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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Mov Disord Clin Pract. Author manuscript; available in PMC 2017 May 1.
Published in final edited form as:
PMCID: PMC4909149
NIHMSID: NIHMS723198

Associations and implications of low health literacy in Parkinson’s Disease

Jori E. Fleisher, MD, MSCE,1,* Krunal Shah, BA,2,^ Whitney Fitts, BA,2,° and Nabila A. Dahodwala, MD, MS2

Abstract

BACKGROUND

Low health literacy (HL) indicates a limited ability to understand and use basic information to make appropriate healthcare decisions. While low HL is associated with higher morbidity, mortality, and healthcare costs in multiple chronic conditions, little is known about HL and its associations in Parkinson’s disease (PD).

METHODS

Cross-sectional study of non-demented adults with PD participating in the National Parkinson Foundation Parkinson’s Outcomes Project at the University of Pennsylvania. Subjects were administered two brief HL assessments—the Rapid Estimate of Adult Literacy in Medicine-Short Form (REALM-SF), a word-recognition test, and the Newest Vital Sign (NVS), a test of literacy, numeracy and understanding of health information—as well as demographic and clinical questionnaires. Adverse outcomes included falls in the 3 months preceding the study visit, and hospital admissions, emergency room visits, infections, or injuries in the preceding year. Caregiver burden was measured using the Multidimensional Caregiver Strain Index.

RESULTS

168 subjects completed both HL screens (mean 65.8 years, 65.5% male, 65.2% Hoehn & Yahr Stage 2). Using the REALM-SF, 97.6% of subjects had adequate HL. Using the NVS, however, 29.8% had low HL, which was associated with older age, lower education, male gender, greater disease severity, and poorer cognition. Low HL was associated with hospital admission and increased caregiver burden.

CONCLUSIONS

Low HL is common and associated with greater caregiver burden and a higher likelihood of hospitalization in patients with PD. Since HL is associated with both disease severity and adverse outcomes, it may be an important, modifiable contributor to morbidity.

Keywords: Parkinson’s disease, health literacy, patient education, caregiver, communication

INTRODUCTION

In 2013, the American Association of Medical Colleges issued a list of competencies integral to physician development1. Included is the capacity to “communicate effectively with patients.” To do so, physicians must recognize low health literacy (HL) as a potential obstacle to clear communication. HL has been defined as the ability to obtain and understand health information in order to make informed decisions regarding health care2, and is a prerequisite comprehension of any health condition. Estimates of low HL prevalence range from 35–80% in elderly and disease-specific cohorts36. Low HL is associated with poorer self-management and higher hospitalization rates, healthcare costs, and mortality among older adults711. HL is often erroneously interpreted as a surrogate marker of education, however many individuals have low HL despite advanced degrees12,13.

Little is known about the prevalence and implications of low HL in Parkinson’s disease (PD). In the first study to our knowledge employing a validated HL screening instrument in a non-demented, general neurology population, nearly 21% had low HL14. This is despite neurologists’ tendency to manage patients with complicated, chronic conditions, requiring extensive communication between providers, patients, and families. In the case of PD, affecting over 1 million adults in the US and often involving multiple pharmacologic and non-pharmacologic interventions15, adequate HL is critical. Adherence relies in part on adequate HL, and increasing regimen complexity is associated with both a higher degree of HL required for comprehension and a higher risk of medication non-adherence13. We therefore sought to establish the validity of two brief HL screens—the Rapid Estimate of Adult Literacy in Medicine-Short Form (REALM-SF)16 and the Newest Vital Sign (NVS)17,18—in a non-demented PD population. We aimed to identify patient factors associated with low HL in this population, explore the relationship between low HL and caregiver burden, and examine the potential role of low HL in the causal pathway between PD and adverse outcomes.

METHODS

Sample

The Institutional Review Board of the University of Pennsylvania approved this study. Subjects included all individuals participating in the National Parkinson Foundation Parkinson’s Outcomes Project (NPF POP) at the University of Pennsylvania’s Parkinson’s Disease and Movement Disorders Center, an NPF Center of Excellence. The study design, inclusion and exclusion criteria, and methods of the POP are detailed elsewhere19. In short, the POP is a worldwide, multicenter, longitudinal study of care practices in the management of individuals with PD. Participants complete an annual evaluation including demographic and clinical information, a review of medical comorbidities and adverse events including falls, injuries, infections, emergency room visits, and hospital admissions occurring over the past year, and a battery of mobility and cognitive tests validated for use in PD. Medications reviewed include dopaminergic agents, antidepressants, and acetylcholinesterase inhibitors. Quality of life is assessed using the Parkinson’s Disease Questionnaire (PDQ-39), covering 8 dimensions of quality of life (QOL). Each dimension is scored from 0–100 with lower scores indicating better QOL, and an overall summary index score (PDSI) is calculated as the average of the 8 subscores, ranging from 0–10020,21. The emotional wellbeing subscore is used as a surrogate measure of depression in this analysis. In addition, care partners are asked to complete the Multidimensional Caregiver Strain Index (MCSI)22,23. The MCSI contains 18 items for a range of 0–72, where higher scores indicate greater strain. For the purposes of this study, individuals with a Montreal Cognitive Assessment (MoCA) score <23 were excluded from analysis24 given the likelihood of significant cognitive impairment confounding any associations with HL.

Interviews

Trained study personnel administered the POP battery, and if the participant was amenable to additional testing, introduced the current study. Participants completed the REALM-SF16, a brief, validated 7-item instrument requiring reading and pronunciation of health-related words (e.g., “Menopause”, “Jaundice”). The REALM-SF is dichotomized at the eighth grade reading level, with 0–6 points corresponding to low HL, and 7 points corresponding to ≥9th grade reading level and adequate HL. The subject was then administered the NVS17,18, a validated 6-item instrument wherein the subject views a nutrition label from an ice cream container. She must answer questions based on the label which test reading, interpretation, and numeracy—critical components of HL. The NVS is scored as 0–3 points indicating low HL, and 4–6 points indicating adequate HL. Both tests have been validated as rapid and reliable screens for low HL in populations of older adults and in cohorts with neurologic disease2527, and have shown good criterion validity with lengthier gold standard measures of HL28. Combined, these screens take <5 minutes and were selected because they are neither timed nor require writing—issues that may confound interpretation of results in a PD population.

Data Analysis

Statistical analyses were performed using STATA version 12.1. Descriptive statistics were calculated for each variable, including performance on both the REALM-SF and NVS assessed as both continuous and categorical variables according to established cut-offs1618. Correlation between the two instruments was calculated using Spearman’s rank correlation. Two-tailed t tests, χ2, and Wilcoxon rank-sum tests were used for bivariate analyses as appropriate, comparing the demographic and clinical characteristics of individuals with low vs. adequate HL. We then constructed a multivariable model to predict NVS scores, with all covariates with a p value of <0.20 in bivariate analyses as candidates for inclusion in the predictive model. Next, we used t tests, χ2, and Wilcoxon rank-sum tests to compare the frequency of adverse outcomes in individuals with low vs. adequate HL. We built multiple logistic regression models to predict the occurrence of individual and any adverse outcomes in one year, incorporating demographic, clinical, HL, and PD-specific factors. Finally, we conducted bivariate analyses as above to identify factors associated with increased caregiver burden and built a multivariable regression model to predict burden based on HL, demographic, and clinical factors. For all models, manual backward stepwise elimination was conducted with each step assessed for confounding. We confirmed the assumptions of linearity and normality of the error distribution for each model. Given the correlation between age and PD duration, each model included only one of these variables to avoid multicollinearity. Model fit was assessed via likelihood ratio testing and a p value <0.05 was considered significant.

RESULTS

Participant Characteristics

A total of 168 individuals participated having a mean age of 65.8 years (SD 8.4) and 65.5% were male. Subjects had a median 16 years of education (interquartile range 15.5–18 years) and 90.5% had a care partner. Table 1 displays the demographic and clinical characteristics of the cohort. Polypharmacy was common, with 75% of subjects taking at least two dopaminergic medications. Antidepressants were taken by 15.6% and acetylcholinesterase inhibitors by 5.4%. Falls occurred in 44.6% of subjects in the past 3 months. Twenty-two percent of the cohort visited an emergency room and 23.2% required hospitalization in the past year for any cause.

Table 1
Characteristics of Participants (n = 168)

Comparison of Health Literacy Screens

The median score on the REALM-SF was 7 out of a possible 7 (range 0–7), with 2.4% of subjects scored as low HL. However, the median score on the NVS was 5 out of a possible 6 (IQR 3–6, range 0–6), with 29.8% of subjects scored as low HL (≤3) as shown in Figure 1. There was no significant correlation between REALM-SF and NVS total scores (Scored continuously: Spearman’s rho = 0.05, p = 0.54; Scored categorically: Kappa 0.03, p = 0.18).

Figure 1
Distribution of Newest Vital Sign Scores

Factors Associated with Low Health Literacy

Based on the ceiling effect demonstrated by the REALM-SF in this highly educated population, the weak correlation between the HL screens, and the more comprehensive assessment of the NVS instrument, all further HL analyses were based on NVS scores alone. In bivariate analyses, 35.45% of men vs. 18.97% of women had low HL (p = 0.026). Older age was significantly associated with low HL, with a median age of 66 vs. 68 years for adequate and low HL groups, respectively (p < 0.001). An educational level of high school or below was more common among individuals with low (12.00%) vs. adequate HL (3.39%, p = 0.067). Subclinical cognitive impairment was more common among those with low HL, with a median of 4 vs. 2 missed points on the MoCA for low and adequate HL, respectively (p <0.001). There was a modest correlation between MoCA and NVS scores (Pearson r=0.28). Finally, there was a trend towards greater disease severity among those with low HL, with 38.00% vs. 27.97% rated as Hoehn & Yahr (H&Y) Stage 3 or higher (p = 0.110)29. After adjusting for depression, low HL remained significantly associated with male gender, high school education or lower, older age, PD severity, and cognitive impairment, as shown in Table 2.

Table 2
Factors associated with Total Score on the Newest Vital Sign

Relationship of Low Health Literacy with Caregiver Burden and Adverse Outcomes

Of 134 subjects with complete data on the MCSI, low HL was associated with greater caregiver burden (median score of 4 and IQR 1–9 in individuals with adequate HL compared to median score of 10 and IQR 2.5–17 in those with low HL, p <0.001). Each point missed on the NVS was associated with a 1.35-point increase in the MCSI score (p = 0.02, 95% CI 0.25–2.44.) After adjustment for PD severity, cognitive function, and multi-comorbidity, however, the relationship between low HL and MCSI was attenuated (0.95-point increase in the MCSI for every NVS point missed, p = 0.086, 95% CI −0.14–2.04) as shown in Table 3.

Table 3
Factors associated with Total Score on the Multidimensional Caregiver Strain Index

There was a 2.6-fold increased odds of hospitalization in individuals with low HL by NVS criteria (p = 0.01, 95% CI 1.23–5.48). Adjusting for multiple comorbidities, social support, and duration of disease, low HL remained significantly associated with hospitalizations (OR 2.89, p <0.01, 95% CI 1.30–6.42). None of the other individual adverse outcomes surveyed—falls, ER visits, injuries, pneumonia, or infection in general—were associated with low HL. As shown in Table 4, after combining all adverse outcomes into a binary summary outcome measure, we found that greater PD severity (H&Y ≥3) and multi-comorbidity were independently associated with adverse events after adjusting for demographics, PD duration, cognitive function, and depression. Low HL was not independently associated with the summary adverse outcomes measure (p = 0.83).

Table 4
Factors associated with Adverse Outcomes in the Past Year*

DISCUSSION

In this cross-sectional study of non-demented individuals with PD, nearly 30% of subjects met NVS criteria for low HL despite high levels of education. Factors independently associated with low HL included male gender, older age, greater disease severity, lower education, and cognitive impairment. Low HL was associated with higher caregiver burden, however this was no longer significant after accounting for PD severity and cognitive impairment. Low HL was, however, independently associated with hospitalizations.

We tested two screening instruments for this study, the REALM-SF and NVS, which have been validated in other disease populations. Our choice of instruments was based upon feedback from prior work using the Short-Form Test of Functional Health Literacy in Adults (S-TOFHLA)30. Although the S-TOFHLA has been extensively validated and used in a prior study of HL in neurology14, we opted for the REALM-SF and NVS here for several reasons. First, subjects in the prior study complained of the time required to take the S-TOFHLA. Furthermore, individuals with PD commented on the difficulty presented by the S-TOFHLA for people with bradykinesia and micrographia. Both the REALM-SF and NVS are shorter than the S-TOFHLA and do not require writing. In addition, the S-TOFHLA is timed and unanswered questions are incorrect. Bradykinesia is thus a potentially significant confounder of S-TOFHLA performance. While both instruments have been compared with and found to have moderate correlation with the S-TOFHLA28,31, one recent study found that REALM-SF and NVS scores were weakly correlated with each other, and similarly noted a prominent ceiling affect with REALM-SF scores28. These results corroborate our finding that 2.4% of our population had low HL by REALM-SF criteria, while 29.8% had low HL according to the NVS.

There are several possible explanations for these divergent findings. The REALM-SF predominantly measures literacy and highly correlates with education16. Our findings on the REALM-SF as compared to the NVS may be explained by our highly educated sample, a pattern consistent with other studies that compare these instruments17,28. It is also interesting to note the higher prevalence of low HL in this sample as compared to our previous finding of 20.5% prevalence in a diagnostically heterogeneous cohort of neurology outpatients14. The two studies are not directly comparable due to the use of different HL screens. Language—the primary cognitive domain tested by the REALM-SF—tends to be relatively spared in PD. However, the shared cognitive domains affected by PD and assessed by the NVS—namely, attention, thought processing, decision making, and working memory32—may explain the poorer NVS performance compared to the REALM-SF. Indeed, the large Health Literacy and Cognition in Older Adults study demonstrated that the relationship between low HL and adverse outcomes was significantly attenuated after adjusting for cognitive function25. The PD-specific declines in particular cognitive domains may cause a rising prevalence of low HL in this population out of proportion to the prevalence attributable to older age. Because of the overlap in these cognitive domains, the NVS may be a rapid and practical screening instrument for subtle cognitive impairment in the PD population, although longitudinal studies to establish reliability and comparison to formal neuropsychological testing are necessary for validation.

Further validating the NVS as a measure of HL in PD were the associations seen with gender, education, age and cognition. Studies in other conditions have shown similar relationships between low HL and male gender, education, age, and cognitive impairment4,6,11. Here, the MoCA serves as a measure of global cognition due to its inclusion in the POP19,33. After excluding individuals with dementia, even subclinical cognitive impairment was significantly associated with low HL. Disease severity—H&Y ≥3—was also associated with low HL. Therefore, clinicians must have a low threshold to suspect poor understanding of complicated disease management plans. Although one may assume that patients coping with PD for several years have greater disease knowledge, the converse may be the case14,34. Advancing age and disease severity make clear, plain language communication critical. Additionally, apathy—a common PD symptom --must be evaluated in future studies to determine its role in the relationship between HL and patient outcomes.

The finding of increased caregiver burden amongst patients with low HL bears consideration. Although bivariate analyses supported this relationship, it was attenuated once known risk factors for caregiver burden, namely, motor disability and cognitive impairment, were included in the model.23 This is of particular interest since our population was limited to individuals without severe cognitive impairment, suggesting that even subclinical cognitive decline remains a powerful influence on caregiver strain. Future work capturing apathy, depression, and other known predictors of high caregiver burden is indicated to better understand the complex relationship between HL and caregiver burden3537. While some caregivers may be able to compensate for the low HL of their charge, data on HL of caregivers themselves is sparse and disheartening3. In studies of patients with congestive heart failure (CHF) and their caregivers, 29–31% of caregivers had low HL38,39. To our knowledge, there are no similar studies involving caregivers of patients with neurologic disease or PD specifically. Future work on the prevalence and implications of low HL amongst caregivers may reveal potential interventions to mitigate patient morbidity and mortality.

In another study of patients with CHF, for example, low HL was independently tied to a greater risk of hospitalization and mortality40. The authors hypothesized that low HL was an indicator of suboptimal self-care behavior. This is particularly detrimental when a medical condition depends on adherence to medications, engaging in physical activity, and monitoring one’s symptoms. Low HL was associated with PD severity, which predicts morbidity, and low HL was also independently associated with hospitalizations. PD severity was strongly associated with all adverse outcomes. Thus, HL may represent a critical and modifiable contributor in the causal pathway in which greater PD severity leads to adverse outcomes.

Self-care in PD frequently involves a complex medication regimen marked by polypharmacy and frequent dosing. In a decade-old study, most individuals with PD were taking ≥2 dopaminergic medications, often dosed ≥3 times daily41. In the current study, that percentage is nearly 75% and does not account for any non-dopaminergics or medications for other conditions. Low HL may account for poor understanding of the need for each prescription and dose, leading to non-adherence. In cardiovascular disease, for example, and in multiple cohorts of elderly patients, low HL has repeatedly been a risk factor for both non-adherence and excess morbidity and mortality8,4245. In two large, multicenter studies of individuals with PD, poor adherence independently predicted worsening disability46,47. Therefore, medication non-adherence may be the critical link in the pathway between HL and disease severity. Future studies are needed to address adherence as an actionable source of excess morbidity and mortality in PD.

Limitations of this study include its cross-sectional design that only allows us to measure associations and not causality among the different variables, and the potential selection bias inherent in drawing participants from an academic referral center. Individuals seen at a specialty clinic may be skewed towards those with greater disease severity. Furthermore, uninsured or underinsured patients may have limited access to such specialty care and are often the most likely to have low HL48. This limitation would bias our results towards the null, underestimating the prevalence and impact of low HL in PD.

Despite the limitations of this study, this remains, to our knowledge, the first investigation of HL in PD and provides evidence for the use of the NVS to measure HL in PD. Based on the NVS, nearly 30% of educated, non-demented individuals with PD failed to demonstrate the skills required for simple healthcare understanding. Low HL was independently associated with hospitalizations. Initiatives exist to begin addressing this situation. The Agency for Healthcare Research and Quality provides helpful resources for engaging in HL-sensitive patient care (www.ahrq.gov/qual/literacy/). Also, the NPF has created a low literacy version of their Aware in Care kit, designed to empower patients and caregivers while hospitalized. Further effort is needed to raise awareness among neurologists of this barrier to care and to create successful, cost-effective interventions to overcome low HL.

Acknowledgments

Funding sources

This study received support from the National Parkinson Foundation Parkinson Outcomes Project and the Parkinson Council. Dr. Fleisher received support from NIH T32-NS-061779. Dr. Dahodwala is supported by NIA K23 AG034236.

The authors thank the patients and caregivers for their participation.

Footnotes

Conflicts of interest:

The authors have no conflicts of interest to report.

AUTHOR ROLES

(1) Research Project: A. Conception, B. Organization, C. Execution

(2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique

(3) Manuscript: A. Writing of the First Draft, B. Review and Critique

J.F.: 1A, 1B, 1C; 2A, 2B, 2C; 3A, 3B

K.S.: 1C; 3B

W.F.: 1C; 3B

N.D.: 1A, 1B; 2A, 2C; 3B

.

Financial disclosures for previous 12 months: Dr. Fleisher received research support from NIH and Parkinson Council. Dr. Dahodwala received research support from NIH, Parkinson Council, National Parkinson Foundation and Teva. Mr. Shah and Ms. Fitts have no disclosures.

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