4.1. Discussion
To the best or our knowledge, this is the first study to develop a model for predicting the HL of community-dwelling older adults. This algorithm-based model was well calibrated by integrating HL-related factors in the model of the European Health Literacy Survey Consortium and is useful in HL risk prediction among older adults. In addition, it has a modest ability to discriminate between older adults with high HL and low HL.
In this study, we integrated variables associated with both medical and public health perspectives in the aforementioned HL model of the European Health Literacy Survey Consortium and proposed a simple scoring algorithm. The scoring system dichotomizes older adults into high-risk (cutoff ≥ 5) and low-risk (cutoff < 5) populations to maximize the sensitivity and specificity of low HL prediction. Based on the proposed cutoff points, among the 92 older adults in the test data set, 63 (68.5%) with a cutoff ≥ 5 were recommended to undergo further HL intervention, although only 31 (62.0%) actually had low HL, resulting in a positive predictive value of 75.6%. Given the importance of early identification and strategy provision for community-dwelling older adults at high risk of low HL, the proposed scoring algorithm proposed can be considered useful in community practice.
This conceptual framework integrating medical and public health perspectives developed by the European Health Literacy Survey Consortium is suitable for exploring the most relevant determinants of HL levels in older adults. Eight predictors were identified to be significantly associated with HL levels: one socio-environmental determinant (i.e., dominant spoken dialect) and seven HL-related factors including health services (i.e., having a family doctor), health cost (i.e., self-paid pneumonia vaccination), health behaviors (i.e., searching online health information), health outcomes (i.e., assistance while visiting a doctor and activities of daily living), participation (i.e., attending health classes), and empowerment (i.e., self-management during illness). The results for seven identified predictors of HL-related factors were consistent with those of previous studies, for example, having a family doctor (7), costs for self-paid vaccination (32), searching online health information (9), functional status such as difficulty in daily activities and assistance while visiting doctors (32, 33), participation in health classes (34), and self-efficacy in disease management (35). However, our study found that personal and situational factors did not affect the HL among older adults. Previous studies have documented that personal determinants of age, education level, and working status as well as situational and environmental determinants including marriage and residential area were significantly associated with HL levels (14, 36). This difference might be because personal and situational determinants were proximal factors of HL, which are influenced and displaced by a more distal and upstream factor (societal and environmental determinants) (37).
Our risk prediction tool provides primary public health workers with an easy-to-use scoring system that examines relevant variables. Users can rapidly predict low HL and thus identify community-dwelling older adults who may require further health assistance by evaluating their HL-related personal, situational, and environmental factors as well as the health behavior and outcomes. Hospitalization and mortality due to poor HL in older adults can be avoided through early identification and intervention. Therefore, this assessment tool should be promptly extended to broader communities.
Our study had some limitations. First, this was a cross-sectional study by convenience sampling from northern, central, and southern Taiwan. Therefore, potential selection bias might also exist. Second, this study relied on the 47-item HLS-EU-Q self-reported questionnaire for the criteria for HL. Further more objective HL assessments might be required to recognize the functional HL in order to avoid the potential for outcome misclassification bias. Third, the high prevalence rate of low HL (54.9%) among our sample may influence the capacity of prediction (i.e., PPV) of this algorithm when applied in other populations. Therefore, when it applies to a population with a lower prevalence of low HL, the older adults with positive results of low HL may in fact have higher HL. Additionally, we excluded older adults who could not pass the Mini-Cog screening or follow instructions to complete the assessment. Our model may, therefore, not be generalizable to the entire population of older adults. Thus, this model is not recommended to be used in individuals with cognitive impairments or dementia who may have difficulty understanding the instructions. Larger population studies with prospective longer term outcome measures are necessary to validate our study.