After reviewing the resources in the course materials, write a 750-1,000-word paper analyzing the biopsychosocial and the biomedical models of health.
Include the following in your writing:
Use three to four current scholarly resources to support your discussion (one of which may be the textbook).
importantAccurate error free detail
4
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Typing Template for APA Papers: A Sample of Proper Formatting for the APA 6th Edition
Student A. Sample
Grand Canyon University:
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1
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Typing Template for APA Papers: A Sample of Proper Formatting for the APA 6th Edition
This is an electronic template for papers written in APA style (American Psychological Association, 2010). The purpose of the template is to help the student set the margins and spacing. Margins are set at 1 inch for top, bottom, left, and right. The type is left-justified only—that means the left margin is straight, but the right margin is ragged. Each paragraph is indented five spaces. It is best to use the tab key to indent. The line spacing is double throughout the paper, even on the reference page. One space is used after punctuation at the end of sentences. The font style used in this template is Times New Roman and the font size is 12.
First Heading
The heading above would be used if you want to have your paper divided into sections based on content. This is the first level of heading, and it is centered and bolded with each word of four letters or more capitalized. The heading should be a short descriptor of the section. Note that not all papers will have headings or subheadings in them.
First Subheading
The subheading above would be used if there are several sections within the topic labeled in a heading. The subheading is flush left and bolded, with each word of four letters or more capitalized.
Second Subheading
APA dictates that you should avoid having only one subsection heading and subsection within a section. In other words, use at least two subheadings under a main heading, or do not use any at all.
When you are ready to write, and after having read these instructions completely, you can delete these directions and start typing. The formatting should stay the same. However, one item that you will have to change is the page header, which is placed at the top of each page along with the page number. The words included in the page header should be reflective of the title of your paper, so that if the pages are intermixed with other papers they will be identifiable. When using Word 2003, double click on the words in the page header. This should enable you to edit the words. You should not have to edit the page numbers.
In addition to spacing, APA style includes a special way of citing resource articles. See the APA manual for specifics regarding in-text citations. The APA manual also discusses the desired tone of writing, grammar, punctuation, formatting for numbers, and a variety of other important topics. Although the APA style rules are used in this template, the purpose of the template is only to demonstrate spacing and the general parts of the paper. The student will need to refer to the APA manual for other format directions. GCU has prepared an APA Style Guide available in the Student Writing Center for additional help in correctly formatting according to APA style.
The reference list should appear at the end of a paper (see the next page). It provides the information necessary for a reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in the paper must appear in your reference list; likewise, each entry in the reference list must be cited in your text. A sample reference page is included below; this page includes examples of how to format different reference types (e.g., books, journal articles, information from a website). The examples on the following page include examples taken directly from the APA manual.
References
American Psychological Association. (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.
Daresh, J. C. (2004). Beginning the assistant principalship: A practical guide for new school administrators. Thousand Oaks, CA: Corwin.
Herbst-Damm, K. L., & Kulik, J. A. (2005). Volunteer support, marital status, and the survival times of terminally ill patients. Health Psychology, 24, 225-229. doi:10.1037/0278-6133.24.2.225
U.S. Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute. (2003). Managing asthma: A guide for schools (NIH Publication No. 02-2650). Retrieved from http://www.nhlbi.nih.gov/
health/prof/asthma/asth_sch
in DEFINING HEALTH PSYCHOLOGY
from Key Concepts in Health Psychology
View article on Credo
MEANING
The development of ideas about the origins and meaning of the terms ‘health’ and ‘illness’ has resulted in the emergence of a number of approaches designed to
encapsulate the primary details of the concept and also provide parameters to its study. These approaches in many ways provide the conceptual pathway that has
resulted in the discipline of health psychology. Think of these models as being formative in the historical development of health psychology as an independent level
of enquiry. It is the parameters detailed in models and approaches developed to conceptualize health and illness that need to be considered, if the distinctiveness of
a psychological approach to health is to be established.
ORIGINS
The study of health, illness and well being has a long history, dating back to the philosophical debates about the relationship between physical (bodily systems) and
psychological systems found in the writings of the Greek philosophers Hippocrates (circa 460–circa 377 BC) and Galen (AD 129–circa 199). In so-called ‘humoral’
theory, these early writers argued that disease or illness arose when the four fluids argued to circulate the physical system (i.e. blood, black bile, yellow bile and
phlegm) were out of balance. Importantly, however, these writers also proposed that there was a relationship between a preponderance of one of the bodily fluids
and bodily temperaments or personality types. In other words, disease was associated with physical factors but these physical factors also affected the mind. The
Middle Ages saw an obsession with demonology and mysticism and reinforced the view that illness was associated only with mental states. With the rise of modern
medicine, however, dualism – the argument that the mind and body are independent and not causally related – became the favoured position, and as such
physicians treated bodily ailments without the need to recognize the role of the mind in illness aetiology.
CURRENT USAGE
The bio-medical model adheres to this formulation. It considers that the mind cannot influence physical systems and vice versa and as such that the mind and
body are completely separate entities. Illness is caused by external agents such as viruses or germs which create physical changes in the bodily system.
Psychological processes are completely independent from any illness or disease process. The biomedical model has provided the mainstay of descriptive
parameters for the study of health and illness for over 300 years. During the last century, however, a number of perspectives have been developed which challenge
the bio-medical perspective. These propose a greater role for psychological and social processes in the aetiology and treatment of illness. Psychosomatic
medicine grew as a branch of psychoanalysis and the study of hysteria developed by Freud and Breuer (Sulloway, 1980). It was observed that some people
showed all the classic indications of neurological damage, such as paralysis of the legs or arms, when there was no underlying physical cause. Freud called this
observation ‘hysterical paralysis’ and argued, on the basis of his famous work studying the patient Anna O, that the ailment was caused by mentalisms i.e. thoughts
about experiences and feelings. While the arguments are intriguing, psychosomatic medicine suffers from its inability to provide sound empirical evidence to link
causally mind matters and physical health (Holroyd and Coyne, 1987).
The biopsychosocial model of health and illness (after Engel, 1980)
Probably the most influential contemporary model of health and illness is the biopsychosocial model which considers that biological, social and psychological
factors interact as dynamic processes in determining the onset, progression and recovery from illness (see Engel, 1977, 1980; Anagnostopoulou, 2005; and also
Figure 1.1).
As you can see in Figure 1.1, the biopsychosocial model proposes that factors ranging from the changing status of molecular structures (i.e. the biological), the
presence of social support (i.e. the social factor) and thoughts and feelings (i.e. the psychological factors) co-vary with each other to produce illness or health. It
therefore rejects the dualist philosophy of the bio-medical model. Donovan (1988) has proposed a biopsychosocial model of addiction which focuses on the
interaction between the biological (e.g. neuroadaptation after the ingestion of an addictive substance), the social (e.g. submitting to peer pressure to use addictive
substances or behaviours, socio-economic class, and so on) and the psychological (e.g. expectancies associated with undertaking a behaviour) to explain the
multifaceted experience of ‘addiction’ (see also Marlatt et al., 1988). The biopsychosocial model also implies a more ‘holist’ approach for the study of health and
illness, as well as interventions designed to prevent people from becoming unhealthy and making ill people well again. While, as a model, the biopsychosocial is
inherently appealing by emphasizing the interplay between various forces and factors in the experience of health and illness, the complexity of these relationships
means that the ‘true’ causal structure of the system may not be derived in a complete state (Armstrong, 1987).
SIGNIFICANCE TO HEALTH PSYCHOLOGY
The bio-medical model, psychosomatic medicine and, in particular, the biopsychosocial model provide the history and parameters within which to view the
development of the discipline called ‘health psychology’ (see health psychology concept – this chapter). These approaches have argued about the relationship
between the mind and body either taking a dualist stance (biomedicine) or monist (biopsychosocial) stance, the later being adopted for the study of psychology and
health.
Further reading
This paper provides a useful introduction to the biopsychological model in the area of addictive behaviours.
This work details how the biopsychosocial model may be applied in interventions in health-related problems.
Donovan, D. M. (1988) Assessment of addictive behaviors: implications of an emerging biopsychosocial model. In Donovan, D. M. and Marlatt, G. A. (eds),
Assessment of Addictive Behaviors. New York: Guilford Press. pp. 3-48.
Engel, G. L. (1980) The clinical application of the biopsychosocial model. American Journal of Psychiatry, 137, 535-544.
See also defining health psychology and health psychology
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28 PTinMOTIONmag.org / September 2018
without reasonable accommodations.
Employers have a legal obligation to
make reasonable worksite and work-
place accommodations that are not an
undue hardship. However, Palisano
cited research indicating that during the
hiring process, employers often have
little guidance and are unaware of, or do
not adhere to, the requirements of the
ADA and other laws.
Further, he said, “Person-workplace
transactions often are not timed or
adapted to build capacity.” Although
assistive technologies “offer promise for
improving work participation,” access
to appropriate assistive technologies
and qualified providers and teams are
frequently limited, he added.
Addressing community living, Palisano
said key considerations are availability,
accessibility, adaptability, and afford-
ability. “Research indicates that young
adults in supported living experience
more variety in community activities
and do preferred activities more
frequently than do young adults living
in group homes.”
Palisano summarized his theme: “The
value proposition of lifecourse health
development is healthy living. Healthy
living involves managing, adjusting,
and adapting to changes in health
capacities and environments.”
To achieve the vision of a preferred
future, Palisano said, “A system sim-
ilar to the pediatric health system is
recommended, whereby the health of
adults with chronic conditions would
be monitored by interprofessional
teams and a care coordinator would be
available to coordinate services.”
He also called for a future in which
physical therapy occurs in real-life
settings. “Research suggests that reha-
bilitation services in clinical settings do
not optimize participation outcomes….
Generalization of learning requires prac-
tice in different contexts, including open
environments that are not predictable.”
“A preferred future that embodies
lifecourse health development is ambi-
tious,” he concluded, “but not beyond
the reach of a profession whose vision
is to transform society and improve the
human experience.”
Maley Lecturer: Health Care Must
Adopt a Biopsychosocial Model
The health care system needs to evolve
from a medical to a biopsychosocial
model, asserted
Robert Palisano
, PT,
ScD, FAPTA, in the 23rd Maley Lecture
at NEXT 2018.
“Healthy living is a societal and systems
issue,” he said. “The focus of the tradi-
tional medical model of health care is
on the individual and acute conditions.”
The title of his lecture was “Lifecourse
Health Development of Individuals with
Chronic Health Conditions: Visualizing
a Preferred Future.” Lifecourse health
development is a biopsychosocial
model—one incorporating biological,
psychological, and social factors.
Palisano is associate dean for research
at the College of Nursing & Health
Professions at Drexel University.
Lifecourse health development
previously was applied to children
and youth with cerebral palsy. In the
Maley Lecture, Palisano extended
the concept to adults with acquired
chronic conditions such as spinal cord
injury, traumatic brain injury, stroke,
multiple sclerosis, Parkinson disease,
and arthritis.
He noted that APTA’s Vision Statement
for physical therapy—transforming
society by optimizing movement to
improve the human experience—served
as the springboard for his presentation.
Palisano defined “lifecourse” as a
progression of socially defined events
and roles in which a person engages.
Health development, he said, occurs
through person-to-environment and
environment-to-person transactions that
change over time. His preferred future,
Palisano said, will be characterized by a
person’s physical, mental, and emotional
wellbeing; participation in desired social
roles throughout life; and achievement
of personal goals.
Using 2 case studies—a boy born
with cerebral palsy and a woman who
contracted polio at 16 months—Palisano
traced their successful transition from
childhood to adulthood and identified
experiences that contributed to their
lifecourse.
He said the transition to adulthood
for youth with disabilities has been
described as “falling off a cliff” due to
lack of preparation, limited support,
lack of skills needed for adult roles, and
disjointed adult services. He noted that
successful transition requires timing
“real-life” experiences and interven-
tions to coincide with the person’s
environment and readiness for change.
“Unfortunately, implementation of
comprehensive and coordinated health
transition services and supports has not
been widely achieved, and finding adult
health care providers is often difficult,”
Palisano said.
He noted that some laws, such as
the Americans with Disabilities Act
(ADA), can help. For example, the ADA
defines “disabled but able to work”
as an individual with a physical or
mental impairment who can perform
essential functions of a job with or
Robert Palisano
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Biopsychosocial model
.
Authors:
Purdy, Elizabeth Rholetter, PhD
Source:
Salem Press Encyclopedia, 2019. 2p.
Document Type:
Article
Subject Terms:
Biopsychosocial model
Abstract:
The biopsychosocial model (BSP) is a method of looking at all biological, psychological, and social influences on human health and the body’s ability to respond to and recover from various diseases. The model examines a continuum of influences that begins with the
biosphere
and encompass society, culture, community, family, and the individual and analyzes their impacts on all the systems that make up the human body. The biopsychosocial model has become part of the medical mainstream; clinicians are taught to examine biological, psychological, and social factors when diagnosing and treating all kinds of health problems and use BSP to treat a wide range of conditions that include but are not limited to cancer, HIV-AIDS, depression, personality disorders, pediatric illnesses and traumas,
post-traumatic stress disorder (PTSD)
, chronic fatigue syndrome, dementia, chronic pelvic pain, and lower
back pain
. The model has also been used effectively by emergency room physicians and acupuncturists.
Full Text Word Count:
1300
Accession Number:
89677525
Database:
Research Starters
Biopsychosocial model
Full Text
The biopsychosocial model (BSP) is a method of looking at all biological, psychological, and social influences on human health and the body’s ability to respond to and recover from various diseases. The model examines a continuum of influences that begins with the biosphere and encompass society, culture, community, family, and the individual and analyzes their impacts on all the systems that make up the human body. The biopsychosocial model has become part of the medical mainstream; clinicians are taught to examine biological, psychological, and social factors when diagnosing and treating all kinds of health problems and use BSP to treat a wide range of conditions that include but are not limited to cancer, HIV-AIDS, depression, personality disorders, pediatric illnesses and traumas, post-traumatic stress disorder (PTSD), chronic fatigue syndrome, dementia, chronic pelvic pain, and lower back pain. The model has also been used effectively by emergency room physicians and acupuncturists.
Holistic health: body, mind, heart, soul. By http://www.yogaartandscience.com/about/about.html Derivative work 1: http://commons.wikimedia.org/wiki/User:Mirzolot2 Derivative work 2: http://commons.wikimedia.org/wiki/User:Creativekiwi [CC-BY-SA-2.5 (http://creativecommons.org/licenses/by-sa/2.5)],
Background
In the twentieth century, the foundation for the biopsychosocial model evolved from conflicts between biological reductionism, which reduced biological explanations to their simplest forms, and traditional psychoanalytic theories, based on the teachings of
Sigmund Freud
(1856–1939), the Austrian
neurologist
. The first American to attempt a more comprehensive understanding of the ways in which the body and the environment influenced one another was Adolf Meyer (1866–1950) of Johns Hopkins University. Roy Grinker (1900–93) of the University of Chicago built on Meyer’s work and is credited with coining the term “biopsychosocial.”
The two individuals most closely associated with the biopsychosocial model are the psychiatrists George Engel (1913–99) and John Romano (1909–94) of the University of Rochester in New York. After suffering the loss of his twin brother, Engel became interested in the high correlation between the loss of a loved one and the onset of various diseases. In 1977, that discovery led Engel to develop the biopsychosocial model that is still used in the twenty-first century. In addition to contributing to the understanding of the biopsychosocial model, Romano, who had founded the university’s Department of Psychiatry in 1946, was a major influence on the overall development of psychiatry in the United States.
Implementations of the biopsychosocial model have focused on patient-centered health care, which has become the norm in both the United States and Canada, and it has been endorsed by the American Academy of Family Physicians, the American College of Physicians, the American Academy of Pediatrics, and the American Osteopathic Association.
Overview
Patient interviews have become one of the most important tools used by physicians as a means of understanding biological, psychological, and social impacts on a patient’s health. Each year physicians conduct between 120,000 and 160,000 patient interviews. A group of physicians at the Centre for Studies in Family Medicine at the University of Western Ontario built on the biopsychosocial model to develop a patient-centered model that is used throughout North America. In a 2000 study conducted at the Centre by Moira Stewart and colleagues, the researchers discovered that Engel’s emphasis on patient interviews held up over time because it continued to be integral to successful outcomes in medical treatment. They found that patient-centered communication was effective in speeding up recovery time, improving mental health, and reducing the need for follow-up medical treatment and referrals to specialists.
In the early twenty-first century, the biopsychosocial model has been widely used in treating chronic illnesses such as cancer and HIV-AIDS. In a 2010 study, Scott M. Debb and David L. Blitz note that the biopsychosocial model is significantly more effective than traditional methods in treating such diseases because it takes biological predispositions, psychological stressors, socioeconomic factors, physiological characteristics, and patient-generated appraisals of all these factors into account. In their examination of chronically ill patients in Atlanta, Chicago, and San Juan, Puerto Rico, Debb and Blitz found that African Americans recovered more slowly from cancer and
HIV/AIDS
than whites and received poorer health diagnoses. This was assumed to be partly due to greater access to the health-care system by whites. However, African Americans expressed more optimism about their health, a fact that researchers posited was linked to higher levels of ethnic identity.
Within the field of physiotherapy, the National Institute for Health and Clinical Excellence has established guidelines for using the biopsychosocial model in conjunction with traditional methods of therapy. The new paradigm calls for increased attention to the overall environment of patients. However, many physiotherapists still lack sufficient training in implementing the model.
The biopsychosocial model also has significant potential for dealing with issues presented by diverse ethnicities that make up the client base of community counselors throughout the world. In 2009, British clinical
psychologist
Waseem Alladin offered a nine-dimensional model for community counseling based on the biopsychosocial model that recognizes respect for human dignity as articulated in the
United Nations Declaration of Human Rights
. For example, understanding social and religious perceptions associated with particular ethnicities is integral to treating individual patients successfully.
Most medical schools teach both the biomedical and biopsychosocial models. When training physicians, the biopsychosocial model emphasizes the need to acknowledge the role that relationships play in an individual’s health; take a patient’s own impressions of their health problems into account; mandate detailed life histories from patients; attempt to identify the most relevant biological, psychological, and social factors in particular cases; and offer treatment based on a multidimensional perspective. For instance, understanding how a person perceives health problems and identifying levels of support available to him or her may determine how well he or she recovers from an illness. Some psychiatrists and psychologists have criticized the biopsychosocial model for various reasons, one of which is that one of the biological, psychological, or social aspects of diagnosis may be underrepresented depending on patients’ subjective experiences or doctors’ own biases. Nonetheless, it continues to be used across the medical spectrum.
Bibliography
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Miller, Suzanne M., ed. Individuals, Families, and the New Era of Genetics: Biopsychosocial Perspectives. New York: Norton, 2006. Print.
Silk, Kenneth R. Biology of Personality Disorders. Washington: APA, 1998. Print.
Stewart, Moira, et al. “The Impact of Patient-Centered Care on Outcomes.” Journal of Family Practice 49.9 (2000). Web. 25 July 2013.
Cultural factors related to adherence to
imatinib in CML: A Mexican perspective
Olga Graciela Cantú-Rodríguez, Mónica Sánchez-Cárdenas, César
Homero Gutiérrez-Aguirre, José Carlos Jaime-Pérez, Consuelo Mancias-Guerra,
Oscar González-Llano, David Gómez-Almaguer
Hematology Service, Hospital Universitario, Universidad Autónoma de Nuevo León, Monterrey, Mexico
Introduction: The advent of imatinib as a therapeutic option of chronic myeloid leukemia (CML) has
transformed this previously highly resistant disease into one that is susceptible to management with oral
drugs that now offer high long-term survival rates. However, achieving an adequate adherence to
treatment regimes is of critical importance. The characteristics of treatment compliance in Mexican
patients have not been determined.
Methods: We evaluated 38 CML patients, members of the Glivec® International Patient Assistance Program
(GIPAP). A bimonthly simplified medication adherence questionnaire was applied and the adherence rate
was calculated by direct tablet counting.
Results: Two groups, one of local patients and another of out-of-town patients, were studied using an 85%
adherence rate as a cut-off. The overall adherence rate was 85.9%. Fifteen patients were considered non-
adherent (39.5%). The group of out-of-town patients presented a higher adherence rate of 92.8% in
contrast with 76.3% in the local population (P= 0.021). The probability of achieving a complete
cytogenetic response at some point of evolution after 8 years of follow-up was 93% in the adherent group
vs. 58% in the group with an adherence rate <85% (P= 0.008). In patients with imatinib failure, the
adherence rate was 75.8% compared to 95.5% (P= 0.008) in the optimal response group.
Conclusions: In Mexican patients with CML, non-adherence to treatment is a cause of the failure to achieve
remission or the subsequent loss of a complete cytogenetic and major molecular response.
Keywords: Treatment, Adherence, Imatinib
Introduction
The introduction of imatinib as a therapeutic option
for chronic myeloid leukemia (CML) completely revo-
lutionized the natural history of the disease.1 From a
progressive disease with a poor prognosis, CLM
evolved into an illness that permits an adequate
quality of life and an overall survival rate greater
than 80% at 5 years.2 Imatinib presents the possibility
of a simple, safe, oral, and highly effective outpatient
treatment, but at considerable cost.3 Despite this and
the fact that the disease is potentially fatal, adherence
may be erratic.
Although there is ample information available
related to various other pathologies, measuring adher-
ence is still a challenge. The lack of standardization
and the difficulty in eliminating bias contribute to
ambiguity and complicate an accurate measurement
of adherence.4
In Mexico, with diseases such as tuberculosis,
diabetes mellitus, and hypertension, non-adherence
to treatment regimes is a public health problem. In
addition to the impact on the clinical course, poor
adherence to treatment results in higher costs,
increased hospitalization rates, and a deterioration in
the doctor–patient relationship.5 In addition to this,
one must consider the potential relationship between
poor adherence to imatinib and the emergence of
drug resistance.
In a previous study we observed a lower molecular
response rate than expected to imatinib treatment,
explained in part by the short follow-up period and
the difficulties encountered in increasing the dose of
imatinib, mainly because of side effects; however,
adherence was not documented in this study.6
Mexico is a developing country with a population of
112 000 000 inhabitants, of which 37 million have an
elementary school education or less. It is also impor-
tant to point out that 35.6% of the population does
not have health insurance.7 Because of these character-
istics and the lack of data available, we considered it
Correspondence to: David Gómez-Almaguer, Servicio de Hematología del
Hospital Universitario, Universidad Autónoma de Nuevo León, Madero y
Gonzalitos s/n, Colonia Mitras Centro, Monterrey, Nuevo Leon CP
64460, Mexico. Email: dgomezalmaguer@gmail.com
© W. S. Maney & Son Ltd 2015
DOI 10.1179/1607845414Y.0000000165 Hematology 2015 VOL. 20 NO. 272
mailto:dgomezalmaguer@gmail.com
mailto:dgomezalmaguer@gmail.com
essential to assess adherence to imatinib as accurately
as possible in a group of underprivileged and unin-
sured patients in order to analyze the factors associ-
ated with good or poor adherence and its impact on
the clinical evolution of CML. A better understanding
of the role of adherence will lead to the creation of pro-
grams focused on strengthening adhesion to treatment
and the integral management of the CML patient.
Methods
Patients
We evaluated 38 patients with a confirmed diagnosis
of CML who were being treated with imatinib and
beneficiaries of the Glivec® International Patient
Assistance Program (GIPAP). Adherence to treatment
was assessed at each medical visit between 1 August
2011 and 15 June 2013. The GIPAP program provided
the necessary medication for treatment of CML based
on imatinib. Each patient was instructed to keep the
empty blisters and return them when he or she received
a new box; the empty boxes returned were registered.
Approval for the study was obtained from the Ethics
Committee of the School of Medicine and the
University Hospital of the Universidad Autónoma
de Nuevo León. Patients gave informed consent in
accordance with the Declaration of Helsinki.
Adherence assessment
Adherence was determined for each patient using two
methods that were different and complementary.
During the follow-up period, the adherence rate was
calculated by the direct counting of tablets (tablets
consumed/tablets prescribed) based on GIPAP
program guidelines and considering medication
adjustments. A bimonthly, simplified medication
adherence questionnaire (SMAQ) was also applied.
This adherence test has been used and validated for
different pathologies with characteristics similar to
CML.8 Each patient was classified as adherent/non-
adherent according to the guidelines established for
the interpretation of the questionnaire. Regarding
exclusion criteria, those who did not undergo at least
two evaluations with the SMAQ adherence test were
eliminated from analysis.
Response to treatment
Before the start of this study, the degree of cytogenetic
response achieved was registered. At the end of the
follow-up period, cytogenetic and molecular response
was determined for each patient in order to detect
changes in disease status and the relationship of
these changes with the adherence rate. Achieved
response was categorized according to criteria estab-
lished by the international guidelines of the
European Network of Chronic Myelogenous
Leukemia. Imatinib failure was defined as progression
from the chronic phase, loss of hematologic, cytoge-
netic, or molecular response, and death from disease.
Optimal response was defined as the presence of at
least a stable molecular response.
Statistical analysis
Groups were compared using the Chi square test/
Fisher’s exact test for categorical data and Student’s
t-test/Mann–Whitney U-test for quantitative data.
The probability of a complete cytogenetic response
(CCyR) was calculated using the cumulative incidence
procedure and the long rank test was used to compare
the groups. A P value <0.05 was considered statisti-
cally significant and all tests were two-sided.
Statistical analysis was carried out using SPSS soft-
ware version 20.0.
Results
We evaluated 38 patients (19 men and 19 women) with
a median age of 42 years (range 21–79) for the study.
At diagnosis, 92.1% of patients were in the chronic
phase of the disease and three patients were diagnosed
in the accelerated phase (7.9%). After a median follow-
up of 241 days (29–301), the mean adherence rate
(MAR) was 85.9% (Table 1). Prior to this study, our
cohort received imatinib for the management of
CML for a median of 41.3 months (92–127). In
patients who had undergone more than 24 months of
treatment, the MAR was significantly lower (80.8%)
Table 1 Patient demographic and clinical characteristics
Adherence
(≥85%) (<85%)
Variable
N= 23
(60.5%)
N= 15
(39.5%) P
Age, median (range) 44 (21–79) 37 (26–65) 0.3
Gender
F/M 12/11 7/8 0.7
Education (years). Mean
(SD)
8.18 (3.7) 7.75 (3.8) 0.7
Distance to hospital 0.09
Local 30% 60%
Foreign 70% 40%
Diagnostic blood count. Mean (SD)
HB (g/dl) 10.2 (2.2) 9.2 (1.9) 0.2
WBCs (103/μl) 192 (12) 332 (24) 0.05
PLTs (103/μl) 501 (46) 380 (37) 0.4
Adverse events record
GI 30% 60% 0.09
Anemia 4% 0% 1.0
Leukopenia 9% 13% 1.0
Thrombocytopenia 35% 13% 0.25
Cutaneous 30.% 33% 1.0
Neurological 4.% 20% 0.28
General 61% 73% 0.50
Dose of imatinib 1.0
400 mg/day 70% 67%
>400 mg/day 30% 33%
Time of prescription 0.001
<24 months 52% 0%
>24 months 48% 100%
Cantú-Rodríguez et al. Treatment adherence on Mexican patients in CML
Hematology 2015 VOL. 20 NO. 2 73
compared to those patients with shorter prescriptions
(96.9%) (P= 0.008).
Regarding the actual dosage, the median was
400 mg (300–800 mg). During follow-up, seven
patients (18.4%) required dose adjustment. In three,
it was necessary to reduce the dose due to the presence
of adverse effects; in four patients the dose was
increased due to failure to respond (MAR 70.4%).
Patients receiving 800 mg daily had the lowest adher-
ence rate (MAR 65.9%). The group of patients that
received ≤4 tablets/day achieved a greater adherence
rate (MAR 87.6%) than patients taking 5–8 tablets/
day (MAR 82.2%), with no statistically significant
difference (P= 0.6). To create contrasting groups, we
established 80%, 85%, and 90% adherence rates as
cut-off levels. Using Cox regression, 85% was estab-
lished as the cut-off, derived from its predictive
ability in terms of the degree of response achieved.
Group 1 (39.5% of patients) had a MAR of 68.1%,
in contrast with Group 2 (MAR 97.5%, N= 23),
with no statistically significant differences in
gender (P= 0.5), age (P= 0.3), or years of education
(P= 0.07). Local patients had a MAR of 76.35%
compared to out-of-town patients 92.85% (P= 0.021).
Throughout their clinical course, 65.8% of patients
presented some adverse event associated with imatinib,
with gastrointestinal symptoms representing the most
common manifestations. The non-adherent group
had higher rates of gastrointestinal events in relation
to adherents (60% vs. 30.4%). Before the start of this
study, the degree of response achieved was registered
to detect which patient failed to show adequate
response levels and the relationship of these response
levels to drug adherence. At baseline, 31.6% of
patients (n= 12) had failed to achieve any degree of
response; of these patients, only three (25%) achieved
complete molecular response (MAR 93.6%).
During follow-up, four patients lost the degree of
response achieved (MAR 81.72%). Of 38 patients,
one was in an accelerated phase and one died
because of disease activity.
Currently, 13 patients (34%) are non-responders
(imatinib failure), 21 patients (55%) have optimal
response (stable MMR), and 4 patients (14%) were
non-evaluable. In patients with imatinib failure, the
MAR was 75.8%, in contrast with the group with
optimal response (MAR 95.5%). This difference was
statistically significant (P= 0.004).
The impact of adhesion on achieving CCyR at some
point during the course of the disease was determined.
In the adherent group (adherence≥ 85%) after 8 years
of follow-up, there was a 93% probability of achieving
a response (HR 3.1; CI 1.2–7.5, P= 0.01), in contrast
with the group categorized as non-adherent with 58%
(P= 0.008) (Fig. 1). An adherence rate of less than
95% was associated with an HR of 2.5 (CI 1.1–5.7,
P= 0.026), while values less than 80% were linked to
an HR of 6.1 (CI 1.4–26.8, P= 0.016).
We applied 161 SMAQ as an adherence test
(median 4; 2–7). A patient was considered non-adher-
ent when at least one questionnaire item indicated that
the drug had not been taken as prescribed. With regard
to the adherence rate established by the SMAQ, the
non-adherent group presented a MAR of 71.7%, in
contrast to the adherent group with 96.2%, a statisti-
cally significant difference (P< 0.001). We analyzed
the correlation between the two methods (adherence
rate obtained by direct tablet counting and the
SMAQ) and obtained a kappa index of 0.728 (P≤
0.001). This showed that there was good concordance
between the two methods.
Discussion
During this study, the MARwas 85.9% after a median
of 3.4 years of treatment based on imatinib; we found
lower rates of adherence related to longer use of the
drug. This behavior mirrors the role of chronic drug
use in pathologies such as hypertension or diabetes.9
The approximate cost of medication with a standard
dose of 400 mg/day is 3800 US$ per month. Our insti-
tution is part of the GIPAP program so that the
patients received the drug for free. As a result, we
were able to analyze the factors that could affect a
lack of adherence after eliminating economic limit-
ations. In contrast with previous evaluations where
younger individuals displayed lower adherence rates,
in our cohort characteristics such as age or gender
were not related to adherence rate.10 We found that
patients who had to travel from their hometowns to
the medical center to receive the drug (distances of
100–600 km) had a significantly higher adherence
rate than local patients (P= 0.02). This could be
because out-of-town patients are afraid of being far
Figure 1 Cumulative probability of complete cytogenetic
response according to level of adherence.
Cantú-Rodríguez et al. Treatment adherence on Mexican patients in CML
Hematology 2015 VOL. 20 NO. 274
from their doctors and therefore more motivated to
carefully follow the instructions, on the other hand,
Jonsson et al. determined a high level of adherence
to imatinib associated with factors such as frequent
contact with a single hematologist, involvement in
decision making, and appropriate information about
the disease.11 These variables were not assessed in
our study. It is important to note that the educational
level of our cohort (mean 8 years) probably had an
important impact on our findings.12
Adherence to imatinib using an electronic monitor-
ing system13 was evaluated in a group of British
patients with CML. Adherence rates in this very differ-
ent population (97.6%) were higher than those in our
study; however, the authors recognize the inability to
completely eliminate measurement bias. In this
setting, measurement of imatinib blood levels may
represent the most accurate method, but it is certainly
difficult and expensive to implement.14 A weakness of
our study is the short time period in which adherence
was measured with relation to time since diagnosis,
which could imply an inaccurate reflection of the
characteristics of attachment present along the
course of the disease. However, the characteristics of
drug adherence tend to remain stable over time, allow-
ing us to assume the presence of such adherence pat-
terns for extended periods of time. The SMAQ has
been widely used in the assessment of adherence to
various drugs.8 It was initially used in the field of anti-
retroviral drugs and it has demonstrated high levels of
detection of patients with poor adherence. In our
population, we were able to detect that 95% of patients
had an adherence rate below 85%.
We retrospectively evaluated the moment when our
patients achieved a CCyR or MMR. After 7 years of
follow up, the likelihood of such a response was
clearly influenced by the degree of adherence (P=
0.008). After 60 months of imatinib use, we found
that our non-adherent population did not exceed a
60% probability of achieving that level of response in
contrast to the adherent group, in which this prob-
ability was higher than 95%.
Despite population differences, other authors have
reported similar data, reinforcing the importance of
adherence to achieve response goals.13,15 Ibrahim
et al.13 determined the role of adherence in the loss
of CCyR once it has been reached. In our population,
patients with imatinib failure had significantly lower
adherence rates (P= 0.008) compared to the group
with optimal response, strengthening the role of
adherence.
The limitations of our study include bias in the
assessment of adherence and a relatively short
follow-up. Ideally the population should be evaluated
from the moment that the diagnosis is established,
with periodic up-dates on the degree of response.
Currently imatinib constitutes the first line of treat-
ment in the management of CML. A decade after its
introduction we still do not know the impact of its
chronic use and if it is realistic to talk of a cure.
Additionally, the constant risk of drug resistance by
new mutations is well known.16,17 If we consider the
economic impact of tyrosine kinase inhibitors, it
is essential to ensure that they are prescribed and
administered optimally and with complete adherence.5
In the majority of Mexican CML patients imatinib is
almost the only choice to obtain sustained remission
and long-term disease-free survival, rendering adher-
ence to this drug essential.
Disclaimer statements
Contributors DG-Awas responsible for designing pro-
tocol and writing the manuscript. OGCR was respon-
sible for designing and writing the protocol and the
manuscript.MSCwas responsible for patients’ attention,
follow-up. CHG-A was responsible for study drug
administration and data analysis. JCJP was responsible
for writing the manuscript. OGL was responsible for
data analysis and Table 1. CMG was responsible for
data analysis and Fig. 1.
Funding None.
Conflicts of interest None.
Ethics approval Approval for the study was obtained
from the Ethics Committee of the School of
Medicine and the University Hospital of the
Universidad Autónoma de Nuevo León. Patients
gave informed consent in accordance with the
Declaration of Helsinki.
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Patient Preference and Adherence 2016:10 669–681
Patient Preference and Adherence
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open access to scientific and medical research
Open Access Full Text Article
http://dx.doi.org/10.2147/PPA.S96241
An ontology for factors affecting tuberculosis
treatment adherence behavior in sub-Saharan Africa
Olukunle Ayodeji
Ogundele1
Deshendran Moodley1
Anban w Pillay1
Christopher J Seebregts1,2
1UKZN/CSiR Meraka Centre for
Artificial intelligence Research and
Health Architecture Laboratory,
School of Mathematics, Statistics
and Computer Science, University
of KwaZulu-Natal, Durban,
KwaZulu-Natal, 2Jembi Health
Systems NPC, Cape Town,
South Africa
Purpose: Adherence behavior is a complex phenomenon influenced by diverse personal,
cultural, and socioeconomic factors that may vary between communities in different regions.
Understanding the factors that influence adherence behavior is essential in predicting which
individuals and communities are at risk of nonadherence. This is necessary for supporting
resource allocation and intervention planning in disease control programs. Currently, there is no
known concrete and unambiguous computational representation of factors that influence tuber-
culosis (TB) treatment adherence behavior that is useful for prediction. This study developed
a computer-based conceptual model for capturing and structuring knowledge about the factors
that influence TB treatment adherence behavior in sub-Saharan Africa (SSA).
Methods: An extensive review of existing categorization systems in the literature was used
to develop a conceptual model that captured scientific knowledge about TB adherence behav-
ior in SSA. The model was formalized as an ontology using the web ontology language. The
ontology was then evaluated for its comprehensiveness and applicability in building predictive
models.
Conclusion: The outcome of the study is a novel ontology-based approach for curating and
structuring scientific knowledge of adherence behavior in patients with TB in SSA. The ontology
takes an evidence-based approach by explicitly linking factors to published clinical studies.
Factors are structured around five dimensions: factor type, type of effect, regional variation,
cross-dependencies between factors, and treatment phase. The ontology is flexible and extend-
able and provides new insights into the nature of and interrelationship between factors that
influence TB adherence.
Keywords: tuberculosis, treatment adherence behavior, influencing factor, conceptual model,
ontology
Introduction
Poor adherence or nonadherence of patients with tuberculosis (TB) to prescribed
treatment is a major contributor to treatment failure.1–3 Treatment adherence behavior
(TAB) is defined as the extent to which a person’s practice of taking medication, follow-
ing a diet, and/or executing lifestyle changes corresponds with agreed recommendations
from a health care provider.4 Thus, poor adherence is the failure of patients with TB
to take medication or follow a diet and lifestyle in accordance with the prescription
given by a health worker.5 Patients with TB who exhibit poor adherence to treatment
over a period of time have a high risk of becoming resistant to prescribed drugs that
may eventually become life-threatening.
Adherence is a complex and dynamic phenomenon with a wide range of interacting
socioeconomic factors impacting on a patient’s adherence behavior.6 These factors vary
in both granularity and the extent of their effects on adherence behavior across different
Correspondence: Olukunle Ayodeji
Ogundele
UKZN/CSIR Meraka Centre for Artificial
intelligence Research and Health
Architecture Laboratory, School of
Mathematics, Statistics and Computer
Science, University of KwaZulu-Natal,
H1 Block, westville Campus, University
Road, Durban 3629, South Africa
Tel +27 78 617 0144
email zinmanship@yahoo.co.uk
Journal name: Patient Preference and Adherence
Article Designation: Review
Year: 2016
Volume: 10
Running head verso:
Ogundele et al
Running head recto:
An ontology for factors affecting TB TAB in sub-Saharan Africa
DOI: http://dx.doi.org/10.2147/PPA.S96241
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Ogundele et al
socioeconomic statuses and geographical regions and can
sometimes have opposite effects under different circum-
stances. Understanding adherence behavior in patients with
TB is important for effective and efficient treatment planning,
and improved understanding of fluctuations in treatment
outcomes in disease-monitoring programs.4 Knowledge of
the pattern of influencing factors and adherence behavior is
also useful for decision support in TB disease control pro-
grams. Taking patients’ subjective treatment experiences into
consideration can facilitate patient-centered interventions
and become a tool to better promote treatment adherence.6
Structured and systematic synthesis of qualitative research
can contribute to improved understanding, interpretation, and
comparison of the growing volume of studies about patients’
adherence to treatment.6
A number of systems have emerged to analyze, structure,
and compare knowledge about influencing factors contribut-
ing to adherence in patients with TB.4,6,7 These systems are
limited in terms of their comprehensiveness and represen-
tational support; some categories are vague and ambiguous,
and there are fundamental semantic differences between the
classification systems, which make them incompatible with
each other. Transforming the current systems into a holistic
formal and computational model is a step toward specifying
a common, consistent, and unambiguous vocabulary and
structure for consolidating the current knowledge around TB
adherence behavior. This consolidated knowledge, or knowl-
edge repository, can form the basis for building adherence
risk predication models for specific communities to identify
knowledge gaps and inform further research studies into
adherence behavior.
To this end, we developed a conceptual model for struc-
turing, curating, and uncovering scientific knowledge about
factors influencing TB adherence. The study presents an
evidence-based model that is essential for clear identifica-
tion and understanding of community-specific factors that
influence TB patients’ adherence and identify communities
at risk.
The conceptual model is formalized as an ontology
and expressed in the web ontology language8 (OWL). An
ontology is an explicit specification of a conceptualization.9
Ontologies have been used successfully to represent concepts
in the public health domain.10–12 OWL is the most widely used
language for expressing and sharing ontologies. It is designed
to represent rich and complex knowledge about things,
groups of things, and relations between things (http://www.
w3.org/2001/sw/wiki/OWL). SNOMED CT (Systematized
Nomenclature of Medicine – Clinical Terms) is represented
as an ontology with OWL.13
The ontology is extendable, can be navigated and queried,
and is useful for computer-based prediction. The ontology
was evaluated for its effectiveness in representing and clas-
sifying factors associated with adherence to TB treatment in
sub-Saharan African (SSA) countries.
The remainder of the paper is organized as follows: the
methods followed in the study are detailed in the “Methodol-
ogy” section. Existing categorization models are reviewed
in the “Review of existing categorization models” section.
The conceptual model is presented in the “Development of
a conceptual model and ontology” section, and the ontology
that is evaluated is described in the “Evaluation of the con-
ceptual model (ontology)” section. Finally, the “Discussion
and conclusion” section is given.
Methodology
Three process steps were used to develop the TB TAB ontol-
ogy. The first step, knowledge acquisition, entailed a review
of the literature on treatment adherence of patients with TB to
identify existing dimensions for classifying influencing factors.
The second step, model development, involved the develop-
ment of a conceptual model using the information extracted
from the literature review and expressing this as an OWL
ontology. The ontology was then evaluated in the final step.
A review of the literature was conducted to provide back-
ground knowledge required for the ontology development
process. The repositories searched included Google Scholar,
Science Direct (Elsevier), SCOPUS, Web of Science,
EBSCO, and PubMed. Keywords such as “Tuberculosis
Treatment Adherence Predictors” OR “Tuberculosis
Medication Adherence Factors” were used to carry out
searches for related literature. The word “treatment” was
also substituted with “drugs” and “medication”. The word
“adherence” was substituted for “compliance”, and the word
“factor” was substituted for “predictor”. Some of the search
phrases used for the search include the following:
• Factors influencing (medication/treatment) (compliance/
adherence) behavior of tuberculosis patient
• Factors influencing tuberculosis patient (poor/non)
(compliance/adherence) with prescribed (medication/
drug)
• Predictors of (drug/medication/treatment) (compliance/
adherence) behavior of tuberculosis patient
• Predictors of tuberculosis patient (poor/non) (compliance/
adherence) to prescribed (drug/medication/treatment)
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An ontology for factors affecting TB TAB in sub-Saharan Africa
Scientific papers were collated and analyzed iteratively
as base knowledge for developing the model. A total of 66
papers were initially identified in the review. Twenty-one
of these were excluded because they did not focus on deter-
mining the influencing factors (predictors) of TB TAB. The
remaining 45 papers were classified into clinical studies or
review papers.
Eight review papers were selected and used as a basis
for formulating the classification dimensions. Five papers
explicitly proposed categorization systems or identified
categories while the remaining three papers supplemented
the general formulation of the final categories.
Thirty-seven papers that reported on clinical studies were
used to identify factors that influence adherence for specific
communities that can be included in the model. Six of these
papers were excluded because they did not focus on factors
that influence TB patients’ adherence. Of the remaining
28 papers, only 14 focused on patients with TB in SSA
countries. These 14 were used to evaluate the model.
The development of the conceptual model involved the
consolidation of the existing categorization systems and identi-
fication of dimensions for representing and structuring factors.
Categorization dimensions were extracted from published
papers through a manual process. A conceptual model that
effectively represents the complexity of factors and objectively
captures existing domain knowledge (from the literature) was
developed using an iterative process. The conceptual model
was formalized into an OWL ontology by following a rigor-
ous ontology engineering method that was adapted from the
Unified Process for Ontology Building14 methodology.
The correctness and comprehensiveness of the ontol-
ogy in capturing and extending knowledge of factors that
influence treatment adherence of patients with TB in SSA
were evaluated and validated. First, a comparative analysis
with the existing categorization was carried out to verify the
representativeness of the model. Second, we validated
the effectiveness of the model in representing the nuances of
the influencing factors by using the model to capture scientific
publications that provide information about patients with TB
in SSA. Finally, we validated the use of the ontology for
building predictive models by using it to construct a Bayesian
decision network model for SSA TB communities.
Review of existing categorization
models
Several categorizations of factors contributing to adherence
behavior have been published.4,6,15 These earlier studies
carried out an assessment of these factors for the purpose of
providing a better understanding of the relationship between
the factors and patients’ adherence, and for proposing appro-
priate intervention strategies. These studies include the World
Health Organization (WHO) study,4 a systematic review and
study by Munro et al,6 and a quantitative literature review
by Jin et al.15 These three studies presented dimensions for
categorizing influencing factors. Additional categorization
concepts that are not evidence based but, nonetheless, are
useful for categorizing influencing factors have been pro-
posed, eg, temporal variation proposed by Castelnuovo16
and Kruk et al.17
The wHO model
A study by the WHO was aimed at structuring appropri-
ate intervention plans for several infectious and chronic
diseases.4 This is the earliest known attempt to consolidate
knowledge about influencing factors for comprehensive
intervention plans for different types of diseases. The study
draws on several qualitative and quantitative studies to
present a categorization with five major categories: patient-
related, socioeconomic, health system, therapy-related, and
condition-related. Second, two categories were presented
based on the type of effect: positive factors that stimulate
patients to adhere more and negative factors that cause a
decrease in adherence.4
Munro et al’s model
Munro et al6 conducted a systematic review of the literature
from 1999 to 2005 and developed a model for categorizing
TB influencing factors. The review was aimed at under-
standing which factors are considered important by patients
with TB, caregivers, and health care providers. A total of
44 articles drawn from different regions of the world were
reviewed. From the study, four main categorization themes
were developed. The four themes are as follows: structural
factors, personal factors, social context factors, and health
service factors.
Jin et al’s model
Jin et al15 identified some categorizations for representing
influencing factors through a systematic review of 102
articles that focused on all types of therapy for several
chronic and infectious diseases. The study examined common
factors causing therapeutic nonadherence from the patient’s
perspective and identified three dimensions for classifying
these factors.
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Ogundele et al
First, they presented five categories based on factor type:
patient-centered, therapy-related, health care system, social
and economic, and disease-related. Second, they presented
three categories based on the type of effect: compliance
increment, compliance decrement, and no effect. Third,
they presented three categories based on difficulties encoun-
tered in measuring the effect and counter intervention of
the factors. They are hard factors, whose impacts are more
quantifiable, and soft factors, whose effects are difficult to
measure and counter.
Temporal concept
Two categories were identified through a review of six
studies carried out by Castelnuovo16 to depict the period
of effect of factors. The categories relate to the treatment
phases of an anti-TB treatment plan. The first is the “intensive
phase”, which is the first 2 months of anti-TB treatment
after the patients are diagnosed with TB. The second is the
“continuation phase”, which starts immediately after the
intensive phase and continues for 4–6 months.16 Other tem-
poral representations are the weekly and monthly categoriza-
tions introduced by Kruk et al.17 They reviewed 14 studies
that focused on the timing of default in low-income countries’
TB treatment.
Challenges of the existing categorization
Variations in the models presented in existing studies pose
challenges for the common and shareable representation of
factors. For instance, the factor type categories identified
across the papers may appear similar, but the description
of the categories and the factors belonging to each category
vary. There are variations in the number of categories
presented under the same dimensions. The WHO4 study
proposed five categories, Munro et al6 developed a model
of four categories, and Jin et al15 identified five categories,
which are similar to the WHO’s categories. Similarly, the
type of effect proposed by the WHO and Jin et al is differ-
ent. Although the WHO proposed three categories, Jin et al
proposed two categories. A comparison of the different
categorization systems is given in Table 1.
Additionally, the naming and definition of existing cat-
egories are inconsistent. There are no generally accepted
names for the categories. For instance, patient-related fac-
tors have different names and meanings across the three
models. They are named as personal factors in Munro et al
and patient-related factors in the WHO and Jin et al. The
WHO’s patient-related-factor category focuses on patient
demographic information and excludes certain lifestyle and
psychological attributes included in Jin et al’s category.
There is also no uniformity in the classification hierar-
chy; some of the existing models introduce subcategories,
while others do not. In the absence of subcategories, fac-
tors are directly grouped under the main categories. Jin
et al introduced two subcategories in their classification
only for the “patient-centered” category, and they are the
demographic and psychological factor categories. Munro et
al used the eight themes as the intermediate groups, but the
relationships with the four themes are not clearly defined.
The WHO report did not provide any subcategories in its
classification.
Finally, none of the categorization systems represent all
the categorization dimensions identified in Table 1. While
some represent more than one dimension in their studies,
others concentrate only on one dimension. Three of the five
studies, WHO,4 Munro et al,6 and Jin et al,15 focused on cat-
egorizing factors, ie, the factor type dimension. Two studies
classified factors according to the type of effect. Two studies
focused solely on the period of effect.
Table 1 Existing influencing factor categorizations
Dimension WHO4 Munro et al6 Jin et al15 Castelnuovo16 Kruk et al17
Factor type Patient-related factors Personal factors Patient-centered factors
Therapy-related factors Health service factors Therapy-related factors
Health system factors Social context factors Health care system factors
Socioeconomic factors Structural factors Social and economic factors
Condition-related factors Disease-related factors
Type of effect Positive factors Compliance increment factors
Negative factors Compliance decrement factors
No-effect factors
Measurement Hard factors
Soft factors
Temporal intensive phase weekly/monthly
Continuation phase
Abbreviation: wHO, world Health Organization.
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An ontology for factors affecting TB TAB in sub-Saharan Africa
Some dimensions are not incorporated across all cat-
egorizations. One of these is the cross-dependency between
influencing factors. Some clinical studies have established
cross-dependencies among factors, ie, a factor’s influence is
dependent on another factor.18
Development of a conceptual model
and ontology
Restructuring existing categorizations
into a common conceptual model
The proposed conceptual model is aimed at representing,
collating, and structuring knowledge found in the literature
in a consistent manner for clear understanding and classifi-
cation of the factors. The model is intended to be used as a
formal basis to develop the ontology. Five dimensions were
identified from the review of existing categorizations. They
have been restructured in order to have a complete and unique
representation of the influencing factors and their application
to patients with TB in SSA. The key elements of the clas-
sification as drawn from the review are factor type, type of
effect, treatment phase, region, and cross-dependency.
Factor type
Factor type represents the grouping of influencing factors
according to the similarity of common terms as presented
in the literature. This type of grouping enables the creation
of a category, sometimes in a hierarchy, to assist in distin-
guishing terms. It is a common dimension for categorizing
influencing factors.
We used the classifications found in the three existing
studies to develop unique and specific factor type catego-
ries. The existing categories were restructured to eliminate
concept overlaps and misrepresented factors. They were
iteratively checked in terms of their effectiveness to classify
factors found in scientific publications.
The process of restructuring the categories involves
matching of existing categories based on the similarity of
names and meaning. Similar factor type categories were
merged to produce a comprehensive category. In addition,
some of the broad categories that represent heterogeneous
factors were split to produce unique categories without
unnecessary overlap. Through this process, seven factor
types were defined and their boundaries were set to facilitate
the inclusion of factors from scientific evidence. They are
patient-centered, social, economic, therapy-related, health
system, lifestyle, and geographical access.
A hierarchical model was introduced to capture the factor
type in a consistent manner. The top level of the hierarchy
includes the main categories, while the second level represents
subgroups of factors. This second level is generated from
some ad hoc groupings found in existing studies. The lowest
level in the hierarchy will represent concrete and measurable
influencing factors. Table 2 shows the proposed model with
new categories developed from the existing models.
The patient-centered category was created by merging
related categories and was redefined. The term patient-
centered was taken from the study by Jin et al15 as against
“patient-related” in the WHO4 and Munro et al’s6 “personal
character”. The category also reflects the definition given
Table 2 Three-level hierarchy of factors based on the factor
type
Top level Middle level Bottom level
Patient-centered Demographic Age group
Sex
Marital status
Knowledge Knowledge of TB
education level
Psychology emotional state
Psychiatric condition
Depression
economic Finance income class
Poverty
employment Job class
employment status
Basic amenities Lack of food
Homelessness
Social Social network Family support
Community network
Stigma-related Perceived stigma
experienced stigma
Belief wellness perceived as cured
Treatment efficacy belief
Therapy Therapy effect Drug adverse effect
Symptoms persistence
Comorbidity Hiv coinfection
Treatment Defaulting history
Treatment alternative
Health system Health care facility Opening hour favorability
Drug availability
Health care staff Staff friendliness
Communication
Gap experience
Lifestyle Substance abuse Alcoholism
Smoking/tobacco usage
Hard drug usage
Healthy living Diet
exercise
Geographical access Location Distance to facility
Dwelling region
Transportation Travel time
Transportation cost
Abbreviations: HIV, human immunodeficiency virus; TB, tuberculosis.
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Ogundele et al
by Munro et al. The new patient-centered category is defined
as the category of influencing factors based on the demo-
graphic attribute of patients and the attitude that defines the
characteristics of the patients. Our patient-centered category
excludes social-related factors from the definitions presented
by Jin et al and Munro et al, interpretation of wellness and
illness, motivation, and beliefs.6,15 In addition, compliance
history and substance abuse included in Jin et al were
excluded, because they are therapy- and lifestyle-related
factors, respectively.
Economic factors were separated from the social factors
following Munro et al’s classification to create two categories.
This will allow for a unique representation of the factors in a
specific category and reveal the potential of a factor to belong
to more than one category. The “social factor” category rep-
resents the social context and situation of a patient while the
“economic factor” category relates to the economic status
and condition of the patient.
The “therapy-related” factor was adopted from WHO
and Jin et al. It represents the category of influencing factors
that relate to therapy difficulty faced by patients and clinical
procedures that facilitate or hinder patients from adhering
to treatment. It also consists of part of the disease-related
factor presented by Jin et al and part of the “health service”
category of Munro et al.
The “health system” category consists of influencing
factors that relate to the performance of health care providers
and accessibility of patients to health care service at the health
facilities. The health system category is directly represented
in categorizations by Jin et al and the WHO.
The “lifestyle” factor is a new category that is introduced to
distinctly cover those factors related to a patient’s lifestyle that
are circumstantial habits developed by patients and are subject
to change, eg, substance abuse, diet, and exercise. Jin et al
classified some of these factors as patient-centered, and the
WHO classified them as “condition-related” factors. Separat-
ing these factors into different categories will allow for a clear
identification of the unhealthy lifestyle-related factors.
A “geographical access” category was also introduced to
represent the category of influencing factors that relate to the
location of health care facilities and the house/workplace of
the patients, and accessibility costs in terms of distance, time,
effort, and financial expenses. This will help in understand-
ing both the financial and nonfinancial burden that relate to
a patient’s geographic access to health facilities.
Type of effect
This category represents the type of effect a factor has on
patients’ TB adherence, and the degree of effect represents
the intensity of influence on a patient with TB. The type of
effect is based on that of the WHO study. Another type was
included based on the “no-effect” type identified in the study
by Jin et al. The three types of effect included in this model
are positive, negative, and neutral effects.
“Positive influencing factor” represents a group of factors
that show significant motivating influence in the improve-
ment of good adherence behavior. These factors are known
to encourage patients to adhere to medication as prescribed
by a health care officer. This category corresponds to the
positive effect4 and compliance increment.15
“Negative influencing factor” represents factors that show
significant demoralizing influence on patients’ attitudes and
cause poor adherence behavior. This category corresponds to
the negative effect (WHO) and compliance decrement.15
“Neutral influencing factors” are a group of factors that
show no significant effect or correlation on patients’ attitude
toward adhering to treatment. This category corresponds to
the no-effect category in the WHO study.
The patients’ state, perception, or experience in rela-
tion to these factors makes the factors negative or positive.
The sex-related factor is based on whether being a male is
a negative influencing factor or being a female is a posi-
tive influencing factor. Therapy-related factors are mostly
based on patient experience. Drug adverse effect, eg, is
based on the treatment experience of the patients receiving
TB treatment and is seen to cause poor adherence. Belief-
related factors are based on the perception of patients about
circumstances or conditions. An example is a patient who
has a strong belief in treatment efficacy (positive influenc-
ing factor) and the lack of this is regarded as a negative
influencing factor.
Treatment phase
The treatment phase factor refers to the stage during which
a factor is influential during treatment. The SSA clinical
cohort studies have considered measuring adherence and the
defaulting rate over different treatment phases. For example,
the two main TB treatment phases are the intensive and
continuation phases of treatment. Previous studies have con-
cluded that there is an increasing trend of poor adherence as
patients go into the continuation treatment phase, and that
more patients tend to default at the continuation than the
intensive phase.17,19,20
Other treatment phases can be included, eg, the “drug
resistance phase factor” represents the category of factors
that are influential during a drug resistance treatment phase
for the treatment of patients resistant to first-line regimen
drugs and can be as long as 2 years.
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An ontology for factors affecting TB TAB in sub-Saharan Africa
Region
The regional variation of the influencing factors describes
the existence of a factor with a significant influence, in
particular, on socioeconomic regions. Although, there is no
existing regional model for influencing factor classification,
several studies have used geographic regions for their clas-
sification. The result of several clinical and review studies
revealed that influencing factors can vary across regions.
Regions can be delineated based on socioeconomic or geo-
graphic similarities. The administrative area is commonly
used for classification and represents geographical regions
with internationally recognized administrative boundaries
and governance, eg, country and provinces. The geographical
region is a representation of regions with physical boundaries
or common geographical/physical features. The region does
not have recognized political boundaries and governance and
represents the communities where the clinical studies were
carried out. Finally, the socioeconomic region is a collection
of regions with social and economic similarities.
Cross-dependency
Although, cross-dependency relationships between influenc-
ing factors are not represented in current categorizations, they
are common in the findings of clinical studies focusing on
influencing factors. A cross-dependency relationship implies
that a certain factor was found to only influence adherence
behavior if another factor was present. Cross-dependency
relationships are represented in a way that they link the “trig-
ger factor” to the factors that are dependent on the trigger
caused by the trigger factor. A “dependent factor” is only
triggered when another factor is present.
For example, suppose some study found that being male
contributes to negative adherence behavior only when there
are unfavorable conditions at work,18 then male sex is rep-
resented as a factor that is triggered by unfavorable working
conditions.
An ontology for TB TAB
The section “Restructuring existing categorizations into a
common conceptual model” presents an abstract conceptual
model for structuring knowledge around adherence. This
subsection describes the TB adherence behavior ontology,
which provides a concrete, formal, and computer-accessible
representation of the conceptual model.
An ontology is a specification of a conceptualization,
provides an unambiguous logic-based model of some
domain of reality, and allows for the representation of rich
and complex knowledge about things, groups of things, and
relations between things.21 Ontologies not only allow for
explicitly capturing, storing, and sharing expert knowledge
but also enable computers to perform automatic reasoning,
consistency checks, data analysis, and decision support.12
Figure 1 provides an overview of the ontology in the
OWL. Key concepts of the model are represented as classes
in the ontology, eg, influencing factor is represented as a
class. The ontology also incorporates a class for evidence
to represent and link published clinical studies that assert
different adherence factors. Relationships between concepts
(classes) are represented as class properties (the arcs between
the nodes in Figure 1). For instance, the “evidence” class is
linked with “influencing factor” by asserts-influencing factor
“object property”.
The factor type dimension is represented as influencing
factor and is a hierarchy of categories of influencing factors.
The type of effect and treatment phases are represented as a
hierarchical object property that links the evidence with the
Figure 1 Overview of the key concepts and relations in the ontology.
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Ogundele et al
influencing factor class. The region dimension is represented
using the “place” class that is linked to evidence in order
to connect each factor to a specific location. Finally, cross-
dependency is represented as an interdependency class and
linked to the evidence and influencing factor.
The developed ontology facilitates the categorization
of influencing factors. It can be applied in the structuring
of influencing factors of adherence behavior for patients
with TB in SSA. It provides links to the information source,
ie, scientific publications, by representing the type and period
of effect as the object property and linking this to the evidence
class. The ontology also maintains knowledge about where
and when these studies were performed, allowing users to
classify factors that fit the profile of their community.
In order to integrate the knowledge adherence with other
knowledge sources, existing ontologies were incorporated
and reused where possible. The evidence class is based on
the evidence ontology22 and the place class is based on the
Geonames23 ontology.
Using the ontology
The rich computational representation of the ontology is
ideally suited to provide a sound basis for developing tools
useful for clinicians and researchers. The ontology was used
to develop a prototype web-based knowledge repository that
allows users to update, navigate, and query the knowledge.
The interface (Figure 2) currently allows users to navigate,
filter, and search for classes and properties in the ontology.
To use the ontology, users navigate or search through
the ontology to discover and select potential factors that are
appropriate for a specific community. A complex search for
an influencing factor can be carried out using a combination
of the classes and class properties in the ontology. Catego-
ries can be navigated to find specific factors that have been
identified by the published literature. Factor properties can
also be filtered, eg, the type of effect can be used to identify
factors that have a specific type of effect, by specifying,
eg, negative influencing factors.
Community-specific influencing factors can be identi-
fied by either specifying a region of interest or describing
the characteristics of the region. Search results will include
factors directly associated with the specified region as well
as those factors that are associated with communities that are
contained within the specified region. For instance, a user
may request for negative factors that can be found in Africa.
By specifying Africa, the repository will include factors
pertaining to communities within countries and geographical
regions within Africa.
Figure 2 The interface for ontology repository.
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An ontology for factors affecting TB TAB in sub-Saharan Africa
The ontology was designed to be extendable. The catego-
ries in the ontology can be extended, a new category can be
defined as equivalent to the collection of existing categories
or factors. This is useful for those users who want to repre-
sent a different classification mechanism or introduce new
categories that are not currently in the ontology. Users can
easily add additional factors and associated scientific papers
to the repository.
Support for Bayesian decision network construction
One of the design goals of the ontology is to aid in the building
of predictive models for specific communities. The ontology
allows for automating the construction of a Bayesian decision
network. A Bayesian network is an annotated directed graph
that encodes probabilistic relationships among distinctions
of interest in an uncertain-reasoning problem.24 In a typical
usage scenario, the modeler would search the repository for
and identify factors that are likely to impact on adherence in
a target community. These factors will then be used to auto-
matically generate the causal structure of a decision network
with default conditional probabilities for that community
(Figure 3). The modeler must still use his/her expertise to
refine and set the weightings of the conditional probabilities,
or degree of effect of each factor. The resultant Bayesian
decision network represents the adherence profile applicable
to that community and may even be used to predict adherence
behavior for individual patients in that community.
Evaluation of the conceptual model
(ontology)
Comparative analysis with existing
categorizations
Table 3 compares the adherence ontology in terms of its
coverage with existing categorizations. The developed ontology
is more comprehensive than the existing categorizations. It
Table 3 Coverage of the ontology compared with existing categorizations
Dimensions WHO4 Munro et al6 Jin et al15 Castelnuovo16 The ontology
Factor type
Type of effect
Treatment phase
Region (gp) (exp)
Difficulty of measurement (imp)
Cross-dependency (imp) (exp)
Total dimensions covered 2 2 4 1 5
Abbreviations: exp, explicit; gp, geopolitical; imp, implicit; WHO, World Health Organization.
Figure 3 A Bayesian decision network for predicting TB TAB.
Abbreviations: TAB, treatment adherence behavior; TB, tuberculosis.
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Ogundele et al
includes five out of six identified dimensions for influencing
factor categorization extracted from the extensive literature
review. Jin et al’s categorization covers four of the dimen-
sions. Both the WHO’s categorization and Munro et al’s
models cover two dimensions. Castelnuovo’s categorization
only covers the treatment phase category.
One important feature that makes the ontology more com-
prehensive than the existing categorizations is the explicit
representation of the region and the cross-dependency dimen-
sions. Both the geographical region and the interdependency
between factors have not been explicitly modeled by existing
categorizations.
Representing findings in SSA communities
We tested the comprehensiveness and effectiveness of the
conceptual model in representing the “nuances” of factors
found in communities in SSA. Factors and their charac-
teristics were extracted from clinical cohort studies that
focused on adherence in TB communities in SSA. A total
of 14 clinical studies found in the SSA region were used in
the identification of factors, which were then classified and
captured in the ontology. The coverage of these factors by
the model was analyzed.
Factor type
The new categories provide a comprehensive range of
factors identified in SSA. First, the newly created patient-
centered category covers ten (71%) of the factors identi-
fied in relation to patients with TB in SSA. This matches
the personal character category defined by Munro et al,
although named differently. This is because our definition
of patient-centered factors is similar to personal character
as it includes demographic and psychological factors.
Patient-centered6 covers 86% of the factors, which is higher
than the new category. Patient-related4 categories only
cover 43% and show a very narrow representation of the
category (Table 4).
The new economic and social categories have a wider
coverage than the “socioeconomic” category presented by the
WHO and Jin et al. Eighty-six percent of the studies identified
factors belonging to these classes. Economic-related
factors are identified in six studies, even with the exclu-
sion of transportation-related factors. The socioeconomic4
covers 64%, the social and economic15 covers 71% of the
factors, while the social context6 covers 14%. The newly
created social category covers 43% of the factors. Simi-
larly, the newly created “economic” category covers 43%,
which makes it lower than condition-related4 (71%) and
“structural”6 factors (64%). This is due to the fact that most
factors in the structural- and condition-related4 categories
are incorporated into the two new categories: geographic
access and lifestyle.
The new health system category covers 26% of the
factors. It covers less than Jin et al’s “health care system”15
which is 43%. This is because not all factors in Jin et al’s
category are represented in the new category. For instance,
lack of accessibility to a health care facility was included
under health care system15 and under geographic access but
was excluded from the new category. The new category
covers more factors than both the health system4 (21%) and
health service6 (14%) categories.
The coverage of therapy-related matches those from
the two studies, which cover 57% of the factors extracted
from SSA studies. Geographic access category has 36%
coverage on influencing factors identified for SSA. Lifestyle
category has 43% coverage on influencing factors identified
for SSA.
The new factor type categorization offers a more com-
plete representation than the existing ones. The categories
are distinct from one another and cover the factors uniquely.
However, certain factors from SSA studies such as the
existence of a direct observation therapy center within the
district,25 false/unknown address,26 and outpatient method20
did not fit into any of the new categories.
Table 4 Analysis of existing and new factor type categories
Influencing factor classifications No of studies (14) in
sub-Saharan Africa, n (%)
Patient-relateda 6 (43)
Personal factorb 10 (71)
Patient-centeredc 12 (86)
Patient-centeredd 10 (71)
Socioeconomica 9 (64)
Social contextb 2 (14)
Social and economicc 10 (71)
Sociald 6 (43)
Condition-relateda 10 (71)
Structuralb 9 (64)
economicd 6 (43)
Therapy-relateda 8 (57)
Therapy-relatedc 8 (57)
Clinical-relatedd 8 (57)
Health systema 3 (21)
Health serviceb 2 (14)
Health care systemc 6 (43)
Health systemd 4 (26)
Disease-relatedc 2 (14)
Lifestyled 6 (43)
Geographic accessd 5 (36)
Notes: aWHO;4 bMunro et al;6 cJin et al1;5 dthe ontology.
Abbreviation: wHO, world Health Organization.
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679
An ontology for factors affecting TB TAB in sub-Saharan Africa
Regional variation
Regional classification of the influencing factors was car-
ried out using countries in SSA with the aim of identifying
influencing factors specific to each of these regions. This
classification revealed knowledge about varying predomi-
nant influencing factors for different countries (Table 5).
Although, there is wide variation in the range of factors
identified for different countries, the most common categories
across all countries are the patient-centered, therapy, and
social-related factors.
Discussion and conclusion
Using a rigorous ontology engineering methodology, we
developed an ontology, the TAB-influencing factors ontol-
ogy, for representing knowledge about factors that influence
TAB in patients with TB. The underlying conceptual model
was developed by reformulating existing categorization
systems from the literature. It incorporates more dimensions
than any of the current categorization systems and was suc-
cessfully used to capture most of the factors that influence
TB adherence behavior in SSA found in the literature.
The ontology takes an evidence-based approach by
explicitly relating each factor to published clinical studies: an
important consideration for health practitioners. It presents the
potential for capturing details of diverse multifaceted influ-
encing factors and their interrelationships and complexities
beyond normal human abstraction, simplification, and compre-
hension. For instance, the diametrically opposing influencing
effects that a specific factor can have under different circum-
stances can be effectively represented in the ontology.
The usefulness of the TAB-IF ontology was demonstrated
in an open, shareable, and extendable web-based knowledge
repository. The ontology formed the computational model
that underpinned the repository and provided advanced
navigation, search, and filtering capabilities. The repository
can be used by program officers to navigate and find potential
factors affecting TB adherence emanating from clinical
studies in similar communities, and to profile communities
and generate risk indices that will help simplify TB patient
monitoring and follow-up activities. The ontology also pro-
vided the basis for the development of a predictive model, a
Bayesian decision network that may be integrated in clinical
decision support tools.
The study presents a novel ontology-based approach for
consolidating and structuring knowledge about TB adherence
behavior. However, a number of limitations of the study should
be noted. Adherence behavior is broad, complex, and difficult
to assess. The current ontology does not claim to be an exhaus-
tive representation of factors that influence TB adherence
behavior. However, the ontology was designed to be extend-
able to reflect custom views and a changing body of knowledge
around TB adherence behavior. Although the conceptual
model contains more dimensions than existing categorization
systems, additional dimensions can be incorporated into the
ontology. The ontology was based on knowledge extracted
from scientific publications, which may not exhaustively reflect
all factors and categorizations experienced in practice.
Possible future research work premised on this study
could be an extension of the ontology to incorporate other
dimensions that are not currently included or supported by the
ontology, eg, “difficulty of measurement”. Further research
is required to qualify the “degree of influence” in a form
that is useful to further categorize and structure influencing
factors. Although the ontology focused on the knowledge
of TB adherence factors in SSA, the approach is potentially
applicable to other diseases and regions where adherence is
Table 5 Regional comparison of predominant influencing factors
Regions Influencing factors category
Burkina Faso27 Alcoholism; defaulting history; TB knowledge
Cameroon20 Stigmatization; wellness perceived as cured
ethiopia19,28,29 Wellness perceived as cured; age group; geographic access; education level; drug adverse effect; social network (family
support); TB knowledge; finance related; alternative treatment
Kenya7 Health care system related; social and economic factor; patient-related factor; alcoholism; therapy-related
Madagascar26 Transportation time; TB knowledge; sex; communication gap experienced
Namibia2 Distance to health care facility; wellness perceived as cured; sex; marital status; education level (literacy); social network (family
support); TB knowledge; drug adverse effect; symptoms persistence; long waiting time; lack of food; substance abuse; lifestyle
Nigeria18 Coinfection (HIV); sex; unfavorable working condition
South Africa30–32 Stigmatization; wellness perceived as cured; alcoholism; tobacco usage (smoking); poverty; incentive expectation at clinic;
symptoms persistence; drug adverse effect; sex; coinfection; psychological distress
Tanzania25 Sex; age group; distance to facility; geographic access
Zambia33 Wellness perceived as cured; TB knowledge; drug availability; drug adverse effect
Abbreviation: TB, tuberculosis.
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Ogundele et al
a significant factor. The proposed ontology can also be used
as a basis to analyze adherence in other diseases such as HIV
and can be extended beyond SSA.
Acknowledgments
This work, including support for the Health Architecture
Laboratory (HeAL) project as well as for DM, CJS, and AWP
and a PhD scholarship to OAO, was funded by grants from
the Rockefeller Foundation (establishing a health enterprise
architecture laboratory, a research laboratory focused on the
application of enterprise architecture and health informatics
to resource-limited settings, grant number: 2010 THS 347)
and the International Development Research Centre (HeAL,
grant number: 106452-001).
Disclosure
The funders had no role in study design and data collec-
tion. The authors report no other conflicts of interest in
this work.
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P E R S P E C T I V E S
The SPUR Model: A Framework for Considering
Patient Behavior
This article was published in the following Dove Press journal:
Patient Preference and Adherence
Kevin Dolgin
Observia, Paris, France
Background: Medication nonadherence is a global problem that requires urgent attention.
Roughly half of all drugs that are prescribed for chronic treatments are not taken by the patients in
question. Initiatives designed to support patients and help them modify their behavior are
enhanced by personalization, and a number of profiling tools exist to help customize such
interventions. Most of these tools were originally designed as paper-based questionnaires, but
the growth of digital adherence technologies (DATs) illuminate the need for the development of
digital profiling systems that can interact with fully automated patient interfaces.
Objective: The objective of this study was to examine existing frameworks from medicine,
psychology, sociology, consumer behavior, and economics to elaborate a comprehensive, quanti-
tative profiling approach that can be used to drive the customization of patient support initiatives.
Results: Building primarily on IcekAjzen’s Theory of Planned Behavior (TPB), the Health Belief
Model (HBM) was used to inform the beliefs about behavior posited in the TPB, while incorporat-
ing established factors regarding self-efficacy in the “control” elements of the TPB and selected
social and psychological factors in the other constituents of themodel. The resulting SPUR (Social,
Psychological, Usage, Rational) framework represents a holistic, profiling tool with detailed,
quantitative outputs that describe a patient’s behavioral risks and the drivers of that risk.
Conclusion: An interactive, digital questionnaire built around SPUR represents
a potentially useful tool for those desirous of building interactive digital support programs
for patients with chronic diseases.
Keywords: adherence, compliance, health beliefs, chronic diseases, review
Introduction
According to the World Health Organization (WHO),1 poor adherence to treatment
of chronic diseases is a worldwide problem of “striking magnitude” and the burden
of poor adherence is growing worldwide as the prevalence of chronic disease
increases. The WHO goes on to point out that the consequences of poor adherence
to long-term chronic therapies are both poor health outcomes and increased health-
care costs. In 2012, global avoidable cost due to non-adherence was estimated at
$269 billion.2 The impact of non-adherence led the WHO to agree with Hayne’s
contention that “increasing the effectiveness of adherence interventions may have
a far greater impact on the health of the population than any improvement in
specific medical treatments”.3
The WHO estimates that roughly 50% of medications prescribed for chronic
diseases are actually taken.1 Even in life-threatening cases adherence rates can be
much lower than expected, with adherence rates measured as low as 77.3% in post-
transplant immunosuppressant drugs4 and 71% for oral oncology drugs.5 A 2012meta-
Correspondence: Kevin Dolgin
Observia, 16 Rue Brancion, Paris 75015,
France
Tel +33 1 81 80 24 50
Email kevin.dolgin@observia-group.com
Patient Preference and Adherence Dovepress
open access to scientific and medical research
Open Access Full Text Article
submit your manuscript | www.dovepress.com Patient Preference and Adherence 2020:14 97–105 97
http://doi.org/10.2147/PPA.S237778
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incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you
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permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
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analysis of adherence in drugs that prevent cardiovascular
disease found an adherence rate of 57% inmore than 370,000
patients.6 Over the past several years, stakeholders in the
healthcare world have intensified their efforts to both under-
stand this issue and to put into place patient support programs
that will help address non-adherence. Addressing non-
adherence through targeting intervention is one of the few
topics on which everyone is in agreement: patients certainly
benefit from increased support and payers would very much
like to reduce overall costs by enhancing adherence to those
drug treatments which they have decided are beneficial;
health-care professionals would like to ensure that the treat-
ments they prescribe are being followed and the pharmaceu-
tical industry benefits by increased sales of their products.
Physicians have traditionally been poor at determining
patient adherence. As early as 1978, Roth et al7 found that
physicians overestimated their patients’ adherence by 400%,
and that the patients too overestimated their own
adherence.
In 2010, Copher et al8 found that physicians overestimated
the number of adherent patients by over 60% and in the
following year Trindade et al9 found similar overestimation
of the adherence rates of IBD patients. In 2016, Clyne et al10
found a weak correlation between physician estimates of
their patients’ adherence and objective measures, as well as
a systematic bias among prescribers to assume that their
patients are more adherent than the norm.
Given their difficulty in perceiving the problem, relying
on health-care professionals alone to address patient non-
adherence can lead to suboptimal outcomes.11 Furthermore,
decisions about whether or not to take medication are typi-
cally made outside of a health-care institution, when the
patient is not in direct contact with health-care professionals.
Many tools have been provided to health-care profes-
sionals to help prescribers more accurately assess adher-
ence. The most widely used of these is the 4-question
Morisky Medication Adherence Scale (MMAS 4).12 This
tool has been and continues to be of great use to health-
care professionals, but it and other tools like it typically
require extra time and effort from often busy professionals.
Both physicians and patients often cite a lack of time
during visits, and surveys such as 2007’s Global Asthma
Physician and Patient Survey13 underscore the need for
more time spent on education and coaching.14 At the same
time, a recent survey carried out in the United States
indicates that only 11% of patients and 14% of physicians
feel that doctors have the time they need to provide excel-
lent care.15 Faced with the need to balance ease of use and
thoroughness of analysis, behavioral profiling tools such as
the MMAS-4 (and the later, 8-question MMAS-8) must
sacrifice the latter to ensure the former. As such, existing
tools have been criticized as being too restrictive to offer
a basis for highly tailored behavioral interventions.16
The availability of digital solutions (Digital Adherence
Technologies: DAT) provides effective new means of iden-
tifying patients at risk of non-adherence and promoting
behavioral change while minimizing demands on physi-
cian time.17–19 However, this technology by its nature
lacks the personalization that can be provided by
a trained human during an interpersonal exchange. This
gives rise to a need for more flexible and personalized
digital support that takes into account each individual’s
behavioral drivers and triggers without the need for human
analysis. Tailored DATs have great potential to support
patients effectively without undue demands on physician
time and with much lower costs than traditional telephone-
based programs, as demonstrated by a 2018 review of the
literature on such technologies with tuberculosis patients20
as well as a 2019 study with hypertensive patients in the
UK.21 These promising approaches warrant further devel-
opment, including the design of DAT-friendly profiling
tools. Such tools would be digital in nature, thorough in
their quantification of the drivers of adherence, predictive
of actual adherence behavior and easily incorporated into
DATs.
The tools that are typically used to determine indivi-
dual patient risk and behavioral needs were designed to be
used by humans and do not incorporate the kind of con-
tinuous and detailed mathematical principles that can inter-
act effectively with digital support programs. For example,
most online retailers, such as Amazon.com, use Bayesian
product recommendation engines such as that described in
US patent 8.255.263 B2.22 Digital patient support pro-
grams could likewise benefit from a similar high degree
of customization, yet they need the detailed, quantifiable
personal profiling that drives them. As pointed out by
Prochaska, Redding, and Evers, “. . . most [health behavior
frameworks] have not even developed constructs that are
subject to such mathematical principles.”23 We believe that
the SPUR (Social, Psychological, Usage, Rational) frame-
work, built on existing behavioral frameworks, can fill this
gap by allowing detailed quantitative measures of estab-
lished adherence behavioral drivers, determined through
an interactive digital questionnaire. Such a questionnaire,
designed from the start to be administered in a digital
setting, can provide engaging intermediate feedback to
patients while providing the kind of driver-specific
Dolgin Dovepress
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Patient Preference and Adherence 2020:1498
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measures that can serve as the foundation for personalized
digital interaction. In order to achieve this in a valid way,
such a tool must be built on solid theoretical frameworks.
Theoretical Frameworks
A number of frameworks are often cited when referring to
patient adherence decision-making. Among the first of
these was the Health Belief Model (HBM),24 first postu-
lated over fifty years ago. This model has been verified in
more recent studies,25 but it does not address the non-
cognitive components of patient behavior, such as medica-
tion costs. Icek Ajzen’s Theory of Planned Behavior
(TPB), and its predecessor the Theory of Reasoned
Action do address these factors and have also often been
applied to healthcare decision-making,26 as has the more
recent derivative of the TPB, the Integrated Behavior
Model.27 Prochaska and Clemente’s Transtheoretical
Model (TTM) has also been a staple model, as has social
cognition theory.28,29 On a more psychological level,
Gérard Reach has postulated elements of relationship to
authority (reactance), and he and others have borrowed
from behavioral economics concepts regarding the ability
to project into the future as determinants of adherence
behavior.30
Outside of medical or even strictly behavioral aca-
demic domains, the field of consumer behavior has gener-
ated interesting frameworks for addressing adherence
problems. Notably, issues of identity, described in the
concept of the extended self, as researched by Russel
Belk,31 provides insights into how individuals view con-
sumption as an extension of self-identity. This has an
impact both on the acceptance of their disease (as
described by Graffigna and Barello 201832) and on their
acceptance of the prescribed treatment.
Patient Support Programs (PSPs) built using these and
other models have proven useful as public health initia-
tives at a population level.33 These models have been used
to build patient-specific support, and they are also often
used to profile patients and provide guidance to health-care
professionals who can then offer tailored advice, educa-
tion, and coaching. Some models have been specifically
designed to profile patients, either in terms of their risk of
non-adherence or the reasons behind this risk. One of the
more widely used frameworks to investigate and affect
patient behavior is the Patient Activation Measure
(PAM).34 The goal of PAM is to measure the degree to
which patients have been “activated” to engage with their
own health; and PAM scores have been strongly correlated
to outcomes.35,36 The Patient Health Engagement model32
purports to go beyond what the authors see as
a “passivizing approach” to patients’ care to examine the
“meaning and lived experiences” of patients, as well as
their emotional and psychological make-up.
While all of these models have demonstrated their
value both at the level of individual prescribers (or provi-
ders) as well as in the design of programs at population
level, none were designed to provide a multi-dimensional
behavioral assessment model that can be used to profile
individuals and interface with a digital agent, such as
a DAT driven by artificial intelligence. What is needed is
a comprehensive model of patient behavior that can both
accurately predict adherence and identify modifiable dri-
vers of health behavior that deliver useful input to digital
coaching and support systems.
In sum, the imperative is to build a model and an
associated profiling tool that successfully meet the follow-
ing criteria: 1) accurately predicts patient adherence to
medication; 2) identifies actionable drivers of adherence
behavior; 3) can be used in the absence of human inter-
pretation so as to inform the customization of purely
digital support programs; and 4) is based on accepted
behavioral models drawn from different domains. Such
a model can then be used both to further investigate the
non-adherence phenomenon at the population level as well
as to provide tailored support to individual patients. By
combining established behavioral models with digital
questionnaire technology, a “grand unified” model can be
created to serve as the basis for the digital questionnaire.
The Proposed Model: SPUR
The core of the SPUR framework is Ajzen’s Theory of
Planned Behavior (TPB). This theory, as seen in Figure 1,
is a well-established approach to consider complex deci-
sion-making. It has been successfully and widely applied
to general healthcare behavior,37 and a smaller number of
studies have shown its utility in predicting medication
adherence,38,39 although its use has been criticized in this
context for being simplistic, too rational in nature, not
taking into account psychological factors such as identity,
and for not having demonstrated impact when applied to
health behavior.40 McEachan et al’s41 2010 meta-analysis
of the use of the TPB determined that the model was
predictive of health behavior and psychological constructs
such as behavioral intent. Using Rothman and Salovey’s
classification of health behaviors into those that prevent,
detect, and cure health problems,42 McEachan et al
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determined that the type of behavior was important in
assessing the applicability of the TPB and particularly, in
the relative importance of its constituents. This was rein-
forced in McEachan et al’s subsequent 2016 meta-
analysis.43 In both of these studies, however, the authors
underlined that no satisfactory study had been made of the
applicability of the TPB in cases of curative behaviors
such as adherence to medication for chronic disease.
The lack of studies examining the TPB in curative
behavior as well as the identified weaknesses of the
approach led us to believe that augmenting the TPB with
health-specific frameworks focusing on curative behavior
can both enhance its applicability in this domain while
further elaborating on it to provide quantifiable outputs
that can then be used to drive algorithms in digital support
programs for chronic disease management.
The TPB describes complex behavior as a function of
attitudes about the behavior (driven by beliefs about the
behavior and tempered by other psychological factors),
subjective norms, and perceived control. Each of these is
weighted to reflect its importance for each individual.
Drawing on the TPB, the SPUR model is focused
specifically on chronic patients’ adherence behavior and
includes elements of ancillary frameworks as mentioned
above.
Specifically, SPUR includes concepts from the HBM to
inform the cognitive elements of attitude formation within
the TPB by detailing the perceived understanding of the
patient and their beliefs about the prescribed behavior.
Commonly cited practical reasons for non-adherence, such
as financial difficulties, complexity of the treatment, etc.1
can inform the control elements of the TPB, typically
referred to as self-efficacy in health behavior, and afore-
mentioned behavioral factors such as reactance and dis-
counting of future benefits can address how beliefs about
behavior combine with the other elements to generate
attitudes. The resulting model is specific to behaviors
regarding chronic diseases and takes into account
a number of established frameworks that have been demon-
strated to influence adherence behavior, generating the fol-
lowing enhanced framework (Figure 2).
The model is called “SPUR”, since the different ele-
ments fall into four major domains: Social, Psychological,
Usage and Rational that are discussed below. The manner
in which these four domains relate to the TPB can be seen
in Figure 3.
Social Factors
Social elements in the case of medical adherence have also
been studied at length. Most of the work in this area has
focussed on perceived social support as a factor in patient
adherence and has shown greater support improves adher-
ence. This has been considered in general44 as well as in
specific cases such as diabetes by Gu, et al45 as well as
Shallcross et al46 in epilepsy and Kim, et al47 in HIV.
Social factors have been considered both with respect
to the impact of those close to the patient and the role of
society as a whole (e.g. the influence of socio-cultural
expectations). This latter definition corresponds more clo-
sely to the TPB’s construct of social norms and can be
clearly seen when considering the changing cultural
acceptability of smoking in many Western societies and
its impact on the prevalence of smoking. Likewise, the
impact of social norms on general health-related behavior
has been well considered (see, for example, Baer, Stacy,
and Larimer48), although the direct impact of norms has
been poorly studied in relation to actual medication
adherence.
Psychological Factors
Many psychological constructs have been examined with
respect to health behavior in cases of chronic disease.
Mental illness itself, such as depression, has long been
linked to non-adherence49–51 and treatment with anti-
depression medication has been shown to increase adher-
ence with depressed HIV patients.52 In SPUR, we have
avoided examining the impact of mental disorders such as
depression, bi-polar or Post-traumatic stress disorder, as
they merit specific treatment beyond the type of behavioral
support that is the focus of our research. We have therefore
focussed on three compelling and well-documented non-
clinical psychological factors: self-concept, reactance and
the discounting of future values.
Beliefs about
behavior
Beliefs about
social norms
Attitude about
behavior
Subjective
norms Action
Beliefs about
control
Perceived
control
Figure 1 The theory of planned behavior.
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Beliefs about
behavior
Beliefs about
social norms
Attitude about
behavior
Subjective
norms Action
Beliefs about
control
Perceived
control
Forgetfulness Financial Capability
Reactance
Identity
Discounting
Perceived
Susceptibility
Perceived
Seriousness
Perceived
Benefits
Perceived
Barriers
Perceived
Threat
Outcome
Expectations
Figure 2 The concepts that constitute SPUR.
Beliefs about
behavior
Beliefs about
social norms
Attitude about
behavior
Subjective
norms Action
Beliefs about
control
Perceived
control
Forgetfulness Financial Capability
Reactance
Identity
Discounting
Perceived
Susceptibility
Perceived
Seriousness
Perceived
Benefits
Perceived
Barriers
Perceived
Threat
Outcome
Expectations
Rational
Psychological
Usage
Social
Figure 3 The SPUR framework in detail.
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The Concept of the Self
In the case of medical adherence, there is a latent tendency
to deny the existence or extent of the illness despite
a cognitive understanding of the facts.53,54 While denial
has been linked to well-being and general health54,55 the
role of denial of personal traits and circumstances in
behavior modification has not been adequately examined.
There is some indication that denial is directly correlated
to non-adherence, particularly in the case of mental illness.
Greenhouse et al29 demonstrated that coping behaviors
associated with denial were inversely correlated with
adherence in bipolar disorder56 and Aldebot and
Weisman de Mamani57 observed that denial led to lower
adherence rates in schizophrenia. Outside of mental ill-
nesses, denial has been identified as a significant contri-
butor to non-adherence in pathologies ranging from
cardiovascular disease58 to HIV.59
We thus hypothesize that questions of denial in the
case of health are driven by conflicts between the pre-
existing self-concept and the new identification with the
disease state, e.g. as a “diabetic”, “cancer patient”, “asth-
matic”, etc. In order to examine the concept of the self
with respect to the consumption of medication, we turn to
consumer behavior and notably, Russel Belk. Belk consid-
ered the effect of possessions on the “extended self” and
the impact of consumption decisions.31,60,61 We believe
that similar concepts can help elucidate a model to under-
stand the impact of medication consumption on identity
and therefore its impact on adherence. Specifically, does
the person accept their illness and their status as “patient”?
We hypothesize that denial of this attribute – equated to
a refusal to incorporate it into their sense of identity – is
detrimental to adherence.
In order to better examine this potential disconnect, it
is useful to begin by considering the patient’s perceived
identity and their concept of self, both from the point of
view of the individual (i.e. Belk) and his or her relations to
others and the community, i.e. Kashima.62 This becomes
particularly relevant for individuals who express a degree
of denial with respect to their disease. Kortte and
Wegener53 have considered specifically denial of illness
among patients across a number of pathologies and have
investigated the psychological literature on the subject,
ranging from Freud to modern thinkers. However, they
have not considered the impact of denial on either sense
of self directly, nor on consumer behavior, such as
adherence.
Reactance
Reactance is the psychological tendency to resist authority.
A number of studies have examined the impact of reactance
on patient adherence. Fogarty and Young63 did not find an
expected correlation between the use of an authoritative tone
by physicians and patient behavior, however, it did lead the
authors to conclude that the underlying degree of psycholo-
gical reactance of the patient was a factor in their adherence
behavior. Gérard Reach’s analysis of diabetic patients’
adherence35 bears this out, demonstrating a strong correlation
between “obedient” behavior (wearing a seatbelt in the back
seat of a car) and adherence, leading him to postulate that
there are two elements to adherence behavior, a “passive”
(i.e. driven by deference to authority) and a “motivational”
element. In 2011, De La Cuevas et al49 determined that
reactance was a stronger driver of adherence behavior than
self-efficacy in patients suffering from depression.
Discounting and Prospect Theory
Researchers in the domain of behavioral economics have
demonstrated the degree to which our asymmetrical valu-
ing of losses over gains affects behavior.64 A number of
researchers have investigated these factors with respect to
adherence behavior.
Zhao et al37 demonstrated a significant difference in
the impact of differential messaging on intended health
behavior across people with different levels of focus on
future outcomes,65 in which patients with high scores in
Strathman’s Consideration of Future Consequences scale
were significantly more sensitive to messages about future
consequences than were subjects with lower scores, who
were more likely to adhere when presented with messages
concerning short-term benefits.
Lebeau et al,66 building on the work of Gérard Reach30
discovered a direct correlation between the discounting of
future gains in type 2 diabetes patients and their measure
of glycated hemoglobin (HbA1c).
Usage
The TPB’s inclusion of perceived control as an important
behavioral driver leads to a consideration of what exactly
this represents in adherence to treatment for chronic dis-
ease. This idea of self-efficacy has been closely studied
and indeed, many patient support initiatives focus on it
exclusively. Issues of control that affect adherence include
non-access to medication due to physical constraints;
financial difficulty;67 difficulty with self-administration,
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which can be the result of a multitude of causes including
anxiety regarding self-injection;68 and forgetfulness.
Rational Factors
The HBM is the most widely and the longest used “expec-
tancy-value” approach to health behavior. The fundamen-
tal behavioral choice is presented as a comparison of the
outcome expectations of health behavior as compared to
the perceived threat of not adopting that behavior. The
former is further subdivided into the perceived benefits
and the perceived threats of the behavior and further sub-
divided into the perceived gravity of the disease coupled
with the perceived susceptibility of the subject to these
nefarious consequences. While the HBM lacks the psycho-
logical, social and practical elements that have since been
studied for their impact on health behavior, it does provide
a good breakdown of the purely rational thought processes
that can inform beliefs about health behavior that can then
be incorporated into the TPB.
Conclusions and Next Steps
By building on the Theory of Planned Behavior, incorporat-
ing behavioral frameworks that are relevant to behavior in
the case of medication adherence for patients with chronic
diseases, the SPUR framework represents a coherent
approach to build a quantifiable questionnaire that can be
used to profile patients for adherence risk while identifying
the drivers behind that risk. The inherently hierarchical struc-
ture of the framework, in which four major sets of drivers:
social, psychological, utilitarian and rational are further bro-
ken down in accordance with relevant theories should allow
both for assessment of an individual’s behavioral risk as well
as analysis of the salient sources of that risk.
The next step will be to build a questionnaire based on
the SPUR framework and test its predictive validity. We
are in the process of doing exactly that and will be carry-
ing out such a study across a range of pathologies in the
near future. It is our hope that SPUR will prove to be
a valuable tool for health-care professionals to determine
the behavioral risks of each patient and construct support
services for them that correspond to their personal beha-
vioral drivers.
Abbreviations
DAT, Digital Adherence Technologies; HBM, Health Belief
Model; PAM, Patient Activation Measure; PSP, Patient
Support Program, SPUR, Social, Psychological, Usage,
Rational; TPB, Theory of Planned Behavior; TTM,
Transtheoretical Model; WHO, World Health Organization.
Acknowledgments
The author would like to thank John D. Piette, Reem
Kayyali, Marie-Eve Laporte and Benoit Arnould for their
input and critical review of the manuscript; Lea Kombargi
for her support in preparing the manuscript; Béatrice
Tugaut for her support in submitting the manuscript.
Author Contributions
The author contributed to data analysis, drafting or revising
the article, gave final approval of the version to be published,
and agrees to be accountable for all aspects of the work.
Ethics Approval and Informed
Consent
This study did not require any ethics approval.
Funding
This study was funded by Observia.
Disclosure
Kevin Dolgin is an employee of Observia. The author
reports no other conflicts of interest in this work.
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Health belief model.
Authors:
Boslaugh, Sarah E., PhD
Source:
Salem Press Encyclopedia, 2019. 1p.
Document Type:
Article
Subject Terms:
Health Belief Model
Abstract:
The health belief model is a value-expectancy theory that uses information about an individual’s values (e.g., how important it is to avoid disease) and expectations (e.g., what results he or she expects from different health behaviors or healthcare interventions) to examine why some individuals take advantage of health programs or alter their behavior to improve their health and others do not. Originally developed by researchers working for the US Public Health Service, the health belief model remains a vital part of health behavior theory today, as public health and medicine have become increasingly focused on the importance of individual behaviors and cultural values and beliefs in health.
Full Text Word Count:
804
Accession Number:
89677562
Database:
Research Starters
Health belief model
Full Text
The health belief model is a value-expectancy theory that uses information about an individual’s values (e.g., how important it is to avoid disease) and expectations (e.g., what results he or she expects from different health behaviors or healthcare interventions) to examine why some individuals take advantage of health programs or alter their behavior to improve their health and others do not. Originally developed by researchers working for the US Public Health Service, the health belief model remains a vital part of health behavior theory today, as public health and medicine have become increasingly focused on the importance of individual behaviors and cultural values and beliefs in health.
Psychologist Albert Bandura, 2005 By bandura@stanford.edu (Albert Bandura) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons
Overview
The health belief model was developed in the United States in the 1950s by Godfrey Hochbaum, Irwin Rosenstock, and colleagues at the US Public Health Service, who were trying to understand why more people did not take advantage of
public health programs
such as tuberculosis screenings. One major change to the health belief model was made in the 1970s, when the concept of
self-efficacy
, meaning the confidence a person feels in their ability to carry out a behavior, was added to the model. In the twenty-first century, the health belief model is used to study not only public health questions but also a wide variety of other health behaviors, including smoking, physical activity, sick-role behavior, and the management of chronic diseases such as
diabetes
.
The health belief model contains the following components: perceived threat, perceived benefits, perceived barriers, cues to action, other modifying variables, and self-efficacy. The components work together to influence an individual’s choice to take action or not.
Perceived threat contains two concepts: susceptibility, or an individual’s belief that he or she is at risk of developing an illness, and perceived severity, or an individual’s belief about the severity of an illness and the consequences of leaving it untreated. In general, an individual who believes that he or she is at risk of a health condition with serious consequences will be more likely to take action, all else held constant. Perceived benefits are the positive results an individual believes will follow from an action, including the probability of reducing the risk of disease. In general, higher perceived benefits make an individual more likely to take action.
Perceived barriers are factors that might dissuade an individual from taking an action that is otherwise regarded as potentially beneficial. Examples of perceived barriers include cost, inconvenience, discomfort, and danger. Cues to action may be either internal (e.g., physical sensations, thoughts, or emotions) or external (e.g., reading a public health brochure or being given advice). Modifying variables are the social, psychological, and demographic factors that may influence an individual’s decision to take action. Self-efficacy, a concept developed by the social psychologist
Albert Bandura
, refers to a person’s belief that he or she can successfully perform a behavior. All things being equal, a person with a higher level of self-efficacy is more likely to attempt to perform a desired behavior, such as quitting smoking.
Bibliography
DiClimente, Ralph J., Laura F. Salazar, and Richard A. Crosby. Health Behavior Theory for Public Health: Principles, Foundations, and Applications. Burlington: Jones, 2013. Print.
Edberg, Mark. Essentials of Health Behavior: Social and Behavioral Theory in Public Health. 2nd ed. Burlington: Jones, 2015. Print.
Glanz, Karen, Barbara K. Rimer, and the National Cancer Institute. Theory at a Glance: A Guide for Health Promotion Practice. 2nd ed. Washington: Dept. of Health and Human Services, Natl. Insts. of Health, 2005. Print.
Glanz, Karen, Barbara K. Rimer, and K. Viswanath, eds. Health Behavior and Health Education: Theory, Research, and Practice. 4th ed. San Francisco: Jossey, 2008. Print.
Hochbaum, Godfrey Martin. Public Participation in Medical Screening Programs: A Socio-psychological Study. Washington: GPO, 1958. Print.
Janz, Nancy K., and Marshall H. Becker. “The Health Belief Model: A Decade Later.” Health Education Quarterly 11.1 (1984): 1–47. Print.
Jones, Christina Jane, Helen Smith, and Carrie Llewellyn. “Evaluating the Effectiveness of Health Belief Model Interventions in Improving Adherence: A Systematic Review.” Health Psychology Review 8.3 (2014): 253–69. Print.
Kim, Hak-Seon, Joo Ahn, and Hae-Kyung No. “Applying the Health Belief Model to College Students’ Health Behavior.” Nursing Research and Practice 6.6 (2012): 551–58. Print.
Pirzadeh, Asiyeh, and Maryam Amidi Mazaheri. “The Effect of Education on Women’s Practice Based on the Health Belief Model about Pap Smear Test.” International Journal of Preventive Medicine 3.8 (2012): 585–90. Print.
Rosenstock, Irwin M., Victor J. Strecher, and Marshall H. Becker. “Social Learning Theory and the Health Belief Model.” Health Education Quarterly 15.2 (1988): 175–83. Print.
Copyright of Salem Press Encyclopedia is the property of Salem Press. The copyright in an individual article may be maintained by the author in certain cases. Content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use. Source: Salem Press Encyclopedia, 2019, 1p
Item: 89677562
MedicalEconomics.com30 MEDICAL ECONOMICS ❚ DECEMBER 10, 2017
by TODD SHRYOCK Editor
aying doctors for outcomes in-
stead of volume may seem to
make sense, but what happens
when patients can’t do their
part to follow prescribed be-
havioral changes or take their
medications?
For some patients, their so-
cioeconomic situation has a
greater impact on their health
than anything their doctor is doing. A Min-
nesota Department of Public Health study
indicates that social determinants aff ect a
larger proportion (40%) of health and well-
being nationally than does clinical care
(10%). Social determinants are typically de-
fi ned as food insecurity, housing, transpor-
tation, education, violence or community
safety, social support, health behaviors and
employment.
Richard Bryce, DO, a primary care phy-
sician in Detroit, has seen the eff ects these
challenges have on his patients at the com-
munity clinic he oversees. “Unfortunately,
a lot of these social challenges play a huge
role in their health,” he says. “When you are
a medical student, you spend so much time
learning about diff erent drugs and surgeries
to improve lives, but on a day-to-day basis,
social determinants play a big role and their
eff ect on outcomes is huge.”
For example, patients may want to ex-
ercise, but are afraid to leave their house
because the neighborhood isn’t safe, says
Bryce. Poverty or lack of education can lead
to poor food choices, even when healthier
foods are readily available.
“Individuals who are unemployed or
homeless can’t aff ord healthcare, and
those living in unsafe neighborhoods with
high rates of violence and/or experiencing
transportation barriers can’t access care
when needed, leading to untreated medical
conditions and resulting in poorer health
outcomes,” says Jay Bhatt, DO, MPH, FACP,
a practicing internist and chief medical of-
fi cer of the American Hospital Association.
“Research has also indicated that many indi-
viduals with food insecurity are at high risk
for chronic diseases such as diabetes and
obesity in some age groups.”
Bhatt is also former managing deputy
commissioner at the Chicago Department
of Public Health where he developed pro-
grams addressing social issues in medical
treatment.
So how does a physician who is respon-
sible for keeping patients healthy deal with
these socioeconomic challenges that stretch
far beyond the walls of the practice, espe-
cially when the fi nancial viability of their
practice may be on the line?
HIGHLIGHTS
Physicians
need to understand
the problems their
patients are facing
before addressing
socioeconomic
challenges, and the
way to do that is to ask.
For patients who
need additional
assistance, smaller
practices can identify
local volunteers who
could serve as a
liaison between the
practice and the social
agencies.
Operations
BUILDING BLOCKS
TO BETTER HEALTH
Overcome socioeconomic obstacles
to improve adherence
http://medicaleconomics.modernmedicine.com/
MedicalEconomics.com 31MEDICAL ECONOMICS ❚ DECEMBER 10, 2017
“You can either look at the problems
faced by patients and ignore them or try to
address them, even if you are not medically
trained to address them,” says Bryce. “So-
cial determinants make keeping patients
healthy hard, but when you can fi nd solu-
tions that are not always medical in nature,
it can be really rewarding.”
IDENTIFYING THE PROBLEM
Physicians need to understand the specifi c
problems their patients are facing before ad-
dressing social issues, and the only way to do
that is to ask, experts say.
Jeremy Long,
MD, MPH, an internist in Denver,
runs a clinic for the underserved
and says it’s necessary to build trust to get
the patient to open up about nonmedical is-
sues that may be aff ecting their health. “Try
to build rapport with them and introduce
them to the whole team to show them how
invested you are,” says Long. “When they see
that, it helps them let their guard down.”
Long’s clinic uses a seven-page intake
form that not only covers the standard med-
ical questions, but details about the patient’s
life. Questions about substance abuse, em-
Social determinants of health Operations
Payer recognition of social determinants of health
While physicians are keenly aware of the social issues that may affect patient adherence, payers
aren’t as quick to acknowledge these factors when it comes to quality metrics. Medical Economics
asked three policy experts to weigh in on what they think federal and commercial payers should do to
recognize social determinants of health when it plays a role in physician reimbursement.
“[The Centers for Medicare
& Medicaid Services (CMS)] should
more adequately risk adjust the metrics
so that providers who are serving
patients with multiple social and
economic barriers to compliance are
not penalized. [The agency] needs to
partner with the U.S. Department of
Housing and Urban Development, the
U.S. Department of Agriculture and
the U.S. Department of Education to
actually address those barriers. CMS
has taken some well-informed steps
in that direction, but it has not gone far
enough.”
—Paula Braveman, MD, MPH
PROFESSOR OF FAMILY AND COMMUNITY MEDICINE
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
“It’s not necessarily [payers] paying
for services. There are other human
service agencies paying for community-
based services and that’s their role. But
it is really: How can you pay to better
coordinate these services and for the
time and staff resources it takes to
more proactively coordinate the things
that we know make a difference in
improving care. That’s something that
could potentially help without being a
total, radical departure.
“Serve as a resource for providers to
look to for standardized screening tools
or other tools to connect to community-
based services. Those are useful roles
for CMS to take into account some of
the challenges physicians face.”
—Pamela Riley, MD, MPH
VICE PRESIDENT OF DELIVERY SYSTEM REFORM
THE COMMONWEALTH FUND
WASHINGTON, D.C.
“In the ideal world, what we would
be saying is that we are expecting the
same quality outcomes regardless of
patients’ social context, but that we
provide additional support—fi nancial
support—to provide the kinds of
services that would mitigate those
social determinants of health. That is a
very diffi cult thing to do without building
the infrastructure around it.
“I think it sends the wrong message
of what we are expecting in the
healthcare system to say, ‘We are
taking these social factors as a given
and therefore not expecting the same
level of health outcomes.’ The goal
has to be equivalent health outcomes,
and I think we need to recognize that
some patients may need more intensive
surrounding and support services
and those need to be built into the
reimbursement system.”
—Jeffrey Levi, Ph.D.
PROFESSOR OF HEALTH POLICY AND MANAGEMENT
MILKEN INSTITUTE SCHOOL OF PUBLIC HEALTH AT
THE GEORGE WASHINGTON UNIVERSITY
WASHINGTON, D.C.
http://medicaleconomics.modernmedicine.com/
MedicalEconomics.com32 MEDICAL ECONOMICS ❚ DECEMBER 10, 2017
Operations Social determinants of health
ployment, insurance style of learning, goals
of care and incarceration are all asked to
gain a complete picture of the person’s life
and the challenges he or she faces.
Bhatt says that physicians can start with
screening questions that take the form of,
“Do you have trouble getting here?”, “What
kind of neighborhood do you live in?” and
“Are you having diffi culty getting food for
yourself or your family?”
“Th ough these questions are personal,
they can help physicians build relationships
with their patients and give them insight
to better understand factors aff ecting their
health,” Bhatt says. Many professional orga-
nizations off er screening tools and guide-
lines, so that’s a good place to start.
Gail Cunningham, MD, FACEP, chief
medical offi cer for University of Maryland St.
Joseph Medical Center, oversees a program
aimed at reducing the hospital’s readmission
rates for patients struggling with psycho-
social issues that aff ect their health. Th e hos-
pital uses a nurse practitioner to interview
patients about the nonmedical challenges in
their lives and helps direct them to commu-
nity resources and nonprofi ts that can help.
“Some patients are honest and some
are a bit delusional about reality,” she says.
“Some patients are fi ercely independent
and don’t want help or don’t think they need
help. Some may decline our service then get
home and realize [dealing with their social
issues is] harder than they thought and will
call us back.”
While it can be challenging to get a full
picture of the patient’s social challenges,
experts agree that the more commitment
physicians show toward the patient’s well-
being, the more they are willing to open up.
But Bryce says to be careful not to stereo-
type, especially about income.
“Social determinants are not always just
about income level,” he says. “If you are not
getting the results you want, it’s important
for the physician to look deeper at the care
model and the patient and why they are
not getting better. Sometimes when you dig
deep, that’s when you’ll fi nd an answer in the
challenges the patient faces.”
PROVIDING SOLUTIONS
Treating patients with challenges that af-
fect their adherence can be frustrating, but
it starts with taking the right attitude. When
a patient has been told multiple times to eat
healthily and make sure they are taking their
meds only to return having done none of it,
the fi rst response from a doctor might be
exasperation. But Bryce says it’s better to
pause and focus on empathy.
“We don’t necessarily understand the life
they live,” says Bryce. “If you try to under-
stand it, it will allow you to better take care
of the patient and decrease the pressure you
put on yourself if you are not getting the re-
sults that you want.”
Th e more doctors are able to understand
What to do with the nonadherent patient
S
hould a physician dismiss
a patient because of
nonadherence alone, even
if every attempt has been
made to deal with not only
clinical issues, but social
issues? Experts say in most cases
the answer is no.
“What I hope not to see is
rampant selection by physicians,
discriminating for patients who
are healthy,” says Ryan Nash, MD,
FACP, an internist and director of
the Center for Bioethics at Ohio
State University. “Patient abandon-
ment is a major problem, so you
want to ensure there is a safe
plan and some sort of transition to
another healthcare professional. It
should be done formally, in writing,
and there should be a set time
period for the transfer.” He recom-
mends seeking legal advice on
drafting a letter that will meet the
requirements of the jurisdiction the
physician is in.
Richard Bryce, DO, a Detroit-
based primary care physician, says
that having patients sign an informal
contract that outlines expectations
when they join the practice can help,
but won’t work for everyone. Some-
times a doctor’s attitude is the most
important factor in patient adher-
ence. “In most cases, if the doctor
can be positive and instill motivation,
the patient will do the best they can.”
One of the issues with a
nonadherent patient is fi guring
out where that patient should
go if they are dismissed from a
practice. “It’s easy to say it’s not
working out at this end, but it’s
incumbent on us as physicians to
work out another place for that
patient,” says Jeremy Long, MD,
MPH, an internist in Denver. Many
nonadherent patients struggle with
behavioral health problems, which
complicates treatment, especially
for the small practice with limited
resources.
The American College of Physi-
cians’ Ethics Manual states in part
that “Continuity of care must be
assured. Abandonment is unethical
and a cause of action under the
law.”
But no matter how complicated
the case, physicians must put in
their best effort. “It is important
to provide patients with enough
information regarding their condi-
tion and your treatment recom-
mendations, and to provide it in a
format that is easy to understand,”
says Jay Bhatt, DO, MPH, FACP,
a practicing internist and chief
medical offi cer of the American
Hospital Association. “Stressing
the signifi cance of the condition
and the need for timely follow-up
should help with patient compli-
ance.”
http://medicaleconomics.modernmedicine.com/
MedicalEconomics.com 33MEDICAL ECONOMICS ❚ DECEMBER 10, 2017
OperationsSocial determinants of health
the challenges patients face in life, the more
that can be used to create a positive attitude
from both the doctor and the patient, he
adds. “Th ere is not a pill out there that is go-
ing to fi x someone who doesn’t have enough
food to eat.”
Patients facing these challenges often
are not in a position to help themselves, so
physicians need to do part—and sometimes
all—of the work to help them. Experts say to
start by researching what resources exist for
the social challenges a practice sees the most.
“Connecting to resources in the commu-
nity requires some initial eff ort, but many
partnerships and activities are already un-
derway,” says Bhatt, adding that reaching
out to the public health department, local
hospitals and social organizations is a good
place to start.
EDUCATING PATIENTS
Bryce says part of the role of the doctor is to
educate patients on help that is available.
For instance, Detroit has a program where
food stamps can be used to buy double the
face value of fresh fruits and vegetables.
Many patients have access to the market,
but just don’t know about it. He adds that
education eff orts aren’t always perfect, but
putting patients in touch with the right re-
source can make a big diff erence.
Some patients will require more hand-
holding than others, but Long says it’s of-
ten ineff ective to just scratch out a name or
phone number of a community resource on
a slip of paper and hand it to the patient. “If
you are in a small practice, I think you have
to start sitting down with people doing the
social services that work in the area and
start networking in a meaningful way.”
Cunningham agrees, noting that time
invested with a representative from a social
service agency may yield a list of services
and contacts for a variety of types of help.
For those patients who need additional as-
sistance, she suggests smaller practices try
to identify local volunteers who could serve
as a liaison between the practice and the so-
cial agencies, helping them fi ll out forms or
setting up interviews.
“Also, ask questions of your patients dur-
ing the visit to identify issues that may have
been missed—‘I’m writing a prescription
now, do you have a way to pick it up and a
way to pay?’” says Cunningham. A call to the
pharmacy may reveal programs to help with
adherence through medication remind-
ers via text message or phone calls, but the
practice may have to do some of the work on
the patient’s behalf.
Long says that his offi ce practices a
model where the patient is shown how to do
something once, with the expecta-
tion the patient can do it the next
time. For example, a patient need-
ing transportation assistance would
be given help fi lling out the applica-
tion and getting the initial appoint-
ment set up. After that, about 80% of
the patients have the knowledge to
continue on their own.
“You have to meet the patient
where they are at,” says Bryce. “For
some patients, you can show them a
program for free food and they take
it from there, but for others, you
have to take it step by step. You don’t
always get the outcomes you want,
but you just do the best you can.”
Th e trust established between
the physician and the patient will
also go a long way toward helping
address any socioeconomic chal-
lenges the patient may face. Th e
more the patient trusts the doctor,
the more open he or she will be to
receiving referrals to help them ad-
dress their needs, says Bhatt.
“Additionally, developing trust-
ing relationships with community
partners is also important,” he says.
“In the partnership, practices should
defi ne roles and responsibilities and
deliverables for each team member.”
Checking up on patients after
their consultation with social servic-
es can help patients feel connected
and cared for, Bhatt adds.
Addressing social determinants
of health is not the job of one phy-
sician alone, but an eff ort must be
made if patients are to overcome
their challenges and if physicians are to
maximize their reimbursement under val-
ue-based care.
“Start off small, build resources and skills
within your team to address these issues,”
says Bhatt, “then start collaborating with
other physicians, local community orga-
nizations, local businesses etc., to help the
most prominent health needs in the com-
munity.”
LOOKING
FOR HELP
Practices with limited
resources don’t have to solve
patient social issues on their
own. Many existing public and
private agencies are willing
to help, but physicians may
need to do some research to
learn where their patients
can obtain assistance. Good
starting points include:
❚ Public health departments
❚ Local hospitals
❚ Pharmacies (for medication
adherence help)
❚ Social organizations
❚ Philanthropic organizations
(such as United Way)
❚ Food banks
❚ Religious organizations
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SPECIAL ISSUE: PHYSICAL HEALTH OF MEN AND BOYS
Men’s Health-Risk and Protective Behaviors: The Effects of Masculinity
and Masculine Norm
s
Dawn M. Salgado, April L. Knowlton, and Brianna L. Johnson
Pacific University
Previous research has examined men’s health in relation to women’s functioning and compared mor-
bidity and mortality rates among specific subgroups of men using demographic features. More recent
research expands these approaches by also examining how men’s thoughts, attitudes, and behaviors
influence health-related attitudes and behaviors. The endorsement and internalization of masculinity is
generally associated with more negative health behaviors and engagement in fewer health-protective
behaviors. However, research to date does not offer a clear and consistent conceptualization of those
specific masculine norms that might facilitate or act as a barrier to men adopting healthier behaviors. The
current study examines data from 376 men between the ages of 18 and 25 to determine whether
health-risk and protective behaviors are predicted by specific masculine norms, when controlling for
demographic variables. Findings suggest men’s endorsement of specific masculine norms predicted more
health-protective than health-risk behaviors, although the proportion of the variance explained by specific
masculine norms was higher for health-risk behaviors than health-protective behaviors. Demographic
variables also predicted both health-risk and protective factors. Results from the current study are
presented within the context of two previous studies (Levant & Wimer, 2014; Levant, Wimer, &
Williams, 2011), highlighting both similarities and additional contributions. Results provide a strong
rationale for considering the influence of masculine norms on men’s health behaviors, especially within
the context of health promotion, prevention, and intervention programs by healthcare providers and
clinicians.
Keywords: men’s health, masculinity, masculine norms, health-risk behaviors, health-protective behaviors
“The body . . . is inescapable in the construction of masculinity, but
what is inescapable is not fixed” (Connell, 1995, p. 56).
The current study addresses the ways health-risk and protective
behaviors are uniquely influenced by men’s internalization and
endorsement of masculine norms within society. Men’s life expec-
tancy is shorter than women’s by 5 to 7 years (Mahalik, Burns, &
Syzdek, 2007; National Center for Health Statistics, 2013). Health
behaviors play an important role in men’s morbidity and mortalit
y.
Men not only engage in about 30 different health behaviors asso-
ciated with risk of injury, disease, or death (Courtenay, 2000a) but
also underutilize available medical services, engage in fewer pre-
ventive care practices, and delay treatment when symptoms of
illness or injury arise (Addis & Mahalik, 2003; Courtenay, Mc-
Creary, & Merighi, 2002; Galdas, Cheater, & Marshall, 2005;
Hegelson, 1995; Levant, Wimer, Williams, Smalley, & Noronha,
2009). Studies with men indicate the level of engagement in health
behaviors may also vary on the basis of race and ethnicity (Cour-
tenay et al., 2002; Thorpe et al., 2015), age (Peak & Gast, 2014),
socioeconomic status (Dolan, 2014), sexual orientation (Drum-
mond, 2005), education and socioeconomic status (Mahalik &
Burns, 2011), and marital status (Mahalik et al., 2007).
Masculinity, Masculine Norms, and Health Behaviors
Since the late 1970s, many clinicians, theorists, and researchers
have proposed explanations for health disparities beyond biologi-
cal or physiological explanations to include factors related to
gender roles, ideologies, norms, and internalized conflict associ-
ated with being male and masculine (Addis & Mahalik, 2003;
Brannon, 1976; Connell, 1995; Courtenay, 2000a, 2000b; de Vis-
ser & McDonnell, 2013; Eisler, 1995; Julty, 1979; Levant, 1996;
Mahalik et al., 2007; O’Neil, 1981; Pleck, 1981; Pleck, 1995;
Sabo, 2001; Smiler, 2004). Parent, Moradi, Rummell, and Tokar
(2011) stated that conformity to masculine norms is distinctive
from personality dimensions (e.g., conscientiousness), masculine
and feminine traits, (e.g., expressiveness), and self-esteem (e.g.,
global). Within this context, the internalization and endorsement of
traditional constructions of masculinity have important conse-
quences for men’s engagement in health-risk and protective be-
Dawn M. Salgado, April L. Knowlton, and Brianna L. Johnson, Depart-
ment of Psychology, Pacific University.
Preliminary results were presented at the 2016 Annual Convention of the
Western Psychological Association in Las Vegas, Nevada. Data discussed
in this article were presented at the 2018 Annual Conventions of the
Western Psychological Association in Portland, Oregon and the Associa-
tion for Psychological Science in San Francisco, California.
Correspondence concerning this article should be addressed to Dawn M.
Salgado, Department of Psychology, Pacific University, 2043 College
Way, Forest Grove, OR 97116. E-mail: dsalgado@pacificu.edu
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Psychology of Men & Masculinities © 2019 American Psychological Association
2019, Vol. 20, No. 2,
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–275 1524-9220/19/$12.00 http://dx.doi.org/10.1037/men0000211
266
mailto:dsalgado@pacificu.edu
http://dx.doi.org/10.1037/men0000211
haviors because they conform to perceptions of how other men
normatively behave (Mahalik et al., 2007). These behaviors be-
come learned and socialized early on and become reinforced
during adolescence and into adulthood (Levant & Wimer, 2014;
Levant, Wimer, & Williams, 2011; Mahalik, & Burns, 2011;
Mansfield, Addis, & Mahalik, 2003; Pleck, Sonenstein, & Ku,
1993).
Over the past 2 decades, masculinity has been consistently
linked to engagement in health behaviors that increase risks asso-
ciated with morbidity and mortality (Courtenay et al., 2002; de
Visser & McDonnell, 2013; Levant, 1992; Levant & Wimer, 2014;
Levant et al., 2011; Mahalik et al., 2007; Mahalik, Good, &
Englar-Carlson, 2003; Mansfield et al., 2003; Morrison, 2012;
Peralta, Tuttle, & Steele, 2010). Mahalik et al. (2007) reported that
men’s support for traditional masculine gender norms was linked
to lower rates of engagement in several health-protective behaviors
including regular seatbelt use, annual physical exams, seeking
psychological help, consuming fruits and vegetables, exercising,
and not engaging in fighting or substance use. These findings are
consistent with other studies documenting the association between
men’s adoption and endorsement of masculine ideals and poorer
eating attitudes and practices (Griffith, Metzl, & Gunter, 2011;
McShane, Salgado, & Bjordahl, 2015), reduced likelihood of being
HIV-tested (Parent, Torrey, & Michaels, 2012), increased steroid
use (Parent & Bradstreet, 2017), and fewer annual screenings
(Morrison, 2012).
Health-protective behaviors (e.g., preventative treatment, self-
care practices, and psychological help-seeking), including the men
who engage in them, are typically characterized as “feminine,”
“weak,” or “gay,” resulting in a paradox whereby attaining mas-
culinity comes at the cost to men’s quality of life, health, and
well-being (Bell, 1984; Bjordahl, Salgado, & Johnson, 2015;
Boon, 2005; Capraro, 2000; Johnson, McShane, & Salgado, 2015;
Mahalik et al., 2003; Mankowski & Maton, 2010; McCusker &
Galupo, 2011).
It is important to understand how men’s health behaviors are
associated with specific masculine norms to identify dimensions
that are particularly beneficial or harmful. Masculine norms are
defined as socially constructed attitudes, standards, expectancies,
and behavioral tendencies associated with being male and mascu-
line (Mahalik et al., 2003). Within the United States, dominant
forms of masculinity are characterized by risk-taking, aggressive-
ness, competitiveness, dominance, status, and focusing on work, as
well as self-reliance, stoicism, having power over women, being a
playboy, and being perceived as heterosexual (Mahalik et al.,
2003; Parent & Moradi, 2009).
Because health outcomes are behaviorally influenced, Courte-
nay (1998, 2000a, 2000b) proposed health behaviors related to
preventive care, substance use, and dietary habits should be of
primary interest to researchers and providers. To the extent men
that endorse masculine norms such as self-reliance, emotional
control, and winning, they are found to engage in more health-risk
and fewer health-protective behaviors (Courtenay, 2000a, 2000b;
Levant et al., 2011; Levant & Wimer, 2014; Mansfield et al.,
2003). Substance use is associated with men’s higher endorsement
of masculine norms associated with risk-taking, less emotional
control, being a playboy, winning, and having power over women
(Iwamoto, Corbin, Lejuez, & MacPherson, 2014; Levant et al.,
2011; Levant & Wimer, 2014). Anger and Stress were associated
with endorsing masculine norms associated with the primacy of
work and self-reliance (Levant et al., 2011) or winning (Levant &
Wimer, 2014), although both studies point to the importance of
emotional control on the management of anger and stress among
men. Himmelstein and Sanchez (2016) reported masculine norms
such as self-reliance can negatively affect health behaviors directly
as well as indirectly when considering how these function in
relation to the minimization of symptoms, doctor distrust, concerns
about privacy, and delays in seeking treatment after experiencing
symptoms of illness or injury. Engaging in health-protective be-
haviors has been linked to less endorsement of masculine norms
associated with being a playboy, risk-taking, and competitiveness
(Levant et al., 2011, Levant & Wimer, 2014).
Although there are some consistent findings across studies re-
garding prediction of health-risk and protective behaviors among
men by specific masculine norms, studies by Levant and Wimer
(2014) and Gerdes and Levant (2018) point to the need for addi-
tional research on this topic. Levant and Wimer (2014) reported
that only 33% of the associations between specific masculine
norms and dimensions of health-risk and protective factors across
two similar studies were replicated, which lowered to 25% (three
of 12 significant associations) when looking at masculine norm
subscales of the Conformity to Masculine Norms Inventory (i.e.,
Winning, Playboy, Primacy of Work, Risk-Taking, Self-Reliance,
and Emotional Control; Parent & Moradi, 2009; Mahalik et al.,
2003). Replicated findings across the two studies indicated that
substance use was predicted lower Playboy subscale scores, Anger
and Stress were predicted by lower Emotional Control subscale
scores, and the Proper Use of Health Care Resources was predicted
less. Findings across both Levant studies (Levant et al., 2011;
Levant & Wimer, 2014) and the more recent content analysis of
associations by Gerdes and Levant (2018) highlight the importance
in having additional research to assist in clarifying the predictive
nature of masculine norms on various health-risk and protective
behaviors.
Current Study
The current study examined the influence of masculine norms
on health-risk and protective behaviors in a university and com-
munity sample of men while relying on previous research findings
(Levant & Wimer, 2014; Levant et al., 2011) as points of com-
parison and contrast in three ways. First, the current study pro-
poses conformity to masculine norms (46-itemed Conformity to
Masculine Norms Inventory [CMNI-46]) would negatively predict
engagement in health behaviors (Health Belief Inventory–20
[HBI-20]), as reported in both Levant studies (Levant & Wimer,
2014; Levant et al., 2011). Previous research examining the effects
of specific masculine norms on health-risk and protective behav-
iors has yielded mixed results (Gerdes & Levant, 2018; Levant &
Wimer, 2014; Levant et al., 2011; Mahalik et al., 2003). As a
result, the second purpose of the current study is to examine
whether associations between specific norms and dimensions of
health-risk behaviors found in both Levant studies (Levant &
Wimer, 2014; Levant et al., 2011) will be replicated. Within this
context, there are three hypotheses:
Hypothesis 1: Substance Use would be predicted by higher
Playboy and lower Winning subscale scores.
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267MASCULINITY, NORMS, AND HEALTH BEHAVIORS
Hypothesis 2: Proper Use of Health Care Resources would be
predicted by lower Risk-taking subscale scores.
Hypothesis 3: Healthy Diet would not be associated with any
of the masculine norm subscales.
The third purpose of the study was to examine whether any
additional masculine norms not previously indicated in the
empirical literature and not replicated across both Levant
studies (Levant & Wimer, 2014; Levant et al., 2011) also
predicted men’s health behaviors in general and their health-
risk and protective behaviors in particular.
Method
Participants
A total of 376 men between the ages of 18 and 25 (M � 20.80,
SD � 2.05) participated in a study on gender and men’s health. A
majority of participants were enrolled in college (n � 281,
74.73%), with the remaining identifying as college graduates (n �
70; 18.62%) or high school/noncollege graduates (n � 25, 6.65%).
Most participants identified as heterosexual (n � 346, 92.02%),
followed by bisexual (n � 18, 4.78%) or gay (n � 11, 2.93%).
Participants reported subjective social status ranged from working
class (n � 4, 1.06%) to lower middle class (n � 67, 17.82%),
middle class (n � 158, 42.02%), upper middle class (n � 132,
35.11%), and upper class (n � 15, 3.99%).
Measures
Demographics. Items assessed participant gender, race/eth-
nicity, sexual orientation, educational status, and age. Subjective
social status was measured using the MacArthur Scale of Subjec-
tive Social Status for Youth (Adler, Epel, Castellazzo, & Ickovics,
2000; Goodman et al., 2001) and asked participants to rate their
familial social status within society by placing an “X” on the rung
of a ladder, with higher rungs representing higher socioeconomic
status. This assessment is valid and appropriate for use for indi-
viduals 12 years and older, reflects minimal differences from the
adult version, and is recommended for participants who may not be
financially independent (Goodman et al., 2001). Previous research
suggests higher subjective social status is associated with health
and well-being (Adler et al., 2000; Adler & Snibbe, 2003; Good-
man et al., 2001).
Health behaviors. The HBI-20 (Courtenay, 1998; Courtenay
et al., 2002; Levant et al., 2011) contains 20 items divided into five
subscales representing both health-protective and health-risk be-
haviors. Health-protective subscales included a Healthy Diet (five
items, � � .79, “I limit the amount of fat I eat”), the Proper Use
of Health Care Resources (two items � � .78, “I fill my medicine
prescriptions immediately”), and Preventive Self-care (seven
items, � � .51, “I check my skin for unusual spots or coloring
every few months”). The lower internal reliability estimate present
in the Preventive Self-care subscale is consistent with previously
published findings (Levant et al., 2011, � � .69; Levant & Wimer,
2014, � � .57) and reflects the preference of content validity over
scale homogeneity to yield the most representative behavioral
items associated with men’s preventative self-care behaviors.
Health-risk behaviors included Anger and Stress (three items, � �
.75, “I get angry and annoyed when I am caught in traffic”), and
Substance Use (three items, � � .67, “I use recreational drugs”).
Participants rated each item on a 7-point Likert scale from 1
(never) to 7 (always), indicating how often they engaged in each
behavior. Total scores for each subscale were developed by sum-
ming items within each subscale, and a total score (20 items � �
.70) was computed by first reverse-scoring risk-behavior items and
then adding to health-protective behavior items. Higher scores on
the HBI-20 and its subscales reflect more engagement in behav-
iors.
Masculine norms. Conformity to masculine norms, defined
as the extent to which men endorse thoughts, attitudes, and behav-
iors associated with male gender norms, was assessed using the
CMNI-46 (Mahalik et al., 2003; Parent & Moradi, 2009, 2011). In
addition to the CMNI total score (� � .86), there were nine
subscales representing specific masculine norms including, Power
over Women (four items, � � .83, “In general, I control the
women in my life”), Violence (six items, � � .83; “Sometimes
violent action is necessary”), Emotional Control (six items, � �
.91, “I tend to keep my feelings to myself”), Winning (six items,
� � .88, “In general, I will do anything to win”), Heterosexual
Self-Presentation (six items, � � .89, “I would be furious if
someone thought I was gay”), Primacy of Work (five items, � �
.78, “My work is the most important part of my life”), Risk-Taking
(five items, � � .86, “I frequently put myself in risky situations”),
Self-reliance (five items, � � .87, “I hate asking for help.”), and
Playboy (four items, � � .81, “If I could, I would frequently
change sexual partners”). Participants responded to each item on a
4-point Likert-type scale from 1 (strongly disagree) to 5 (strongly
agree), and the items were reverse-scored as needed and then
summed within and across each masculine norm. Higher scores on
the total CMNI-46 and each specific masculine norm reflect higher
levels of conformity.
Procedure
After receiving institutional review board approval, participants
were recruited through a variety of means, including undergradu-
ate psychology courses, Internet websites, social media, and list-
servs, to participate in a study on gender- and health-related
attitudes and behaviors. They were provided a link to an online
survey through Survey Monkey. Interested participants reviewed
and signed the informed consent, were asked to verify they met the
inclusion criteria for participation (e.g., over 18 and male), and
then directed to the survey. The survey took approximately 15 to
20 min to complete, and participants were provided with contact
information for the principal investigator of the study and an
overview of the study. Participants had the option to be directed to
a separate, unlinked questionnaire for entering their contact infor-
mation to be entered into a raffle for one of three $50 gift cards,
and students were able to print out a certificate of research partic-
ipation in the event they were able to receive credit within courses.
Results
Preliminary Analyses
Bivariate correlations between the CMNI-46 and HBI-20 total
scores, health-risk behaviors, and health-protective behaviors were
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268 SALGADO, KNOWLTON, AND JOHNSON
conducted (samples sizes ranged from 375 to 376, one-tailed). The
CMNI-46 was negatively correlated with HBI-20 total scores,
r � �.09, p � .05, and health-risk behaviors (Anger and Stress,
r � .34, p � .001; Substance Use, r � .17, p � .001). Mixed
results between masculinity and health-protective behaviors were
observed with Preventive Self-care being positively correlated
(r � .15, p � .001), whereas Healthy Diet and the Proper Use of
Health Care Resources were not significant.
Descriptives and correlations between health behaviors, demo-
graphics, and masculine norms are shown in Table 1. A Healthy
Diet was associated with being older but was not associated with any
of the masculine norms. The Proper Use of Health Care Resources
was associated with being younger, lower subjective social status,
lower Emotional Control and Self-Reliance and higher Winning,
Heterosexual Self-presentation, and Primacy of Work subscale scores.
Preventive Self-care was associated with being younger, heterosexual,
higher Winning, Heterosexual Self-presentation, and Primacy of
Work and lower Emotional Control subscale scores. Anger and Stress
were not associated with demographics but were correlated with
higher Power over Women, Violence, Winning, Heterosexual self-
presentation, and Self-reliance subscale scores. Substance Use was
associated with identifying as a nonracial/ethnic minority, and higher
Violence, Risk-Taking, and Playboy subscale scores.
Bivariate correlations between demographics, HBI-20, and
CMNI-46 were also conducted (N � 376, two-tailed). Engagement
in healthier behaviors was associated with being heterosexual, r �
.10, p � .05, and lower subjective social status, r � �.12, p � .02.
CMNI-46 total scale scores were associated with being younger
(r � �.14, p � .001), identifying as a racial/ethnic minority (r �
.12, p � .001) and as a heterosexual, r � .15, p � .01. Younger
men reported higher subscale scores for Risk-Taking, Heterosex-
ual Self-presentation, and Primacy of Work than older men in the
sample. Compared to men who identified as sexual minorities,
heterosexual men reported higher Power over Women and Het-
erosexual Self-presentation subscale scores. Individuals identify-
ing as racial/ethnic minorities reported higher Power over Women,
Heterosexual Self-presentation, and Primacy of Work subscale
scores. Higher subjective social status was correlated with lower
Primacy of Work subscale scores.
Regression Analyses for HBI-20 Total
Hierarchical multiple regressions were conducted with HBI-20
total scores, as well as health-protective and risk behaviors. For
each outcome variable, demographic variables were included in
the first step, followed by CMNI-46 total in Step 2. The second set
of multiple regressions were also conducted for the same outcomes
variables and demographic variables but included the set of mas-
culine norms at Step 2 rather than the CMNI total scale score. The
current study met the minimum criteria and guidelines for the
number of participants and predictors (Green, 1991; Harris, 1985;
Wilson VanVoorhis & Morgan, 2007). Multicollinearity diagnos-
tics were assessed, and variance inflation factor scores did not
exceed 10, and tolerance scores were above .20 across all regres-
sions.
For HBI-20 total, the final model resulted in an adjusted R2 �
.04, �R2 � .02, F(5, 375) � 4.12, p � .01, and was associated with
being younger, being heterosexual, and having lower subjective
social status, as well as lower conformity to masculine norms
(CMNI-46). Results from the second set of regressions, which
included the set of masculine norms, are shown in Table 2. Total
HBI-20 was associated with identifying as heterosexual and a
lower subjective social status, as well as lower Self-reliance and
higher Playboy and Winning subscale scores.
Regression Analyses on Risk Health Behaviors
Health-risk behaviors were characterized by experiences of An-
ger and Stress and Substance Use. In the first set regressions,
CMNI-46 total was associated with Anger Stress, as well as
Substance Use. For Anger and Stress, the final model resulted in
an adjusted R2 � .11, �R2 � .12, F(5, 375) � 10.27, p � .001, and
associated with more conformity to masculine norms but was not
predicted by demographic variables. For Substance Use, the final
model resulted in an adjusted R2 � .05, �R2 � .04, F(5, 375) �
4.56, p � .001, and associated with being a nonracial/ethnic
minority and more conformity to masculine norms as well. The
second set of regressions are presented in Table 2. For anger and
stress, demographics were not significant predictors, whereas
Power over Women, Heterosexual Self-presentation, and Self-
reliance were predictive. Substance Use was higher among men
who did not identify as a Racial/ethnic minority and those who
scored higher on the Risk-taking and Playboy subscales. In sum-
mary, engagement in health-risk behaviors was associated with
higher Power over Women, Heterosexual Self-presentation, Self-
reliance, Playboy, and Risk-taking subscale scores. Health-risk
behaviors were not associated with the Violence, Primacy of
Work, Winning, or Emotional Control subscales.
Regression Analyses on Protective-Health Behaviors
Health-protective behaviors included a Healthy Diet, Proper Use
of Health Care Resources, and Preventive Self-care. In the first set
of regressions, total CMNI-46 scores did not predict Healthy Diet
or the Proper Use of Health Care Resources but predicted more
Preventive Self-care. For Healthy Diet, the final model resulted in
an adjusted R2 � .03, �R2 � .00, F(5, 374) � 3.25, p � .001, and
was only predicted by being older. For Proper Use of Health Care
Resources, the final model resulted in an adjusted R2 � .09, �R2 �
.00, F(5, 375) � 7.99, p � .001, and associated with being
younger, being heterosexual, and having lower subjective social
status. For Preventive Self-care, the final model resulted in an
adjusted R2 � .04, �R2 � .01, F(5, 375) � 4.18, p � .001, and
associated with being younger and higher CMNI-46 scores.
The second set of regressions are shown in Table 3. For Healthy
Diet, age was the only significant predictor across all demograph-
ics and masculine norms. The Proper Use of Health Care Re-
sources was associated with being younger and having lower
social status, lower Power over Women and Self-reliance and
higher Winning and Heterosexual Self-presentation subscale
scores. More engagement in Preventive Self-care behaviors were
reported by younger men and participants with lower Emotional
Control and Winning subscale scores. Violence, Risk-taking, Pri-
macy of Work, and Playboy subscales were not significant across
both health-protective behaviors. In summary, more engagement
in health-protective behaviors (excluding Healthy Diet) was re-
ported among men who were younger, heterosexual, and from a
lower socioeconomic status, as well as who scored lower on the
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269MASCULINITY, NORMS, AND HEALTH BEHAVIORS
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270 SALGADO, KNOWLTON, AND JOHNSON
Emotional Control, Power over Women, and Self-reliance and
higher Winning subscale scores.
Discussion
The current study examined the predictive effects of masculinity
and masculine norms on health behaviors in general, as well as
health-risk and protective behaviors while relying on findings from
two previous studies (Levant & Wimer, 2014; Levant et al., 2011).
This was accomplished in three ways: (a) examining whether
conformity to masculine norms (CMNI-46) predicted overall
health behaviors, as well as health-risk and protective behaviors,
(b) examining whether consistent findings between associations of
specific masculine norms and health behaviors reported by Levant
& Wimer, 2014 and Levant et al. (2011) were replicated in the
current study, and (c) examining whether masculine norms that
were previously unreported might predict engagement in health-
risk and protective behaviors.
CMNI-46 and Men’s Health Behaviors
We proposed conformity to masculine norms (CMNI-46) would
negatively predict engagement in health behaviors (HBI-20), as
well as increased engagement in health-risk and decreased engage-
ment in health-protective behaviors. Similar to previous studies
(Addis & Mahalik, 2003; Courtenay, 2000b; Levant & Wimer,
2014; Levant et al., 2011; Mahalik, Lagan, & Morrison, 2006;
Pleck, Sonensterin, & Ku, 1994), conformity to masculine norms
(CMNI-46) was negatively associated with health behaviors (HBI-
20). Although conformity to masculine norms (CMNI-46) pre-
dicted both health-risk behaviors (i.e., Anger and Stress, Substance
Use), it only predicted one of the three health-protective factors,
Preventive self-care. These findings offer new evidence when
compared with findings reported in the study Levant and Wimer
(2014), who reported health-risk and protective behaviors were not
predicted by the conformity to masculine norms (CMNI-46) total
score.
Associations Between Specific Masculine Norms and
Men’s Health Behaviors
A second purpose of the current study was to examine the three
replicated associations between specific norms measured in the
CMNI-46 and health-risk and protective behaviors (Levant &
Wimer, 2014; Levant et al., 2011). In the two previous studies, (a)
Substance Use was associated with higher Playboy subscale
scores, (b) Anger and Stress was predicted by higher Emotional
Control, (c) the Proper Use of Healthcare Resources was associ-
ated with lower Risk-Taking subscale scores, and (d) Healthy Diet
was not associated with any of the CMNI-46 subscale scores.
Across these three findings, only the first and last results were
replicated in the current study.
The third purpose of the study was to provide additional evi-
dence on whether masculine norms are more likely to have a
protective or detrimental effect on men’s health and to examine
whether men’s health-risk and protective behaviors were predicted
by specific masculine norms not previously examined or found to
be inconsistent (Levant & Wimer, 2014; Levant et al., 2011). Both
Gerdes and Levant (2018) and Levant and Wimer (2014) reported
that masculine norms, as assessed by total the number of subscale
scores, were more likely to predict health-risk behaviors as com-
pared with health-protective behaviors. The current study presents
Table 2
Regression Analyses Predicting Health-Risk and Total Health Behavior Inventory by Demographic and Masculine Norms
Anger and Stress Substance Use Total
B SE � t p B SE � t p B SE � t p
Step 1
Age �.06 .07 �.05 �0.89 .38 .05 .06 .04 0.80 .42 �.40 .23 �.09 �1.75 .08†
Heterosexual .17 .51 .02 0.34 .74 �.35 .46 �.04 �0.75 .46 3.56 1.73 .11 2.06 .04�
Racial/ethnic minority .19 .31 .03 0.62 .54 �.62 .28 �.11 �2.19 .03� .77 1.06 .04 0.73 .47
Subjective social status .09 .09 .05 1.01 .31 .03 .08 .02 0.33 .74 �.80 .30 �.14 �2.69 .01�
Adjusted R2 �.00 .01 .03�
F for Step 1 .66 1.61 3.46��
Step 2
Age .00 .06 .00 0.05 .96 .05 .06 .04 0.88 .38 �.35 .22 �.08 �1.54 .12
Heterosexual �.57 .49 �.06 �1.18 .24 �.35 .45 �.04 �0.77 .44 3.55 1.72 .11 2.07 .04�
Racial/ethnic minority �.00 .30 �.00 �0.01 .99 �.53 .27 �.10 �1.94 .05† .28 1.05 .01 0.26 .79
Subjective social status .14 .08 .08 1.65 .10 .02 .08 .01 0.24 .81 �.69 .29 �.12 �2.34 .02�
Power over Women .22 .07 .20 3.29 .00�� �.05 .06 �.04 �0.73 .46 �.48 .24 �.12 �1.98 .05†
Violence .06 .04 .08 1.48 .14 .04 .04 .05 1.02 .31 �.15 .15 �.06 �1.00 .32
Emotional Control �.06 .04 �.08 �1.50 .14 �.01 .03 �.01 �0.22 .83 �.16 .13 �.07 �1.20 .23
Winning .07 .04 .09 1.62 .11 �.02 .04 �.03 �0.44 .66 .37 .15 .14 2.47 .01�
Heterosexual Self-presentation .10 .04 .16 2.60 .01� .02 .03 .03 0.45 .66 .14 .13 .06 1.03 .31
Primacy of Work .02 .06 .02 0.29 .77 �.01 .05 �.01 �0.23 .82 .34 .20 .09 1.70 .09†
Risk-taking .01 .05 .01 0.21 .83 .24 .05 .26 5.17 .00�� �.24 .18 �.07 �1.34 .18
Self-reliance .13 .05 .14 2.55 .01� .09 .05 .10 1.92 .06† �.47 .18 �.15 �2.66 .01�
Playboy .01 .05 .01 0.26 .79 .20 .05 .23 4.36 .00�� �.41 .17 �.13 �2.36 .02�
Adjusted R2 change .16�� .15 .09��
Adjusted R2 full model .14 .14�� .10
F for full model 5.60�� 5.64�� 4.12��
† p � .10. � p � .05. �� p � .01.
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
Ps
yc
ho
lo
gi
ca
l
A
ss
oc
ia
tio
n
or
on
e
of
its
al
lie
d
pu
bl
is
he
rs
.
T
hi
s
ar
tic
le
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
271MASCULINITY, NORMS, AND HEALTH BEHAVIORS
the opposite pattern of results with more significant associations
between masculine norms and health-protective behaviors than
health-risk behaviors (6 versus 5, respectively). However, it is also
worth noting that a higher proportion of the variance in health-risk
behaviors was accounted for by masculine norms (i.e., 16% in
Anger and Stress and 15% in Substance Use) when compared with
that of health-protective behaviors (i.e., 9% in the Proper Use of
Health Care Resources and 7% in Preventive self-care services).
Levant and Wimer (2014), and more recently Gerdes and Levant
(2018), highlighted findings in which specific masculine norms
predicted either health-risk or protective behaviors or both in
opposite directions. Results from the current study suggest that
health-risk behaviors were uniquely predicted higher Risk-taking
and Playboy subscale scores, whereas health-protective behaviors
were uniquely predicted by the Winning and Emotional Control
subscales. Power over Women and Self-reliance subscale scores
predicted not only more engagement with health-risk behaviors but
also less engagement with health-protective behaviors. These find-
ings suggest these specific masculine norms may be particularly
detrimental to men’s health and well-being. Interestingly, Hetero-
sexual Self-presentation was the only masculine norm found to
predict more engagement in both health-risk and health-protective
behaviors.
As reported by Levant and Wimer (2014), most of the associ-
ations between men’s health and specific masculine norms were
not replicated across their two studies (67% of associations using
two measures of masculine norms and 75% of associations using
only those norms assessed by the CMNI-46). As a result, the
current study provides additional evidence on those specific mas-
culine norms most relevant to men’s health-risk and protective
behaviors. Primacy of Work and Violence did not predict health-
risk or protective behaviors in men, unlike findings previously
published. Anger and Stress were predicted by higher Power over
Women, Heterosexual self-presentation, and Self-reliance subscale
scores, only the latter of which had been previously reported.
Substance Use was predicted by higher Risk-taking subscale
scores in both the current study and results from the second study
reported by Levant and Wimer (2014). The Proper Use of Health
Care Resources was predicted by higher Winning (opposite direc-
tion reported in previous research) and lower Self-reliance and
Power over Women subscale scores. Preventive Self-care was
associated with higher Winning and lower Emotional Control
subscale scores.
Taken together, these findings suggest that research on this topic
would benefit from reports on the number of specific masculine
norms that predicted health-risk as compared with protective be-
haviors, the unique proportion of variance explained by the set of
masculine norms for each health-risk and protective behaviors, and
whether (and in what direction) specific masculine norms uniquely
or simultaneously predict health-risk and/or protective behaviors.
Research by Himmelstein and Sanchez (2016) suggests that tradi-
tional masculine norms act as barriers to men’s engagement in
Preventive Self-care, appearing as problem minimization, distrust
of health care providers and privacy, and longer delays in seeking
treatment after injury or symptoms are experienced.
Limitations
The generalizability of the results in the current study could be
strengthened with a more diverse sample population (e.g., race/
Table 3
Regression Analyses Predicting Health Prevention Behaviors by Demographic and Masculine Norms
Healthy Diet Proper Use of Healthcare Resources Preventive Self-care
B SE � t p B SE � t p B SE � t p
Step 1
Age .40 .11 .19 3.72 .00�� �.60 .13 �.23 �4.54 .00�� �.20 .07 �.16 �3.05 .00��
Heterosexual .06 .81 .00 0.08 .94 2.00 .10 .10 2.01 .05� 1.12 .50 .12 2.24 .03�
Racial/ethnic minority .36 .50 .04 0.73 .47 �.10 .61 �.01 �0.16 .87 .12 .31 .02 0.39 .70
Subjective social status .17 .14 .06 1.23 .22 �.72 .17 �.21 �4.19 .00�� �.11 .09 �.07 �1.27 .20
Adjusted R2 .03�� .08�� .03��
F for Step 1 3.74�� 9.53�� 3.90��
Step 2
Age .43 .11 .20 3.89 .00�� �.54 .13 �.21 �4.22 .00�� �.17 .07 �.13 �2.54 .01�
Heterosexual �.11 .84 �.01 �0.13 .90 1.57 .99 .08 1.59 .11 .88 .51 .09 1.75 .08†
Racial/ethnic minority .22 .52 .02 0.43 .67 �.42 .61 �.03 �0.69 .49 �.02 .31 .00 �0.08 .94
Subjective social status .22 .14 .08 1.51 .13 �.66 .17 �.19 �3.91 .00�� �.06 .09 �.03 �0.65 .51
Power over Women .13 .12 .07 1.06 .29 �.34 .14 �.14 �2.45 .02� �.02 .07 �.02 �0.31 .75
Violence �.10 .07 �.08 �1.32 .19 .06 .09 .04 0.74 .46 .01 .04 .01 0.14 .89
Emotional Control .00 .07 .00 0.04 .97 �.14 .08 �.10 �1.87 .06† �.08 .04 �.12 �2.15 .03�
Winning .08 .07 .07 1.14 .25 .21 .09 .13 2.41 .02� .12 .04 .16 2.69 .01�
Heterosexual Self-presentation .00 .07 .00 0.07 .95 .19 .08 .15 2.45 .02� .05 .04 .09 1.38 .17
Primacy of Work .08 .10 .04 0.78 .44 .14 .12 .06 1.17 .24 .10 .06 .08 1.59 .11
Risk-taking .01 .09 .00 0.07 .94 �.05 .10 �.03 �0.53 .59 .09 .05 .09 1.65 .10
Self-reliance .03 .09 .02 0.36 .72 �.29 .10 �.15 �2.84 .01� �.01 .05 �.02 �0.28 .78
Playboy �.06 .09 �.04 �0.65 .51 �.13 .10 �.07 �1.28 .20 �.02 .05 �.02 �0.30 .76
Adjusted R2 change .02 .09�� .07��
Adjusted R2 full model .02 .16 .08
F for full model 1.61† 6.33�� 3.41��
† p � .10. � p � .05. �� p � .01.
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272 SALGADO, KNOWLTON, AND JOHNSON
ethnicity, sexual orientation, and subjective social status). The
study focused on men between the ages of 18 and 25 to understand
early patterns that could be addressed and incorporated into health
programming and interventions to improve health outcomes later
on. Additional research would also benefit from examining the
function of masculine norms in a more diverse age range of men
because research suggests gender norms may vary throughout
development (Twenge, 1997). Levant and Wimer (2014) sug-
gested some differences in the prediction of men’s behaviors might
be due to sample characteristics across their two studies.
Previous studies on men’s health behavior include additional
variables (e.g., multiple measures of masculine norms, longer
measures, and perceptions of men’s normative health behaviors) to
predict a higher proportion of the variance in men’s engagement in
health-risk and protective behaviors. In the current study, the first
set of regressions with CMNI-46 total scores accounted for only
between 4% and 11% across the HBI-20-total score and the five
health-risk and protective factors, which increased to between 7%
and 9% when all nine masculine norms were included.
Although the CMNI has been influential at understanding the
attitudes and behaviors of men (Parent & Moradi, 2011; Parent &
Smiler, 2013), additional research on the psychometrics of the
HBI-20 is warranted given low internal consistency estimates for
some of the subscales (Levant & Wimer, 2014). Results from the
Preventive Self-Care subscale of the HBI-20 should be interpreted
cautiously given its low � coefficient (�.70) but were included in
the current study, given its purpose was to compare and contrast
findings from two previously published articles (Levant & Wimer,
2014; Levant et al., 2011).
Conclusion
Harrison (1978) stated masculinity is dangerous for your health.
In support, results from the current study suggest that conformity
to masculine norms (CMNI-46) predicted lower engagement in
health behaviors. However, more complex representations of the
associations between masculinity and health is needed, especially
when considering the effects of specific masculine norms compos-
ing most multidimensional measures of masculinity. Gough (2013)
suggested that masculinity can function to both constrain and
facilitate health behaviors among men. In the current study, some
masculine norms were found to uniquely predict either health-
protective (i.e., Emotional Control, Winning) or risk behaviors
(i.e., Playboy, Risk-taking); others (i.e., Power over Women, Self-
reliance) might be characterized as particularly detrimental to
men’s health and well-being because they predict less engagement
in health-protective behaviors and more engagement in health-risk
behaviors; and a few (i.e., Primacy of Work, Violence) were not
associated with health-protective or risk behaviors in the current
study. In their recent study, Gerdes and Levant (2018) urged future
researchers to present findings on the predictive nature of specific
masculine norms on men’s health behaviors in general and health-
risk and protective behaviors in particular. The current study offers
one example of how future researchers might approach to under-
standing these associations and clarifying specific norms associ-
ated with masculinity that might be detrimental or beneficial to
men’s health and well-being.
Implications
The current study suggests health-related interventions with
boys and men that focus on men’s emotional expressiveness,
winning at all costs, or being one’s best could be important topics
for inclusion. For example, tapping into masculine norms associ-
ated with winning and success might encourage men to engage in
the Proper Use of Health Care Resources as a way to succeed in
being healthy. However, the findings also suggest interventions
and programs for boys and men need to address what Brooks and
Silverstein (1995) called the “darker side of masculinity,” namely,
heterosexist and sexist attitudes associated with masculinity, so
health and well-being are not predicated on the marginalization
and domination of other men and women.
Leone, Rovito, Mullin, Mohammed, and Lee (2017) suggest
men’s prevention, promotion, and intervention programs may ben-
efit from gender-specific programming informed by masculine
norms. Within this context, some health-enhancing masculine
norms may act as a core programmatic feature (e.g., Winning)
while other commonly endorsed norms associated with lower
engagement in health behaviors are reframed (e.g., Self-reliance),
and norms associated with harmful attitudes towards others are
addressed (e.g., Playboy, Violence). At the same time, researchers
are pointing to the need for more understanding on how to recruit
men into participating in health-related interventions (Farrimond,
2012; Houle et al., 2017; Mansfield et al., 2003), capture those
features most relevant to men (Calasanti, Pietilä, Ojala, & King,
2013; Neukrug, Britton, & Crews, 2013; Parent & Bradstreet,
2017; Robertson & Baker, 2017), and how to best respond to
barriers men report having (Boman & Walker, 2010; Mahalik et
al., 2007). In summary, health care providers and clinicians would
benefit from accounting for aspects of masculinity when develop-
ing prevention and intervention efforts targeting boys and men.
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tqmp.03.2.p043
Received November 1, 2017
Revision received February 8, 2019
Accepted February 26, 2019 �
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275MASCULINITY, NORMS, AND HEALTH BEHAVIORS
http://dx.doi.org/10.1037/1524-9220.4.1.3
http://dx.doi.org/10.1007/s10464-009-9288-y
http://dx.doi.org/10.1007/s10464-009-9288-y
http://dx.doi.org/10.3149/jmh.0202.93
http://dx.doi.org/10.3149/jmh.0202.93
http://dx.doi.org/10.1037/a0021071
http://dx.doi.org/10.1037/a0021071
http://dx.doi.org/10.1037/a0024186
http://dx.doi.org/10.1037/a0024186
http://dx.doi.org/10.1002/j.1556-6676.2013.00109.x
http://dx.doi.org/10.1002/j.1556-6676.2013.00109.x
http://dx.doi.org/10.1002/j.2164-4918.1981.tb00282.x
http://dx.doi.org/10.1177/1359105317692144
http://dx.doi.org/10.1037/a0015481
http://dx.doi.org/10.1037/a0021904
http://dx.doi.org/10.1037/a0023837
http://dx.doi.org/10.1037/a0023837
http://dx.doi.org/10.1037/a0027642
http://dx.doi.org/10.1037/a0028067
http://dx.doi.org/10.1177/2158244014558044
http://dx.doi.org/10.1177/2158244014558044
http://dx.doi.org/10.1177/1077801210363539
http://dx.doi.org/10.1007/BF01420798
http://dx.doi.org/10.1177/0017896916645558
http://dx.doi.org/10.1023/B:SERS.0000011069.02279.4c
http://dx.doi.org/10.1097/FCH.0000000000000078
http://dx.doi.org/10.1097/FCH.0000000000000078
http://dx.doi.org/10.1007/BF02766650
http://dx.doi.org/10.1007/BF02766650
http://dx.doi.org/10.20982/tqmp.03.2.p043
http://dx.doi.org/10.20982/tqmp.03.2.p043
Masculinity, Masculine Norms, and Health Behaviors
Current Study
Method
Participants
Measures
Demographics
Health behaviors
Masculine norms
Procedure
Results
Preliminary Analyses
Regression Analyses for HBI-20 Total
Regression Analyses on Risk Health Behaviors
Regression Analyses on Protective-Health Behaviors
Discussion
CMNI-46 and Men’s Health Behaviors
Associations Between Specific Masculine Norms and Men’s Health Behaviors
Limitations
Conclusion
Implications
References
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