Skip to main content
NCBI home page
Preventive Medicine Reports logoLink to Preventive Medicine Reports
. 2025 Jul 16;57:103179. doi: 10.1016/j.pmedr.2025.103179

Sociodemographic disparities in health literacy among American adults: A national survey study

Yiqing “Skylar” Yu a,⁎, Ana Altares b, Alyssa Leib b, Laura L Bellows c, Christopher Berry d, Dan J Graham a,e, Megan P Mueller b
PMCID: PMC12302880  PMID: 40727277

Abstract

Objectives

Health literacy is associated with various health behaviors and outcomes. The recent distribution of health literacy across different sociodemographic groups in the U.S. is unknown. This study aims to investigate sociodemographic differences in health literacy among U.S. adults using the Newest Vital Sign (NVS) assessment.

Methods

We conducted an online survey in 2022 using CloudResearch, involving 2829 participants. This survey included the NVS to measure health literacy and information on sociodemographic factors.

Results

Over 60 % of participants demonstrated inadequate (low to moderate) health literacy. Significant associations between health literacy and gender (p < .01), age (p < .01), ethnicity (p = .02), race (p < .01), education level (p < .01), residential region (p < .01), and household income (p = .04) were found. Males, Black or African American, Asian, Hispanic or Latino individuals, those with lower income, and those residing in the Northeast, South, and West regions exhibited lower health literacy compared to their counterparts. Additionally, a positive correlation between age and health literacy was observed, with the highest health literacy level among adults aged 65 and older. Education level showed a non-linear relationship with health literacy, peaking among those with job-specific training post-high school.

Conclusion

The study highlights sociodemographic disparities in health literacy. Targeted interventions and policies are needed to address these gaps, improve health outcomes, and reduce economic burdens associated with low health literacy. Future research should consider additional factors, such as digital literacy and language barriers, to provide a more comprehensive understanding of health literacy.

Keywords: Health literacy, Sociodemographic differences, Newest vital sign, Online survey, Health disparity

Highlights

  • •

    Over 60 % of U.S. adults demonstrated inadequate health literacy in this study.

  • •

    Health literacy varies significantly by gender, age, ethnicity, race, and income.

  • •

    Older adults (65+) showed the highest health literacy among all age groups.

  • •

    Education level and regional differences influence health literacy disparities.

  • •

    Targeted interventions are needed to address health literacy gaps and disparities.

1. Introduction

Health literacy is associated with health behaviors, such as physical activity (Suka et al., 2015), illness management (Reynolds et al., 2019; Guo et al., 2020), medication adherence (Alsaedi and McKerinan, 2021), and preventive behaviors (e.g., COVID-19 masking) (Patil et al., 2021; McCafferry et al., 2020). Health literacy is the ability to gather and comprehend relevant information to make health-related decisions (Institute of Medicine, 2004), which includes mathematical skills, reading, and interpreting health information, and the ability to apply knowledge in making health decisions (Nutbeam and Lloyd, 2021). To assess health literacy, researchers focus on three literacy scales: prose (understanding information from sentences), document (interpreting noncontinuous texts in various formats), and quantitative ability (performing numeric computations) (Kutner et al., 2006). Health literacy varies across sociodemographic groups and may underlie health disparities, as low health literacy is associated with poor health outcomes including poor glycemic control among patients with type 2 diabetes (White et al., 2022) and poor overall health (Lastrucci et al., 2019; Lans et al., 2023).

A robust analysis of health literacy from the 2003 National Assessment of Adult Literacy (Kutner et al., 2006) highlighted stark differences in health literacy by race, ethnicity, gender, age, language spoken at home, education level, and income. Hispanic adults had the lowest health literacy scores, while White and Asian/Pacific Islander adults had the highest. Older adults and those with lower incomes and education levels had lower health literacy scores compared to their counterparts. These findings highlight the existence of disparities in health literacy across certain racial/ethnic groups, immigrants, and those with lower English proficiency (Fleary and Ettienne, 2019; Sepassi et al., 2023; Becerra et al., 2017).

Racial/ethnic disparities in health literacy are produced by larger structural injustices, such as differences in access to education, economic opportunities, and social services (Cabellos-García et al., 2020). In the healthcare setting, reduced awareness or acknowledgement of disparities in health literacy may lead to poor communication between healthcare providers and patients (Kelly and Haidet, 2007), resulting in poor health outcomes (Saha et al., 2008). Gaps in health literacy not only impact individual health outcomes, but also broader national economic measures. Low health literacy costs an additional $106–238 billion annually in health care costs (Vernon et al., 2007). Understanding disparities in health literacy and their impact on lower socioeconomic individuals and racial/ethnic minorities is critical to informing healthcare policies and interventions.

Existing research on health literacy represents specific populations (e.g., patients, college students) (Ickes and Cottrell, 2010; Peterson et al., 2011), outside of the U.S. (Sørensen et al., 2015; Rajah et al., 2019; Murray et al., 2008), or is now dated (Kutner et al., 2006; Rikard et al., 2016). Moreover, many health literacy measurement tools are burdensome to participants (containing >30 questions) (Rajah et al., 2019) and/or require significant staffing to conduct (Kutner et al., 2006). This study used the Newest Vital Sign (NVS) (Weiss et al., 2005), a quick health literacy screening tool, to more efficiently measure health literacy in a larger, more nationally representative sample than in previous studies (Weiss et al., 2005; Osborn et al., 2007). This study provides a nationally representative assessment of disparities in health literacy in the U.S., expanding on research in the past 20 years. This is noteworthy as the way that health information is consumed has drastically changed with advancing technology (Jia et al., 2021). Given the lack of recent studies, historical evidence of disparities in health literacy (Fleary and Ettienne, 2019; Sepassi et al., 2023; Becerra et al., 2017), and importance of health literacy for health outcomes (Lastrucci et al., 2019; Lans et al., 2023), this study evaluated sociodemographic differences in health literacy in a nationally representative sample of U.S. adults. This research presents an opportunity to identify current gaps in health literacy in the US. In doing so, these findings enable researchers and practitioners to better reach specific groups, understand how they consume health information, and design more effective strategies to improve health literacy.

2. Methods

2.1. Participants

We conducted an anonymous online survey in April/May 2022 via CloudResearch Prime Panels (previously Turk Prime; Prime Research Solutions LLC; Flushing, NY; 2015–2022), accessing multiple research participant panel platforms in the U.S. Data sourced from this platform is reliable, valid, high-quality, and replicable (Douglas et al., 2023), with samples representative of larger populations. CloudResearch's built-in screening process SENTRY detects fraudulent and inattentive respondents to ensure high levels of response quality (Berry et al., 2022). This study was part of a larger research project examining perceptions of health claims on restaurant menus.

Survey respondents (n = 3730) were a panel census-matched by age and gender. Approximately half of the sample was recruited from low-income households (≤ $38,437; the cut point for low-income households from the PEW Research Center's American Trends Panel survey in 2021 dollars (Brown et al., 2020)). Because we could only target recruitment efforts on income (and not household size), we used one income cut point value for recruitment, later categorizing participants as low-income based on their responses about household income and size.

Eligible participants were ≥ 18 years old, had eaten from a restaurant in the past month, and consented to participate. The survey was given in English to English-speaking participants. The final analysis included 2829 participants (Fig. 1). Participants received compensation from the panel platform. This study was approved by the Colorado State University Institutional Review Board.

Fig. 1.

Fig. 1

Open in a new tab

Participant flow diagram for inclusion in final analytic sample of adult restaurant patrons (United States, 2022).

2.2. Health literacy

We assessed health literacy using the NVS (Weiss et al., 2005), a quick screening tool to assess individuals' prose and document literacy and numeracy using a nutrition label and six questions, through an online survey. The NVS has been validated against the Rapid Estimate of Adult Literacy in Medicine (Weiss et al., 2005) and Test of Functional Health Literacy in Adults (Osborn et al., 2007), used with diverse populations including Korean Americans and Black women (Shealy and Threatt, 2016; Kim et al., 2020; Hepburn et al., 2021), and validated for online administration (Mackert et al., 2017; Mansfield et al., 2018). Participants viewed an ice-cream nutrition label and responded to six questions (e.g., “If you eat the entire container, how many calories will you eat?”). Correct answers were coded as 1, incorrect answers as 0; total scores ranged from 0 to 6. A score of 0–1 suggests a high likelihood (50 % or more) of limited literacy (“low literacy”); 2–3 indicates the possibility of limited literacy (“moderate literacy”); 4–6 indicates adequate literacy (“high literacy”).

Free-response questions were graded manually (See A.1). For the peanut allergy question (question 6), any variations of “allergy,” “peanut (oil),” “nuts,” or responses suggesting correct understanding were accepted. Among this sample, the NVS demonstrated good internal consistency with online administration and manual coding (Cronbach's α = 0.75), comparable to in-person administration (Cronbach's α = 0.76) (Weiss et al., 2005), indicating reliable measurement with our online format and grading criteria.

NVS score was categorized into inadequate (< 4) and adequate (≥ 4) levels for descriptive purposes based on previous research (Jia et al., 2022; Muscat et al., 2022) and was treated as a continuous variable in regression models (described below).

2.3. Sociodemographic factors

We collected sociodemographic information on self-reported gender, age, race, ethnicity, education level, and current residential region, to describe sample characteristics (See Table 1 for all sociodemographic response options). To understand income differences, participants were categorized as low-income if their household income was <185 % federal poverty level (FPL), considering household size and annual income (Toossi and Jones, 2023). In addition to evaluating age as a continuous variable in a regression model, differences in health literacy between age groups were explored, aligned with a large-scale study on health literacy by Berens et al. (Berens et al., 2016).

Table 1.

Mean health literacy scores and between-group differences across sociodemographic characteristics among adult restaurant patrons (United States, 2022; N = 2829).+

Grouping Demographic Variables n (%) NVS Mean (SD)
Gender
 Female a 1466 (51.82) 3.1 (1.9)
 Male a 1341 (47.40) 2.7 (2.0)
 Non-binary/third gender 14 (0.49) 2.7 (2.1)
 Prefer not to say 8 (0.28) 3.3 (2.0)
Hispanic or Latino
 Yes a 310 (10.96) 2.4 (1.9)
 No a 2500 (88.37) 3.0 (2.0)
 Prefer not to say 19 (0.67) 2.2 (1.8)
Race
 American Indian or Alaska Native 32 (1.13) 2.8 (1.9)
 Asian a 106 (3.75) 2.2 (1.8)
 Native Hawaiian or Other Pacific Islander 11 (0.39) 2.2 (1.5)
 Black or African American b,c 307 (10.85) 2.0 (1.7)
 White/Caucasian a,c 2170 (76.71) 3.1 (2.0)
 Multiracial b 121 (4.28) 2.8 (1.9)
 Some other race 60 (2.12) 2.4 (1.8)
 Prefer not to answer 22 (0.78) 2.5 (1.9)
Age
 18–29 years a,b 480 (16.97) 2.4 (1.9)
 30–45 years c,d 813 (28.74) 2.3 (2.0)
 46–64 years a,c,e 894 (31.60) 3.3 (1.9)
 65 years and older b,d,e 642 (22.69) 3.5 (1.7)
Education Level
 8th grade or less 13 (0.46) 1.9 (1.6)
 Some high school a,b,c,d,e 114 (4.03) 2.0 (1.5)
 High school graduate f,g,h,i, 709 (25.06) 2.5 (1.8)
 Job-specific training programs a,f,j,k 115 (4.07) 3.6 (1.8)
 Some college but no degree b,g,l,m 628 (22.20) 3.3 (1.9)
 Associate's degree c,h 339 (11.98) 3.2 (1.9)
 Bachelor's degree d,i,j,l 576 (20.36) 3.0 (2.1)
 Graduate degree e,k,m 335 (11.84) 2.8 (2.2)
Current Region++
 Midwest a,b,c 619 (21.88) 3.2 (1.9)
 Northeast a 532 (18.81) 2.8 (1.9)
 South b 1059 (37.43) 2.8 (1.9)
 West c 619 (21.88) 2.8 (2.1)
Annual Household Income
 Under 185 % Federal Poverty Level a 1108 (39.54) 2.7 (1.9)
 Above 185 % Federal Poverty Level a 1694 (60.46) 3.0 (2.0)
Open in a new tab

Notes: The same superscripts next to each level of each sociodemographic variable indicate that there is statistically significant difference between the two groups (p < .05). Health Literacy was measured using the Newest Vital Sign (NVS), a six-item screening measure (higher scores indicate higher health literacy). SD = Standard Deviation. GED = General Education Development.

+

All groups except income included the full sample. Income sample was 2802.

++

Participants self-selected the region of the country they currently live in. According to the United States Census Bureau categories, here are the states included in each region. Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. South: Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, Washington D.C., and West Virginia. West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

2.4. Statistical analysis

Descriptive statistics (means, standard deviations, and percentages), ANOVAs, and t-tests assessed trends in health literacy scores overall and by sociodemographic groups. Tukey-Kramer adjustments for multiple comparisons for variables with more than two categories were used. The relationship between age as a continuous variable and health literacy score using correlation analysis was also explored. To test the primary research question, the association between each sociodemographic factor and health literacy was assessed via two methods: 1) ordinal logistic regression and 2) multiple linear regression, while controlling for all other assessed variables. Diagnostic plots for the regression model were used to assess linearity, normality, homoscedasticity, and independence of residuals. Ordinal logistic regressions were run because of data non-linearity and the discrete nature of the NVS score. The outcomes of the ordinal logistic regression were consistent with the results from the multiple linear regression model (see A.2), and the later model was chosen for better interpretability. Robust standard errors were also used in multiple linear regression models due to heteroscedasticity. All analyses were conducted in 2023 using RStudio (RStudio Software, PBC, Boston, MA; version 2023.06.2).

3. Results

Analysis of 2829 participants demonstrated statistically significant associations between health literacy and various sociodemographic factors (Table 1). Participants ranged in age from 18 to 93 years (Mean = 48.5, SD = 17.5). Over 60 % demonstrated inadequate (low to moderate) health literacy (NVS scores <4).

The average NVS score was 2.9 (SD = 2.0) out of 6 (see A.3 for correctness rate for each question); 1130 participants (39.9 %), had adequate literacy (score 4–6), 839 (29.7 %) had moderate literacy (score 2–3), and 860 (30.4 %) had low literacy (score 0–1).

There were significant differences in health literacy scores by gender, race, ethnicity, household income, residential region, age, and education level (Table 1). Specifically, males had significantly lower health literacy scores (Mean = 2.7) than females (Mean = 3.1). Participants identifying as Asian (Mean = 2.2) and Black (Mean = 2.0) had significantly lower health literacy scores compared to White individuals (Mean = 3.1); those who identified as Black also had lower mean health literacy scores than those who identified as multiracial (Mean = 2.8). Small sample size for some minority racial groups, such as “Native Hawaiian or Other Pacific Islander” (n = 11) limited our ability to discern statistical differences. Participants identifying as Hispanic or Latino also had significantly lower health literacy scores (Mean = 2.4) than those identifying as non-Hispanic or Latino (Mean = 3.0). Participants residing in the U.S. Midwest had significantly higher health literacy scores (Mean = 3.2) compared to those residing in the Northeast (Mean = 2.8), South (Mean = 2.8), and West (Mean = 2.8). Participants with household incomes exceeding 185 % FPL reported significantly higher health literacy scores on average (Mean = 3.0) than those below 185 % FPL (Mean = 2.7). We found a moderate positive correlation between age and health literacy (r = 0.25, p < .01), with older individuals (65+) having the highest scores (Mean = 3.5) compared to all other age groups; participants aged 46–64 (Mean = 3.3) had significantly higher scores than those under 46 (Meanaged 18–29 = 2.4, Meanaged 30–45 = 2.3).

Education level exhibited a non-linear association with health literacy score (Fig. 2). Participants with job-specific training after high school had the highest score (Mean = 3.6), significantly higher than those with some high school education (p < .01), high school graduates (p < .01), as well as those with bachelor's degrees (p = .04) and graduate degrees (p < .01). Those with some college education but no degree had the second-highest scores (Mean = 3.3), significantly higher than those with some high school (p < .01), high school graduate (p < .01), bachelor's degree (p = .04), and graduate degree (p = .01) (see Table 1).

Fig. 2.

Fig. 2

Open in a new tab

Relationship between education level and health literacy among adult restaurant patrons, showing an inverted-U pattern (United States, 2022).

Note: The error bars represent 95 % confidence intervals. NVS = Newest Vital Sign. The NVS is a six-item screening measure of health literacy. Higher scores indicate greater health literacy.

To understand the unique contribution of each sociodemographic factor on health literacy, the association between each of the above sociodemographic factors and health literacy levels adjusting for all other sociodemographic factors was examined. Table 2 compares the single-predictor regression model for each sociodemographic factor and the full regression model with all factors. All previously mentioned predictors remained statistically significantly associated with health literacy scores (p < .05) in our multiple linear regression model. The model accounted for a modest proportion of the variance in health literacy (R2 = 0.13).

Table 2.

Multiple linear regression results predicting health literacy from sociodemographic characteristics among adult restaurant patrons (United States, 2022).

 Simple linear regression
 Multiple linear regression
Demographic Variables β SE p-value β SE p-value
Intercept 2.24 0.16 <0.01
Gender (ref. = Female)
 Intercept 3.12 0.05 <0.01
 Male −0.46 0.07 <0.01 −0.39 0.07 <0.01
 Non-binary / third gender −0.41 0.52 0.43 0.13 0.53 0.80
 Prefer not to say 0.13 0.69 0.85 1.13 0.85 0.19
R2 = 0.01
Hispanic or Latino (ref. = No)
 Intercept 2.98 0.04 <0.01
 Prefer not to say −0.77 0.45 0.09 −1.13 0.41 <0.01
 Yes −0.61 0.12 <0.01 −0.30 0.13 0.02
R2 = 0.01
Race (ref. = White/Caucasian)
 Intercept 3.10 0.04 <0.01
 American Indian or Alaska Native −0.35 0.34 0.31 −0.07 0.32 0.82
 Asian −0.87 0.19 <0.01 −0.66 0.19 <0.01
 Black or African American −1.11 0.12 <0.01 −0.83 0.11 <0.01
 Multiracial −0.28 0.18 0.12 0.06 0.18 0.75
 Native Hawaiian or Other Pacific Islander −0.91 0.58 0.12 −0.73 0.40 0.07
 Some other race −0.71 0.25 <0.01 −0.05 0.28 0.85
 Prefer not to answer −0.60 0.41 0.15 0.13 0.44 0.78
R2 = 0.04
Age
 Intercept 1.53 0.10 <0.01
 Age 0.03 0.002 <0.01 0.02 0.002 <0.01
R2 = 0.06
Education (ref. = high school graduate)
 Intercept 2.48 0.07 <0.01
 8th grade or less −0.56 0.54 0.30 −0.46 0.49 0.25
 Some high school −0.48 0.19 0.01 −0.39 0.16 0.01
 Job-specific training programs 1.09 0.19 <0.01 0.87 0.17 <0.01
 Some college but no degree 0.82 0.11 <0.01 0.70 0.10 <0.01
 Associate's degree 0.70 0.13 <0.01 0.58 0.12 <0.01
 Bachelor's degree 0.48 0.11 <0.01 0.35 0.11 <0.01
 Graduate degree 0.30 0.13 0.02 0.14 0.14 0.32
R2 = 0.04
Current region (ref. = Midwest)
 Intercept 3.22 0.08 <0.01
 Northeast −0.41 0.12 <0.01 −0.33 0.11 <0.01
 South −0.40 0.10 <0.01 −0.19 0.09 0.04
 West −0.42 0.11 <0.01 −0.16 0.11 0.14
R2 = 0.01
Annual Household Income (ref. = above 185 Federal Poverty Level)
 Intercept 3.04 0.05 <0.01
 Under 185 Federal Poverty Level −0.33 0.08 <0.001 −0.16 0.08 0.04
R2 = 0.01 R2 = 0.13
Open in a new tab

Notes: “ref.” means reference group for each variable in the regression model. Robust standard error was applied in the multiple linear regression model. SE = Standard Error. Health Literacy was measured using the Newest Vital Sign (NVS), a six-item screening measure (higher scores indicate higher health literacy).

4. Discussion

4.1. Findings

This study utilized the NVS via an online survey to assess health literacy among a large sample of adults representative of the broader U.S. adult population in regard to age, gender, and race/ethnicity. Only 39.9 % of participants demonstrated adequate health literacy, with the average participant correctly answering three out of six NVS questions. This finding aligns with the national health literacy levels in other countries or regions (Sørensen et al., 2015; Rajah et al., 2019; Murray et al., 2008).

Consistent with prior research, these findings underscore the influence of racial/ethnic, gender, and income disparities in health literacy (Fleary and Ettienne, 2019; Sepassi et al., 2023; Becerra et al., 2017). Black or African American and Asian individuals exhibited lower health literacy compared to White/Caucasian individuals. Females showed higher health literacy than males, consistent with existing literature (Kutner et al., 2006). Sun and colleagues (Sun et al., 2022) identified factors such as number of children and self-efficacy for managing chronic disease as associated with males' health literacy, while age and chronic disease treatment were associated with females' health literacy. However, the small number of participants identifying as non-binary or preferring not to say limits the ability to draw reliable conclusions about health literacy within gender-diverse groups. Future research is needed to explore gender disparities in health literacy.

Midwest residents demonstrated significantly higher health literacy compared to other regions. It is possible that the results are confounded by the test being administered only in English, as the Midwest has the lowest proportion of non-native English speakers compared to other regions (Korhonen, 2023). Sentell and Braun (Sentell and Braun, 2012) found low health literacy associated with limited English proficiency in California. The National Assessment of Adult Literacy (Kutner et al., 2006) also found that U.S. adults who spoke only English during childhood had higher health literacy than those who spoke another language or multiple languages. It is possible that some non-native English-speaking participants in this study scored lower than they would have if the assessment was available in their native language. We did not assess participants' native language in this study. Future studies should include multiple languages to provide a more inclusive and representative understanding of health literacy.

Contrary to previous findings, this study revealed a positive correlation between age and health literacy, with the highest rates among participants aged ≥65, even after controlling for other sociodemographic factors. A large-scale cross-sectional study (Berens et al., 2016) involving 1946 German adults found a negative correlation between age and perceived health literacy. Similarly, a 2016 systematic review and meta-analysis (Kobayashi et al., 2016) reported a strong negative association between age and health literacy, specifically in reading comprehension, reasoning, and numeracy skills, but only a weak association with medical vocabulary among participants aged ≥50 years. Differences in findings may stem from different assessment tools and the online distribution of our survey. Older adults participating in online research panels might be more digitally literate than the general population of the same age, and consistent internet use and digital literacy are linked to higher health literacy (Kobayashi et al., 2015; Luo et al., 2024; Duren-Winfield et al., 2015). This suggests that our findings may be more representative of the populations that use internet consistently. Future research should incorporate other relevant confounders, such as digital literacy.

Additionally, we found an inverted-U shaped trend between education and health literacy. Health literacy peaked at the level of job-specific training and participants at both the lower and higher ends of educational achievement showed lower health literacy. Participants with a degree beyond high school still had higher health literacy than those with a high school degree or below, but unlike prior studies demonstrating a linear positive association between education level and health literacy (Kutner et al., 2006; Cha et al., 2014). These findings suggest a more nuanced relationship. The NVS, which assesses health literacy through tasks like interpreting nutrition labels, may not align with traditional skills developed through formal education or the emphasis on numerical skills may vary by education level. Berens and colleagues (Berens et al., 2016) found education alone did not significantly predict health literacy when controlling for various sociodemographic factors. This implies that education, while influential, should be considered alongside other sociodemographic variables. It is important to note that the National Assessment of Adult Literacy (Kutner et al., 2006), which identified a linear positive association between education level and health literacy, utilized a more comprehensive assessment encompassing 28 health literacy tasks. This disparity suggests that a brief health literacy assessment tool, such as the NVS, might not capture all facets of health literacy as accurately as a more comprehensive instrument covering multiple constructs, potentially accounting for the differences observed in our results.

4.2. Limitations

This study has several limitations. First, we administered the NVS online using only the English version. Participants with limited English proficiency or digital literacy may not have participated in this survey, leading to underrepresentation of groups more vulnerable to low health literacy and limiting generalizability. Follow-up on ambiguous responses in the final open-ended NVS questions was not possible, which may have affected assessment accuracy. Additionally, using an online survey platform may introduce selection bias. Although our sampling strategy aimed for representativeness among U.S. adults, CloudResearch respondents are generally younger, more educated, less likely to have children, and have lower proportions of Black or African Americans and Hispanic or Latino populations (Chandler et al., 2019). These respondents are also likely experienced survey takers, which may not accurately reflect some of the broader population's characteristics. However, CloudResearch tends to have a more representative sample and higher response quality than other platforms (Berry et al., 2022). Lastly, while the regression model explained a modest proportion of variance (R2 = 0.13), unmeasured factors such as health system familiarity, trust in healthcare, or digital engagement may also contribute to individuals' health literacy.

4.3. Strengths and implications

This study fills a gap in the literature by assessing health literacy using the NVS in a representative sample of U.S. adults based on age, gender, race, and ethnicity. It provides current population-level adult health literacy metrics, and shows that health literacy varies across sociodemographic characteristics.

These variations have implications for health-related behaviors and outcomes. Health professionals can improve health outcomes, especially among marginalized groups, by tailoring communications to meet patients where they are. This study highlights persistent sociodemographic disparities in health literacy in the U.S., signaling a critical need to address these disparities. Promoting health literacy can improve well-being of marginalized populations and reduce healthcare costs, creating a more equitable and efficient healthcare system. Given the disparities in health literacy and the implications for health outcomes (Suka et al., 2015; Reynolds et al., 2019; van der Gaag et al., 2022), it is critical to investigate population-level health literacy educational programs and interventions. Special emphasis should be placed on effectively reaching and benefiting historically marginalized groups to improve healthy equity and justice for these audiences.

4.4. Conclusion

This study identified sociodemographic disparities in health literacy among U.S. adults using the NVS, a brief health literacy screening tool. Over 60 % of participants exhibited inadequate health literacy, with lower levels observed among males, racial and ethnic minority groups, those from lower-income households, and regions outside the Midwest. Health literacy showed a non-linear relationship with education, indicating nuance that may be missed by studies utilizing fewer educational categories. Additionally, older adults showed higher health literacy, possibly due to greater digital literacy than their non-participant peers. These findings highlight the need for targeted interventions and policies to improve health literacy, particularly for marginalized groups.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

CRediT authorship contribution statement

Yiqing “Skylar” Yu: Writing – review & editing, Writing – original draft, Formal analysis, Data curation, Conceptualization. Ana Altares: Writing – review & editing, Writing – original draft, Data curation. Alyssa Leib: Writing – review & editing, Writing – original draft, Methodology, Data curation. Laura L. Bellows: Writing – review & editing, Funding acquisition, Conceptualization. Christopher Berry: Writing – review & editing, Funding acquisition, Conceptualization. Dan J. Graham: Writing – review & editing, Funding acquisition, Conceptualization. Megan P. Mueller: Writing – review & editing, Supervision, Investigation, Funding acquisition, Conceptualization.

Ethics approval and consent to participate

The protocol (IRB #2894) was approved by Colorado State University Institutional Review Board. All participants provided electronic consent before starting the survey.

Funding sources

This project was funded by Colorado State University Food Science and Human Nutrition Research and Innovation Fund.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Not applicable.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2025.103179.

Appendix A. Supplementary data

Supplementary material: Table A.1 Grading criteria for each item on the Newest Vital Sign (United States, 2022). Table A.2 Comparison of linear regression and ordinal logistic regression models predicting health literacy among adult restaurant patrons (United States, 2022). Table A.3 Proportion of correct responses to each Newest Vital Sign item among adult restaurant patrons (United States, 2022).

mmc1.docx (28.1KB, docx)

Data availability

Data will be made available on request.

References

  1. Alsaedi R., McKerinan K. Literature review of type 2 diabetes management and health literacy. Diabetes Spectr. 2021;34(4):399–406. doi: 10.2337/ds21-0014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Becerra B.J., Arias D., Becerra M.B. Low health literacy among immigrant Hispanics. J. Racial Ethn. Health Disparities. 2017;4(3):480–483. doi: 10.1007/s40615-016-0249-5. [DOI] [PubMed] [Google Scholar]
  3. Berens E.M., Vogt D., Messer M., Hurrelmann K., Schaeffer D. Health literacy among different age groups in Germany: results of a cross-sectional survey. BMC Public Health. 2016;16(1):1151. doi: 10.1186/s12889-016-3810-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Berry C., Kees J., Burton S. Drivers of data quality in advertising research: differences across MTurk and professional panel samples. J. Advert. 2022;51(4):515–529. [Google Scholar]
  5. Brown K.P., Horowitz Juliana Menasce, Anna About half of lower-income americans report household job or wage loss due to Covid-19 [Internet] Pew Res. Center. 2020 https://www.pewresearch.org/social-trends/2020/04/21/methodology-32-2/ [cited 2024 Jul 22]. Available from: [Google Scholar]
  6. Cabellos-García A.C., Castro-Sánchez E., Martínez-Sabater A., Díaz-Herrera M.Á., Ocaña-Ortiz A., Juárez-Vela R., et al. Relationship between determinants of health, equity, and dimensions of health literacy in patients with cardiovascular disease. IJERPH. 2020;17(6):2082. doi: 10.3390/ijerph17062082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cha E., Kim K.H., Lerner H.M., Dawkins C.R., Bello M.K., Umpierrez G., et al. Health literacy, self-efficacy, food label use, and diet in young adults. Am. J. Hlth. Behav. 2014;38(3):331–339. doi: 10.5993/AJHB.38.3.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chandler J., Rosenzweig C., Moss A.J., Robinson J., Litman L. Online panels in social science research: expanding sampling methods beyond mechanical Turk. Behav. Res. Ther. 2019;51(5):2022–2038. doi: 10.3758/s13428-019-01273-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Douglas B.D., Ewell P.J., Brauer M. Data quality in online human-subjects research: comparisons between MTurk, prolific, CloudResearch, Qualtrics, and SONA. PLoS One. 2023;18(3) doi: 10.1371/journal.pone.0279720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Duren-Winfield V., Onsomu E.O., Case D.L., Pignone M., Miller D., Jr. Health literacy and computer-assisted instruction: usability and patient preference. J. Health Commun. 2015;20(4):491–498. doi: 10.1080/10810730.2014.976322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fleary S.A., Ettienne R. Social Disparities in Health Literacy in the United States. HLRP: Health Literacy Res. Pract. 2019;3(1) doi: 10.3928/24748307-20190131-01. Available from:. [cited 2023 Nov 16] [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Guo X. mei, Zhai X., Hou B. ru. Adequacy of health literacy and its effect on diabetes self-management: a meta-analysis. Aust. J. Prim. Health. 2020;26(6):458–465. doi: 10.1071/PY20079. [DOI] [PubMed] [Google Scholar]
  13. Hepburn M., Bautista C., Feinn R. Health promotion behaviors among urban black women. West. J. Nurs. Res. 2021;43(11):1001–1009. doi: 10.1177/0193945920988785. [DOI] [PubMed] [Google Scholar]
  14. Ickes M.J., Cottrell R. Health literacy in college students. J. Am. Coll. Heal. 2010;58(5):491–498. doi: 10.1080/07448481003599104. [DOI] [PubMed] [Google Scholar]
  15. Institute of Medicine . National Academies Press; 2004. Health Literacy: A Prescription to End Confusion; p. 367. [PubMed] [Google Scholar]
  16. Jia X., Pang Y., Liu L.S. Online health information seeking behavior: a systematic review. Healthcare. 2021;9(12) doi: 10.3390/healthcare9121740. https://www.mdpi.com/2227-9032/9/12/1740 Available from: [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jia J., Levy D.E., McCurley J.L., Anderson E., Gelsomin E.D., Porneala B., et al. Health literacy, numeracy, and health promotion: a secondary analysis of the Choosewell 365 workplace trial. Am. J. Prev. Med. 2022;63(1):93–101. doi: 10.1016/j.amepre.2021.12.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kelly P.A., Haidet P. Physician overestimation of patient literacy: a potential source of health care disparities. Patient Educ. Couns. 2007;66(1):119–122. doi: 10.1016/j.pec.2006.10.007. [DOI] [PubMed] [Google Scholar]
  19. Kim M.T., Kim K.B., Ko J., Murry N., Xie B., Radhakrishnan K., et al. Health literacy and outcomes of a community-based self-help intervention. Nurs. Res. 2020;69(3):210–218. doi: 10.1097/NNR.0000000000000409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kobayashi L.C., Wardle J., Wagner C. von. Internet use, social engagement and health literacy decline during ageing in a longitudinal cohort of older English adults. J. Epidemiol. Community Health. 2015;69(3):278–283. doi: 10.1136/jech-2014-204733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kobayashi L.C., Wardle J., Wolf M.S., Von Wagner C. Aging and functional health literacy: a systematic review and meta-analysis. GERONB. 2016;71(3):445–457. doi: 10.1093/geronb/gbu161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Korhonen V. Statista. 2023. https://www.statista.com/statistics/312940/share-of-us-population-speaking-a-language-other-than-english-at-home-by-state/ [cited 2024 Jun 5]. Share of U.S. population speaking a language besides English at home 2021, by state. Available from:
  23. Kutner M., Greenberg E., Ying J., Paulson C. National Center for Education Statistics. 2006. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2006483 [cited 2023 Nov 16]. The Health Literacy of America's Adults: Results from the 2003 National Assessment of Adult Literacy. Available from:
  24. Lans A., Bales J.R., Borkhetaria P., Schwab J.H., Verlaan J.J., Rossi L.P., et al. Impact of Health Literacy on Self-Reported Health Outcomes in Spi...: Ingenta Connect. Spine. 2023;48(7):E87–E93. doi: 10.1097/BRS.0000000000004495. [DOI] [PubMed] [Google Scholar]
  25. Lastrucci V., Lorini C., Caini S., Florence Health Literacy Research Group, Bonaccorsi G. Health literacy as a mediator of the relationship between socioeconomic status and health: a cross-sectional study in a population-based sample in Florence | PLOS ONE. PLoS One. 2019;14(12) doi: 10.1371/journal.pone.0227007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Luo X., Pu H., Wang S., Zhong D., Liu F., Li Z. Influence of internet use on Chinese residents’ health: the mediating role of health knowledge. Technol. Soc. 2024;1(76) [Google Scholar]
  27. Mackert M., Champlin S., Mabry-Flynn A. Exploring college student health literacy: do methods of measurement matter? J. Stud. Aff. Res. Pract. 2017 doi: 10.1080/19496591.2017.1289095. [cited 2024 Jun 11]; Available from: [DOI] [Google Scholar]
  28. Mansfield E.D., Wahba R., Gillis D.E., Weiss B.D., L’Abbé M. Canadian adaptation of the newest vital sign©, a health literacy assessment tool. Public Health Nutr. 2018;21(11):2038–2045. doi: 10.1017/S1368980018000253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. McCafferry K., Dodd R., Cvejic E., Ayre J., Batcup C., Isautier J., et al. Disparities in COVID-19 related knowledge, attitudes, beliefs and behaviours by health literacy | medRxiv. Public Health Research & Practice. 2020 doi: 10.17061/phrp30342012. 220–06. [DOI] [PubMed] [Google Scholar]
  30. Murray T.S., Hagey J., Willms D., Shillington R., Desjardins R. Health Literacy in Canada: A Healthy Understanding. 2008. https://escholarship.org/uc/item/890661nm [cited 2024 Jun 5]; Available from:
  31. Muscat D.M., Cvejic E., Bell K., Smith J., Morris G.M., Jansen J., et al. The impact of health literacy on psychosocial and behavioural outcomes among people at low risk of cardiovascular disease. Prev. Med. 2022;156 doi: 10.1016/j.ypmed.2022.106980. [DOI] [PubMed] [Google Scholar]
  32. Nutbeam D., Lloyd J.E. Understanding and responding to health literacy as a social determinant of health. Annu. Rev. Public Health. 2021;42:159–173. doi: 10.1146/annurev-publhealth-090419-102529. [DOI] [PubMed] [Google Scholar]
  33. Osborn C.Y., Weiss B.D., Davis T.C., Skripkauskas S., Rodrigue C., Bass P.F., et al. Measuring adult literacy in health care: performance of the newest vital sign. Am. J. Health Behav. 2007;31(1):S36–S46. doi: 10.5555/ajhb.2007.31.supp.S36. [DOI] [PubMed] [Google Scholar]
  34. Patil U., Kostareva U., Hadley M., Manganello J.A., Okan O., Dadaczynski K., et al. Health literacy, digital health literacy, and COVID-19 pandemic attitudes and behaviors in U.S. college students: implications for interventions. Int. J. Environ. Res. Public Health. 2021;18(6):3301. doi: 10.3390/ijerph18063301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Peterson P.N., Shetterly S.M., Clarke C.L., Bekelman D.B., Chan P.S., Allen L.A., et al. Health literacy and outcomes among patients with heart failure. JAMA. 2011;305(16):1695–1701. doi: 10.1001/jama.2011.512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Rajah R., Hassali M.A.A., Murugiah M.K. A systematic review of the prevalence of limited health literacy in southeast Asian countries. Public Health. 2019;167:8–15. doi: 10.1016/j.puhe.2018.09.028. [DOI] [PubMed] [Google Scholar]
  37. Reynolds R., Smoller S., Allen A., Nicholas P.K. Health literacy and health outcomes in persons living with HIV disease: a systematic review. AIDS Behav. 2019;23:3024–3043. doi: 10.1007/s10461-019-02432-9. [DOI] [PubMed] [Google Scholar]
  38. Rikard R.V., Thompson M.S., McKinney J., Beauchamp A. Examining health literacy disparities in the United States: a third look at the National Assessment of adult literacy (NAAL) BMC Public Health. 2016;16(1):975. doi: 10.1186/s12889-016-3621-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Saha S., Beach M.C., Cooper L.A. Patient centeredness, cultural competence and healthcare quality. J. Natl. Med. Assoc. 2008;100(11):1275–1285. doi: 10.1016/s0027-9684(15)31505-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Sentell T., Braun K.L. Low health literacy, limited English proficiency, and health status in Asians, Latinos, and other racial/ethnic groups in California. J. Health Commun. 2012;17(sup3):82–99. doi: 10.1080/10810730.2012.712621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sepassi A., Garcia S., Tanjasiri S., Lee S., Bounthavong M. Predicted health literacy disparities between immigrant and US-born racial/ethnic minorities: a Nationwide study. J. Gen. Intern. Med. 2023;38(10):2364–2373. doi: 10.1007/s11606-023-08082-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shealy K.M., Threatt T.B. Utilization of the newest vital sign (NVS) in practice in the United States. Health Commun. 2016;31(6):679–687. doi: 10.1080/10410236.2014.990079. [DOI] [PubMed] [Google Scholar]
  43. Sørensen K., Pelikan J.M., Röthlin F., Ganahl K., Slonska Z., Doyle G., et al. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU) Eur. J. Pub. Health. 2015;25(6):1053–1058. doi: 10.1093/eurpub/ckv043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Suka M., Odajima T., Okamoto M., Sumitani M., Igarashi A., Ishikawa H., et al. Relationship between health literacy, health information access, health behavior, and health status in Japanese people. Patient Educ. Couns. 2015;98(5):660–668. doi: 10.1016/j.pec.2015.02.013. [DOI] [PubMed] [Google Scholar]
  45. Sun S., Lu J., Wang Y., Wang Y., Wu L., Zhu S., et al. Gender differences in factors associated with the health literacy of hospitalized older patients with chronic diseases: a cross-sectional study. Front. Public Health. 2022;10 doi: 10.3389/fpubh.2022.944103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Toossi S., Jones J.W., editors. The Food and Nutrition Assistance Landscape: Fiscal Year 2022 Annual Report. 2023. p. 34. (Economic Information Bulletin No. 255) [Google Scholar]
  47. van der Gaag M., Heijmans M., Spoiala C., Rademakers J. The importance of health literacy for self-management: a scoping review of reviews. Chronic Illn. 2022;18(2):234–254. doi: 10.1177/17423953211035472. [DOI] [PubMed] [Google Scholar]
  48. Vernon J., Trujillo A., Rosenbaum S., DeBuono B. Low health literacy: implications for National Health Policy. Health Policy Manage. Faculty Publ. 2007 https://hsrc.himmelfarb.gwu.edu/sphhs_policy_facpubs/172 Available from: [Google Scholar]
  49. Weiss B.D., Mays M.Z., Martz W., Castro K.M., DeWalt D.A., Pignone M.P., et al. Quick assessment of literacy in primary care: the newest vital sign. Ann. Fam. Med. 2005;3(6):514–522. doi: 10.1370/afm.405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. White A., Buschur E., Harris C., Pennell M.L., Soliman A., Wyne K., et al. Influence of Literacy, Self-Efficacy, and Social Support on Diabetes-Related Outcomes Following Hospital Discharge. Diabetes, Metabolic Syndrome Obesity. 2022;15:2323–2334. doi: 10.2147/DMSO.S327158. [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.

Supplementary Materials

Supplementary material: Table A.1 Grading criteria for each item on the Newest Vital Sign (United States, 2022). Table A.2 Comparison of linear regression and ordinal logistic regression models predicting health literacy among adult restaurant patrons (United States, 2022). Table A.3 Proportion of correct responses to each Newest Vital Sign item among adult restaurant patrons (United States, 2022).

mmc1.docx (28.1KB, docx)

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Data will be made available on request.

Articles from Preventive Medicine Reports are provided here courtesy of Elsevier

RESOURCES