Objective: Health literacy (HL) is the degree of individuals' capacity to access, understand, appraise and apply health information and services required to make appropriate health decisions. This study aimed to establish a predictive algorithm for identifying community-dwelling older adults with a high risk of limited HL.
Design: A cross-sectional study.
Setting: Four communities in northern, central and southern Taiwan.
Participants: A total of 648 older adults were included. Moreover, 85% of the core data set was used to generate the prediction model for the scoring algorithm, and 15% was used to test the fitness of the model.
Primary And Secondary Outcome Measures: Pearson's χ test and multiple logistic regression were used to identify the significant factors associated with the HL level. An optimal cut-off point for the scoring algorithm was identified on the basis of the maximum sensitivity and specificity.
Results: A total of 350 (54.6%) patients were classified as having limited HL. We identified 24 variables that could significantly differentiate between sufficient and limited HL. Eight factors that could significantly predict limited HL were identified as follows: a socioenvironmental determinant (ie, dominant spoken dialect), a health service use factor (ie, having family doctors), a health cost factor (ie, self-paid vaccination), a heath behaviour factor (ie, searching online health information), two health outcomes (ie, difficulty in performing activities of daily living and requiring assistance while visiting doctors), a participation factor (ie, attending health classes) and an empowerment factor (ie, self-management during illness). The scoring algorithm yielded an area under the curve of 0.71, and an optimal cut-off value of 5 represented moderate sensitivity (62.0%) and satisfactory specificity (76.2%).
Conclusion: This simple scoring algorithm can efficiently and effectively identify community-dwelling older adults with a high risk of limited HL.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627398 | PMC |
| http://dx.doi.org/10.1136/bmjopen-2020-045411 | DOI Listing |
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