Personal Health Literacy and Life Simple 7: The Multi-Ethnic Study of Atherosclerosis

Abstract

Background:

Personal Health literacy (PHL) is essential in cardiovascular risk management. Hindrances in PHL can lead to poor cardiovascular outcomes.

Purpose:

To investigate whether limited PHL is associated with lower likelihoods of i) overall cardiovascular health and ii) individual cardiovascular health components as defined by the American Heart Association’s Life Simple (LS7).

Methods:

Multi-Ethnic Study of Atherosclerosis participants (N=3719; median age[range]: 59[45–84]) completed a PHL questionnaire in 2016–2018. PHL was classified as limited (score ≥10) or adequate (score <10). LS7 components were measured in 2000–2002. Robust Poisson regression was employed to compute prevalence ratios and 95% confidence intervals (PR[95%CI]) of LS7 measures.

Results:

14.7% of participants had limited PHL. Limited PHL was associated with lower likelihoods of optimal LS7 (0.69[0.50, 0.95], p=0.02) and average LS7 (0.95[0.88, 1.02], p=0.15) after adjustment. Limited PHL was significantly associated with a 7% lower likelihood of ideal fasting blood glucose level after adjustment (0.93[0.89, 0.98], p<0.01).

Discussion:

Limited PHL was modestly associated with suboptimal cardiovascular health and elevated blood glucose, independent of income and education.

Translation to Health Education Practice:

Health educators and providers should equitably address PHL barriers to improve cardiovascular management and quality of care for patients and communities.

BACKGROUND

Personal health literacy (PHL) has recently been defined by the US Department of Health and Human Services in Healthy People 2030 as “the degree in which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others”.^1,2 PHL incorporates multiple skillsets (e.g. reading, math, and comprehension), and may be critical to the prevention and optimal management of chronic illnesses such as cardiovascular disease.^3,4 Access to health services, treatment adherence, and communication between patients and health care professionals are essential activities where both personal and organizational HL skills are required.^1,4 Limitations in HL are problematic, as they can prevent patients from managing their health properly and can also impair the ability of health providers to deliver optimal patient care, ultimately contributing to poorer health.^3,4 A 2003 assessment from the United States Department of Education reports up to 36% of US adults have basic or below basic PHL skills, while approximately 14% are considered to have below basic PHL.^3,5 Unfortunately, more recent nationally representative data are not available. As a social determinant of health, limited PHL underlies health disparities among racial and ethnic minorities, persons of low socioeconomic and educational statuses, and the elderly⁴—subgroups of the US population that frequently experience a greater burden of cardiovascular and other diseases.⁶

A 2018 Scientific Statement from the American Heart Association (AHA) underscores the potential impact of PHL on cardiovascular disease in the US. In that Statement, the authors highlight how deficiencies in PHL can hinder effective primary and secondary prevention of cardiovascular diseases.⁴ Furthermore, intervention trials have shown that enhancing disease education improved key cardiovascular risk factors, including better glycemic control^7,8 and decreased body mass index (BMI).⁹ In observational studies, lower PHL has been associated with smoking,¹⁰ poorer blood pressure management,¹¹ and cardiovascular disease risk and mortality.^12,13 While informative, key limitations of previous studies include recruiting participants with existing chronic disease conditions,^14,15 small sample sizes,^7–11 and a lack of generalizability due to limited racial/ethnic or geographic diversity.^8–10 Therefore, there is a need to understand the relationship between PHL and biologic and behavioral factors associated with cardiovascular health in a large, community-based, and racially and ethnically diverse cohort.

PURPOSE

In this study, we investigate the association between personal health literacy and cardiovascular health defined by AHA’s Life Simple 7 (LS7) in the Multi-Ethnic Study of Atherosclerosis (MESA). We hypothesize that limited PHL, compared to adequate PHL, will be associated with i) lower likelihoods of average and optimal overall cardiovascular health defined by LS7, and ii) lower likelihoods of intermediate and ideal levels of individual cardiovascular health components also defined by LS7.

METHODS

Description of Cohort

MESA is a large, ongoing population-based epidemiologic cohort study aimed at prospectively examining clinical outcomes and etiologic factors of atherosclerosis across six communities in the US.^16,17 The study comprised of 6,814 participants who at baseline (2000–2002) were aged 45–84 years, were free of clinical cardiovascular disease and self-identified as Black, Chinese, Hispanic, or White.¹⁶ Participants were recruited across six field centers: St. Paul, MN; Chicago, IL; Baltimore, MD; New York City, NY; Forsyth County, NC; and Los Angeles, CA.^16,17 With the goal of establishing a cohort demographically representative of the source communities, each of the six field centers recruited participants from at least two of the aforementioned self-identified race/ethnicity groups.¹⁶ Since baseline, five comprehensive clinical examinations (denoted as “Exams”) and over 20 follow-up phone assessments have occurred. Pertaining to our investigation, we used PHL data collected during the 18^th and 19^th follow-up phone calls (2016–2018) and cardiovascular health data from the baseline exam (Exam 1; 2000–2002).

Health Literacy Assessment

PHL was assessed using an over-the-phone health literacy questionnaire administered during the 18^th and 19^th MESA Follow-Up phone calls. Participants were contacted with phone numbers they provided to MESA during their most recent phone follow-up. Trained MESA personnel administered the questionnaire, which was available in English, Spanish, Cantonese, or Mandarin, depending on the preferred language of the participant.

Shown in Table 1, the four-item questionnaire assessed participant perception of PHL skills on a five-point scale. Items 1–3 examined the skills of literacy, comprehension and confidence in completing medical forms and understanding health information.^4,18,19 Item 4 assessed health numeracy, the ability to utilize numbers and basic arithmetic to complete health-related tasks.^4,18–20 Items 1–3 were derived from the validated Rapid Estimate of Adult Literacy in Medicine (REALM; Area under the Receiver Operating Characteristic (AUROC) range: 0.72–0.84)²¹ and Test of Functional Health Literacy in Adults (TOFHLA) health literacy instruments (AUROC range in English: 0.63–0.74^21–23; AUROC range in Spanish: 0.65–0.76²⁴). As described in the previous studies, the comparison standards for these items was “inadequate” and “inadequate/marginal health literacy” in the REALM or short-TOPHLA assessments.^21–24 Similarly, the fourth health numeracy item was validated against the Newest Vital Signs (NVS) instrument with the comparison standard of “limited” and “limited/marginal” (AUROC range: 0.78–0.83).²⁵ Items 1–3 have five possible responses scored from 1–5 while the Item 4 has four possible responses scored 1, 2, 4, and 5 (see Table 1). Higher scores for each item attributed to greater difficulty completing a specific literacy/numeracy task. We then summed each item score to calculate an overall PHL score ranging from 0–20, with higher scores corresponding to greater PHL difficulty overall. Although standardized cutpoints specific to our questionnaire instrument have not been developed yet, we dichotomized overall PHL scores into “limited PHL” (score ≥10) and “adequate PHL” (score <10) as done previously in MESA.²⁰

Table 1:

Distribution of personal health literacy questionnaire responses, n=3719; MESA

	Never	Rarely	Sometimes	Often	Always
Score	1	2	3	4	5
Item 1: How often do you have someone help you read materials received from your doctor? (HL)	77.0% (2862)	7.2% (267)	7.1% (265)	4.1% (152)	4.7% (173)
	Never	Rarely	Sometimes	Often	Always
Score	1	2	3	4	5
Item 2: How often do you have problems learning about your health condition because of difficulty reading materials received from your doctor? (HL)	77.2% (2872)	8.5% (315)	6.6% (244)	3.6% (134)	4.1% (154)
	Extremely	Quite a bit	Somewhat	A little bit	Not at all
Score	1	2	3	4	5
Item 3: How confident are you filling out medical forms by yourself? (HL)	71.0% (2642)	12.7% (473)	7.2% (269)	3.8% (140)	5.2% (195)
	Very easy	Easy		Hard	Very hard
Score	1	2		4	5
Item 4: In general, how easy or hard do you find it to understand medical statistics? (HN)	47.4% (1762)	35.8% (1333)		11.7% (435)	5.1% (189)

Abbreviations: HL=health literacy; HN=health numeracy

Cardiovascular Health Measures

We assessed cardiovascular health using the American Heart Association’s LS7 metric at MESA Exam 1 (2000–2002), as has been done previously.^14,15 LS7 was developed to promote cardiovascular health through quantifying levels of seven major cardiovascular risk factors: four biological health components (BMI, blood pressure, total cholesterol, blood sugar) and three health behavior components (healthy diet composition, physical activity level, smoking history).²⁶

During Exam 1, trained clinical examiners collected participant BMI (kg/m²) via anthropometry, mean resting systolic and diastolic blood pressures (mmHg) from the brachial artery after a five-minute rest (three measurements taken, the latter two averaged for analysis), and phlebotomy (after an 8-hour fast) to allow for measurement of total cholesterol levels (mg/dL) and fasting blood sugar (mg/dL) through the glucose oxidase method using a Vitros Analyzer (Johnson & Johnson Clinical Diagnostics, Rochester, NY).^14,27 MESA examiners also ascertained physical activity level (minutes of moderate and/or vigorous activity per week), smoking history (never, former, current), and healthy diet composition (based on a food frequency questionnaire) via self-reporting.¹⁴ Additional details about MESA data collection procedures are available at https://www.mesa-nhlbi.org/.

We classified each of the seven components using LS7 criteria and scoring (0 points: poor, 1 point: intermediate, 2 points: ideal).²⁶ We then summed individual component scores to compute an overall cardiovascular health score ranging from 0–14, with higher scores corresponding to better cardiovascular health. Lastly, we categorized composite scores using the LS7 criteria for overall cardiovascular health (0–7 points: inadequate, 8–11 points: average, 12–14 points: optimal; see Table 2).

Table 2:

Life Simple 7 components definitions and distribution by personal health literacy status, n=3719; MESA

LS7 components	Poor (0 points)	Intermediate (1 point)	Ideal (2 points)
Smoking status	Current	Former	Never
Limited PHL	8.6% (47)	1.6% (9)	89.7% (490)
Adequate PHL	11.9% (378)	2.0% (62)	86.1% (2733)
Total	11.4% (425)	1.9% (71)	86.7% (3223)
BMI	≥ 30 kg/m2	25–29.9 kg/m2	< 25 kg/m2
Limited PHL	24.5% (134)	42.1% (230)	33.3% (182)
Adequate PHL	31.7% (1005)	38.7% (1227)	29.7% (941)
Total	30.6% (1139)	39.2% (1457)	30.2% (1123)
Physical activity	0 min/week	<150 min/week moderate or <75 min/week vigorous	≥150 min/week moderate or ≥75 min/week vigorous
Limited PHL	28.6% (156)	18.9% (103)	52.6% (287)
Adequate PHL	18.7% (592)	17.7% (563)	63.6% (2018)
Total	20.1% (748)	17.9% (666)	62.0% (2305)
Healthy diet score*	0–1 pts	2–3 pts	4–5 pts
Limited PHL	54.4% (297)	45.4% (248)	0.2% (1)
Adequate PHL	60.7% (1925)	38.9% (1235)	0.4% (13)
Total	59.7% (2222)	39.9% (1483)	0.4% (14)
Total cholesterol	≥240 mg/dL	200–239 mg/dL	<200 mg/dL
Limited PHL	9.9% (54)	41.8% (228)	48.4% (264)
Adequate PHL	9.6% (304)	43.9% (1394)	46.5% (1475)
Total	9.6% (358)	43.6% (1622)	46.8% (1739)
Blood pressure	SBP ≥140 mmHg or DBP ≥90 mmHg	SBP 120–139 mmHg or DBP 80–89 mmHg or treated to goal	SBP <120 mmHg and DBP <80 mm Hg
Limited PHL	27.3% (149)	43.2% (236)	29.5% (161)
Adequate PHL	19.3% (613)	40.2% (1275)	40.5% (1285)
Total	20.5% (762)	40.6% (1511)	38.9% (1446)
Fasting blood sugar	≥ 126 mg/dL	100–125 mg/dL	< 100 mg/dL
Limited PHL	12.5% (68)	22.0% (120)	65.6% (358)
Adequate PHL	4.8% (152)	14.9% (472)	80.3% (2549)
Total	5.9% (220)	15.9% (592)	78.2% (2907)
Overall LS7 status	Inadequate (0–7 points)	Average (8–11 points)	Optimal (12–14 points)
Total %, (n)	25.4% (944)	65.4% (2432)	9.2% (343)

Abbreviations: LS7=Life Simple 7; PHL=personal health literacy

^*Healthy diet score consistent with Dietary Approaches to Stop Hypertension (DASH) eating pattern (see Lloyd-Jones et al. 2010) and is comprised of five parts: (i) ≥4.5 cups/day of fruits and vegetables, (ii) ≥2 servings/week of fish, (iii) ≥3 servings/day of whole grains, (iv) <36 oz/week of sugar-sweetened beverages, and (v) <1500 mg/day of sodium.

Covariates and Additional Variables

Sociodemographic factors of participant age, sex, race/ethnicity, study center location, total gross family income in the past 12 months (<$35,000, $35,000-$75,000, ≥$75,000), and highest educational attainment (<high school, high school diploma/GED, some college, bachelors/associates degree, graduate/professional degree) were obtained from self-reported data from Exam 1. Data on participant birthplace (in the US, outside the US) and primary language spoken at home (English, Spanish, and Chinese) were incorporated as measures of immigration status and acculturation, also derived from Exam 1. Lastly, we utilized existing data on prevalence of dementia at Exam 6 to exclude participants with prevalent dementia at the time the PHL questionnaire was administered.

Study Design and Statistical Analysis

PHL was first assessed at the 18^th and 19^th follow-up phone calls. We utilized these PHL data as a proxy for Exam 1 PHL in cross-sectional analyses with Exam 1 LS7. Of the n=4028 participants who completed the PHL questionnaire, we excluded i) n=115 participants with incomplete Exam 1 variables (115 missing income data, 9 missing education and birthplace data), ii) n=147 individuals missing LS7 measures, and iii) n=47 participants with prevalent dementia prior to Exam 6. This resulted in a final analytical sample size of n=3719 (Figure 1).

Figure:

Sample size flowchart in personal health literacy-Life Simple 7 study, MESA

We first assessed baseline demographics in the total sample and by PHL status. We then employed modified Poisson regression with robust variance estimation to compute prevalence ratios and 95% confidence intervals (PR [95% CI]) for overall LS7 and individual LS7 components comparing limited PHL versus the adequate PHL reference group. Since each LS7 outcome consists of three levels, we ran separate regressions to compute PRs of average vs inadequate and optimal vs inadequate overall LS7, as well as PRs of intermediate vs poor and ideal vs poor LS7 components, all by PHL status.

We first ran a univariate analysis in Model 1 regressing LS7 by PHL. We then adjusted for age, race/ethnicity (as a social construct for racism), sex, and field center in Model 2. We further adjusted for place of birth and language spoken at home (operationalized as English, Chinese/Spanish) in Model 3. Lastly, we further adjusted for income and education in Model 4.

We conducted analyses using SAS (version 9.4; SAS Institute Inc., Cary, NC). We considered two-sided p-values lower than 0.05 to indicate statistical significance.

Ethical Considerations and Data Availability

All MESA participants provided written informed consent. The Institutional Review Board at all MESA study centers approved the use of health data for scientific research purposes. Under appropriate permissions, MESA data are available via the National Institutes of Health National Heart, Lung, and Blood Institute-sponsored Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) https://biolincc.nhlbi.nih.gov/.

RESULTS

Participant Characteristics

Among n=3719 participants, 54% were female, 42% identified as White, and mean age at Exam 1 was 59.3 years (SD=9.1). Mean HL score was 6.4 (SD=3.7; range=4 to 19), while limited PHL was present in 14.7% of participants in the study (Table 3). Compared to those with adequate PHL, individuals with limited HL tended to be older, were more likely to be female, identify as Hispanic or Chinese, less likely to speak English as a primary language at home, have an annual income of less than $35,000, and more likely to have a high school education/GED or less. Participants with limited PHL tended to have poorer cardiovascular health overall than individuals with adequate PHL, as shown by the lower unadjusted mean LS7 score (8.5 [SD=2.0] vs 8.9 [SD=2.0]). Regarding cardiovascular risk factors and LS7 components, individuals with limited PHL were more likely to be less physically active, have higher blood pressure, and higher fasting blood glucose levels compared those with adequate PHL (Table 3).

Table 3:

Baseline participant characteristics by personal health literacy status, n=3719; MESA

Characteristics % (N)	Total Sample 100% (3719)	Adequate PHL 85.3% (3173)	Limited PHL 14.7% (546)
Mean health literacy score (±SD)	6.4 (3.7)	5.0 (1.4)	14.4 (2.8)
Health literacy score range (min-max)	5 (4–19)	6 (4–10)	8 (11–19)
Exam 1 age (±SD)	59.3 (9.1)	58.4 (8.7)	64.4 (9.7)
Sex
Male	45.8% (1705)	46.6% (1479)	41.4% (226)
Female	54.2% (2014)	53.4% (1694)	58.6% (320)
Race/ethnicity
Black	24.1% (897)	26.5% (842)	10.1% (55)
Chinese	13.8% (512)	10.7% (340)	31.5% (172)
Hispanic	20.3% (756)	16.9% (537)	40.1% (219)
White	41.8% (1554)	45.8% (1454)	18.3% (100)
Field center location
Baltimore, MD	14.5% (539)	15.9% (503)	6.6% (36)
Chicago, IL	20.3% (754)	23.1% (734)	3.7% (20)
Forsyth County, NC	14.5% (541)	15.0% (477)	11.7% (64)
Los Angeles, CA	19.3% (718)	13.0% (411)	56.2% (307)
New York City, NY	15.0% (559)	16.1% (511)	8.8% (48)
St. Paul, MN	16.3% (608)	16.9% (537)	13.0% (71)
Primary language spoken at home
Chinese	10.9% (407)	7.6% (242)	30.2% (165)
English	79.3% (2951)	86.2% (2734)	39.7% (217)
Spanish	9.7% (361)	6.2% (197)	30.0% (164)
Annual income
< $35,000	36.5% (1357)	30.6% (970)	70.9% (387)
$35,000 to $75,000	35.7% (1327)	38.1% (1210)	21.4% (117)
≥ $75,000	27.8% (1035)	31.3% (993)	7.7% (42)
Highest level of education completed
Less than high school	13.1% (488)	8.1% (257)	42.3% (231)
High school diploma or GED	16.8% (623)	15.4% (489)	24.5% (134)
Some college	23.9% (888)	25.3% (803)	15.6% (85)
Bachelors or associates degree	25.0% (929)	27.1% (861)	12.5% (68)
Graduate or professional degree	21.3% (791)	24.0% (763)	5.1% (28)
Cardiovascular disease risk factors
Current smokers	11.4% (425)	11.9% (378)	8.6% (47)
Body mass index, kg/m2 (±SD)	28.1 (5.4)	28.3 (5.4)	27.4 (4.9)
Physical activity, minutes per week (±SD)	381.9 (540.4)	396.5 (545.1)	297.1 (504.6)
Total cholesterol, mg/dL (±SD)	194.9 (34.8)	194.9 (34.8)	194.9 (35.2)
Systolic blood pressure, mmHg (±SD)	123.2 (20.0)	122.3 (19.6)	128.2 (21.3)
Diastolic blood pressure, mmHg (±SD)	71.7 (10.0)	71.7 (10.0)	71.5 (9.8)
Fasting blood glucose, mg/dL (±SD)	94.5 (26.4)	93.2 (25.0)	102.5 (32.0)
Life Simple 7 components
Smoking status
Poor (never smokers)	11.4% (425)	11.9% (378)	8.6% (47)
Intermediate (former smokers)	1.9% (71)	2.0% (62)	1.6% (9)
Ideal (never smokers)	86.7% (3223)	86.1% (2733)	89.7% (490)
Body mass index
Poor (≥ 30 kg/m2)	30.6% (1139)	31.7% (1005)	24.5% (134)
Intermediate (25–29.9 kg/m2)	39.2% (1457)	38.7% (1227)	42.1% (230)
Ideal (< 25 kg/m2)	30.2% (1123)	29.7% (941)	33.3% (182)
Physical activity
Poor (0 min/week)	20.1% (748)	18.7% (592)	28.6% (156)
Intermediate (<150 min/week moderate or <75 min/week vigorous)	17.9% (666)	17.7% (563)	18.9% (103)
Ideal (≥150 min/week moderate or ≥75 min/week vigorous)	62.0% (2305)	63.6% (2018)	52.6% (287)
Healthy diet score*
Poor (0–1 points)	59.7% (2222)	60.7% (1925)	54.4% (297)
Intermediate (2–3 points)	39.9% (1483)	38.9% (1235)	45.4% (248)
Ideal (4–5 points)	0.4% (14)	0.4% (13)	0.2% (1)
Total cholesterol
Poor (≥240 mg/dL)	9.6% (358)	9.6% (304)	9.9% (54)
Intermediate (200–239 mg/dL)	43.6% (1622)	43.9% (1394)	41.8% (228)
Ideal (<200 mg/dL)	46.8% (1739)	46.5% (1475)	48.4% (264)
Blood pressure
Poor (SBP ≥140 mmHg or DBP ≥90 mmHg)	20.5% (762)	19.3% (613)	27.3% (149)
Intermediate (SBP 120–139 mmHg or DBP 80–89 mmHg or treated to goal)	40.6% (1511)	40.2% (1275)	43.2% (236)
Ideal (SBP <120 mmHg and DBP <80 mm Hg)	38.9% (1446)	40.5% (1285)	29.5% (161)
Fasting blood sugar
Poor (≥126 mg/dL)	5.9% (220)	4.8% (152)	12.5% (68)
Intermediate (100–125 mg/dL)	15.9% (592)	14.9% (472)	22.0% (120)
Ideal (<100 mg/dL	78.2% (2907)	80.3% (2549)	65.6% (358)
Overall Life Simple 7 scores
Mean CVH score (±SD)	8.9 (2.0)	8.9 (2.0)	8.5 (2.0)
CVH score range (min-max)	13.0 (0.0–13.0)	13.0 (0.0–13.0)	11.0 (2.0–13.0)
Overall Life Simple 7 categories
Inadequate (0–7 points)	25.4% (944)	24.5% (776)	30.8% (168)
Average (8–11 points)	65.4% (2432)	65.7% (2085)	63.6% (347)
Ideal (12–14 points)	9.2% (343)	9.8% (312)	5.7% (31)

Abbreviations: SBP = systolic blood pressure; DBP = diastolic blood pressure. Note: column percentages are presented.

^*Healthy diet score consistent with Dietary Approaches to Stop Hypertension (DASH) eating pattern¹⁸ and is comprised of five parts: (i) ≥4.5 cups/day of fruits and vegetables, (ii) ≥2 servings/week of fish, (iii) ≥3 servings/day of whole grains, (iv) <36 oz/week of sugar-sweetened beverages, and (v) <1500 mg/day of sodium.

Prevalence Ratios of Life Simple 7

Prevalence ratios and 95% confidence intervals for the relationships between PHL status, overall LS7, and individual LS7 components are presented in Table 4. In univariate modeling, we observed that limited PHL, as compared to adequate PHL, was associated with significantly lower likelihoods of average and optimal overall LS7. Negative associations remained consistently significant after stepwise adjustments for demographic and immigration status variables. With further adjustments for education and income level (Model 4), limited PHL was significantly associated with a lower likelihood of optimal LS7 (0.69 [0.50, 0.95], p=0.02), and a non-significantly lower likelihood of average LS7 (0.95 [0.88, 1.02], p=0.15).

Table 4:

Prevalence ratios and 95% confidence intervals of overall and individual LS7 components, n=3719; MESA

Overall Life Simple 7
	Average vs Inadequate (ref.)			Optimal vs Inadequate (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=2861	n=515		n=1088	n=199
Model 1	Ref.	0.92 (0.87, 0.99)	0.02	Ref.	0.54 (0.39, 0.76)	<0.01
Model 2	Ref.	0.92 (0.85, 0.98)	0.02	Ref.	0.56 (0.41, 0.76)	<0.01
Model 3	Ref.	0.91 (0.84, 0.98)	0.01	Ref.	0.55 (0.40, 0.76)	<0.01
Model 4	Ref.	0.95 (0.88, 1.02)	0.15	Ref.	0.69 (0.50, 0.95)	0.02
Individual Life Simple 7 Components
Smoking	Intermediate vs Poor (ref.)			Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=440	n=56		n=3111	n=537
Model 1	Ref.	1.14 (0.60, 2.17)	0.69	Ref.	1.04 (1.01, 1.07)	0.01
Model 2	Ref.	0.99 (0.47, 2.10)	0.99	Ref.	0.97 (0.94, 1.00)	0.08
Model 3	Ref.	0.95 (0.43, 2.11)	0.91	Ref.	0.96 (0.93, 0.99)	0.02
Model 4	Ref.	0.90 (0.41, 1.99)	0.80	Ref.	0.98 (0.94, 1.01)	0.23
Body mass index	Intermediate vs Poor (ref.)			Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=2232	n=364		n=1946	n=316
Model 1	Ref.	1.15 (1.05, 1.25)	<0.01	Ref.	1.19 (1.07, 1.32)	<0.01
Model 2	Ref.	1.05 (0.95, 1.16)	0.32	Ref.	0.94 (0.84, 1.05)	0.28
Model 3	Ref.	1.01 (0.91, 1.11)	0.91	Ref.	0.92 (0.83, 1.03)	0.15
Model 4	Ref.	1.00 (0.90, 1.11)	0.97	Ref.	0.96 (0.86, 1.08)	0.51
Physical activity	Intermediate vs Poor (ref.)			Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=1155	n=259		n=2610	n=443
Model 1	Ref.	0.82 (0.69, 0.96)	0.01	Ref.	0.84 (0.78, 0.90)	<0.01
Model 2	Ref.	0.94 (0.78, 1.13)	0.51	Ref.	0.90 (0.83, 0.98)	0.01
Model 3	Ref.	0.97 (0.81, 1.18)	0.78	Ref.	0.96 (0.89, 1.04)	0.30
Model 4	Ref.	1.03 (0.85, 1.25)	0.79	Ref.	1.01 (0.93, 1.09)	0.78
Healthy diet*	Intermediate + Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value
	n=2222	n=1497
Model 1	Ref.	1.16 (1.05, 1.28)	<0.01
Model 2	Ref.	0.92 (0.82, 1.03)	0.14
Model 3	Ref.	0.90 (0.80, 1.01)	0.08
Model 4	Ref.	0.92 (0.81, 1.04)	0.17
Total cholesterol	Intermediate vs Poor (ref.)			Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=1698	n=282		n=1779	n=318
Model 1	Ref.	0.98 (0.93, 1.05)	0.62	Ref.	1.00 (0.95, 1.06)	0.96
Model 2	Ref.	0.99 (0.92, 1.06)	0.71	Ref.	1.02 (0.96, 1.09)	0.48
Model 3	Ref.	0.99 (0.93, 1.06)	0.85	Ref.	1.02 (0.96, 1.09)	0.45
Model 4	Ref.	1.00 (0.93, 1.07)	0.97	Ref.	1.04 (0.98, 1.12)	0.22
Blood pressure	Intermediate vs Poor (ref.)			Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=1888	n=385		n=1898	n=310
Model 1	Ref.	0.91 (0.83, 0.99)	0.03	Ref.	0.77 (0.69, 0.86)	<0.01
Model 2	Ref.	1.05 (0.95, 1.15)	0.34	Ref.	0.92 (0.82, 1.02)	0.12
Model 3	Ref.	1.05 (0.95, 1.16)	0.31	Ref.	0.91 (0.81, 1.01)	0.09
Model 4	Ref.	1.06 (0.96, 1.18)	0.24	Ref.	0.93 (0.83, 1.05)	0.24
Fasting blood sugar	Intermediate vs Poor (ref.)			Ideal vs Poor (ref.)
	Adequate PHL	Limited PHL	p-value	Adequate PHL	Limited PHL	p-value
	n=624	n=188		n=2701	n=426
Model 1	Ref.	0.84 (0.75, 0.95)	<0.01	Ref.	0.89 (0.85, 0.93)	<0.01
Model 2	Ref.	0.85 (0.75, 0.97)	0.02	Ref.	0.92 (0.88, 0.96)	<0.01
Model 3	Ref.	0.86 (0.76, 0.99)	0.03	Ref.	0.92 (0.88, 0.96)	<0.01
Model 4	Ref.	0.90 (0.79, 1.03)	0.14	Ref.	0.93 (0.89, 0.98)	<0.01

Model 1: crude; Model 2: model 1 + age, race/ethnicity, sex, field center; Model 3: model 2 + place of birth (US/outside US) and primary language spoken at home (English/Chinese or Spanish); Model 4: model 3 + income and education; PHL=personal health literacy.

^*Intermediate and ideal diet categories were combined due to small samples and compared to the poor diet category

As for individual LS7 components, we observed limited PHL to be associated with lower likelihoods of non-smoking, healthy diet, normal fasting blood glucose levels in crude models. Associations attenuated and became non-significant for most measures with partial covariate adjustment. After full covariate adjustment (Model 4), limited PHL was significantly associated with 7% lower prevalence of ideal blood glucose level (0.93 [0.89, 0.98], p<0.01), and a 10% non-significant lower prevalence of intermediate blood glucose level (0.90 [0.79, 1.03], p=0.14). No clear patterns emerged for the other LS7 components.

DISCUSSION

In this study, we found that limited personal health literacy was associated with a lower likelihood of optimal overall cardiovascular health among adults from a diverse community-based cohort. This association persisted even after accounting for demographic variables, acculturation factors, education and income. Furthermore, limited PHL was associated with a significantly lower likelihoods of ideal smoking status, diet, and blood glucose levels in crude models, however only fasting blood glucose remained statistically significant in fully adjusted models. Overall, these findings suggest that limited PHL may be linked to adverse cardiovascular health status and poor risk factor management.

Our study adds to the existing literature on PHL and cardiovascular risk factors and disease. Previous studies of patients with cardiovascular diseases highlight the role PHL may have in managing such conditions. Among patients with myocardial infarction, those with below-basic PHL had a greater likelihood of 30-day hospital readmission and greater frequencies of cardiovascular comorbidities and other outcomes (i.e. diabetes, heart failure, stroke, vascular disease) compared to patients with above-basic PHL.¹² In a cohort of heart failure patients, individuals with low PHL observed nearly a 2-times greater risk of mortality compared to those with adequate PHL after adjusting for potential confounders.¹³ In both of these instances, and in the present study, associations were independent of educational achievement, a key health determinant as indicated in Healthy People 2030,²⁸ thus providing additional support that the concept of PHL is a unique construct from education, as described in the Scientific Statement by AHA.⁴

In the present analysis, participants with limited PHL were less likely to have optimal glucose levels. These observational findings compliment data from previous intervention studies that have shown that enhancing health literacy through individualized health counseling and disease management programs led to improved glycemic control among patients with diabetes.^7,8 Observational studies of diabetes patients also found associations between health literacy and diabetic control. For instance, in a cohort study, patients with type 2 diabetes who experienced difficulty completing health forms or reading medical materials were more likely to have hypoglycemic events compared to patients who did not find such tasks challenging.²⁹ In a cross-sectional study of diabetic patients (86% with type 2, 14% with type 1), those with limited PHL skills more frequently exhibited poor glycemic control and engaged less in disease-management behaviors including tracking carbohydrate intake, glucose monitoring, and insulin administration.³⁰ Taken together, these findings suggest that developing strategies to improve PHL, possibly via interventions aimed at improving glycemic control, may lead to better risk factor management and reduced risk of diabetic complications.

Our findings highlight the importance of addressing limitations in HL to improve cardiovascular health and overall quality of life. Healthy People 2030 outlines multiple overarching objectives aimed at expanding proficiency in HL skills and reducing the negative burden of poor HL across the US population.^1,2 HL by its very nature is a complex social health determinant, intersecting with numerous aspects of education, culture, and socioeconomics.⁴ Furthermore, HL is strongly connected to the health infrastructure that patients regularly use.⁴ Given these intricacies, we agree with Healthy People 2030’s recommendation that a multifaceted approach is needed to address limited HL across two possible domains: at the community level and at the healthcare level.

At the local level, community health promoters are critical in bridging the gap between populations experiencing HL challenges and healthcare institutions. Health promoters can provide valuable perspectives on the structural barriers and HL inequities that underserved populations face.^31,32 Such inequities, which are prevalent among communities of color, are largely driven by educational and economic disadvantages, inadequate access to healthcare, and other social health determinants tied to systemic racism and historical trauma.^4,20,26,33 Importantly, these same factors also contribute to poorer cardiovascular profiles among communities of color.³⁴ Therefore, there is a need for health promoters to develop and sustain appropriate health education programs that adhere to the competencies outlined by the National Commission for Health Education Credentialing (NCHEC). For example, when developing such programs, health promoters should consider the unique challenges and needs of the communities they serve (sub-competencies 1.1.2: “identify priority population(s)”; 1.3.2: “Determining the knowledge, attitudes, beliefs, skills, and behaviors that impact the health and health literacy of the priority populations”).³⁵ A large proportion of the population is at risk for CVD, particularly among communities of color.³⁴ When considering cardiovascular health promotion, there is a need for tailored assessments of individual populations in order to better understand specific barriers and opportunities to improve cardiovascular health literacy and outcomes in these priority populations. Promoters can also play a role in program implementation by establishing safe and positive educational settings (3.2.1: “Create an environment conducive to learning”), as well as in monitoring to track program success and direction (3.3.1: “Monitor progress in accordance with the timeline”; 3.3.2: “Assess progress in achieving objectives”, 3.3.3: “Modify interventions as needed to meet individual needs”).³⁵ The U.S. Diabetes Prevention Program serves as one model for applying NCHEC competencies to reduce risk of diabetes, an important CVD risk factor, among at-risk individuals.³⁶ Last, health promoters can take part in advocacy efforts which can help extend the reach and impact of education programming (5.2.1: Identify existing coalitions and stakeholders that favor and oppose the proposed policy, system, or environmental change and their reasons”; 5.2.3: “ Create formal and/or informal alliances, task forces, and coalitions to address the proposed change”).³⁵ The American Heart Association’s Policy Research arm actively collaborates with community health promoters, as well as health providers and lawmakers, through research and scientific reports to help develop policies and maintain programs aimed at identifying structural barriers of cardiovascular health and enhancing disease prevention at the population level (i.e. Scientific Statement on Health Literacy, Life’s Simple 7, updated risk factor management guidelines).^4,37–39 By constructing meaningful educational programs and collaborations for advocacy, community health promoters can be an essential resource in helping individuals with limited HL navigate through a complex healthcare landscape.^31,40,41

At the health institution level, providers are often unaware of the HL hurdles that patients face. Some health professionals assume most patients are capable of fully understanding the information and/or care they provide to them.⁴⁰ This can be true for management of CVD risk factors, such as control of blood pressure and glucose. Similarly, providers may use complicated language filled with medical jargon when describing treatments and procedures, making it difficult for even the average patient to comprehend.⁴⁰ In separate opinion pieces by Allen-Meares et al. and Polster, the authors recommend health professionals should screen for HL, use simple language (preferably at a 6^th grade reading level or lower), and incorporate visual aids when communicating medical data and treatment regimens with patients.^40,41 In doing so, materials and communication from providers should be developed with respect and cultural humility of patients.⁴¹ Additionally, such materials must be efficiently interpreted into the languages of the patients that are being served.^40,41 Lastly, NCHEC competency recommendations should be consider for institution level HL programming as well as cardiovascular health research and guidelines outlined by the AHA.^4,35,38 Taken altogether, a multifaceted approach is necessary in facilitating effective action towards eliminating HL barriers and improving cardiovascular and general health.

Our investigation has several strengths. First, this study is novel in its assessment of the relationship between PHL status and cardiovascular health in a diverse well-enumerated, non-patient cohort derived from a community-based population. Second, the PHL questionnaire used in this investigation and previously in MESA²⁰ is unique for the following reasons: i) it captures both health literacy and numeracy skills required in key health access and management activities; ii) it is available in multiple languages commonly spoken by MESA participants; iii) it consists of items derived from the frequently used and validated REALM and TOPHLA assessments;^21–25 and iv) compared to more comprehensive tools such as the TOPHLA⁴², our short four-item questionnaire measured PHL with relatively little participant burden. Third, we adjusted for primary language spoken at home and place of birth as acculturation factors for potential confounding in our statistical models, as done before in MESA.²⁰ And fourth, LS7 is a widely used metric of cardiovascular health and has been part of a major effort by the AHA to enhance more holistic primary and secondary prevention approaches of cardiovascular disease as well as to improve heart health among adults.^26,38

Our study also has a number of limitations. First, PHL was based on subjective assessment. In addition to REALM and TOPHLA, several more objective tools used to measure various components of PHL, as well as assessments specific to particular health conditions and/or populations do exist but were not assessed in MESA as they would be associated with greater participant burden. Second, PHL was only available at one time point in MESA (Exam 6), non-concurrent with LS7 collected in the baseline Exam 1. Therefore, it was not possible to examine the prospective associations between PHL and change in LS7 metrics, as selection bias may have occurred. Importantly, we did exclude participants with evidence of dementia at visit 6, as PHL may have changed for these individuals since Exam 1. Third, our specific four-item PHL questionnaire has not yet been tested for general validity, however validity of the components has been demonstrated.^21–25 Future validation studies in other cohorts are needed. Fourth, the AHA has recently released an new version LS7 titled “Life’s Essential 8”, an updated cardiovascular metric similar to LS7 that now includes healthy sleep.⁴³ Although we recognize sleep as an important aspect of cardiovascular health, we were unable to incorporate it in our analysis since sleep data was not collected during MESA Exam 1. Fifth, for some analyses, the precision of our effect estimates was poor, likely driven by low statistical power. And lastly, although our sample is relatively diverse, our findings may not entirely be generalizable to the broader US population, particularly young and middle-aged adults.

TRANSLATION TO HEALTH EDUCATION PRACTICE

Limited personal health literacy is associated with suboptimal cardiovascular health status as well as poor fasting blood glucose levels, even after accounting for covariates such as education and income. In accordance with the goals of Healthy People 2030, future intervention-based solutions are needed to develop personal health literacy in order to enhance cardiovascular health long term. Findings from this study support recommendations in Healthy People 2030 that health educators and health care practitioners should identify health literacy limitations of their community members and patients, respectively, and use equitable approaches to reduce barriers and improve patient health literacy as a means to improve health management and quality of care.