CI week 1 assignment

Please read the article by Ghaddar et al. on “Understanding the Intention to Use Telehealth Services in Underserved Hispanic Border Communities: Cross-Sectional Study” (see attached). Identify, using the following attached word document format 6310-Week1-Assignment1_Format x

Gap in the literature that the study addresses
Research question(s)
Study design
Population studied
Predictor variable(s)
Outcome variable(s)
Results (provide answer to the research question(s) based on study findings).

I encourage you to view the sample assignment attached and answers above to better understand what is expected for this assignment.

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Week 1 – Assignment 1 Rubric

Ghaddar et al.
Understanding the Intention to Use Telehealth Services in Underserved Hispanic Border Communities: Cross-Sectional Study.

Part	Answers	Points Earned
Gap in the literature			10
Research question
Study design
Population studied
Predictor variable(s)		15
Outcome variable(s)
Results	30
Total	100

Week 1 – Assignment 1 Rubric

Ghaddar et al. Health Insurance Literacy and Awareness of the Affordable Care Act in a Vulnerable
Hispanic Population.

Part Answers Points Earned
Gap in the literature Not many studies (a) have looked at health insurance literacy

(HIL) and its relationship with the ACA (5) or (b) have assessed
the health insurance literacy of Hispanic consumers (5).

Research question • What factors contribute to health insurance literacy in
vulnerable Hispanic communities? (3)

• Is ACA knowledge associated with health insurance literacy
among vulnerable groups along the Texas-Mexico border? (7)

Study design Cross-sectional 10
Population studied Target population (preferred answer): Hispanic (4) vulnerable

groups (4) along the Texas-Mexico border (2)
OR
Accessible population: Hispanic (4) attendees at Operation Lone
Star (4) in Hidalgo County (2)
Note to coaches: either one of these answers is considered
correct.

Predictor variable(s) Health insurance literacy (7), sociodemographic characteristics
(2), health literacy (2), health status (2), political affiliation (2).

Outcome variable(s) ACA knowledge 15
Results • Almost 70% of participants knew nothing/very little about the

ACA. (10)
• Multivariate analyses revealed that no/very little ACA

knowledge was associated with
o low levels of confidence “choosing health insurance

plans” (full sample) and “comparing plans” (U.S.-born
sub-sample) (10)

o low income levels (5)
o not having a diabetes diagnosis (5)

Total 100

Patient Education and Counseling 101 (2018) 2233–2240

Contents lists available at ScienceDirect

Patient Education and Counseling

journal homepage: www.elsevier.com/locate/pateducou

Health insurance literacy and awareness of the Affordable Care Act in a
vulnerable Hispanic population

Suad Ghaddara,*, Jihyun Byunb, Janani Krishnaswamic

a Department of Health and Biomedical Sciences, The University of Texas Rio Grande Valley, Edinburg, USA
b School of Human Ecology, The University of Texas at Austin, Austin, USA
c Department of Pediatrics and Preventive Medicine, The University of Texas Rio Grande Valley, Edinburg, USA

A R T I C L E I N F O A B S T R A C T

Article history:
Received 2 March 2018
Received in revised form 25 August 2018
Accepted 29 August 2018

Keywords:
Health insurance literacy
Affordable Care Act

Objective: The Patient Protection and Affordable Care Act (ACA) has allowed millions of Americans to
obtain coverage. However, many, especially minorities, remain uninsured. With mounting evidence
supporting the importance of health insurance literacy (HIL), the purpose of this cross-sectional study is
to examine the association between HIL and ACA knowledge.
Methods: We conducted 681 in-person interviews with participants at a community health event along
the Texas-Mexico border in 2015, after the conclusion of the ACA’s second enrollment period. To assess
HIL, we used the Health Insurance Literacy Measure, reflecting consumers’ confidence to choose,
compare, and use health insurance. We assessed ACA knowledge through the following question: “How
much would you say you know about this health reform law?” Logistic regression was used to examine
the association between HIL and ACA knowledge after controlling for several covariates.
Results: Almost 70% of participants knew nothing/very little about the ACA. Multivariate analyses
revealed that no/very little ACA knowledge was associated with low levels of confidence “choosing health
insurance plans” (OR:0.55; 95%CI:0.40-0.75) (full sample) and “comparing plans” (OR:0.56; 95%CI:0.32-
0.96) (U.S.-born sub-sample).
Conclusion: No/little ACA knowledge is associated with lower levels of HIL.
Practice Implications: Promoting HIL is an essential step towards improving healthcare access.

1. Introduction

The United States’ health care system has faced an array of
challenges, including high costs, health inequities, and high
uninsured rates, among others. The Patient Protection and
Affordable Care Act (ACA), a monumental health reform effort
also known as Obamacare, aimed to address several of the system’s
shortcomings, most importantly the high rate of the uninsured
which stood at 17% of the population (51 million people) in 2009
[1]. The ACA’s passage in 2010 has allowed more than 20 million
Americans to obtain coverage under its provisions [2]. These
include expanding Medicaid (government health care coverage for
low-income individuals) eligibility in certain states, providing
subsidies for qualifying individuals to purchase private health

* Corresponding author at: Department of Health and Biomedical Sciences, The
University of Texas Rio Grande Valley, 1201 W. University Dr., Edinburg, TX, 78539,
USA.

E-mail addresses: suad.ghaddar@utrgv.edu (S. Ghaddar),
jhbyun376@utexas.edu (J. Byun), janani.krishnaswami@utrgv.edu
(J. Krishnaswami).

https://doi.org/10.1016/j.pec.2018.08.033
0738-3991/© 2018 Elsevier B.V. All rights reserved.

insurance plans in the health insurance marketplaces, prohibiting
barriers to enrollment based on pre-existing conditions, and
increasing the cut-off age for young adults to stay on a parent’s
plan to age 26. Many individuals, however, remain uninsured,
especially among minority populations and particularly among
Hispanics. Despite considerable outreach efforts and correspond-
ing major enrollment gains, 28% of non-elderly Hispanics remain
uninsured [2]. In comparison, and for the same time period, only
9% and 15% of non-elderly, non-Hispanic whites and blacks were,
respectively, uninsured [2]. Several reasons and persistent
challenges to the lack of health coverage remain, including low
population awareness of the ACA law, its enrollment guidelines
and provisions [3,4]. However, health insurance literacy may be an
even more fundamental factor influencing coverage gaps [5–10].

Health insurance literacy (HIL) is defined as “the degree to which
individuals have the knowledge, ability, and confidence to find and
evaluate information about health plans, select the best plan for
their own (or their family’s) financial and health circumstances,
and use the plan once enrolled [11].” Similar to the evidence
supporting an association between low health literacy and poor
health outcomes [12], research is starting to reveal that poor

http://crossmark.crossref.org/dialog/?doi=10.1016/j.pec.2018.08.033&domain=pdf

mailto:suad.ghaddar@utrgv.edu

mailto:jhbyun376@utexas.edu

mailto:janani.krishnaswami@utrgv.edu

https://doi.org/10.1016/j.pec.2018.08.033

http://www.sciencedirect.com/science/journal/07383991

www.elsevier.com/locate/pateducou

https://95%CI:0.32

https://95%CI:0.40-0.75

2234 S. Ghaddar et al. / Patient Education and Counseling 101 (2018) 2233–2240

familiarity with a health insurance program, such as Medicare, is
associated with a less effective utilization of healthcare services,
and consequently poorer health outcomes [13]. Evidence is
mounting supporting the importance of HIL in determining health
insurance status, healthcare utilization, and health behaviors,
among others [6,14,15]. However, few studies have explored HIL
within the ACA context, or assessed HIL in Hispanic communities
[14]. The purpose of this study is to assess health insurance literacy
in a vulnerable Hispanic community and to examine whether ACA
knowledge is associated with levels of health insurance literacy.

2. Methods

2.1. Study setting and data collection

Data for this study was collected from participants at Operation
Lone Star (OLS), an annual public health emergency preparedness
exercise along the Texas-Mexico border. The event is a collabora-
tion between various organizations, including local departments of
health, the Texas Department of State Health Services, the U.S.
military, institutions of higher education, and a myriad of
community organizations and volunteers. The event also provides
free primary, dental, and vision healthcare services to community
residents. In 2015, OLS events and services took place during the
week of July 27–31 at five locations across the South Texas Border
from Brownsville to Laredo. Data collection for this study took
place at one of the locations in Hidalgo County (home to over
800,000 people) [16] which was attended by almost 3000 county
residents (children and adults) over the course of the week. As in
other Texas-Mexico border counties, the overwhelming majority of
the population is of Hispanic or Latino origin (92%) [16]. The county
is characterized by high poverty rates (a third of the population
lives below the federal poverty level) and low educational
attainment (36% of individuals 25 years and over do not have a
high school degree) [16]. Lack of healthcare coverage is a main
challenge with 43% of individuals 18–64 years old being uninsured
in 2015 [17].

We employed a convenience sampling design. Data was
collected in-person by trained student interviewers, some of
whom were bilingual (English and Spanish). Students approached
OLS attendees, who were waiting to receive health services at
various stations, with information about the study and invited
them to participate. Based on the participant’s preferred language,
interviews were conducted in either English or Spanish. After
completing the anonymous interview, participants were provided
with educational material about diabetes and a bottle of water. All

Table 1
Health Insurance Literacy Measure18.

Selecting health insurance scales
Scale 1. Confidence: Choosing a health plan
How confident are you that . . . ?
Six statements on which respondents rate their level of confidence choosing a hea
1: Not at all confident, 2: Slightly confident, 3: Moderately confident, 4: Very confi

Scale 2. Comparing health plans
When comparing health insurance plans, how likely are you to . . . ?
Seven statements on which respondents indicate the likelihood of a behavior relat
1: Not at all likely, 2: Somewhat likely, 3: Moderately likely, 4: Very likely

Using health insurance scales
Scale 3: Confidence: Using a health plan
How confident are you that . . . ?
Four statements on which respondents rate their level of confidence about using h
1: Not at all confident, 2: Slightly confident, 3: Moderately confident, 4: Very confi

Scale 4: Being Proactive
When using your health insurance plan, how likely are you to . . . ?
Four statements on which respondents indicate the likelihood of being proactive w
1: Not at all likely, 2: Somewhat likely, 3: Moderately likely, 4: Very likely

study procedures were approved by the Institutional Review Board
at The University of Texas-Pan American (now The University of
Texas Rio Grande Valley).

2.2. Measurements

The survey instrument included questions assessing socio-
demographic characteristics, knowledge of the ACA, health
insurance literacy, ehealth literacy, and health status, among
others. The survey instrument was translated to Spanish. We used
existing Spanish translations when available (e.g., Census ques-
tions). For those items where no Spanish translation was available,
a Spanish native speaker translated the survey items. These were in
turn reviewed and modified by a Spanish professor with broad
academic knowledge and experience in the linguistic usage of both
Spanish and English in the region as well as a deep cultural
understanding of the target population.

2.2.1. Dependent variable: ACA knowledge
We assessed ACA knowledge by the question “How much would

you say you know about this health reform law?” Response options
included: nothing, very little, just some, a fair amount, or a great
deal. We recoded the survey responses into a dichotomous variable
(1, nothing/very little knowledge; 0, otherwise). The question
mirrored that used in a nationally-representative sample [8]
allowing us to compare our results to other studies.

2.2.2. Independent variable: health insurance literacy
We utilized the Health Insurance Literacy Measure (HILM) [18]

to assess health insurance literacy. The HILM is a valid and reliable
measure of “consumers’ ability to select and use private health
insurance.” It includes 21 items divided into two scales: selecting
insurance and using insurance, each of which encompasses two
subscales (Table 1). We administered the using health insurance
scales (Scales 3 and 4) only to those individuals who reported
having healthcare coverage.

2.2.3. Covariates
Our analysis controlled for several covariates.

2.2.3.1. Sociodemographic characteristics. We used standard self-
report U.S. Census measures to assess a range of sociodemographic
characteristics. These included gender, age, ethnicity (being of
Hispanic or Latino origin), country of birth, educational
attainment, income, and health insurance status.

lth insurance plan.
dent

ed to their behavior when choosing a health plan

ealth insurance.
dent

hen using their health insurance.

S. Ghaddar et al. / Patient Education and Counseling 101 (2018) 2233–2240 2235

2.2.3.2. Health literacy. We used two health literacy measures
assessing different aspects of the concept. For health literacy, we
used the Single Item Literacy Screener [19] which helps identify
limited reading ability, an important aspect of health literacy.
Participants responded to the question: “How confident are you
filling out forms by yourself?” Response options included: not at
all, a little bit, somewhat, quite a bit, extremely. We considered
those with “extreme” or “quite a bit” of confidence to have
adequate levels of health literacy. We assessed eHealth literacy
using eHEALS, an 8-item scale, designed to “measure consumers’
combined knowledge, comfort, and perceived skills at finding,
evaluating, and applying electronic health information to health
problems’’ [20]. Respondents indicated their level of agreement on
a 5-point Likert-type scale (1 “Strongly disagree” to 5 “strongly
agree”). Higher scores on the summation of responses reflect
higher levels of eHealth literacy. The reliability and validity of
eHEALS has been established in both English and Spanish [20,21].
Cronbach’s α for the scale was 0.96 for our Spanish-speaking
subsample (N = 495) and 0.94 for our English-speaking subsample
(N = 172).

2.2.3.3. Health status. Given that poor health and the presence of
chronic conditions may represent unmet healthcare needs and,
thus, may generate more interest in health coverage as well as
awareness of coverage options, we included two measures for
health status. We assessed general health status using a validated

Table 2
Participant characteristics by ACA knowledge.

Sociodemographic variables n % Know som
a fair am

ACA Knowledge 681 31%
Interview language 681
English 177 26 44
Spanish 504 74 26
Of Hispanic/Latino origin 667
Yes 662 99 31
No 5 1 20
Country of birth 666
U.S.-born 172 26 41
Foreign-born 494 74 28
Gender 672
Male 133 20 38
Female 539 80 29
High school graduate 667
Yes 328 49 39
No 339 51 24
Income < $20K 653 Yes 548 84 27 No 105 16 54 Uninsured 680 Yes 79 12 43 No 601 88 29 Self-rated health status 647 Poor/fair 367 57 27 Good/very good/excellent 280 43 37 Diabetes diagnosis 644 Yes 111 17 39 No 533 83 30 Adequate health literacy 666 Yes 246 37 39 No 420 63 26 Political affiliation 651 Yes 219 34 38 No 432 66 27

n Mean (SD) Mean
(SD)

Age (range 18–80) 666 38.78 (12.51) 37.35
(12.03)

eHeals (range 8–40) 667 21.30 23.91
(9.58) (10.03)

question from the Behavioral Risk Factor Surveillance System
(BRFSS) [22] asking respondents to rate their health (excellent,
very good, good, fair, poor). We recoded the health status question
as a dichotomous variable (1, fair or poor health; 0, otherwise). We
also checked whether participants had a diabetes diagnosis using
the BRFSS question that asked whether they had ever been told by
a health professional that they had diabetes.

2.2.3.4. Political affiliation. Given the politicized nature of the
health reform debate in the U.S., we asked about the political
affiliation of participants. We expect that those with any type of
affiliation (Republican, Democrat, Independent) will be more likely
to know about the ACA relative to those with no affiliation.

2.3. Data analysis

We analyzed data using SPSS (Version 24) [23]. Descriptive
analyses generated participant characteristics. We conducted
bivariate tests (two-sided chi-square and t tests, where appropri-
ate) to examine the association between ACA knowledge and
different variables. To assess the internal reliability of the HILM
scales in English and Spanish, we used the Cronbach’s alpha
coefficient; a coefficient above 0.80 for basic research tools reflects
adequate internal consistency [24]. We ran logistic regressions to
examine the association between ACA knowledge and health

e, Know nothing or very little (%) p
ount, or a great deal (%)

69%
<.001

56
74

.592
69
80

.001
59
72

.058
62
71

<.001 61 76

<.001 73 45

.012
57
71

.005
73
63

.060
61
70

<.001 61 74

.006
62
73
Mean p
(SD)
39.43 .048
(12.68)
20.15 <.001 (9.15)

2236 S. Ghaddar et al. / Patient Education and Counseling 101 (2018) 2233–2240

insurance literacy controlling for the different covariates. We used
a Type I error rate of 0.05.

3. Results

3.1. Sample characteristics

We interviewed 681 attendees at OLS. Table 2 presents
participant characteristics by ACA knowledge (know some, a fair
amount, or a great deal vs. know nothing or very little). Almost 70%
of participants knew nothing or very little about the ACA. The
majority of the interviews were conducted in Spanish with only
26% conducted in English. Participants were overwhelmingly of
Hispanic or Latino origin (99%) and female (80%). Almost three-
quarters were foreign-born, primarily in Mexico. Low educational
attainment and poverty were characteristic of the population
served at OLS with only half having a high school degree or its
equivalent and 84% reporting annual household incomes below
$20,000. Lack of healthcare coverage represented a serious
problem among participants with 88% being uninsured. More
than half (57%) reported fair or poor health and 17% had a diabetes
diagnosis. Two-thirds did not have a political affiliation. The
average age was 39 years. eHEALS scores ranged from 8 to 40 with
an average score of 21. Knowing nothing or very little about the
ACA was associated with speaking Spanish, being foreign-born, not
having a high school degree, reporting an annual household
income below $20,000, being uninsured, having fair or poor health,
having inadequate health literacy, and not being affiliated with a
political party. No or little ACA knowledge was also associated with
older age and lower levels of eHealth literacy.

3.2. Health Insurance Literacy Measure

3.2.1. Reliability analysis
Table 3 reports the internal consistency findings for the HILM

scales for the overall sample, the English-speaking, and the
Spanish-speaking samples and compares them to those from the
HILM development study [18]. Internal consistency, as measured
by Cronbach’s alpha coefficient, was high. The internal reliability of
the scales was comparable to that reported by Paez et al. [18].

3.2.2. Health insurance literacy and ACA knowledge
Table 4 presents the means and standard deviations for the four

HILM scales along with t-tests examining the association between
ACA knowledge and health insurance literacy. ACA knowledge was
significantly associated with health insurance literacy as measured
by Scales 1 and 2 of the HILM. Scales 3 and 4 were administered
only to the insured sub-sample given that the questions refer to

Table 3
Reliability measures.

Scale Cronbach’s α N Cronbach’s α
Paez et al.18

Scale 1. Confidence: Choosing a health plan 0.89 512 0.93
English 0.89 145
Spanish 0.88 367

Scale 2. Comparing health plans 0.92 556 0.96
English 0.92 151
Spanish 0.92 405

Scale 3: Confidence: Using a health plan 0.85 73* 0.93
English 0.89 40
Spanish 0.78 33

Scale 4: Being Proactive 0.89 77* 0.80
English 0.89 42
Spanish 0.88 35

* Scales 3 and 4 were administered to only those with healthcare coverage.

health insurance usage. The relationship between these two
subscales and ACA knowledge was not significant, potentially due
to the much smaller sample size.

3.3. Multivariate logistic regression analyses

To examine the association between ACA knowledge and HIL,
we conducted binary logistic regressions controlling for individu-
al-level variables associated with ACA knowledge. Table 5 (HILM
Scale 1) and 6 (HILM Scale 2) report the results with odds ratios
(OR) and 95% confidence intervals (CI). After controlling for
individual-level variables potentially associated with ACA knowl-
edge, logistic regression analyses revealed that higher levels of
health insurance literacy, as measured by HILM (Scales 1 and 2),
were associated with decreased odds of no or little ACA knowledge.
Scale 1 (confidence choosing insurance) demonstrated a higher
level of significance relative to Scale 2 (comparing health plans),
<0.001 versus 0.058, respectively. Income was also associated with ACA awareness. Participants who reported annual household incomes below $20,000 were twice as likely to have no or little knowledge of the ACA. A diabetes diagnosis was also a significant variable. Participants with a diabetes diagnosis were half as likely to have no or ACA knowledge. When including Scale 2 (comparing health plans) of the HILM in the model (Table 6), being female was also related to no or little ACA knowledge.

Given the area’s sociodemographic profile, there is a possibility
that some of OLS attendees are not U.S. citizens and, therefore, not
eligible for purchasing health insurance under the ACA. As a
consequence, this group may not be interested in knowing about
the ACA. To rule out that possibility, we repeated the analyses for
the sub-sample that was U.S.-born (Panel B of Tables 5 and 6), being
fully aware that many of those who are foreign-born may still be
citizens. The association between ACA knowledge and Scale 1 of
HIL continued to retain significance (OR:0.48; 95%CI:0.26-0.87).
Scale 2, which was marginally significant for the overall sample,
became significant in the U.S.-born subsample (OR:0.56; 95%
CI:0.32-0.96).

4. Discussion and conclusion

4.1. Discussion

This study suggests that low levels of health insurance literacy
predict no or poor knowledge of the ACA in a low-income,
predominantly Hispanic population along the Texas-Mexico
border, independent of sociodemographic factors and health
status. Overall, awareness of the ACA was very low in an
underserved Hispanic community. Even after the conclusion of
two ACA enrollment periods, over two-thirds of participants still
reported knowing nothing or very little about the health reform
law. This lack of awareness was more pronounced among those
with lower levels of health insurance literacy, as measured by two
scales reflecting confidence choosing health plans and comparing
health plans. Those with low income levels were also more likely to
have no or little knowledge of the ACA while those who had a
diabetes diagnosis were less likely to have no or little ACA
knowledge.

The low level of ACA knowledge, though more pronounced in
our sample, is consistent with other findings in the literature. In an
analysis of a nationally-representative sample conducted a few
weeks before the introduction of the health insurance exchanges,
24% of respondents knew a great deal/fair amount about the ACA
compared to 14% in our sample. When comparing ACA knowledge
among the uninsured, a group that is expected to be more
interested in obtaining healthcare coverage, the corresponding
awareness numbers were surprisingly lower (17% in the national

https://CI:0.32-0.96

https://95%CI:0.26-0.87

S. Ghaddar et al. / Patient Education and Counseling 101 (2018) 2233–2240 2237

Table 4
Health insurance literacy by ACA knowledge.

Total sample Know some, Know nothing or very little (%) p
a fair amount, or a great deal (%)

Confidence choosing (n,(%)) 512 (100%) 166 (32%) 346 (68%)
Mean (SD) 2.15 (0.78) 2.47 (0.79) 1.99 (0.73) <.001 Comparing plans (n,(%)) 556 (100%) 181 (33%) 375 (67%) Mean (SD) 2.41(0.87) 2.68 (0.91) 2.29 (0.83) <.001 Confidence using (n,(%))* 73 (100%) 33 (45%) 40 (55%) Mean (SD) 2.81 (0.84) 2.91 (0.80) 2.73 (0.87) .355 Being proactive (n,(%))* 77 (100%) 36 (47%) 41 (53%) Mean (SD) 2.92 (0.94) 3.07 (0.90) 2.79 (0.97) .201

* Scales 3 and 4 were administered to only those with healthcare coverage.

Table 5
Logistic regression results: ACA knowledge (dependent variable) and confidence
choosing insurance.

Panel A Odds ratio 95% Confidence Interval p
Total sample: n = 465

Confidence choosing insurance 0.55 (0.40, 0.75) <.001 Income < $20K 2.00 (1.16, 3.45) .006 Has diabetes 0.53 (0.30, 0.93) .028 Model fit: p-value: <.001; Nagelkerke R Square: 0.18

Panel B Odds ratio 95% Confidence Interval p
U.S.-born sub-sample: n = 130

Confidence choosing insurance 0.48 (0.26, 0.87) .016
eHEALS 0.94 (0.89, 1.00) .051
Model fit: p-value: <.001; Nagelkerke R Square: 0.34

Table 6
Logistic regression results: ACA knowledge (dependent variable) and comparing health plans.

Panel A Odds ratio 95% Confidence Interval p
Total sample: n = 510

Comparing health plans 0.77 (0.59, 1.00) .058
Income < $20K 1.94 (1.16, 3.25) .011 Has diabetes 0.51 (0.29, 0.88) .015 Female 1.66 (1.02, 2.69) .041

Model fit: p-value: <.001; Nagelkerke R Square: 0.15

Panel B U.S.-born sub-sample: n = 130 Odds ratio 95% Confidence Interval p

Comparing health plans 0.56 (0.32, 0.96) .035
Female 2.85 (1.09, 7.46) .033
Model fit: p-value: <.001; Nagelkerke R Square: 0.31

sample [8] compared to 13% for our uninsured subsample and 10%
for our uninsured U.S.-born subsample). In another study focusing
on ACA awareness in West Virginia, familiarity with the health
insurance marketplace under the ACA improved from 2013 to 2014,
yet 29% were still “not at all familiar” and 25% were “not too
familiar” in 2014 [3]. The lack of ACA knowledge in our sample is
more prominent and concerning given that our data was collected
after the conclusion of two enrollment periods in the health
insurance marketplace, where one would expect more exposure to
have occurred through outreach campaigns, media coverage, and
word-of-mouth.

The measurement of health insurance literacy varies across
studies. Many studies utilize objective knowledge-based questions
on key insurance concepts (e.g., premium, deductible, co-pay,
provider networks) through true/false statements, definitions,
and/or multiple choice questions [6,8,14,15,25]. Other studies have
utilized subjective measures such as rating an individual’s level of
confidence in understanding health insurance terms [5] or a
limited number of questions from the HILM [3]. However, no
consistent number of questions or cut-off points for defining

adequate HIL is available across studies. In contrast, HILM, despite
its limitations, represents a multi-dimensional measure that
captures multiple domains of health insurance literacy. The only
other study to our knowledge that utilizes scales 1 and 2 of the
HILM is one where HIL is assessed pre and post an intervention
aiming to enhance HIL [26]. Question scores for most of the
statements were lower for our sample compared to the 83% White,
non-Hispanic sample in the other study.

Few studies specifically examined the relationship between
ACA knowledge and HIL. Among the few that did, similar findings
were reported where familiarity of the marketplace was positively
associated with health insurance literacy [3].

The positive relationship between income levels and ACA
knowledge is consistent with other studies where ACA knowledge
and HIL were higher among higher income brackets [8]. The
finding that a diabetes diagnosis was associated with higher

likelihood of at least some ACA knowledge may reflect unmet
healthcare needs among this group, thus, generating more interest
in seeking information about healthcare coverage options. It might
also indicate more experience with the healthcare system.

4.1.1. Limitations
Our study has several limitations. First, similar to other cross-

sectional designs, we merely establish an association, and not
causality, between ACA knowledge and HIL. We also cannot
establish the directionality of the relationship, as it is likely that
knowledge of the ACA will also influence HIL. Second, cultural
dynamics may have influenced comprehension and relevance of
HILM measures. In Mexico, health insurance is universal and
nationally owned, and bears little resemblance to the U.S. market-
driven insurance system. However, the relationship between ACA
knowledge and HIL maintained significance when considering only
U.S.-born individuals, mitigating this possibility. Third, the
assessment of HILM’s psychometric properties was conducted in
a majority White, non-Hispanic population group and to our
knowledge has not been tested in Spanish-speaking groups. Our

https://2.41(0.87

2238 S. Ghaddar et al. / Patient Education and Counseling 101 (2018) 2233–2240

reliability analyses, however, reflects high internal consistency of
the scales. We also ran the analyses separately for the Spanish- and
English-speaking subgroups (not reported) and had similar
findings regarding the significance of the association between
the two HILM scales and ACA knowledge. Fourth, our ACA
knowledge assessment is measured by one subjective knowledge
question at a time when there are multiple components and
provisions to the ACA. This is especially problematic given that
comparisons between subjective and objective measures of ACA
knowledge revealed that people were not as knowledgeable as
they thought they were [8]. This, however, may be mitigated by the
fact that self-assessments of knowledge in our sample were
generally low and, therefore, unlikely to be overestimated. Finally,
given the large number of uninsured participants and, conse-
quently, the small number of those who have used insurance, it
was difficult to assess the associations between ACA knowledge
and scales 3 (confidence using health insurance) and 4 (using
health insurance/being proactive) of the HILM.

4.2. Conclusion

In this study of low-income, predominantly Hispanic individu-
als in a U.S.-Mexico border community, little or no knowledge of
the ACA was significantly associated with low health literacy levels.
This association was significant even after controlling for other
factors that could influence ACA knowledge, including sociodemo-
graphic factors and health status. This suggests that health
insurance literacy exerts an independent effect on knowledge of
major healthcare reform policies such as the ACA.

National initiatives, such as Healthy People 2020, the 2011
Department of Health and Human Services Disparities Action Plan,
and elements of the ACA, frequently include a goal of addressing
widening health disparities in the United States, which are among
the largest in the developed world [27,28]. Promoting access to
primary care and preventive services for high-risk, vulnerable
populations has been posited as one pathway to equitably improve
health outcomes, while reducing unnecessary tertiary- and
emergency care spending on advanced disease [29]. Moreover,
health insurance is a social determinant of health shown to
increase financial security, access to preventive and primary care,
and treatment for chronic conditions [29,30]. Uninsured popula-
tions are more likely to be low-income, non-white, and less likely
to report good health [28].

The ability of health policies to exert intended population
health benefits depends on participation and adoption by eligible
individuals. This is especially true in vulnerable communities
facing high burden of disease and disability, such as Hispanics in
border communities, who can benefit from policies that influence
access and entry into healthcare. While healthcare reform policies
such as the ACA may intend to increase healthcare and insurance
access for vulnerable communities, those with low health
insurance literacy may not actually know about potentially helpful
provisions, coverage options, and health reform efforts, and thus
remain uninsured [6]. Despite achieving significant gains in
coverage after the passage of the ACA, Hispanics are least likely
among all ethnic groups to have insurance [2,4,31]. This is
especially unfortunate given that Hispanics represent the fastest-
growing U.S. minority, are more likely than any other ethnic group
to delay needed care due to cost, and experience significant health
disparities in prevalence and severity of diseases such as diabetes,
colon cancer and cardiovascular disease – diseases which can be
potentially prevented, treated and alleviated through regular
primary care [28].

Low health insurance literacy is one potential pathway
influencing disparity in ACA-related coverage. Low health insur-
ance literacy predicted little or no knowledge of the ACA reform;

low awareness could in turn prevent eligible individuals from
accessing ACA enrollment opportunities. Of note, a serious lack of
ACA knowledge among participants in our Hispanic sample was
found even after the conclusion of two enrollment periods in the
health insurance marketplace.

Our study findings are mirrored in other vulnerable communi-
ties around the world. Mounting evidence is pointing to inade-
quate understanding of health insurance concepts as a barrier to
the success and sustainability of various health insurance schemes
in low- and middle-income countries [32]. In the Lucknow region
in India, low health insurance literacy has an indirect potential
impact, distinct from affordability, on the purchase of private
health insurance through its contribution to negative perceptions
about health insurance [33]. Across West Africa, education, a likely
contributor to a better understanding of the benefits of health
insurance, is an independent predictor of enrolling and remaining
in plans [34,35]. In Ghana, negative beliefs and attitudes towards
health insurance decrease the odds of remaining insured for the
richest quintile [35]. Such evidence underscores the need for
promoting health insurance literacy to ensure that government
efforts to expand access, whether through free or subsidized
coverage, achieve their goal of equity in the provision of health
care.

4.3. Practice implications

The relationship between HIL and ACA knowledge seen in our
study highlights the need for healthcare reform policies to more
strongly emphasize supporting educational programs. While
various assistance programs were established with the ACA to
provide outreach and education [36] and were successful in
reaching millions [37–39], there appears to specifically be a need
for improved, culturally-appropriate outreach efforts in vulnerable
settings, such as the low-income, Hispanic border community
surveyed in this study. Additionally, this study underscores the
need for outreach programs to increase general knowledge of
health insurance (e.g., HIL), rather than simply providing a policy-
specific education, such as determination of eligibility. Integrating
health insurance education within health delivery systems,
specifically those serving low-income communities (e.g., federally
qualified health centers) will not only promote awareness of health
insurance options but also promise to support more effective
utilization of healthcare services. Several programs and tools have
been recently developed and tested, with promising results in
terms of enhancing HIL and assisting with healthcare coverage
decisions [25,26,40,41]

Finally, it is possible that increasing general knowledge of
health insurance, a key prerequisite to “entry” into healthcare, may
serve to increase a patient’s confidence and self-efficacy to enroll in
relevant programs enabled by healthcare reform and to navigate
the healthcare system. In this manner, HIL can be seen as a type of
education that functions similarly to other recognized pathways
linking the social determinants of education, health literacy and
health. Further research is needed to elucidate these possible
mediating mechanisms between HIL and enrollment in healthcare
programs.

Funding

This work was supported by the College of Health Sciences and
Human Services (now the College of Health Professions) at the
University of Texas Rio Grande Valley (previously The University of
Texas-Pan American). The College played no role in study design; in
the collection, analysis and interpretation of data; in the writing of
the manuscript; and in the decision to submit the manuscript for
publication.

S. Ghaddar et al. / Patient Education and Counseling 101 (2018) 2233–2240 2239

Declaration of interest

None.

Acknowledgements

This research would not have been possible without the
assistance of many individuals and organizations: Hidalgo County
Health and Human Services Department (Mr. Eduardo Olivarez,
Ms. Lauren Garcia, Ms. Brenda Salázar and Ms. Nancy Treviño);
Palmview High School leadership (Dr. Armando Ocaña and Mr. Luis
García); Dr. John Gonzalez, Associate Professor of Social Work at
the University of Texas Rio Grande Valley (UTRGV), for incorporat-
ing OLS data collection within his Research for the Social Services
course (SOCW 4311); Social Work students (SOCW 4311, 2015
Summer II) for their assistance with data collection; Ms. Patricia
Garcia, research assistant, for securing and organizing health
educational materials for distribution to OLS participants; Ms.
Alma Arteaga for helping with various administrative tasks;
student interviewers and volunteers (Patricia Garcia, Reem
Ghaddar, and Elsa Suarez); Ms. Ameera Khan for assistance with
data entry; Dr. Elvia Ardalani, Professor of Writing and Language
Studies at UTRGV, for assistance with Spanish translations; Dr. John
Ronnau, Dean of the College of Health Sciences and Human
Services at the time of the study, for his unwavering support
throughout all phases of the project.

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Health insurance literacy and awareness of the Affordable Care Act in a vulnerable Hispanic population

1 Introduction
2 Methods
2.1 Study setting and data collection
2.2 Measurements
2.2.1 Dependent variable: ACA knowledge
2.2.2 Independent variable: health insurance literacy
2.2.3 Covariates
2.2.3.1 Sociodemographic characteristics
2.2.3.2 Health literacy
2.2.3.3 Health status
2.2.3.4 Political affiliation

2.3 Data analysis
3 Results
3.1 Sample characteristics
3.2 Health Insurance Literacy Measure
3.2.1 Reliability analysis
3.2.2 Health insurance literacy and ACA knowledge
3.3 Multivariate logistic regression analyses
4 Discussion and conclusion
4.1 Discussion
4.1.1 Limitations
4.2 Conclusion
4.3 Practice implications
Funding
Declaration of interest
Acknowledgements
References

Original Paper

Understanding the Intention to Use Telehealth Services in
Underserved Hispanic Border Communities: Cross-Sectional
Study

Suad Ghaddar1, PhD; Kristina P Vatcheva2, PhD; Samantha G Alvarado3, MSHS; Laryssa Mykyta4, PhD
1Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, United States
2School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley, Brownsville, TX, United States
3School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX, United States
4Social, Economic and Housing Statistics Division, Health and Disability Statistics Branch, US Census Bureau, Washington, DC, United States

Corresponding Author:
Suad Ghaddar, PhD
Department of Health and Biomedical Sciences
University of Texas Rio Grande Valley
EHABW 2.206
1201 W. University Dr.
Edinburg, TX,
United States
Phone: 1 956 665 5269
Email: suad.ghaddar@utrgv.edu

Abstract

Background: Despite the United States having one of the leading health care systems in the world, underserved minority
communities face significant access challenges. These communities can benefit from telehealth innovations that promise to
improve health care access and, consequently, health outcomes. However, little is known about the attitudes toward telehealth in
these communities, an essential first step toward effective adoption and use.

Objective: The purpose of this study is to assess the factors that shape behavioral intention to use telehealth services in underserved
Hispanic communities along the Texas-Mexico border and examine the role of electronic health (eHealth) literacy in telehealth
use intention.

Methods: We used cross-sectional design to collect data at a community health event along the Texas-Mexico border. The area
is characterized by high poverty rates, low educational attainment, and health care access challenges. Trained bilingual students
conducted 322 in-person interviews over a 1-week period. The survey instrument assessed sociodemographic information and
telehealth-related variables. Attitudes toward telehealth were measured by asking participants to indicate their level of agreement
with 9 statements reflecting different aspects of telehealth use. For eHealth literacy, we used the eHealth Literacy Scale (eHEALS),
an 8-item scale designed to measure consumer confidence in finding, evaluating, and acting upon eHealth information. To assess
the intention to use telehealth, we asked participants about the likelihood that they would use telehealth services if offered by a
health care provider. We analyzed data using univariate, multivariate, and mediation statistical models.

Results: Participants were primarily Hispanic (310/319, 97.2%) and female (261/322, 81.1%), with an average age of 43 years.
Almost three-quarters (219/298) reported annual household incomes below $20,000. Health-wise, 42.2% (136/322) self-rated
their health as fair or poor, and 79.7% (255/320) were uninsured. The overwhelming majority (289/319, 90.6%) had never heard
of telehealth. Once we defined the term, participants exhibited positive attitudes toward telehealth, and 78.9% (254/322) reported
being somewhat likely or very likely to use telehealth services if offered by a health care provider. Based on multivariate
proportional odds regression analysis, a 1-point increase in telehealth attitudes reduced the odds of lower versus higher response
in the intention to use telehealth services by 23% (OR 0.77, 95% CI 0.73-0.81). Mediation analysis revealed that telehealth
attitudes fully mediated the association between eHealth literacy and intention to use telehealth services. For a 1-point increase
in eHEALS, the odds of lower telehealth use decreased by a factor of 0.95 (5%; OR 0.95, 95% CI 0.93-0.98; P<.001) via the increase in the score of telehealth attitudes.

J Med Internet Res 2020 | vol. 22 | iss. 9 | e21012 | p. 1https://www.jmir.org/2020/9/e21012
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Conclusions: Telehealth promises to address many of the access challenges facing ethnic and racial minorities, rural communities,
and low-income populations. Findings underscore the importance of raising awareness of telehealth and promoting eHealth
literacy as a key step in fostering positive attitudes toward telehealth and furthering interest in its use.

(J Med Internet Res 2020;22(9):e21012) doi: 10.2196/21012

KEYWORDS

telehealth; eHealth literacy; health information technologies

Introduction

The United States has one of the leading health care systems in
the world, offering highly specialized and technologically
advanced medical care. At the same time, the US health care
system faces many challenges, especially when serving
vulnerable communities (eg, low socioeconomic status groups,
minority populations, and uninsured people). Primary among
these challenges is access to care, including lack of health
insurance coverage [1], shortages of primary and specialty care
providers [2], transportation difficulties [3], and language
barriers [4,5], among others. With recent advances in
technology, telehealth promises to address many of these access
challenges.

Telehealth is commonly defined as “the use of electronic
information and telecommunications technologies to support
and promote long-distance clinical health care, patient and
professional health-related education, public health, and health
administration” [6]. This definition encompasses a broad scope
of remote health care services (eg, telemedicine, telemonitoring,
mobile health [mHealth] apps, patient portals). For the purposes
of this study, telehealth as presented to participants and
supported by the statements assessing their attitudes is more
representative of telemedicine rather than other health
information technologies (HIT). We opted to use the term
telehealth in the survey because of its broader scope and higher
likelihood of public recognition.

Several models, such as the technology acceptance model [7]
and the unified theory of acceptance and use of technology [8],
have been developed to depict consumer interest and willingness
to use technology. With the expansion of technology into the
health care sector, these models, with various modifications,
have been applied to the adoption of HIT such as mHealth apps,
patient portals, telemonitoring, and telemedicine [9]. Only
recently have efforts been directed at developing technology
use models specific to the health context [10,11]. In addition to
the common key concepts across previous models (attitudes,
behavioral intention, and behavior), HIT-specific models expand
the focus from the technology’s features to incorporate end-user
characteristics (eg, health status, internet self-efficacy) and the
realm of social influence. The role of electronic health (eHealth)
literacy, commonly defined as “consumers’ combined
knowledge, comfort, and perceived skills at finding, evaluating,
and applying electronic health information to health problems”
[12], has rarely been integrated in these models [11]. Yet
evidence has been mounting in support of its role in the adoption
of various HIT apps such as patient portals [13,14] and mHealth
[15,16]. In line with conceptual models depicting end-user
characteristics influencing behavioral intention to use HIT

through the mediating effects of perceived ease of use, perceived
usefulness, and attitudes [10], we hypothesize that eHealth
literacy will exhibit a similar indirect effect on the behavioral
intention to use telehealth.

Most telehealth research in the United States has initially
focused on telehealth adoption from the perspective of health
care providers [17-21] and health systems [20,22] or on policies
[23] and reimbursement models that facilitate its adoption [24].
The consumer/patient perspective has just recently been more
extensively considered. However, most patient research on
telehealth has been conducted at the international level
[21,25,26] or has focused on white, non-Hispanic populations
within the United States [27]. The perspective and characteristics
of individuals from vulnerable US minority communities has
not received much attention, although there are a few notable
exceptions focused primarily on African Americans [28,29].
Given that Hispanics are the largest minority group in the United
States [30], it is important to understand the factors that
influence their acceptance of telehealth, especially given that
acceptance is a predictor of adoption [31]. This entails
examining several dimensions, primary among which are
end-user characteristics and attitudes toward health technology.
Thus, the purpose of this study is to assess the factors that shape
behavioral intentions to use telehealth services in vulnerable,
marginalized Hispanic communities along the Texas-Mexico
border and examine the role of eHealth literacy in telehealth
intention use.

Methods

Study Setting
We collected data from participants at Operation Lone Star
(OLS), a joint military and civilian public health emergency
preparedness exercise that takes place annually along the
Texas-Mexico border. OLS is a cooperative effort between the
Texas Department of State Health Services, Cameron County
Public Health Department, Hidalgo County Health and Human
Services, City of Laredo Health Department, the Texas State
Guard, and various community volunteer organizations. The
event brings free health care services to area residents. These
include child immunizations, sports physicals, hearing
screenings, vision screenings for prescription glasses, diabetes
and blood pressure screenings, and dental services, among
others.

In 2018, OLS events took place during the week of July 23-27
at 6 locations across 4 border counties (Cameron, Hidalgo, Starr,
and Webb). For this study, we collected data at one of the two
Hidalgo County sites, which provided services to 2294 children
and adult county residents over the course of OLS week. Hidalgo

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County is the largest county along the Texas-Mexico border; it
is home to almost 850,000 people, the overwhelming majority
of whom are of Hispanic or Latino origin (92%) [30]. The
county is characterized by high poverty rates (almost a third of
the population lives below the federal poverty level) and low
educational attainment (36% of individuals aged 25 years and
over do not have a high school degree) [30]. Lack of health care
coverage is a major access challenge with 43% of individuals
aged 18 to 64 years being uninsured in 2018 [32].

Recruitment and Data Collection
Data were collected in person by students participating in a
special course-based undergraduate research experience, two
graduate research assistants, and the first author. All data
collection team members completed training in the ethical
conduct of research, survey administration, and interviewing
techniques, as well as additional requirements for participation
at OLS. Most team members were bilingual (English and
Spanish).

We employed a convenience sampling design to recruit
participants. The data collection team approached event
attendees waiting to receive health services at various stations,
provided them with information about the study and invited
them to participate. The 15- to 20-minute interviews were
conducted in either English or Spanish, based on the
participant’s preferred language. After completing the
anonymous interview, participants were provided with a
drawstring bag, a bottle of water, and a chance to enter in a
raffle for one of sixty $50 gift cards from a local grocery store.

All study procedures were approved by the institutional review
board at the University of Texas Rio Grande Valley (UTRGV).

Survey Instrument
The survey instrument included questions assessing
sociodemographic information, health status, eHealth literacy,
and telehealth-related variables measuring attitudes and
behavioral intentions to use telehealth. For sociodemographic
characteristics and health status variables, we used questions
from existing national surveys (eg, US Census Bureau, Centers
for Disease Control and Prevention [CDC]) for which existing
Spanish translation was available. For the remaining variables,
where no Spanish translation was available, a bilingual (English
and Spanish) graduate student translated the survey. The survey
was then piloted by bilingual and native Spanish-speaking
interviewers who assessed participant understanding of both
the English and Spanish versions. Minor modifications were
made to reflect the area’s culture and local linguistic Spanish
use.

We provided participants with the following definition before
asking about the two main telehealth-related variables (outcome
measure: behavioral intention to use telehealth; predictor
measure: attitudes toward telehealth): Telehealth uses
technology to access and manage health care outside of doctors’
offices or clinics. Some examples are receiving care from your
health care provider by video, remote monitoring of blood
pressure or heart rate, or checking your laboratory results online.

Outcome Measure: Behavioral Intention to Use
Telehealth Services
We assessed the behavioral intention to use telehealth by the
question, “How likely are you to use telehealth services if they
were offered by your provider?” Response options included:
very likely, somewhat likely, not very likely, and would not use
telehealth services.

Predictor Measures

Attitudes Toward

Telehealth

We assessed attitudes toward telehealth by asking respondents
to rate their level of agreement (5-point Likert scale: 1=strongly
disagree to 5=strongly agree) with 9 statements reflecting
different aspects of telehealth use such as perceived ease of use,
perceived usefulness, and perceived cost effectiveness. The
statements were adopted with minor modifications from a study
on patient telemedicine readiness in a Louisiana oncology
practice [28]; the instrument was developed based on the
technology acceptance model [7,33] and the fit between
individuals, task, and technology framework [34]. The
summated scale (range 9 to 45 with higher scores reflecting
more positive attitudes) demonstrated good internal consistency
(Cronbach alpha of .794); the internal consistency was lower
for the Spanish surveys (Cronbach alpha of .757) as compared
with the English surveys (Cronbach alpha of .839) but still above
the acceptable .70 threshold value [35].

Telehealth Readiness
We assessed telehealth readiness with 3 questions related to the
methods participants used to (1) make an appointment with their
health care provider, (2) communicate with their health care
provider, and (3) keep track of their personal health information.
A participant was considered telehealth-ready if they had
communicated with their health care provider to make an
appointment or discussed their test results via a website and/or
email or kept track of their personal health information using
an online system.

eHealth Literacy
We used the 8-item eHealth Literacy Scale (eHEALS) to assess
eHealth literacy [12]. For each item, respondents indicated their
level of agreement on a 5-point Likert scale (1=strongly disagree
to 5=strongly agree). Higher scores on the summation of
responses (range 8 to 40) reflect higher levels of eHealth
literacy. The reliability and validity of eHEALS has been
previously established in both English and Spanish [12,36].
Cronbach alpha, assessing the internal consistency of eHEALS,
was .916 for our sample (.904 and .924 for the English- and
Spanish-speaking subsamples, respectively).

Sociodemographic

Variables

We assessed several sociodemographic variables, including age,
sex, employment status, marital status, educational attainment,
country of birth, ethnicity, annual household income, and health
care coverage.

Internet Knowledge and Skills
We used 2 questions to examine participant internet knowledge
and skills. The first question (dichotomized yes/no) asked

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participants whether they have “ever gone online to access the
internet or to send and receive emails.” The second question
asked respondents to self-rate their internet skills. Responses
were dichotomized as yes=fairly skilled, very skilled, or expert
and no=not at all skilled or not very skilled.

Health Status
To assess the health of participants, we used 2 measures. The
first measure examined general health status through the CDC’s
validated self-rated health status measure [37,38]: “Would you
say that in general your health is?” The 5 response categories
were excellent, very good, good, fair, and poor. We
dichotomized the responses: 1=fair or poor health versus
0=otherwise. The second measure, chronic condition, assessed
the presence of chronic health conditions by asking participants
whether they have ever been told by a health professional that
they had diabetes, asthma, heart disease, cancer, or arthritis.
Those reporting a diagnosis of one or more conditions were
coded as 1=having at least one chronic condition versus
0=otherwise.

Statistical Analysis
We used descriptive analyses (frequencies and percentages for
categorical variables and means and standard deviations for
continuous variables) to summarize and examine study data by
the levels of the outcome variable (intention to use telehealth
services). To take into account the ordinal nature of the outcome
variable (that there is a clear ordering of the levels but with
unknown absolute distances between them) and the fact that
the number of the ordered levels is fewer than 5, we conducted

bivariate and multivariate proportional odds regression analyses
[39,40]. To identify independent factors associated with the
intention to use telehealth services, we fitted proportional odds
regression models with variables selected based on the bivariate
analyses and controlling for potential confounders. We assessed
for potential multicollinearity and 2-way interactions between
the variables included in the models. Model-based adjusted
odds ratios (ORs) for lower versus higher response levels for
the intention to use telehealth services and their respective 95%
confidence intervals were estimated. The assumption of the
proportional odds model that the effects of any explanatory
variables are proportional across any response levels were tested
using the score test and likelihood ratio test. To select a good
model, we used Akaike information criterion.

To test for the potential mediation effect of telehealth attitudes
on the effect of eHealth literacy on the intention to use telehealth
services (Figure 1), we conducted mediation analysis. A
mediator is a variable M (eg, telehealth attitudes) that falls into
the casual pathway between an independent variable X (eg,
eHealth literacy) and an outcome variable Y (eg, intention to
use telehealth services) and at least partially explains the effects
of X on Y. To examine a potential mediation effect, we
decomposed the total effect of eHealth literacy on the intention
to use telehealth services (c path) into two causal paths, direct
effect (c′ path between eHealth literacy and intention to use
telehealth services not passing through telehealth attitudes) and
indirect effect (path between eHealth literacy and the intention
to use telehealth services passing through telehealth attitudes)
[41].

Figure 1. Path diagram of the hypothesized mediation model.

The focus of the mediation analysis was to evaluate and estimate
the indirect effect of eHealth literacy on the intention to use
telehealth services using the product of coefficients approach,
c=ab and employing the method proposed by VanderWeele et
al [42] by fitting the regression models (1) and (2) below in the
settings of an ordinal outcome and a continuous mediator:

where equation (1) is a linear regression equation for the
continuous mediator M on the explanatory variable X and a
covariate Z with intercept i0 and slopes a and b, respectively,

and a random error ε~N(0,σ2); and equation (2) is a proportional
odds regression model for the log of probability of a smaller
response Y≤j compared with the probability of a larger response
Y>j on independent variable X with regression coefficient c′,
mediator variable M with regression coefficient b, and covariate
Z with regression coefficient d*, lj are the intercepts, and
j=1,2,3,4 is the number of the ordered categories in the outcome
variable Y.

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Under the assumptions that the reference level in the outcome
variable is common, the model is correctly specified, and the
mediator follows a normal distribution with a constant
conditional variance, the natural indirect effect (NIE) and natural
direct effect (NDE) for an exposure X on the outcome Y
comparing any two X=x and X=x* for a proportional odds
model are given by:

NIE ≈ exp {ab(x–x*)} (3)

NDE ≈ exp {c′(x–x*)} (4)

The standard errors of the log of the aforementioned effects
were estimated using the Delta method [42,43].

Proportion of mediated effect in the total effect was computed
as:

Multivariate regression and mediation analyses were conducted
using complete case analysis under the missing at random
(MAR) assumptions. The 294 participants used to build the
model did not differ from the original sample in terms of
sociodemographic characteristics: age, gender, education level,
marital status, employment status, language, income, and health
insurance status.

All statistical analyses were generated using SAS software
version 9.4 (SAS Institute Inc). All statistical tests were 2-sided
and performed at .05 significance level.

Results

Sample Characteristics
Table 1 reports sample characteristics by the levels of the
outcome variable (intention to use telehealth services). Most of
the participants were female (261/322, 81.1%), Hispanic
(310/319, 97.2%), and of low socioeconomic status (219/298,
73.5% reported annual household incomes below $20,000).
Only 59.3% (191/322) were high school graduates and 41.6%
(131/315) were employed. Around a third (117/320, 36.6%)
were born in the United States; a similar percentage (126/322,
39.1%) chose to complete the survey in English. Over half of
participants (181/322, 56.2%) were married. The average age
was 43 (SD 14.1) years. Not surprisingly, given the nature of
the event where free health care services are the main attraction,
only a fifth (65/320, 20.3%) were insured. A large proportion
(136/322, 42.2%) self-rated their health as fair or poor,
considerably higher than the corresponding numbers of 30%
and 21% at the county and state levels, respectively [44]. Over

a third (124/321, 38.6%) indicated having at least one chronic
health condition.

Regarding familiarity with technology, only 59.9% (193/322)
indicated having gone online to access the internet or
send/receive emails. Almost 40% (118/306, 38.6%) self-rated
their internet skills as not skilled at all or not very skilled. As a
result, a small proportion of participants exhibited telehealth
readiness: only 23.9% (77/322) reported having used technology
to communicate with their health care provider or keep track of
their personal health information. While only 9.4% (30/319)
had heard of telehealth, once provided with a telehealth
definition, 4 in 5 respondents were either very likely (137/322,
42.5%) or somewhat likely (117/322, 36.3%) to use telehealth
services, if offered by their health care provider. The remainder
were either not very likely (25/322, 7.8%) or indicated that they
would not use these services (43/322, 13.4%). The average
eHEALS score was 28 (SD 7.1, range 8 to 40) and the average
telehealth attitudes score was 30 (SD 5.9, range 13 to 45).

Bivariate analyses (Table 1), using proportional odds regressions
to predict lower versus higher intentions to use telehealth
services, revealed that only country of birth (P=.01), language
in which survey was administered (P=.01), eHEALS score
(P=.03), and telehealth attitudes (P<.001) were significantly associated with the intention to use telehealth services.

Overall, respondents reported positive attitudes toward the use
of telehealth. The majority indicated agreement with the idea
that telehealth can save time and money and provide access to
specialized care (Figure 2). An area of concern for more than
half of respondents (169/321, 52.6%) was related to their ability
to understand the physician through a telehealth video or call.

The significant variables based on bivariate analysis, with the
exception of language, were then included in a multivariate
proportional odds regression model. Given the high level of
collinearity between language and country of birth (r=.732),
we opted to include only the country of birth for its more
objective level of measurement relative to the language variable.
The language variable was based on the language in which
participants chose to complete the survey (English or Spanish);
while indicative of language preference, it does not necessarily
measure the level of English language proficiency or correspond
to language measures in other studies or national datasets.
Country of birth, on the other hand, is a standard measure and
allows for comparability across studies [45]. To ensure that the
language variable did not impact the results differently, we
repeated the analysis with language instead of country of birth
and the results were similar.

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Table 1. Sample characteristics by the levels of the intention to use telehealth services if offered by a health care provider.

P valueaWould not use
(n=43)

Not very likely
(n=25)

Somewhat likely
(n=117)

Very likely
(n=137)

Total sample
(n=322)

Variables

Categorical, n (%)

.5438 (88)21 (84)91 (78)111 (81)261 (81)Female

.5841 (95)24 (100)112 (97)133 (98)310 (97)Hispanic

.3330 (81)18 (72)71 (65)100 (79)219 (74)Incomeb <$20K

.5623 (54)19 (76)71 (61)78 (57)191 (59)High school graduate

.7010 (24)12 (50)56 (48)53 (40)131 (42)Employed

.0118 (43)11 (46)49 (42)39 (29)117 (37)US-born

.0121 (48)14 (56)51 (44)40 (29)126 (39)English survey

.9227 (63)11 (44)66 (56)77 (56)181 (56)Married

.8410 (23)4 (16)22 (19)29 (21)65 (20)Insured

Health status

.2624 (56)9 (36)48 (41)55 (40)136 (42)Fair/poor

.3716 (37)9 (36)42 (36)57 (42)124 (39)Chronic condition

Internet skills

.4521 (49)19 (76)77 (66)76 (56)193 (60)Gone onlinec

.5318 (46)9 (38)35 (31)56 (43)118 (39)Not/not very skilled

Telehealth

.698 (19)6 (24)30 (26)33 (24)77 (24)Telehealth ready

.921 (2)4 (16)14 (12)11 (8)30 (9)Heard of telehealth

Continuous, mean (SD)

.6246 (17)41 (12)41 (14)44 (14)43 (14)Age in years

.0325 (9)29(6)28 (7)28 (7)28 (7)eHEALSd

<.00123 (6)28 (5)30 (4)34 (5)30 (6)Telehealth attitudes

aWald chi-square test.
bAnnual household income.
cGone online to access the internet or to send and receive emails.
deHEALS: eHealth Literacy Scale.

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Figure 2. Participant responses to attitudinal statements related to telehealth use.

As shown in Table 2, eHEALS was no longer significantly
associated with the intention to use telehealth (P=.68). The
telehealth attitudes variable maintained significance, indicating
that a 1-point increase in telehealth attitudes reduced the odds
of lower versus higher response in the intention to use telehealth

services by 23% (OR 0.77, 95% CI 0.73-0.81). In addition,
US-born participants, compared with foreign-born participants,
had 2.20 (95% CI 1.35-3.58) times higher odds of lower versus
higher response in the intention to use telehealth services.

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Table 2. Multivariate proportional odds regression model of factors associated with lower versus higher response to the intention to use telehealth
services (n=294).

P valuebORa (95% CI)Coefficient estimate (SE)Variable

.681.01 (0.97-1.04)0.01 (0.02)eHEALSc

<.0010.77 (0.73-0.81)–0.28 (0.03)Telehealth attitudes

.0022.20 (1.35-3.58)0.79 (0.25)US-born

aOR: odds ratio.
bWald chi-square test.
ceHEALS: eHealth Literacy Scale.

Mediation Analysis
Bivariate analysis (Table 1) revealed that eHealth literacy, as
measured by eHEALS, was significantly associated with the
intention to use telehealth services (P=.03). After controlling
for the effect of country of birth, eHEALS remained
significantly associated with the outcome (P=.02). The
assumptions of the linear regression model (telehealth attitudes
regressed on eHEALS and country of birth) for normal
(Shapiro-Wilk test P value=.07) and homoscedastic errors
(White test P value=.16) were satisfied. Linear regression
analysis showed that eHEALS was significantly associated with
the hypothesized mediator, telehealth attitudes (P=.002),
controlling for the effect of country of birth (Table 3). Based
on the proportional odds regression model, eHEALS was no
longer associated (P=.68) with the intention of telehealth use

after adjusting for telehealth attitudes and country of birth (Table
3). This indicated that telehealth attitudes fully mediated the
association between eHEALS and intention to use telehealth
services. Using the Delta method, the estimated NIE of eHEALS
on the intention to use telehealth was significant (OR 0.95, 95%
CI 0.93-0.98; P<.001). For a 1-point increase in eHEALS, the odds of lower use of telehealth services decreased by a factor of 0.95 (5%) via the increase in the score of telehealth attitudes, controlling for the effect of country of birth. The estimated proportion of mediated effect of eHEALS in the total effect was 117.87% (Table 3). The fact that the direct effect c′ was opposite in sign to the indirect ab is known as inconsistent mediation [46] because the mediator acts like a suppressor variable. For the same reason, the estimated proportion of the mediated effect was greater than 1 [46].

Table 3. Adjusted estimated effects based on mediation analysis conducted with linear and proportional odds regression models (n=294).a

P valueORb (95% CI)Coefficient estimate (SE)Variable

.002N/Ac0.18 (0.05)eHealth literacy (a coefficient)

<.0010.77 (0.73-0.81)–0.28 (0.03)Telehealth attitudes (b coefficient)

.681.01 (0.97-1.04)0.01 (0.02)eHealth literacy (direct effect, c′ coefficient)

<.0010.95 (0.93-0.98)–0.05 (0.01)eHealth literacy (indirect effect, ab coefficients)

aAll effects are adjusted for country of birth.
bOR: odds ratio.
cCoefficient was estimated using linear regression.

Discussion

Principal Findings
This study examined the attitudes of vulnerable minority groups
toward telehealth and assessed the factors that shape their
intention to use telehealth services. Findings revealed that
marginalized Hispanic communities along the Texas-Mexico
border have had limited exposure to telehealth. Despite that,
participants exhibited generally positive attitudes toward
telehealth which, in turn, were associated with a higher
likelihood of using telehealth services if offered by one’s health
care provider. Additionally, these positive attitudes mediated
the relationship between eHealth literacy and the intention to
use telehealth, highlighting the important role that eHealth
literacy plays in shaping attitudes and, ultimately, telehealth
acceptance.

The positive association between attitudes toward telehealth
and intention to use telehealth services is in line with the basic
concept underlying the different technology acceptance
frameworks [7,47,48], where reactions to using a certain
information technology (attitudes, perceived usefulness,
perceived ease of use) impact the behavioral intention to use
that technology. Studies specific to HIT also support that
pathway [10,25,26].

While health information technology acceptance and adoption
models have evolved over time to integrate end-user
characteristics, these models have rarely considered eHealth
literacy. Kim and Park [10] include the technological/computer
literacy domain of eHealth literacy [49,50], which they term as
HIT self-efficacy. In their model, HIT self-efficacy is
conceptualized to affect perceived usefulness and ease of use,
which in turn shape attitudes and the behavioral intention to use
HIT. Our findings are in line with that causal pathway, although

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eHealth literacy is a much broader concept that encompasses
many other skills beyond technological literacy and includes
functional, communicative, critical, and transactional eHealth
literacy skills [49]. This highlights the need to integrate eHealth
literacy, with its multidimensional characteristics, in HIT
conceptual frameworks. This is especially relevant as the
evidence is mounting on eHealth literacy’s role in extending
the digital divide to health care [51,52] and, just as importantly,
in facilitating the adoption of various eHealth apps. For example,
our finding of the significance of eHealth literacy to telehealth
use intention mirrors other findings where eHealth literacy has
been found to have a significant association with the use of or
the intention to use other consumer eHealth platforms such as
patient portals [13,14] and mHealth [15,16].

Limitations
There are several limitations to this study. First, our outcome
variable, the behavioral intention to use telehealth, was assessed
by the question, “How likely are you to use telehealth services
if they were offered by your provider?” This may be interpreted
to assume that a participant has a health care provider, a
questionable premise given the high rate of uninsurance in our
sample. However, it is worth noting that despite being uninsured,
participants interact with health care providers in a variety of
traditional and nontraditional settings. Some have providers
through local safety net clinics; data collected at the same event
in 2015 from participants with an almost identical
sociodemographic profile showed that 54% of the uninsured
had received health care services at the local county health clinic
[53]. Area residents also seek health care services across the
border in Mexico; a population-based survey of 1405 Texas
border county residents revealed that respondents, especially
the uninsured, regularly sought health care services in Mexico,
with 38% of respondents reporting a doctor’s visit within the
past 12 months [54]. Furthermore, many uninsured community
members interact with health care providers for their children’s
health care services; while 43% of Hidalgo County’s population
between the ages of 18 and 64 years did not have health care
coverage in 2018, the corresponding rate for those under age
19 years was 13.6% [32], reflecting the higher insurance rates
among children through the Children’s Health Insurance
Program (CHIP) or children’s Medicaid.

Second, similar to many health information technology
acceptance and adoption studies, data were collected at a single
time point, with no experimental manipulation or random
assignment, resulting in the inability to establish causality.

Third, to our knowledge, there are no previous studies that
evaluate the causal relationship between eHealth literacy,
telehealth attitudes, and intention to use telehealth services.
Therefore, our mediation analysis was not based on a
theoretically defined causal chain of variables. However, some
models depicting the causal pathway between end-user
characteristics and the intention to use HIT include variables
that can serve as proxies for eHealth literacy such as HIT
self-efficacy [10]; the depicted causal pathways in these models
support our findings. It is worth noting that these studies also
use cross-sectional data.

Fourth, our introduction of telehealth to participants did not
take into account the cost of telehealth services, a highly relevant
factor for poor, uninsured communities. Another limitation is
that most participants had not heard of telehealth. Thus, attitudes
and the intention to use telehealth reflected a hypothetical
scenario to most participants. Nevertheless, such positive
attitudes point to a window of opportunity that can be reinforced
by well-designed virtual platforms that take into account the
eHealth literacy skills of the target population.

Finally, the sociodemographic homogeneity of the sample did
not allow for capturing the impact of ethnicity, educational
attainment, socioeconomic status, and other sociodemographic
factors on the intention to use telehealth; such factors have been
shown to exhibit varying levels of influence on the use of
eHealth [55]. However, given our focus on vulnerable minority
groups, this homogeneity allows us to better control for such
effects. One exception is that the overrepresentation of females
in our sample, a reflection of event attendance, may limit the
generalizability of our findings to males. While the higher
representation of women relative to men at the event may be
an indication of working schedules and women assuming
responsibilities for child immunizations and physicals, it may
also be reflective of the well-documented gender differences in
accessing health care and adhering to preventive care guidelines
[56,57]. Such differences may potentially extend to the
intentions to use telehealth services.

Future Research
Multiple venues exist to expand our knowledge on telehealth
use. First, it is important to strengthen the evidence on the causal
pathways leading to telehealth adoption by incorporating
longitudinal research designs [9]. Although our mediation
analysis only showed mathematically that telehealth attitudes
were a significant mediator of the effect of eHealth literacy on
the intention to use telehealth services, this finding provides a
strong basis for testing the causal link through future
longitudinal data or experimental designs. In addition, it is
important to expand the literature on the intention to use
telehealth to include actual adoption and use. The recent
adoption of telehealth due to the COVID-19 pandemic provides
a fertile ground for research on the facilitators and barriers to
telehealth use in vulnerable communities. Future research should
also address the development of attitudinal measures toward
telehealth that exhibit good psychometric properties. Most
studies in the literature, similar to our study, use various sets of
statements that reflect the particular research question and
setting [28,58,59]. Furthermore, it is important to incorporate
additional factors such as social influence [9] and privacy and
data security issues [60] and explore their impact on telehealth
acceptance and adoption. The latter is especially important since
perceived privacy and security concerns have been shown to
impact the intention to use telehealth services [61]. Finally, it
is essential to explore telehealth’s role in extending services to
the uninsured and investigate delivery and reimbursement
models specifically targeting this population.

Conclusions
To our knowledge, this study is among the first to focus
primarily on a vulnerable Hispanic population and use mediation

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analysis to explore the role of eHealth literacy on the behavioral
intention to use telehealth. Our findings contribute significantly
to promoting telehealth adoption in communities where access
to health care services is a major challenge and an understanding
of end-user characteristics is key to successful intervention
design and adoption.

Telehealth promises to address many of the access challenges
facing ethnic and racial minorities, rural communities, and

low-income populations. Understanding the factors that
influence its acceptance and, subsequently, its adoption is an
essential first step to designing culturally relevant platforms
that take into account key characteristics of these communities.
Raising awareness about telehealth and developing interventions
that target eHealth literacy skills promise more positive attitudes
and more willingness to engage in telehealth use.

Acknowledgments
We would like to thank the following individuals and organizations for their help and support, without which this study would
not have been possible: Hidalgo County Health and Human Services Department and Pharr-San Juan-Alamo (PSJA) Independent
School District leadership for facilitating and supporting our participation at OLS at the PSJA Early College High School site;
UTRGV Health and Human Performance (HLTH 3305, Summer III) students for their assistance with data collection and data
entry; Ms Alejandra Hernandez, HLTH 3305 student, for securing promotional items for distribution to OLS participants; Ms
Yajaira Ayala, graduate research assistant, for assistance with coordination of OLS participation logistics, data collection, and
data entry; and Mr Juan Carlos Ayala and Ms Sylvia Hernandez, departmental administrative assistants, for administrative support.
We would also like to thank Ms Reem Ghaddar for proofreading multiple versions of the manuscript. This study was partially
funded by the College of Health Professions at UTRGV and by UTRGV’s Collaborative on Population Health Innovation and
Improvement (CoPHII). The College and CoPHII played no role in study design; in the collection, analysis, and interpretation
of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.

Authors’ Contributions
SG led the design of the study, data collection, and manuscript preparation. KV conducted the statistical analyses and drafted the
statistical methodology and results. SGA assisted with the literature review, coordination of study logistics, data collection, data
cleaning, and descriptive analyses. LM assisted with the design of the survey and with manuscript preparation. All authors
reviewed multiple drafts of the manuscript and edited and approved the final manuscript.

Conflicts of Interest
None declared.

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Abbreviations
CDC: Centers for Disease Control and Prevention
CoPHII: Collaborative on Population Health Innovation and Improvement
eHEALS: eHealth Literacy Scale
eHealth: electronic health
HIT: health information technologies
MAR: missing at random
mHealth: mobile health
NDE: natural direct effect
NIE: natural indirect effect
OLS: Operation Lone Star
OR: odds ratio
PSJA: Pharr-San Juan-Alamo
UTRGV: University of Texas Rio Grande Valley

Edited by G Eysenbach; submitted 09.06.20; peer-reviewed by L Marceau; comments to author 10.07.20; revised version received
15.07.20; accepted 26.07.20; published 03.09.20

Please cite as:
Ghaddar S, Vatcheva KP, Alvarado SG, Mykyta L
Understanding the Intention to Use Telehealth Services in Underserved Hispanic Border Communities: Cross-Sectional Study
J Med Internet Res 2020;22(9):e21012
URL: https://www.jmir.org/2020/9/e21012
doi: 10.2196/21012
PMID:

©Suad Ghaddar, Kristina P Vatcheva, Samantha G Alvarado, Laryssa Mykyta. Originally published in the Journal of Medical
Internet Research (http://www.jmir.org), 03.09.2020. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly
cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright
and license information must be included.

J Med Internet Res 2020 | vol. 22 | iss. 9 | e21012 | p. 13https://www.jmir.org/2020/9/e21012
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