Identifying Health Literacy in Kansas Using the Behavioral Risk Factor Surveillance System

Abstract

Health literacy continues to be an important research topic as part of population-based assessments for overall health issues. The objective of this continuation study was to examine the health literacy rates and health outcomes as measured by the Kansas Behavioral Risk Factor Surveillance System (BRFSS) survey. A cross-sectional research design was used. Health literacy data were extracted from the state-specific module of the BRFSS telephone survey. Demographic and health status variables were extracted from the core BRFSS dataset. The association between demographic and health status characteristics with health literacy was obtained using weighted samples in multivariable logistic regression models. As in the previous study, most respondents had moderate health literacy (61.1%), followed by high health literacy (31.4%) and low health literacy (7.5%). The demographic variables of interest included race, marital status, home ownership, insurance status, metropolitan status code, survey language, veteran status, education, employment, income, sex, and age. The health status variables included general health rating, presence of chronic conditions, and length of time since last check-up. Findings include individuals with low levels of health literacy were nearly 7 times as likely to be unsure of at least one health condition than those with high health literacy and demonstrate a broad gap in people’s ability to communicate accurate information to health-care providers. Results can inform future efforts to build programs that address health disparities issues including low health literacy to provide equitable health-care services. There is a continued need for support for the creation of health literate programs.

Keywords

health literacy population-based assessment chronic conditions

Introduction

Health literacy continues to be an important research topic impacting overall health.^1–5 Health literacy has been defined as by Ratzan and Parker as “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.”⁶ Recent studies have confirmed that socioeconomic status, age, race, cognition, and education level are contributing factors of health literacy levels.^7,8

Health disparities are differences in health that occur due to social, economic, or environmental disadvantages. Health literacy has been found to be associated with many of the similar factors as health disparities. Health literacy rates may explain some of the variation in health disparities that would be otherwise linked to other socioeconomic factors, such as education or income.⁹ Some literature suggests that the individuals who are likely to fall victim to social disparities, which in turn lead to worse health outcomes, are also more likely to have lower levels of health literacy.¹⁰ Incorporating health literacy as part of adult education may be a promising method to addressing health equity issues.^11,12

Historically, there has been an absence of discussion in the literature of what a health literate population looks like, or how it is to be assessed.¹³ Recent studies have shown success using the Behavioral Risk Factor Surveillance System (BRFSS) and a three-item screening tool to measure health literacy at the population level.^8,14 The objective of this continuation study was to examine the health literacy rates and health outcomes as measured by the Kansas BRFSS survey.

Methods

Study Population

Analyzed data included the responses of Kansas residents who participated in the 2014 Kansas BRFSS and completed the health literacy assessment portion of the survey. The study was reviewed and approved by a university institutional review board.

Measures

The demographic variables of interest included race, marital status, home ownership, insurance status, metropolitan status code, survey language, veteran status, education, employment, income, sex, and age. The health status variables included general health rating, presence of chronic conditions, and length of time since last check-up.

Cumulative scores were calculated for responders who answered all three health literacy questions. We categorized respondents in the following manner: low health literacy (scores 3–8), moderate health literacy (scores 9–14), and high health literacy (score = 15). Therefore, all analyses utilized this trichotomized health literacy variable.

To assess each respondent’s ability to answer chronic health questions, any “yes” or “no” answer was considered as “able to answer the question.” Answers of “I don’t know/not sure” were categorized as “unable to answer the question.” Missing values or unasked questions were excluded from analyses.

Statistical Analysis

All data were analyzed using IBM SPSS Statistics version 24 for complex samples. The specific weighting methodology conformed to that prescribed by the Centers for Disease Control and Prevention for the version of the survey that contained health literacy questions. Chi-square tests were used to evaluate the significance of bivariate associations for each demographic variable, health status variable, and health literacy with the respondent’s ability to answer all the questions regarding chronic health conditions. For continuity with previous work, we include bivariate associations of demographic variables, health status variables, and the ability to answer each chronic health question with respondent’s health literacy.

To obtain the adjusted multivariable model for the association between the demographic, health status, and health literacy variables and the ability to answer questions about chronic health conditions, variables with an associated p value of ≤.20 were selected for inclusion in the initial multivariable model for complex samples.¹⁵ All predictors selected were assessed for their associations with other predictor variables. Variables with significant associations (Spearman’s rho ≥0.3) were assessed in separate models to avoid issues of multicollinearity.

Results

Compared with previously published studies on health literacy,^8,16 differences in respondents’ sex, age, race, education level, marital status, home ownership, income, insurance coverage, rurality of residence, general health rating, and the time since their last check-up were associated with differences in health literacy (Table 1). Germane to our study’s purpose is the proportion of individuals with low health literacy who were unable to answer questions regarding their history of chronic health conditions. Low levels of health literacy were found in a greater proportion of individuals who could not answer questions regarding their history of heart attack (17.3% vs. 7.0%, p < .001), coronary disease (12.3% vs. 7.1%, p < .001), stroke (20.5% vs. 7.1%, p < .001), cancer (33.9% vs. 7.5%, p = .042), chronic obstructive pulmonary disease (15.0% vs. 7.0%, p < .001), depressive disorder (12.9% vs. 6.2%, p < .001), kidney disease (8.2% vs. 7.5%, p = .001), and diabetes (14.1% vs. 6.7%, p < .001).

Table 1.

Weighted Demographic and Health Status Characteristics of Kansas BRFSS Sample in Relation to Their Health Literacy (N = 1,944,274).

	Low HL		Moderate HL		High HL
Variable	N	%	N	%	N	%	p
Overall	146,666	7.5	1,187,318	61.1	610,291	31.4
Sex							<.001
Male	88,129	9.2	606,077	63.5	260,127	27.3
Female	58,537	5.9	581,352	58.7	350,164	35.4
Age							<.001
18–24 years	18,874	6.9	207,028	75.2	49,510	18.0
25–34 years	19,705	6.1	199,498	61.8	103,578	32.1
35–44 years	22,100	7.1	171,242	55.3	116,210	37.5
45–54 years	32,074	9.6	179,886	54.0	120,992	36.3
55–64 years	24,362	7.4	193,971	59.0	110,505	33.6
65 years and more	29,552	7.9	235,803	62.9	109,496	29.2
Race							<.001
N-H White	89,571	5.7	967,363	61.9	505,892	32.4
N-H Black	5,559	5.5	59,418	59.1	35,597	35.4
N-H other	4,613	8.2	36,892	65.3	15,008	26.6
N-H multirace	3,449	8.8	20,994	53.6	14,722	37.6
Hispanic	42,249	24.9	93,771	55.3	33,700	19.9
Marital status							<.001
Partnered	76,568	6.6	677,014	58.2	410,452	35.3
Not partnered	69,990	9.0	505,506	65.3	198,372	25.6
Education							<.001
Did not graduate high school	51,752	27.8	114,420	61.4	20,223	10.8
High school graduate	54,405	10.3	346,500	65.9	124,793	23.7
Some college or technical school	32,839	4.8	439,990	64.0	214,241	31.2
Graduated college or technical school	6,899	1.3	285,171	52.5	250,658	46.2
Employment							<.001
Employed full time	66,372	5.7	692,566	59.5	404,590	34.8
Not employed	13,822	15.1	56,991	62.3	20,723	22.6
Homemaker	10,973	9.1	70,848	58.6	39,167	32.4
Student	2,770	2.4	90,065	79.3	20,792	18.3
Retired	23,821	7.1	206,243	61.8	103,490	31.0
Unable to work	24,729	22.6	64,498	58.9	20,315	18.5
Income							<.001
<$15,000	23,491	17.2	87,995	64.3	25,327	18.5
$15,000 to <$25,000	32,548	12.9	168,057	66.9	50,787	20.2
$25,000 to <$35,000	15,976	7.9	124,615	61.4	62,343	30.7
$35,000 to <$50,000	13,269	4.7	188,846	66.9	80,366	28.5
$50,000+	22,680	2.9	430,286	55.5	322,531	41.6
Home ownership							<.001
Own	81,400	5.9	833,414	60.3	467,880	33.8
Rent	52,838	12.0	279,378	63.3	108,850	24.7
Other arrangement	12,428	11.2	67,786	61.2	30,501	27.5
Insurance status							<.001
Has health-care coverage	103,414	6.1	1,023,766	60.8	556,816	33.1
Does not have health-care coverage	40,778	16.4	156,073	62.6	52,354	21.0
Metropolitan status code							<.001
In MSA city code	26,873	8.0	195,205	58.4	112,096	33.5
Within MSA city suburb but not city center	16,330	4.0	239,317	58.7	151,852	37.3
Outside MSA	37,728	9.5	249,618	62.9	109,638	27.6
Veteran status							0.476
Yes	19,081	8.3	134,338	58.6	75,993	33.1
No	127,585	7.4	1,051,431	61.4	533,686	31.2
General health rating							<.001
Good or better health	81,689	5.0	1,003,022	61.1	556,244	33.9
Poor or fair health	63,774	21.3	181,287	60.6	54,047	18.1
Length of time since last check-up							0.001
Within past year	85,506	6.6	777,967	60.1	431,214	33.3
Within past 2 years	18,270	7.0	161,294	62.1	80,134	30.9
Within past 5 years	11,189	6.9	107,890	66.3	43,716	26.9
5+ years	14,677	10.4	90,360	64.0	36,258	25.7
Never had check-up	6,445	21.4	16,624	55.3	7,010	23.3
Ability to answer questions regarding chronic conditions
Ever told you had a heart attack?							<.001
Yes/no	129,844	7.0	1,135,196	61.2	588,696	31.8
Unsure	14,067	17.3	47,570	58.5	19,667	24.2
Ever told you have angina or coronary disease?							<.001
Yes/no	131,171	7.1	1,126,462	61.3	580,338	31.6
Unsure	10,879	12.3	48,990	55.5	28,422	32.2
Ever told you had a stroke?							<.001
Yes/no	133,383	7.1	1,148,833	61.1	598,302	31.8
Unsure	12,014	20.5	35,616	60.7	1,1035	18.8
Ever told you have asthma?							0.305
Yes/no	119,430	7.3	1,012,792	61.5	514,054	31.2
Unsure	27,236	9.3	170,242	58.2	95,127	32.5
Do you have asthma now?							0.053
Yes/no	7,735	8.4	50,939	55.2	33,666	36.5
Unsure	16,572	8.7	114,802	60.1	59,643	31.2
Ever told you have skin cancer?							0.810
Yes/no	138,610	7.6	1,107,081	61.0	569,925	31.4
Unsure	7,901	6.2	79,040	62.3	39,934	31.5
Ever told you have cancer (any type)?							0.042
Yes/no	145,125	7.5	1,185,520	61.1	609,083	31.4
Unsure	1,541	33.9	1,798	39.5	1,208	26.6
Ever told you have COPD, emphysema, or chronic bronchitis?							<.001
Yes/no	126,078	7.0	1,104,217	61.0	580,091	32.0
Unsure	18,408	15.0	74,649	61.0	29,379	24.0
Ever told you have arthritis, gout, fibromyalgia, or lupus?							0.405
Yes/no	145,890	7.5	1,182,501	61.2	605,230	31.3
Unsure	776	7.3	4,816	45.2	5,061	47.5
Ever told you have a depressive disorder?							<.001
Yes/no	96,938	6.2	949,962	60.9	512,403	32.9
Unsure	48,163	12.9	228,744	61.1	97,679	26.1
Ever told you have kidney disease?							0.001
Yes/no	141,177	7.5	1,152,165	60.9	597,269	31.6
Unsure	4,071	8.2	33,355	67.0	12,329	24.8
Ever told you have diabetes?							<.001
Yes/no	115,571	6.7	1,064,191	61.5	549,751	31.8
Unsure	29,845	14.1	121,189	57.4	59,924	28.4

Abbreviations: HL, health literacy; MSA, metropolitan statistical area; BRFSS, Behavioral Risk Factor Surveillance System; COPD, chronic obstructive pulmonary disease; NH, Non-Hispanic.

The ability to answer all the BRFSS questions on chronic health conditions was significantly associated with the respondent’s marital status, income, and health literacy level (Table 2). When compared with the highest earning group ($50,000+), a reported income of $15,000 to $25,000 was associated with greater than threefold (12.9% vs. 3.5%) the proportion of respondents who could not answer all questions regarding chronic health conditions. For respondents earning $25,000 to $35,000, this increased to a nearly fourfold difference (13.2% vs. 3.5%). Partnered respondents were better able to answer all the questions than nonpartnered respondents (94.7% vs. 88.5%, p = .004). Individuals with low levels of health literacy were nearly 7 times as likely to be unsure of at least one health condition than those with high health literacy (22.0% vs. 3.3%) and almost 3 times as likely as those with moderate health literacy (22.0% vs. 7.4%).

Table 2.

Weighted Demographic and Health Status Characteristics of Kansas BRFSS Sample in Relation to Their Ability to Answer All Questions Regarding Chronic Health Conditions (N = 326,972).

	Answered all questions		Unable to answer all questions
Variable	N	%	N	%	p
Overall	300,068	91.8	26,904	8.2
Sex					.877
Male	119,652	92.0	10,386	8.0
Female	180,416	91.6	16,518	8.4
Age					.116
18–24 years	56,188	92.7	4,401	7.3
25–34 years	57,770	87.2	8,451	12.8
35–44 years	53,782	92.5	4,385	7.5
45–54 years	46,783	96.6	1,624	3.4
55–64 years	44,532	94.1	2,787	5.9
65 years and more	41,013	88.6	5,256	11.4
Race					.904
N-H White	231,046	92.3	19,143	7.7
N-H Black	24,160	87.9	3,320	12.1
N-H other	10,232	91.9	896	8.1
N-H multirace	12,609	92.0	1,099	8.0
Hispanic	21,119	90.8	2,147	9.2
Marital status					.004
Partnered	162,949	94.7	9,126	5.3
Not partnered	136,266	88.5	17,633	11.5
Education					.101
Did not graduate high school	32,454	95.1	1,687	4.9
High school graduate	81,372	87.4	11,683	12.6
Some college or technical school	112,304	91.7	10,147	8.3
Graduated college or technical school	72,195	95.7	3,242	4.3
Employment					.417
Employed full time	170,674	91.1	16,609	8.9
Not employed	23,385	90.7	2,396	9.3
Homemaker	16,348	94.2	1,002	5.8
Student	17,027	99.2	131	0.8
Retired	36,484	90.8	3,703	9.2
Unable to work	33,937	92.1	2,919	7.9
Income					.007
<$15,000	37,434	93.8	2,474	6.2
$15,000 to <$25,000	45,932	87.1	6,821	12.9
$25,000 to <$35,000	28,896	86.8	4,405	13.2
$35,000 to <$50,000	40,811	94.9	2,179	5.1
$50,000+	98,945	96.5	3,615	3.5
Home ownership					.093
Own	183,569	95.0	9,692	5.0
Rent	92,650	88.0	12,588	12.0
Other arrangement	18,306	85.9	3,016	14.1
Insurance status					.504
Has health-care coverage	247,689	91.6	22,829	8.4
Does not have health-care coverage	50,404	93.9	3,300	6.1
Metropolitan status code					.433
In MSA city code	51,653	91.3	4,948	8.7
Within MSA city suburb but not city center	49,886	92.2	4,201	7.8
Outside MSA	49,949	95.7	2,250	4.3
Veteran status					.715
Yes	26,200	92.9	2,013	7.1
No	272,961	91.7	24,781	8.3
General health rating
Good or better health	225,762	91.9	19,942	8.1
Poor or fair health	73,621	91.4	6,962	8.6
Length of time since last check-up					.129
Within past year	189,470	93.4	13,493	6.6
Within past 2 years	46,397	91.0	4,578	9.0
Within past 5 years	26,048	92.9	2,004	7.1
5+ years	24,229	88.6	3,121	11.4
Never had check-up	3,391	69.9	1,459	30.1
Health literacy level					<.001
Low health literacy	21,165	78.0	5,962	22.0
Moderate health literacy	157,300	92.6	12,557	7.4
High health literacy	91,944	96.7	3,183	3.3

Abbreviations: MSA, metropolitan statistical area; BRFSS, Behavioral Risk Factor Surveillance System.

Due to their association (p≤0.20) with the ability to answer questions regarding chronic health conditions, age, income, marital status, home ownership, education, time from last check-up, and health literacy were selected for inclusion in a multivariable model. However, significant bivariate associations between education, income, marital status, and home ownership required us to enter each in a separate model with health literacy, time since last check-up, and age as covariates. Tables 3 to 5 summarize the results for each model that could converge on a final solution. The model containing age, time since last check-up, health literacy, and home ownership was unable to converge on a solution and is not represented in these tables.

Table 3.

Adjusted Associations Between Age, Income, Time Since Last Check-up, Health Literacy Level, and Ability to Answer All Chronic Health Condition Questions on the BRFSS.^a

Variable	Unable to answer questions about chronic health
Variable	OR [95% CI]	p
Age
18–24 years	0.77 [0.24, 2.47]	.663
25–34 years	0.81 [0.20, 3.29]	.762
35–44 years	1.56 [0.60, 4.06]	.358
45–54 years	0.63 [0.19, 2.09]	.449
55–64 years	0.88 [0.34, 2.27]	.791
65 years and more	REF
Income
<$15,000	1.74 [0.51, 5.93]	.375
$15,000 to <$25,000	3.57 [1.47, 8.66]	.005
$25,000 to <$35,000	4.21 [1.54, 11.56]	.005
$35,000 to <$50,000	0.84 [0.21, 3.35]	.809
$50,000+	REF
Length of time since last check-up
Within past year	0.84 [0.32, 2.21]	.720
Within past 2 years	0.86 [0.27, 2.74]	.801
Within past 5 years	0.54 [0.09, 3.12]	.493
5+ years	REF
Health literacy level
Low health literacy	4.21 [1.31, 13.49]	.016
Moderate health literacy	1.98 [0.79, 4.94]	.144
High health literacy	REF

Abbreviation: BRFSS, Behavioral Risk Factor Surveillance System.

Psuedo R² values for this model are: Cox and Snell = 0.046, Nagelkerke = 0.118, McFadden = 0.095.

Table 4.

Adjusted Associations Between Age, Education, Time Since Last Check-up, Health Literacy Level, and Ability to Answer All Chronic Health Condition Questions on the BRFSS.^a

Variable	Unable to answer questions about chronic health
Variable	OR [95% CI]	p
Age
18–24 years	0.56 [0.22, 1.44]	.230
25–34 years	0.55 [0.14, 2.09]	.376
35–44 years	0.93 [0.39, 2.22]	.863
45–54 years	0.34 [0.11, 1.05]	.061
55–64 years	0.58 [0.25, 1.34]	.200
65 years and more	REF
Education
Graduated college or technical school	6.42 [0.83, 49.98]	.076
Some college or technical school	8.09 [1.10, 59.64]	.040
High school graduate	14.02 [1.84, 106.84]	.011
Did not graduate high school	REF
Length of time since last check-up
Within past year	0.74 [0.29, 1.86]	.519
Within past 2 years	0.77 [0.27, 2.16]	.614
Within past 5 years	0.46 [0.08, 2.68]	.387
5+ years	REF
Health literacy level
Low health literacy	7.65 [2.32, 25.22]	.001
Moderate health literacy	2.20 [0.91, 5.33]	.081
High health literacy	REF

Abbreviation: BRFSS, Behavioral Risk Factor Surveillance System.

Psuedo R² values for this model are: Cox and Snell = 0.048, Nagelkerke = 0.122, McFadden = 0.098.

Table 5.

Adjusted Associations Between Age, Marital Status, Time Since Last Check-up, Health Literacy Level, and Ability to Answer All Chronic Health Condition Questions on the BRFSS.a

Variable	Unable to answer questions about chronic health
Variable	OR [95% CI]	p
Age
18–24 years	0.59 [0.23, 1.56]	.290
25–34 years	0.49 [0.14, 1.75]	.275
35–44 years	0.93 [0.38, 2.25]	.870
45–54 years	0.31 [0.10, 0.98]	.046
55–64 years	0.53 [0.24, 1.19]	.125
65 years and more	REF
Marital status
Partnered	0.64 [0.32, 1.28]	.207
Not partnered	REF
Length of time since last check-up
Within past year	0.77 [0.30, 1.94]	.571
Within past 2 years	0.83 [0.28, 2.41]	.725
Within past 5 years	0.43 [0.08, 2.39]	.337
5+ years	REF
Health literacy level
Low health literacy	5.79 [1.91, 17.49]	.002
Moderate health literacy	2.32 [0.94, 5.72]	.067
High health literacy	REF

Abbreviation: BRFSS, Behavioral Risk Factor Surveillance System.

Psuedo R² values for this model are: Cox and Snell = 0.032, Nagelkerke = 0.082, McFadden = 0.065.

Discussion

This study confirms the use of the BRFSS survey to measure variables associated with health disparities.^17–19 This study also reinforces the findings that race, education, employment, income, sex, age, and health status are related to health literacy. Although other studies have used BRFSS state data,²⁰ as of this publication, no states have measured health literacy using the BRFSS survey as a multiyear study. This study confirms many of the previous findings from 2012 BRFSS data and extends the results by examining the proportion of individuals with low health literacy who were unable to answer questions regarding their history of chronic health conditions.

Findings included individuals with low levels of health literacy were nearly 7 times as likely to be unsure of at least one health condition than those with high health literacy (22.0% vs. 3.3%) and almost 3 times as likely as those with moderate health literacy (22.0% vs. 7.4%). These findings highlight a broad gap in people’s ability to communicate accurate information to health-care providers. The implication of this study’s findings is a key to the development of health literacy initiatives and communication programs that target people with low health literacy.

Limitations

This study utilized cross-sectional data from one Midwestern state with validated methodology.^8,14,21,22 Several limitations should be considered. Self-reported survey data are subject to possible response bias. However, other research has found that self-reported health literacy rates have been demonstrated as reliable.²³ Respondents may have encountered survey fatigue which may have affected their response to the telephone questions. Furthermore, the health literacy questions are used as a screening tool and do not access particular gaps in skill or knowledge. The data for this study were limited to questions constructed for the supplemental arm of the survey. Casual judgments and study results are limited due to the cross-sectional nature of the data. Finally, this study included results from one Midwestern state which may not be generalizable to other states with differing populations.

Conclusions

This study has demonstrated the importance of assessing health literacy by incorporating a three-item health literacy assessment into an optional state-specific module of the BRFSS. In addition, health literacy rates for the state have been identified and associations with variables typically collected through the BRFSS survey are confirmed. These findings highlight the need for programs to assist individual comprehend, communicate, and manage health issues. There is currently a dearth of evidence-based programs for rural, Midwestern populations to provide appropriate knowledge and skills to address health literacy and health disparities. Interventions and programs could be designed to model the behaviors of people with high health literacy. Certainly, educational initiatives directed at multiple levels are necessary to continue to address health disparities, improve health outcomes, and provide equitably health services to all people.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this study was provided by the Aging Institute, Wichita State University, Kansas Department of Health and Environment, and the Kansas BRFSS.

Author Biographies

Amy K. Chesser, PhD, holds a degree inHealth Communication and is an associate professor in the Department of Public Health Sciences at Wichita State University. Her research focusses on health literacy, older adults and women's health issues.

Jared Reyes, PhD, in Communication Sciences and Disorders is the research program manager at the Envision Research Institute in Wichita, KS.

Nikki Keene Woods, is an associate professor in Public Health Sciences. She is a maternal and infant health educator and researcher at Wichita State University.

References

Attygalle

Perera

Jayamanne

BDW.

Mental health literacy in adolescents: ability to recognise problems, helpful interventions and outcomes. Child Adolesc Psychiatry Ment Health 2017; 11: 38.

Batista

Lawrence

Sousa

Oral health literacy and oral health outcomes in an adult population in Brazil. BMC Public Health 2017; 18: 60.

Carels

Selensky

Rossi

et al . A novel stepped-care approach to weight loss: the role of self-monitoring and health literacy in treatment outcomes. Eat Behav 2017; 26: 76–82.

Leon-Gonzalez

Garcia-Esquinas

Paredes-Galan

et al.

Health literacy and health outcomes in very old patients with heart failure. Rev Esp Cardiol (Engl Ed) 2017; 71: 178–184.

Muller

Rowsell

Stuart

et al . Effects on engagement and health literacy outcomes of web-based materials promoting physical activity in people with diabetes: an international randomized trial. J Med Internet Res 2017; 19: e21.

Ratzan

Parker

RM.

Introduction. In: Selden

Zorn

Ratzan

et al. (eds) National library of medicine current bibliographies in medicine: health literacy. Bethesda, MD: National Institutes of Health, U.S. Department of Health and Human Services, 2000, p. 4.

Cutilli

CC.

Health literacy in geriatric patients: an integrative review of the literature. Orthop Nurs 2007; 26: 43–48.

Keene Woods

Chesser

AK.

Validation of a single question health literacy screening tool for older adults. Gerontol Geriatr Med 2017; 3: 2333721417713095.

Schillinger

Barton

Karter

et al.

Does literacy mediate the relationship between education and health outcomes? A study of a low-income population with diabetes. Public Health Rep 2006; 121: 245–254.

10.

Mantwill

Monestel-Umana

Schulz

PJ.

The relationship between health literacy and health disparities: a systematic review. PLoS One 2015; 10: e0145455.

11.

Chervin

Clift

Woods

et al . Health literacy in adult education: a natural partnership for health equity. Health Promot Pract 2012; 13: 738–746.

12.

Hasnain-Wynia

Wolf

MS.

Promoting health care equity: is health literacy a missing link? Health.

Serv Res 2010; 45: 897–903.

13.

Guzys

Kenny

Dickson-Swift

et al . A critical review of population health literacy assessment. BMC Public Health 2015; 15: 215.

14.

Chesser

Keene Woods

Smothers

et al . Health literacy and older adults: a systematic review. Gerontol Geriatr Med 2016; 2: 2333721416630492.

15.

Bursac

Gauss

Williams

et al . Purposeful selection of variables in logistic regression. Source Code Biol Med 2008; 3: 17.

16.

Chesser

Reyes

Smothers

K, K

et al . Integrating health literacy questions into a statewide Behavioral Risk Factor Surveillance System (BRFSS). Questionnaire 2014; 7: 96–103.

17.

Desmond

Jackson

Waterbor

JW.

Disparities in cancer survivorship indicators in the deep south based on BRFSS data: recommendations for survivorship care plans. South Med J 2017; 110: 181–187.

18.

Shikdar

Suzuki

Racial disparities in menu-labeling usage: analysis of the 2012 Behavioral Risk Factor Surveillance System (BRFSS) sugar-sweetened beverage and menu-labeling module. J Racial Ethn Health Disparities 2017; 5: 514–521.

19.

Tung

Baig

Huang

et al . Racial and ethnic disparities in diabetes screening between asian americans and other adults: BRFSS 2012-2014. J Gen Intern Med 2017; 32: 423–429.

20.

McGuire

Bouldin

Andresen

et al . Examining modifiable health behaviors, body weight, and use of preventive health services among caregivers and non-caregivers aged 65 years and older in Hawaii, Kansas, and Washington using 2007 BRFSS. J Nutr Health Aging 2010; 14: 373–379.

21.

Kohlmann

BRFSS and beyond: methodological quality of the measures from the Behavioral Risk Factor Surveillance System. Soz Praventivmed 2001; 46: S1–S2.

22.

Nelson

Holtzman

Bolen

et al . Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soz Praventivmed 2001; 46: S3–S42.

23.

Sarkar

Schillinger

Lopez

et al . Validation of self-reported health literacy questions among diverse English and Spanish-speaking populations. J Gen Intern Med 2011; 26: 265–271.