Mean Health Literacy Score Research Articles

Health literacy, numeracy, and interpretation of graphical breast cancer risk estimates

Objective Health literacy and numeracy are necessary to understand health information and to make informed medical decisions. This study explored the relationships among health literacy, numeracy, and ability to accurately interpret graphical representations of breast cancer risk. Methods Participants ( N = 120) were recruited from the Facing Our Risk of Cancer Empowered (FORCE) membership. Health literacy and numeracy were assessed. Participants interpreted graphs depicting breast cancer risk, made hypothetical treatment decisions, and rated preference of graphs. Results Most participants were Caucasian (98%) and had completed at least one year of college (93%). Fifty-two percent had breast cancer, 86% had a family history of breast cancer, and 57% had a deleterious BRCA gene mutation. Mean health literacy score was 65/66; mean numeracy score was 4/6; and mean graphicacy score was 9/12. Education and numeracy were significantly associated with accurate graph interpretation ( r = 0.42, p < 0.001 and r = 0.65, p < 0.001, respectively). However, after adjusting for numeracy in multivariate linear regression, education added little to the prediction of graphicacy ( r 2 = 0.41 versus 0.42, respectively). Conclusion In our highly health-literate population, numeracy was predictive of graphicacy. Practice implications Effective risk communication strategies should consider the impact of numeracy on graphicacy and patient understanding.

Health Literacy in College Students

Objective: The purpose of this study was to assess the health literacy levels, and the potential importance of healthy literacy, of college students. Participants: Courses were randomly selected from all upper level undergraduate courses at a large Research I university to obtain a sample size of N = 399. Methods: During the 2007–2008 school year, students were administered the Test of Functional Health Literacy (TOFHLA), which assessed both numeracy and reading comprehension utilizing real-life health care materials. The numeracy scale (n = 17) assessed understanding of prescription labels, appointment slips, and glucose monitoring using actual hospital forms and labels for prescription vials. Ten prompts were given, and participants had to answer quantitatively. The reading comprehension scale (n = 50) assessed understanding of health care texts through 3 reading passages, including instructions for preparation for an upper gastrointestinal (GI) series, Medicaid patient rights and responsibilities, and hospital informed consent agreement. A modified Cloze procedure was employed; every fifth to seventh word in each passage was omitted and the participant selected the best word to fill in the blank from a list of 4 possible choices. Results: The mean health literacy score was 93.83, which was considered adequate functional health literacy. In terms of importance of health literacy to the students, it was found that a majority valued its importance (89.3%). Conclusions: Indeed this group of university students would seem to have good health literacy levels that would allow them to navigate the health care system. However, many students still had problems with individual TOFHLA items, particularly the numeracy questions.

Developing Predictive Models of Health Literacy

INTRODUCTIONLow health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy.METHODSWe analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having ‘above basic’ proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S.RESULTSAll variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone.CONCLUSIONSMultivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL.