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Health Literacy Mediates the Relationship Between Educational Attainment and Health Behavior: A Danish Population-Based Study

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Individuals with a lower education level frequently have unhealthier behaviors than individuals with a higher education level, but the pathway is not fully understood. The aim of this study was to investigate whether health literacy mediates the association between educational attainment and health behavior (smoking, physical inactivity, poor diet) and obesity. The study included respondents ages 25 years or older drawn from a large population-based survey conducted in 2013 (N = 29,473). Two scales from the Health Literacy Questionnaire were used: (a) Understanding health information well enough to know what to do and (b) Ability to actively engage with health care providers. Multiple mediation analyses were conducted using the Karlson-Holm-Breen method. The study showed that health literacy in general and the ability to understand health information in particular mediated the relationship between educational attainment and health behavior, especially in relation to being physically inactive (accounting for 20% of the variance), having a poor diet (accounting for 13% of the variance), and being obese (accounting for 16% of the variance). These findings suggest that strategies for improving health behavior and reducing health inequalities may benefit from adopting a stronger focus on health literacy within prevention, patient education, and other public health interventions.
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Journal of Health Communication
International Perspectives
ISSN: 1081-0730 (Print) 1087-0415 (Online) Journal homepage: http://www.tandfonline.com/loi/uhcm20
Health Literacy Mediates the Relationship
Between Educational Attainment and Health
Behavior: A Danish Population-Based Study
Karina Friis, Mathias Lasgaard, Gillian Rowlands, Richard H. Osborne & Helle
T. Maindal
To cite this article: Karina Friis, Mathias Lasgaard, Gillian Rowlands, Richard H. Osborne
& Helle T. Maindal (2016): Health Literacy Mediates the Relationship Between Educational
Attainment and Health Behavior: A Danish Population-Based Study, Journal of Health
Communication, DOI: 10.1080/10810730.2016.1201175
To link to this article: http://dx.doi.org/10.1080/10810730.2016.1201175
Published online: 26 Sep 2016.
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Health Literacy Mediates the Relationship Between Educational
Attainment and Health Behavior: A Danish Population-Based
Study
KARINA FRIIS
1
, MATHIAS LASGAARD
1,2
, GILLIAN ROWLANDS
3,4
, RICHARD H. OSBORNE
5
, and HELLE T. MAINDAL
3,6
1
DEFACTUMPublic Health and Health Services Research, Central Denmark Region, Aarhus, Denmark
2
Department of Psychology, University of Southern Denmark, Odense, Denmark
3
Department of Public Health, Aarhus University, Aarhus, Denmark
4
Institute of Health and Safety, Newcastle University, Newcastle upon Tyne, United Kingdom
5
Centre for Population Health Research, Deakin University, Geelong, Australia
6
Steno Health Promotion Centre, Steno Diabetes Centre, Gentofte, Denmark
Individuals with a lower education level frequently have unhealthier behaviors than individuals with a higher education level, but the
pathway is not fully understood. The aim of this study was to investigate whether health literacy mediates the association between
educational attainment and health behavior (smoking, physical inactivity, poor diet) and obesity. The study included respondents ages
25 years or older drawn from a large population-based survey conducted in 2013 (N= 29,473). Two scales from the Health Literacy
Questionnaire were used: (a) Understanding health information well enough to know what to do and (b) Ability to actively engage with
health care providers. Multiple mediation analyses were conducted using the Karlson-Holm-Breen method. The study showed that health
literacy in general and the ability to understand health information in particular mediated the relationship between educational attainment
and health behavior, especially in relation to being physically inactive (accounting for 20% of the variance), having a poor diet (accounting
for 13% of the variance), and being obese (accounting for 16% of the variance). These findings suggest that strategies for improving health
behavior and reducing health inequalities may benefit from adopting a stronger focus on health literacy within prevention, patient
education, and other public health interventions.
The persistence of social inequality in health is a major concern in
public health (Mackenbach, 2012). In general, individuals with low
education levels have a poorer health status than well-educated
individuals, which is indicated by worse self-reported health and
physical functioning as well as by higher levels of morbidity and
disability and a shorter life expectancy (Diderichsen et al., 2012;
Gallo et al., 2012; Mackenbach, 2012; Ullits et al., 2015). It is
frequently reported that people with low educational attainment
have higher rates of unhealthy behaviors (such as smoking, physi-
cal inactivity, and poor diet) and obesity than people with higher
levels of education (Buck & Frosini, 2012; Laaksonen et al., 2008;
Lantz, Golberstein, House, & Morenoff, 2010; Marmot, 2005;
McFadden, Luben, Wareham, Bingham, & Khaw, 2008; Nandi,
Glymour, & Subramanian, 2014; Nordahl et al., 2014; Stringhini
et al., 2010,2011). In order to target these disparities, it is important
to understand how educational attainment is related to health
behavior. A number of competing mechanisms may mediate the
relationship between education and health behavior. Such mechan-
isms include work and economic conditions as well as sociopsy-
chological resources (Cutler & Lleras-Muney, 2010), but the
pathway is not fully understood. To reduce inequality in health
and to facilitate the development of targeted and effective interven-
tions, a clearer conceptualization and empirical investigation of the
pathways between education and health behavior is needed.
Health literacy is defined by the World Health Organization
as the cognitive and social skills that determine personsmoti-
vation and ability to gain access to, understand, and use infor-
mation in ways that promote and maintain good health
(Nutbeam, 1986). Health literacy brings together many concepts
that relate to what people need in order to make effective
decisions about health for themselves and their families. It is
well established that education level is associated with health
literacy level (Barber et al., 2009; Beauchamp et al., 2015; Bo,
Friis, Osborne, & Maindal, 2014; Paasche-Orlow, Parker,
Gazmararian, Nielsen-Bohlman, & Rudd, 2005; Van Der
Heide, Wang, et al., 2013). Some studies have shown that
inadequate health literacy is associated with unhealthy behaviors
such as smoking, physical inactivity, and poor diet (Adams
et al., 2013; Geboers, De Winter, Luten, Jansen, & Reijneveld,
2014; Husson, Mols, Fransen, Van De Poll-Franse, & Ezendam,
2015; Speirs, Messina, Munger, & Grutzmacher, 2012;Von
Wagner, Knight, Steptoe, & Wardle, 2007), although Wolf,
Gazmararian, and Baker (2007) found that limited health literacy
is not independently associated with some health behaviors.
Address correspondence to Karina Friis, DEFACTUMPublic
Health and Health Services Research, Central Denmark Region,
DK-8200 Aarhus N, Denmark. E-mail: karina.friis@stab.rm.dk
Journal of Health Communication, 00: 17, 2016
Copyright © Taylor & Francis Group, LLC
ISSN: 1081-0730 print/1087-0415 online
DOI: 10.1080/10810730.2016.1201175
Some authors have suggested that health literacy is a mediator
between education level and health outcomes (Howard, Sentell, &
Gazmararian, 2006; Lee, Tsai, Tsai, & Kuo, 2010; Nielsen-Bohlman,
Panzer, & Kinding, 2004; Paasche-Orlow et al., 2005; Schillinger,
Barton, Karter, Wang, & Adler, 2006; Van Der Heide, Rademakers,
et al., 2013). As poor health behaviors affect health outcomes
directly, it is reasonable to assume that health literacy also mediates
the relationship between education and health behavior. Yet to the
best of our knowledge, this relationship has not been investigated in
population-based studies. Health literacy may mediate the effects of
education on health behavior through a number of mechanisms.
Previous studies have shown that inadequate health literacy (mea-
sured in terms of health-related reading ability and numeracy) is
associated with poor problem-solving ability, low self-efficacy, low
motivation, and poor knowledge of how to perform self-care beha-
viors (Amalraj, Starkweather, Nguyen, & Naeim, 2009; Geboers
et al., 2014;Hussonetal.,2015;Kaminski&Good,1998;
Osborn, Paasche-Orlow, Bailey, & Wolf, 2011; Paasche-Orlow &
Wolf, 2007; Schillinger et al., 2006;Torres&Marks,2009;
Verhoeven & Snow, 2001;Wolfetal.,2004). It is important to
know whether health literacy does indeed mediate the relationship
between education level and health behavior. If this is the case, adults
with a lower formal education level may benefit from health lit-
eracyinformed interventions and from health services that are
responsive to the needs of populations with limited health literacy.
Using a large population-based survey, this study aimed to
investigate whether health literacy mediates the association
between education level and health behavior (smoking, physical
inactivity, poor diet) and obesity. Figure 1 illustrates the model used
for the mediation analyses. Two distinct health literacy dimensions
from the 9-dimension Health Literacy Questionnaire (HLQ) were
used: (a) Understanding health information well enough to know
what to do and (b) Actively engage with health care providers
(Osborne, Batterham, Elsworth, Hawkins, & Buchbinder, 2013).
Methods
Study Design and Data Collection
The study was based on data from respondents ages 25 years or
older. The data were drawn from the 2013 Danish health and
morbidity survey called How Are You?Geographically speaking,
Denmark is divided into five administrative regions. The present
study comprised data from one of these regionsthe Central
Denmark Regionwhere approximately 22% of the Danish popu-
lation resides. The population of the Central Denmark Region has a
similar demographic composition (gender, age, and marital status)
and similar health and social factors as the total Danish population
(Christensen, Davidsen, Ekholm, Pedersen, & Juel, 2014).
The survey consisted of a county-stratified random sample of
46,354 persons who were drawn from the Danish Civil
Registration System using as a key the unique personal identifica-
tion number given to each Danish citizen. People were invited to
complete a postal or a Web-based questionnaire. Three reminders
were issued. Data were collected by the Central Denmark Region
between February and April 2013. A total of 29,473 people
(63.6%) completed and returned the questionnaire. The personal
identification number was used by Statistics Denmark to link both
respondents and nonrespondents to the Danish national registers.
Weights were used to account for differences in selection probabil-
ities and response rates. These weights were constructed using a
model-based calibration approach based on register information
from Statistics Denmark. Data were weighted to represent the
population of the Central Denmark Region.
Measures
Health Literacy
The HLQ (Osborne et al., 2013) is a widely used measure of health
literacy that has been translated into many European and Asian
languages. It was developed using a validity-driven approach
including in-depth grounded consultations, psychometric analyses,
and cognitive interviews. The HLQ consists of nine scales. The
translation and cultural adaption of the questions from English into
Danish followed a rigorous forward-backward translation proce-
dure and cognitive testing to ensure cross-cultural validity.
In the present study, two of the nine HLQ scales were
included: Understanding health information well enough to
know what to do and Actively engage with health care provi-
ders. Given that population surveys have limited space for
survey questions, only these two scales were selected that cov-
ered two distinct elements of health literacy that we hypothe-
sized would provide valuable perspectives within a larger
general population health and morbidity survey. Each scale
comprised five items for which participants indicated their
response on a 4-point scale: 1 = very difficult,2=difficult,
3=easy, and 4 = very easy. Scale scores were calculated as
the mean of the five item scores and then standardized to range
from 1 (lowest ability) to 4 (highest ability) to ensure consis-
tency with the response options. If responses to more than two
items in a scale were missing for an individual, the scale score
for that individual was regarded as missing. As a result of this,
1,962 observations (6.7%) were excluded from the
Understanding health information scale and 1,925 observations
(6.5%) were excluded from the Actively engage with health care
providers scale. Cronbachs alpha coefficients indicated that the
internal consistency of both scales was high: Understanding
health information (α= .87) and Actively engage with health
care providers (α= .91).
Fig. 1. Model of mediation analysis.
2K. Friis et al.
Health Behavior
Three measures of health behavior (smoking, physical inactivity,
poor diet) were used. Respondents who smoked on a daily basis
were classified as smokers. Furthermore, respondents were classi-
fied as physically inactive if during a typical week they were not
physically active for a minimum of 30 minutes per day as recom-
mended by the Danish Health and Medicines Authority (Kiens
et al., 2007). Dietary habits were assessed using the Dietary
Quality Score (Toft, Kristoffersen, Lau, Borch-Johnsen, &
Jorgensen, 2007), which classifies the quality of the diet in relation
to cardiovascular risk. The scale consists of 25 items, including
questions about type of bread spread, fats used for cooking, and
how often the participants consumed selected food items (including
fish, meat, fruits, and vegetables). Poor diet was defined by a low
amount of fruit, vegetables, and fish and a high amount of satu-
rated fat.
Obesity
Self-reported height and weight were used to calculate body
mass index, and obesity was defined as a body mass index of
30 kg/m
2
or more.
Educational Attainment
The participants were asked about their highest level of completed
school education and any further higher level education.
Participants were classified into two educational categories: (a)
low level of education and (b) medium/high level of education.
Low level of education included basic education (primary and
lower secondary school). Medium/high level included education
levels above the low level (vocational education; upper secondary
school; and short-, medium-, and long-term higher education).
Demographic and Socioeconomic Factors
Data on age, gender, ethnic background, and marital status were
collected from national registers to avoid missing data.
Respondents were defined as Danish if they had Danish citizen-
ship or if at least one of their parents was a Danish citizen. Marital
status referred to whether an individual was married or not.
Ethics
The study was approved by the Danish Data Protection Agency
(Reference No. 2007-58-0010) and was conducted in accor-
dance with the Helsinki Declaration. Information about the
survey was provided to potential participants in writing and
via the Web. The participantsvoluntary completion and return
of the survey questionnaires constituted implied consent.
Statistical Analysis
Prior to the multiple mediation analyses, we used regression
analyses to test (a) the association between education level and
the two health literacy scales, (b) the association between the two
health literacy scales and each of the four health behavior mea-
sures, and (c) the association between education level and each of
the four health behavior measures (see Figure 1). All bivariate
analyses identified significant associations (data not shown).
To determine the indirect effect of health literacy on the
association between education and each of the four dependent
variables (smoking, physical inactivity, poor diet, obesity), we
conducted multiple mediation analyses using the Karlson-Holm-
Breen STATA command (Breen, Karlson, & Holm, 2013;
Kohler, Karlson, & Holm, 2011). This command decomposes
the total effect into the direct effect (the effect of the independent
variable [education level] on the dependent variable [health
behavior] while controlling for mediating variables [health
literacy scales]) and the indirect effect (the effect of the inde-
pendent variable on the dependent variable through mediating
variables; Breen et al., 2013). All mediation analyses were
further adjusted for age, gender, ethnic background, and marital
status. Significance was set at p< .05. Statistical analyses were
performed using STATA 13.
Results
A total of 18.6% of the respondents had low levels of education
(see Table 1). In total, 17.6% were daily smokers, 18.9% were
physically inactive, 12.3% had a poor diet, and 15.6% were obese.
Low educational attainment significantly predicted daily
smoking (total effect; see Table 2), even when the two health
literacy scales were included as mediating factors (direct effect).
Both health literacy scales were significant mediators in the
association between educational attainment and daily smoking
(see Table 3), but the contributing factor was relatively small
(Understanding health information: 6.6%, Actively engage with
health care providers: 4.5%).
Low educational attainment was a significant predictor of
physical inactivity (total effect; see Table 2), even when the
two health literacy scales were included as mediating factors
(direct effect). Moreover, both health literacy scales were sig-
nificant mediators in the association between educational attain-
ment and physical inactivity (see Table 3). The Understanding
health information scale accounted for 20.1% of the variance
between educational attainment and physical inactivity, whereas
the Actively engage with health care providers scale accounted
for only 5.4% of the variance. Hence, Understanding health
information had the strongest indirect effect on physical
inactivity (78.8%).
Low educational attainment was also a significant predictor
of having an unhealthy diet (total effect), even when the health
literacy scales were included as mediating factors (direct effect;
see Table 2). The understanding health information scale
mediated the association between education and having an
unhealthy diet (accounting for 13.3% of the variance), whereas
the Actively engage with health care providers scale did not
contribute significantly to the total effect of the association
between educational attainment and healthy diet (see Table 3).
Finally, Table 2 shows that low educational attainment was
also a significant predictor of obesity (total effect), even when
the two health literacy scales were used as mediating factors
(direct effect). Both health literacy scales were significant med-
iators in the association between education and obesity (see
Table 3). The Understanding health information scale accounted
for 16.2% of the total variance, whereas the Actively engage
with health care providers scale accounted for only 4.2% of the
variance. Of the two scales, Understanding health information
had the strongest indirect effect on obesity (79.6%).
Health Literacy and Health Behavior 3
Discussion
To our knowledge, this is the first population-based study
examining whether components of health literacy mediate the
often-reported associations between education and a number
of important health behaviors. We found that pertinent ele-
ments of health literacy do indeed act as mediators in the
relationship between education and health behavior.
Specifically, among people reporting that they are physically
inactive, have a poor diet, and/or are obese, the ability to
understand health information accounted for a substantial per-
centage of the total association with educational attainment.
Of the two health literacy scales, Understanding health infor-
mation was clearly the stronger mediating factor. A reason for
this may be that this scale reflects a basic set of competencies
needed for people to become equipped with knowledge
through reading and comprehension of information and
instructions about health. The other scale, Actively engage
with health care providers, may have a less direct or a down-
stream impact on health behaviors. People whose skills do not
allow them to properly understand health information may be
less exposed to common health information, and they may not
have the skills needed to comprehend and act on health-
promoting communication (Nutbeam, 2008;Roberts,2015).
Our study shows that compared with the other health behaviors
explored, health literacy plays only a small part in mediating the
relationship between education and smoking. The underlying
causes for this may relate to the fact that in Denmark policy
regulations and mass media campaigns relating to tobacco use
have been in place for more than two decades. Regardless of their
health literacy levels, most persons are therefore aware of the
health-related consequences of smoking. Instead, certain cultural
factors and normative beliefs in people with a low educational
attainment may in part explain the strong social gradient in
smoking status (Mackenbach, 2012).
Our study also shows that even though the association between
educational attainment and health behavior is partly mediated by
health literacy, educational attainment remains associated with all
four health behavior factors when two indicators of health literacy
are taken into account. This association remains even after adjust-
ment for age, gender, ethnic background, and marital status.
Hence, the present study suggests that the two indicators of health
literacy measured contribute to the link between education and
Table 2. Direct effect of education on health behaviors and indirect
effect of health literacy on the association between education and
health behaviors.
Dependent variable and effect
a
OR [CI] SE Z p
Smoking
Total effect 1.86 [1.68, 2.05] 0.09 12.23 .000
Direct effect 1.73 [1.57, 1.92] 0.09 10.65 .000
Indirect effect 1.07 [1.05, 1.10] 0.01 5.88 .000
Physical inactivity
Total effect 1.57 [1.43, 1.72] 0.07 9.46 .000
Direct effect 1.40 [1.27, 1.54] 0.07 6.95 .000
Indirect effect 1.12 [1.10, 1.15] 0.01 9.40 .000
Unhealthy diet
Total effect 2.37 [2.12, 2.65] 0.13 15.16 .000
Direct effect 2.10 [1.88, 2.36] 0.12 12.83 .000
Indirect effect 1.13 [1.10, 1.16] 0.02 8.25 .000
Obesity
Total effect 1.72 [1.56, 1.90] 0.09 10.67 .000
Direct effect 1.54 [1.39, 1.71] 0.08 8.28 .000
Indirect effect 1.12 [1.09, 1.15] 0.01 8.67 .000
Note. All estimates are adjusted for age, gender, ethnic background, and marital
status. OR = odds ratio; CI = confidence interval.
a
Total effect: The effect of the independent variable (education) on the dependent
variable (specific health behaviors) when not controlling for mediating vari-
ables (health literacy). Direct effect: The effect of the independent variable
(education) on the dependent variable (specific health behaviors) when con-
trolling for mediating variables (health literacy). Indirect effect: The effect of
the independent variable (education) on the dependent variable (specific health
behaviors) through mediating variables (health literacy).
Table 1. Characteristics of individuals who participated in the 2013
health and morbidity survey (N= 29,473).
Characteristic n%
a
MSD
Demographic and socioeconomic factors
Age 52.1 16.3
Gender
Male 14,045 49.4
Female 15,448 50.6
Ethnicity
Danish 28,400 93.6
Not Danish 1,073 6.4
Educational attainment
Low 5,507 18.6
Medium/high 23,037 81.4
Marital status
Married 19,828 58.9
Not married 41.1
Health literacy
Understanding health information well
enough to know what to do
3.1 0.6
Actively engage with health care
providers
3.1 0.6
Health behavior
Smoking
Daily smoking 4,856 17.6
Not daily smoking 23,971 82.4
Physical activity
Physically inactive 5,253 18.9
Not physically inactive 23,484 81.1
Diet
Poor diet 3,268 12.3
Not poor diet 24,872 87.7
Obesity
Obese (BMI 30) 4,602 15.6
Not obese (BMI <30) 24,057 84.4
Note. BMI = body mass index.
a
All percentages are weighted on register data to represent the population of the
Central Denmark Region, 2013.
4K. Friis et al.
health behavior. Still, the measures do not offer a complete
explanation of the pathway. The full construct of health literacy,
as defined by the HLQ, includes seven other independent scales
that may also be strong determinants. Other variables, such as
social norms, workplace environments, knowledge, stressors/
resources, and work status/income, that differ between educa-
tional groups could also be part of the mechanisms explaining
the association between education and health behavior, as found
in other studies (Cutler & Lleras-Muney, 2010; Godin et al., 2010;
Layte & Whelan, 2009; Matsuyama et al., 2011; Mulder, De
Bruin, Schreurs, Van Ameijden, & Van Woerkum, 2011). For
instance, individuals with low educational attainment may have
poorer health behaviors because they face different constraints,
have different beliefs about the impact of their behavior, or have
different norms than individuals with higher levels of education
(Cutler & Lleras-Muney, 2010).
To date, most research on the association between health
literacy and health behavior has used available measures of func-
tional health literacy (Adams et al., 2013; Geboers et al., 2014;
Husson et al., 2015; Speirs et al., 2012; Von Wagner et al., 2007;
Wolf et al., 2007). With the development of new health literacy
measures, a much broader range of the health literacy concept can
now be measured. In this study we sought to measure difficulties
people have in understanding health information and also difficul-
ties they have in interacting with health care providers. The two
different measures of health literacy are self-reported and capture a
dynamic state depending on how the individual person perceives
his or her current situation. It is important to note that the way in
which persons respond to questions about their ability to under-
stand health information and actively engage with health care
providers varies depending on the presence or absence of demands
related to their specific health conditions and the complexity of the
immediate health care system with which they engage (Batterham,
Hawkins, Collins, Buchbinder, & Osborne, 2016; Edwards, Wood,
Davies, & Edwards, 2012).
This study has some limitations. First, it is important to note
that our findings are based on cross-sectional data, and therefore
no conclusions about temporality or causation can be made.
Second, the ability and motivation to fill out a health survey
may be viewed as a health literacy competency in itself; thus,
the most vulnerable groups may have been excluded from our
study. The study is also limited because it included only two of
the nine defined scales of the HLQ. Thus, it suffers from con-
struct underrepresentation (Buchbinder et al., 2011). We can
therefore draw conclusions only about the two scales we mea-
sured and not about health literacy overall. Application of the
complete tool in this large population survey was not possible
for practical reasons. Future research may be strengthened
through the measurement of the full range of health literacy
indicators and may therefore generate a more complete under-
standing of any health literacy strengths and limitations that
individual persons have independent of their educational attain-
ment and how these strengths and limitations determine their
health behavior and health status.
Conclusion
Health literacy, particularly the ability to understand health
information, is a mediator in the relationship between educa-
tional attainment and health behavior, especially in relation to
being physical inactive, having a poor diet, and being obese.
The findings of the present study indicate that strategies for
improving public health and reducing health inequalities may
be improved through a stronger focus on health literacy. Health
literacy is very closely linked to education and health inequal-
ities. Interventions aimed at improving health behavior and
health status have the potential to become more targeted and
effective when informed by robust data on the health literacy of
the target populations.
Funding
The data collection was funded by the Central Denmark
Region. This study was partly funded by the pharmaceutical
company MSD Denmark. Funding was provided as an unrest-
ricted research grant. Richard H. Osborne was funded in part
through National Health and Medical Research Council of
Australia Senior Research Fellowship No. APP1059122.
Table 3. Contribution of each health literacy mediator on the association between education and health behaviors
Dependent variable and health literacy mediating variable Coefficient SE p
Contribution to the
indirect effect (%)
Contribution to
the total effect (%)
Smoking
Understanding health information well enough to know what to do 0.14 0.06 .015 59.5 6.6
Actively engage with health care providers 0.14 0.05 .008 40.5 4.5
Physical inactivity
Understanding health information well enough to know what to do 0.31 0.06 .000 78.8 20.1
Actively engage with health care providers 0.12 0.08 .015 21.3 5.4
Unhealthy diet
Understanding health information well enough to know what to do 0.41 0.07 .000 96.3 13.3
Actively engage with health care providers 0.02 0.07 .739 3.7 0.5
Obesity
Understanding health information well enough to know what to do 0.30 0.06 .000 79.6 16.2
Actively engage with health care providers 0.11 0.06 .042 20.4 4.2
Health Literacy and Health Behavior 5
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Health Literacy and Health Behavior 7
... People with problematic health literacy had difficulties searching, understanding, assessing, and applying health-related information [18]. Researchers in Denmark also supported these trends -based on general health literacy, the most common is the problematic level, and the least common is the excellent level of health literacy [19]. This suggests that among patients in European countries, low health literacy is predominant. ...
... In 2016, researchers from Denmark reported that health literacy and the ability to understand health information are mediators between education and health behaviour. Patients with lower health literacy were less active in their health management [19]. Therefore, the analysis shows that health literacy, health indicators, and personal functioning are interrelated. ...
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Background Health literacy is defined as a person's ability to find, understand, and use health-related information when making health-related decisions. Patients with lower health literacy more frequently face difficulties when they have health issues or need medical help. Such patients are less likely to visit health care facilities and receive less help, which subsequently leads to higher hospitalization and mortality rates. Patients with better health literacy skills pay more attention to their health behaviours. Methods This is a cross-sectional survey conducted in two primary health care centres—one public and one private—in Lithuania. The study enrolled patients who were visiting family physicians (n = 399). The study used the Health Literacy Survey European Questionnaire (HLS-EU-Q47). Calculation of means and two independent samples were used for statistical analysis, and a correlation coefficient was calculated. Results The majority (40.6%) of respondents had problematic health literacy, while only 7% had excellent health literacy. Better health literacy was observed among younger patients (aged below 30 years), residing in urban areas, having higher education, and living with a partner. Inadequate or problematic health literacy was noted among 83.6% of respondents aged 59 years and older; similar rates were also observed among patients with basic or primary education (76.1%), secondary education (76.6%), and divorced patients (86%). Respondents with better health literacy also had better health behaviours (p < 0.05). Conclusions Health literacy is influenced by age, residence, education, and family status. Patients with better health literacy also reported better health behaviours.
... Third, our results indicate that the most commonly used measures to assess the association between health literacy and smoking were the NVS 38 54,55,79,81 . However, in the meta-analysis to assess the associations between smoking and health literacy assessed by NVS, REALM and TOFHLA, they were not significant. ...
... ,53,74,86,90,96 , REALM20,42,76,82,83,85,97 , TOFHLA39,[43][44][45]52,[60][61][62]76,80,91,93,94 , the European Health Literacy Survey Questionnaire (HLS-EU-Q)41,47,51,57,65,69,87 , and the Health Literacy Questionnaire (HLQ)54,55,79,81 . In addition, health literacy assessment instruments in various languages were used to assess the association between health literacy and smoking-related behaviors or issues, such as the 14-item health literacy scale for Japanese adults and Communicative and Critical Health Literacy in Japan 58,86 , Chinese Citizen Health Literacy Questionnaire in China71,72 , and Korean Health Literacy Instrument in Korea 68 . ...
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Introduction Numerous studies have reported associations between health literacy and smoking-related behaviors or issues. However, no literature review has been conducted to synthesize these associations. Therefore, this review aimed to assess the associations between health literacy and smoking-related behaviors or issues. Material and Methods We searched published literature in four electronic databases (PubMed, CINAHL Plus, Scopus, and Web of Science) from inception to 22 February 2021. The search was limited to articles written in English and published in scientific journals. The reference lists of identified articles and Google Scholar were also manually searched. The extracted data regarding the association between health literacy and smoking was subjected to meta-analysis using Review Manager software (Review Manager, version 5.4.1). The results of the meta-analysis are reported as a weighted odds ratios (ORs) with 95% confidence interval (CI). Heterogeneity and publication bias were assessed using the Cochrane chi-squared test and I 2 value, and the funnel plot, respectively. Results The initial database search yielded 1266 articles. Fourteen additional articles were obtained through a manual search. Finally, 66 articles were included in the analysis. The meta-analysis showed that 22 studies had a pooled OR (95% CI) for smoker of 1.49 (95% CI: 1.25–1.79) in the inadequate health literacy group, compared with the adequate health literacy group. There was a high heterogeneity (p
... Better reading and comprehension skills are associated with better formative education, which is related to schooling level, a marker and social determinant of health 35 . HL is thus related to one's schooling, reflecting on their health behaviors; consequently, developing health literacy can reduce health inequalities 36 . ...
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Aim: This study analyzes factors associated with dimensions of health literacy (HL) functional, communicative and critical among public health service users with chronic non-communicable diseases. Methods: A cross-sectional analytical research was carried out in Piracicaba, São Paulo, Brazil, with adults and older adults attending Family Health Units (FHU). Data were collected by oral exam (CPOD and CPI) and a questionnaire on systemic conditions, sociodemographic factors, health behaviors and HLS (HLS-14). The outcomes consisted of functional, communicative, and critical HL dimensions dichotomized by median (high and low), which were analyzed by chi-square test (p<0.05) to find associations with the variables studied. Results: The study sample comprised 238 FHU users with 62.7 (± 10.55) mean age, of which 47.5% (n=113) showed high functional HL, 50.0% (n=119) high communicative HL, and 46.2% (n=110) high critical HL. High functional HL was associated with men (p<0.05). Functional and communicative HL were associated with having higher education (p<0.001 and p=0.018, respectively). High communicative and critical HL were associated with regular use of dental and medical services (p<0.05). Individuals with low functional HL were more likely to present poor tooth brushing (p=0.020). High HL (in all three dimensions) was associated with regular flossing and having more teeth (p<0.05). Conclusion: Functional, communicative and critical HL were associated with health behaviors and clinical outcomes, whereas the functional dimension was also associated with sociodemographic factors. HL dimensions allowed to differentiate health-related factors.
... The first galvanizing term to help with this notion is educational attainment. In this way, educational attainment is a function of literacy, and is characterized by health literacy research (Berkman et al., 2020;Friis et al., 2016). Health literacy has been used to describe the connected topics of educational attainment and mortality, educational attainment and quality of life, and parental education and child health outcomes (Declaration of Alma-Ata, 2008). ...
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Structural barriers embedded within American society contribute to health inequities and social determinants of health (SDOH) in ways that systematically influence one’s ability to succeed and to maintain a healthy overall quality of life in the United States. This article leverages educational attainment as an upstream SDOH factor that can be used to address downstream implications of population health equity. As providers learn to prescribe more innovative treatments that directly influence SDOH, an exploration is made to develop an intervention that integrates education, public health, and medicine as systems in a coordinated process to increase educational attainment for vulnerable populations. This article develops and analyzes the use of health equity management (HEM) model as a conceptual framework to identify precursors for educational attainment and provide an equitable solution for mending the educational attainment gap. It provides theoretical framing, conceptualizes stakeholder engagement, and creates a conceptual framework for identifying and addressing population health issues with education prescriptions. Operationalizing an educational prescription intervention will utilize provider-based screening methods to decrease the gaps in educational attainment by fostering partnerships between education, public health, and medicine. HEM identifies ideal partnership relationships to increase educational attainment and address long-standing quality of life issues, with a primary focus on coordinated activities among systems. Incorporating provider expertise into upstream educational decision-making legitimizes educational attainment as a critical component of population health equity. For many Americans, this is a necessary call to action to demand real structural change to ensure prosperity for all. An educational prescription intervention is a step towards increasing population health equity.
... It should be remembered that access to education is not the same elsewhere; when examining urban versus rural outcomes in COVID-19, the disparities are markedly evident when considering county-level data [62]. Concerning the relationship between COVID-19 and educational level, it is acknowledged that education and engagement in health behaviors are positively associated [63], and low education is associated with unhealthy behaviors [64]. However, several external factors may be examined, and additional measures will have to be studied each time to test for this relationship [65]. ...
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The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.
... Twitter users from socioeconomically vulnerable counties showed lower attention on perceived severity and susceptibility of COVID-19. Low educational attainment could lead to low health literacy, which increases difficulties in understanding health information (Friis, Lasgaard, Rowlands, Osborne, & Maindal, 2016;Paakkari & Okan, 2020). People with lower health literacy were more likely to report less perceived susceptibility to COVID-19 (Bailey et al., 2020). ...
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Social media analysis provides an alternate approach to monitoring and understanding risk perceptions regarding COVID‐19 over time. Our current understandings of risk perceptions regarding COVID‐19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) as the pandemic has evolved. Data are also limited regarding the impact of social determinants of health (SDOH) on COVID‐19‐related risk perceptions over time. To address these knowledge gaps, we extracted tweets regarding COVID‐19‐related risk perceptions and developed indicators for the three dimensions of risk perceptions based on over 502 million geotagged tweets posted by over 4.9 million Twitter users from January 2020 to December 2021 in the United States. We examined correlations between risk perception indicator scores and county‐level SDOH. The three dimensions of risk perceptions demonstrate different trajectories. Perceived severity maintained a high level throughout the study period. Perceived susceptibility and negative emotion peaked on March 11, 2020 (COVID‐19 declared global pandemic by WHO) and then declined and remained stable at lower levels until increasing once again with the Omicron period. Relative frequency of tweet posts on risk perceptions did not closely follow epidemic trends of COVID‐19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention to perceived severity and susceptibility of COVID‐19 than those from wealthier counties. Examining trends in tweets regarding the multiple dimensions of risk perceptions throughout the COVID‐19 pandemic can help policymakers frame in‐time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in the United States.
... Education provides opportunity for upward mobility including greater financial, social, and psychological resources, ultimately reducing health disparities [60]. Education is also associated with health literacy [66] and may impact a person's ability to identify their mental health needs and, presumably, to choose an evidence-based therapy, including CT. In addition, more years of education are commonly associated with better retention and a greater treatment response in CT {27, [67][68][69][70] although other studies indicate positive outcomes regardless of education levels [25,26,67]. ...
Article
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Purpose Social determinants of health (SDOH) encompass the range of conditions in a person’s environment that impacts health and health outcomes. We summarize the literature examining the intersection of SDOH and cognitive therapy (CT) and provide concrete clinical guidance for incorporating SDOH into a cognitively oriented case conceptualization and implementation of CT. Recent findings We begin by providing a brief overview of cognitive theory, the impact of SDOH on clinical presentations, and current literature examining SDOH and CT. We then offer a step-by-step approach to incorporate attention to SDOH into assessment, case conceptualization, and delivery of CT. Finally, we explicitly examine five key domains central to SDOH including: health care, social and community context, neighborhood and built environment, education, and economic stability. Within each domain, case examples are provided to highlight possible cognitions and schemas related to SDOH that warrant consideration as possible targets for intervention in CT. Summary SDOH undoubtedly intersect with mental health outcomes, and attending to this bidirectional relationship over the course of CT can enhance outcomes. The empirical research evaluating this intersectionality is sparse, and there is little clinical guidance for implementing CT using a SDOH-informed approach. This critical gap in the knowledge base on SDOH-informed CT is particularly relevant when working with minoritized populations for whom disparities across SDOH have been demonstrated. Practical recommendations for therapists are offered to bolster the ability to better provide culturally sensitive care that incorporates attention to SDOH.
Article
Aim: To examine the associations between three social determinants of health (SDOH) and recurrence of AF after ablation. Methods: We selected patients who underwent a first ablation after an incident hospital diagnosis of AF between 2005 and 2018 from the entire Danish population. Educational attainment, family income, and whether the patient was living alone were assessed at the time of ablation. We used cause-specific proportional hazard models to estimate hazard ratios (HR) with 95% CI adjusted for age and sex. In secondary analyses, we adjusted for comorbidities, antiarrhythmic medication, and prior electrical cardioversion. Results: We selected 9,728 patients (mean age 61 years, 70% men), and 5,881 patients had AF recurrence over an average of 1.37 years after ablation (recurrence rate 325.7 (95%CI 317.6-334.2) per 1000 person-years). Lower education (HR 1.09 [1.02-1.17] and 1.07 [1.01-1.14] for lower and medium vs. higher), lower income (HR 1.14 [1.06-1.22] and 1.09 [1.03-1.17] for lower and medium vs. higher), and living alone (HR 1.07 [1.00-1.13]) were associated with increased rates of recurrence of AF. We found no evidence of interaction between sex or prior HF with SDOH. The association between family income and AF recurrence was stronger among patients <65 years compared to those aged ≥65 years. The associations between SDOH and AF recurrence did not persist in the multivariable model. Conclusions: AF was more likely to recur among patients with lower educational attainment, lower family income, or those living alone. Multidisciplinary efforts are needed to reduce socioeconomic inequity in the effect of ablation.
Article
Issue addressed: The literature provides evidence that maternal health is strongly linked with noncommunicable diseases (NCDs) and their associated risk factors. Enabling women with the asset of health literacy may help to reduce the intergenerational impact of NCDs. However, little is known about the health literacy of pregnant women and women with young children in Tasmania and globally. This study aimed to identify the health literacy status of pregnant women and women with young children (0-8 years) living in Tasmania and describe their health literacy status according to their demographic characteristics. Methods: An online cross-sectional survey was undertaken. The survey included demographic questions and a health literacy questionnaire (HLQ). The description of demographic differences across the HLQ scales focused on effect sizes (ES) for standardised differences in mean health literacy scores. The differences found to be statistically significant at p <0.05 were also included. Results: 194 participants completed the survey with a mean age of 35.3 years. 73.2% were married, 16.5% were pregnant, 93% had one or more children and 81.5% were university educated. For the first five HLQ scales (score range 1-4), the lowest overall score was seen for the scale 'Actively managing my health' (mean= 2.96; SD= 0.54). For the last four scales (score range 1-5), the lowest overall score was seen for the scale 'Navigating the healthcare system' (mean=3.75, SD= 0.67). Non-pregnant women, women with children, women with chronic health conditions and non-married women experienced more health literacy challenges. Conclusion: Women in our study showed various strengths and challenges with mean scores varying across the nine HLQ scales. Understanding the health literacy needs of women will enable health services to co-design solutions and interventions capable of responding to the evolving health needs of pregnant women and women with young children. This approach will ensure that codesigned solutions can engage the end-user in healthy lifestyle practices and the solutions are sustainable. SO WHAT?: We must shift away from a "one size fits all" approach to tailor services to respond to the differing health literacy needs of pregnant women and women with young children to support healthy lifestyle practices and reduce the NCD burden.
Article
Aims To examine (i) the sex-specific associations between three social determinants of health (SDOH) and use of ablation after incident atrial fibrillation (AF), and (ii) the temporal trends in these associations. Methods and results We conducted a nationwide cohort study of patients with an incident hospital diagnosis of AF between 2005 and 2018. SDOH at the time of AF diagnosis included three levels of educational attainment, tertile groups of family income, and whether the patient was living alone. Outcome was catheter ablation for AF. We used cause-specific proportional hazard models to estimate hazard ratios (HR) with 95% CI and adjusted for age. To examine temporal trends, we included an interaction term between the exposure and calendar years. Among 122 276 men, those with lower education (HR 0.49 [95%CI 0.45–0.53] and 0.72 [0.68–0.77] for lower and medium vs. higher), lower income (HR 0.31 [0.27–0.34] and 0.56 [0.52–0.60] for lower and medium vs. higher), and who lived alone (HR 0.60 [0.55–0.64]) were less likely to receive AF ablation. Among 98 476 women, those with lower education (HR 0.45 [0.40–0.50] and 0.83 [0.75–0.91] for lower and medium vs. higher), lower income (HR 0.34 [0.28–0.40] and 0.51 [0.46–0.58] for lower and medium vs. higher), and who lived alone (HR 0.67 [0.61–0.74]) were less likely to receive AF ablation. We found no evidence of temporal trends in the associations. Conclusions In the Danish universal healthcare system, patients with AF who had lower educational attainment, lower family income, or living alone were less likely to undergo AF ablation.
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People's health behaviours are widely known to affect their health and risk of mortality. Less is known about how these behaviours cluster together in the population and how multiple lifestyle risk patterns have changed over time between different population groups. Focusing on changes in the English population between 2003 and 2008, this paper considers these questions in relation to policy and practice. Using data from the Health Survey for England, we examined how four lifestyle risk factors-smoking, excessive alcohol use, poor diet, and low levels of physical activity-co-occur in the population and how this distribution has changed over time. We found that the overall proportion of the population that engages in three or four of these unhealthy behaviours has declined significantly, from around 33 per cent of the population in 2003 to around 25 per cent by 2008. However, these reductions have been seen mainly among those in higher socioeconomic and educational groups: people with no qualifications were more than five times as likely as those with higher education to engage in all four poor behaviours in 2008, compared with only three times as likely in 2003. The health of the overall population will improve as a result of the improvement in these behaviours, but the poorest and those with least education will benefit least, leading to widening inequalities and avoidable pressure on the NHS. If policy-makers, public health commissioners and the NHS wish to address health inequalities, they will therefore need to find effective ways to help people in lower socioeconomic groups to reduce the number of unhealthy behaviours they have. This is likely to work only if a holistic approach to policy and practice is adopted that addresses lifestyles that encompass multiple unhealthy behaviours. At a policy level, this is likely to mean moving beyond siloed approaches to public health behaviour policies, in which the focus is on renewing strategies on individual lifestyle risks one at a time, as this ignores how behaviours are actually distributed in the population. A more integrated approach to behaviour change is required that links more closely to inequalities policy and is focused more directly on the government's stated goal to 'improve the health of the poorest, fastest'.
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Nearly half of all American adults—90 million people—have difficulty understanding and acting upon health information. The examples below were selected from the many pieces of complex consumer health information used in America. • From a research consent form: “A comparison of the effectiveness of educational media in combination with a counseling method on smoking habits is being examined.” (Doak et al., 1996) • From a consumer privacy notice: “Examples of such mandatory disclosures include notifying state or local health authorities regarding particular communicable diseases.” • From a patient information sheet: “Therefore, patients should be monitored for extraocular CMV infections and retinitis in the opposite eye, if only one infected eye is being treated.” Forty million Americans cannot read complex texts like these at all, and 90 million have difficulty understanding complex texts. Yet a great deal of health information, from insurance forms to advertising, contains complex text. Even people with strong literacy skills may have trouble obtaining, understanding, and using health information: a surgeon may have trouble helping a family member with Medicare forms, a science teacher may not understand information sent by a doctor about a brain function test, and an accountant may not know when to get a mammogram. This report defines health literacy 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” (Ratzan and Parker, 2000). However, health literacy goes beyond the individual obtaining information. Health literacy emerges when the expectations, preferences, and skills of individuals seeking health information and services meet the expectations, preferences, and skills of those providing information and services. Health literacy arises from a convergence of education, health services, and social and cultural factors. Although causal relationships between limited health literacy and health outcomes are not yet established, cumulative and consistent findings suggest such a causal connection. Approaches to health literacy bring together research and practice from diverse fields. This report examines the body of knowledge in this emerging field, and recommends actions to promote a health-literate society. Increasing knowledge, awareness, and responsiveness to health literacy among health services providers as well as in the community would reduce problems of limited health literacy. This report identifies key roles for the Department of Health and Human Services as well as other public and private sector organizations to foster research, guide policy development, and stimulate the development of health literacy knowledge, measures, and approaches. These organizations have a unique and critical opportunity to ensure that health literacy is recognized as an essential component of high-quality health services and health communication.
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Background Recent advances in the measurement of health literacy allow description of a broad range of personal and social dimensions of the concept. Identifying differences in patterns of health literacy between population sub-groups will increase understanding of how health literacy contributes to health inequities and inform intervention development. The aim of this study was to use a multi-dimensional measurement tool to describe the health literacy of adults in urban and rural Victoria, Australia. Methods Data were collected from clients (n = 813) of 8 health and community care organisations, using the Health Literacy Questionnaire (HLQ). Demographic and health service data were also collected. Data were analysed using descriptive statistics. Effect sizes (ES) for standardised differences in means were used to describe the magnitude of difference between demographic sub-groups. Results Mean age of respondents was 72.1 (range 19–99) years. Females comprised 63 % of the sample, 48 % had not completed secondary education, and 96 % reported at least one existing health condition. Small to large ES were seen for mean differences in HLQ scales between most demographic groups. Compared with participants who spoke English at home, those not speaking English at home had much lower scores for most HLQ scales including the scales ‘Understanding health information well enough to know what to do’ (ES −1.09 [95 % confidence interval (CI) -1.33 to −0.84]), ‘Ability to actively engage with healthcare providers’ (ES −1.00 [95 % CI −1.24, −0.75]), and ‘Navigating the healthcare system’ (ES −0.72 [95 % CI −0.97, −0.48]). Similar patterns and ES were seen for participants born overseas compared with those born in Australia. Smaller ES were seen for sex, age group, private health insurance status, number of chronic conditions, and living alone. Conclusions This study has revealed some large health literacy differences across nine domains of health literacy in adults using health services in Victoria. These findings provide insights into the relationship between health literacy and socioeconomic position in vulnerable groups and, given the focus of the HLQ, provide guidance for the development of equitable interventions.
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Socioeconomic inequalities in mortality pose a serious impediment to enhance public health even in highly developed welfare states. This study aimed to improve the understanding of socioeconomic disparities in all-cause mortality by using a comprehensive approach including a range of behavioural, psychological, material and social determinants in the analysis. Data from The North Denmark Region Health Survey 2007 among residents in Northern Jutland, Denmark, were linked with data from nationwide administrative registries to obtain information on death in a 5.8-year follow-up period (1(st)February 2007- 31(st)December 2012). Socioeconomic position was assessed using educational status as a proxy. The study population was assigned to one of five groups according to highest achieved educational level. The sample size was 8,837 after participants with missing values or aged below 30 years were excluded. Cox regression models were used to assess the risk of death from all causes according to educational level, with a step-wise inclusion of explanatory covariates. Participants' mean age at baseline was 54.1 years (SD 12.6); 3,999 were men (45.3%). In the follow-up period, 395 died (4.5%). With adjustment for age and gender, the risk of all-cause mortality was significantly higher in the two least-educated levels (HR = 1.5, 95%, CI = 1.2-1.8 and HR = 3.7, 95%CI = 2.4-5.9, respectively) compared to the middle educational level. After adjustment for the effect of subjective and objective health, similar results were obtained (HR = 1.4, 95%CI = 1.1-1.7 and HR = 3.5, 95%CI = 2.0-6.3, respectively). Further adjustment for the effect of behavioural, psychological, material and social determinants also failed to eliminate inequalities found among groups, the risk remaining significantly higher for the least educated levels (HR = 1.4, 95%CI = 1.1-1.9 and HR = 4.0, 95%CI = 2.3-6.8, respectively). In comparison with the middle level, the two highest educated levels remained statistically insignificant throughout the entire analysis. Socioeconomic inequality influenced mortality substantially even when adjusted for a range of determinants that might explain the association. Further studies are needed to understand this important relationship.
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Background: Health literacy is a multidimensional concept covering a range of cognitive and social skills necessary for participation in health care. Knowledge of health literacy levels in general populations and how health literacy levels impacts on social health inequity is lacking. The primary aim of this study was to perform a population-based assessment of dimensions of health literacy related to understanding health information and to engaging with healthcare providers. Secondly, the aim was to examine associations between socio-economic characteristics with these dimensions of health literacy. Methods: A population-based survey was conducted between January and April 2013 in the Central Denmark Region. Postal invitations were sent to a random sample of 46,354 individuals >25 years of age. Two health literacy dimensions were selected from the Health Literacy Questionnaire (HLQ™): i) Understanding health information well enough to know what to do (5 items), and ii) Ability to actively engage with health care providers (5 items). Response options ranged from 1 (very difficult) to 4 (very easy). We investigated the level of perceived difficulty of each task, and the associations between the two dimensions and socio-economic characteristics. Results: A total of 29,473 (63.6%) responded to the survey. Between 8.8%, 95% CI: 8.4-9.2 and 20.2%, 95% CI: 19.6-20.8 of the general population perceived the health literacy tasks as difficult or very difficult at the individual item level. On the scale level, the mean rating for i) understanding health information was 3.10, 95% CI: 3.09-3.10, and 3.07, 95% CI: 3.07-3.08 for ii) engagement with health care providers. Low levels of the two dimensions were associated with low income, low education level, living alone, and to non-Danish ethnicity. Associations with sex and age differed by the specific health literacy dimension. Conclusion: Estimates on two key dimensions of health literacy in a general population are now available. A substantial proportion of the Danish population perceives difficulties related to understanding health information and engaging with healthcare providers. The study supports previous findings of a socio-economic gradient in health literacy. New insight is provided on the feasibility of measuring health literacy which is of importance for optimising health systems.
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Inadequate health literacy is a common problem among older adults and is associated with poor health outcomes. Insight into the association between health literacy and health behaviors may support interventions to mitigate the effects of inadequate health literacy. The authors assessed the association of health literacy with physical activity and nutritional behavior in community-dwelling older adults. The authors also assessed whether the associations between health literacy and health behaviors are mediated by social cognitive factors. Data from a study among community-dwelling older adults (55 years and older) in a relatively deprived area in The Netherlands were used (baseline n¼643, response: 43%). The authors obtained data on health literacy, physical activity, fruit and vegetable consumption, and potential social cognitive mediators (attitude, self-efficacy, and risk perception). After adjustment for confounders, inadequate health literacy was marginally significantly associated with poor compliance with guidelines for physical activity (OR=1.52, p=.053) but not with poor compliance with guidelines for fruit and vegetable consumption (OR=1.20, p=.46). Self-efficacy explained 32% of the association between health literacy and compliance with physical activity guidelines. Further research may focus on self-efficacy as a target for interventions to mitigate the negative effects of inadequate health literacy.
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The concept of ‘health literacy’ refers to the personal and relational factors that affect a person's ability to acquire, understand and use information about health and health services. For many years, efforts in the development of the concept of health literacy exceeded the development of measurement tools and interventions. Furthermore, the discourse about and development of health literacy in public health and in clinical settings were often substantially different. This paper provides an update about recently developed approaches to measurement that assess health literacy strengths and limitations of individuals and of groups across multiple aspects of health literacy. This advancement in measurement now allows diagnostic and problem-solving approaches to developing responses to identified strengths and limitations. In this paper, we consider how such an approach can be applied across the diverse range of settings in which health literacy has been applied. In particular, we consider some approaches to applying health literacy in the daily practice of health-service providers in many settings, and how new insights and tools – including approaches based on an understanding of diversity of health literacy needs in a target community – can contribute to improvements in practice. Finally, we present a model that attempts to integrate the concept of health literacy with concepts that are often considered to overlap with it. With careful consideration of the distinctions between prevailing concepts, health literacy can be used to complement many fields from individual patient care to community-level development, and from improving compliance to empowering individuals and communities.
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Background: The objectives of the study were to examine the prevalence of health literacy (HL) among colorectal cancer (CRC) survivors and the relation between HL and health behaviors and to explore whether or not HL and health behaviors are independently associated with health-related quality of life (HRQoL) and mental distress. Methods: This analysis is part of a longitudinal, population-based survey among CRC survivors diagnosed between 2000 and 2009 and registered by the Eindhoven Cancer Registry. Data collected during the second data wave was used (n = 1643; response rate 83%). Patients filled out a screening question on subjective functional HL, questions on health behaviors, HRQoL (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30), and mental distress (Hospital Anxiety and Depression Scale). Results: Subjective HL was low among 14%, medium among 45%, and high among 42% of the participants. CRC survivors with low HL were more often smokers and did not meet the prescribed physical activity guidelines compared with survivors with medium or high HL. CRC survivors with low HL reported statistically significantly lower levels of mental and physical HRQoL and higher distress levels compared with survivors with medium and high HL. HL, in addition to sociodemographic and clinical characteristics and health behaviors, significantly explained 1.5-6.2% of the variance in HRQoL and mental distress levels. Partial mediation is indicated for HRQoL and feelings of depression, but not for anxiety. Conclusion: Low subjective functional HL among CRC survivors is associated with lower levels of physical activity, higher frequency of smoking, poorer HRQoL, and more mental distress. HL and health behaviors have both a unique as well as an overlapping contribution to the explained variances of HRQoL and mental distress.