Health Literacy
The association between health literacy and indicators of cognitive impairment in a diverse sample of primary care patients

https://doi.org/10.1016/j.pec.2013.07.006Get rights and content

Abstract

Objectives

To confirm the association of health literacy scores as measured by Health Literacy Assessment Using Talking Touchscreen Technology (Health LiTT) with cognitive ability and education. To determine whether this association differs by cognitive task.

Methods

Cognitive impairment was measured using the Mini-Cog, which combines a delayed word recall task (WRT) and a clock drawing task (CDT) to yield an overall classification of normal versus cognitively impaired. Participants were recruited from primary care clinics that provide care to underserved patients.

Results

Participants (n = 574) were predominantly non-Hispanic black (67%) with a mean age of 46 years, 50% did not have health insurance, 56% had a high school education or less and 21% screened positive for cognitive impairment. Overall cognitive ability and education were significantly associated with health literacy after adjusting for other variables, including race/ethnicity and physical health. We observed a stronger association between the CDT and health literacy than between the WRT and health literacy.

Conclusion

By confirming hypothesized associations, this study provides additional support of the validity of Health LiTT.

Practice implications

Health LiTT is a reliable and valid tool that researchers and clinicians can use to identify individuals who might have difficulty understanding health information.

Introduction

Health Literacy Assessment Using Talking Touchscreen Technology (Health LiTT) is a new computer-based tool that can be used in clinical practice and research to assess patients’ health literacy [1], [2]. For the purpose of this measurement tool, we define health literacy as the capacity to read and comprehend health-related print material, identify and interpret information presented in graphical format (charts, graphs and tables), and perform arithmetic operations in order to make appropriate health and care decisions [3]. Health LiTT was created to measure a breadth of literacy levels and can be administered as a short form test or using computer adaptive testing (CAT). CAT uses computer algorithms to select the best test items based on responses to previous items. This approach minimizes the assessment length, while maximizing the precision of the measurement of health literacy. As a new tool, it is important to know how Health LiTT is related to other commonly used measures in the health care setting: (1) cognitive ability, and (2) years of education.

Health LiTT has been shown to be acceptable to a wide variety of patients, including those who are computer naïve and older [2], and initial evidence supports the validity of this new tool [1], [4]. Validation of a new measurement tool requires building a weight of evidence demonstrating that the instrument is measuring the construct of interest and that the scores behave as hypothesized [5]. Two variables consistently shown to be independently associated with better health literacy in numerous previous studies are normal cognitive ability and higher educational attainment [6], [7], [8], [9], [10], [11], [12], [13]. The relationship between health literacy, cognitive ability, and education is complex and the causal direction of the associations is difficult to tease apart [8]. However, demonstrating independent associations of cognitive ability and education with health literacy as measured by Health LiTT would further support the validity of this new measure.

The overall objective of this study was to determine whether the established associations between health literacy, education, and cognitive ability are confirmed when health literacy is measured by the new Health LiTT. The strength of association between health literacy and cognitive ability may vary by how these constructs are measured [13]. Thus, a secondary objective was to assess whether the association between health literacy and cognitive ability differed by type of cognitive task.

Section snippets

Participants

Data for this unplanned secondary analysis were from a sample of primary care patients who participated in a study to develop and calibrate Health LiTT [1], [2]. Participants in the parent study were recruited from two urban and two suburban primary care clinics that provide care to underserved patients, many of whom do not have health insurance. Two recruitment methods were used at both clinics: flyers posted near the reception desk and direct invitation by a research assistant in the waiting

Participants

All patients who were approached appeared to have sufficient cognitive function to provide informed consent and sufficient ability to interact with the touchscreen computer [2]. No patients were excluded based on an inability to provide consent or interact with the computer. Enrollment rates were very high at both the suburban (90.3%) and urban (90.0%) sites. For those who chose not to participate, the primary reason given was not enough time to complete the assessment. Overall Mini-cog

Cognitive impairment

The proportion of participants who screened positive for cognitive impairment by the Mini-Cog in our study was 20.6%, which is quite high considering that the prevalence of dementia in the general population as determined by the Mini-Cog is only 6.4% [15]. Performance on tests of cognitive function may vary with social conditions over the lifecourse or by race/ethnicity, educational attainment, education quality, and literacy skills [22], [23], [24]. It is possible that in a vulnerable

Role of funding

This study was supported in part by Grant Number R01-HL081485 from the National Heart, Lung, and Blood Institute. Portions of this manuscript were presented at the Health Literacy 2nd Annual Research Conference, Bethesda MD, October 18–19, 2010.

Conflicts of interest

No conflicts of interest were reported by any of the authors.

Acknowledgements

The authors thank Katy Wortman for data management; Patricia O’Neil and Lindsay Norgaard for scoring the Mini-Cog clock drawings; Seung Choi, Jeremiah Aakre, Shane Pankratz and Paul Novotny for statistical analysis support; and David Eton for reviewing an earlier version of this manuscript. Finally, we thank all of the patients who participated in this study.

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