HEALTH LITERACY AND CLINICAL PREVENTIVE BEHAVIOR AMONG OLDER
AFRICAN AMERICANS IN ALLEGHENY COUNTY
by
Kristin L. Macfarlane
BS, BA, University of Pittsburgh, 2012
Submitted to the Graduate Faculty of
Behavioral and Community Health Sciences
Graduate School of Public Health in partial fulfillment
of the requirements for the degree of
Master of Public Health
University of Pittsburgh
2012
UNIVERSITY OF PITTSBURGH
GRADUATE SCHOOL OF PUBLIC HEALTH
This essay is submitted
by
Kristin Macfarlane
on
December 20, 2012
and approved by
Essay Advisor:
Steven M. Albert, PhD, MSPH
______________________________________
Professor and Chair
Department of Behavioral and Community Health Sciences
Graduate School of Public Health
University of Pittsburgh
Essay Reader:
Jeanine Buchanich, PhD
______________________________________
Research Assistant Professor
Deputy Director, Center of Occupational Biostatistics and Epidemiology
Department of Biostatistics
Graduate School of Public Health
University of Pittsburgh
ii
Copyright © by Kristin L. Macfarlane
2012
iii
Steven M. Albert, PhD
HEALTH LITERACY AND CLINICAL PREVENTIVE BEHAVIOR AMONG
OLDER AFRICAN AMERICANS IN ALLEGHENEY COUNTY
Kristin L. Macfarlane, MPH
University of Pittsburgh, 2012
The public health relevance of this essay is to contribute to the body of research that seeks to
elucidate the causal link between health literacy and health outcomes. This study also addresses
the national issue of low clinical preventive services utilization among older adults.
Low functional health literacy is associated with negative health outcomes, but the causal
link as to why this is the case is not well understood. Clinical preventive service use has been
identified as a possible mechanism, but there is a dearth of research concerning this topic. For
older adults, who face higher risk for concomitant illnesses and higher risk for disability, clinical
preventive service use is a key component of retaining health and functional independence.
These services, however, are highly underutilized by older adults.
This study utilizes secondary, cross-sectional baseline data collected from the Boosting
Minority Involvement (BMI) study, a community-based cohort study intended to increase
minority participation in research. Participants included ninety-one African American men and
women aged 60 years and older, living in low-income community settings. Health literacy was
measured with the Short Test of Functional Health Literacy in Adults (S-TOFHLA) and clinical
preventive behavior was assessed as a percentage of clinical tests in which the participant
engaged as identified by the Prevention in Practice Report:“10 Keys” of Healthy Aging survey.
Multivariate linear regression analysis showed that health literacy did not significantly
predict percent clinical preventive behavior, controlling for covariates. The number of
iv
comorbidites one experienced emerged as the only significant predictor of clinical preventive
behavior. A Spearman’s Rho correlation revealed that functional health literacy and number of
comorbidities were positively associated with percent clinical preventive behavior. Comorbidity
index was not related to one’s level of health literacy.
While this study is a good first step, further research is necessary to elucidate the
relationship between functional health literacy and clinical preventive behavior in an effort to
establish a tenable avenue for intervention to improve health outcomes.
v
TABLE OF CONTENTS
1.0
FUNCTIONAL HEALTH LITERACY ............................................................................ 1
1.1
HEALTH LITERACY IN THE UNITED STATES ......................................... 2
1.2
MEASURING HEALTH LITERACY ............................................................... 3
1.3
HEALTH OUTCOMES ....................................................................................... 4
1.4
VULNERABLE POPULATIONS ....................................................................... 5
1.5
THEORY & BEHAVIOR .................................................................................... 7
1.6
CLINICAL PREVENTIVE SERVICES UTILIZATION AMONG
OLDER ADULTS ................................................................................................ 9
2.0
1.7
THE ROLE OF THE PUBLIC HEALTH PROFESSIONAL ....................... 10
1.8
PURPOSE OF THIS RESEARCH PROJECT ................................................ 12
METHODS ......................................................................................................................... 14
2.1
DATA COLLECTION ....................................................................................... 14
2.2
MEASURES ........................................................................................................ 15
2.3
3.0
2.2.1
Percent Clinical Preventive Behavior .......................................................... 15
2.2.2
S-TOFHLA ..................................................................................................... 16
2.2.3
Covariates ....................................................................................................... 17
STATISTICAL ANALYSIS .............................................................................. 17
RESULTS ........................................................................................................................... 20
vi
3.1
BASELINE CHARACTERISTICS .................................................................. 20
3.2
DESCRIPTIVE STATISTICS........................................................................... 22
3.3
PERCENT CLINICAL PREVENTIVE BEHAVIOR .................................... 24
3.4
4.0
3.3.1
Predictors of percent clinical preventive behavior ..................................... 24
3.3.2
Correlation across percent clinical preventive behavior ........................... 26
S-TOFHLA SCORE ........................................................................................... 27
3.4.1
Predictors of S-TOFHLA score .................................................................... 27
3.4.2
Correlation between comorbidity index and S-TOFHLA score ............... 29
DISCUSSION ..................................................................................................................... 30
4.1
HEALTH LITERACY AND CLINICAL PREVENTIVE BEHAVIOR....... 30
4.2
COMORBIDITY INDEX AND CLINICAL PREVENTIVE BEHAVIOR .. 31
4.3
STUDY LIMITATIONS .................................................................................... 33
4.4
FUTURE DIRECTIONS .................................................................................... 34
APPENDIX A: INSTRUMENTS .............................................................................................. 37
APPENDIX B: DATA ANALYSIS ........................................................................................... 41
BIBLIOGRAPHY ....................................................................................................................... 42
vii
LIST OF TABLES
Table 1. Characteristics of study population, N=91 ..................................................................... 21
Table 2. Percent clinical preventive behavior by gender .............................................................. 23
Table 3. Multivariate linear regression on percent clinical preventive behavior .......................... 24
Table 4. Correlation across percent clinical preventive behavior ................................................. 26
Table 5. Mulitvariate linear regression on S-TOFHLA score ...................................................... 28
Table 6. Correlation of comorbidity index and S-TOFHLA score ............................................... 29
Table A.1. Prevention in Practice Report: “10 Keys” of Healthy Aging survey .......................... 37
Table A.2. Selection of items used to assess health literacy from the S-TOFHLA ...................... 40
Table B.1. Tests of normality ....................................................................................................... 41
viii
PREFACE
The author wrote this essay in an attempt to contribute to the theoretical underpinnings of
the link between health literacy and health outcomes and as an exercise in applying the concepts
learned as a student of the University of Pittsburgh’s Graduate School of Public Health. The
author’s interest in both senior health and educational intervention to improve health spawned
the selection of this specific topic
The author would like to thank Dr. Steven, M. Albert for his constant mentorship and
support and Dr. Jeanine Buchanich for her statistical expertise and guidance. The author would
also like to acknowledge her husband, Brandon Lenoir, and her mother, Sandra Macfarlane, for
their support and their contribution as proofreaders.
ix
1.0
FUNCTIONAL HEALTH LITERACY
This project explores the pivotal role health literacy plays in the preventive health of elderly
African Americans. For the purpose of this study, health literacy is defined 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” (United States Department of Health
and Human Services [DHHS], 2011; Ratzan & Parker, 2000; Nielsen-Bohlman, Panzer, &
Kindig, 2004). This definition encompasses not only one’s ability to acquire information through
speaking, writing, and communication but also one’s ability to comprehend and successfully
apply information to one’s own health management.
Health literacy underlies one’s ability to perform routine tasks in the healthcare system,
such as following medical instructions, taking prescription medication appropriately, and
completing health insurance forms (Williams, Parker, & Baker, 1995; Baker, Parker & Williams,
1996; Powers, 1998; Kutner, Greenberg, Jin, & Paulson, 2006). Seemingly straightforward
undertakings like communicating one’s symptoms to a health provider and comprehending
appointment slips become difficult when a person functions below a proficient level of health
literacy, and can result in adverse health outcomes (Nielsen-Bohlman et. al., 2004).
Without an understanding of the language of health, Americans may find it difficult to
make decisions such as where to seek services and which treatment options to pursue. Arguably,
one’s level of health literacy impacts one’s ability to navigate today’s complex health care
system and to actively participate in one’s own health care, which is increasingly necessary as
1
the delivery of health related services shifts to a model of consumerism in which decisionmaking power is delegated to individuals (McCray, 2012; Parker, 2000). Unfortunately, limited
health literacy is a problem in the United States (Berkman et al., 2011).
1.1
HEALTH LITERACY IN THE UNITED STATES
According to the National Assessment of Adult Literacy study, which dedicated a portion of the
study to the examination of health-specific literacy skills, only 12% of adults 16 and older in the
United States have a proficient level of health literacy, meaning that these individuals have the
optimal skill set to execute complex activities like finding abstract information to solve problems
and synthesizing and analyzing multiple pieces of information from a document (Kutner et al.,
2006). Participants were classified into one of four performance levels based upon their
assessment score as follows (ordered highest to lowest): proficient, intermediate, basic, and
below basic. Most Americans, 53%, have intermediate health literacy, which indicates that they
are able to complete moderately challenging tasks such as making simple inferences about
complex information and use it to solve problems. The results also show that 22% of Americans
function at a basic level where they are able to understand and use information that is short and
simple for everyday tasks, and 14% percent have below a basic level, meaning that their skills
range from nonliterate to understanding only simple forms of information (Kutner et al., 2006)
Overall, as level of health literacy decreased in the NAAL study, so did levels of selfreported health, which indicates the 88% of Americans who fall below the proficient level of
literacy may have difficulties comprehending and applying health information to decisions that
affect their health (Kutner et al. 2006). We know that low functional health literacy contributes
2
to poor health outcomes, health disparities and higher healthcare costs and we can infer from the
NAAL study that a majority of Americans experience suboptimal health outcomes and a larger
economic burden as a result of limited literacy skills (Agency for Healthcare Research and
Quality [AHRQ], 2011; Berkman et al., 2004; Howard, Sentell, & Gazmararian, 2006; Friedland,
1998; Lee, 1999). Increasing functional health literacy levels may therefore be an advantageous
mechanism through which to enhance the well-being of the American public.
As such, numerous organizations have identified increasing health literacy as a national
priority.
Healthy People, a nationwide health-promotion and disease prevention program,
recognized improving consumer health literacy as an objective of both 2010 and 2020 (DHHS,
2000; DHHS, 2011). The Institute of Medicine (IOM), the National Institutes of Health (NIH)
and the American Medical Association (AMA) have also promoted health literacy research and
intervention development as a priority (Berkmen et al., 2011). The United States Department of
Health and Human Services (DHHS) also released a National Action Plan to Improve Health
Literacy, which emphasizes a multidisciplinary approach to making health information
accessible and understandable (DHHS, 2010).
1.2
MEASURING HEALTH LITERACY
The Short Test of Functional Health Literacy in Adults (S-TOFHLA), is a common tool used by
researchers to evaluate one’s level of health literacy (Baker, Williams, Parker, Gazmararian, &
Nurss, 1999). This tool is a shortened version of the original 67-item Test of Functional Health
Literacy in Adults (TOFHLA), and is commended for its brevity, high degree of validity and
reliability, and high degree of correlation to other health literacy tests (Baker et al., 1999). S3
TOFHLA is generally the tool of choice as it takes nearly half the time to complete when
compared to the TOFHLA. It is superior to tests such as the Rapid Estimate of Adult Literacy in
Medicine (REALM) because it is able to test comprehension within context as opposed to simply
testing the ability to pronounce words in isolation (Baker et al., 1999)
The S-TOFHLA integrates common vernacular and situations that occur in the healthcare
setting into questions, and classifies participants according to their prospective level of health
literacy as follows (highest to lowest): adequate, marginal, inadequate. Those who have an
adequate level of health literacy are able to complete and understand most of the situations and
materials they encounter in the health setting. Marginal health literacy indicates that one may
have difficulty with complex tasks, written materials and medical instructions. Those with
inadequate health literacy may find it difficult to perform simple tasks such as reading and
interpreting prescription labels and appointment slips (AMA, 1999; Parker, 2000).
1.3
HEALTH OUTCOMES
Health literacy is an important determinant of health and has even been suggested to play a more
important role in predicting health than age, level of education, race, income and employment
status (Berkman et al., 2011). Studies have shown that low health literacy is linked to poorer
understanding and management of health conditions, lower adherence to medication, less
preventive care, and lower levels of participation in health-related decision making, which
certainly contribute to adverse health outcomes (Gazmararian et al., 2003; Williams et al., 1998a;
Williams et al. 1198b; Scott et al., 2002; Berkman et al., 2004). Low health literacy is also
associated with higher hospitalization rates and emergency room visits, higher risk of mortality,
4
and poorer overall physical and mental health (Baker et al., 2004; Wolf et al., 2005). The causal
link for why inadequate health literacy is related to such negative health outcomes, however,
remains elusive.
Among the mechanisms proposed by the current literature are disease knowledge, selfefficacy, self-management skills, risk behaviors, and appropriate/timely use of clinical
preventive services, and differences in healthcare access (Schillinger et al., 2002; Baker, 2002;
Scott, et al., 2002; Wolf, Gazmararian & Baker, 2007; Gazmararian, Williams, Baker & Peel,
2003). In a literature review of health literacy, healthcare use and health outcomes, The Agency
for Healthcare Research and Quality (AHRQ) called for further research to determine
mechanisms as to how exactly health literacy impacts health outcomes (Berkman et al., 2004).
1.4
VULNERABLE POPULATIONS
Enhancing health literacy has been proposed as a means to reduce or eliminate racial and
socioeconomic health disparities, which is a major national goal (Sudore et al., 2006; Saha ,2006;
Howard, Sentell & Gazmararian, 2006, Sentell & Haplin, 2006). Racial minorities, seniors, lowincome individuals, and those with less than a high school education experience a
disproportionate burden of morbidity and mortality in the United States (AHRQ, 2011). Many of
these subpopulations also exhibit lower levels of functional health literacy and may be
particularly vulnerable to poor health outcomes for this reason (AHRQ, 2004; AHRQ, 2011).
These populations include seniors, racial minorities (specifically African American [AAs], and
Hispanic populations), those living below the poverty level, those who have less than high school
equivalency, and those who do not speak English as a first language (Kutner et al., 2006;
5
Berkman et al., 2011). Since functional health literacy has proven to be a better predictor of
health status and health behavior than demographic and socioeconomic variables, this may be a
tenable avenue to closing gaps in health inequalities (Scott et al. 2002; Williams et al., 1998a;
Bennet et al., 1998; Lindau et al., 2002).
Seniors comprise a particularly vulnerable sub-population as limited health literacy is
prevalent among older adults and appears concurrently with an increased risk of acquiring
multiple chronic diseases and related disabilities that accompany an increase in age (Baker,
Gazmararian, Sudano, & Patterson, 2000; Williams et al., 1995; Weiss, Reed & Kligman, 1995;
Kramarow, Lubitz, Lentzner, & Gorina, 2007; Hoffman, Rice & Sung ,1996). Elderly patients
with chronic diseases and limited health literacy have less knowledge of their disease and worse
self-management skills than those that operate at a proficient level (Williams, et al 1998a). Those
with limited literacy also experience more difficulty in interpreting health messages and taking
prescription medication properly, which can seriously impact one’s health status (Ratzan &
Parker, 2000).
When faced with managing a chronic condition and/or multiple comorbidities, it is
essential to have the knowledge and skills required to maintain health and prevent adverse
outcomes. Considering that health literacy generally declines as one ages (Baker, Gaznararuabm
Sydabim, & Patterson, 2000; Benson & Foreman, 2002), it is likely that these individuals
experience greater difficulty in managing their health and therefore suffer from worse health
outcomes than their younger counterparts.
The identification of these subgroups is important when considering the fact that health
literacy may play a role in attenuating health disparities by acting as a direct correlate of health
outcomes (Sudore et al., 2006; Saha, 2006). By modifying levels of health literacy within
6
disproportionately burdened subgroups, poor health outcomes may be reduced concurrently with
health inequalities, but we must first understand the link between health literacy and health
outcomes within these groups.
1.5
THEORY & BEHAVIOR
Most of the proposed mechanisms linking health literacy to health outcomes, as listed at the end
of section 1.2, are either constructs found in behavioral change and education theories (i.e.
knowledge and self-efficacy), or are actual behaviors (see Glanz, Rimer, &Viswanath, 2008).
General behavioral modification, therefore, offers a more comprehensive mechanism to explore
this relationship, and it is worth examining the role of health literacy in prompting behavioral
change as a means of attenuating negative health outcomes.
Health literacy is associated with health behaviors (Cho, Lee, Arozullah, Critteden,
2008), defined by Gochman (1997) as “those personal attributes such as beliefs, expectations,
motives, values, perceptions, and other cognitive elements; personality characteristics, including
affective and emotional states and traits; and overt behavior patterns, actions, and habits that
relate to health maintenance, to health restoration, and to health improvement” (p. 169).
Although this definition is daunting as many components appear to contribute to one’s health
behavior, theory succeeds in simplifying these factors into constructs that are used to explain
how behavioral change works.
Common substantiated theories used to describe behavioral modification are the health
belief model, the theory of reasoned action, the transtheoretical model, and social cognitive
theory (Glanz et al., 2008). Many behavioral interventions combine constructs from multiple
7
theories. Theory-based interventions are more effective in promoting and sustaining behavioral
change than those that are not (Doak, Doak, Leanord & Root, 1996). Thus, health literacy
interventions that utilize theory to promote behavioral modification will increase the likelihood
of success. Before constructing an intervention based on theoretical constructs, however, we
must identify behaviors that are substantially impacted by health literacy.
There is evidence that low health literacy is associated with lower use of services that are
primarily delivered within a clinical setting, which includes screening tests, counseling, and
immunizations (Scott et al., 2002). Clinical preventive services are essential to maintaining and
improving health by detecting disease in earlier, more treatable stages and preventing the onset
and/or progression of illness (Benson & Aldrich, 2012). These services can also reduce the risk
of chronic disease, which is currently the primary cause of mortality, in addition to reducing
healthcare costs (Maciosek et al., 2010; Benson & Aldrich, 2012; Cohen & Neumann, 2009). For
older adults, who face a higher risk for concomitant illnesses and higher risk for disability,
clinical preventive service use is a key component of remaining healthy and independent
(Kramarow, Lubitz, Lentzner & Gorina, 2007; Hoffman et al., 1996,).
Despite the established benefits and the fact that most of these services are covered by
Medicare and Medicaid, clinical preventive services are highly underutilized by older adults,
which leads us to the question of what role health literacy may play in the decisions to engage in
these preventive clinical behaviors (Centers for Medicare & Medicaid Services, 2011; Cho et al.,
2008; Centers for Disease Control and Prevention [CDC],2012; CDC, 1998).
8
1.6
CLINICAL PREVENTIVE SERVICES UTILIZATION AMONG
OLDER ADULTS
Older adults are the fastest growing age group in the United States as the baby boomers are
transitioning into their retirement years, making the role of clinical preventive services
increasingly important to keeping this population healthy and functionally independent (CDC,
2009). Only 25% of those aged 50-64 and fewer than 40% of those 65 and older are up to date on
the core clinical preventive services recommended by the U.S. Preventative Services Task Force
(USPSTF), which encompass a number of screening tests and immunizations (DHHS 2010;
CDC 2012). Increasing the proportion of older adults who are up to date on these services has
been deemed a priority by Healthy People 2020; increasing health literacy among seniors may be
an effective way to address this issue (Tebo, 2011; DHHS, 2011).
Functional health literacy is associated with an increased likelihood of engaging clinical
preventive behaviors including cancer screening and vaccinations ( Scott, et al., 2002; Lindau, et
al., 2001; Friedman, Corwin, Dominick, & Rose, 2009, Baker et al., 1997). It is therefore no
surprise that higher literacy is also associated with earlier diagnosis of disease, which decreases
the risks related to limited treatment options (Bennett et al., 1998, CDC, 2012). Clinical
preventive services are also important for the health maintenance of those who already have a
chronic condition, as individuals with hypertension or diabetes need to monitor their blood
pressure or blood glucose on a regular basis (Benson & Aldrich, 2011).
Those with lower health literacy, however, may experience literacy-related barriers to
preventive service utilization such as poor understanding of screening, negative attitudes about
clinical services, limited understanding of risk factors and the role of preventive behavior, and
fear or embarrassment of discussing clinical services with health professionals (Berkman et al.
9
2004; American Medical Association [AMA], 1999; Lindau et al., 2001; Rogers, Wallace &
Weiss, 2006; Friedman et al., 2009; Baker et al.,1996, CDC, 1999; Dolan et at., 2004). Another
concern is that many older adults are unaware of USPSTF recommendations (AHRQ, 2004).
Thus, it would appear that improving health literacy may increase use of clinical preventive
services by combating the barriers described above. There is, however, an overall dearth of
research concerning the relationship between health literacy and clinical preventive services
(AHRQ, 2011).
The CDC acknowledges that, “one challenge is to educate older adults about the value of
preventive services and motivate them to get the clinical preventive services they need” (Benson
& Aldrich, 2012). If people are able to access and apply health-related information, they can
acquire the knowledge, positive attitudes, motivation, and/or the self-efficacy necessary to make
the decision to seek and utilize these clinical services. This is where the role of public health
professionals comes into play.
1.7
THE ROLE OF THE PUBLIC HEALTH PROFESSIONAL
The public is constantly bombarded with complex health information presented as verbal and
written guidelines, regulations, educational materials, and medical instructions from which
individuals need to pull and apply information that is relevant to their own lives. One must
construct a knowledge base that serves as a foundation to evaluate information quality, analyze
risks and benefits, calculate dosages, interpret test results, and seek additional relevant health
information. This is a lot to ask of a population in which half of adults find it difficult to
understand and use health information (AMA, 1999, Kutner et al., 2006).
10
Moreover, many sources of health-related information, such as educational materials and
health messages, are written above the reading level of the average American or are presented in
a way that is not accessible (Davis et al., 1990; Meade, Diekmann, & Thornhill, 1992). While
much of this discussion has focused on the individual, public health professionals must also take
responsibility for recognizing and helping those with limited functional health literacy. This is
particularly important because it is not uncommon for those with poor literacy skills to conceal
difficulties to avoid shame, embarrassment, or stigmatization (Parikh et al., 1996; Baker et al.,
1996). Health professionals need to find and act on the most effective avenues to create a more
health literate nation.
DHHS’s National Action Plan to Improve Health Literacy (NAPIHL) encourages solving
this problem through multiple disciplines that operate at many levels of the socio-ecological
model. Systematic goals for improvement include increasing healthcare access and
communication, as well as supporting the partnering of stakeholders and the development of
policy changes. The NAPIHL also highlights the role of public health professionals in
development and dissemination of health information that is easy to understand, useable, and
culturally and linguistically relevant. Of primary importance is the construction of actionable
health messages; otherwise the message can become irrelevant or confusing to the public. DHHS
supports an increase in adult education efforts as well an increase in research that focuses on the
development and dissemination of effective health literacy interventions. Evidence-based health
literacy interventions are largely lacking (AHRQ, 2011)
11
1.8
PURPOSE OF THIS RESEARCH PROJECT
This study examines the relationship between functional health literacy and clinical preventative
behavior among African Americans (AAs) aged 60 and older and who live in low income
housing in Allegheny County. This study targets demographics that typically possess lower
levels of health literacy, lower rates of clinical prevention service utilization, and poor health
outcomes.
Studies have shown that functional health literacy is related to health outcomes, but the
causal link is not definitively understood.
Clinical preventive service utilization has been
proposed as a possible mediating factor as these services play a role in preventing onset and
progression of disease as well as detecting illness in its early treatable form, although research is
limited.
We know that older adults who engage in clinical preventive behaviors have a higher
probability of remaining healthy and functionally independent (Cranksaw, Rabiner, & O’Keefe
2003). If health literacy is identified as a substantial contributing factor, it may be a viable
method of increasing preventive service utilization, thus increasing positive health outcomes.
Studying components that contribute to individual clinical preventive services is not
generalizable to all clinical preventive services.
For example, poor knowledge and
misperceptions about prostate cancer screening do not necessarily translate as barriers to blood
pressure screening. Older adults are susceptible to multiple chronic conditions, thus
comprehensive approaches to disease prevention and management may be more valuable than
targeting individual diseases and the relevant protective behaviors. As such, examining the
knowledge, motivation and intentions behind overall clinical preventive behaviors may be more
relevant to developing effective interventions. Although most studies observe health literacy in
12
relation to each individual clinical procedure or test, this research assumes holistic approach and
evaluates total clinical preventive behavior.
This method considers whether or not individuals with higher health literacy are more
likely to engage in general clinical preventive behavior, which suggests the use of skills like
information seeking and informed decision-making. These skills also underlie behavioral
constructs such as knowledge and motivation relative to individual clinical tests/procedures. To
the author’s knowledge, this is the first study that investigates overall clinical preventive
behavior in relation to functional health literacy.
In an effort to contribute to the body of research that influences health literacy
interventions, this study tests the following hypotheses:
Hypothesis 1
1Ho: Health literacy does not predict clinical percent behavior, holding
covariates constant.
1Ha: Health literacy does predict clinical percent behavior, holding covariates
constant.
Hypothesis 2
2Ho: Health literacy is not positively associated with percent clinical prevention
behavior.
2Ha: Health literacy is positively associated with percent clinical prevention
behavior.
13
2.0
2.1
METHODS
DATA COLLECTION
This study utilizes secondary data collected from the Boosting Minority Involvement (BMI)
study conducted at the University of Pittsburgh for which Steven M. Albert, Chair of the
Department of Behavioral and Community Health Sciences at the University of Pittsburgh
Graduate School of Public Health, granted access and permission for use. The objective of BMI
was to increase research participation of senior African Americans (AAs) living in low income
communities in health assessments. Recruitment sites included four community centers and four
low-income senior housing buildings sponsored by Allegheny County, which were located in
Pittsburgh, PA.
These areas were targeted due to a high concentration of AAs and a
geographical placement within low income areas.
Eligible participants lived in county-sponsored senior housing or attended community
centers. The study sample for this research included AA adults aged 60 years and older.
Participants were excluded if they were blind, rendering them unable to complete the STOFHLA. The BMI protocol was approved by the University of Pittsburgh Institutional Review
Board prior to implementation and all participants provided written informed consent.
14
2.2
MEASURES
Although BMI was designed as a cohort study with measurements taken at baseline, 6 months,
and 12 months, this research uses only baseline data and is therefore cross-sectional in nature.
Assessments were conducted via in-person interviews and all measures were determined by selfreport.
2.2.1 Percent Clinical Preventive Behavior
Percent clinical preventive behavior was assessed by selecting clinical-specific items from the
Prevention in Practice Report: “10 Keys” to Healthy Aging survey, which was a key component
of an educational initiative to promote healthy aging in McKeesport, Pennsylvania (Robare et al.,
2011; Newman et al., 2010). The items used for this study are highlighted in yellow in Table
A.1 in Appendix A. This survey was designed by the Center for Aging and Population HealthPrevention Research Center (CAPH-PRC) within the University of Pittsburgh’s Graduate School
of Public Health and was based on the 10 dimensions necessary to maintain health in older
adults, which were founded on epidemiological, clinical, and laboratory research. The clinical
dimensions that were used included prevention of bone loss and muscle weakness, control of
blood pressure, regulation of blood sugar, participation in cancer screening, regular
immunizations, and lowering cholesterol.
Percent clinical preventive behavior was calculated for each individual by dividing the
sum of clinical preventive behaviors by the number of clinical tests for which one was eligible.
Answers to each item were dichotomized (yes or no) to indicate if the person received the test. A
percentage score as opposed to a sum is more appropriate due to variations in clinical test
15
relevance as there are gender specific tests (i.e. mammography) and eligibility requirements (i.e.
immunizations).
Clinical preventive behaviors used to calculate an overall percentage consisted of
knowing one’s blood pressure, blood sugar, LDL cholesterol; receipt of an annual influenza
vaccination and pneumococcal vaccination within one’s lifetime; acquisition a bone mineral
density test, colon cancer screening test within one’s lifetime; and attainment of a prostate
specific antigen (PSA) test for males, and for females, a mammogram within the past year and a
pap test or pelvic exam in the past 2 years. Although some items ask for test result rather than
whether the test was done, awareness of the results suggests that the relevant clinical test was
employed.
2.2.2 S-TOFHLA
The S-TOFHLA was used to assess participants’ level of health literacy (Baker et al.,
1999). This instrument utilizes a modified Cloze procedure for 36 items; every fifth to seventh
word of a health-related passage is deleted and replaced with a blank space. The participant
must select a word to fill the blank from a multiple choice bank of four answers for which there
is only one correct answer. Table A.2 in Appendix A displays a number of S-TOFHLA items
used to assess health literacy as an example. Items for which the participant selected the correct
answer were coded as “1” while incorrect or incomplete answers received a “0.” Correct
answers were summed to generate a score for each individual on a scale that ranges from 0-36
and classifies each participant’s level of health literacy as follows: 0-16 items correct =
inadequate, 17-22 items correct = marginal, and 23-36 items correct = adequate.
16
2.2.3 Covariates
Gender, age, and education (dichotomized into <high school and >high school) were included as
explanatory variables as these variables have been demonstrated to be related to preventive
health service use among seniors (CDC, 1998).
A comorbidity index was calculated by
summing self-reported illness, which included cerebrovascular disease, coronary heart disease,
congestive heart failure, hypertension, diabetes mellitus, depression, COPD or asthma,
osteoporosis, depression, macular degeneration, glaucoma, peripheral neuropathy, Parkinson’s
disease, and history of cancer. The comorbidity index was an important component for which to
adjust because of its relationship to clinical prevention behaviors. For example, a person with
diabetes may be more likely than others to keep track of their blood sugar than other participants.
Likewise, a person with a previous cancer diagnosis may engage in cancer screening as a result
of a higher perceived susceptibility.
2.3
STATISTICAL ANALYSIS
Prior to statistical analysis, the data were cleaned to detect, correct, and remove inconsistencies.
Cleaning, descriptive statistics and statistical analyses were performed using IBM SPSS
Statistics 19.
A Shapiro-Wilk test was used to assess the normality of percent clinical prevention
behavior and S-TOFHLA score to facilitate the selection of statistical analysis methods. This
test revealed that neither variable came from a normally distributed population (see Table B.1 in
Appendix B).
17
Multivariate linear regression was employed to test the hypothesis that health literacy
predicts percent clinical preventive behavior. Predictor variables were forced into models based
on sound theoretical reasons for their selection (see Studenmund & Cassidy, 1987). I chose this
method because normality is not required for linear regression analysis and because this method
was the most theoretically justifiable model (Field, 2009). Two models were employed, the first
of which included age, education, gender and S-TOFHLA scores predictors of clinical preventive
behavior. The second model included comorbidity index to account for the association between
particular conditions and relevant clinical preventive services.
Non-parametric correlation analysis was used to explore the independent relationship
between S-TOFHLA and comorbidity index across percent preventive behavior. Correlation
differs from linear regression analysis because it tests for association between two variables; one
cannot be used to predict the other (Field, 2009). Due to the non-normal distribution of percent
clinical preventive behavior, a Spearman’s rho correlation was used to detect significant,
monotonic associations that may be overlooked in linear regression analysis. A one-tailed test
was employed due to theoretical justification to suspect a positive relationship between each
variable (S-TOFHLA score and comorbidity index) and percent clinical preventive behavior.
The result of regression and correlation analysis between S-TOFHLA score and percent
clinical preventive behavior spawned an exploratory analysis of the relationship between
comorbidity index and S-TOFHLA score. Number of comorbidities may be a moderator of the
relationship between health literacy and preventive service use. Patients with health conditions
have been shown to access health-related information, thus allowing for the modification of
one’s health literacy (Castleton et al., 2010). An individual with multiple illnesses may have
greater clinical health-related knowledge or self-efficacy due to more exposure to clinical
18
services and the healthcare setting out of necessity. Therefore, multivariate linear regression
analysis was used to explore comorbidity index as a predictor of health literacy. Two models
were employed to predict S-TOFHLA. The first model included age, gender, education and
comorbidity index. The second model included percent clinical preventive behavior to control
for the possibility that increased exposure to the clinical setting may also influence health
literacy.
The use of a nonparametric correlation test was necessary due to the non-normal
distribution of S-TOFHLA score.
A Spearman’s rho correlation was used to detect any
significant relationship between comorbidity index and S-TOFHLA score that may not be
apparent in regression analysis. A one-tailed test was employed due to theoretical justification to
suspect a positive relationship between comorbidity index and S-TOFHLA.
19
3.0
3.1
RESULTS
BASELINE CHARACTERISTICS
Baseline characteristics of the 91 AA participants are presented in Table 1. The sample was
predominantly female (80.2%); 18.7% of the population had less than a high school level of
education, 35.2% had a high school equivalency, and 46.2% of participants had educational
experience beyond high school. Median S-TOFHLA score and percent clinical preventive
behavior were reported because these variables were not normally distributed. The median STOFHLA score was 34 on a scale of 0-36 points. Most participants had an adequate level of
health literacy (92.3%). Only 3.3% of participants had an inadequate level of health literacy and
4.4% had a marginal level of health literacy. The median percent clinical preventive behavior
was 62.5% out of a possible 100%, with a range of 0-89%. The mean comorbidity index was 3.2
self-reported illnesses per participant. Most participants had hypertension (78%) and/or arthritis
(68.1%). Congestive heart failure (7.7%), depression (6.6%) and Parkinson’s disease (2.2%)
were the least reported illnesses.
20
Table 1. Characteristics of study population, N=91
Characteristic
N (%)
72.4 + 7.4; range: 60-95
Age (mean years + S.D.)
Gender
Female
Asfdas
Male
Afsdafs
Asfdafas
Education
fadsdfas
Asfdasf
< High school
Afsadf
School
High school equivalent
Any vocational school
Any college
Graduate or professional school
73 (80.2)
18 (19.8)
17
32
4
35
3
S-TOFHLA Score (mean + S.D.)
(18.7)
(35.2)
(4.4)
(38.5)
(3.3)
31.6 + 7; median 34; range 0-36
Health literacy level
Inadequate
Marginal
Adequate
3 (3.3)
4 (4.4)
84 (92.3)
Percent clinical prevention behavior
(mean + S.D.)
57.8 + 20.1; median 62.5; range 0-89
Comorbidity index (mean + SD)
3.2 + 1.9; range 0-10
Comorbidities
Cerebrovascluar disease
G
Diabetes mellitus
Hypertension
Coronary heart disease
Congestive heart failure
Macular degeneration
Glaucoma
G
Depression
Arthritis
Osteoporosis
COPD or asthma
Peripheral neuropathy
Parkinson’s disease
History of cancer
10
31
71
22
7
12
15
6
62
13
15
12
2
13
21
(11.0)
(34.1)
(78.0)
(24.0)
(7.7)
(13.2)
(18.5)
(6.6)
(68.1)
(14.3)
(16.5)
(13.2)
(2.2)
(14.3)
3.2
DESCRIPTIVE STATISTICS
Descriptive statistics for each item used to calculate percent clinical preventive behavior are
presented by gender in Table 2. A larger percentage of females (74%) knew their last blood
pressure reading when compared with their male counterparts (50%). A higher percentage of
women (60.3%) also had a bone mineral density test compared to men (38.9%) A higher
percentage of males received an annual influenza vaccine (88.9%) and pneumococcal vaccine
(94.1%) compared to females (65.8% for each). Men had a slightly higher percentage (72.2%) of
colon cancer screening when compared to women (60.3%) The percentage of men and women
who knew their last blood sugar reading and who knew their LDL cholesterol was similar at
about 33% and 67% respectively. A two-tailed t-test was run for each of the items listed above to
detect differences in test/procedure acquisition by gender. Knowing one’s blood pressure was
the only item that approached statistical significance (p< .10). Gender differences, however, were
not present at the standard level of alpha=.05 for any one item.
For gender-specific tests, 94.4% of men had a PSA test within the last year, 72.3% of
women received a mammogram within the past year and 69.6% of women had a papanicolaou
test or pelvic exam within the past two years. As a whole, men had a slightly higher median
percent clinical preventive behavior (62.5) than women (55.6), although a Mann-Whitney U test
revealed that this difference was not statistically significant.
22
Table 2. Percent clinical preventive behavior by gender
Clinical prevention behavior (timeframe of receipt of clinical test specified by instrument)
Receipt of test and/or knowledge of test result
Male (%)
Female (%)
Total (%)
Last blood pressure reading
Yes
No
50.0
50.0
74.0
26.0
69.2
30.8
Last blood sugar reading
Yes
No
33.3
66.7
32.9
67.1
33.0
67.0
LDL cholesterol
Yes
No
11.1
88.9
12.3
87.7
12.1
87.9
Usually receive influenza vaccine (annual)
Yes
No
88.9
11.1
65.8
34.2
70.3
29.7
Pneumococcal vaccine (ever)
Yes
No
94.1
5.9
65.8
34.2
71.1
28.9
Bone mineral density test (ever)
Yes
No
38.9
61.1
60.3
39.7
56.0
44.0
Colon cancer screening (ever)
Yes
No
72.2
27.8
60.3
39.7
62.6
37.4
Received prostate specific antigen (PSA)
tests
Test (within past year)
Yes
No
94.4
5.6
Received mammogram (within past year)
Yes
No
72.6
27.4
Received pap test or pelvic exam
(within past two years)
Yes
No
69.6
31.0
Total clinical prevention behavior (percent)
(mean + S.D, median)
60.1+16.2
median 62.5
62.5
23
57.2 + 21
median 55.6
57.8 + 20.1
median 62.5
3.3
PERCENT CLINICAL PREVENTIVE BEHAVIOR
3.3.1 Predictors of percent clinical preventive behavior
Linear regression analysis was used to test the hypothesis that S-TOFHLA predicts percent
clinical preventive behavior, adjusting for covariates. Two models were employed, both of which
incorporated age, education, and gender as demographic characteristics shown to be related to
clinical preventive behavior (CDC, 1998). Comorbidity index was added as a predictor in the
second model. Table 3 displays the results of both models.
Table 3. Multivariate linear regression on percent clinical preventive behavior
Predictor
Variable
S-TOFHLA
score
Age
Education
Gender
Comorbidity
index
Constant
F
p-value
R
R2
Adjusted R2
Model 1a
Coefficient
(B-value)
.410
Standardized
Coefficient
.143
.248
5.711
-5.915
.091
.112
-.118
Model 2b
P-value Coefficient
(B-value)
.224
.431
27.159
.873
.484
.198
.039
-.006
.406
.316
.306
.243
4.503
-6.689
3.11**
.344
20.501
2.343
.048*
.348
.121
.069
Standardized
Coefficient
.150
P-value
.089
.088
-.133
.288
.397
.413
.230
.006
.185
.459
Dependent: percent clinical preventive behavior
a
Predictors: (constant), S-TOFHLA score, age, education, gender
b
Predictor’s (constant), S-TOFHLA score, age, education, gender, comorbidity index
*p<.05, p<.01
Model 1 does not significantly explain the variation in percent clinical preventive
behavior.
Additionally, neither S-TOFHLA score, age, education, nor gender significantly
predicted percent clinical preventive behavior when controlling for the respective covariates.
24
When comorbidity index is added, the F statistic (2.343) is significant, (p=.048, p< .05),
specifying that this model is better at predicting the outcome of percent clinical preventive
behavior than using the mean as a best guess. Model 2 significantly predicts 12.1% of the
variance of percent clinical preventive behavior (R2=.121). Generalizability of the model is
assessed by calculating the difference between R2 and the adjusted R2 (.121-.069=.052), which
indicates that 5.2% less of the variance of the percent clinical preventive behavior would be
explained had this model been derived from a population when compared with this sample.
Thus, this model is fairly generalizable.
The only statistically significant predictor in Model 2 is comorbidity index (p=.006, p<
.01), while holding all other variables constant, suggesting that this variable may play a more
substantial role in predicting percent clinical preventive behavior than S-TOFHLA score, age,
gender and education. A positive relationship exists; an increase of one medical condition is
associated with an increase in 3% in preventive behavior (B-value=3.111), which is a relatively
large magnitude. This model indicates that participants that have a larger number of illnesses
engage in a higher percentage of clinical preventive behavior.
It is worth noting that even though S-TOFHLA score is not significant at alpha=.05, this
variable approaches significance in Model 2 and has a higher standardized coefficient (Beta =
.288), and a lower p-value (.185) than education, gender and age. This trend is also present in
Model 1 and suggests that clinical preventive behavior increases as health literacy increases.
Health literacy may therefore play a more important role as a predictor than other demographic
covariates, which is consistent with the findings of other studies (Scott et al. 2002; Williams et
al., 1998a; Bennet et al., 1998; Lindau et al., 2002). However, a lack of statistical significance
25
leads us to accept the null hypothesis that health literacy does not predict clinical percent
behavior, holding covariates constant.
3.3.2 Correlation across percent clinical preventive behavior
Correlation analysis was used to test the hypothesis that S-TOFHLA score is positively
associated with percent clinical preventive behavior.
Comorbidity index was added to the
analysis as a result of its relationship to preventive behavior, which was discovered in regression
analysis (Section 3.3.1). The results of Spearman’s rho correlation analysis across percent
clinical preventive behavior and S-TOFHLA score is summarized in Table 4.
Table 4. Correlation across percent clinical preventive behavior
Variable
Spearman’s rho correlation
coefficient,
P-value
(non-parametric)
S-TOFHLA score
Comorbidity index
.221, .018*
.229, .029*
(one-tailed test), *=p<.05, **p<.01
Percent clinical preventive behavior is significantly correlated with S-TOFHLA score,
(rs=.221, p<.05). The coefficient of determination is small (rs2=.049) and accounts for 4.9% of
the proportion of variance in the ranks that two variables share. We reject the null hypothesis
and conclude that health literacy and percent preventive behavior have a positive, monotonic
relationship, meaning that as one increases, so does the other.
Comorbidity index is also significantly correlated with S-TOFHLA score (rs =.229,
p<.05) and accounts for 5.2% of the proportion of variance in the ranks that the two variables
share (rs2=.052). This relationship is also positive, but the correlation coefficient of comorbidity
index and percent clinical preventive behavior (.229) is slightly larger than that of the S26
TOFHLA score and percent clinical preventive behavior (.221).
The magnitude of both
coefficients is relatively small, but the number of illnesses one has appears to have a somewhat
larger association with clinical preventive behavior when compared to health literacy.
3.4
S-TOFHLA SCORE
3.4.1 Predictors of S-TOFHLA score
The results presented in section 3.3 initiated an exploratory analysis of the relationship between
comorbidity index and S-TOFHLA score. Linear regression analysis was used to test the
hypothesis that the number of comorbidities one experiences predicts S-TOFHLA score,
adjusting for covariates. Two models were employed, both of which incorporate age, education,
and gender as demographic characteristics shown to be related to health literacy (Kutner et al.,
2006). Percent clinical preventive behavior was added as a predictor in the second model
because a significant correlation between S-TOFHLA score and clinical preventive behavior was
detected in section 3.3.2. The results are displayed in Table 5.
27
Table 5. Multivariate linear regression on S-TOFHLA score
Predictor
Variable
Comorbidity
index
Age
Education
Gender
Percent clinical
preventive
behavior
Constant
F
p-value
R
R2
Adjusted R2
Model 1a
Coefficien
t
-.076
Standardized
Coefficient
-.020
-.058
2.551
6.317**
.095
.143
.363
Model 2b
P-value Coefficient
20.076*
4.575
.002
.419*
.175
.137
.836
-.223
Standardized
Coefficient
-.060
.544
.152
.000
-.068
2.283
6.507**
.048
-.073
.128
.374
.137
.027
18.685*
4.05
.002
.439*
.192
.145
P-value
.474
.211
.000
.185
.562
.040
Dependent: S-TOFHLA score
a
Predictors: (constant), comorbidity index, age, education, gender
b
Predictors: (constant), comorbidity index, age, education, gender, % clinical preventive behavior
*p<.05, **p<.01
Both Model 1 and Model 2 significantly explain the variation in S-TOFHLA score at
alpha=.05. When percent clinical preventive behavior is added to Model 2, the proportion
variation of S-TOFHLA score explained by the model jumps from 17.5% in Model 1 to 19.2% in
Model 2, specifying that Model 2 is a slightly better predictor of S-TOFHLA score than Model 1.
Generalizability of the model is assessed by calculating the difference between R 2 and the
adjusted R2 (.192-.145=.047) , which indicates that only 4.7% less of the variance of the percent
clinical preventive behavior would be explained had this model been derived from a population
as opposed to a sample. This model is therefore fairly generalizable.
Gender is the only significant independent predictor of S-TOFHLA score (p=.000, p<
.01). In light of limitations in sample size and distribution, trends among predictor variables
approaching significance were examined. Percent clinical preventive behavior approaches
significance (.185), and has a higher standardized coefficient (Beta=.137) than all other
28
explanatory variables and thus may have more of an effect on health literacy than comorbidity
index, age, and education. Comorbidity index had the smallest standardized coefficient (Beta= .060) and a high p-value and therefore does not appear to affect S-TOFHLA score. We therefore
accept the null hypothesis that comorbidity index does not predict health literacy.
3.4.2 Correlation between comorbidity index and S-TOFHLA score
Non-parametric correlation analysis was used to explore the independent relationship between
comorbidity index and S-TOFHLA score. Due to the non-normal distribution of S-TOFHLA
score, a Spearman’s rho correlation was used to detect general associations that may have been
overlooked in linear regression analysis, which is presented in Table 6.. Table 3 showed that
percent clinical preventive behavior was positively associated with S-TOFHLA score and, thus,
was not included in Table 6
Table 6.Correlation of comorbidity index and S-TOFHLA score
Independent
Variable
Spearman’s rho correlation
coefficient,
(non-parametric)
Comorbidity index
P-value
.068, .261
(one-tailed test), *p<.05, **p<.01
Comorbidity index was not statistically associated with S-TOFHLA score. A very small
positive trend does exist (rs=.068), but we conclude that the number of illnesses one has is not
positively associated with one’s level of health literacy.
29
4.0
4.1
DISCUSSION
HEALTH LITERACY AND CLINICAL PREVENTIVE BEHAVIOR
This study found that functional health literacy does not predict the percentage of
relevant clinical prevention behaviors in which one engages, adjusting for covariates. It appears,
however, that health literacy approaches significance as a predictor. Small sample size may have
limited the study from finding statistical significance. Even so, there is still an 18.5% chance of
obtaining the observed result of the S-TOFHLA score as a predictor variable by chance even if
no effect exists. The relationship is not statistically significant, but the trend is consistent with
the findings of other studies for individual preventive procedures like cancer screening and
immunizations (Peterson et al., 2011; Scott et al., 2002; Lindau et al., 2001).
Although we cannot conclude that health literacy predicts clinical preventive behavior,
these variables are positively correlated, meaning that as one variable increases, so does the
other. The magnitude of the relationship, however, is relatively small (rs =.221), and the analysis
did not control for explanatory variables. A limitation of this statistical test is that it is indicative
only of association and does not express causality; it is not possible to determine the direction of
the relationship in a correlation. In order to elucidate the foundation for this positive correlation,
clinical preventive behavior was also tested as a predictor of health literacy. Although percent
30
clinical preventive behavior was not a significant predictor, the trend once again reflected a
positive association.
The positive regression coefficients may indicate that each partially contributes to the
other. Those who are exposed to one or more clinical preventive behaviors, whether as a result
of contact with health professionals or a heightened awareness of health-related risks, have the
opportunity to increase health-related knowledge at that particular appointment. This potential
increase in health literacy and/or more experience with clinical services may then cause
individuals to engage in more clinical preventive behaviors. Therefore, a cycle may exist where
each variable positively affects the other, which would account for the presence of an
association. Multivariate linear regression appeared to be the best fit for this study, although a
significant, non-parametric correlation suggests the possibility that health literacy and clinical
preventive behavior possess a relationship that may be more accurately expressed by a more
complex regression equation.
One study, however, cannot completely confirm or refute the association between health
literacy and percent clinical preventive behavior, and further research is needed to detangle this
obscure relationship.
4.2
COMORBIDITY INDEX AND CLINICAL PREVENTIVE BEHAVIOR
Number of comorbidities was the only significant predictor of percent clinical preventive
behavior, while controlling for covariates. It was also independently, positively associated with
clinical preventive behavior in a correlation analysis. When studying the use of clinical tests in a
senior population, we must consider the fact that individuals may already have a moderate to
31
high level of contact with health care professionals due to a higher prevalence of health issues.
This sample had a mean comorbidity index of 3.2, indicating that many individuals were
managing multiple health conditions. Perhaps these individuals were exposed to the healthcare
setting more often out of necessity, and consequently had a greater opportunity for
communication about clinical preventive behavior and health management with their health care
professionals. In fact, studies have documented that physician recommendation or
encouragement may play a substantial role in one’s behavioral intention and decision to engage
in clinical preventive procedures, which may overshadow the effect of health literacy on clinical
preventive behavior if one exists (Guerra, Dominguez, & Shea, 2005, Lewis & Jenson, 1996;
Aiken, West, Woodward, & Reno, 1994; Drake et al., 2004). Another possibility is that the
particular conditions exhibited by this sample were closely related to the specific clinical tests
and procedures used to calculate percent clinical preventive behavior. For example, a number of
clinical procedures are applicable to those with a history of cancer, heart disease and diabetes
mellitus (i.e. cancer screening, and knowing one’s blood pressure and blood sugar), but are not
necessarily as relevant to Parkinson’s and arthritis patients. Hypertension was the most prevalent
condition within the sample and diabetes mellitus ranked third, which lends support to this
explanation.
Comorbidity index neither predicted nor was associated with health literacy. While this
variable predicted percent clinical preventive behavior, it was not likely due to its influence over
health literacy in this study. This reduces the possibility that increased exposure to healthcare
due to necessity might increase exposure to medical terminology and information as previously
theorized.
32
4.3
STUDY LIMITATIONS
This study has a number of limitations. First, this study is restricted to AA men and women aged
60 and older, living in a low-income, community setting and is not representative of the general
population. The results may not be generalizable to other ethnicities, younger individuals, or to
different income levels and living situations. There are limitations in data collection as this study
utilizes secondary data that were not collected for the purpose of this analysis. Additionally, the
cross-sectional nature of the data does not allow for the assessment of temporality or causality.
Characteristics of the sample also act as a limitation. This group had a relatively large
proportion of individuals that had adequate health literacy (92.3%), which contributed to a highly
skewed distribution. Given that only 7 out of 91 participants had below adequate health literacy
and only 3 of those 7 fell into the inadequate category, it is difficult to detect any effects even if
they exist. This could be due to self-selection bias as those with lower health literacy skills may
have opted to not participate in this study. It could also be the result of the relatively high
education levels of this sample (71.3% > high school).
It should be noted that this is the first attempt to calculate overall percent clinical
preventive behavior. As such, internal consistency and reliability have not been tested. A caveat
emerges in the use of the “Prevention in Practice: 10 Keys to Healthy Aging survey.” Questions
that cover blood pressure, blood sugar and LDL cholesterol ask the participants for the result of
the test as opposed to whether or not they received the test. It may be more difficult to recall an
actual number, which may affect the accuracy of the answer. We can, however, infer that
knowledge of these numbers conveys either a receipt of the test or a sense of desire to manage
one’s health, which are both clinically-related preventive behaviors. Future studies can phrase
question to explicitly capture the utilization of services.
33
It is also important to acknowledge that there are items on the survey that address tests
and procedures that do not match U.S. Preventive Services Task USPSTF guidelines (see Moyer,
2012; USPTF, 2002). Guidelines, however, are constantly changing and are sometimes different
across multiple national organizations, which is not only confusing to the general population, but
also impedes the design of a consistent instrument that assesses holistic clinical preventive
behavior. The CAPH-PRC is part of the network for Centers for Disease Control and Prevention
(CDC) Prevention Research Centers and is therefore a reputable resource in the field of healthy
aging As such, clinical items on the survey were deemed relevant to healthy aging and were
included in the analysis regardless of USPSTF recommendation
Lastly, although the sample size is adequate according to the number of predictors (Field,
2009), it was relatively small considering the high variability within S-TOFHLA score and
percent clinical preventive behavior observations.
This sample size therefore may not be
adequate for drawing robust conclusions.
4.4
FUTURE DIRECTIONS
Although this study establishes a positive association between health literacy and clinical
preventive behavior and comorbidity index as a significant predictor of percent clinical
preventive behavior, further research is needed to detangle the relationship of these three
variables. The previously described limitations may have reduced the ability to detect effects. It
is notable that other studies have encountered a similar issue of a large proportion of individuals
with high functional health literacy (see Wolf et al., 2007).
34
We, therefore, cannot make
definitive conclusions from this study and further research is necessary to clarify the relationship
between health literacy and health outcomes.
Future research should ensure the collection of an adequate sample of those that fall into
inadequate and marginal levels of health literacy. Additionally, the collection of demographics of
those who choose not to participate may be useful in elucidating any self-selection bias and may
provide a foundation for including these individuals in future studies.
Most importantly,
longitudinal cohort studies should be employed to detect causality between health literacy,
preventive behaviors and comorbidities. Most of the current health literacy research utilizes
cross-sectional data, which does not provide an adequate understanding of how health literacy is
related to health behavior or health outcomes (Dewalt et al., 2004).
Current research focuses on the identification of constructs related to specific clinical
preventive procedures/tests, which are not generalizable to all clinical preventive behaviors. This
helps us understand barriers that underlie interventions targeted to particular preventive services,
but loses the scrutiny of how people work as health consumers.
There is a need for the
development of instruments that measure holistic preventive behavior in order to gain a greater
understanding of how increasing health literacy might impact behavioral change and health
outcomes.
The current health care system has become consumer-centric, and individuals need to
make more proactive decisions regarding their own health. Providing health information is not
enough. Health professionals need to equip the population with the appropriate skills to seek
health-related information and apply that information to make informed decisions. Identifying
how health literacy may influence a person’s decision to engage in general preventive behaviors,
especially among populations who are disproportionately affected by negative health outcomes,
35
is necessary to developing effective and comprehensive interventions. More theoretically-based
studies should address the effect of literacy on behavioral change to further explore the
relationship of health literacy to health outcomes.
36
APPENDIX A
INSTRUMENTS
A.1
PERCENT CLINICAL PREVENTIVE BEHAVIOR
Table A.1 displays items from the Prevention in Practice Report: “10 Keys” to Healthy Aging
survey, which was used to calculate percent clinical preventive behavior. Only clinical-specific
items, which are highlighted in yellow, were used in the analysis.
Table A.1 Prevention in Practice Report: “10 Keys” to Healthy Aging survey
10 Keys to Healthy
Aging
1
1
Systolic
Blood
Pressure
2
Smoking
2
10 Key Self Rating
One answer for each question.
1. What was your last blood pressure?
________/________ Don’t Know
1.1 Are you on blood pressure medication?
Yes
No
Don’t Know
1.2 Do you have Hypertension?
Yes
No
Don’t Know
2. Do you smoke (cigarettes, cigars, pipes, etc)?
Yes
No
Sometimes
2.1 If yes, do you want to quit?
Yes
No
Sometimes
Table A.1 continues onto page 38
37
Prostate Cancer
(men)
Breast Cancer
Screenings:
Cervical
(women)
Colon Cancer
(Men and Women)
4
Immunizations
3. Have you had a Prostate Specific Antigen (PSA)
test in the past year?
Yes
No
Don’t Know
3._
Have you had a mammogram within the past year?
Yes
No
Don’t Know
Does Not Apply
3._ Have you had a Pap test or Pelvic exam in the past 2
years?
Yes
No
Don’t Know
Does Not Apply
3._
4.
Have you ever been screened for Colon Cancer?
Yes
No
Don’t Know
Do you usually get a yearly flu shot?
Yes
No
4
5
Blood
Glucose
5
LDL Cholesterol
6
6
Physical
Activity
7
7
4.1 Have you ever had a pneumonia shot?
Yes
No
Don’t Know
5. What was your last blood sugar reading?
__________mg/dl
Don’t Know
5.1 Do you have Diabetes?
Yes
No
Don’t Know
5.2 Are you on prescribed medication for Diabetes?
Yes
No
Don’t Know
6. What is your LDL cholesterol level? ___________
mg/dl
Don’t Know
6.1 Are you on prescribed cholesterol lowering
medication?
Yes
No
Don’t Know
7.
How many hours a week do you spend performing
household chores, preparing meals, and shopping?
_________ hours
Don’t Know
7.1 Not including the above, how many hours a week do
you exercise?
__________ hours
Don’t Know
Table A.1 continues onto page 39
38
8
8
Bone
Loss &
Muscle Weakness
9
Social
Contact
9
1
Depression
10
8.
Do you think that your height has decreased?
Yes
No
Don’t Know
8.1 Have you ever had a bone density test?
Yes
No
Don’t Know
8.2 Are you on prescribed medication for bone loss?
Yes
No
Don’t Know
8.3 Are you able to walk a ¼ mile ?
Yes
No
Sometimes
9. Do you have social contact in the community at
least once a week?
Yes
No
Sometimes
10. Do you have a good outlook on life?
Yes
No
Sometimes
10.1 Have you been diagnosed with Depression?
Yes
No
Don’t Know
10.2 Are you on medication for Depression?
Yes
No
Don’t Know
39
A.2
HEALTH LITERACY
Table A.2 displays a selection of items from the Short Test of Functional Health Literacy in
Adults. The participants must select the correct answer to fill each blank from a bank of four
questions. For example, the correct answer to ‘Your doctor has send you to have a __________
x ray’ is ‘a. stomach.’ All other answers are considered incorrect.
Table A.2 Selection of items used to assess health literacy from the S-TOFHLA
a.
b.
c.
d.
Your doctor has sent you to have a ___________ x-ray.
a. stomach
b. diabetes
c. stitches
d. germs
You must have an ______________ stomach when you come for _________.
a. asthma
a. is
b. empty
b. am
c. incest
c. if
d. anemia
d. it
The x-ray will _________ from 1-3 __________ to do.
a. take
a. beds
b. view
b. brains
c. talk
c. hours
d. look
d. diets
40
APPENDIX B
DATA ANALYSIS
B.1
DISTRIBUTION
Table B.1 displays tests of normality for S-TOFHLA score and percent clinical preventive
behavior. The Shapiro-Wilk test shows that neither variable comes from a normally distributed
population (p<.05.) These tests help facilitate the selection of statistical tests.
Table B.1 Tests of normality
Variable
S-TOFHLA score
Percent clinical
preventive behavior
Kolmogorov-Smirnova
Statistic df
Sig.
.281
91
.000
.159
91
.000
a. Lilliefors Significance Correction
41
Shapiro-Wilk
Statistic
.559
.933
df
91
91
Sig.
.000
.000
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