Abstract
BackgroundPeople with low health literacy (HL) are at increased risk of poor health outcomes, and receive less benefit from healthcare services. However, healthcare practitioners can effectively adapt healthcare information if they are aware of their patients’ HL. Measurements are available to assess HL levels but may not be practical for use within primary care settings. New alternative methods based on demographic indicators have been successfully developed, and we aim to test if such methodology can be applied to routinely collected consultation records.MethodsSecondary analysis was carried out from a recently completed prospective cohort study that investigated a primary care population who had consulted about a musculoskeletal pain problem. Participants completed questionnaires (assessing general health, HL, pain, and demographic information) at baseline and 6 months, with linked data from the participants’ consultation records. The Single Item Literacy Screener was used as a benchmark for HL. We tested the performance of an existing demographic assessment of HL, whether this could be refined/improved further (using questionnaire data), and then test the application in primary care consultation data. Tests included accuracy, sensitivity, specificity, and area under the curve (AUC). Finally, the completed model was tested prospectively using logistic regression producing odds ratios (OR) in the prediction of poor health outcomes (physical health and pain intensity).ResultsIn total 1501 participants were included within the analysis and 16.1% were categorised as having low HL. Tests for the existing demographic assessment showed poor performance (AUC 0.52), refinement using additional components derived from the questionnaire improved the model (AUC 0.69), and the final model using data only from consultation data remained improved (AUC 0.64). Tests of this final consultation model in the prediction of outcomes showed those with low HL were 5 times more likely to report poor health (OR 5.1) and almost 4 times more likely to report higher pain intensity (OR 3.9).ConclusionsThis study has shown the feasibility of the assessment of HL using primary care consultation data, and that people indicated as having low HL have poorer health outcomes. Further refinement is now required to increase the accuracy of this method.
Highlights
People with low health literacy (HL) are at increased risk of poor health outcomes, and receive less benefit from healthcare services
This dataset was chosen for this study because consultations for musculoskeletal pain conditions are common in primary care, and most often require significant patient/healthcare engagement and self-management over time, potentially placing HL as an important factor for this patient population [21,22,23]
In total 16.1% of the cohort (n = 241) were classified as low HL using the Single Item Literacy Screener (SILS) measure, and inspection of the comparison between those with adequate and low HL in Table 2 shows those within the low HL category have; older age, more females, lower age at leaving full time education, higher level of sleep problems, lower mental health score, higher presence of mobility issues, increased comorbidity, more likely to live alone, and a greater percentage of being off work due to health reasons
Summary
People with low health literacy (HL) are at increased risk of poor health outcomes, and receive less benefit from healthcare services. In terms of the identification of those with low HL, historically HL was assessed through the creation of specific comprehension tasks often included within large population surveys [8, 11], concerns arose in terms of the practicality of such measures due to the time required to complete This led to the development of shorter tests or simpler screening questions, and these methods were proven to have accuracy compared to “Gold Standard” measures (e.g. Newest Vital Sign [12], Single Item Literacy Screener [13]). An example is the Demographic Assessment of Health Literacy (DAHL) which has been shown to have acceptable approximation to HL assessment [11, 18]
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