Abstract
Background and Aims
The spectrum of health literacy in metabolic dysfunction–associated steatotic liver disease (MASLD) patients and its relationship to disease severity are largely unknown. We performed an exploratory cross-sectional study regarding health literacy in this population.
Methods
The Health Literacy Questionnaire (HLQ) was administered to 101 individuals with MASLD attending an ambulatory hepatology clinic; data were obtained from medical records. Scores per HLQ scale were related to disease severity by Spearman coefficient, t-test, and multivariate regression. Clustering analyses were performed. Significance was set at P < .05.
Results
In HLQ part 1 (scored 1–4), the highest scoring scale was feeling understood and supported by health-care providers (mean ± standard deviation) (3.43 ± 0.6). In HLQ part 2 (scored 1–5), the highest scoring scale was understanding health information well enough to know what to do (4.12 ± 0.57). In HLQ part 1, the lowest scoring scale was actively managing health (2.98 ± 0.6). In HLQ part 2, the lowest scoring scale was navigating the health system (3.83 ± 0.71). A decreased ability to actively manage health was associated with higher liver steatosis (r = −0.29, P = .01) and body mass index (r = −0.38, P = <.001). A decreased ability to understand health information (r = −0.24, P = .04) was associated with higher liver stiffness. Higher education was positively correlated with finding (r = 0.25, P = .01), appraising (r = 0.24, P = .01), and understanding (r = 0.34, P = <.001) health information. Two main clusters of health literacy patterns were observed; one with higher education, higher health literacy, and less severe MASLD.
Conclusion
Lower education and health literacy are associated with more advanced MASLD and metabolic dysfunction.
Keywords: Metabolic Dysfunction–Associated Steatotic Liver Disease (MASLD), Health Literacy, Social Determinants of Health, HLQ, Disparities
Introduction
Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic disorder with a waxing and waning course, with some patients progressing to cirrhosis and eventually hepatic decompensation or hepatocellular cancer.1 Optimal management of MASLD includes both management of the liver disease and related comorbidities given the interplay of these conditions on metabolic health.1,2 Those who have access to health services receive clinical care and medical advice at clinic visits; however, there is often a large amount of information to absorb and some patients may not be ready or able to do so. From a patient perspective, having access to reliable and relevant information and being able to identify and synthesize such information is important for making decisions and managing one’s health and well-being, as are the skills, ability, confidence, and resources to make informed decisions and enact them.
According to the World Health Organization, health literacy “represents the personal knowledge and competencies that accumulate through daily activities, social interactions and across generations.”3 Broadly, it represents “the personal characteristics and social resources needed for individuals and communities to access, understand, appraise, and use information and services to make decisions about health, or that have implications for health.”4 Low health literacy has been associated with worse health outcomes (eg, high mortality and poor overall health status) and higher use of health-care services.4,5 As a result, this is now a priority area of focus both for the World Health Organization and the US Department of Health and Human Services as part of the Healthy People 2030 initiative.6,7
Several studies have indicated that low health literacy is a problem for individuals with chronic liver diseases.8, 9, 10, 11, 12, 13, 14 However, there remains a paucity of information related to the overall status of health literacy in patients with MASLD and its relationship to markers of disease severity.
The objective of the current study was to describe the spectrum of health literacy and its relationship to the severity of both MASLD and metabolic comorbidities in a cohort of patients with MASLD attending a tertiary care hepatology clinic. The hypothesis was that low health literacy would be related to having more advanced liver disease, greater obesity, and poorer glycemic control. The aims of this pilot study were to (1) describe the overall health literacy profile of the study population and identify specific areas of high and low health literacy, (2) establish the relationships of health literacy levels and dimensions to measures of liver disease severity and also associated metabolic comorbidities, and (3) identify subpopulations (ie, clusters) of individuals with similar patterns of health literacy levels and establish their shared knowledge gaps and related disease profiles. The intent of this study was to serve as a pilot to provide direction for future large-scale studies to provide confirmatory information.
Materials and Methods
Study Population
The population for this cross-sectional study was adults with MASLD presenting to a tertiary care hepatology ambulatory clinic. MASLD was defined by the presence of metabolic comorbidities such as obesity, type 2 diabetes mellitus (T2DM), hypertension, high triglycerides, or low high-density lipoprotein cholesterol along with evidence of steatosis (through imaging, continuous attenuation parameter [CAP] measurement by transient elastography, or biopsy) and without other cause of steatosis.15 Exclusion criteria were refusal to participate in the study, age <18 years, florid alcohol-related liver disease, pregnancy, incarceration, and inability to speak English. Study participation was voluntary.
Study Conduct
The study was conducted at a tertiary care center’s (Virginia Commonwealth University Health System) ambulatory hepatology clinic in 2023. This study was reviewed and approved by the Virginia Commonwealth University Institutional Review Board and designated as an exempt study (HM20027550). Participants were recruited at the time of outpatient clinic visits. All potentially eligible individuals were approached for participation to minimize bias. The institutional review board granted a waiver for written consent of participants. All participants verbally consented and signed a Health Insurance Portability and Accountability Act release or provided agreement online (for participants who participated remotely). Those who agreed to participate were given the option of taking the survey on paper, on an electronic tablet, or have the survey verbally administered by a research coordinator. Several participants completed the survey remotely and were provided a secure Health Insurance Portability and Accountability Act–compliant Redcap16,17 hyperlink that meets the national data security standards.
Assessment of Health Literacy
Health literacy was assessed using the Health Literacy Questionnaire (HLQ). The HLQ is a widely used and extensively validated, multidimensional measure of health literacy.4,18 It assesses nine scales of health literacy: (1) feeling understood and supported by health-care providers (HCPs); (2) having sufficient information to manage one’s health; (3) actively managing one’s health; (4) social support for health; (5) appraisal of health information; (6) ability to actively engage with HCPs; (7) navigating the health-care system; (8) ability to find good health information; and (9) understand health information well enough to know what to do.18 Each health literacy scale is comprised of 4–6 individual Likert-scale items, for a total of 44 questions.18 The part 1 survey items represent the health literacy scales 1–5 utilize a response scale from 1 to 4, where 1 is strongly disagree, 2 is disagree, 3 is agree, and 4 is strongly agree.18 The part 2 Likert response scale for the survey items measuring scales 6–9 range from 1 to 5, where 1 is cannot do or always difficult, 2 is usually difficult, 3 is sometimes difficult, 4 is usually easy, 5 is always easy.18 The overall score for each scale was calculated as the mean score of the associated individual scale items.4 Higher scores indicate higher health literacy on that scale.4
Demographic Characteristics and MASLD-related Measures
Clinical, demographic, and laboratory data were obtained from clinical records for each participating individual. Specifically, information on how the MASLD diagnosis was made, Fibrosis-4 Index (FIB-4) scores, complete blood counts, hepatic and basic metabolic panel-related laboratory data, hemoglobin A1C (HbA1C), lipid panel, reported alcohol use, and comorbidity profile were captured. The severity of the MASLD was assessed by the liver stiffness measurement (LSM) measured by vibration-controlled transient elastography. The degree of steatosis was measured by CAP via vibration-controlled transient elastography or histological assessment of liver biopsies. In addition, participants provided their date of birth, gender identity, highest level of education, type of insurance coverage, race, and ethnicity.
Statistical Methods
A total recruitment goal of 100 participants was planned set for this study. For any HLQ scale to be correlated with LSM with a correlation coefficient of 0.3, a sample size of 84 or 112 participants was expected to provide a power of 0.8 or 0.9, respectively, keeping significance at 0.05.
The study population was described by conventional descriptive statistics. To define the health literacy in the study population, the distribution of HLQ scores and their mean were quantified. Bivariate Spearman correlation was used to define the relationship between HLQ scales and other determinants of metabolic health (LSM, body mass index [BMI], aspartate transaminase, alanine transaminase, etc.). HLQ scores across social determinants of health (education, insurance status, etc.) and metabolic/liver health (LSM, FIB-4 severity categories, BMI tertiles, etc.) were compared using t-test followed by post hoc adjustment with Tukey multiple comparisons of means.
Multivariate regression analyses with LSM, CAP, FIB-4, BMI and HbA1C as outcome variables were used to relate HLQ scores to liver health parameters. Regressions were further adjusted for covariates including presence of T2DM, age, race, education level, and insurance status. Logistic regression analyses were used to further identify HLQ parameters related to fibrosis stage groups. A P value of .05 was considered significant.
To identify subpopulations based on health literacy patterns, an exploratory cluster analysis was performed using hierarchical clustering with average linkage based on Euclidean distance (R function hclust). Optimal number of clusters was chosen based on the maximum number of methods that agreed on the same optimal number of clusters (R package NbClust). Results were visualized using the principal components approach to evaluate the interactions of demographic, clinical, and laboratory data and other measured social determinants of health and HLQ score profiles.
Results
Sample Characteristics
A total of 101 individuals participated in the study (Table 1). The (mean ± standard deviation) age was 56.96 ± 12.50 years, with 70% of participants over the age of 50 years. There were a greater number of women (61.4%), and the study population was predominantly White (75.2%). The mean LSM was 10.6 kPa, while the mean CAP score was 297 db/m. The mean FIB-4 was 2.18. Seventy-six individuals had at least some college education.
Table 1.
Sample Demographic and Biometric Characteristics of Entire Cohort
| Characteristic | Total study population: n = 101 n (%) or M ± SD |
|---|---|
| Age | 56.96 ± 12.50 |
| Males | 39 (38.6) |
| Race | |
| White | 76 (75.2) |
| Non-White | 23 (22.8) |
| Multiracial | 2 (2) |
| Hispanic | |
| Yes | 5 (5.0) |
| No | 95 (94.1) |
| N/A | 1 (1) |
| Education status | |
| High school or less | 24 (23.8) |
| Any college (4 y or less) | 53 (52.5) |
| Over 4 y college | 23 (22.8) |
| N/A | 1 (1) |
| Insurance status | |
| Public (Medicaid, Medicare, Tricare) | 53 (52.5) |
| Private (employer provided, marketplace) | 45 (44.6) |
| Other | 3 (3) |
| Mean LSM Kpa | 10.67 ± 11.06 |
| LSM strata | |
| <8 Kpa | 46 (45.5) |
| 8.1–15 Kpa | 20 (19.8) |
| >15 Kpa | 12 (11.9) |
| N/A | 23 (22.8) |
| Mean FIB-4 | 2.18 ± 2.53 |
| FIB-4 strata | |
| <1.3 | 52 (51.5) |
| 1.3–2.67 | 27 (26.7) |
| >2.67 | 22 (21.8) |
| Mean CAP (db/m) | 297.53 ± 60.37 |
| Aspartate transaminase (IU/l) | 41.06 ± 30.35 |
| Alanine transaminase (IU/l) | 46.90 ± 32.85 |
| Alk Phos (IU/l) | 88.99 ± 48.14 |
| Total bilirubin (mg/dl) | 0.70 ± 0.36 |
| Albumin (gm/dl) | 4.29 ± 0.37 |
| INR | 1.05 ± 0.11 |
| MELD score | 8.20 ± 2.06 |
| NAFLD activity score | 4.16 ± 1.59 |
| Fibrosis stage | |
| 0 | 7 (6.9) |
| 1 | 4 (4) |
| 2 | 9 (8.9) |
| 3 | 8 (7.9) |
| 4 | 10 (9.9) |
| N/A | 63 (62.4) |
| BMI (kg/m2) | 34.72 ± 7.89 |
| Type 2 diabetes | 44 (43.6) |
| Hypertension | 63 (62.4) |
| Number of metabolic comorbidities | |
| 0 | 13 (12.9) |
| 1 | 22 (21.8) |
| 2 | 39 (38.6) |
| 3 | 27 (26.7) |
| Hemoglobin A1C (%) | 6.16 ± 1.09 |
| Hypothyroidism | 14 (13.9) |
| Mental health condition | 29 (28.7) |
Note that vibration-controlled transient elastography–related data were available in 78 individuals, and liver histology was available in 38 individuals. LSM is measured by fibroscan. Fibrosis stage denotes fibrosis stage per biopsy.
INR, international normalized ratio; M, mean; MELD, model for end stage liver disease; n, number; NAFLD, non-alcoholic fatty liver disease; N/A, value not available; SD, standard deviation.
Profile of Health Literacy Levels
The scores for each of the HLQ scales with their corresponding means and standard deviation are shown in Table 2. In part 1 of the HLQ, participants had the highest scores for feeling understood and supported by their HCPs (mean 3.43). Participants had the lowest scores for actively managing their health (mean 2.98), with the poorest scores for making time to be healthy (mean 2.78). Participants also had relatively lower scores for having sufficient information to manage their health (mean 3.09). Importantly, despite over 70% of participants having some college education, the overall scores for items related to ability to appraise health information were relatively low (mean 3.09).
Table 2.
Mean Scores per HLQ Scale and Corresponding Items for Entire Cohort
| M ± SD | |
|---|---|
| HLQ part 1 (response scale 1–4) | |
| Scale 1: Feeling understood and supported by HCPs | 3.43 ± 0.60 |
| Items | |
| I have at least one HCP who… | 3.49 ± 0.76 |
| I have at least one HCP I can… | 3.44 ± 0.71 |
| I have the HCPs I need to help me work out what I need… | 3.32 ± 0.66 |
| I can rely on at least one… | 3.50 ± 0.64 |
| Scale 2: Having sufficient information to manage my health | 3.09 ± 0.63 |
| Items | |
| I feel I have good information about health… | 3.37 ± 0.67 |
| I have enough information to help me deal… | 3.10 ± 0.71 |
| I am sure I have all the information I… | 2.89 ± 0.79 |
| I have all the information I need to… | 3.00 ± 0.81 |
| Scale 3: Actively managing my health | 2.98 ± 0.60 |
| Items | |
| I spend quite a lot of time actively managing… | 2.98 ± 0.79 |
| I make plans for what I need to do to be… | 3.09 ± 0.65 |
| Despite other things in my life, I make time… | 2.78 ± 0.77 |
| I set my own goals about health and fitness | 3.00 ± 0.68 |
| There are things that I do regularly… | 3.05 ± 0.73 |
| Scale 4: Social support for health | 3.20 ± 0.60 |
| Items | |
| I can get access to several people who… | 3.36 ± 0.67 |
| When I feel ill, the people around me really… | 2.97 ± 0.78 |
| If I need help, I have plenty of people I… | 3.20 ± 0.72 |
| I have at least one person… | 3.19 ± 0.81 |
| I have strong support from… | 3.30 ± 0.76 |
| Scale 5: Appraisal of health information | 3.09 ± 0.54 |
| Items | |
| I compare health information from different… | 3.23 ± 0.71 |
| When I see new information about health, I… | 3.07 ± 0.71 |
| I always compare health information from… | 3.07 ± 0.75 |
| I know how to find out if the health… | 3.03 ± 0.69 |
| I ask HCPs about the quality… | 3.07 ± 0.68 |
| HLQ part 2 (response scale 1–5) | |
| Scale 6: Ability to actively engage with HCPs | 4.11 ± 0.65 |
| Items | |
| Make sure that HCPs understand… | 3.83 ± 0.82 |
| Able to discuss your health concerns with a… | 4.30 ± 0.69 |
| Have good discussions about your health… | 4.26 ± 0.82 |
| Discuss things with HCPs… | 4.04 ± 0.76 |
| Ask HCPs questions to get… | 4.14 ± 0.75 |
| Scale 7: Navigating the health-care system | 3.83 ± 0.71 |
| Items | |
| Find the right health care | 3.62 ± 0.81 |
| Get to see the HCPs you need to | 3.99 ± 1.00 |
| Decide which HCP you need… | 3.95 ± 0.87 |
| Make sure you find the right place to get… | 3.98 ± 0.79 |
| Find out what health-care services you are… | 3.66 ± 0.95 |
| Work out what is the best care for you | 3.75 ± 0.81 |
| Scale 8: Ability to find good health information | 3.97 ± 0.60 |
| Items | |
| Find information about health problems | 3.92 ± 0.76 |
| Find health information from several… | 4.07 ± 0.77 |
| Get information about health so you are… | 3.99 ± 0.78 |
| Get health information in words you… | 3.99 ± 0.78 |
| Get health information by yourself | 3.87 ± 0.74 |
| Scale 9: Understand health information well enough to know what to do | 4.12 ± 0.57 |
| Items | |
| Confidently fill medical forms in the correct… | 4.34 ± 0.77 |
| Accurately follow the instructions from… | 4.14 ± 0.71 |
| Read and understand written health… | 3.96 ± 0.88 |
| Read and understand all the information on… | 4.00 ± 0.76 |
| Understand what HCPs are… | 4.21 ± 0.65 |
Mean scores of full cohort for each HLQ scale and each scale’s corresponding items. Items are truncated; full items are available from the authors of the HLQ.18 Low scores are considered to reflect poor health literacy, and high scores reflect better health literacy.
M, mean; n, number; SD, standard deviation.
In part 2 of the HLQ, the participants felt they were able to understand health information well enough to know what to do (mean 4.12) and had the ability to actively engage with HCPs (mean 4.11) but had the poorest scores for the ability to navigate the health-care system (mean 3.83).
Relationship of Health Literacy to Demographic and Other Determinants of Health
There were no statistically significant differences in the distribution and mean scores for the nine scales of the HLQ by race, age, and gender (Table 3). Those with public insurance (vs private) had higher scores for actively managing their health (Table 3). The scores for navigating the health-care system, finding health information, and understanding health information were directly related to educational status (Table 3), with those with the highest education also having the best scores (Figure 1).
Table 3.
Scores of Each HLQ Scale by Various Clinical and Demographic Characteristics
| HPS (M ± SD) | HSI (M ± SD) | AMH (M ± SD) | SS (M ± SD) | CA (M ± SD) | AE (M ± SD) | NHS (M ± SD) | FHI (M ± SD) | UHI (M ± SD) | |
|---|---|---|---|---|---|---|---|---|---|
| Age (y) | |||||||||
| 29–45 (n = 18) | 0.30 ± 0.87 | 0.06 ± 1.08 | −0.04 ± 1.19 | 0.25 ± 0.84 | 0.24 ± 0.78 | 0.20 ± 0.99 | −0.15 ± 1.16 | 0.02 ± 1.00 | 0.05 ± 1.00 |
| 46–65 (n = 56) | 0.01 ± 0.89 | −0.02 ± 0.98 | −0.11 ± 0.95 | −0.04 ± 1.04 | −0.01 ± 1.00 | −0.06 ± 0.93 | 0.04 ± 0.92 | 0.02 ± 0.92 | −0.03 ± 1.00 |
| >65 (n = 27) | −0.23 ± 1.25 | 0.01 ± 1.03 | 0.25 ± 0.95 | −0.09 ± 1.02 | −0.13 ± 1.13 | −0.02 ± 1.17 | 0.01 ± 1.07 | −0.05 ± 1.19 | 0.02 ± 1.04 |
| P | .223 | .960 | .296 | .486 | .475 | .642 | .785 | .963 | .958 |
| Race | |||||||||
| White (n = 76) | −0.02 ± 0.97 | −0.10 ± 1.02 | −0.02 ± 0.99 | −0.07 ± 0.96 | −0.02 ± 0.91 | −0.10 ± 1.07 | −0.09 ± 1.07 | −0.05 ± 1.06 | −0.03 ± 1.04 |
| Non-White (n = 23) | 0.04 ± 1.14 | 0.27 ± 0.92 | 0.11 ± 1.06 | 0.21 ± 1.14 | 0.07 ± 1.31 | 0.29 ± 0.64 | 0.26 ± 0.67 | 0.11 ± 0.79 | 0.12 ± 0.92 |
| Multiracial (n = 2) | 0.32 ± 0.89 | 0.65 ± 0.56 | −0.47 ± 0.71 | 0.33 ± 0.00 | 0.01 ± 0.26 | 0.29 ± 1.52 | 0.24 ± 1.32 | 0.72 ± 0.94 | −0.22 ± 0.49 |
| P | .875 | .196 | .703 | .441 | .931 | .245 | .322 | .473 | .769 |
| Gender | |||||||||
| Male (n = 39) | 0.11 ± 0.86 | 0.06 ± 0.92 | −0.01 ± 0.93 | 0.16 ± 0.84 | −0.04 ± 0.71 | 0.22 ± 0.92 | 0.14 ± 1.01 | −0.08 ± 0.94 | −0.08 ± 1.01 |
| Female (n = 62) | −0.07 ± 1.08 | −0.04 ± 1.05 | 0.01 ± 1.05 | −0.10 ± 1.08 | 0.02 ± 1.15 | −0.14 ± 1.03 | −0.09 ± 0.99 | 0.05 ± 1.04 | 0.05 ± 1.00 |
| P | .376 | .625 | .939 | .210 | .755 | .084 | .283 | .552 | .519 |
| Education | |||||||||
| HS or less (n = 24) | 0.01 ± 0.75 | 0.04 ± 0.91 | −0.01 ± 0.82 | −0.03 ± 0.79 | −0.16 ± 0.79 | 0.09 ± 0.95 | 0.06 ± 1.00 | −0.25 ± 1.06 | −0.25 ± 1.02 |
| Any college (n = 53) | −0.09 ± 1.15 | −0.13 ± 1.04 | −0.04 ± 1.00 | −0.07 ± 1.17 | −0.05 ± 1.10 | −0.11 ± 1.07 | −0.19 ± 1.02 | −0.08 ± 0.99 | −0.15 ± 0.97 |
| >4 y college (n = 23) | 0.26 ± 0.83 | 0.32 ± 0.93 | 0.13 ± 1.2 | 0.23 ± 0.74 | 0.26 ± 0.96 | 0.21 ± 0.86 | 0.41 ± 0.87 | 0.47 ± 0.84 | 0.63 ± 0.84 |
| P | .377 | .186 | .781 | .484 | .318 | .405 | .053 | .030 | .002 |
| Insurance | |||||||||
| Public (n = 53) | −0.07 ± 1.18 | −0.03 ± 1.09 | 0.22 ± 0.98 | −0.07 ± 1.13 | 0.03 ± 1.08 | 0.01 ± 1.01 | 0.00 ± 1.00 | −0.03 ± 1.04 | 0.00 ± 1.03 |
| Private (n = 45) | 0.07 ± 0.79 | 0.02 ± 0.92 | −0.27 ± 0.98 | 0.04 ± 0.85 | −0.03 ± 0.95 | 0.00 ± 1.02 | −0.02 ± 1.03 | 0.00 ± 0.98 | −0.04 ± 0.98 |
| Other (n = 3) | 0.11 ± 0.42 | 0.25 ± 0.39 | 0.14 ± 1.02 | 0.55 ± 0.19 | −0.05 ± 0.21 | −0.07 ± 0.64 | 0.17 ± 0.95 | 0.50 ± 0.38 | 0.59 ± 1.01 |
| P | .766 | .884 | .050 | .552 | .951 | .990 | .954 | .681 | .578 |
| No T2DM (n = 55) | −0.07 ± 1.01 | 0.02 ± 1.04 | 0.05 ± 1.10 | 0.00 ± 1.02 | 0.10 ± 0.93 | −0.06 ± 1.06 | 0.01 ± 1.06 | 0.14 ± 0.96 | 0.10 ± 1.00 |
| T2DM (n = 44) | 0.05 ± 1.00 | 0.01 ± 0.97 | −0.04 ± 0.88 | −0.04 ± 0.99 | −0.11 ± 1.08 | 0.07 ± 0.94 | 0.01 ± 0.94 | −0.15 ± 1.05 | −0.10 ± 1.01 |
| P | .540 | .979 | .673 | .831 | .305 | .501 | .963 | .149 | .336 |
| HTN (n = 63) | 0.19 ± 1.03 | 0.06 ± 1.06 | 0.05 ± 1.18 | 0.08 ± 1.00 | 0.13 ± 1.01 | 0.21 ± 0.99 | 0.13 ± 1.01 | 0.21 ± 0.97 | 0.15 ± 1.04 |
| No HTN (n = 38) | −0.11 ± 0.97 | −0.03 ± 0.97 | −0.03 ± 0.88 | −0.05 ± 1.00 | −0.08 ± 0.99 | −0.13 ± 0.99 | −0.08 ± 1.00 | −0.13 ± 1.00 | −0.09 ± 0.97 |
| P | .142 | .661 | .696 | .516 | .313 | .097 | .327 | .101 | .237 |
| Hist. Stage | |||||||||
| F0 (n = 7) | −0.78 ± 0.88 | −0.48 ± 0.70 | −0.20 ± 0.60 | −0.05 ± 0.73 | −0.17 ± 1.07 | −0.26 ± 0.63 | −0.49 ± 1.16 | −0.52 ± 0.97 | −0.62 ± 0.55 |
| F1 (n = 4) | 0.32 ± 0.80 | 0.06 ± 1.38 | 0.45 ± 1.28 | 0.58 ± 0.68 | 0.29 ± 0.93 | 0.44 ± 0.67 | 0.42 ± 0.86 | 0.05 ± 1.05 | 0.47 ± 0.90 |
| F2 (n = 9) | −0.49 ± 1.59 | 0.12 ± 1.41 | −0.08 ± 1.39 | −0.08 ± 1.58 | −0.17 ± 1.28 | 0.19 ± 1.13 | 0.37 ± 0.80 | 0.31 ± 0.68 | 0.01 ± 1.08 |
| F3 (n = 8) | 0.32 ± 0.77 | 0.25 ± 0.97 | 0.28 ± 0.81 | 0.45 ± 0.68 | 0.20 ± 0.97 | 0.13 ± 0.55 | 0.16 ± 0.65 | −0.32 ± 0.76 | −0.09 ± 0.70 |
| F4 (n = 10) | −0.06 ± 0.86 | −0.10 ± 0.97 | 0.10 ± 0.8 | −0.17 ± 0.52 | −0.14 ± 0.71 | −0.24 ± 1.18 | −0.13 ± 0.99 | −0.11 ± 1.41 | −0.36 ± 1.14 |
| P | .243 | .745 | .794 | .514 | .870 | .622 | .331 | .556 | .375 |
| Fib-4 | |||||||||
| <1.3 (n = 52) | −0.01 ± 0.99 | −0.09 ± 1.07 | −0.18 ± 1.05 | −0.01 ± 0.97 | 0.02 ± 1.00 | 0.00 ± 0.98 | −0.07 ± 1.03 | 0.05 ± 0.95 | 0.09 ± 0.96 |
| 1.3–2.67 (n = 27) | 0.00 ± 1.14 | 0.15 ± 0.99 | 0.18 ± 0.96 | 0.07 ± 1.15 | 0.03 ± 1.21 | 0.11 ± 0.88 | 0.20 ± 0.78 | 0.02 ± 0.96 | −0.02 ± 1.06 |
| >2.67 (n = 22) | 0.02 ± 0.88 | 0.02 ± 0.84 | 0.21 ± 0.89 | −0.06 ± 0.92 | −0.09 ± 0.72 | −0.13 ± 1.21 | −0.09 ± 1.17 | −0.13 ± 1.17 | −0.19 ± 1.03 |
| P | .995 | .603 | .161 | .895 | .897 | .709 | .481 | .789 | .538 |
| LSM (kPa) | |||||||||
| <8 (n = 46) | 0.01 ± 0.99 | 0.09 ± 1.06 | 0.08 ± 1.10 | 0.09 ± 1.04 | 0.16 ± 0.98 | 0.29 ± 0.95 | 0.15 ± 1.01 | 0.19 ± 0.96 | 0.23 ± 1.02 |
| 8.1–15 (n = 20) | 0.20 ± 0.90 | 0.02 ± 0.82 | 0.07 ± 0.78 | 0.13 ± 0.67 | 0.11 ± 0.81 | −0.18 ± 0.66 | −0.30 ± 0.90 | −0.05 ± 0.78 | −0.17 ± 0.78 |
| >15 (n = 12) | −0.38 ± 0.66 | −0.30 ± 0.86 | −0.11 ± 0.79 | −0.31 ± 0.80 | −0.30 ± 0.93 | −0.33 ± 1.09 | −0.15 ± 1.03 | −0.45 ± 0.98 | −0.49 ± 0.80 |
| P | .245 | .460 | .846 | .371 | .325 | .042 | .213 | .099 | .040 |
| BMI tertile | |||||||||
| High (n = 33) | −0.14 ± 1.11 | −0.14 ± 1.06 | −0.46 ± 1.06 | −0.13 ± 1.10 | −0.14 ± 1.11 | −0.02 ± 0.90 | −0.05 ± 0.90 | −0.01 ± 0.94 | 0.03 ± 0.86 |
| Medium (n = 34) | 0.00 ± 1.03 | 0.00 ± 0.87 | −0.02 ± 0.99 | −0.05 ± 1.00 | −0.09 ± 1.10 | −0.07 ± 0.80 | −0.03 ± 0.86 | −0.05 ± 1.11 | −0.05 ± 1.02 |
| Low (n = 34) | 0.14 ± 0.85 | 0.14 ± 1.07 | 0.46 ± 0.73 | 0.18 ± 0.90 | 0.22 ± 0.75 | 0.08 ± 1.26 | 0.08 ± 1.22 | 0.05 ± 0.97 | 0.02 ± 1.12 |
| P | .529 | .527 | .001 | .410 | .285 | .819 | .852 | .922 | .938 |
Scores for each scale standardized (z score). LSM is measured by fibroscan. Hist. stage denotes fibrosis stage per biopsy.
AE, ability to actively engage with HCPs; AMH, actively managing my health; CA, appraisal of health information; FHI, ability to find good health information; HPS, feeling understood and supported by HCPs; HSI, having sufficient information to manage my health; HTN, hypertension; M, mean; n, number; NHS, navigating the health-care system; SD, standard deviation; SS, social support for health; UHI, understand health information well enough to know what to do.
Figure 1.
Bar plot of median Z transformed HLQ scales by education level. Each panel corresponds to individual HLQ scale. Education level categories are depicted on the x axis; the height of the bar on the y axis represents median Z transformed score for each scale. AE, ability to actively engage with HCPs; AMH, actively managing my health; CA, appraisal of health information; FHI, ability to find good health information; HPS, feeling understood and supported by HCPs; HSI, having sufficient information to manage my health; NHS, navigating the health-care system; SS, social support for health; UHI, understand health information well enough to know what to do.
Relationship of Health Literacy to Measures of Liver Disease and Metabolic Dysfunction
Correlation between HLQ scores and markers of metabolic health and liver disease was obtained (Table 4). There was an inverse relationship between CAP and scores for actively managing health (P < .05) and social support (P < .01). Additionally, there was an inverse relationship between LSM and the ability to understand health information (P < .05). Higher BMI was correlated with lower scores for all HLQ scales, with scores for actively managing health (r = −0.38, P < .01) and social support (r = −0.2, P < .05) reaching statistical significance. Similarly, HbA1C scores were inversely related to scores for 8 of 9 HLQ scales, including a statistically significant inverse correlation with ability to find good health information (r = −0.259, P < .05). Mean HLQ scores for strata based on severity of MASLD based on histologic stage, FIB-4, and LSM are provided in Table 3. The LSM measurements were higher in those with low education status compared to those with the highest level of education (Figure 2). The scores did not vary based on the presence of T2DM or hypertension, while the scores for actively managing health were poorer in those with obesity (P < .01) (Table 3).
Table 4.
Pairwise Correlations of HLQ Scale Scores With Measures of Liver Disease and Metabolic Health
| HPS | HSI | AMH | SS | CA | AE | NHS | FHI | UHI | |
|---|---|---|---|---|---|---|---|---|---|
| Education | 0.181 | 0.129 | 0.112 | 0.138 | 0.242a | 0.078 | 0.110 | 0.252a | 0.336b |
| Aspartate transaminase | 0.08 | 0.125 | 0.127 | 0.119 | 0.018 | −0.039 | −0.054 | −0.061 | −0.118 |
| Alanine transaminase | 0.011 | 0.048 | −0.029 | 0.079 | −0.067 | −0.06 | −0.138 | −0.129 | −0.166 |
| CAP | −0.126 | −0.150 | −0.286a | −0.302b | −0.21 | −0.131 | −0.096 | 0.002 | 0.084 |
| LSM | −0.122 | −0.178 | −0.137 | −0.097 | −0.199 | −0.158 | −0.128 | −0.187 | −.237a |
| FIB-4 | 0.017 | 0.116 | 0.167 | 0.051 | −0.023 | 0.054 | 0.1 | 0.013 | −0.049 |
| Fibrosis stage | 0.208 | 0.119 | 0.036 | −0.103 | −0.038 | −0.052 | 0.019 | 0.077 | −0.006 |
| NAS | 0.021 | −0.076 | −0.102 | 0.227 | 0.153 | −0.126 | 0.083 | 0.062 | 0.074 |
| BMI | −0.138 | −0.169 | −.381b | −.200a | −0.075 | −0.129 | −0.152 | −0.018 | −0.033 |
| T2DM | 0.058 | −0.037 | −0.094 | −0.022 | −0.129 | 0.052 | 0.016 | −0.137 | −0.105 |
| HbA1C | 0.089 | −0.092 | −0.051 | −0.005 | −0.154 | −0.109 | −0.079 | −.259a | −0.167 |
| LDL-C | −0.115 | 0.041 | −0.036 | 0.041 | 0.076 | −0.166 | −0.058 | 0.111 | 0.071 |
| Triglycerides | −0.137 | 0.026 | −0.189 | 0.048 | −0.044 | −0.087 | −0.056 | 0.025 | 0.04 |
| HDL-C | 0.072 | 0.109 | 0.194 | 0.136 | 0.184 | 0.128 | 0.114 | .267a | .230a |
Spearman coefficients are shown. LSM is measured by fibroscan.
AE, ability to actively engage with HCPs; AMH, actively managing my health; BMI, body mass index; CA, appraisal of health information; CAP, continuous attenuation parameter; FHI, ability to find good health information; HbA1c, hemoglobin A1C; HDL-C, high-density lipoprotein cholesterol; HPS, feeling understood and supported by HCPs; HSI, having sufficient information to manage my health; LDL-C, low-density lipoprotein cholesterol; NAS, NAFLD activity score; NHS, navigating the health-care system; SS, social support for health; UHI, understand health information well enough to know what to do.
Correlation is significant at the 0.05 level (2-tailed).
Correlation is significant at the 0.01 level (2-tailed).
Figure 2.
Box plots of measures of liver disease severity and metabolic health by education level. Each panel represents an individual measure of liver disease (KPA and CAP) or metabolic health (BMI, HbA1C); x axis represents level of education; y axis represents level of health indicator. HbA1C, hemoglobin A1C; KPA, kilopascals (unit of liver stiffness measurement per elastography).
To further determine if the relationships noted on simple correlation analysis described previously would still hold after adjusting for comorbidity profile including the presence of T2DM, age, race, education level, and insurance status, separate multivariate regression analyses were performed with HLQ scale scores and these factors as covariates and the LSM, CAP, FIB-4, BMI, and HbA1C as outcome variables. Following adjustment, the presence of T2DM and non-White race approached significance for CAP values (Supplementary Table 1). There was a strong inverse relationship between scores for actively manage health and BMI (−4.83; P < .001). Actively managing health was positively associated with FIB-4 (0.998; P = .025). HLQ scores were not correlated to HbA1C in this cohort after adjustment for confounding variables.
Clustering Analysis to Identify Subpopulations With Similar HLQ Profiles
Clustering analysis identified 5 clusters of individuals within the study cohort; these clusters are visualized on principal component plots (Supplementary Figure 1). The majority of individuals (93%) fell into 1 of 2 clusters; the characteristics of these clusters are provided in Table 5. They were distinguished by varying educational status. Those in the group with more individuals with high school or less education had a trend for higher CAP, LSM, and BMI but did not differ with respect to FIB-4 or HbA1C. The mean HLQ scores for cluster 2 (lower education status) were significantly lower than in cluster 1 for all scales. All cluster 1 HLQ scores were higher as compared to cluster 2 scores; thus, cluster 1 can be defined as the higher health literacy cluster.
Table 5.
Sample Demographic, Biometric, and HLQ Characteristics of 2 Largest Clusters of Patients
| Cluster 1 (n = 41) n (%) or M ± SD |
Cluster 2 (n = 53) n (%) or M ± SD |
P | |
|---|---|---|---|
| Age (y) | 58.05 ± 12.12 | 56.25 ± 12.68 | .487 |
| Males | 16 (39.0) | 21 (39.6) | 1.000 |
| Race | .766 | ||
| White | 29 (70.7) | 41 (77.4) | |
| Non-White | 11 (26.8) | 11 (20.8) | |
| Multiracial | 1 (2.4) | 1 (1.9) | |
| Educationa | .13 | ||
| High school or less | 7 (17.1) | 16 (30.2) | |
| College <4 y | 18 (43.9) | 30 (56.6) | |
| College >4 y | 16 (39) | 6 (11.3) | |
| N/A | 0 (0%) | 1 (1%) | |
| Insurance | .654 | ||
| Public | 22 (53.7) | 27 (50.9) | |
| Private | 17 (41.5) | 25 (47.2) | |
| Other | 2 (4.9) | 1 (1.9) | |
| BMI (kg/m2) | 34.07 ± 9.26 | 35.53 ± 6.74 | .378 |
| T2DM | 17 (41.5) | 25 (47.2) | .349 |
| Hypertension | 23 (56.1) | 37 (69.8) | .248 |
| LDL-C (mg/dl) | 100.23 ± 37.70 | 92.98 ± 39.15 | .412 |
| HDL-C (mg/dl) | 50.72 ± 13.49 | 45.17 ± 11.56 | .055 |
| Triglycerides (mg/dl) | 139.03 ± 75.37 | 140.44 ± 84.07 | .938 |
| HbA1C (%) | 5.97 ± 0.82 | 6.39 ± 1.25 | .106 |
| CAP (db/m) | 291.34 ± 55.74 | 304.9 ± 64.86 | .352 |
| LSM (kPa) | 7.47 ± 4.08 | 13.38 ± 14.14 | .025 |
| LSM strata | .307 | ||
| <8 | 21 (51.29) | 21 (39.6) | |
| 8.1–15 | 9 (22) | 11 (20.8) | |
| >15 | 2 (4.9) | 9 (17) | |
| N/A | 9 (22) | 12 (22.6) | |
| FIB-4 | 2.38 ± 3.10 | 2.06 ± 2.07 | .554 |
| FIB-4 strata | .997 | ||
| <1.3 | 21 (51.2) | 27 (50.9) | |
| 1.3–2.67 | 11 (26.8) | 14 (26.4) | |
| >2.67 | 9 (22) | 12 (22.6) | |
| NAFLD activity score | 4.23 ± 1.30 | 4.06 ± 1.83 | .770 |
| Fibrosis stage | .460 | ||
| 0 | 1 (2.4) | 6 (11.3) | |
| 1 | 12 (4.9) | 2 (3.8) | |
| 2 | 4 (9.8) | 4 (7.5) | |
| 3 | 5 (12.2) | 3 (5.7) | |
| 4 | 3 (7.3) | 7 (1.2) | |
| N/A | 26 (63.4) | 31 (58.5) | |
| HLQ scale scores | Cluster 1 (M ± SD) | Cluster 2 (M ± SD) | P |
| HPS | 3.79 ± 0.32 | 3.28 ± 0.46 | <.001 |
| HSI | 3.59 ± 0.38 | 2.83 ± 0.45 | <.001 |
| AMH | 3.39 ± 0.46 | 2.75 ± 0.39 | <.001 |
| SS | 3.56 ± 0.40 | 3.08 ± 0.45 | <.001 |
| CA | 3.49 ± 0.34 | 2.86 ± 0.38 | <.001 |
| AE | 4.47 ± 0.44 | 3.91 ± 0.54 | <.001 |
| NHS | 4.26 ± 0.47 | 3.57 ± 0.51 | <.001 |
| FHI | 4.40 ± 0.39 | 3.66 ± 0.44 | <.001 |
| UHI | 4.54 ± 0.48 | 3.85 ± 0.32 | <.001 |
First 5 HLQ scales (HPS, HSI, AMH, SS, CA) are scored from 1 to 4, and second 4 scales (AE, NHS, FHI, UHI) are scored from 1 to 5. Higher scores represent higher health literacy. LSM is measured by fibroscan.
AE, ability to actively engage with HCPs; AMH, actively managing my health; CA, appraisal of health information; FHI, ability to find good health information; fibrosis stage, fibrosis stage per biopsy; HDL-C, high-density lipoprotein cholesterol; HPS, feeling understood and supported by HCPs; HSI, having sufficient information to manage my health; LDL-C, low-density lipoprotein cholesterol; M, mean; n, number; N/A, value not available; NHS, navigating the health-care system; SD, standard deviation; SS, social support for health; UHI, understand health information well enough to know what to do.
Average scores of various demographic, biometric, and HLQ, characteristics from the 2 principal clusters of participants within the cohort.
Discussion
Health literacy is considered by many as a high priority area of research due to its impact on individuals’ health and well-being via several mechanisms (eg, access to resources and relevant, timely information for decisions).4,6,7 In addition to limited access to HCPs on a daily basis, there is also a large amount of information that is publicly available including misinformation and sometimes conflicting information about diet, exercise, value of medications, etc., making it challenging for affected individuals to find, sort through, and extract the information needed to make health-related decisions. Health literacy may take on greater importance in the ability of affected individuals, who often have multiple comorbid diseases, to manage their health, which in turn could affect long-term outcomes. The current pilot study provides novel information on the key dimensions of health literacy in individuals with MASLD and provides direction for future research.
The current study identified that lower HLQ scores along multiple scales were associated with more severe hepatic steatosis and fibrosis, obesity, and metabolic dysfunction. This demonstrates that health literacy may be a contributing factor to the severity of MASLD and metabolic dysfunction. It is however important to note that health literacy is not a causal factor driving stellate cell fibrogenesis but rather an upstream factor affecting the patient’s ability to engage in their health care in an active manner, which in turn may affect the trajectory of disease by influencing the metaboinflammatory biological drivers of fibrosis. These data further underscore the complexity of factors modulating disease severity and indicate that those with the greatest steatosis burden and liver stiffness have poorer health literacy scores suggesting opportunities for improvement in future definitive studies across various MASLD subpopulations. They also provide a rationale for larger studies to better understand the influence of health literacy on the MASLD course.
Another notable finding is that the ability to “actively managing one’s health” showed clear inverse relationships with BMI and CAP scores. Furthermore, there was a disconnect between understanding how to manage health and being able to actively do so. Among the questions assessing for actively managing health, the lowest scoring question was “despite other things in my life, I make time….” These data suggest that the ability to navigate and implement healthy living practices is impaired and provides additional direction for future research on both the role of health literacy in the ability of individuals to engage in sustainable lifestyle modification toward a healthier lifestyle which would be expected to benefit both the liver and associated cardio–renal–metabolic comorbidities often present in individuals with MASLD.
Additionally, those with 4 years or more of college generally had higher health literacy levels across the majority of scales and also better metabolic status and less severe liver disease (Table 3; Figures 1 and 2) (Figures 1 and 2). While theoretically higher education should be associated with higher health literacy due to better reading and writing skills, ability to search for and find information and interpret data, and better financial status allowing greater access to health care, the current study was not designed to dissect these mechanistic underpinning of this relationship. It is important, however, that these observations are not used to perpetuate biases against individuals with lower formal education levels but rather to recognize and address the diverse needs of patients in a compassionate and equitable manner and provides direction for future research addressing the needs of those with lower formal education.
It is important to note that the current study, is a pilot study and the population was predominately Caucasian; as such the data are not definitive or entirely generalizable. Despite these limitations, they do provide novel information on health literacy using an extensively validated comprehensive health literacy assessment tool and provide foundational information for future studies.
Conclusion
This pilot study indicates that health literacy is associated with the severity of MASLD and related metabolic parameters. These provide a rationale for future studies to obtain more detailed insights into these relationships and to explore causal links and the contribution of health literacy gaps to the ability to follow a healthy lifestyle, which in turn impacts development and progression of MASLD. Such studies would provide direction toward health literacy–informed approaches for care delivery to improve long-term outcomes. While this was a pilot study and the data are not definitive, they are a foundational step toward full assessment of the implications of health literacy in MASLD.
Footnotes
Acknowledgments: Preliminary data for this study (including poster and abstract) were presented in part at the annual meeting of the American Association for Study of Liver Diseases in San Diego 2024. It is original work and is not under consideration for publication elsewhere.
Authors’ Contributions: Alana Saddic: Methodology, formal analysis, investigation, writing - original draft, writing - review and editing, visualization, project administration. Jessica N. Mittler: Conceptualization, methodology, formal analysis, writing - review and editing, supervision, project administration, funding acquisition. Ekaterina Smirnova: Formal analysis, data curation, writing - review and editing, visualization. Deborah Diaz Granados: Conceptualization, methodology, formal analysis, writing - review and editing, supervision, project administration, funding acquisition. Arun J. Sanyal: Conceptualization, methodology, formal analysis, resources, writing - review and editing, supervision, project administration, funding acquisition.
Conflicts of Interest: These authors disclose the following: Dr Arun J. Sanyal has stock options in Tiziana, Inversago, Rivus, NorthSea, and Durect. He has served as a consultant to Novo Nordisk, Eli Lilly, Boehringer Ingelhiem, Inventiva, Gilead, Takeda, LG Chem, Hanmi, Corcept, Surrozen, Poxel, 89 Bio, Boston Pharmaceuticals, Regeneron, Merck, Alnylam, Aligos, Akero, Myovant, Salix, Avant Sante, NorthSea Pharma, Madrigal, Path AI, Histoindex, Astra Zeneca, Abbvie, and Zydus. His institution has received grants from Intercept, Novo Nordisk, Boehringer Ingelhiem, Eli Lilly, Merck, Takeda, Salix, Inventiva, Gilead, Akero, Hanmi, Histoindex, and 89Bio. He receives royalties from Wolter Kluwers (UptoDate) and Elsevier. Alana Saddic has stock options in Eli Lilly and McKesson. She received an abstract award for travel from The AASLD Foundation. The remaining authors disclose no conflicts.
Funding: Funding was provided by a grant from Novo Nordisk; a grant from NIDDK to Dr Arun Sanyal (RO1 DK R01 DK129564); intramural funds from the Stravitz-Sanyal Institute for Liver Disease and Metabolic Health at VCU School of Medicine; [Clinical and Translational Science Award] (award no. UM1TR004360) from the National Center for Advancing Translational Sciences. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or Novo Nordisk. The use of REDCap16,17 in this study to collect and manage data is supported by Clinical and Translational Science Award listed above.
Ethical Statement: This study was reviewed and approved by the Virginia Commonwealth University Institutional Review Board and designated as an exempt study (HM20027550).
Reporting Guidelines: STROBE checklist.
Material associated with this article can be found in the online version at https://doi.org/10.1016/j.gastha.2025.100873.
Supplementary materials
Figure A1.
References
- 1.Rinella M.E., Neuschwander-Tetri B.A., Siddiqui M.S., et al. AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 2023;77(5):1797–1835. doi: 10.1097/HEP.0000000000000323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Li A.A., Ahmed A., Kim D. Extrahepatic manifestations of nonalcoholic fatty liver disease. Gut Liver. 2020;14(2):168–178. doi: 10.5009/gnl19069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Health promotion glossary of terms 2021. World Health Organization; Geneva: 2021. Licence: CC BY-NC-SA 3.0IGO. [Google Scholar]
- 4.Dodson S., Beauchamp A., Batterham R.W., et al. 2017. Ophelia Toolkit: a step-by-step guide for identifying and responding to health literacy needs within local communities. [Google Scholar]
- 5.Berkman N.D., Sheridan S.L., Donahue K.E., et al. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97. doi: 10.7326/0003-4819-155-2-201107190-00005. [DOI] [PubMed] [Google Scholar]
- 6.Health literacy development for the prevention and control of noncommunicable diseases: volume 1. Overview. World Health Organization; Geneva: 2022. [Google Scholar]
- 7.U.S. Department of Health and Human Services Health Literacy in Healthy People 2030. Healthy People 2030. https://health.gov/healthypeople/priority-areas/health-literacy-healthy-people-2030
- 8.Figueroa G., Castañeda S., McLean H., et al. Low health literacy, lack of knowledge, and self-control hinder healthy lifestyles in diverse patients with steatotic liver disease. Dig Dis Sci. 2024;69(2):384–398. doi: 10.1007/s10620-023-08212-9. [DOI] [PubMed] [Google Scholar]
- 9.Volk M.L., Fisher N., Fontana R.J. Patient knowledge about disease self-management in cirrhosis. Am J Gastroenterol. 2013;108(3):302–305. doi: 10.1038/ajg.2012.214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kaps L., Hildebrand K., Nagel M., et al. Risk factors for poorer health literacy in patients with liver cirrhosis. PLoS One. 2021;16(7) doi: 10.1371/journal.pone.0255349. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kaps L., Omogbehin L., Hildebrand K., et al. Health literacy in gastrointestinal diseases: a comparative analysis between patients with liver cirrhosis, inflammatory bowel disease and gastrointestinal cancer. Sci Rep. 2022;12(1) doi: 10.1038/s41598-022-25699-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Freundlich Grydgaard M., Bager P. Health literacy levels in outpatients with liver cirrhosis. Scand J Gastroenterol. 2018;53(12):1584–1589. doi: 10.1080/00365521.2018.1545045. [DOI] [PubMed] [Google Scholar]
- 13.Jacobsen B.G., Lauridsen M.M., Grønkjaer L.L. Knowledge needs in patients with liver disease: a qualitative study. BMC Nurs. 2023;22(1):406. doi: 10.1186/s12912-023-01580-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gulati R., Nawaz M., Pyrsopoulos N.T. Health literacy and liver disease. Clin Liver Dis. 2018;11(2):48–51. doi: 10.1002/cld.690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Rinella M.E., Lazarus J.V., Ratziu V., et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78(6):1966–1986. doi: 10.1097/HEP.0000000000000520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Harris P.A., Taylor R., Thielke R., et al. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Harris P.A., Taylor R., Minor B.L., et al. The REDCap consortium: building an international community of software partners. J Biomed Inform. 2019;95 doi: 10.1016/j.jbi.2019.103208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Osborne R.H., Batterham R.W., Elsworth G.R., et al. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ) BMC Public Health. 2013;13:658. doi: 10.1186/1471-2458-13-658. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.