Laurie T. Martin|Teague Ruder|José J. Escarce|Bonnie Ghosh?Dastidar|Daniel Sherman|Marc N. Elliott|Chloe E. Bird|Allen Fremont|Charles Gasper|Arthur Culbert|Nicole Lurie
Developing Predictive Models of Health Literacy
RAND Corporation|RAND Corporation|RAND Corporation|RAND Corporation|American Institutes for Research|RAND Corporation|RAND Corporation|RAND Corporation|Missouri Foundation for Health|Missouri Foundation for Health|RAND Corporation
low health literacy lhl remains formidable barrier improving health care quality outcomes given lack precision single demographic characteristics predict health literacy administrative burden inability existing health literacy measures estimate health literacy population level lhl largely unaddressed public health clinical practice help overcome limitations developed two models estimate health literacy analyzed data 2003 national assessment adult literacy naal using linear regression predict mean health literacy scores probit regression predict probability individual basic proficiency predictors included gender age raceethnicity educational attainment poverty status marital status language spoken home metropolitan statistical area msa length time us variables except msa statistically significant lower educational attainment strongest predictor linear regression model probit model accounted 30 21 variance health literacy scores respectively nearly twice much variance accounted either education poverty alone multivariable models permit accurate estimation health literacy single predictors models applied readily available administrative census data produce estimates average health literacy identify communities would benefit appropriate targeted interventions clinical setting address poor quality care outcomes related lhl