Do Subjective Measures Improve the Ability to Identify Limited Health Literacy in a Clinical Setting?
Published Date:2015 Sep-Oct
Source:J Am Board Fam Med. 28(5):584-594.
Pubmed Central ID:PMC4987705
Funding:1U13AG048721-01/AG/NIA NIH HHS/United States
3U54CA153460-03S1/CA/NCI NIH HHS/United States
KL2 RR024994/RR/NCRR NIH HHS/United States
KL2 TR000450/TR/NCATS NIH HHS/United States
KM1CA156708/CA/NCI NIH HHS/United States
P30 CA91842/CA/NCI NIH HHS/United States
P30 DK092950/DK/NIDDK NIH HHS/United States
P50CA95815/CA/NCI NIH HHS/United States
R01 CA168608/CA/NCI NIH HHS/United States
R21 HS020309/HS/AHRQ HHS/United States
TL1 TR000449/TR/NCATS NIH HHS/United States
TL1RR024995/RR/NCRR NIH HHS/United States
U54 CA155496/CA/NCI NIH HHS/United States
U54CA153460/CA/NCI NIH HHS/United States
U58DP0003435/DP/NCCDPHP CDC HHS/United States
UL1 RR024992/RR/NCRR NIH HHS/United States
UL1 TR000448/TR/NCATS NIH HHS/United States
Existing 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.
We 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.
The 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%.
Findings 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.
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