large data sets to assess the relationships between FHL and health status, utilization, expenditures, and satisfaction. Furthermore, the model can be used to identify individuals at high risk of inadequate FHL, which will enable targeting of educational interventions that address FHL deficiencies.