Arthritis Research UK Primary Care Centre, Research Institute for Primary Care & Health Sciences, Keele University, Staffordshire, ST5 5BG, UK.
Midlands Partnership NHS Foundation Trust, St Georges' Hospital, Stafford, ST16 3AG, UK.
BMC Fam Pract. 2019 Jul 18;20(1):101. doi: 10.1186/s12875-019-0994-8.
People 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.
Secondary 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).
In 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).
This 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.
健康素养水平较低的人（HL）更有可能出现不良健康后果，并且从医疗保健服务中获益较少。然而，如果医疗保健从业者了解患者的 HL，他们可以有效地调整医疗保健信息。目前有可用于评估 HL 水平的测量方法，但在初级保健环境中可能不实用。已经成功开发了基于人口统计学指标的新替代方法，我们旨在测试这种方法是否可应用于常规收集的咨询记录。
对最近完成的一项前瞻性队列研究进行了二次分析，该研究调查了因肌肉骨骼疼痛问题就诊的初级保健人群。参与者在基线和 6 个月时完成了问卷（评估一般健康状况、HL、疼痛和人口统计学信息），并与参与者的咨询记录中的数据相关联。使用单一项识字筛查器作为 HL 的基准。我们测试了现有的 HL 人口统计学评估的性能，是否可以进一步改进/提高（使用问卷数据），然后测试其在初级保健咨询数据中的应用。测试包括准确性、敏感性、特异性和曲线下面积（AUC）。最后，使用逻辑回归在预测不良健康结果（身体健康和疼痛强度）方面对完成的模型进行前瞻性测试，生成比值比（OR）。
总共纳入了 1501 名参与者进行分析，其中 16.1%被归类为 HL 水平较低。对现有人口统计学评估的测试显示性能不佳（AUC 0.52），使用来自问卷的附加组件进行细化可改进模型（AUC 0.69），而仅使用咨询数据的数据的最终模型仍有改善（AUC 0.64）。在预测结果方面，对该最终咨询模型的测试表明，HL 水平较低的患者报告健康状况较差的可能性高 5 倍（OR 5.1），报告疼痛强度更高的可能性高近 4 倍（OR 3.9）。
这项研究表明，使用初级保健咨询数据评估 HL 是可行的，HL 水平较低的人健康状况更差。现在需要进一步改进以提高该方法的准确性。
