Genetically competent care for those with chronic illnesses
Locate at least one scholarly journal article that discusses your subtopic (I have attached the required articles for this week you can choose from as well)
-Identify your subtopic (Genetically competent care for those with chronic illness)and provide a brief summary of your journal article on how this topic relates to nursing practice.
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Genetically competent care for those with chronic illnesses
-What is the nurse’s role in providing care in relation to your subtopic and the overarching theme of advocacy?
-What ethical implications should be considered with regard to genetics and genomics for nursing practice? Why?
A 3-paragraph (at least 350 words)
Be sure to use
evidence
from the readings and include
in-text citations
.
Utilize
essay-level
writing practice and skills, including the use of
transitional material
and
organizational frames
.
Avoid quotes;
paraphrase
to incorporate evidence into your own writing.
A
reference list
is required. Use the most
current evidence
(usually ≤ 5 years old).
Mental illness
crispr and dna
medication and genetics
March-April 2016 • Vol. 25/No. 2 91
Alexandra Plavskin, MS, RN, is Clinical Instructor, Hunter College, New York, NY.
Genetics and Genomics of Pathogens:
Fighting Infections with Genome-
Sequencing Technology
G enetics is “the study ofheredity” (World HealthOrganization [WHO], 2002,
para. 1), while genomics is defined
as “the study of genes and their
functions, and related techniques”
(para. 2). An expanded definition of
genomics indicates “genetics scruti-
nizes the functioning and composi-
tion of the single gene whereas
genomics addresses all genes and
their interrelationships in order to
identify their combined influence
on the growth and development of
the organism” (WHO, n.d., para. 3).
Population genetics explores trait
changes in a population and poten-
tial contributing factors (Gillespie,
2010). Phylogenetics is the study of
evolutionary relatedness between
organisms (Wiley & Lieberman,
2011).
Background
The study of human genetics and
genomics is imperative because the
leading causes of mortality in the
United States all have a genetic
component, including cancer, heart
disease, and diabetes (Calzone et al.,
2010). However, the study of genet-
ics and genomics of pathogens also
can have substantial impact on clin-
ical practice. The study of patho –
gens can help identify sources of
infection and manage outbreaks of
health care-associated infections
(HAIs), one of the top 10 causes of
death in the United States (Calfee,
2012; Peleg & Hooper, 2010).
Approximately 5% of persons ad –
mitted to hospitals develop HAIs;
these patients have prolonged hos-
pitalization as well as increased
morbidity and mortality (Calfee,
2012). A retrospective 5-year med-
ical record review of unexplained
hospital deaths in one health care
institution determined 31% may be
due to HAIs (Calfee, 2012; Morgan,
Lomotan, Agnes, McGrail, &
Roghmann, 2010). HAIs are also a
tremendous financial burden to
health care institutions. Scott (2009)
estimated U.S. hospitals spend $28-
$45 billion annually in direct med-
ical costs for HAIs. These estimates
do not include indirect costs, such
as loss of productivity or associated
costs incurred by patients or their
family members (Calfee, 2012).
Despite awareness of antimicro-
bial-resistant organisms and efforts
to contain them, resistant strains
are emerging and spreading. De –
velopment of new antibiotics is not
keeping pace with the spread of
antimicrobial-resistant infection
(ARI) (Kishony & Collins, 2014).
Individuals who develop these in –
fections are at risk for increased
morbidity and mortality, including
prolonged hospital stay, delayed
recovery, or recurrent infection
(Neidell et al., 2012; Roberts et al.,
2009). Methicillin-resistant Staph –
ylococcus aureus alone causes more
deaths annually in the United
States (~19,000) than Parkinson’s
disease, emphysema, homicide, and
HIV/AIDS combined. In addition,
the estimated annual cost of ARI to
the U.S. health care system is $21-
$34 billion (Infectious Diseases
Society of America [IDSA], 2011). A
number of factors may contribute
to increased antimicrobial resist-
ance: overprescription of antimicro-
bial medications, overuse of empiric
antimicrobial therapy, the majority
of prescriptions for antimicrobial
therapy being written by prescribers
who are not infectious disease spe-
cialists, use of antibiotics for self-
limiting viral infections, and use of
antibiotics in livestock feed (IDSA,
2011; Roberts et al., 2009). Strict
adherence to guidelines for infec-
tion control is a national priority to
combat HAIs (Septimus et al.,
2014). In addition, genomic analy-
ses of bacteria can provide insight
into sources of infection, character-
istics of organisms, and how health
Alexandra Plavskin
Discussions of clinical genetics and genomics often focus on screen-
ing for disease-causing genes in humans and the promise of target-
ed therapies. Another important area of research is analysis of
pathogen genomes. Genetics and genomics-based approaches,
such as population genomics and phylogenetics, provide insight
into mechanisms of resistance, sources of infections, and pathogen
transmission routes.
Instructions for Continuing Nursing Education Contact Hours appear on page 95.
March-April 2016 • Vol. 25/No. 292
care personnel can combat the
spread of infections (Köser et al.,
2012; Snitkin et al., 2012).
Genetics and Genomics of
Pathogens
Several tools help researchers
study genetics and genomics of
pathogens. Whole genome sequen –
cing (WGS) determines the entire
sequence of an organism’s DNA.
WGS of populations of bacteria
allows researchers to study patterns
of antibiotic resistance and patho gen
transmission (Köser et al., 2012).
Using population genetic approach-
es, researchers can identify these pat-
terns in large-scale studies of resist-
ance following antibiotic adminis-
tration across multiple pa tient
cohorts. Alternatively, by construct-
ing transmission maps, they can use
genetic patterns in an infectious out-
break to track patho gen transmis-
sion in one health care institution.
The contribution of genetics and
genomics to the understanding of
broad-scale spread of infections and
individual patient-to-patient trans-
mission is discussed. Possible influ-
ence of population genetics and use
of transmission maps on nursing
practice also are discussed.
Population Genetics of
Antibiotic Resistance
Population genetics studies found
clarithromycin-resistant commensal
bacteria persist in gastrointestinal
(GI) flora years after completion of
antibiotic therapy (Andersson &
Hughes, 2010; Sjölund, Wreiber,
Andersson, Blaser, & Engstrand,
2003). Commensal bacteria live in
the GI system without causing
adverse symptoms for the human
host. Another study revealed
amoxi cillin-resistant bacteria persist
in oral cavities of children who
have not taken amoxicillin in the
past 3 months; some of those bacte-
rial isolates were also resistant to
erythromycin, penicillin, and tetra-
cycline (Ready et al., 2004; Sommer
& Dantas, 2011). These studies indi-
cated antibiotic resistance can occur
even if patients complete the entire
antibiotic course and take the med-
ication as directed. Pathogens can
also share virulence or resistance to
antibiotics through transfer of
genetic material between organisms
(lateral gene transfer) (Harrison &
Brockhurst, 2012; Smillie et al.,
2011). Population genetics allows
researchers and clinicians to identify
patterns of prevalence and inci-
dence in patho gens. Examples of
these patterns include obtaining
and harboring antibiotic-resistant
bacteria months to years after taking
antibiotics as prescribed, as well as
bacterial ability to be resistant to
several other antibiotics (Kritsotakis,
Tsioutis, Roumbelaki, Christidou, &
Gikas, 2011; Ready et al., 2004).
Thus, previous antibiotic use may
make patients more susceptible to
antibiotic resistance against not
only the prescribed antibiotic, but
also other antibiotics. Nurses should
include previous antibiotic use in
the patient’s medication history.
They also should monitor patients
continuously for signs and symp-
toms of infection, as previous antibi-
otic use may influence effectiveness
of other antibiotics.
Transmission Maps
Another important application
of genome-sequencing technology
is the use of transmission maps.
Transmission maps attempt to iden-
tify how infectious agents spread by
documenting the pathogen’s gen –
omic information, infected patients
and their location, and/or any
shared equipment. An outbreak
of carbapenem-resistant Klebsiella
pneumoniae in a National Institutes
of Health Clinical Center promoted
the use of whole genome bacterial
sequencing and an algorithm to
reconstruct the transmission of the
infection (Snitkin et al., 2012).
Researchers began by collecting iso-
lates from multiple sites on the
body of the patient initially identi-
fied with the infection (index
patient). They collected isolates
from the groin, urine, and throat by
using bronchoalveolar lavage. Cul –
tures were collected from several
locations because bacteria continu-
ously evolve even when occupying
one host. Therefore, bacteria in one
anatomical region may differ genet-
ically from bacteria in another loca-
tion. Researchers then collected
samples from other patients and
sequenced the bacterial genomes.
The sequencing information was
used to determine the evolutionary
history of bacterial mutations, and
was combined with epidemiologic
information to construct a trans-
mission map.
Phylogenetics, the study of evolu-
tionary relatedness between organ –
isms, is important when analyzing
pathogen transmission because even
small changes in genetic sequence
provide a great deal of information
(Sleator, 2013). Changes in genetic
code, regardless of their effect on the
organism, act as markers and can be
used by researchers to trace the line-
age of a pathogen. This process
allows researchers to understand the
spread of infections (see Figure 1).
Identifying the gene sequence (via
whole genome sequencing) then cre-
ating a transmission map of the
pathogen’s location and genetic
changes can help researchers deter-
mine who contracted the infection
first and possibly where he or she
contracted it (see Figure 2).
Genetic information can be used
to identify unexpected modes of
transmission when epidemiologic
data are lacking, and can be used
during outbreaks to guide infection
control strategies. In the sample
hypothetical transmission map in
Figure 2, patient 1 was most likely
the source of infection for patients 2
and 3. This conclusion is drawn
from analysis of genetic mutations
seen in Figure 1. This information
would not be available solely from
epidemiologic data. Figure 3 demon –
strates patients 2 and 3 were in the
Emergency Department (ED) simul-
taneously. They may have shared
equipment or were seen by the
same health care providers, both
possible sources for the spread of
infection. Conversely, patients 1
and 3 were not on the same unit
during their hospital stay, but
genetic analysis indicated patient 3
was infected with a strain of bacte-
ria from patient 1. This indicates a
source of infection (equipment,
staff person, or other patient) has
yet to be identified and may contin-
ue to spread the infection. Supple –
March-April 2016 • Vol. 25/No. 2 93
menting the epidemiologic data
with genomic analyses allows
researchers to identify possible
routes and sources of infections
more effectively. Snitkin and col-
leagues (2012) also described how
transmission maps can help identi-
fy the spread of infections.
For example, whole genome se –
quencing of a ventilator culture
identified K. pneumoniae bacteria on
a ventilator that was cleaned twice
with a quaternary ammonia solu-
tion and once with bleach. Despite
being cleaned three times, this ven-
tilator was the source of K. pneumo-
niae infection for a patient in the
intensive care setting (Snitkin et al.,
2012). However, whole genome
sequencing and phylogenetic analy –
sis demonstrated bacteria from the
ventilator were related closely to
those isolated in another infected
patient. Analysis of the timing of
infection occurrence and bacterial
genome revealed the ventilator was
the source of the infection, even
though it was cleaned three times
prior to additional use. Despite
knowledge about the source of
infection, many questions remain.
The infection may have resulted
from incorrect technique in clean-
ing the ventilator, bacterial resist-
ance to some cleaning agents, or a
combination of both influences.
Nurses and nurse researchers can
play a vital role in gathering addi-
tional information, and educating
health care staff and patients about
pathogen spread and prevention
strategies.
Implications for Nursing
Practice
Nurses are instrumental in stop-
ping the spread of infections by
testing new hospital protocols or
encouraging use of existing infec-
tion control practices. An impor-
tant application is testing new hos-
pital protocols for use of surveil-
lance cultures. Protocols can be
adapted to incorporate use of trans-
mission maps and population genet-
ics of bacteria. For example, during
institutional outbreaks, health care
leaders can consider unique charac-
teristics of the causative agent (e.g.,
Genetics and Genomics of Pathogens: Fighting Infections with Genome-Sequencing Technology
FIGURE 1.
Genetic Mutations
T A C G C
G C C G C
G C A G C
G C C C C
G C A G A
Ancestor
Patient 1
Patient 2
Patient 3
Patient 4
Patient 1 Strain: A T T C C G G
Patient 2 Strain: A T TA C G G
Mutations in bacteria isolated from four patients and the ancestral (original) strain.
Hypothetical data are used to demonstrate how genomic analysis can be used to
derive the order of infection. Shaded squares represent new mutations relative to the
ancestral strain. For example, bacteria cultured from patient 1 have two new muta-
tions relative to the ancestral strain (column 1 the mutation was T → G; column 2 the
mutation was A →C). Bacteria cultured from patient 2 have one new mutation relative
to patient 1 (column 3). Bacteria cultured from patient 4 have one new mutation rel-
ative to patient 2 (column 5). Bacteria cultured from patient 3 also have one new
mutation relative to patient 1, but it is in a different location than the mutation in patient
2 (column 4); this indicates the mutation was most likely derived from patient 1, not
patients 2 or 4.
FIGURE 2.
Transmission Map
Transmission map constructed from hypothetical data in Figure 1. Bacteria cultured
from patient 1 were closest to the ancestral DNA, with the lowest number of new
mutations. Bacteria cultured from patients 2 and 4 are related mutations, while bac-
teria cultured from patient 3 are an independent lineage. Genomic analysis also indi-
cates bacteria from patient 2 most likely infected patient 4, deduced from the number
of new mutations. Due to a variable incubation period, the time between contracting
the infection and diagnosis may vary.
Ancestor Patient 1
Patient 3
Patient 2 Patient 4
Time when infection was contracted
March-April 2016 • Vol. 25/No. 294
its likelihood to be transmitted by
asymptomatic carriers, ability to
form biofilms, or antimicrobial re –
sistance) (Mah, 2012). Many of these
characteristics can be determined
quickly by whole genome sequenc-
ing of pathogens. Culturing isolates
through large-scale surveillance can
identify pathogens; cultures should
be obtained from patients, health-
care staff, and equipment by nurses
and hospital epidemiology staff
(Snitkin et al., 2012). Applications of
genetics and genomics can also be
used to strengthen existing infection
control practices.
Information provided by genetic
testing of bacteria can guide educa-
tional interventions for patients,
family members, and health care
staff. Nurses play an important role
in patient and family education.
Sequencing of bacterial genomes
provides insight into how an infec-
tion is being transmitted through a
healthcare institution, including
the likely source of infection, order
of subsequent infections, and
strains of bacteria infecting individ-
ual patients (Snitkin et al., 2012).
Nurses can use this information to
educate patients and family mem-
bers about ways to prevent infec-
tion, such as correct handwashing
techniques and the differences
among air, droplet, and contact pre-
cautions. In addition, nurses can
provide education about possible
asymptomatic colonization, the
incubation period, and signs and
symptoms of infection. Nurses can
champion quality improvement
initiatives to educate health care
staff about a particular strain of
infection, method of spread and
strategies to prevent its spread, and
where to obtain additional informa-
tion. Healthcare leaders can review
equipment and room cleaning pro-
cedures with environmental servic-
es staff, unlicensed assistive person-
nel, and nurses to reinforce the
importance of comprehensive de –
contamination. They also can test
available antimicrobial agents for
effectiveness against a causative
organism.
Clinical applications of genetics
and genomics are also a tremen-
dous opportunity for interprofes-
sional collaboration (Calzone,
Jenkins, Prows, & Masny, 2011).
Figure 3 demonstrates how epi-
demiologic data alone may be
insufficient to analyze and manage
infection outbreaks. In that exam-
ple, patients 2 and 3 were simulta-
neously present in the ED; however,
they did not contract the infection
from each other. Nurses, epidemiol-
ogists, physicians, geneticists, and
other healthcare professionals must
work together to understand and
combat the spread of pathogens.
The author recommends this inter-
professional approach include the
following: (a) patient, family, and
health care staff education; (b) coor-
dinated effort to collect and se –
quence surveillance cultures; (c)
vigilant use of isolation; (d) identifi-
cation of at-risk patients; (e) identi-
fication of signs/symptoms of infec-
tion; (f) determination of appropri-
ate treatment plans; (g) administra-
tion of treatment and supportive
patient care; and (h) continuous
vigilant patient assessment. Pop –
ulation genetics and whole genome
sequencing of bacteria are at the
cutting edge of biology and health
care, and tremendous opportunities
are available for clinicians and
researchers to collaborate to im –
prove patient outcomes.
Knowledge about genetics and
genomics continues to expand rap-
idly. In response to this, nurse lead-
ers from clinical, academic, and
research settings worked collabora-
tively to identify essential genetic
and genomic competencies and
FIGURE 3.
Patient Location
This diagram demonstrates how whole genome sequencing and the use of transmis-
sion maps can provide information that is not available through epidemiologic analy-
sis alone. This figure uses the same hypothetical data presented in Figures 1 and 2.
Patients 2 and 3 were in the emergency department and were diagnosed with the
infection. However, genomic analysis (see Figure 1) demonstrates patients 2 and 3
do not have the same ancestral strain of the infection. They were unlikely to get it
from each other, although they were in the same location. However, patient 3 most
likely contracted the infection from patient 1 although they were not in the same loca-
tion during their hospital stay. This may indicate another infection source has yet to
be identified (equipment, staff, or patient) and infection may continue to spread.
Intensive Care Unit
Emergency Department
Legend
Patient 1 Patient 2 Patient 3
March-April 2016 • Vol. 25/No. 2 95
outcome indicators for nurses in all
clinical specialties, roles, and prac-
tice settings (Calzone et al., 2011;
Jenkins & Calzone, 2007). Human
genetics and genomics provide a
number of important clinical tools,
such as analysis of known gene vari-
ants (e.g., BRCA 1, BRCA 2, APC,
and BLMash) and use of pharmaco –
genomics to decrease adverse med-
ication events (e.g., warfarin dos-
ing) (Kamatani et al., 2013; Raskin
et al., 2011; Relling & Klein, 2011;
Roy, Chun, & Powell, 2011). Under –
standing pathogen genetics and
genomics is also important to
decrease the spread of infection and
promote patient safety. Essential
genetic and genomic competencies
for nurses include interprofessional
collaboration, patient advocacy,
ability to interpret genetics and
genomics information or services
for patients, identification of pa –
tients who may benefit from genet-
ics and genomics information or
services, and evaluation of the
impact and effectiveness of genetics
and genomics strategies on patient
outcomes (Calzone et al., 2011;
Jenkins & Calzone, 2007). Under –
standing gene tics and genomics of
patho gens will help nurses work
collaboratively with other health
care professionals to decrease trans-
mission of pathogens, educate
patients and families, and promote
improved patient outcomes.
Future Research
Use of transmission maps and
population genetics provides a clin-
ically relevant way to apply genetics
and genomics to health care prac-
tice (Rostved et al., 2013; Snitkin et
al., 2012). Currently, they can be
used to identify infection sources
and methods of pathogen transmis-
sion. Future research can address
pathogen behavior in different
patient populations and clinical
environments. Research about how
pathogens respond to treatments
and develop resistance can be used
to anticipate the formation of resist-
ance, promote use of effective tar-
geted therapies, and develop new
therapies. Being able to obtain data
about sources of infection, method
of transmission, the path of the
infection, and bacterial evolution in
different environments can im –
prove management of infectious
disease in health care institutions.
Conclusion
Existing infection protocol prac-
tices such as hand hygiene are the
mainstay of infection control. How –
ever, application of genetics and
genomics, such as whole genome
sequencing of pathogens, creation of
transmission maps, and population
genetics, can help researchers and
health care personnel identify
sources of infection and study how
in fections spread. Col laborative
efforts and incorporation of genetics
and genomics can help the health
care team fight multidrug- resistant
pathogens and promote patient
safety.
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Genetics and Genomics of Pathogens: Fighting Infections with Genome-Sequencing Technology
Instructions For Continuing Nursing Education Contact Hours
Genetics and Genomics of Pathogens: Fighting Infections
with Genome-Sequencing Technology
Deadline for Submission: April 30, 2018 MSN J1606
To Obtain CNE Contact Hours
1. For those wishing to obtain CNE contact hours, you must read the
article and complete the evaluation through the AMSN Online
Library. Complete your evaluation online and print your CNE
certificate immediately, or later. Simply go to www.amsn.org/library
2. Evaluations must be completed online by April 30, 2018. Upon
completion of the evaluation, a certificate for 1.3 contact hour(s) may
be printed.
Learning Outcome
After completing this learning activity, the learner will be able to discuss
the role of genetics and genome-sequencing technology in the treatment
of infections.
The author(s), editor, editorial board, content reviewers,
and education director reported no actual or potential
conflict of interest in relation to this continuing nursing
education article.
This educational activity is jointly provided by Anthony J.
Jannetti, Inc. and the Academy of Medical-Surgical Nurses
(AMSN).
Anthony J. Jannetti, Inc. is accredited as a provider of
continuing nursing education by the American Nurses
Credentialing Center’s Commission on Accreditation.
Anthony J. Jannetti, Inc. is a provider approved by the
California Board of Registered Nursing, provider number
CEP 5387. Licensees in the state of California must retain
this certificate for four years after the CNE activity is
completed.
This article was reviewed and formatted for contact hour
credit by Rosemarie Marmion, MSN, RN-BC, NE-BC,
AMSN Education Director.Fees — Member: FREE Regular: $20
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individual use.
Genomics is changing personal healthcare
and medicine: the dawn of iPH
(individualized preventive healthcare)
Ruty Mehrian-Shai1 and Juergen K. V. Reichardt2,3*
Abstract
This opinion piece focuses on the convergence of information technology (IT) in the form of personal monitors, especially
smart phones and possibly also smart watches, individual genomic information and preventive healthcare and medicine.
This may benefit each one of us not only individually but also society as a whole through iPH (individualized preventive
healthcare). This shift driven by genomic and other technologies may well also change the relationship between patient
and physician by empowering the former but giving him/her also much more individual responsibility.
Keywords: Human genomics, Individual information, Personalized medicine, Medical education, Health care cost
Costs for healthcare in most countries are rising rapidly
and account for a sizeable fraction of a country’s GDP
(gross domestic product) [1]. This trend is most evident
in the USA where the fraction of GDP spent on health-
care has doubled from 8.2 % in 1980 to 16.2 % in 2012
[1]. This generally rising trend is noticeable in Australia
as well [1], although it is not as pronounced with an in-
crease from 5.8 % of GDP in 1980 to 8.6 % in 2011.
Clearly, this escalation is not sustainable and hence can-
not continue indefinitely. Healthcare must be sustain-
able. In fact, a significant burden is expended towards
the end of life [2] suggesting that a more preventive ap-
proach may be beneficial.
We propose here that a convergence of information
technology epitomized by individual monitors, incl.
smart phones and smart watches, and genomics in the
form of personal genomic information, especially on dis-
ease susceptibility, will result in new health information
accessible to each individuum.
The four converging areas leading to what we propose
to call individualized preventive healthcare (iPH) are:
First, ongoing rapid advances in personal monitors,
e.g. monitoring heart rate or tracking day to day activity,
e.g. smart phones and smart watches allow individuals to
collect, monitor and collate relevant health information
personally. These data can then be analyzed through on-
line world-wide searches, e.g. “Googling”, by the patient
him/herself before seeing a physician. There are also
significant ethical issues associated with these new devel-
opments [3] which must be carefully considered and
addressed.
Furthermore, genome sequencing is now approaching
a cost of just $1000 [4]. This price, which is continu-
ously falling, will put one’s own whole human genome
DNA sequence and its information at individual finger-
tips. Clearly, such genomic disease-related risk informa-
tion must be accompanied by appropriate and careful
interpretation and counselling [5]. In any case, individual
genomic information can be used to identify risks which
can then be mitigated if not eliminated altogether. Of
course, these developments in genomic science again
put the patient at the very heart of the matter by allow-
ing him/her to search for information, e.g. by Googling,
before seeing a physician to prevent (or at least slow)
disease.
Third, the microbiome [6] which is intrinsically per-
sonal and largely determined genomically also has be-
come of considerable interest and will find its way into
modern medical practice, perhaps again by patients
Googling information. In fact, because of the significant
role of the gut microbiota in human physiology and
* Correspondence: jreichardt@yachaytech.edu.au
2Division of Tropical Health and Medicine, James Cook University, Townsville,
QLD, Australia
3Present Address: Yachay Tech University, San Miguel de Urcuquí, Ecuador
Full list of author information is available at the end of the article
disease [6], new and unique opportunities will arise for
personal control of the gut flora. This will result in novel
strategies to prevent and treat diseases including cancer,
inflammatory bowel disease (IBD), diabetes, heart dis-
ease, allergy and perhaps even mental illness. The patho-
genesis of disease can be influenced also by various
epigenomic factors: microbiota, food intake, stress level
and physical activity. All these factors can be monitored,
investigated and evaluated.
We also note that the US NIH/NCI initiative on per-
sonalized medicine [7] to accelerate precision medicine
and the plan to monitor genetic and environmental fac-
tors of “cohort” of 1 million or more Americans will set
the basis of the multifactorial disease “warning” machin-
ery and provide valuable new insights.
Lastly, there is also an urgent need for credible and
trusted sources of medical information on the Internet
for individual patients to access and inform themselves.
This important issue has been addressed already, e.g. [8],
but will require constant attention, especially from all of
us, the medical professionals. Similarly, relationships
with patients are apt to change if they “arm” themselves
with Googled information.
Conclusion
In conclusion, we believe that iPH (individualized prevent-
ive healthcare) which arises from a convergence of per-
sonal monitors, incl. information technology (IT),
genomics, incl. the microbiome and vastly expanded infor-
mation available online will offer not only great individual
benefits by improving health through personalized infor-
mation and prevention but also significant cost savings in
the long run for healthcare. Furthermore, iPH may radic-
ally alter the relationship between physicians and patients.
This will give patients not only increased information but
also significant individual responsibility. Future research,
education and thoughtful discourse should prepare indi-
viduals, medical practitioners, scientists, (health) econo-
mists if not societies at large for these important changes.
Competing interests
There are no competing interests to declare.
Authors’ contributions
JKVR conceived and wrote the manuscript, whilst RMS commented on it and
contributed ideas as well. Both authors read and approved the final
manuscript.
Acknowledgement
JKVR gratefully acknowledges the opportunity to develop these ideas at
James Cook University whilst also visiting the MedUni Vienna and the TU
Dresden.
Author details
1Pediatric Hemato-Oncology, Chaim Sheba Medical Center, Ramat Gan, Israel.
2Division of Tropical Health and Medicine, James Cook University, Townsville,
QLD, Australia. 3Present Address: Yachay Tech University, San Miguel de
Urcuquí, Ecuador.
Received: 29 September 2015 Accepted: 31 October 2015
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CLINICAL SCHOLARSHIP
Multi-Ethnic Minority Nurses’ Knowledge and Practice
of Genetics and Genomics
Bernice Coleman, PhD, ACNP-BC, FAHA, FAAN1, Kathleen A. Calzone, PhD, RN, APNG, FAAN2, Jean Jenkins,
PhD, RN, FAAN3, Carmen Paniagua, EdD, MSN, CPC, ANP, ACNP-BC, AGACNP-BC, APNG-BC, FAANP4,
Reynaldo Rivera, DNP, RN, NEA-BC5, Oi Saeng Hong, RN, PhD, FAAN6, Ida Spruill, PhD, RN, LISW, FAAN7,
& Vence Bonham, JD8
1 Research Scientist II, Nursing Research and Development, Nurse Practitioner, Heart Transplant and Mechanical Assist Device Programs, Heart
Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
2 Senior Nurse Specialist, Research, National Institutes of Health, National Cancer Institute, Center for Cancer Research, Genetics Branch, Bethesda, MD,
USA
3 Clinical Advisor, National Institutes of Health, National Human Genome Research Institute, Bethesda, MD, USA
4 Adult Acute Care Nurse Practitioner & Adult Gerontology Acute Care Nurse Practitioner, Advanced Practice Nurse Geneticist, Department of
Emergency Medicine, University of Arkansas for Medical Sciences, College of Medicine, Little Rock, AR, USA
5 Director of Nursing Innovation, New York-Presbyterian Hospital, New York, NY, USA
6 Professor, University of California at San Francisco, School of Nursing, Community Health Systems, San Francisco, CA, USA
7 Assistant Professor, Medical University of South Carolina, College of Nursing, Carleston, SC, USA
8 Associate Investigator, Social and Behavioral Research Branch, National Institutes of Health, National Human Genome Research Institute, Bethesda,
MD, USA
Key words
Minority nurses, nursing, genetics, survey,
nursing practice
Correspondence
Dr. Bernice Coleman, Nursing Research and
Development, Cedars Sinai Medical Center,
8700 Beverly Blvd., Los Angeles, CA 90048.
E-mail: bernice.coleman@cshs.org
Accepted: February 20, 2014
doi: 10.1111/jnu.12083
Abstract
Purpose: Exploratory studies establishing how well nurses have integrated
genomics into practice have demonstrated there remains opportunity for ed-
ucation. However, little is known about educational gaps in multi-ethnic mi-
nority nurse populations. The purpose of this study was to determine minority
nurses’ beliefs, practices, and competency in integrating genetics-genomics in-
formation into practice using an online survey tool.
Design: A cross-sectional survey with registered nurses (RNs) from the partic-
ipating National Coalition of Ethnic Minority Organizations (NCEMNA). Two
phases were used: Phase one had a sample of 27 nurses who determined the
feasibility of an online approach to survey completion and need for tool revi-
sion. Phase two was a main survey with 389 participants who completed the
revised survey. The survey ascertained the genomic knowledge, beliefs, and
practice of a sample of multi-ethnic minority nurses who were members of
associations comprising the NCEMNA.
Methods: The survey was administered online. Descriptive survey responses
were analyzed using frequencies and percentages. Categorical responses in
which comparisons were analyzed used chi square tests.
Findings: About 40% of the respondents held a master’s degree (39%) and
42% worked in direct patient care. The majority of respondents (79%) re-
ported that education in genomics was important. Ninety-five percent agreed
or strongly agreed that family health history could identify at-risk families,
85% reported knowing how to complete a second- and third-generation fam-
ily history, and 63% felt family history was important to nursing. Conversely,
50% of the respondents felt that their understanding of the genetics of com-
mon disease was fair or poor, supported by 54% incorrectly reporting they
thought heart disease and diabetes are caused by a single gene variant. Only
30% reported taking a genetics course since licensure, and 94% reported in-
terest in learning more about genomics. Eighty-four percent believed that their
ethnic minority nurses’ organizations should have a visible role in genetics and
genomics in their communities.
Conclusions: Most respondents felt genomics is important to integrate into
practice but demonstrated knowledge deficits. There was strong interest in the
need for continuing education and the role of the ethnic minority organiza-
tions in facilitating the continuing education efforts. This study provides evi-
dence of the need for targeted genomic education to prepare ethnic minority
nurses to better translate genetics and genomics into practice.
Clinical Relevance: Genomics is critical to the practice of all nurses, most
especially family health history assessment and the genomics of common com-
plex diseases. There is a great opportunity and interest to address the genetic-
genomic knowledge deficits in the nursing workforce as a strategy to impact
patient outcomes.
As the proliferation of knowledge and understanding of
genomics accelerates, it becomes clearer that understand-
ing heritability and its intersection with environment has
now become foundational to nursing science, theory, and
practice. Genetic and genomic literacy now distinguishes
all nursing professionals as state-of-the-art academicians,
researchers, and clinicians who will provide the best care
possible. We are emerging into an era whereupon nursing
assessments, interventions, and the promotion of well-
ness will only attain scientific merit with the translation
of genomic knowledge to practice. Health care increas-
ingly demands that the registered nurse (RN) use ge-
nomic information and technology when designing and
providing care to those concerned about health or dis-
ease. These expectations have direct implications for RN
preparatory curricula, as well as for the 2.9 million prac-
ticing nurses (U.S. Department of Health and Human
Services, Health Resources and Services Administration,
2010).
Complex diseases such as cardiovascular and heart dis-
ease, diabetes, and cancer have disproportionally affected
racial and ethnic minority populations (National Center
for Health Statistics, 2012). While genetics research ex-
plores single gene disorders, the scientific discoveries now
inclusive of genomics are beginning to illuminate all ge-
netic variation in the human genome and the environ-
mental influences on health outcomes for persons with
complex chronic diseases. A transformative change in the
genomic knowledge of disease pathophysiology has pro-
duced a knowledge gap for nurses. A previous study as-
sessed nurses’ knowledge of genomics integration into
practice (Calzone et al., 2012; Calzone, Jenkins, Culp,
Bonham, & Badzek, 2013); however, the study was not
representative of ethnic minority nurses. In fact, very lit-
tle is known about genomic knowledge gaps of minor-
ity nurses (Spruill, Coleman, & Collins-McNeil, 2009).
These findings support the need for further investigation
of multi-ethnic minority nurses’ knowledge and practice
of genetics and genomics.
Background
The National Coalition of Ethnic Minority Nurse Asso-
ciations (NCEMNA) was incorporated in 1998 as a uni-
fied voice in nursing for the elimination of health dispari-
ties for ethnic minority populations. This national nursing
collaboration represents 350,000 nurses and is composed
of five ethnic minority nursing organizations. Its member
organizations are:
� Asian American/Pacific Islander Nurses Association,
Inc. (AAPINA)
� National Alaska Native American Indian Nurses As-
sociation, Inc. (NANAINA)
� National Association of Hispanic Nurses, Inc.
(NAHN)
� National Black Nurses Association, Inc. (NBNA)
� Philippine Nurses Association of America, Inc.
(PNAA)
The goals of the NCEMNA focus on development of
a cadre of ethnic nurses reflecting the nation’s diver-
sity, advocating for cultural competence, and accessible
and affordable health care. This coalition of ethnic mi-
nority nurse organizations collectively supports the de-
velopment of professional and educational advancement
of ethnic nurses, and the education of consumers, health-
care professionals, and policy makers on health issues of
ethnic minority populations. The NCEMNA’s primary ob-
jective is to develop ethnic minority nurse leaders in ar-
eas of health policy, practice, education, and research.
Through this approach, the endorsement of best nursing
practice models inclusive of genetics-genomics, educa-
tion, and research to improve the health of minority pop-
ulations is paramount (NCEMNA, 2013). One of the first
initiatives that the NCEMNA undertook was implement-
ing strategies to increase minority nurse participation and
success in research careers at the doctoral level. An area
determined as a collective interest to the NCEMNA mem-
ber organizations was the need to improve the health
of the representative ethnic minority patient populations
through research. Given the anticipated emerging major-
ity of these minority populations, the NCEMNA member
organizations identified the need to increase minority fac-
ulty and doctorally prepared nurses conducting research
through mentorship. Nurses from the NCEMNA member
organizations received competitive grants to participate
in the mentorship program that culminated in a yearly
conference where genetic-genomic information was pre-
sented as a foundational contributor to common diseases
found in ethnic patient populations represented by the
NCEMNA member organizations.
Representatives from the National Human Genome
Research Institute (NHGRI) and the National Cancer
Institute (NCI) along with the primary investigator of
this current work have presented on genetics and ge-
nomics at the National NCEMNA conferences. The re-
sponse and interest in genomic topics led to the interest
in gathering baseline information from these representa-
tive nursing groups regarding how ethnic minority nurses
utilized genetic-genomic core competencies and informa-
tion in their practice. Fundamental to this undertaking
was the establishment and endorsement of the Essential
Nursing Competencies and Curricula Guidelines for Ge-
netics and Genomics in October 2006 and expanded in
2008, and an established strategic implementation plan
that focused on practicing nurses, regulatory oversight
of nursing practice, and academic preparation of nurses
(Consensus Panel on Genetic/Genomic Nursing Compe-
tencies, 2006, 2009).
Theoretical Framework
The theoretical framework guiding this study was
Rogers’ Diffusion of Innovations (DOI; Rogers, 2003).
This theory consists of four components: (a) the inno-
vation, which in this study is genomics; (b) dissemi-
nation communication channels; (c) time; and (d) the
social system, which in this study is the minority nurs-
ing community. Factors that influence diffusion of the
innovation are antecedents and consist of adopter char-
acteristics as well as their attitudes. Adopters in this
study are the minority nurses, and their characteristics
include their genomic competency. Attitudes are the un-
derlying beliefs the adopters hold about the innovation
(i.e., genomics).
Study Aims
The ultimate goal of this collaborative project was to
assure that in this genomic era of health care, ethnic
minority nurses are prepared to assure quality care in
a diverse population that has concerns/experiences with
health disparities. Study aims were approached in two
phases to allow for testing of the study instrument fol-
lowed by administration of the instrument in the target
population.
Phase One Pilot Test Aims
1.1. Establish the feasibility of an online survey method
of data collection.
1.2. Evaluate the degree of respondent burden and sur-
vey response rates to establish whether this method of
data collection would be adequate for future target pop-
ulation implementation.
Phase Two Aims
2.1. Determine minority nurses’ beliefs, practices, and
competency of integrating into practice genomic informa-
tion related to common multifactorial diseases.
2.2. Assess knowledge of human genetic variation and
the use of patient characteristics, including ethnicity, gen-
der, genes, and race in diagnostics, treatment, and referral
decisions.
Analysis of aim 2.2 will be reported in a subsequent
article.
The NCEMNA Board approved moving forward with
a plan to utilize the diverse expertise of the NCEMNA
communities to create a genetics-genomics initiative. The
NANAINA chose to abstain from participation in this re-
search. Representatives from NCEMNA were identified to
organize this initiative with representatives of the NHGRI
and NIH. This study was approved by the Cedars Sinai
Institutional Review Board as well as the NIH Office of
Human Subjects Research.
Materials and Methods
Instrument
The survey instrument used in this study was collab-
oratively developed by all investigators. Multiple tele-
phone meetings were held to identify the process and re-
quired survey content to benchmark the genetic-genomic
knowledge of nurses via a membership survey. The fi-
nal draft survey is a compilation of the following five
instruments, which have been combined, reviewed, and
pretested by the research team.
1. The knowledge, attitude, and interest of African
American nurses toward genetics (Spruill et al.,
2009).
2. Bonham and Sellers’ Genetic Variation Knowledge
Assessment Index (GKAI; Bonham, Sellers, & Wool-
ford, submitted for publication).
3. Bonham and Sellers’ Health Professionals Beliefs
about Race (HPBR) scale.
4. Bonham and Sellers’ Racial Attributes in Clinical
Evaluation (RACE) scale.
5. The Genetics and Genomics in Nursing Practice
(GGNPS; Calzone et al., 2012).
The first survey instrument, the knowledge, beliefs,
and practices of African American nurses of genetics, was
designed to assess the interest, knowledge, and practice of
genetics and genomics among African American Nurses.
At tool construction, both face validity and construct va-
lidity were obtained using a panel of experts to evaluate
the items of the tool to ensure the construct was cap-
tured (Spruill et al., 2009). The Cronbach α standardized
is 0.652 for this 21-item survey instrument.
The survey instrument used in this study also included
questions modified from a study with physicians to eval-
uate nurses’ knowledge of genetic variation using the
Genetic Variation Knowledge Assessment Index (GKAI).
The GKAI scores range from 0 to 6, mean 3.28 (SD =
1.17) and was found to be symmetric and unimodal.
To evaluate nurses’ utilization of race in clinical prac-
tice, questions from the exploratory Health Profession-
als Beliefs about Race (HPBR; HPBR-BD, α = 0.69, four
items, and HPBR-CD α = 0.61, three items) and Racial
Attributes in Clinical Evaluation (RACE) scales (α = 0.86,
seven items; Bonham et al., submitted for publication).
In addition to the instruments described in the preced-
ing paragraph, the survey utilized for this study included
questions from the GGNPS instrument (Calzone et al.,
2012; Jenkins, Woolford, Stevens, Kahn, & McBride,
2010). This survey tool is constructed to evaluate Rogers
DOI theoretical domains, including attitudes, receptivity,
confidence, competency, knowledge, decision, and adop-
tion. Instrument validation was performed using struc-
tural equation modeling, which confirmed that the in-
strument items aligned with the domains of the DOI
(Jenkins et al., 2010).
The final compiled study instrument included seven
sections assessing beliefs, knowledge, practice, use of race
or ethnicity, education, and demographics. There were
a total of 61 questions, including multiple choice, di-
chotomous (yes or no), and Likert scale questions. The
questions were consistent with the Essentials of Genetic
and Genomic Nursing Competencies and assessed fam-
ily history utilization as well as the genomics of com-
mon disease, which represent knowledge and practice
expected of all RNs irrespective of their role, level of aca-
demic training, or specialty in which they practice (Con-
sensus Panel on Genetic/Genomic Nursing Competen-
cies, 2009). The selection of family history as evidence of
practice integration was intentional because family his-
tory collection falls within the scope of practice of all RNs
and is not cost or technology dependent.
Data Collection
Phase One. The target population consisted of nurses
attending the March 2009 NCEMNA conference. Nurses
of all levels of academic preparation, role, and clinical
specialty were invited to participate in the online survey
methodology assessing genetic and genomic knowledge,
belief, and skills. The only member organization exclu-
sion was NANAINA per their request. Conference leaders
provided notice to the 125 participants about the pilot
testing study, inviting them to test the instrument online.
No individual nurses were approached. Rather, interested
conference attendees self-selected to participate.
During Phase One pilot testing, computers were made
available at the NCEMNA annual meeting. A researcher
was stationed by the computer with an access code to as-
sist with survey access. A target of 30 participants was
desired for the study pilot phase. Prior to participation,
each participant was informed of the study aims and pro-
vided his/her verbal consent. In addition, upon launching
the survey online, the participant also had a written con-
sent as part of the instructions prior to encountering any
survey questions.
Phase Two. The following NCEMNA Associations
chose to participate: AAPINA, NAHN, NBNA, and PNAA.
Recruitment of study participants was done through each
participating NCEMNA member association. A link to the
survey was posted on the NCEMNA website as well as
each participating NCEMNA member association website.
Recruitment consisted of email announcements to associ-
ation constituencies as well as notifications through asso-
ciation newsletters. The survey offered no incentives. The
survey was open for a total of 10 months, with slightly
varying start dates for each association.
Instructions for the survey included the phone num-
bers and email addresses of study investigators to contact
with any questions. Participants also received instructions
that the survey was voluntary, no identifying informa-
tion would be collected or stored, and they could skip any
question.
Eligibility was limited only to licensed RNs who ac-
cessed the online survey. Membership in an NCEMNA
participating association was not required. Inclusion and
exclusion criteria were the same for both Phase One and
Phase Two studies.
Survey data were collected using the online survey
tool SurveyMonkey (SurveyMonkey, Inc., Palo Alto, CA,
USA). The survey took approximately 20 min for com-
pletion and collected no personal identifying information.
All data were stored in a password-protected file that was
available only to study investigators.
Statistical Analysis
Data were analyzed using SAS 9.3 (SAS Institute Inc.,
Cary, NC, USA). The answers to all survey questions were
summarized using descriptive statistical techniques. Chi-
squared tests were used to assess the relationships be-
tween survey items with categorical responses. The level
of significance was α = 0.05, and all tests of statistical sig-
nificance were two tailed.
Results
Phase One
A total of 27 participants completed the online sur-
vey. Participants found the length of the survey to be
just right. On average, participants spent 23 min com-
pleting the survey. There were some technical problems
with obtaining online access that were remedied during
Phase One of the study. The majority agreed or strongly
agreed that the directions for survey completion were
adequate 70% (n = 16/23), the survey was organized
86% (n = 20/23), the survey was easy to navigate 69%
(n = 16/23), question sequence was clear and predictable
70% (n = 16/23), terminology was consistent and ap-
propriate 82% (n = 19/23), and the survey was tech-
nically easy to complete 78% (n = 18/23). Most (82%,
n = 18/22) indicated that there were no questions
worded in a way that were not sensitive to their ethnic
group. Survey tool modifications were made based on
recommendations from the participants to enhance re-
spondent response by decreasing the number of survey
items. The final instrument for use in Phase Two con-
sisted of seven sections and a total of 61 questions.
Phase Two
Demographic and work characteristics of par-
ticipants. A total of 392 respondents completed an
online survey located on their nursing organization’s
website in Phase Two of the study. Excluding three in-
eligible participants reporting a highest nursing degree of
a licensed practical nurse, a total of 389 were included
in the data analysis. Table 1 summarizes the characteris-
tics of the eligible nurses. Participants’ ages ranged from
Table 1. Demographic Characteristics of Study Participants
Percent of time spent seeing patients (n = 311)
Mean 51%
Range 0–100%
NCEMNA organization affiliation (n = 305)
Asian American/Pacific Islander Nurses Association 37 (12%)
National Association of Hispanic Nurses 53 (17%)
National Black Nurses Association 109 (36%)
Philippine Nurses Association of America 112 (37%)
23 to 82 years, with a mean of 52 years, the majority
were female (93%, n = 304/326). The majority of par-
ticipants were Asian (43%, n = 138/322) and African
American (33%, n = 107/322). Eighteen percent (n =
60/329) stated that they considered themselves to be His-
panic/Latino, and 8% (n = 27/322) reported that they
were White. The majority (39%, n = 130/331) reported
their highest level of education was a master’s degree,
35% (n = 115/331) had a baccalaureate degree, 16%
(n = 53/331) held a doctoral degree, 8% (n = 28/331)
had an associate degree, and 2% (n = 5/331) were
diploma prepared. The primary work setting reported was
a hospital (68%, n = 163/241). The average number of
years they had worked in nursing was 20 years, and more
than half (51%, n = 166/326) had worked at their cur-
rent work setting for over 10 years. Forty-two percent
(n = 139/330) indicated their primary role was patient
care, 22% (n = 71/330) were in education, and 19% (n =
63/330) were in administration.
Beliefs. The majority of respondents felt it was very
important (79%, n = 301/383) or somewhat important
(19%, n = 71/383) for nurses to become more educated
about the genomics of common disease. The most fre-
quent advantages of integrating genomics into practice
identified included better decisions about recommenda-
tions for preventive services (87%, n = 332/383), bet-
ter treatment decisions (73%, n = 280/383), improved
services to patients (68%, n = 259/383), better ad-
herence to clinical recommendations by patients (56%,
n = 216/383), and genetic risk triaging (46%, n =
177/383). The highest reported potential disadvantages to
integrating genomics into practice included that it would
increase insurance discrimination (61%, n = 224/366),
genetics could increase patient anxiety about risk (52%,
n = 191/366), and it would be not reimbursable or too
costly (49%, n = 181/366).
Knowledge. Self-reported genetic knowledge as-
sessments are provided in Table 2. Half of the partici-
pants (50%, n = 182/364) felt their understanding of the
genetics of common diseases was poor or fair. The ma-
jority (95%, n = 371/389) agreed or strongly agreed that
family history could help to identify at-risk families and
85% (n = 323/381) knew how to complete it. The major-
ity had completed a family history for themselves (74%,
n = 279/378) and 51% (n = 195/381) had collected one
for a family member.
Responses varied by disease as to the degree to which
nurses felt genetics had clinical relevance to a wide range
of common health conditions. For example, only 54%
(n = 191/353) reported that hemochromatosis, an inher-
ited condition, had a great deal to do with genetics. The
majority correctly identified that genetic risk (e.g., as indi-
cated by family history) has clinical relevance for breast,
colon, and ovarian cancers; coronary heart disease; and
diabetes. However, 54% of respondents (n = 105/193)
thought diabetes and heart disease are caused by a single
gene variant, which is incorrect.
Practice. When presented with the option to identify
what was important to consider when delivering nursing
care, genes (29%, n = 53/185) and insurance (10%, n =
37/362) were the two lowest items identified as essential.
Other items scored as more essential to consider included
race (52%, n = 196/376), gender (53%, n = 196/371),
age (63%, n = 231/369), and family history (63%, n =
238/375).
Seventy-two percent (n = 274/380) also reported
collecting family histories for patients in their prac-
tice setting. When a patient indicated a disorder in
the family, nurses always collected the age of diagno-
sis (64%, n = 231/361), the relationship to the patient
Table 2. Knowledge Measures
Measure n (%)
Understanding of genetics of common diseases (n = 364)
Excellent 6 (2%)
Very good 47 (13%)
Good 129 (35%)
Fair 149 (41%)
Poor 33 (9%)
Do you think that genetic risk (e.g., as indicated by family
health history) has clinical relevance for breast cancer?
(n = 378)
Correct 378 (100%)
Incorrect 0 (0%)
Do you think that genetic risk (e.g., as indicated by family
health history) has clinical relevance for colon cancer?
(n = 375)
Correct 366 (98%)
Incorrect 9 (2%)
Do you think that genetic risk (e.g., as indicated by family
health history) has clinical relevance for coronary heart
disease? (n = 372)
Correct 333 (98%)
Incorrect 9 (2%)
Do you think that genetic risk (e.g., as indicated by family
health history) has clinical relevance for diabetes? (n =
376)
Correct 372 (99%)
Incorrect 4 (1%)
Do you think that genetic risk (e.g., as indicated by family
health history) has clinical relevance for ovarian
cancer? (n = 369)
Correct 354 (96%)
Incorrect 15 (4%)
The DNA sequences of two randomly selected healthy
individuals of the same sex are 90%–95% identical. (n =
208)
Correct 82 (39%)
Incorrect 126 (61%)
Most common diseases such as diabetes and heart
disease are caused by a single gene variant. (n = 193)
Correct 88 (46%)
Incorrect 105 (54%)
Genetics course since licensure (n = 356)
Yes 123 (35%)
No 233 (65%)
(91%, n = 330/363), race or ethnic background (77%,
n = 242/315), age at death from the condition (65%,
n = 237/362), as well as maternal and paternal lineages
(77%, n = 278/359).
With regard to family history specific knowledge el-
ements, nurses with higher levels of education tended
to accurately report that a family history should include
age at diagnosis of condition (p = .0146). More years
of practice influenced the collection by nurses of stan-
dard family history information that also included race or
ethnic backgrounds (p = .0197), age at death from con-
ditions (p = .0268), and age at diagnosis of condition
(p = .0009). Most nurses (98%, n = 380/386) agreed
or strongly agreed that family health histories could
be used to teach patients and family members about
the importance of genetics-genomics and disease pre-
vention. However, there was no relationship between
the proportion of work time spent seeing patients and
the perceived value of family history, use of family his-
tory, or variable collected (i.e., age, relationship, race, or
lineages).
Genetics and genomics education. Only 35%
(n = 123/356) indicated that they had taken a course that
included genetics as a major component since they ob-
tained their nursing license. While the majority of nurses
(94%, n = 335/357) indicated that they intended to learn
more about genetics, only 30% (n = 107/352) knew
whether there were any courses on genetics available
to them. More than half (55%, n = 196/358) identi-
fied workshops that included a mixture of presentations
and group activities as the preferred format for learning
about genetics. Overall, most (90%, n = 318/354) would
encourage NCEMNA or their organization to support
a genetics and genomics awareness initiative and 81%
(n = 289/357) responded that they would attend train-
ing if offered at their annual conference. Similarly, 84%
(n = 297/354) believed that their national organization
should have a visible role in genetics-genomics in their
community.
Discussion
This study assessed the knowledge, beliefs, and prac-
tice of a sample of multi-ethnic minority nurses re-
cruited through NCEMNA for Phase One and through
the NCEMNA Member associations for Phase Two. Phase
One of the study showed the feasibility of an online sur-
vey method of data collection, indicating minimal diffi-
culty and taking an average of 23 min to complete. In-
strument modifications were made based on respondent
recommendations to assure accurate and complete re-
sponses from the broader membership, and the investiga-
tors chose to enhance response by decreasing the number
of survey items.
In Phase Two, it was determined that most respondents
in this study felt genetics-genomics are important to in-
tegrate into practice, but they demonstrated knowledge
deficits. The majority of respondents felt it was very im-
portant (79%) for nurses to become more educated about
the genomics of common disease. Half of the participants
felt their understanding of the genetics of common dis-
eases was poor or fair. They indicated a strong interest in
learning more, with 94% reporting that they intended
to learn more about genetics. Study participants were
also supportive (90%) of encouraging a genetics and ge-
nomics awareness initiative.
These results were very similar to those reported re-
cently from a study of nurses responding to an American
Nurses Association (ANA) study (Calzone et al., 2013).
Both studies included similar populations (NCEMNA,
93% female, n = 304/326; ANA, 96% female, n =
461/481; NCEMNA, average age 52 years; ANA, average
age 51 years). However, this study population had differ-
ent ethnicity/race characteristics, enhancing the under-
standing of differences in knowledge, beliefs, and prac-
tices of genetics and genomics for all nurses. This study
included more nurses who were Asian (NCEMNA, 43%,
n = 138/322; ANA, 2%, n = 8/476); Black/African Amer-
ican (NCEMNA, 33%, n = 107/322; ANA, 3%, n =
14/476); Hispanic (NCEMNA, 18%, n = 60/329; ANA,
2%, n = 8/478); and fewer who were White (NCEMNA,
8%, n = 27/322; ANA, 89%, n = 424/476). There were
also more nurses with advanced degrees in nursing who
participated in this study (NCEMNA: master’s degrees
39%, n = 130/331, doctoral degrees 16%, n = 53/331;
ANA: master’s degrees 31%, n = 148/483, doctoral de-
grees 2%, n = 39/483). The two populations also dif-
fered in their primary role, indicating variation in the
number of nurses involved in research (NCEMNA, 6%,
n = 20/330; ANA, 4%, n = 16/427) and administration
(NCEMNA, 19%, n = 63/330; ANA, 9%, n = 38/427).
Most nurses indicated that their primary role was pa-
tient care (NCEMNA, 42%, n = 139/330; ANA, 54%, n =
231/427).
A correlation was found between higher academic ed-
ucation and years in nursing, which increased family
history collection in practice. Seventy-two percent (n =
274/380) reported collecting family histories for patients
in their practice setting. This result is higher than that re-
ported in other nursing populations. The National Nurs-
ing Workforce Study conducted through the ANA found
that nurses who indicated they actively saw patients
(60%, n = 216/359) had rarely or never assessed a fam-
ily history in the preceding 3 months (Calzone et al.,
2013). Additional study is needed to ascertain the basis
for these differences, which could be associated differ-
ences in the question asked between these two surveys,
with the ANA asking about family history utilization in
the past 3 months, whereas the NCEMNA survey asked
about use of family history at any time in practice. The
perceived value of family history may also contribute to
this difference, or the difference may be the direct re-
sult of family history education initiatives undertaken by
some NCEMNA member associations, but data on these
specific details were not assessed in this study.
Three hundred and five study participants stated that
they belonged to one (299/305) or more than one
(6/305) NCEMNA member organization. Two identi-
fied themselves as American Indian/Alaska Native, even
though NANAINA members did not participate in this
study. So either those participants were a member of
another organization or answered independently. There
were also 39 who reported their race or ethnicity as
other and their write-in answers indicated mixed race re-
sponses. Those who specified their race or ethnicity as
White may have been of mixed race or felt this choice
best reflected their race or ethnicity.
Health disparities in chronic diseases such as can-
cer (Wallace, Martin, & Ambs, 2011), cardiovascular
disease (Cambien & Tiret, 2007; Kathiresan & Srivas-
tava, 2012), and diabetes are mediated by complex gene
interactions (Tekola-Ayele, Adeyemo, & Rotimi, 2013;
Zorka et al., 2013), which are changing the manage-
ment of chronic disease in vulnerable populations. In
the 10 years since the Human Genome Project was com-
pleted, rapid changes in genetic technology have resulted
in substantial changes in the care of patients with these
and other chronic diseases, which disproportionally af-
fected racial and ethnic minority groups (Goldenberg
et al., 2013; Wallace et al., 2011). This rapid infusion of
genetic-genomic knowledge and changes in clinical prac-
tice present both a burden and opportunity for multi-
ethnic minority nurses.
Nurses remain trusted healthcare providers (Gallop
Poll, 2012). The nurse–patient professional relationship is
foundationally supported by perceived professional com-
petencies and caring attributes that underpin this trust
(Dinc & Gastmans, 2012). Health disparities have pre-
vailed in minority populations despite policy initiatives
and new knowledge in genetics and genomics (Agency
for Healthcare Research and Quality, 2012). Establishing
a culturally competent nursing workforce is suggested as
a key component to improving communication and the
patient-centered trust relationship with minority popula-
tions (Viseanath & Ackerson, 2011). Within these pop-
ulations, culture, race, and perceived discrimination can
negatively affect the interpretation of communication de-
livered by healthcare providers (Subban, Terwoord, &
Schuster, 2008). An important component to establish-
ing a trust relationship is the healthcare provider us-
ing culturally competent communications with minor-
ity populations that support patient engagement of the
value of genetic and genomic information in their health
care. Radwin, Cabral, and Woodworth (2013) conducted
a study using path analysis in a multi-ethnic sample of
in-patient cancer patients. The investigators sought to de-
termine what contributed to the development of trust in
the population of African Americans, Caucasian Ameri-
cans, Hispanics, Native Hawaiians or Pacific Islanders, and
American Indians or Alaska Natives. Data were collapsed
into two ethnic groups—Caucasians and all other ethnic
groups combined. For the multiminority groups only, re-
sponsiveness and proficiency were positively related to
greater trust in nurses.
The multi-ethnic minority nurse sample in this study
reported gaps in genetic and genomic knowledge. These
gaps are similar to findings from a study conducted using
a sample collected through the ANA, and both studies
demonstrate that education is required in basic genetic
and genomic core concepts (Calzone et al., 2013). The
majority of the current sample (65%) had not received
continuing education with a focus on genetics and ge-
nomics. Respondents in this study were interested, open,
and motivated to engage in education that would sup-
port proficiency in genetics and genomics. These find-
ings support the need for further genetics-genomics ed-
ucation in this multi-ethnic study population, consistent
with similar findings in an African American nurse sam-
ple (Powell-Young & Spruill, 2013) that clearly demon-
strated the need for a focused education in both the for-
mal and continuing education areas. Patients expect that
care providers approach them knowledgeable about their
conditions, sensitive about their culture, and aware of
their sometimes intergenerational experiences of unequal
and sometimes discriminatory care that may predicate
perceptions of mistrust (Benkert, Peters, Pate, & Dinardo,
2007). This is the first study investigating multi-ethnic
minority nurses’ knowledge of genetics and genomics.
Designing education for this population of nurses who are
requisite in the knowledge and culture of caring for mi-
nority populations is a first and crucial step to preparing
nurses that may well influence health disparity outcomes.
Limitations
Ascertainment bias is recognized as a limitation of this
study. Nurses were recruited through the minority nurs-
ing organizations that are members of NCEMNA. There-
fore, nurses self-identifying as minority nurses that are
members or follow activities of these organizations would
have been notified of the study. Furthermore, partici-
pants may have had some motivation to complete the
survey, which could include concern about genomics or
other influencing factors. As such, the findings cannot
be generalized to the overall minority nurse community.
However, this is the first study of its kind despite this lim-
itation, so the insights gleaned from the data can still be
useful in planning targeted education for this diverse con-
stituency.
Notably, the study population consisted of a highly ed-
ucated group of nurses, with 39% holding a master’s
degree and 16% a doctoral degree. These education lev-
els differ drastically from the national nursing work-
force. Overall, 13% of nurses of any race hold a mas-
ter’s or doctoral degree. By race and ethnic categories,
13% of Caucasian/Non-Hispanics, 15% of Black/African
American/Non-Hispanics, 11% of Hispanic/Latino/any
race, and 8% of Asian/Non-Hispanics hold a master’s or
doctoral degree (U.S. Department of Health and Human
Services, Health Resources and Services Administration,
2010). Additional study is clearly needed in a more rep-
resentative minority nurse population. Overall, the data
indicate that study participants had a strong interest in
gaining knowledge and or refining knowledge about ge-
netics and genomics. This information more broadly in-
forms the NCEMNA Board and member organizations on
the need, scope, and optimal design of a collaborative
NCEMNA member organizations education initiative in
genetics-genomics.
Lastly, participants were informed that they could skip
any survey item. As such, the per-question response rate
varied. To assess this further, the dataset was queried to
ascertain whether there was a pattern to the per-question
response rate. Overall, the lowest response rates were as-
sociated with Sections 6 and 7 located at the end of the
survey. Section 6 correlates with the knowledge, use, and
beliefs about race and genetic variation items (Bonham
and Sellers’ GKAI, Bonham and Sellers’ HPBR scale, and
Bonham and Sellers’ RACE scale). Of these three instru-
ments in Section 6, the GKAI had the lowest response
rates. Section 7 consisted of the demographic questions.
Overall, this analysis revealed that participants seemed to
respond less as the survey progressed, with over 13% of
responders answering no question, as opposed to earlier
sections, where this rate was 6% to 7%. Additional psy-
chometrics on the instrument are needed to inform re-
finement of the tool, including reduction in the number
of items.
Conclusions
This study was designed to determine minority nurses’
beliefs, practices, and competency of integrating into
practice genomics information related to common multi-
factorial diseases. This goal was supported by the leader-
ship of NCEMNA and provided the opportunity to assess
minority nurses’ knowledge of human genetic variation
and the use of patient characteristics, including ethnic-
ity, gender, genes, and race in diagnostics, treatment, and
referral. This study population had different ethnicity or
race characteristics, more nurses with advanced degrees,
and higher proportions reporting primary functional ar-
eas such as research or administrative than previous stud-
ies. However, genomic knowledge deficits in the nurs-
ing workforce revealed in this study were similar to that
found in other nurses previously reported. Therefore, the
recommendation is that genomics education is needed by
all nurses. Only then can we assure in this genomic era of
health care that nurses as integral members of the work-
force are prepared to deliver responsible, effective, and
accountable care that includes genomics.
Acknowledgements
This research was supported by the Intramural Re-
search Programs of the National Institutes of Health, Na-
tional Cancer Institute, and National Human Genome Re-
search Institute.
Clinical Resources
� Essentials of Genetic and Genomic Nursing: Com-
� Genetics and Genomics Competency Center for Ed-
ucation: http://www.g-2-c-2.org/
� Greco, K., Tinley, S., & Seibert, D. (2012). Essen-
tial Genetic and Genomic Competencies for Nurses
with Graduate Degrees. American Nurses Associa-
tion and International Society of Nurses in Genet-
ics: http://www.genome.gov/Pages/Health/Health-
CareProvidersInfo/Grad Gen Comp
� U.S. Surgeon General’s My Family Health Por-
trait: https://familyhistory.hhs.gov/fhh-web/home.
action
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research/findings/nhqrdr/nhdr12/index.html
Benkert, R., Peters, R., Pate, N., & Dinardo, E. (2007). Trust of
nurse practitioners and physicians among African
Americans with hypertension. Journal of the American
Academy of Nurse Practitioners, 20, 273–280.
Bonham, V. L., Sellers, S. L., & Woolford, S. (submitted for
publication). Physicians’ knowledge, use and beliefs about
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Liewehr, D., . . . McBride, C. (2012). Survey of nursing
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Reproduced with permission of the copyright owner. Further reproduction prohibited without
permission.
Disparities in Diabetes: The Nexus of Race, Poverty,
and Place
Darrell J. Gaskin, PhD, Roland J. Thorpe Jr, PhD, Emma E. McGinty, PhD, MS, Kelly Bower, RN, PhD, Charles Rohde, PhD,
J. Hunter Young, MD, MHS, Thomas A. LaVeist, PhD, and Lisa Dubay, PhD, ScM
In the United States, 25.6 million or 11.3% of
adults aged 20 years and older had diabetes in
2010.1 Non-Hispanic Blacks had the highest
prevalence at 12.6% compared with non-
Hispanic Whites at 7.1%.1 Traditional expla-
nations for the observed race disparity in
diabetes prevalence include differences in
health behaviors, socioeconomic factors, family
history of diabetes, biological factors, and
environmental factors.2—4 Little work has been
conducted to understand how individual and
environment-level factors operate together to
produce disparities in diabetes prevalence.
A relatively new line of research has begun
to show that risk of diabetes is associated with
neighborhood attributes that are also associ-
ated with race. Auchincloss et al. found that
higher diabetes rates were related to lack of
availability of neighborhood resources that
support physical activity and healthy nutri-
tion.5 Schootman et al. found that poor housing
conditions were associated with diabetes prev-
alence.6 Black neighborhoods are more likely
to be characterized by these risk factors
(i.e., having food deserts, being less likely to
have recreational facilities, and tending to have
lower-quality housing than White neighbor-
hoods).7—18 As such it stands to reason that
failing to adjust national estimates of diabetes
prevalence for these social conditions might
influence perceptions of diabetes disparities.
LaVeist et al. compared disparities in diabetes
in an urban, racially integrated, low-income
community with a national sample from the
National Health Interview Survey.19,20 They
found that when urban Whites and Blacks
resided in the same low-income community,
the race disparity in diabetes prevalence dis-
appeared, largely because the prevalence rate
for Whites increased substantially.19 Ludwig
et al. used data from the Moving to Opportuni
ty
demonstration project and found a lower
prevalence of diabetes among low-income
adults who moved from high-poverty
neighborhoods to low-poverty neighborhoods
compared with low-income adults who moved
from a high-poverty neighborhood to another
high-poverty neighborhood.21 Findings from
these studies suggest the need to further ex-
plore the role of place in race disparities in
diabetes.
We explored whether the nexus of race,
poverty, and neighborhood
racial composition
and poverty concentration illuminates the race
disparities in diabetes. Specifically, we exam-
ined (1) whether diabetes prevalence increases
in predominantly Black neighborhoods com-
pared with predominantly White neighbor-
hoods, (2) whether diabetes prevalence is
higher in poor neighborhoods than in nonpoor
neighborhoods, and (3) whether the impact
of neighborhood racial composition and pov-
erty concentration on the risk of diabetes varies
by race. We hypothesized that residential
segregation and concentrated poverty (1) in-
crease Black individuals’ exposure to environ-
mental risks associated with poor health, (2)
reduce their access to community amenities
that promote good health and healthy behaviors,
and (3) limit their access to social determinants
that promote good health such as quality jobs,
education, public safety, and social net-
works.7,22—24
METHODS
The National Health and Nutrition Exami-
nation Survey (NHANES) was designed to de-
termine the health, functional, and nutritional
status of the US population. Since 1999,
NHANES has been conducted as a continuous,
annual survey with public use data files re-
leased in 2-year increments. Each sequential
series of this cross-sectional survey is a nation-
ally representative sample of the civilian non-
institutionalized population that consists of
an oversample of participants aged 12 to 19
years, participants aged 60 years and older,
Mexican Americans, Blacks, and low-income
individuals.25 Each of these surveys used
a stratified, multistage probability sampling
design.25 Data were collected from respon-
dents in 2 phases. The first phase consisted
of a home interview in which information
Objectives. We sought to determine the role of neighborhood poverty and
racial composition on race disparities in diabetes prevalence.
Methods. We used data from the 1999–2004 National Health and Nutrition
Examination Survey and 2000 US Census to estimate the impact of individual
race and poverty and neighborhood racial composition and poverty concentra-
tion on the odds of having diabetes.
Results. We found a race–poverty–place gradient for diabetes prevalence for
Blacks and poor Whites. The odds of having diabetes were higher for Blacks than
for Whites. Individual poverty increased the odds of having diabetes for both
Whites and Blacks. Living in a poor neighborhood increased the odds of having
diabetes for Blacks and poor Whites.
Conclusions. To address race disparities in diabetes, policymakers should
address problems created by concentrated poverty (e.g., lack of access to
reasonably priced fruits and vegetables, recreational facilities, and health care
services; high crime rates; and greater exposures to environmental toxins).
Housing and development policies in urban areas should avoid creating high-
poverty neighborhoods. (Am J Public Health. 2014;104:2147–2155. doi:10.2105/
AJPH.2013.301420)
RESEARCH AND PRACTICE
November 2014, Vol 104, No. 11 | American Journal of Public Health Gaskin et al. | Peer Reviewed | Research and Practice | 2147
regarding the participant’s health history,
health behaviors, health utilization, and risk
factors were obtained. The second phase was
a medical examination. At the conclusion of the
home interview participants were invited to
receive a detailed physical examination at
a mobile examination center.25 Among those
who participated in the physical examination,
a nationally representative subset underwent
laboratory tests, including measurement of
fasting glucose.
We linked the NHANES data to 2000 US
Census data26 to measure the residential seg-
regation and concentrated poverty within
respondents’ census tract of residence. Because
we accessed the respondents’ census tract
information, the analysis was performed at the
National Center for Health Statistics (NCHS)
Research Data Center under the supervision
of NCHS staff to preserve the privacy, confi-
dentiality, and anonymity of the NHANES
respondents. In this analysis we used the
combined 1999—2004 data sets of adults who
completed the household interview, physical
examination, and laboratory components. We
restricted the analysis to Blacks (n = 1202) and
non-Hispanic Whites (n = 3201) who were
aged 25 years and older.
Key Dependent Variable and Independent
Variables
We identified persons with diabetes as re-
spondents who had a fasting glucose of 126
milligrams per deciliter or higher, had hemo-
globin A1c values of 6.5% or higher, or
reported taking medications for diabetes. We
excluded persons with normal glycemic values
who reported taking metformin from this
definition. Independent variables of interest
were individual race, individual poverty status,
neighborhood racial composition, and neigh-
borhood poverty concentration. Race was self-
reported in the NHANES as either non-Hispanic
African American/Black or non-Hispanic
White. We measured poverty status 2 ways.
The poverty—income ratio is a ratio of house-
hold income to the federal poverty level (FPL)
and is based on the respondent’s household
income and size.27 Poverty—income ratio was
coded as a 5-level categorical variable that
indicates each individual’s household poverty
ratio (below 100% of FPL [poor], 100% to
199% of FPL (near-poor), 200% to 299% of
FPL, 300% to 399% of FPL and greater than or
equal to 400% of FPL). We used this categori-
zation in our race—place model. Also, we used
a binary poverty variable indicating whether
individuals had household incomes between
0% and 199% of FPL or greater than or equal
to 200% of the FPL in our poverty—place
model.
We used the respondent’s census tract to
measure neighborhood characteristics because
census tracts are small, permanent, statistical
subdivisions within a county that range from
1500 to 8000 persons who are similar with
respect to population characteristics, economic
status, and living conditions. We designated
neighborhood racial composition as predomi-
nantly White, Black, or other race (Asian or
Hispanic) if that group was greater than 65% of
the census tract’s population. We designated
the racial composition of a neighborhood as
integrated if at least 2 groups were each more
that 35% of the census tract’s population. We
classified neighborhoods as having concen-
trated poverty if greater than or equal to 20%
of families in the census tract had incomes
below the FPL.
Other covariates included demographic
variables (age and gender), socioeconomic fac-
tors (education and health insurance status),
and family history of diabetes. We measured
age as a continuous variable. We included age
and age squared to control for nonlinearities.
We coded gender as a dichotomous variable.
We coded educational attainment as 5 cate-
gories (< 9 years of school, 9 to 12 years of
school but no diploma, high-school graduate or
general equivalency diploma, some college, or
college graduate or higher). We coded health
insurance coverage as 4 categories (privately
insured, Medicare, Medicaid or other govern-
ment coverage, or uninsured). We also con-
trolled for self-reported family history of
diabetes, if the respondent had any biological
relatives (grandparents, parents, brothers, or
sisters) who had been told by a health pro-
fessional that they had diabetes.
Statistical Analysis
We conducted bivariate analysis comparing
the diabetes prevalence across the categories
for each of our main independent variables.
We used the 2-by-N v2 test to determine
proportional differences by diabetes status. We
estimated a series of logistic regression models
to assess the intersection between diabetes
disparities and individual race and poverty and
neighborhood racial composition and poverty
concentration. The base model included all of
our key independent variables and covariates.
The race—place model interacted individual
race with neighborhood racial composition. To
do this, we created a variable with 8 categories:
White in White neighborhood, White in Black
neighborhood, White in other race neighbor-
hood, White in integrated neighborhood, Black
in Black neighborhood, Black in White neigh-
borhood, Black in other race neighborhood,
and Black in integrated neighborhood.
The poverty—place model combined indi-
vidual poverty with neighborhood poverty. We
created a variable with 4 categories: nonpoor
in nonpoor neighborhood, poor in nonpoor
neighborhood, nonpoor in poor neighborhood,
and poor in poor neighborhood. The race—
poverty—place model combined individual race
and poverty with neighborhood poverty. We
created a variable with 8 categories: nonpoor
White in nonpoor neighborhood, nonpoor
White in poor neighborhood, poor White in
nonpoor neighborhood, poor White in poor
neighborhood, nonpoor Black in nonpoor
neighborhood, nonpoor Black in poor neigh-
borhood, poor Black in nonpoor neighbor-
hood, and poor Black in poor neighborhood.
The sampling design for the NHANES is
a complex, stratified, multistage probability
sample of noninstitutionalized individuals.
Therefore, we developed sample weights to
account for both the differential probability of
being sampled and differential response rates.
We applied sample weights to account for the
differential probability of being selected, non-
response adjustments, and adjustments to na-
tional control totals in the NHANES.28
We adjusted parameter estimates and stan-
dard errors for the multistage sampling design
with Taylor linearization methods. Following
the algorithm described by the NCHS,29 we
created a 6-year sample weight variable by
assigning two thirds of the 4-year weight for
1999—2002 if the person was sampled in
1999—2002 or assigning one third of the
2-year weight for 2003—2004 if the person
was sampled in 2003—2004. We used the SVY
commands in Stata version 12 (StataCorp LP,
College Station, TX) to produce nationally
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representative estimates and appropriate stan-
dard errors for all estimation.
RESULTS
The prevalence of diabetes varied with the
key independent variables and covariates
(Table 1). Blacks had a higher rate of diabetes
than Whites (0.123 vs 0.084; P = .03). The
prevalence of diabetes was inversely related to
household poverty level. Adults in poor and
near-poor households had the highest rates of
diabetes (0.12 and 0.127), followed by adults
between 200% and 299% FPL (0.108), fol-
lowed by adults between 300% and 399%
FPL (0.087), followed by adults in households
greater than or equal to 400% FPL (0.054).
Adults in predominantly Black neighborhoods
had higher rates of diabetes than those in
predominantly White neighborhoods (0.13 vs
0.084; P = .019). This neighborhood difference
is similar to the individual race difference.
When we combined individual race with
neighborhood racial composition, we found
that Blacks living in Black neighborhoods,
Blacks living in integrated neighborhoods, and
Blacks living in White neighborhoods had
significantly higher rates of diabetes (0.134,
0.123, and 0.106) than Whites in White
neighborhoods (0.083). When we combined
individual poverty with neighborhood poverty
concentration, we found that, compared with
nonpoor adults in nonpoor neighborhoods,
poor adults in poor and nonpoor neighbor-
hoods had higher rates of diabetes. When we
categorized adults by their race, poverty status,
and neighborhood poverty concentration, we
found that individual and neighborhood pov-
erty status were associated with diabetes for
Blacks and Whites.
Nonpoor Whites had lower rates of diabetes
than Blacks and poor Whites. Nonpoor Whites
in poor and nonpoor neighborhoods had sim-
ilar diabetes rates. There was a place gradient
for poor Whites. Poor Whites in poor neigh-
borhoods had the highest diabetes rates (0.15),
but the diabetes rate was lower for poor Whites
in nonpoor neighborhoods (0.121). For Blacks
there appears to be a race—poverty—place
gradient with nonpoor Blacks in nonpoor
neighborhoods having the lowest rates of di-
abetes (0.100), followed by poor Blacks in
nonpoor neighborhoods (0.114), nonpoor
TABLE 1—Diabetes Prevalence by the Independent Variables: 1999–2004 National Health
Race–place individual race and neighborhood racial composition
White in White neighborhood (Ref) 6114 0.083 (0.070, 0.096)
White in Black neighborhood 42 0.072 (0.000, 0.216) .874
White in other race neighborhood 128 0.123 (0.021, 0.224) .451
White in integrated neighborhood 895 0.083 (0.046, 0.121) .994
Black in Black neighborhood 1194 0.134 (0.104, 0.165) .002
Black in White neighborhood 554 0.106 (0.059, 0.153) .0258
Black in other race neighborhood 72 0.108 (0.000, 0.223) .681
Black in integrated neighborhood 785 0.123 (0.083, 0.164) .048
Poverty–place individual poverty and neighborhood poverty
concentration
Nonpoor in nonpoor neighborhood (Ref) 4866 0.701 (0.058, 0.082)
Poor in nonpoor neighborhood 2149 0.120 (0.095, 0.145) <.001
Nonpoor in poor neighborhood 760 0.089 (0.048, 0.130) .339
Poor in poor neighborhood 1109 0.140 (0.010, 0.179) .003
Race–place–poverty individual race and poverty and
neighborhood
poverty concentration
Nonpoor White in nonpoor neighborhood (Ref) 4119 0.068 (0.056, 0.080)
Nonpoor White in poor neighborhood 275 0.062 (0.014, 0.111) .828
Poor White in nonpoor neighborhood 1743 0.121 (0.095, 0.147) <.001
Poor White in poor neighborhood 350 0.150 (0.071, 0.219) .043
Nonpoor Black in nonpoor neighborhood 667 0.100 (0.061, 0.141) .125
Nonpoor Black in poor neighborhood 485 0.136 (0.074, 0.198) .03
0
Poor Black in nonpoor neighborhood 406 0.114 (0.057, 0.170) .132
Poor Black in poor neighborhood 759 0.129 (0.129, 0.083) .011
Gender <.001
Male 5137 0.069 (0.058, 0.080)
Female 4652 0.110 (0.091, 0.129)
Continued
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Blacks in poor neighborhoods (0.136), and
then poor Blacks in poor neighborhoods
(0.129).
The base model determined if individual
covariates and neighborhood racial composi-
tion and poverty concentration separately in-
fluence the odds of having diabetes (Table 2).
We found that only household poverty status,
gender, and family history were significant
predictors. Neighborhood racial composition
and poverty concentration did not indepen-
dently influence the odds of having diabetes.
Compared with adults living at greater than or
equal to 400% FPL, the odds of having di-
abetes were 1.93 (95% confidence interval
[CI] = 1.21, 3.07) for the near-poor and 1.93
(95% CI = 1.09, 3.45) for the poor. The odds
of males having diabetes were 2.02 (95% CI =
1.59, 2.56) compared with females. The odds
of having diabetes among those with a family
history of diabetes were 3.27 (95% CI = 2.54,
4.21) compared with those without a family
history of diabetes.
The results from the race—place models
tested whether the odds of having diabetes
were related to adults’ racial identity relative to
the racial composition of their neighborhood
(Table 2). In this model, individual poverty
status, gender, and family history were still
significant predictors and similar in magnitude
to the base model; however, only Blacks in
integrated neighborhoods had greater odds
of having diabetes than Whites in White
neighborhoods (OR = 2.13; 95% CI = 1.26,
3.60). The other race—place indicator variables
were statistically insignificant.
The results from the poverty—place models
tested whether odds of having diabetes were
related to adults’ poverty status relative to their
neighborhood’s poverty concentration (Table 3).
We found that poor adults in nonpoor and
poor neighborhoods had greater odds of hav-
ing diabetes than nonpoor adults in nonpoor
neighborhoods. The odds of having diabetes
for poor adults in poor neighborhoods were
higher than for poor adults in nonpoor neigh-
borhoods (1.98 vs 1.67). Also, individual race
was significant in this model. The odds of
having diabetes were 1.59 (95% CI = 1.11,
2.28) times greater for Blacks than for Whites.
Finally, in the race—poverty—place model,
we categorized adults by their individual race,
individual poverty status, and neighborhood
poverty concentration. Similar to the bivariate
analysis, we found evidence of a race—poverty—
place gradient for poor Whites and nonpoor
Blacks in the logistic analysis. We found that,
compared with nonpoor Whites in nonpoor
neighborhoods, poor Whites in poor
TABLE 1—Continued
Family history of diabetes <.001
History of diabetes 4600 0.122 (0.103, 0.142)
No history of diabetes 5137 0.054 (0.043, 0.065)
Educational attainment
< 9th grade 775 0.195 (0.130, 0.259) .067
9th–12th grade, no diploma 1547 0.124 (0.090, 0.159) .006
Predominantly Black neighborhood 0.93 (0.45, 1.93) . . .
Predominantly other race neighborhood 1.16 (0.63, 2.14) . . .
Integrated neighborhood 1.30 (0.90, 1.88) . . .
Race–place individual race and neighborhood
racial composition
White in White neighborhood (Ref) . . . 1.00
White in Black neighborhood . . . 1.70 (0.24, 11.87)
White in other race neighborhood . . . 1.32 (0.34, 5.11)
White in integrated neighborhood . . . 1.32 (0.78, 2.24)
Black in Black neighborhood . . . 1.44 (0.92, 2.25)
Black in White neighborhood . . . 1.78 (0.87, 3.66)
Black in other race neighborhood . . . 1.30 (0.31, 5.55)
Black in integrated neighborhood . . . 2.13** (1.26, 3.60)
Continued
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neighborhoods were the most disadvantaged
(OR = 2.51; 95% CI = 1.31, 4.81). The size of
the disadvantage was smaller for poor Whites
in nonpoor neighborhoods (OR = 1.73; 95%
CI = 1.16, 2.57). Compared with nonpoor
Whites in nonpoor neighborhoods, poor Blacks
in poor neighborhoods and nonpoor Blacks in
poor neighborhoods were similarly disadvan-
taged (OR = 2.45; 95% CI = 1.50, 4.01; and
OR = 2.49; 95% CI = 1.48, 4.19, respectively).
The size of the disadvantage was slightly lower
for poor Blacks in nonpoor neighborhoods
(OR = 2.34; 95% CI = 1.22, 4.46), and lower
for nonpoor Blacks in poor neighborhoods
(OR = 2.08; 95% CI = 1.26, 3.44). Although
the CIs overlap, the overall trends suggest that
there is a place gradient for poor Whites and
Blacks.
We estimated the predicted diabetes preva-
lence for the race—poverty—place categories
with adjustment for age, gender, socioeconomic
status, and diabetes family history (Figure 1).
We found that, for Whites, diabetes prevalence
was associated with individual poverty status,
and for poor Whites, neighborhood poverty
was associated with higher risk. For Blacks,
diabetes risk was associated with individual
and neighborhood poverty status ranging from
6.2% to 8.9%. However, neighborhood pov-
erty had a stronger association with diabetes
risk for nonpoor Blacks.
DISCUSSION
This study provides evidence that place
matters for Blacks and poor Whites. Living in
high-poverty neighborhoods increases the odds
of having diabetes for Blacks and poor Whites
but not for nonpoor Whites. Blacks and poor
Whites have higher odds of diabetes than
nonpoor Whites; however, living in poor
neighborhoods increases their odds further
such that poor Whites living in poor neigh-
borhoods are most disadvantaged. Our findings
are consistent with those of the Moving to
Opportunity demonstration project, which
demonstrated that enabling families to move
from high-poverty neighborhoods to low-
poverty neighborhoods improved their lives
along several dimensions, including general
health status, mental status, obesity rates, and
diabetes rates.21 Findings from a long-term
follow-up survey showed that Moving to
Opportunity participants who relocated to
low-poverty neighborhoods experienced
a 26% reduction in glycated hemoglobin level
of 6.5% or higher.30 A possible cause for this
reduction was changes in eating habits to
include more fruits and vegetables and an
increase in the amount of exercise.30
Why does living in a poor neighborhood
increase the odds of having diabetes for Blacks
and poor Whites? A recent report issued by
the Joint Center for Political and Economic
Studies showed that 46% of urban Blacks and
67% of poor urban Blacks live in high-poverty
neighborhoods (poverty rate > 20%) com-
pared with 11% of urban Whites and 30% of
poor urban Whites.31 The Exploring Health
Disparities in Integrated Communities study
reported that when poor Blacks and Whites
live in an integrated poor community, they
have similar diabetes prevalence (10.4% vs
10.5%).20 The narrowing of the disparities was
attributable to the White residents of this poor
community having higher rates of diabetes.
Other analyses of the Exploring Health Dis-
parities in Integrated Communities data found
similar results for obesity, hypertension, and
use of health services.19 The authors concluded
that community-level social and environmental
factors contribute to national race disparities
in diabetes. However, there are relatively few
integrated and economically balanced census
tracts in the United States (425 out of 66 438
in 2000). Concentrated poverty is not as large
a problem for Whites as it is for Blacks. Poor
Whites typically do not live in poor neighbor-
hoods. Black poverty is more concentrated
than White poverty; hence, poor Blacks have
greater exposure to negative neighborhood-
level health risks.
Poor Black neighborhoods may contribute
to higher diabetes prevalence because of the
decreased availability of healthy food and
limited walkability. These neighborhoods are
often referred to as “food deserts” because of
limited access to a supermarket or large gro-
cery store. Poor Black neighborhoods are more
Family history of diabetes 3.27*** (2.54, 4.21) 2.94*** (2.22, 3.88)
Educational attainment
< 9th grade 1.19 (0.79, 1.79) 1.01 (0.60, 1.70)
9th–12th grade, no diploma 1.08 (0.71, 1.64) 1.00 (0.63, 1.58)
High-school graduate (Ref) 1.00 1.00
Some college 1.12 (0.79, 1.57) 1.07 (0.75, 1.54)
‡ college graduate 0.64 (0.36, 1.13) 0.61 (0.33, 1.14)
Health insurance status
Private insurance (Ref) 1.00 1.00
Medicare 1.26 (0.92, 1.72) 1.29 (0.90, 1.84)
Medicaid, SCHIP, or other government insurance 1.05 (0.63, 1.77) 0.90 (0.51, 1.58)
No insurance 0.77 (0.51, 1.16) 0.65 (0.36, 1.17)
Note. CI = confidence interval; FPL = federal poverty level; OR = odds ratio; SCHIP = state children’s health insurance
program. The models controlled for age and quadratic age, which were significant predictors (P < .001).
*P < .05; **P < .01; ***P < .001.
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likely to be “food deserts.” One study in Detroit
found that poor Black neighborhoods were
farther from supermarkets than poor White
neighborhoods.8 Another study found that
chain supermarkets were half as likely to be
located in predominantly Black neighborhoods
than in predominantly White neighborhoods.9
Several studies found that food available in
low-income and minority communities was
more expensive and of a lower quality.10—16
Morland and Filomena found that a lower
proportion of stores in predominantly Black
neighborhoods carried fresh produce, except
for bananas, potatoes, okra, and yucca.17 Blacks
in poor neighborhoods consume fewer fruits
and vegetables than people in middle-income,
racially integrated neighborhoods.32 This is
important because consumption of leafy green
vegetables is associated with a 14% reduced
risk of type 2 diabetes.33 There is strong
evidence suggesting that the walkability of
neighborhoods is positively associated with
physical activity and walking behaviors of
adults.34 In addition, residents of highly walk-
able neighborhoods are less likely to be over-
weight or obese.34—36
We did not find strong associations be-
tween diabetes prevalence and an individual’s
racial identity and the neighborhood racial
composition. Likewise, we did not find strong
associations between diabetes and an indi-
vidual’s poverty status and the neighbor-
hood’s poverty rate. Although there was
evidence of an individual race effect, neigh-
borhood racial composition does not seem to
have an effect on the odds of having diabetes.
The higher rate of diabetes prevalence among
Blacks in Black neighborhoods observed in
the bivariate analysis did not persist in the
multivariable models. The observed bivariate
association was probably because of the pre-
ponderance of poor Blacks living in poor
Black neighborhoods, rather than the neigh-
borhood’s racial composition. Hence, we be-
lieve the community-level risk factors that
elevate diabetes risk are associated with
problems of concentrated poverty in minority
communities. As concluded in a recent Joint
Center for Political and Economic Studies
report, “place matters for minority communi-
ties not because they are predominantly Black
or Latino but rather because they are
impoverished.”31(p26)
Limitations
Our study was based on a nationally repre-
sentative sample with an objective measure of
diabetes from the NHANES. Despite these
strengths, the study has a few limitations.
This study is a cross-sectional analysis and,
therefore, cannot infer causality. Also, our
findings are generalizable only to Blacks and
Whites. Future work should consider His-
panics, particularly Mexican Americans, who
have high diabetes prevalence compared with
TABLE 3—Estimated Odds Ratios of Having Diabetes With Control for the Nexus of Poverty–
Place and Race–Poverty–Place: 1999–2004 National Health and Nutrition Examination
Survey and 2000 US Census
Variable
Poverty–Place Model,
OR (95% CI)
Race–Poverty–Place Model,
OR (95% CI)
Individual race
White (Ref) 1.00 . . .
Black 1.59* (1.11, 2.28) . . .
Poverty–place individual poverty and neighborhood
poverty concentration
Nonpoor in nonpoor neighborhood (Ref) 1.00 . . .
Poor in nonpoor neighborhood 1.67** (1.14, 2.44) . . .
Nonpoor in poor neighborhood 1.26 (0.72, 2.21) . . .
Poor in poor neighborhood 1.98* (1.16, 3.39) . . .
Race–place–poverty individual race and poverty and
neighborhood poverty concentration
Nonpoor White in nonpoor neighborhood (Ref) . . . 1.00
Nonpoor White in poor neighborhood . . . 1.07 (0.44, 2.59)
Poor White in nonpoor neighborhood . . . 1.73** (1.16, 2.57)
Poor White in poor neighborhood . . . 2.51** (1.31, 4.81)
Nonpoor Black in nonpoor neighborhood . . . 2.08** (1.26, 3.44)
Nonpoor Black in poor neighborhood . . . 2.49*** (1.48, 4.19)
Poor Black in nonpoor neighborhood . . . 2.34* (1.22, 4.46)
Poor Black in poor neighborhood . . . 2.45*** (1.50, 4.01)
Gender
Female (Ref) 1.00 1.00
Male 2.15 (1.63, 2.85) 2.15*** (1.63, 2.84)
Family history of diabetes
No family history of diabetes (Ref) 1.00 1.00
Family history of diabetes 2.95*** (2.21, 3.92) 2.94*** (2.21, 3.91)
Educational attainment
< 9th grade 1.04 (0.62, 1.73) 1.05 (0.63, 1.74)
9th–12th grade, no diploma 1.03 (0.65, 1.64) 1.05 (0.66, 1.66)
High-school graduate (Ref) 1.00 1.00
Some college 1.05 (0.73, 1.49) 1.05 (0.74, 1.49)
‡ college graduate 0.55 (0.30, 1.01) 0.55 (0.30, 1.01)
Health insurance status
Private insurance (Ref) 1.00 1.00
Medicare 1.34 (0.94, 1.90) 1.33 (0.92, 1.89)
Medicaid, SCHIP, or other government insurance 0.96 (0.54, 1.71) 0.97 (0.55, 1.72)
No insurance 0.70 (0.39, 1.27) 0.70 (0.40, 1.23)
Note. CI = confidence interval; OR = odds ratio; SCHIP = state children’s health insurance program. The models controlled for
age and quadratic age, which were significant predictors (P < .001).
*P < .05; **P < .01; ***P < .001.
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Whites. The analysis pools 6 years (1999—
2004) of data from the NHANES to obtain
adequate sample sizes to study neighborhood
effects. However, this assumes that these asso-
ciations remained stable over time. Also, we
used the 2000 US Census data to measure
neighborhood racial composition and poverty
concentration, and this assumes that these
measures remained stable in the census tract
throughout the study period. The analysis
combines individual and area-level data, which
could lend itself to multilevel modeling.
However, after we controlled for the NHANES
complex survey design, there was a small
number of observations sharing the same
census tract.
Conclusions
Consistent with the health and socioeco-
nomic gradient literature,37—39 we found that
individual poverty status matters for diabetes
prevalence in both Blacks and Whites. There-
fore, policies that address individual poverty
(e.g., increasing the minimum wage, job training
and employment, quality of public education
systems, access to higher education, access to
health care) will reduce diabetes risk for
Blacks and Whites. Because Blacks have
lower socioeconomic status relative to
Whites, these policies can reduce race dis-
parities in diabetes. However, neighborhood
poverty matters for Blacks. Policies should
focus on improving poor neighborhoods in
an effort to reduce the Black—White disparity
in diabetes.
Impoverished communities are character-
ized by an overall lack of community-level
resources, from grocery stores, parks and rec-
reation facilities, quality schools, and public
transportation options to public safety alterna-
tives, resilient local businesses, employment
opportunities, and accessible and integrated
health care system.18,23,40—42 Poor communi-
ties are also at greater risk for environmental
toxins that have a negative impact on health.43
In addition, poor communities lack the political
and economic power to improve these condi-
tions. It is the responsibilities of local, state, and
federal governments to recognize the disad-
vantages created by concentrated poverty,
especially for minority communities. City
planners should use zoning regulations and
urban design standards to avoid creating
neighborhoods and communities where pov-
erty is concentrated. Policymakers should work
with local leaders to adopt and implement
policies and programs to address community-
level factors.
Finally, as the US Department of Housing
and Urban Development continues its policy of
revitalizing poor urban communities under the
Choice Neighborhoods programs, more re-
search is needed to understand the mecha-
nisms by which changes in neighborhood
poverty influence diabetes risk. Choice Neigh-
borhoods is designed to transform a high-
poverty neighborhood into a mixed-income
neighborhood by redesigning existing public
housing; improving access to quality education,
transportation, health care, recreation
and other community services; improving
access to jobs; investing in local businesses; and
reducing crime.44 Policymakers need to know
what neighborhood-level factors matter most
for residents of poor communities. j
0.035 0.037
0.058
0.082
0.069
0.082
0.077
0.081
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Nonpoor White in
nonpoor
neighborhood
Nonpoor White in
poor
neighborhood
Poor White in
nonpoor
neighborhood
Poor White in
poor
neighborhood
Nonpoor Black in
nonpoor
neighborhood
Nonpoor Black in
poor
neighborhood
Poor Black in
nonpoor
neighborhood
Poor Black in
poor
neighborhood
Pr
ed
ic
te
d
P
ro
b
ab
ili
ty
Note. These are predicted probabilities with adjustment for age, gender, family history of diabetes, educational attainment, and insurance status.
FIGURE 1—Predicted probabilities of diabetes prevalence by race, poverty, and place category: 1999–2004 National Health and Nutrition
Examination Survey and 2000 US Census.
RESEARCH AND PRACTICE
November 2014, Vol 104, No. 11 | American Journal of Public Health Gaskin et al. | Peer Reviewed | Research and Practice | 2153
About the Authors
Darrell J. Gaskin, Roland J. Thorpe Jr, Emma E. McGinty,
and Thomas A. LaVeist are with the Hopkins Center for
Health Disparities Solutions and the Department of Health
Policy and Management at the Johns Hopkins Bloomberg
School of Public Health, Baltimore, MD. Kelly Bower is
with the Hopkins Center for Health Disparities Solutions
and the Department of Community Public Health, Johns
Hopkins School of Nursing, Baltimore. Charles Rohde is
with the Hopkins Center for Health Disparities Solutions
and the Department of Biostatistics at the Johns Hopkins
Bloomberg School of Public Health. J. Hunter Young is with
the Department of Epidemiology at the Johns Hopkins
Bloomberg School of Public Health and the Welch Center
for Prevention, Epidemiology and Clinical Research in the
Johns Hopkins School of Medicine, Baltimore. Lisa Dubay is
with the Urban Institute, Washington, DC.
Correspondence should be sent to Darrell J. Gaskin, PhD,
Hopkins Center of Health Disparities Solutions, Department
of Health Policy and Management, Johns Hopkins Bloom-
berg School of Public Health, 624 N Broadway, Suite 441,
Baltimore, MD 21205 (e-mail: dgaskin@jhsph.edu). Re-
prints can be ordered at http://www.ajph.org by clicking the
“Reprints” link.
This article was accepted April 24, 2013.
Contributors
D. J. Gaskin and L. Dubay were the principal investi-
gators of this project. D. J. Gaskin conceptualized and
designed the analysis plan for this article. E. E. McGinty
and K. Bower conducted the literature review.
E. E. McGinty managed the data and conducted the
statistical analysis. D. J. Gaskin, R. J. Thorpe Jr, E. E.
McGinty, K. Bower, C. Rohde, J. H. Young, and
T. A. LaVeist helped interpret results. D. J. Gaskin,
R. J. Thorpe Jr, and E. E. McGinty drafted the article.
All authors were involved in reviewing and editing the
final draft of the article.
Acknowledgments
This research was supported by the National Heart, Lung
and Blood Institute (grant 5R01HL092846-02).
The analysis was conducted at the Research Data
Center of the National Center for Health Statistics.
Note. The findings and conclusions are those of the
authors and do not necessarily represent the views of the
National Center for Health Statistics or the Centers for
Disease Control and Prevention.
Human Participant Protection
The institutional review board at the Johns Hopkins
Bloomberg School of Public Health approved the study.
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Review
E ffe c ts o f N u rs e -M a n a g e d P ro to c o ls in th e O u tp a tie n t M a n a g e m e n t o f
A dults W ith C h ro n ic C onditions
A System atic Review and M eta-analysis
R yan J. S h a w , P h D , RN; J e n n ife r R. M c D u f f ie , PhD ; C ris tin a C. H e n d rix , D N S , NP; A lis o n Edie, D N P , FNP; L in d a L in d s e y -D a v is , P h D , RN;
A v is h e k N a g i, M S ; A n d rz e j S. K o sin ski, PhD ; an d Joh n W . W illia m s Jr., M D , M H S c
Background: C h an ges in fe d e ra l h e a lth p o lic y are p ro v id in g m o re
access t o m ed ica l care f o r persons w ith c h ro n ic disease. P ro v id in g
q u a lity care m a y re q u ire a te a m a p p ro a c h , w h ic h th e A m e ric a n
C o lle g e o f Physicians calls th e “m e d ic a l h o m e .” O n e n e w m o d e l
m a y in v o lv e n u rs e -m a n a g e d p ro to cols.
Purpose: T o d e te rm in e w h e th e r n u rs e -m a n a g e d p ro to c o ls are e f
fe c tiv e f o r o u tp a tie n t m a n a g e m e n t o f a d u lts w ith diabetes, h y p e r
te n s io n , an d h y p e rlip id e m ia .
Data Sources: MEDLINE, C o c h ra n e C e n tra l R egister o f C o n tro lle d
Trials, EMBASE, a n d CINAHL fro m Jan ua ry 1 9 8 0 t h ro u g h January
2 0 1 4 .
Study Selection: T w o review e rs used e lig ib ility c rite ria t o assess all
title s , ab stracts, a n d fu ll te x ts an d resolved dis a g re e m e n ts by dis
cussion o r b y c o n s u ltin g a th ird review e r.
Data Extraction: O n e re v ie w e r d id d a ta a b s tra c tio n s a n d q u a lity
assessments, w h ic h w e re c o n firm e d b y a s econd review e r.
Data Synthesis: F rom 2 9 5 4 studies, 1 8 w e re in c lu d e d . A ll studies
used a reg istere d nurse o r e q u iv a le n t w h o titra te d m e d ic a tio n s by
f o llo w in g a p ro to c o l. In a m e ta-a na lysis, h e m o g lo b in A 1c level d e
creased b y 0 .4 % (9 5 % C l, 0 .1 % t o 0 . 7 % ) (n = 8); systolic and
d ia s to lic b lo o d pressure decreased b y 3 .6 8 m m H g (C l, 1 .0 5 to
6.31 m m H g ) an d 1 .5 6 m m H g (C l, 0 .3 6 t o 2 .7 6 m m H g),
re s p ective ly (n = 12); to ta l cho le s te ro l level decreased b y 0 .2 4
m m o l/L (9 .3 7 m g /d L ) (C l, 0 . 5 4 – m m o l/L decrease t o 0 .0 5 – m m o l/L
increase [ 2 0 .7 7 – m g / d L decrease t o 2 . 0 2 – m g / d L increase]) (n = 9);
a n d lo w -d e n s ity -lip o p ro te in c h o le ste rol level decreased b y 0.31
m m o l/L (1 2 .0 7 m g /d L ) (C l, 0 . 7 3 – m m o l/L decrease t o 0 .1 1 – m m o l/L
increase [ 2 8 .2 7 – m g / d L decrease t o 4 . 1 3 – m g / d L increase]) (n = 6).
Limitation: Studies had lim ite d de s c rip tio n s o f th e in te rv e n tio n s an d
p ro to c o ls used.
Conclusion: A te a m a p p ro a c h t h a t uses n u rs e -m a n a g e d p ro to c o ls
m a y ha ve p o s itiv e e ffe c ts o n th e o u tp a tie n t m a n a g e m e n t o f a d u lts
w ith c h ro n ic c o n d itio n s , such as diabetes, h y p e rte n s io n , an d
h y p e rlip id e m ia .
Primary Funding Source: U.S. D e p a rtm e n t o f V e te ra n s A ffa irs.
Ann Intern Med. 2014;161:113-121. d o i:10.7 326 /M 13 -256 7 www.annals.org
For author affiliations, see end o f text.
M edical management of chronic illness consumes 75% of every health care dollar spent in the United States
(1). Thus, provision of economical and accessible— yet
high-quality— care is a major concern. Diabetes mellitus,
hypertension, and hyperlipidemia are prime examples of
chronic diseases that cause substantial morbidity and mor
tality (2, 3) and require long-term medical management.
For each of these disorders, most care occurs in outpatient
settings where well-established clinical practice guidelines
are available (4—7). Despite the availability o f these guide
lines, there are important gaps between the care recom
mended and the care delivered (8-10). The shortage of
primary care clinicians has been identified as 1 barrier to
the provision of comprehensive care for chronic disease
(11, 12) and is an impetus to develop strategies for expand
ing the roles and responsibilities o f other interdisciplinary
team members to help meet this increasing need.
The patient-centered medical home concept was de
veloped in an effort to serve more persons and improve
chronic disease care. It is a model of primary care transfor
mation that builds on other efforts, such as the chronic
care model (13), and includes the following elements:
patient-centered orientation toward the whole person,
team-based care coordinated across the health care system
and community, enhanced access to care, and a systems-
based approach to quality and safety. Care teams may in
clude nurses, primary care providers, pharmacists, and be-
w w w .annals.org
havioral health specialists. An organizing principle for care
teams is to utilize personnel at the highest level of their skill
set, which is particularly relevant given the expected in
crease in demand for primary care services resulting from
the Patient Protection and Affordable Care Act.
W ith this increased demand, the largest health care
workforce, registered nurses (RNs), may be a valuable asset
alongside other nonphysician clinicians, including physi
cian assistants, nurse practitioners, and clinical pharma
cists, to serve more persons and improve chronic disease
care. Robust evidence supports the effectiveness o f nurses
in providing patient education about chronic disease and
secondary prevention strategies (14-19). W ith clearly de
fined protocols and training, nurses may also be able to
order relevant diagnostic tests, adjust routine medications,
and appropriately refer patients.
O ur purpose was to synthesize the current literature
describing the effects o f nurse-managed protocols, includ-
S ee a ls o :
E d ito r ia l c o m m e n t ……………………………………………………………153
W e b – O n ly
S u p p le m e n t s
C M E q u iz
15 July 2014 Annals of Internal Medicine I Volume 161 • Number 2 [ 1 1 3
R e v i e w Nurse-Managed Protocols in Managing Outpatients W ith Chronic Conditions
Figure 1. S u m m a r y o f e v id e n c e s e a rc h a n d s e le c tio n .
I n c l u d e d ( n = 2 0 )
U n i q u e s t u d i e s : 1 8
C o m p a n i o n a r t i c l e s : 2 *
* Methods or follow-up articles.
ing medication adjustment, for the outpatient manage
ment o f adults with common chronic conditions, namely
diabetes, hypertension, and hyperlipidemia.
M e t h o d s
W e followed a standard protocol for all steps o f this
review. A technical report that fully details our methods
and presents results for all original research questions
is available at www.hsrd.research.va.gov/publications/esp
/reports.cfm.
D a t a S o u r c e s a n d S e a r c h e s
In consultation with a master librarian, we searched
M ED LIN E (via PubMed), Cochrane Central Register of
Controlled Trials, EMBASE, and CINAHL from 1 Janu
ary 1980 through 31 January 2014 for English-language,
peer-reviewed publications evaluating interventions that
compared nurse-managed protocols with usual care in
studies targeting adults with chronic conditions (Supple
ment 1, available at www.annals.org).
W e selected exemplary articles and used a Medical
Subject Heading analyzer to identify terms for “nurse pro
tocols.” W e added selected free-text terms and validated
search terms for randomized, controlled trials (RCTs) and
quasi-experimental studies, and we searched bibliographies
o f exemplary studies and applicable systematic reviews for
missed publications (15, 17, 2 0 -2 9 ). To assess for publi
cation bias, we searched ClinicalTrials.gov to identify com
pleted but unpublished studies meeting our eligibility
criteria.
S t u d y S e l e c t i o n , D a t a E x t r a c t i o n , a n d Q u a l i t y
A s s e s s m e n t
Two reviewers used prespecified eligibility criteria to
assess all titles and abstracts (Supplement 2, available at
1 1 4 15 July 2014 Annals o f Internal Medicine Volume 161 • Number 2
www.annals.org). Eligibility criteria included the involve
ment of an RN or a licensed practical nurse (LPN) func
tioning beyond the usual scope of practice, such as adjust
ing medications and conducting interventions based on a
written protocol. Potentially eligible articles were retrieved
for further evaluation. Disagreements on inclusion or ex
clusion were resolved by discussion or a third reviewer.
Studies excluded at full-text review are listed in Supple
ment 3 (available at www.annals.org). Abstraction and
quality assessment were done by 1 reviewer and confirmed
by a second. We piloted the abstraction forms, designed
specifically for this review, on a sample of included articles.
Key characteristics abstracted included patient descriptors,
setting, features of the intervention and comparator, match
between the sample and target populations, extent of the
nurse interventionist’s training, outcomes, and quality ele
ments. Supplements 4 and 5 (available at www.annals.org)
summarize quality criteria and ratings, respectively.
Because many studies were done outside the United
States, we queried the authors o f such studies about the
education and scope of practice o f the nurse intervention
ists. Authors were e-mailed a table detailing the credential-
ing and scope of practice of various U.S. nurses and asked
to classify their nurse interventionist.
D a t a S y n t h e s i s a n d A n a l y s i s
The primary outcomes were the effects of nurse-
managed protocols on biophysical markers (for example,
glycosylated hemoglobin or hemoglobin A lc [HbAlc]), pa
tient treatment adherence, nurse protocol adherence,
adverse effects, and resource use. W hen quantitative syn
thesis (that is, meta-analysis) was feasible, dichotomous
outcomes were combined using odds ratios and continuous
outcomes were combined using mean differences in
random-effects models. For studies with unique but con
ceptually similar outcomes, such as ordering a guideline-
indicated laboratory test, we synthesized outcomes across
conditions if intervention effects were sufficiently homoge
neous. We used the Knapp and H artung method (30, 31)
to adjust the SEs of the estimated coefficients.
For categories with several potential outcomes (for ex
ample, biophysical markers) that may vary across chronic
conditions, we selected outcomes for each chronic condi
tion a priori: H bA lc level for diabetes, blood pressure (BP)
for hypertension, and cholesterol level for hyperlipidemia.
In 1 example (32), we imputed missing SDs using esti
mates from similar studies.
We computed summary estimates of effect and evalu
ated statistical heterogeneity using the Cochran Q and I 2
statistics. We did subgroup analyses to examine potential
sources o f heterogeneity, including where the study was
conducted and intervention content. Subgroup analyses in
volved indirect comparisons and were subject to confound
ing; thus, results were interpreted cautiously. Publication
bias was assessed using a ClinicalTrials.gov search and fun-
w w w .a n n a ls .o r g
E x c l u d e d a t t h e t i t l e / a b s t r a c t
le v e l ( n = 2 6 1 5 )
E x c l u d e d ( n = 3 1 9 )
N o t E n g l i s h , w e s t e r n i z e d c o u n t r y ,
o r f u l l p u b l i c a t i o n : 5 5
N o a d u l t s w i t h d i s e a s e o f i n t e r e s t
o r c o n d u c t e d in a n o u t p a t i e n t
m e d i c a l s e t t i n g : 2 9
I n e l i g i b l e s t u d y d e s i g n o r
c o m p a r a t o r : 7 5
N o i n t e r v e n t i o n o f in t e r e s t : 1 5 3
N o o u t c o m e o f in t e r e s t : 7
S e a r c h r e s u l t s o f
r e f e r e n c e s ( n = 2 9 5 4 )
R e t r i e v e d f o r
f u l l – t e x t r e v i e w
( n = 3 3 9 )
Nurse-Managed Protocols in Managing Outpatients With Chronic Conditions R e v i e w
nel plots when at least 10 studies were included in the
analysis.
W hen quantitative synthesis was not feasible, we ana
lyzed data qualitatively. We gave more weight to evidence
from higher-quality studies with more precise estimates of
effect. The qualitative syntheses identified and documented
patterns in efficacy and safety of the intervention across
conditions and outcome categories. We analyzed potential
reasons for inconsistency in treatment effects across studies
by evaluating variables, such as differences in study popu
lation, intervention, comparator, and outcome definitions.
W e followed the approach recommended by the
Agency for Healthcare Research and Quality (33) to eval
uate the overall strength of the body o f evidence. This
approach assesses the following 4 domains: risk o f bias,
consistency, directness, and precision. These domains were
considered qualitatively, and a summary rating o f high,
moderate, low, or insufficient evidence was assigned.
R o le o f th e F u n d in g Source
The Veterans Affairs Quality Enhancement Research
Initiative funded the research but did not participate in the
conduct of the study or the decision to submit the manu
script for publication.
R e s u l t s
O ur electronic and manual searches identified 2954
unique citations (Figure 1). O f the 23 potentially eligible
studies, 4 were excluded because we could not verify
whether nurses had the authority to initiate or titrate med
ications and the author did not respond to our query for
clarification (34—37). We excluded a trial of older adults in
which we could not differentiate the target illnesses (38).
Approximately two thirds of the authors we contacted for
missing data or clarification responded.
We included 18 unique studies (23 004 patients) that
focused on patients with elevated cardiovascular risk (Ta
ble) (32, 3 9 -5 5 ). O f these, 16 were RCTs and 2 were
controlled before-and-after studies on diabetes (49, 53).
The comparator was usual care in all but 1 study, in which
a reverse-control design was used, and each intervention
served as the control for the other. Eleven studies were
done in Western Europe and 7 in the United States. Me
dian age o f participants was 58.3 years (range, 37.2 to 72.1
years) based on 16 studies. Approximately 47% of the par
ticipants were female. Race was not reported in 84% o f the
studies. Supplement 5 gives detailed study characteristics.
No outstanding studies were identified through Clinical-
Trials.gov. Supplement 6 provides funnel plots that assess
publication bias (available at www.annals.org).
Overall, these studies displayed moderate risk of bias.
Two studies were judged as having a high risk o f bias
because o f inadequate randomization (44, 53), 12 were
moderate risk (32, 3 9 – 4 1 , 43, 47-52, 54), and 4 were low
risk (42, 45, 46, 55). O ther design issues affecting risk-of-
bias ratings were possible contamination from a concurrent
Table. Study and Patient Characteristics of Included
Diabetes, Hypertension, and Hyperlipidem ia Studies
Characteristic Cardiovascular Risk
Studies, n ( % )
Total
Studies 18
Patients* 23 004
Design
RCT 16 (89)
Non-RCT 2 ( 1 1 )
Location
U nited States 7 ( 3 9 )
W estern Europe 11 (61)
S etting
General medical hospital 12 (67)
Specialty hospital 3 (17)
Primary clinic and specialty hospital 2 ( 1 1 )
Telephone- and clinic-delivered care 1 (5.5)
Risk o f b ia s /q u a lity
L o w /g o o d 4 ( 2 2 )
M o d e ra te /fa ir 12 (67)
H ig h /p o o r 2 (11)
RCT — randomized, controlled trial.
* Number of patients represents the total mean of 22 839 and 23 170 because in
1 included study (30), hypertension and hyperlipidemia results were reported on 2
different but overlapping populations due to randomization,
t Range, 14-36 mo.
$ Clinical certification or diabetes nurse educator.
§ Motivational interviewing.
w w w .annals.org
15 July 2014 Annals of Internal Medicine Volume 161 •Number 2 1 1 5
R e v i e w Nurse-Managed Protocols in Managing Outpatients W ith Chronic Conditions
F i g u r e 2 . Effects of nurse-managed protocols on hemoglobin A1c level.
Study, Year (Reference) Nurse Protocols Total, n Usual Care
Total, n
Mean (SD) Mean
A u b e rte ta l, 1 9 9 8 (4 0 ) 7.10 (1.33) 51 8.20
H ouw e lin g et al, 2009 (47) -1 .5 0 (1.35) 46 -0 .9 0
H ouw e lin g et al, 2011 (46) -0 .0 9 (1.07) 102 0.03
M acM ahon e t al, 2009 (48) -0 .3 4 (0.97) 94 0.12
O ‘H are et al, 2004 (52) -0 .2 3 (1.42) 182 -0 .2 0
Taylor e t al, 2003 (32) -1 .1 4 (1.35) 61 -0 .3 5
W allym ahm ed et al, 2011 (54) 9.30 (1.40) 40 9.70
Summary ( /2 = 69 .8% )
(SD)
W eighted Mean
Difference
(95% Cl), %
-1 .1 0 (-1 .6 2 t o -0 .5 8 )
-0 .1 5 (-0 .3 3 to 0.03)
-0 .6 0 (-1 .1 5 t o -0 .0 5 )
-0 .1 2 (-0 .4 3 to 0.19)
-0 .4 6 (-0 .7 4 t o -0 .1 8 )
-0 .0 3 (-0 .3 4 to 0.28)
-0 .7 9 (-1 .2 4 t o -0 .3 4 )
-0 .4 0 (-0 .9 9 to 0.19)
-0 .4 0 (-0 .7 0 t o -0 .1 0 )
intervention, unblinded outcome assessors, and incomplete
outcomes data.
Characteristics o f the Interventions
All 18 study interventions used a protocol and re
quired the nurse to titrate medications; however, only 11
reported that the nurse was independently allowed to ini
tiate new medications. All but 1 study (55) provided the
actual algorithm or citation. An RN (not an advanced
practice RN) was the interventionist in all U.S. studies; a
nurse with an equal scope o f practice was the intervention
ist in the non-U.S. studies. N o studies reported use of
LPNs. In 14 studies, interventions were delivered in a
nurse-led clinic (3 9 -4 2 , 44, 4 6 -5 4 ). Supervisors were
nearly always physicians. O f the studies reporting nurses’
training, 3 used specialists (for example, diabetes-certified),
10 used RNs with study-specific training, and 1 used nurse
case managers with experience in coordinating long-term
care.
Nurse protocols included additional components, such
as education or self-management, in 16 studies. Two stud
ies (41, 47) did not report additional intervention. Baseline
characteristics showed that patients with diabetes had an
elevated H bAlc level of approximately 8.0% or greater.
Most patients with hypertension had moderate hyperten
sion, and patients with hyperlipidemia had borderline high
lipid levels. Outcomes were assessed at 6 to 36 months,
with most studies reporting outcomes at 12 months or
longer.
D iabetes O utcom es
O f the 15 studies done in patients with diabetes, 10
RCTs (2633 patients) targeted glucose control. Figure 2
shows the forest plot o f the random-effects meta-analysis
on H bA lc level. Compared with usual care, nurse-managed
protocols decreased H bA lc levels by 0.4% (95% C l, 0.1%
to 0.7%) (n = 8) and effects varied substantially (Q =
23.19; I 2 = 70%). In the 2 non-RCTs (49, 53) not in
cluded in Figure 2, effects of the protocols on H bA lc level
1 1 6 15 July 2014 Annals o f Internal Medicine Volume 161 • Number 2
were larger and in the same direction but had higher vari
ability. Thus, nurse-managed protocols were associated
with a highly variable mean decrease in H bA lc level.
O ther diabetes-related performance measures were
rarely reported (Supplement 6). In 1 controlled before-
and-after study (53), achieving target eye examination, uri
nary m icroalbumin-creatinine ratio, and foot examination
goals was reported to reach 80% to 100% using nurse-
managed protocols. A second study (49) found a nonsig
nificant increase in intervention patients achieving eye and
foot examination goals compared with control participants.
Reduction in the proportion of patients with an H bA lc
level o f 8.5% or greater was achieved in 1 study (odds
ratio, 1.69 [Cl, 1.25 to 2.29]) (49).
BP O utcom es
Fourteen studies reported BP outcomes: 13 RCTs
(10 362 patients) and 1 non-RCT (885 patients). Re
stricted to the 12 RCTs specifically addressing BP (10 224
patients), the intervention decreased systolic BP by 3.68
mm Hg (Cl, 1.05 to —6.31 mm Hg) and diastolic BP by
1.56 mm H g (Cl, 0.36 to 2.76 mm Hg), with high vari
ability (72 > 70%) (Figures 3 and 4). Funnel plots sug
gested possible publication bias with systolic but not dia
stolic BP (Supplement 6). Overall, nurse-managed
protocols were associated with a mean decrease in systolic
and diastolic BP.
Eleven of the 18 studies focused on achieving various
target BPs: 10 RCTs (9707 patients) and 1 non-RCT (885
patients). W hen the analysis was restricted to RCTs, nurse-
managed protocols were more likely to achieve target BP
than control protocols (odds ratio, 1.41 [Cl, 0.98 to
2.02]), but these results could have been due to chance,
and treatment effects were highly variable (Q = 35.20;
/ 2 = 74%) (Supplement 7, available at www.annals.org).
Using the summary odds ratio and median event rate from
the control group of the trials that implemented nurse pro
tocols, we estimated the absolute treatment effect as a risk
w w w . a n n a l s . o r g
Nurse-Managed Protocols in Managing Outpatients W ith Chronic Conditions R e v i e w
difference o f 120 more patients achieving target total BP
per 1000 patients (Cl, 6 fewer to 244 more). Funnel plots
suggested some asymmetry but no clear publication bias.
H y p e r l i p i d e m i a O u t c o m e s
Fifteen studies reported hyperlipidemia outcomes: 13
RCTs (14 817 patients) and 2 non-RCTs (1114 patients).
O f these, 9 RCTs (3494 patients) specifically addressed
total cholesterol levels and 6 RCTs specifically addressed
low-density lipoprotein levels (1095 patients). In analyses
restricted to these trials, the intervention was associated
with a decrease in total cholesterol level. Total cholesterol
levels decreased by 0.24 mmol/L (9.37 mg/dL) (Cl, 0.54-
mmol/L decrease to 0.05-mmol/L increase [20.77-mg/dL
decrease to 2.02-mg/dL increase]) [n = 9), and low-
density lipoprotein cholesterol levels decreased by 0.31
mmol/L (12.07 mg/dL) (Cl, 0.73-mmol/L decrease to
0.11-mmol/L increase [28.27-mg/dL decrease to 4.13-
mg/dL increase]) (n = 6), with marked variability in inter
vention effects (72 > 89%) (Figure 4). Effects o f nurse-
managed protocols on total and low-density lipoprotein
cholesterol levels from the 2 non-RCTs (49, 53) were in
the same direction. Reductions in total cholesterol level
were not statistically significant. Overall, nurse-managed
protocols were associated with a mean decrease in total and
low-density lipoprotein cholesterol levels.
All 11 studies (9221 patients) targeting various total
cholesterol levels were included in the quantitative analysis
(Supplement 7). Nurse-managed protocols were statisti
cally significantly more likely to achieve target total choles
terol levels than control protocols (odds ratio, 1.54 [Cl,
Figure 3 . Effects o f n u rs e -m a n a g e d p ro tocols on systolic (to p ) an d d ia s to lic ( b o tto m ) b lo o d pressure.
Study, Year (Reference) Nurse Protocols Total, n Usual Care Total, n
Mean (SD)
Mean (SD)
Bebb et al, 2007 (41) 143.30 (19.50) 743 143.10 (17.70) 677
Bellary et al, 2008 (42) 134.30 (20.36) 868 134.60 (20.36) 618
Denver et al, 2003 (44) 141.10 (19.30) 59 151.00 (21.90) 56
Houweling et al, 2009 (47) -8.60 (20.54) 46 -4.00 (14.91) 38
Houweling et al, 2011 (46) -7.40 (17.82) 102 -5.60 (16.45) 104
MacMahon et al, 2009 (48) -10.50 (17.45) 94 1.70 (19.39) 94
N ew et al, 2003 (51) 147.00 (20.23) 506 149.00 (20.23) 508
New et al, 2004 (50) 142.00 (24.00) 2474 142.17 (24.00) 2531
O’Hare et al, 2004 (52) -6.69 (21.24) 182 -2.11 (17.47) 179
Rudd et al, 2004 (55) -14.20 (16.23) 69 -5.70 (18.59) 68
Taylor et al, 2003 (32) 4.40 (17.45) 61 8.60 (19.39) 66
Wallymahmed et al, 2011 (54) 115.00 (13.00) 40 124.00 (14.00) 41
Summary (/2 = 75.1%)
– 2 0
I “1
-1 5 -1 0 – 5 0
Weighted Mean Difference, mm Hg
Weighted Mean
Difference
(95% Cl), mm Hg
0.20 (-1.73 to 2.13)
-0.30 (-2.40 to 1.80)
-9.90 (-17.46 t o -2.34)
-4.60 (-12.20 to 3.00)
-1.80 (-6.49 to 2.89)
-12.20 (-17.47 t o -6.93)
-2.00 (-4.49 to 0.49)
-0.17 (-1.50 to 1.16)
-4.58 (-8.59 to -0,57)
-8.50 (-14.35 t o -2.65)
-4.20 (-10.61 to 2.21)
-9.00 (-14.88 t o -3.12)
-3.68 (-6.31 t o -1.05)
Study, Year (Reference) Nurse Protocols Total, n Usual Care Total, n
Mean (SD) Mean (SD)
Bebb et al, 2007 (41) 78.20 (10.20) 743 77.90 (10.40) 677
Bellary et al, 2008 (42) 78.40 (8.63) 868 80.31 (8.63) 618
Denver et al, 2003 (44) 79.90 (10.60) 59 82.20 (12.40) 56
Houweling et al, 2009 (47) -1.40 (9.09) 46 -2.40 (7.61) 38
Houweling et al, 2011 (46) -3.20 (10.18) 102 -1.00 (9.26) 104
MacMahon et al, 2009 (48) -5.90 (8.72) 94 -0.51 (9.69) 94
New et al, 2003 (51) 74.00 (11.29) 506 74.79 (11.29) 508
New et al, 2004 (50) 78.20 (16.06) 2474 78.11 (16.06) 2531
O’Hare et al, 2004 (52) -3.14 (10.56) 182 0.28 (10.00) 179
Rudd et al, 2004 (55) -6.50 (10.00) 69 -3.40 (7.90) 68
Taylor et al, 2003 (32) 2.20 (10.00) 61 1.90 (9.30) 66
Wallymahmed et al, 2011 (54) 65.00 (9.00) 40 69.00 (9.00) 41
Summary (/2 = 75.1 %)
Weighted Mean
Difference
(95% Cl), mm Hg
0.30 (-0.77 to 1.37)
-1.91 (-2.80 t o -1.02)
-2.30 (-6.53 to 1.93)
1.00 (-2.57 to 4.57)
-2.20 (-4.86 to 0.46)
-5.39 (-8.03 to -2.75)
-0.79 (-2.18 to 0.60)
0.09 (-0.80 to 0.98)
-3.42 (-5.54 t o -1.30)
-3.10 (-6.12 t o -0.08)
0.30 (-3.07 to 3.67)
-4.00 (-7.92 to -0.08)
-1.56 (-2.76 t o -0.36)
I—————- 1—————–
-1 0 – 5 0
Weighted Mean Difference, mm Hg
w w w .a n n a ls .o r g 15 July 2014 Annals o f Internal Medicine Volume 161 • Number 2 1 1 7
R e v i e w Nurse-Managed Protocols in Managing Outpatients W ith Chronic Conditions
F ig u re 4. E ffe c ts o f n u r s e – m a n a g e d p ro to c o ls o n t o t a l c h o le s te r o l ( t o p ) a n d l o w – d e n s i t y lip o p r o t e in c h o le s te r o l ( b o t t o m ) le v e ls .
Summary U2 = 90.8%)
166.00 (38.60) 40 200.80 (38.60) 41
Weighted Mean
Difference
(95% Cl), mg/dL
-3.00 (-14.14 to 8.14)
1.15 (-1.54 to 3.84)
-24.33 (-30.82 to -17.84)
19.30 (2.59 to 36.01)
-1.93 (-11.47 to 7.61)
-20.47 (-31.18 to -9.76)
-10.81 (-16.99 to -4.63)
-9.10 (-18.67 to 0.47)
-34.80 (-51.61 to -17.99)
-9.37 (-20.77 to 2.02)
—–1—–
– 4 0 – 2 0 0 2 0
Weighted Mean Difference, mg/dL
Study, Year (Reference) Nurse Protocols Total, n Usual Care Total, n
Mean (SD) Mean (SD)
Weighted Mean Difference, mg/dL
Weighted Mean
Difference
(95% Cl), mg/dL
2.00 (-7.70 to 11.70)
-24.71 (-30.21 t o -19.21)
11.59 (-0.69 to 23.87)
-20.46 (-31.17 t o -9.75)
-12.90 (-23.53 to -2.27)
-27.03 (-40.49 to -13.57)
-12.07 (-28.27 to 4.13)
To convert mg/dL to mmol/L, multiply by 0.0259.
1.02 to 2.31]), with substantial variability in treatment
effects (Q = 71.59; / 2 = 86%). Using the summary odds
ratio and median event rate from the control group of the
RCTs, we estimated the absolute treatment effect as a risk
difference o f 106 more patients achieving target total cho
lesterol levels per 1000 patients (Cl, 5 to 196). Funnel
plots did not suggest publication bias (Supplement 6).
P a tie n t A d h e re n c e to T r e a tm e n t
Behavioral adherence was reported in 4 studies (39,
43, 48, 49). In 1 study, the rate o f daily medication adher
ence (±SE) for the intervention group during the 6-month
study was 80.5% ± 23.0% compared with 69.2% ±
31.1% for the usual care group (P = 0.03) (55). When
reported, effects on lifestyle changes and medication adher
ence showed an overall pattern of small positive effects
associated with nurse-managed protocols.
A d h e re n c e to P ro to cols
Two studies (39, 52) reported data on nurses’ adher
ence to treatment protocols. W hen compared with usual
care, nurses instituted pharmacologic therapy for lipid
management more often (39). O ’Hare and colleagues (52)
found that hypoglycemic agents and antihypertensives, in-
1 1 8 15 July 2014 Annals o f Internal Medicine Volume 161 • Number 2
eluding angiotensin-converting enzyme inhibitors, angio
tensin II antagonists, and statins, were started or doses were
increased by nurses following treatment protocols more of
ten than in usual care groups.
A d v e rs e Effects
The included studies had few reports on adverse effects
associated with nurse-managed protocols. Only 1 study on
diabetes in a U.S. H M O (40) reported adverse effects.
Severe low blood glucose events were identical (1.5%) at
baseline and increased similarly— 2.9% in the control
group compared with 3.1% in the intervention group (P =
0.158).
R esource U se
Resource use was reported in only 3 studies (45, 47,
51). Houweling and colleagues (47) found total salary costs
(±SE) to be significantly lower in the intervention group
(€114.6 ± €50.4) than in the control group (€138.3 ±
€48.3; P < 0.001). In this same study, total costs for med
ication were reported to be lower in the intervention
groups (€136.3 ± €91.9) than in the control group
(€149.0 ± €94.4; P > 0.05) at study completion.
w w w .an n als.o rg
Nurse-Managed Protocols in Managing Outpatients W ith Chronic Conditions R e v i e w
Inpatient costs were reported to be substantially lower
in 2 other studies. O ne study (45) estimated total inpatient
costs for the intervention group at $869 535 compared
with $1 702 682 for the control group (P = 0.02). The
second study (51) reported decreases in costs by sex, with
the intervention groups achieving a decrease o f $606 for
men and $888 for women. Further, total outpatient costs
were reported at $1 237 270 in the nurse-managed proto
col group compared with $1 381 900 in the control group
( P = 0.47) (51).
S u b g ro u p A n a ly s is
W e did subgroup analyses comparing studies that were
conducted in the United States compared with other coun
tries, had targeted H bA lc alone compared with multiple
conditions, and incorporated self-management plans com
pared with those that did not. These analyses showed
greater effects on decreasing H bA lc level only for studies
done on diabetes management in the United States (—0.92
vs. —0.23; P — 0.01). Treatment variability was reduced
in these subgroups. Therefore, some variability in diabetes
care may be explained by country or specificity o f the in
tervention. For BP and cholesterol, subgroup analysis
found no statistically significant differences in treatment
effects. We planned to conduct subgroup analyses examin
ing the intervention primarily by clinic visits compared
with telephone calls, but variability in the results was
insufficient.
D i s c u s s i o n
Nurse-managed protocols in the studies we examined
had a consistently positive effect on chronically ill patients.
Hemoglobin A lc levels decreased by approximately 0.4%
(moderate strength of evidence [SOE]). Systolic and dia
stolic BP decreased by 4 mm Hg and 2 mm Hg, respec
tively (moderate SOE). Total cholesterol levels decreased
by 0.24 mmol/L (9.37 mg/dL), and low-density lipopro
tein cholesterol levels decreased by 0.31 mmol/L (12.07
mg/dL) (low SOE). Im portant differences were found in
treatment effects across studies for most outcomes. Sub
group analyses explained little of this variability and
showed differences only for effects on H bA lc level between
non—U.S.-based and U.S.-based studies. Effects o f nurse-
managed protocols on lifestyle changes and medication ad
herence were reported infrequently, but when reported,
they showed an overall pattern o f small positive effects (low
SOE).
T he SOE was insufficient to estimate a treatment ef
fect for all other outcomes: protocol adherence, adverse
effects, and resource use. Indirect evidence (for example,
proportion o f patients prescribed the indicated medication)
suggests reasonable adherence to the protocol by nurses.
Although these studies showed protocol adherence by
nurses in intervention groups compared with control par
ticipants, the SOE on nurse adherence was judged to be
insufficient. Further, only 1 o f the 18 studies reported ad-
w w w . a n n a l s . o r g
verse effects (40); therefore, the SOE was judged to be
insufficient to determine the effect of nurse-managed pro
tocols on adverse effects in treatment studies about chronic
disease. Finally, resource use was reported in only 3 studies
(45, 47, 51), so the evidence is insufficient to determine
any effect.
O ur study has many strengths, including a protocol-
driven review, a comprehensive search, careful quality as
sessment, and rigorous quantitative synthesis methods.
However, our report and the literature also have limita
tions. Because inclusion criteria required medication titra
tion, we may have missed studies in which nurses had
autonomy to practice in other capacities beyond their
scope of practice. We did not include studies of inpatient
settings in which nurses might often use protocols. The
literature lacked detailed descriptions of the interventions
and protocols used. Studies had limited descriptions of in
tervention intensity; treatment adherence; nurses’ educa
tion levels, training, or supervision; protocol adherence;
adverse effects; and resource use. Eleven of the 18 studies
were done in countries outside the United States, which
may limit applicability to U.S practices. O ther perfor
mance measures were rarely reported. Studies were limited
to the use of RNs; there was no report of using LPNs.
Finally, the reported outcomes varied across studies and
contributed to unexplained variability.
W ith changes in federal health policy, new models are
needed to provide more accessible and effective chronic
disease care. The implementation of a patient-centered
medical home model will play a critical role in reconfigur
ing team-based care and will expand the responsibilities of
team members. O ur review shows that team approaches
using nurse-managed protocols help improve health out
comes among patients with moderately severe diabetes, hy
pertension, and hyperlipidemia. In addition, RNs can suc
cessfully titrate medications according to protocols for
these conditions. Similar results were found on the effects
o f quality improvement strategies on glycemic control in
type 2 diabetes where case managers did not have to wait
for physician approval to adjust medications (56). Further
research is needed to understand the effects o f nurse-
managed protocols in caring for complex or unstable pa
tients. Supplement 8 (available at www.annals.org) pres
ents a detailed table of identified evidence gaps and a
framework for future research.
As the largest health care workforce group, nurses are
in an ideal position to collaborate with other team mem
bers in the delivery o f more accessible and effective chronic
disease care. Team members, such as clinical pharmacists,
may also be able to serve in similar capacities and in areas
with limited health care resources (57). Thus, health care
systems will need to balance the benefits and costs associ
ated with each team member and determine who is best
suited to take on these expanded roles. Results from our
review suggest that nurse-managed protocols have positive
15 July 2014 Annals o f Internal Medicine Volume 161 • Number 2 1 1 9
Review Nurse-Managed Protocols in Managing Outpatients With Chronic Conditions
effects on outpatient care of adults with chronic
conditions.
F ro m D u rh a m V eterans Affairs C e n te r for H e a lth Services Research in
P rim ary Care; G eriatric Research, E d u c a tio n , a n d C linical C e n te r, D u r
h a m V eterans Affairs M edical C e n te r; a n d D u k e U niversity, D u rh a m ,
N o r th C arolina.
D is c la im e r: T h e c o n te n t is solely th e responsibility o f th e au th o rs a n d
does n o t necessarily represent th e official views o f U .S. D e p a rtm e n t o f
V eterans Affairs o r D u k e U niversity. Ail w ork herein is original. All
au th o rs m eet th e criteria for a u th o rsh ip , in c lu d in g acceptance o f resp o n
sibility for th e scientific c o n te n t o f th e m a n u scrip t.
A c k n o w le d g m e n t: T h e au th o rs th a n k C o n n ie S ch ard t, M LS, for help
w ith the litera tu re search a n d retrieval a n d Liz W in g , M A , for editorial
assistance.
F in a n c ia l S upport: T h is re p o rt is based o n research c o n d u c te d by th e
Evidence-based Synthesis P ro g ram (ESP) C e n te r located at the D u rh a m
V eterans Affairs M edical C e n te r, D u rh a m , N o r th C a ro lin a, w h ich is
fu n d e d by th e D e p a rtm e n t o f V eterans Affairs, V eterans H e a lth A d m in
istration, O ffice o f R esearch a n d D ev elo p m en t, H e a lth Services Research
a n d D ev e lo p m e n t (VA-ESP P roject 0 9 -0 1 0 ; 20 1 3 ). T h e first au th o r, D r.
R yan Shaw, was su p p o rte d by a D e p a rtm e n t o f V eterans Affairs H e a lth
Services Research a n d D ev e lo p m e n t O ffice o f A cadem ic Affiliations
n u rsin g p o std o c to ral research aw ard (T P P -2 1 -0 2 1 ).
D isclosu res: D r. W illiam s reports grants fro m V eterans Affairs H e a lth
Services Research a n d D e v e lo p m e n t d u rin g th e c o n d u c t o f th e study.
A u th o rs n o t n a m e d here have disclosed n o conflicts o f interest.
D isclosures can be view ed at w w w .ac p o n lin e .o rg /au th o rs/icm je/C o n flict
O f ln te re s tF o tm s .d o ? m s N u m = M 13-2567.
R equests f o r S in g le R eprints: R yan J. Shaw, P h D , R N , H e a lth Services
Research a n d D ev e lo p m e n t (1 5 2 ), 411 W e st C h a p el H ill Street, Suite
6 0 0 , D u rh a m , N C 2 7 7 0 1 ; e-m ail, ryan.shaw @ duke.edu.
C u rre n t a u th o r addresses a n d a u th o r c o n trib u tio n s are available at
w w w .annals.org.
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