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Doing Business 2015
Going Beyond Efficiency
Comparing Business regulations for domestiC firms in 189
eConomies
A World Bank Group Flagship Report
1 2 t h e d i t i o n

© 2014 International Bank for Reconstruction and Development
/ The World Bank
1818 H Street NW, Washington DC 20433
Telephone: 202-473-1000; Internet: www.worldbank.org
Some rights reserved
1 2 3 4 17 16 15 14
This work is a product of the staff of The World Bank with
external contributions. The findings, interpretations, and
conclusions
expressed in this work do not necessarily reflect the views of
The World Bank, its Board of Executive Directors, or the
governments
they represent. The World Bank does not guarantee the accuracy
of the data included in this work. The boundaries, colors,
denomina-
tions, and other information shown on any map in this work do
not imply any judgment on the part of The World Bank
concerning the
legal status of any territory or the endorsement or acceptance of
such boundaries.
Nothing herein shall constitute or be considered to be a
limitation upon or waiver of the privileges and immunities of
The World Bank,

all of which are specifically reserved.
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including for commercial purposes, under the following
conditions:
Attribution—Please cite the work as follows: World Bank.
2014. Doing Business 2015: Going Beyond Efficiency.
Washington, DC: World
Bank. DOI: 10.1596/978-1-4648-0351-2. License: Creative
Commons Attribution CC BY 3.0 IGO
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All queries on rights and licenses should be addressed to the
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ISBN (paper): 978-1-4648-0351-2
ISBN (electronic): 978-1-4648-0352-9
DOI: 10.1596/978-1-4648-0351-2
ISSN: 1729-2638
Cover design: Corporate Visions, Inc.

Doing Business 2015
COMPARING BUSINESS REGULATIONS FOR DOMESTIC
FIRMS IN 189 ECONOMIES
A World Bank Group Flagship Report
1 2 T H E D I T I O N
Doing Business 2015
Resources on the
Doing Business website
Current features
News on the Doing Business project
http://www.doingbusiness.org
Rankings
How economies rank—from 1 to 189
http://www.doingbusiness.org/rankings
Data
All the data for 189 economies—topic
rankings, indicator values, lists of

regulatory procedures and details
underlying indicators
http://www.doingbusiness.org/data
Reports
Access to Doing Business reports as well
as subnational and regional reports,
reform case studies and customized
economy and regional profiles
http://www.doingbusiness.org/reports
Methodology
The methodologies and research papers
underlying Doing Business
http://www.doingbusiness.org/methodology
Research
Abstracts of papers on Doing Business
topics and related policy issues
http://www.doingbusiness.org/research
Doing Business reforms
Short summaries of DB2015 business
regulation reforms, lists of reforms since

DB2008 and a ranking simulation tool
http://www.doingbusiness.org/reforms
Historical data
Customized data sets since DB2004
http://www.doingbusiness.org/custom-query
Law library
Online collection of business laws and
regulations relating to business
http://www.doingbusiness.org/law-library
Contributors
More than 10,700 specialists in 189
economies who participate in Doing
Business
/contributors/doing-business
Entrepreneurship data
Data on business density (number of
newly registered companies per 1,000
working-age people) for 139 economies

/exploretopics/entrepreneurship
Distance to frontier
Data benchmarking 189 economies to
the frontier in regulatory practice
/distance-to-frontier
Information on good practices
Showing where the many good
practices identified by Doing Business
have been adopted
/good-practice
Doing Business iPhone app
Doing Business at a Glance—presenting
the full report, rankings and highlights
for each topic for the iPhone, iPad and
iPod touch
/special-features/iphone

Doing Business 2015
v Foreword
1 Overview
1 5 About Doing Business
24 What is changing in Doing Business?
33 Reforming the business environment in 2013/14
Case studies
47 Starting a business
The growing efficiency of company registries
53 Zoning and urban planning
Understanding the benefits
60 Registering property
Measuring the quality of land administration systems
67 Getting credit
The importance of registries
76 Protecting minority investors
Going beyond related-party transactions

83 Paying taxes
Trends before and after the financial crisis
90 Enforcing contracts
How judicial efficiency supports freedom of contract
96 Resolving insolvency
Measuring the strength of insolvency laws
102 Highlights from the Doing Business research conference
109 References
1 1 4 Data notes
146 Distance to frontier and ease of doing business ranking
152 Summaries of Doing Business reforms in 2013/14
167 Country tables
231 Labor market regulation data
252 Acknowledgments
Contents
Doing Business 2015 is the 12th in a
series of annual reports investigating
the regulations that enhance business
activity and those that constrain it.
Doing Business presents quantitative

indicators on business regulations
and the protection of property rights
that can be compared across 189
economies—from Afghanistan to
Zimbabwe—and over time.
Doing Business measures regulations
affecting 11 areas of the life of a
business. Ten of these areas are
included in this year’s ranking on the
ease of doing business: starting a
business, dealing with construction
permits, getting electricity, registering
property, getting credit, protecting
minority investors, paying taxes,
trading across borders, enforcing
contracts and resolving insolvency.
Doing Business also measures labor
market regulation, which is not included
in this year’s ranking.
Data in Doing Business 2015 are current
as of June 1, 2014. The indicators are
used to analyze economic outcomes
and identify what reforms of business
regulation have worked, where and why.
Doing Business 2015
Foreword
How to use Doing Business indicators and how not to

The public discourse on eco-nomic policy is overwhelmingly
focused on fiscal measures,
monetary interventions, welfare pro-
grams and other such highly visible
instruments of government action.
Thus when an economy does poorly, a
disproportionate amount of our debate
centers on whether or not it needs a
fiscal stimulus, whether there should be
liquidity easing or tightening, whether
its welfare programs have been too
profligate or too paltry and so on.
What gets much less attention but is
equally—and, in some situations, even
more—important for an economy’s
success or failure is the nuts and
bolts that hold the economy together
and the plumbing that underlies the
economy.

The laws that determine how easily a
business can be started and closed,
the efficiency with which contracts are
enforced, the rules of administration
pertaining to a variety of activities—
such as getting permits for electricity
and doing the paperwork for exports
and imports—are all examples of the
nuts and bolts that are rarely visible
and in the limelight but play a critical
role. Their malfunctioning can thwart
an economy’s progress and render
the more visible policy instruments,
such as good fiscal and monetary poli-
cies, less effective. Just as the Space
Shuttle Challenger broke apart on
takeoff from Cape Canaveral, Florida,
on January 28, 1986, not because (as

was later realized) something major
had gone wrong but because a joint
held together by a circular nut called
the O-ring had failed, an economy can
be brought down or held back by the
failure of its nuts and bolts. The World
Bank Group’s Doing Business report
is an annual statement of the state
of the nuts and bolts of economies
around the world and, as such, is one of
the most important compendiums of
information and analysis of the basis
of an economy’s effective day-to-day
functioning and development.
Creating an efficient and inclusive
ethos for enterprise and business
is in the interest of all societies. An
economy with an efficient bureaucracy

and rules of governance that facilitates
entrepreneurship and creativity among
individuals, and provides an enabling
atmosphere for people to realize
their full potential, can enhance living
standards and promote growth and
shared prosperity. It can also help
in creating an environment in which
standard macroeconomic policies are
more effective and course through the
economy more easily. After decades
of debate there is now some conver-
gence in economics about the roles
of the market and the state. To leave
everything to the free market can lead
to major economic malfunction and
elevated levels of poverty, and have
us be silent witnesses to, for instance,

discrimination against certain groups.
Moreover, there is a logical mistake that
underlies the market fundamentalist
philosophy. To argue that individuals
and private businesses should have
all the freedom to pursue what they
DOING BUSINESS 2015vi
wish and that government should not
intervene overlooks the fact that gov-
ernment is nothing but the outcome
of individual actions. Hence the edict
is internally inconsistent. Fortunately,
market fundamentalism has, for the
most part, been relegated to the mar-
gins of serious policy discourse.
Turning to the other extreme, it is now
widely recognized that to have the

state try to do it all is a recipe for eco-
nomic stagnation and cronyism. In any
national economy there are too many
decisions to be made, and too great a
variety of skills and talents scattered
through society, for any single author-
ity to take effective charge.
It is true that government should inter-
vene in the market to help the disadvan-
taged, to keep inequality within bounds,
to provide public goods and to create
correctives for market failures such
as those stemming from externalities,
information asymmetries and systemic
human irrationalities.1 But over and
above these, government also has the
critical responsibility to provide a nimble
regulatory setup that enables ordinary

people to put their skills and talents
to the best possible use and facilitates
the smooth and efficient functioning
of businesses and markets.2 It is this
critical role of providing an enabling
and facilitating ethos for individual tal-
ent and enterprise to flourish—which
includes an awareness of where not to
intervene and interfere—that the Doing
Business report tries to measure. There
is no unique way of doing this, and there
are plenty of open conceptual questions
one has to contend with. In brief, by its
very nature Doing Business has all the
ingredients of being both important and
controversial, and it has lived up to both
qualities in ample measure.
SWITCHING SIDES
As an independent researcher and,

later, as Chief Economic Adviser to the
Indian government, I used, criticized,
valued and debated the Doing Business
report, unaware that I would be at the
World Bank one day and hence be
shifted from the side of the consumer
to that of the manufacturer of this
product. This shift has given me a
360-degree view of Doing Business and,
along with that, an awareness of its
strengths and weaknesses, which oth-
ers, luckier than I, may not have.
Its greatest strength is its transpar-
ency and adherence to clearly stated
criteria. Doing Business takes the same
set of hypothetical questions to 189
economies and collects answers to
these. Thus, for instance, when check-

ing on an economy’s efficacy in “enforc-
ing contracts,” it measures the time,
cost and procedures involved in resolv-
ing a hypothetical commercial lawsuit
between 2 domestic firms through a
local court. The dispute involves the
breach of a sales contract worth twice
the size of the income per capita of
the economy or $5,000, whichever is
greater. This meticulous insistence on
using the same standard everywhere
gives Doing Business a remarkable
comparability across economies.
However, this same strength is inevi-
tably a source of some weaknesses. It
means that, contrary to what some
people believe, Doing Business is not
based on sample surveys of firms. It is

not feasible, at least not at this stage,
to conduct such surveys in 189 econo-
mies. A lot of the Doing Business data
are based on careful collection of de jure
information on what an economy’s laws
and regulations require. Further, even
when, based on a study of one economy
or a cluster of economies, some measure
is found to be an important determinant
of the ease of doing business, it may not
be possible to put this measure to use
unless a way is found to collect informa-
tion on it from all 189 economies.
Nor does the fact that the same mea-
sures are collected for all economies
automatically mean that they are the
right measures. The same measure
may be more apt for one economy and

less so for another. As Ken Arrow once
pointed out, the medieval English law
under which no one was allowed to sleep
on park benches applied to both pau-
pers and aristocrats, but since the latter
typically did not consider the use of park
benches for napping, it was amply clear
that this horizontally anonymous law
was actually meant for only one class of
people, namely the poor.3
Another problem arises from the fact
that the overall ease of doing business
ranking is an aggregation of 10 com-
ponent indicators—measuring how
easy it is (in the economy concerned)
to start a business, deal with construc-
tion permits, get electricity, register
property, get credit, pay taxes, trade

across borders, enforce contracts and
resolve insolvency and how strong the
protections for minority investors are.
Further, each of these 10 component
indicators is itself an amalgam of
several even more basic measures. The
way all this is aggregated is by giving
each basic measure the same weight to
get to each component indicator, and
then giving an equal weight to each of
the 10 component indicators to get to
the final score. Questions may indeed
be asked about whether it is right
to give the same weight to different
indicators.4 Is an economy’s speed at
1. There is evidence that human beings are not just frequently
irrational but have certain systema tic propensities to this, which
can be and has been used to
exploit individuals (Akerlof and Shiller 2009; Johnson 2009).
By this same logic, these irrationalities can be used to promote
development and growth. The

next World Development Report (World Bank, forthcoming), to
be published in December 2014, is devoted to this theme.
2. This convergent view can increasingly be found in
microeconomics books, such as Bowles (2006); Basu (2010);
and Ferguson (2013).
3. Arrow 1963.
4. There is a lot of research on the choice of weights when
aggregating and on the algebra of ranking; see, for example,
Sen (1977); Basu (1983); and Foster,
McGillivray and Seth (2012).
viiFOREWORD
giving an electricity connection to a
new enterprise as important as its
ability to enforce contracts efficiently?
Further, the measures count both the
time taken to get certain permits and
clearances and also the number and
intricacy of procedures. These also
entail weights.
There is a way of doing away with
weights, an approach that involves

declaring one economy to be ranked
above another only if it dominates
the other in all 10 indicators. This is
referred to as the criterion of vector-
dominance, and its properties have
been studied and are well understood.
The trouble with this criterion is that
it leads to incompleteness in rankings.
For many pairs of economies it will not
be possible to treat either as ranked
above the other; nor can we, in such
cases, declare the 2 to be equally good
in terms of the ease of doing business.
This is illustrated in the figure, which
ranks a small cluster of economies by
using vector-dominance in terms of the
10 indicators. A downward line between
2 economies represents dominance, and

2 economies that cannot be connected
by a downward line cannot be compared
with each other. Hence Singapore is
unequivocally ranked above Ireland,
which is ranked above Cyprus and so on.
Singapore is also ranked above Latvia.
Similarly, New Zealand is ranked above
Latvia, which is above Morocco and
Benin, and so on. Singapore and New
Zealand, which are this year’s winner
and runner-up in our ordinal ranking,
cannot, however, be ranked in terms of
vector-dominance; nor can we rank New
Zealand and Ireland.5
It is true that the figure shows only
a small segment of the quasi-order
over the 189 economies; but even if we
showed the full set, the picture would

be populated with pairs of economies
that cannot be ranked. That is indeed
the disadvantage of vector-dominance.
When it pronounces judgment, it does
so with great authority, but it achieves
this at the cost of total reticence over
large domains.
What I suspected when I was a user
of Doing Business, and now know, is
that a significant number of the top
30 economies in the ease of doing
business ranking come from a tradition
where government has had quite a
prominent presence in the economy,
including through the laying out of
rules to regulate different dimensions
of the activities of the private sector.
However, all these economies have

an excellent performance on the
Doing Business indicators and in other
international data sets capturing
various dimensions of competitiveness.
The top-performing economies in the
ease of doing business ranking are
therefore not those with no regulation
but those in which governments have
managed to create rules that facilitate
interactions in the marketplace without
needlessly hindering the development
of the private sector.6 Ultimately, Doing
Business is about smart regulations
that only a well-functioning state can
provide. The secret of success is to
have the essential rules and regulations
in place—but more importantly to have
a good system of clearing decisions
quickly and predictably, so that small

and ordinary businesses do not feel
harassed.
To get to an evaluation of this, one has
to make choices, such as what to include
and what to exclude and what weights
to use. This has been done in creating
the Doing Business measures, and effort
is being made to improve on these.
Excessive taxation, for instance, can
dampen incentives and adversely affect
an economy’s functioning. But this
does not mean that the lower the tax
rates and collections, the better. There
are economies where the tax revenue
to GDP ratio is so low that it hampers
the government’s ability to regulate
efficiently, invest in infrastructure and
provide basic health and education

services to the poor. With that in mind,
the Doing Business team changed the
indicator that used to treat a lower
tax rate as better. Three years ago a
threshold was set such that economies
with tax rates below this threshold are
not rewarded. This has reduced the bias
in favor of economies that choose not
to levy even a reasonable tax on private
companies.
Our attention has been drawn to many
critiques by the Independent Panel
on Doing Business, chaired by Trevor
Manuel, which submitted its report in
2013.7 Following this report a decision
was made to set a 2-year target to
improve the methodology of Doing
Business without damaging the overall

integrity of this valuable publication.
The Doing Business team is in the midst
Ranking by vector-dominance
Singapore
Ireland
Cyprus
Senegal
New Zealand
Latvia
Morocco
Benin
5. This example of vector-dominance is based only on the top 2
economies in this year’s ease of doing business ranking. The
figure was constructed as
follows: First, all economies were sorted by their ranking, and
the first economy for which all 10 indicator rankings are lower
than those of Singapore was
identified: Ireland. The process was then repeated for Ireland,
and so on for all 189 economies. Second, the analysis was
replicated, this time starting
with New Zealand. Third, all pairs of economies in the figure
were compared (for example, the horizontal line between
Singapore and Latvia means that
Singapore vector-dominates Latvia and all economies connected
with a vertical line under Latvia).

6. See Besley and Burgess (2004).
7. The report by the Independent Panel on Doing Business is
available on its website at http://www.dbrpanel.org/.
DOING BUSINESS 2015viii
of such an exercise, and it is hoped that
independent researchers, wherever in
the world they happen to be, will join in
the task of refining and improving this
important document.
STRENGTHS AND
WEAKNESSES
While the 2-year task of improving the
methodology continues, it is worth being
clear that there is no such thing as the
best, all-encompassing indicator. As
a consequence, responsibility rests as
much with the users of the ease of doing
business ranking as with its producers
to make sure that it is a valuable

instrument of policy. Controversy has
often arisen from reading more into
the ranking or indicator than what it
actually captures. It has been pointed
out, critically, that there are economies
that do poorly on the Doing Business
indicators but that nevertheless get
a lot of foreign direct investment
(FDI) from global corporations. These
examples are usually nothing more
than a reminder that an economy has
many more aspects than the features
that are tracked and measured by the
Doing Business report. The flow of FDI
into an economy is facilitated by having
a better doing business ethos, true, but
FDI flows can be thwarted by other
policy weaknesses; and, conversely, an

economy with poor performance on the
Doing Business indicators may make up
for it in other ways so as to attract
large FDI inflows. The fact that there
are examples of economies that do not
do well on the Doing Business indicators
but continue to receive flows of FDI
shows that private corporations do not
make this mistake; they will decide on
the basis of a range of factors.
Another common criticism is implicit
in the question, If economy x is grow-
ing fast, why does it not rank high on
the ease of doing business? First, if
the ease of doing business ranking
were constructed in such a way that
it had a very high correlation with
GDP or GDP growth, there would be

little reason to have a new ranking. We
would be able to get our result from
looking at GDP or GDP growth tables.
Second, this question is often rooted in
the common mistake, already noted,
of treating the ease of doing busi-
ness ranking as an all-encompassing
measure of an economy’s goodness.
It is not. An economy can do poorly on
Doing Business indicators but do well in
macroeconomic policy or social welfare
interventions. In the end, Doing Business
measures a slender segment of the
complex organism that any modern
economy is. It attempts to capture a
segment that is representative of other
general features of the economy (and
effort will be made to improve on this),

but the fact remains that an economy
can undo the goodness or badness of
its performance on Doing Business indi-
cators through other policies.
Moreover, economic efficiency is not
the only measure by which we evalu-
ate an economy’s performance.8 Most
of us value greater equality among
people; the ease of doing business
ranking is not meant to measure suc-
cess on that scale. We value better
health, better education, literature
and culture; the ease of doing business
ranking is not meant to capture these
either. It is a mistake to treat this as a
criticism of the ease of doing business
ranking; it is simply a reminder that life
is a many-splendored thing, and the

Doing Business report tries to capture
one aspect of the good life. The need is
to resurrect that once-popular expres-
sion, “ceteris paribus.” Other things re-
maining the same, an economy should
try to improve its score underlying the
ease of doing business ranking.
In putting the ease of doing business
ranking to use in crafting policy, it
is important to keep in mind these
caveats, strengths and weaknesses.
Ultimately, the Doing Business indicators
are meant to simply hold up a mirror to
economies. A poor score should alert a
government that it ought to examine
its regulatory structure. On the basis of
this it may decide to change some regu-
latory features and policies in ways that

may not even directly affect its ease of
doing business ranking but nevertheless
improve the economy’s performance. If
this happens, and there is some evidence
that it does, the Doing Business report
would be serving its purpose. There are
governments that attract a lot of talent
into their bureaucracy but nevertheless
do not have an efficient administration
because the bureaucrats get trapped in
their arcane rules of engagement. This
is a report that can be of great value to
such governments. And it is gratifying
that a large number of governments
have put it precisely to such use.
Promoting a well-functioning, competi-
tive private sector is a major undertak-
ing for any government, especially for

one with limited resources and techni-
cal capabilities. It requires long-term
comprehensive policies targeting mac-
roeconomic stability; investment in in-
frastructure, education and health; and
the building of technological and entre-
preneurial capacity. A well-functioning
political system—one in which the gov-
ernment is perceived to be working in the
public interest while managing scarce
resources in a reasonably transparent
way—plays a central role. Removing
administrative barriers and strengthen-
ing laws that promote entrepreneurship
and creativity—both of which are within
the power of governments to do—can
set an economy on the path to greater
prosperity and development. There is

compelling evidence that excessively
burdensome regulations can lead to
large informal and less-productive sec-
tors, less entrepreneurship and lower
rates of employment and growth.
8. See Stiglitz, Sen and Fitoussi (2009); World Bank (2014a);
and World Bank and IMF (2014).
ixFOREWORD
CARDINALITY,
ORDINALITY, RANKINGS
AND RATINGS
One feature of the report that has
received a lot of attention is its use of
rankings. Ultimately, what the report
does is to provide a table with a simple
ordinal ranking of all 189 economies.
After a lot of debate and discussion
a decision was made to stay with the
overall ranking, even though other,

cardinal features of the exercise are at
the same time being strengthened, as
will be explained shortly.
It was in 2005 that the World Bank
Group management decided to start
ranking economies on the ease of
doing business because it recognized
the value of benchmarking exercises
in generating interest among policy
makers in reform.9 In an area that
had received little attention from
policy makers before the …
Journal of Aging and Health
1 –26
© The Author(s) 2015
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0898264315589576
jah.sagepub.com

Article
Racial Differences in
Minnesota Nursing
Home Residents’ Quality
of Life: The Importance
of Looking Beyond
Individual Predictors
Tetyana P. Shippee, PhD1,
Carrie Henning-Smith, MPH, MSW1,
Taeho Greg Rhee, AM1, Robert N. Held, MBA2,
and Robert L. Kane, MD1
Abstract
Objectives: The aim of this study is to investigate raci al
differences in
nursing home (NH) residents’ quality of life (QOL) at the
resident and
facility levels. Method: We used hierarchical linear modeling to
identify
significant resident- and facility-level predictors for racial
differences in
six resident-reported QOL domains. Data came from the
following: (a)
resident-reported QOL (n = 10,929), (b) the Minimum Data Set,
and (c)
facility-level characteristics from the Minnesota Department of
Human
Services (n = 376). Results: White residents reported higher
QOL in five
of six domains, but in full models, individual-level racial
differences remained
only for food enjoyment. On the facility level, higher
percentage of White

residents was associated with better scores in three domains,
even after
adjusting for all characteristics. Discussion: Racial differences
in QOL exist
on individual and aggregate levels. Individual differences are
mainly explained
1University of Minnesota, Minneapolis, USA
2Minnesota Department of Human Services, Minneapolis, USA
Corresponding Author:
Tetyana P. Shippee, Division of Health Policy and Management,
School of Public Health,
University of Minnesota, 420 Delaware Street SE, MMC 729,
Minneapolis, MN 55455, USA.
Email: [email protected]
589576 JAHXXX10.1177/0898264315589576Journal of Aging
and HealthShippee et al.
research-article2015
mailto:[email protected]
2 Journal of Aging and Health
by health status. The finding that facility racial composition
predicts QOL
more than individual race underscores the importance of
examining NH
structural characteristics and practices.
Keywords
long-term care, quality of life, racial disparities, nursing home
Introduction

Members of racial/ethnic minority populations have been
underrepresented in
nursing homes (NHs) and other formal long-term care (LTC)
services (Mui,
Choi, & Monk, 1998). However, over the past decade, the
proportion of older
adults from minority groups residing in NHs has quickly grown
(Agency for
Healthcare Research and Quality [AHRQ], 2000). Racial/ethnic
minority
groups tend to receive poorer quality of care (QOC) in LTC
settings (Smith,
Feng, Fennell, Zinn, & Mor, 2007), although this may be due to
patterns of use
of a subset of facilities rather than outright discrimination in
admission
(Konetzka & Werner, 2009). Despite the increase in use of NH
services by
racial/ethnic minority groups, we know little about their quality
of life (QOL)
in these settings and how it compares to their White
counterparts.
NH quality should not be solely defined as QOC, which is
typically mea-
sured by staff-reported clinical outcomes. Instead, NH quality
should include
resident-reported measures of QOL, which is multidimensional,
incorporat-
ing aspects of residents’ lives and experiences, physical
environment and
comfort, relationships with staff and other residents, food and
meal enjoy-
ment, social engagement, and mood (Kane, 2003; Shippee,
Henning-Smith,
Kane, & Lewis, 2013). Yet, most research on racial differences

in NH quality
has focused on QOC (Allsworth, Toppa, Palin, & Lapane, 2005;
Christian,
Lapane, & Toppa, 2003; Fennell, Miller, & Mor, 2000; Hughes
& Lapane,
2004). Many of these studies have been limited to specific
medical condi-
tions and clinical procedures, such as diabetes (Allsworth et al.,
2005) and
medical management for stroke (Allsworth et al., 2005;
Christian et al., 2003;
Hughes & Lapane, 2004).
In contrast, this study uses a multidimensional, validated,
resident-reported
measure of QOL for the entire population of Medicaid-certified
NHs in
Minnesota, which is one of the few states in the nation to
collect such mea-
sures (Shippee et al., 2013). We use cumulative inequality (CI)
theory
(Ferraro & Shippee, 2009) to examine whether NH residents’
QOL differs by
racial characteristics—and if so, whether these differences are
explained by
resident- or facility-level predictors. At the individual level, the
outcome of
Shippee et al. 3
interest is whether individual QOL is related to race. At the
structural level,
the outcome of interest is whether facility-level QOL is
associated with facil-

ity racial composition and individual QOL. Ours is the first
study to look at
resident- and facility-level correlates of racial differences in
NH QOL; our
findings provide insights for policy and practice as the NH
population contin-
ues to diversify.
Racial Differences on Individual and Facility Levels
Few studies explicitly address racial differences in NH QOL.
Resident-level
differences in NHs’ QOC include more use of physical
restraints among
Black residents compared with White residents (Cassie &
Cassie, 2013), less
use of specialized dementia care among minority residents
(Sengupta,
Decker, Harris-Kojetin, & Jones, 2012), and lower rates of flu
vaccination for
Black residents versus White residents (Cai, Feng, Fennell, &
Mor, 2011). A
few, mainly qualitative, studies find generally lower QOL for
minority older
adults (Engle, Fox-Hill, & Graney, 1998; Ryvicker, 2011; Wu &
Barker,
2008). For example, a study examining needs and preferences of
Black and
White NH residents close to death found that QOL was equally
important for
Black and White adults, but that Black residents reported more
untreated pain
(Engle et al., 1998). Another study found that older Asian NH
residents
reported lack of cultural understanding (e.g., culturally
inappropriate food)

and need for more social engagement (Wu & Barker, 2008).
Another qualita-
tive study found that residents in a NH serving a predominantly
racial/ethnic
minority population had lower quality of resident–staff
interaction because
the facility was less focused on promoting resident-centered
care and because
staff members were compared with those in a facility serving
predominately
White residents, had less resident individual information, and
were less
trained on how to communicate with residents (Ryvicker, 2011).
Overall,
minority NH residents, especially Black older adults, tend to
have lower
functional status (Jones, Sonnenfeld, & Harris-Kojetin, 2009),
which affects
QOL (DuBeau, Simon, & Morris, 2006; Shippee et al., 2013).
Therefore,
these residents may also report lower QOL ratings compared
with their White
counterparts.
Especially needed is better understanding of how facility-level
predictors
affect racial differences in QOL, particularly as racial
segregation is an
important reason for racial disparities in health care (Greene,
Blustein, &
Weitzman, 2006; Hayanga et al., 2009). A growing body of
literature shows
that minorities tend to be disproportionately served by hospitals
with lower
QOC (Jha, Fisher, Li, Orav, & Epstein, 2005). Research on
disparities in LTC

has demonstrated that long-standing policies of racial and
socioeconomic
segregation have resulted in segregated NH facilities (Smith et
al., 2007,
2008). NHs that house higher proportions of Black residents
often have seri-
ous quality deficiencies, lower staffing ratios, and greater
financial vulnera-
bility (Stone, 2011). Also, minority neighborhoods have fewer
NHs than
predominately White neighborhoods (Smith et al., 2007), and
the NHs that do
operate in high-poverty neighborhoods, with a higher proportion
of minority
elderly residents, have higher numbers of closures and fare
worse on quality
performance measures (Feng et al., 2011b). Although these
studies demon-
strate troubling disparities in QOC for minority older adults,
few studies have
examined differences in QOL by race/ethnicity.
Existing literature focuses mostly on racial differences in
admission to
NHs and QOC. Black older adults have historically used NHs
(and other
forms of LTC) at a lower rate than their White counterparts
(Feng, Fennell,
Tyler, Clark, & Mor, 2011a), despite bearing higher disability
burden. This
difference may reflect personal choice, as well as difficulty

accessing LTC.
However, trends now indicate that Black older adults use NHs
more than
White older adults (Smith et al., 2008). Black older adults
admitted to NHs
are in worse health than their White counterparts (Cagney &
Agree, 2005), in
part because they are less likely to have a primary care provider
and more
likely to delay care (Ferraro & Shippee, 2008). Cultural
preferences may
explain differences in timing of admission; some research shows
different
norms around extended family providing care (Cagney & Agree,
2005) and
historical mistrust of health care by minorities, especially Black
older adults
(Gamble, 1997). Payment source may be another obstacle; most
Black older
adults rely on Medicaid (Sengupta et al., 2012), and some NHs
prefer to
admit residents who are not on Medicaid.
Theoretical Framework
We draw on the CI theory (Ferraro & Shippee, 2009) to
understand racial
differences in QOL in NHs. CI theory is a middle-range theory
that synthe-
sizes elements from stress process (Pearlin, Schieman, Fazio, &
Meersman,
2005), cumulative disadvantage (Dannefer, 2003), and life-
course perspec-
tives (Elder,1998) to focus on how inequality is generated in
social systems
and plays out over the life course. We view the link between

race/ethnicity
and QOL as resting on both individual and structural levels or
resulting from
the interplay “between institutional arrangements and individual
life trajecto-
ries” (O’Rand, 1996, p. 230). Despite this being a cross-
sectional study, we
believe CI theory is useful because it helps understand
disparities in LTC as
an outcome of lifetime processes of disadvantage/advantage. CI
theory is
useful to our study in at least two respects.
Shippee et al. 5
First, the theory emphasizes the role of accumulated risk and
available
resources for individual outcomes. Race/ethnicity is confounded
with socio-
economic status and is related to socially determined barriers
and resultant
health risks which might result in different patterns of LTC use.
Black older
adults (who constitute the majority of our non-White sample)
have a greater
likelihood of depending on Social Security benefits as their only
source of
income and of depending on Medicaid for health insurance
compared with
their White counterparts which might result in different options
of facilities
for LTC (Sengupta et al., 2012). Therefore, they may experience
limited
access to high quality NHs and worse health status when

admitted. Race/
ethnicity is also related to one’s personal cultural attitudes and
beliefs about
health and the health care system. A history of racial
discrimination and
exploitation of vulnerable populations has led to greater distrust
in health
care providers and health care systems among minority
populations than that
of their White counterparts (Boulware, Cooper, Ratner, LaVeist,
& Powe,
2003). Research shows systematic differences in the QOC
received by White
versus Black older adults in LTC (Cai et al., 2011).
Second, CI theory emphasizes that inequality has an
institutional charac-
ter, with attention to how structural arrangements, not simply
individual char-
acteristics or choices, maintain or exacerbate inequality on a
variety of
outcomes, including health care. Racial disparities in QOC on
the facility
level include greater numbers of deficiencies and lower staffing
ratios in NHs
with more minority residents (Smith et al., 2007). In addition,
CI theory
states that neighborhood context contributes to the development
of inequality
through access to care and available social support, among other
factors
(Ferraro, Shippee, & Schafer, 2009). Research shows that
minority individu-
als, because of their lower socioeconomic status, are more
likely to live in
impoverished, often racially segregated neighborhoods,

characterized by
resource-poor institutions and health care providers (Smith et
al., 2007). Lack
of these resources can result in a variety of health problems for
the residents
including health hazards (Cagney, Browning, & Wen, 2005),
delayed access
to primary care (Latkin & Curry, 2003), and lower QOC (Jha et
al., 2005).
NHs that are most likely to serve poor and non-White
populations are char-
acterized by limited resources, reliance on Medicaid, and poor
QOC (Mor,
Zinn, Zngelelli, Teno, & Miller, 2004) and are at greatest risk
of closure
(Feng et al., 2011b).
Based on CI theory and the existing literature, we hypothesized
that on the
individual level, minority residents in NHs would report lower
individual
QOL due to accumulated health and socioeconomic
disadvantages over their
life course, compared with their White counterparts (Hypothesis
1 [H1]). On
the facility level, we expected that NHs that serve a higher
proportion of
White older adults would have higher average QOL scores
compared with
facilities that serve a higher proportion of non-White residents
(Hypothesis 2

[H2]). However, there may be individual differences in QOL
based on the
facility-level racial composition. For example, non-White
residents may have
higher QOL in a facility with others “like them,” whereas White
residents
may do worse in a facility with a higher percentage of non-
White residents.
Thus, we also examine the effect of facility racial composition
on individual
QOL scores, building on the classic work on group composition
and indi-
vidual outcomes in education (e.g., Rosenberg & Simmons,
1971). We com-
bine multidimensional resident-reported data on QOL with
clinical data from
the Minimum Data Set (MDS) and facility-level characteristics
from the
Minnesota Department of Human Services (DHS) to investigate
these
hypotheses.
Method
Sample and Data Sources
This study used data from three sources: (a) self-reported
resident interviews
using a multidimensional measure of QOL, (b) resident clinical
data from the
MDS version 2.0, and (c) facility-level characteristics from
reports to the
Minnesota DHS.
Self-reported resident QOL was compiled from the Resident
Quality of

Life and Satisfaction With Care Survey. The interview is
conducted annually
via two-stage random sampling, in which facilities provide a list
of long-stay
and short-stay residents. The interview sample includes separate
random
samples of long- and short-stay residents at each facility (Vital
Research,
2010). Residents were eligible for either list if they were not in
isolation due
to communicable illness and if they or their guardian did not
decline partici-
pation. In 2010, 96% of residents (27,724) were eligible to
participate, and
58% (16,187) were sampled to be approached. Of these, 15%
had unsuccess-
ful interview attempts with the most common reasons being
inability to
respond (5%), refusal (4%), and severe cognitive impairment
(2%), leaving a
survey response rate of 85% (n = 13,433). An average of 35
interviews per
facility were completed (Vital Research, 2010).
Face-to-face interviews used a 52-item survey covering
previously vali-
dated QOL domains (Kane, 2003; Kane et al., 2003). The survey
uses a sim-
plified yes/no binary response structure to facilitate inclusion of
respondents
with mild to moderate cognitive impairment (except for mood
items, which
use a Likert-type scale from 1 to 4). The majority of
respondents missed only
one to five out of 52 items (58%), with 14% responding to all
52 items.

Shippee et al. 7
Patterns of missing data differed by resident characteristics,
with older, lon-
ger-stay, and more cognitively impaired residents being less
likely to have a
complete survey. We addressed missing values by using the
multiple imputa-
tion approach in Stata via the “mi” procedure (Rubin, 1996;
StataCorp LP,
2011). Findings were robust to alternate strategies of handling
missing data,
including list-wide deletion.
Resident clinical data were drawn from the mandatory MDS 2.0
data, for
all NH residents with a QOL report in 2010. Most independent
variables had
little missing data (less than 6%). Facility-level characteristics
come from
facility reports to the DHS. Specific facility characteristics are
described
below. We had no missing data on facility characteristics. For
2010, our full
models used 10,969 resident surveys (385 non-White).
Measurement of QOL
Our measure of QOL consisted of six QOL domains:
environment, personal
attention, food enjoyment, engagement, negative mood, and
positive mood,
plus a summary score, which includes all domains. This work is

based on the
original conceptual work by Kane et al. (2003) with updated
factor analyses
to better match the Minnesota sample and revised survey
instrument (Shippee
et al., 2013). Prior work has found that mood is strongly
associated with QOL
(Kane et al., 2003) and positive and negative mood load in
factor analyses as
two independent domains (Shippee et al., 2013).
The environment domain includes four items addressing ease of
navi-
gating one’s room, arrangement of personal items, and the
ability to take
care of one’s possessions. Personal attention includes six items
asking
residents whether staff treat them politely and with respect,
whether they
are handled gently, whether they can get help when needed, and
whether
they feel listened to. Food enjoyment includes three items
asking residents
whether they enjoy the food and mealtimes and whether or not
their favor-
ite foods are served in the facility. Engagement includes nine
items asking
whether there are activities that the resident enjoys, whether
staff members
know what the resident likes, whether they feel known as a
person by staff
and other residents, and whether they would consider any staff
or other
residents to be friends. Negative mood includes six items asking
residents
how often in the past 2 weeks they have been bored, angry,

worried, sad,
afraid, or lonely. Negative mood scores are re-scaled so that
higher scores
indicate less negative mood. Finally, positive mood includes
three items
asking residents how often in the past 2 weeks they have felt
peaceful,
interested in things, and happy. In factor analyses, all domains
are loaded
with alpha scores >.60.
Resident-Level Variables
Our selection of resident-level characteristics includes
demographic and
health characteristics that have been shown to affect NH
residents’ QOL
(Shippee et al., 2013).
Our main sociodemographic variable of interest is
race/ethnicity, mea-
sured as White versus non-White. Although it would be ideal to
examine
QOL for each racial/ethnic group separately, our data were
limited by the
number of non-White NH residents (only 3% of residents were
non-White).
Of those who were non-White, 55% were Black, 24% were
Native American,
11% were Hispanic, and 10% were Asian. Other
sociodemographic charac-
teristics include age, gender, educational attainment (high

school degree or
more vs. less than high school), marital status (married vs.
widowed/divorced/
never married), living arrangement prior to entering the facility
(lived alone,
lived with others, transferred from another facility), length of
stay (in years),
and payment source on admission (Medicaid, Medicare, and
private insur-
ance/self-family pay).
Health characteristics include difficulties with activities of
daily living
(scored from 0 to 28; high score indicates more impairment;
Morris, Fries, &
Morris, 1999), Alzheimer’s disease, mood/anxiety disorder,
count of chronic
conditions (scored from 0 to 4 or more, includes cancer,
Parkinson’s disease,
multiple sclerosis, stroke, arthritis, diabetes mellitus, and hip
fracture), and
cognitive status (1 = better cognitive performance,
corresponding to score of
0-3 on the original measure vs. 0 = score of 4-6 corresponding
to higher cog-
nitive impairment; Morris et al., 1994).
Facility-Level Variables
In addition to the percentage of minority residents, facility-
level characteris-
tics included structural features that may affect QOL (Shippee,
Hwanhee,
Henning-Smith, & Kane, 2014). We included physical
environment (size and
structure) and organizational factors of the service system (care

delivery and
staffing) in our models (Lucas et al., 2007). Physical
environment characteris-
tics included location (rural, metropolitan, and micropolitan),
resident acuity
level (an aggregate measure of resident clinical severity,
derived from the
Minnesota Case Mix Classification Index, a score based on
Resource
Utilization Groups, which are determined by MDS items and
have been shown
to predict utilization among NH residents), percentage of
private rooms, size
(fewer than 75 beds vs. 75 beds or more), and ownership (for -
profit, non-
profit, and government). Organizational characteristics
consisted of care
delivery and staffing measures. These include staff retention
(percentage of
Shippee et al. 9
direct care staff not leaving each year) and hours of care per
resident day by
different staff specialties (e.g., activity staff, licensed social
workers, Certified
Nursing Assistants (CNAs), Registered Nureses (RNs), and
Licensed Practical
Nurses (LPNs)). To see the impact of QOC on QOL, we include
a quality
improvement score (a measure of performance by the Centers
for Medicare
and Medicaid (CMS) based on health inspections, QOC rating,
and staffing

combined into an overall rating on a 1 to 5 scale). Finally, we
included a mea-
sure of participation in Minnesota’s Performance-Based
Incentive Payment
Program (PIPP), which allows facilities to apply for funding for
self-initiated
quality improvement projects. Participation in PIPP may
indicate a facility’s
motivation to improve QOL for residents, as well as their
organizational abil-
ity to do so.
Analytic Plan
The analysis was divided into three main stages. First, we
examined racial
differences for each QOL domain (on the bivariate level). We
estimated racial
differences using Pearson’s chi-square test of significance.
Second, to evalu-
ate the relationship between individual race/ethnicity and each
QOL domain
on the resident level, we used hierarchical linear models
(HLMs), adjusted
for relevant covariates. Because residents are nested within
NHs, resident
characteristics may be correlated with NH characteristics. HLM
was suitable
because it accounts for “within-group” correlation and provides
better infer-
ences (Raudenbush & Bryk, 2002). Third, to investigate racial
differences in
QOL on the facility level, we examined the role of racial
composition (per-
centage of non-White residents in each NH) for aggregate
facility-level QOL

using ordinary least squares (OLS) regression, with QOL scores
and other
characteristics aggregated at the mean for each facility. Finally,
we used
HLM to estimate the relationship between percentage of White
(on the facil-
ity level) and individual resident-reported QOL, controlling for
resident- and
facility-level covariates. We used “xt” family of commands in
Stata v.12 for
multivariate HLM analyses.
Results
Table 1 presents descriptive statistics and bivariate tests of
racial differences.
At the bivariate level, non-White NH residents rated QOL lower
in all
domains but environment and the summary score. A number of
significant
differences between White and non-White NH residents
emerged. The non-
White NH residents were approximately 14 years younger than
their White
counterparts. Non-White residents were also less likely to be
female, tended
Table 1. Descriptive Statistics for Dependent and Independent
Variables.
Range White Non-White

Quality of life domains
Environment 0-4 3.13 3.28
Personal attention 0-6 5.27 5.01***
Food 0-3 2.32 2.07***
Engagement 0-9 6.37 6.01*
Negative mood 0-6 4.27 4.16*
Positive mood 0-3 2.30 2.20*
Summary score 0-31 23.68 22.73
Resident-level characteristics
Race/ethnicity
Non-Hispanic White 0-1 1.00 0.00***
Non-Hispanic Black/African American 0-1 0.00 0.55***
Native American 0-1 0.00 0.24***
Asian 0-1 0.00 0.10***
Hispanic 0-1 0.00 0.11***
Age 21-111 84.38 70.82***
Female 0-1 0.70 0.53***
Married 0-1 0.21 0.12***
High school education 0-1 0.66 0.64
Prior living arrangement
Lived alone 0-1 0.39 0.28***
Transferred from another facility 0-1 0.26 0.24***
Activities of daily living impairments 0-28 14.08 12.23***
Alzheimer’s disease 0-1 0.12 0.06***
Anxiety/mood disorders 0-3 0.65 0.62
Count of chronic conditions 0-4 1.10 1.19*
Good cognitive performance (vs. impaired) 0-1 0.88 0.88
Length of stay (years) 0-46 3.06 3.81***
Insurance coverage
Private only 0-1 0.14 0.05***
Medicaid 0-1 0.43 0.85***
Medicare 0-1 0.38 0.08***
Dual 0-1 0.05 0.02***
Facility-level characteristics
Metropolitan status
Rural 0-1 0.28 0.07***

Metro 0-1 0.52 0.82***
Micro 0-1 0.20 0.12***
(continued)
Shippee et al. 11
to have longer lengths of stay, and were much more likely to be
covered by
Medicaid, compared with White NH residents. White NH
residents had more
functional dependency and higher rates of Alzheimer’s than
their non-White
counterparts (opposite of our expected findings based on CI
theory). Non-
White NH residents lived in larger facilities, which had fewer
private rooms
compared with those occupied by White NH residents. Non-
White NH resi-
dents were more likely to reside in for-profit NHs than White
NH residents,
and most non-White NH residents were in facilities located in
urban areas,
compared with White NH residents.
To evaluate the association between individual race and
individual QOL,
we estimated HLMs predicting each individual QOL domain,
plus the sum-
mary score, controlling for resident and facility characteristics,
with residents
nested within facilities (Table 2).
We found that individual resident characteristics explained most

of the
variability in QOL, and thus showed the effect of resident-
related covariates
only (facility-related predictors accounted for only 3% of the
variability in
Range White Non-White
Acuity level (case-mix) 0.63-1.41 1.05 1.03***
Percent private rooms 0-100 39.09 29.42***
Active bed count 15-397 93.41 116.41***
Ownership
For-profit 0-1 0.26 0.43***
Non-profit 0-1 0.64 0.54***
Government 0-1 0.10 0.03***
Staff retention 0.28-0.97 0.74 0.78***
Staff direct care hours (per resident day)
Activities staff 0-0.62 0.24 0.20***
CNAs 0-4.23 2.37 2.10***
Licensed mental health/social workers 0-1.22 0.11 0.14***
LPNs 0.13-1.63 0.72 0.75***
RNs 0-1.53 0.43 0.49***
Quality improvement score 1-5 3.03 2.92*
Participation in PIPP 0-1 0.19 0.18
n 10,538 385
Note. Mean for continuous variables and proportion for
categorical variables. PIPP =
Performance-Based Incentive Payment Program.
Differences between White and non-White significant at *p <
.05. **p < .01. ***p < .001.
Table 1. (continued)

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https://doi.org/10.1177/0898264318767778
Journal of Aging and Health
1 –19
© The Author(s) 2018
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DOI: 10.1177/0898264318767778
journals.sagepub.com/home/jah
Original Article

Disparities in Nursing
Home Use and Quality
Among African
American, Hispanic,
and White Medicare
Residents With
Alzheimer’s Disease and
Related Dementias
Maricruz Rivera-Hernandez, PhD1,
Amit Kumar, PhD1, Gary Epstein-Lubow, MD1,2,
and Kali S. Thomas, PhD1,3
Abstract
Objective: This article examines differences in nursing home
use and
quality among Medicare beneficiaries, in both Medicare
Advantage and fee-
for-service, newly admitted to nursing homes with Alzheimer’s
disease and
related dementias (ADRD). Method: Retrospective, national,
population-
based study of Medicare residents newly admitted to nursing
homes with
ADRD by race and ethnic group. Our analytic sample included
1,302,099
nursing home residents—268,181 with a diagnosis of ADRD—
in 13,532
nursing homes from 2014. Results: We found that a larger share
of
Hispanic Medicare residents that are admitted to nursing homes
have ADRD
compared with African American and White beneficiaries. Both
Hispanics
1Brown University, Providence, RI, USA

2Center for Alzheimer’s Disease and Memory Care Hebrew
SeniorLife, Boston, MA, USA
3Providence VA Medical Center, RI, USA
Corresponding Author:
Maricruz Rivera-Hernandez, Investigator in Health Services,
Policy, and Practice,
Brown University, Box G-S121-6, Providence, RI 02903, USA.
Email: [email protected]
767778 JAHXXX10.1177/0898264318767778Journal of Aging
and HealthRivera-Hernandez et al.
research-article2018
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2 Journal of Aging and Health 00(0)
and African Americans with ADRD received care in segregated
nursing
homes with fewer resources and lower quality of care compared
with
White residents. Discussion: These results have implications for
targeted
efforts to achieve health care equity and quality improvement
efforts among
nursing homes that serve minority patients.
Keywords
Alzheimer’s disease and dementia, nursing home disparities,
Hispanics with

dementia, disparities among dementia residents
Introduction
The proportion of Hispanic and African American older adults
using nursing
homes has increased dramatically since the late 1990s (Feng,
Fennell, Tyler,
Clark, & Mor, 2011). In 10 years, the proportion of African
American and
Hispanic residents grew by 10% and 55%, respectively (Feng et
al., 2011).
Poor quality of nursing home care received by these groups has
been docu-
mented, with newer studies continuing to report disparities in
the quality of
care and quality of life in these settings (Hefele et al., 2017;
Shippee, Henning-
Smith, Kane, & Lewis, 2015; Smith, Feng, Fennell, Zinn, &
Mor, 2007).
Recently, a major concern expressed by the Centers of Medicare
and
Medicaid has focused on the similarly poor quality of nursing
home care for
persons with Alzheimer’s disease and related dementias
(ADRD) and ways to
improve the quality of care for these individuals (Centers for
Medicare &
Medicaid Services [CMS], 2017d). Existing literature suggests
that cognitive
impairment is a predictor of institutionalization and a large
percentage of
people with dementia may enter a nursing home before death
(Gaugler, Yu,
Krichbaum, & Wyman, 2009; Schulz et al., 2004; Yaffe et al.,

2002). This
may be particularly important among African Americans and
Hispanics, who
are more likely to have ADRD than Whites (Mayeda et al.,
2014; Potter et al.,
2009; Samper-Ternent et al., 2012). Despite the increased
utilization of nurs-
ing homes and the higher rates of ADRD by Hispanics and
African Americans,
we know little about the quality of nursing home care among
African
Americans and Hispanics with ADRD and how it compares to
Whites, espe-
cially in the Medicare program that covers 95% of people with
ADRD
(Alzheimer’s Association, 2016).
One of the goals of Healthy People 2020 is to improve the
quality of life
for people with ADRD, which relates to quality of long-term
care (Healthy
People 2020, 2014; Kane, 2001). However, long-term care is
accompanied
Rivera-Hernandez et al. 3
by critical gaps and challenges regarding care for people with
ADRD, and
addressing these challenges requires implementing practices
that promotes
person-centered care and enhances the quality of life for every
nursing home
resident with ADRD (CMS, 2017d; Shih, Concannon, Liu, &
Friedman,

2014). In addition, two goals of the National Alzheimer’s
Project Act (NAPA)
include enhancing care quality and efficiency and improving
data to track
progress (Khachaturian, Khachaturian, & Thies, 2012). Among
the different
strategies that NAPA highlighted to achieve these goals are
reducing and
eliminating disparities in access to quality health care and to
increase the
availability and quality of data collected and reported on
racial/ethnic minor-
ity populations (Office of the Assistant Secretary for Planning
and Evaluation,
2015). Thus, there is a pressing need to characterize care and
outcomes of
nursing home residents with ADRD.
Research on ADRD using national samples and diverse
populations is
limited. In fact, many studies have been published on the
difficulty of
recruiting minority populations with ADRD to conduct research
(Barnes &
Bennett, 2014; Grill & Galvin, 2014). The absence of research
evidence for
this fast-growing group presents significant challenges for
formulating
appropriate policies and interventions to improve quality of
care, health out-
comes, and quality of life for minority nursing home residents
with ADRD
(Alzheimer’s Association, 2004; Khachaturian et al., 2012). The
contribu-
tion of this study is an examination of national rates and health
care dispari-

ties in nursing home care among residents with ADRD. Because
there are
few datasets available to study quality of long-term care for
people with
ADRD, specifically among Hispanics, using national nursing
home assess-
ment and survey data is pivotal in understanding patterns of
racial disparities
in nursing homes.
The present study uses the Institute of Medicine’s conceptual
framework
applied to mental health (McGuire, Alegria, Cook, Wells, &
Zaslavsky,
2006). Under this framework, operation of the health care
system and dis-
crimination can contribute to disparities among ADRD
populations. Sources
of disparities include provider practices, insurance plans, and
other health
care factors that result in lower quality of care for minority
groups. In addi-
tion, minority groups may be subject to discrimination,
receiving lower qual-
ity of care by providers (Smith, 1990). There is evidence that
racial and ethnic
groups have less access to mental health services and needed
care, and receive
lower quality of care when treated (Office of the Surgeon
General, Center for
Mental Health Services, & National Institute of Mental Health,
2001). Using
multiple national databases containing information about
Medicare benefi-
ciaries and nursing home measures, our study examines
individual- and facil-

ity-level characteristics to assess differences in nursing home
utilization and
care among Hispanic, African American, and White Medicare
residents with
ADRD.
Method
We used multiple sources of national data from 2014.
Information on the rate
of admission to nursing homes and nursing home residents’
characteristics
came from the Minimum Data Set (MDS). The MDS resident
assessment
instrument has nearly 400 data elements, including information
on cognitive
and physical function, psychosocial well-being, mood, disease
diagnoses,
health conditions, special treatments, and medication use. The
assessments
are reported for all residents admitted to Medicare and/or
Medicaid-certified
nursing homes. Repeated evaluations of the reliability of the
MDS provided
at least adequate values on most scales (Kosar, Thomas, Inouye,
& Mor,
2017; Morris et al., 1990; Phillips et al., 1997; Thomas, Dosa,
Wysocki, &
Mor, 2017). Admission and discharge assessment dates help
determine the
time period spent in the nursing home and have been validated

against
Medicare claims (Rahman, Tyler, Acquah, Lima, & Mor, 2014).
We identi-
fied people with a diagnosis of ADRD by using MDS Section I
for Active
Diagnoses. We included any individual who had an active
diagnosis of
Alzheimer’s disease, non-Alzheimer’s dementia, or one of the
following
ICD-9 codes listed: 290.0, 290.10, 290.11, 290.12, 290.13,
290.20, 290.21,
290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.10, 294.11,
294.20, 294.21,
331.0, 331.11, 331.19, 331.2, 331.82, 331.7, or 797 (Thomas,
Baier, et al.,
2017). The Master beneficiary summary file (MBSF) contains
demographics,
Medicare Advantage enrollment, and dual eligibility (Research
Data
Assistance Center, 2017). The Certification and Survey Provider
Enhanced
Reporting (CASPER) system (CMS, 2017e), Long-Term Care:
Facts on Care
in the US (LTC focus; 2017) and Nursing Home Compare
(NHC) Five-Star
Ratings databases provide nursing home-level information on
ownership,
size, staffing, and 30-day rehospitalization rates (CMS, 2017b).
These vari-
ables are described below. These files were linked to each other
using facil-
ity-level identifiers that allow us to create historical person-
level utilization
records matched to facility characteristics.
Variables

Study measures from the Master Beneficiary Summary File
(MBSF). Demo-
graphic characteristics include age (less than 65 years, 65-84,
and 85 and
above), sex, enrolled in Medicare Advantage, Medicaid and
Medicare dual
eligibility (eligibility for Medicaid coverage for at least 1
month). In addition,
we used the race/ethnicity variable from this file, which has
high sensitivity,
positive predictive value, and kappa for Whites, African
Americans, and His-
panics (Eicheldinger & Bonito, 2008).
Study measures from the MDS
Delirium. Delirium was identified using the Confusion
Assessment
Method Criteria. This method defines delirium as present if it
was indicated
that a patient was reported to have shown an acute change in
delirium symp-
toms, inattention, and either disorganized thinking or an altered
level of con-
sciousness included in Section C (Inouye et al., 1990; Kosar et
al., 2017;
Wei, Fearing, Sternberg, & Inouye, 2008; Wong, Holroyd-
Leduc, Simel, &
Straus, 2010).

Cognitive function. This measure was identified using the
Cognitive Func-
tion Scale, comprised of the Brief Interview for Mental Status
(BIMS) and the
Cognitive Performance Scare (CPS) in Section C. The scores
identified resi-
dents who are cognitively intact (those who completed the
BIMS and scoring
between 13 and 15 points) or with severe (those who did not
complete the
BIMS and have a CPS score of 5 or 6), moderate (those scoring
0-7 on the
BIMS or a 3-4 on the CPS), and mild impairment (those with a
BIMS score
of 8-12 or a CPS score of 0-2; Thomas, Dosa, et al., 2017).
Aggressive behavior. This measure was obtained using the
Aggressive
Behavior Scale (ABS), which is calculated using items from
Section E,
including verbal abusive, physical abusive, disruptive, and
resisting care.
Each item receives a score of 0 to 3 indicating that the behavior
was not
exhibited in the last week (score of 0), or that it occurred 1 to 3
days (score
of 1), 4 to 6 days (score of 2), or daily (score of 3). The items
were cal-
culated with scores ranging from 0 to 12. Then aggressive
behavior scores
were divided into four categories, including none (residents
with score of 0),
moderate (those with scores of 1 to 2), severe (those with scores
of 3 to 5),
and very severe (residents with scores of 6 to 12; Perlman &
Hirdes, 2008).

Severe functional impairment. This was indicated when the
score of the
Activities of Daily Living (ADL) Scale is greater than 23. The
28-point ADL
scale includes items for bed mobility, transfer, locomotion on
unit, dressing,
eating, toilet use, and personal hygiene. Residents were rated
from 0 to 4 as
being able to do the activity independently, with supervision,
with limited
assistance, with extensive assistance, or being totally
dependent. Scores were
summarized to create a composite score, ranging from 0 to 28,
to characterize
physical function (Wysocki, Thomas, & Mor, 2015).
Admission source. We characterized residents’ sources of
admission using
Item A1800 from the MDS, which captures whether admissions
came from
the community, another nursing home, acute hospital,
psychiatric hospital,
inpatient rehabilitation facility, hospice, or other.
Long-stay resident. We defined residents as long-stay if they
remained in
a nursing home for more than 100 days (American Health Care
Association,
2017a).
Study measures from LTC focus

Rehospitalization rate (adjusted). This measure was calculated
at the facil-
ity level using the observed rate of rehospitalization within 30
days, divided
by the expected rate of rehospitalizations within 30 days and
then multi-
plied by the national rate. Expected rate for the facility is
calculated using
a predictive model that adjusts from 33 demographic and
clinical variables
from the MDS. These variables included functional status,
prognosis, clinical
conditions, diagnosis, services, and treatment (American Health
Care Asso-
ciation, 2017b).
Other variables at the facility level included percentage of
Hispanic resi-
dents and percentage of African American residents admitted to
the facility
during 2014, percentage of residents whose primary support is
Medicaid,
whether the facility is for profit, whether the facility is part of a
chain, and
whether the facility has Alzheimer’s Special Care Unit (also
drawn from
OSCAR or CASPER data [CMS, 2017e]).
Study measures from CMS’ NHC
Nursing home five-star quality rating. This is a composite
measure of three
domains: health inspections, staffing levels, and quality
measure. Nursing
homes receive a score between 1 (lower quality than average)

and 5 (higher
quality than average; CMS, 2017b).
Analysis
We examined rates of Medicare beneficiaries newly admitted
residents to
nursing homes with ADRD, by race and ethnic group. We also
explored dif-
ferent quality indicators for nursing homes and compared
facility character-
istics for residents with ADRD stratified by race and ethnic
group. Our
analysis included new admissions defined as the beginning of a
nursing home
stay where the person had not had a nursing home stay within
the past 2
years. We identified 1,370,123 new admissions among Medicare
beneficia-
ries in 2014. These were 1,164,714 (85.0%) admissions among
White
beneficiaries, 139,068 (10.2%) among African American
beneficiaries, and
66,341 (4.8%) among Hispanic beneficiaries. After merging
with the datasets
and dropping cases with missing data, our analytic sample
included 1,302,099
(880,040 admissions from Whites vs. 106,002 from African
Americans vs.
47,876 from Hispanics). There were 268,181 admissions with
ADRD diagno-

sis (85.1% Whites vs. 10.3 African Americans vs. 4.6%
Hispanics) from
13,532 nursing homes in 2014. One-way analysis of variance
and chi-square
were used to assess differences among groups.
Brown’s Center for Gerontology and Healthcare Research has
access to
these data under a CMS data use agreement. The Brown
Institutional Review
Board approved our use of these data.
Results
The results showed that 20.6% of all Medicare beneficiaries
newly admitted
to a nursing home in 2014 had an ADRD diagnosis. Overall, a
slightly higher
percentage of Hispanic residents were diagnosed with ADRD
compared with
African Americans or White residents. Approximately, 22.2% of
Hispanic
residents had an ADRD diagnosis compared with approximately
20.2% of
African Americans and 20.6% of Whites had (p < .001).
Descriptive statistics for newly admitted Medicare beneficiaries
with
ADRD stratified by race and ethnic group are displayed in Table
1. A higher
percentage of White residents with ADRD were older than their
African
American or Hispanic counterparts. Approximately, 50.1%
Whites, 36.9%
African Americans, and 39.8% Hispanics were 85 years or
older. However, a

larger proportion of African Americans and Hispanics had
severe functional
impairment (i.e., an ADL scale score greater than 23) compared
with Whites
(18.3% vs. 18.4% vs. 9.6%; p < .001, respectively). In addition,
minorities
had greater rates of cognitive impairment but were less likely to
have severe
behavioral disturbances or to have a delirium diagnosis than
their White
counterparts. For instance, 12.2% of African American and
12.9% of Hispanic
were severely cognitively impaired, whereas 9.5% of White
residents were in
this category. By contrast, African Americans and Hispanics
were 1.9 per-
centage points (95% confidence interval [CI] = [–2.5, –1.5]
percentage
points) and 3.9 percentage points (95% CI = [–4.7, –3.3]
percentage points)
less likely to have delirium.
Hispanics were more likely to become long-stay residents
compared with
African Americans and Whites (33.9% vs.32.0% vs. 32.2%). In
addition,
minorities were more likely to be enrolled in Medicare
Advantage and to be
dually eligible for Medicare and Medicaid. About 20.9% and
22.7% of
African American and Hispanic residents were enrolled in
Medicare

Table 1. Descriptive Characteristics of Medicare Beneficiaries
Newly Admitted to
a Nursing Home With a Dementia and/or Alzheimer’s Disease
Diagnosis in 2014,
by Race and Ethnicity (n = 268,181).
Whites
~84.9%
African
Americans
~10.0%
Hispanics
~5.1%
Age, %
<65 2.06 5.06 3.47
65-84 47.87 58.06 56.70
85+ 50.06 36.88 39.83
Female, % 63.60 61.72 59.66
Severe functional
impairment, %
9.56 18.28 18.40
ADL Scale Score, M (SD) 17.85 (4.90) 18.63 (5.39) 18.96
(5.20)
Cognitive function, %a
Intact 17.90 14.91 14.30
Mildly impaired 26.51 24.63 22.89
Moderately impaired 46.05 48.28 49.87
Severely impaired 9.54 12.18 12.95

Delirium, %a 12.39 10.42 8.42
Aggressive behaviors, %a
None 79.59 81.39 84.00
Moderate 13.38 12.62 10.82
Severe 5.64 4.83 4.15
Very severe 1.38 1.16 1.03
Admitted from, %
Community 10.77 6.92 9.60
Nursing home 2.02 1.49 1.81
Hospital 83.94 88.62 85.93
Other 3.27 2.97 2.65
Became a long-stay resident, % 32.23 32.01 33.87
Medicare Advantage
enrollment, %
17.41 20.94 22.70
Dually eligible for Medicare
and Medicaid, %
27.16 54.25 68.73
Note. Authors’ analysis of data from the 2014 Medicare Master
Beneficiary Summary File,
the Minimum Data Set, the Master Beneficiary Summary File,
the Certification and Survey
Provider Enhanced Reporting system, and Long-Term care:
Facts on Care in the US.
Differences across groups are significant at the p ≤ .05 level.
ADL Score (Scale 0-28 with
0 = complete independence, 28 = complete dependence); ADL =
activities of daily living;
ABS = Aggressive Behavior Scale; CFS = Cognitive Function
Scale.
aDelirium, ABS, and CFS contain missing values (84,690 cases,

15,357 cases, and 32,501 cases,
respectively).
Advantage compared with 17.4% Whites. Finally, the large
majority of
minorities were dually eligible; African Americans and
Hispanics were 27.1
and 41.6 percentage points more likely to be dual-enrolled as
compared with
Whites (95% CI = [26.4, 27.7] and [40.6, 42.5], respectively) .
The characteristics of nursing homes to which these residents
were admit-
ted are displayed in Table 2. Segregation in nursing homes was
high among
minorities. African Americans with ADRD were admitted to
facilities where
35.3% of residents were also African Americans. Similarly,
there was a high
concentration of Hispanics (29.2%) in facilities where
Hispanics with ADRD
were admitted residents. In addition, Whites were more likely to
be admitted
to facilities with Alzheimer’s special care units (21.5%)
compared with either
African Americans (16.3%) or Hispanics (12.7%). Finally,
White residents
were admitted to nursing homes with slightly better quality in
several mea-
sures than their counterparts. The average nursing home 30-day
rehospital-
ization rate was 17.1% for facilities where Whites were

admitted compared
with 19.1% and 18.1% for facilities where African Americans
and Hispanics
were admitted, respectively. (All differences across groups are
significant at
p < .001). Similarly, facilities where White residents received
care were less
likely to be for profit compared with facilities where African
American or
Hispanic residents received care (69.4% vs. 79.7% vs.81.6%; p
< .001).
Finally, CMS nursing home star ratings on average were 3.5 for
nursing
homes where Whites were admitted, 3.5 for nursing homes
where Hispanics
Table 2. Characteristics of Nursing Homes Where Medicare
Beneficiaries With
a Dementia and/or Alzheimer’s Disease Diagnosis Were
Admitted, by Race and
Ethnic Group (n = 268,181).
Whites
African
Americans Hispanics
% Hispanic residents 2.57 (6.39) 4.30 (8.57) 28.71 (29.22)
% African American residents 7.37 (11.08) 35.36 (25.80) 11.17
(14.48)
Alzheimer’s special care unit 21.47 (41.06) 16.29 (36.93) 12.72
(33.33)
For profit 69.44 (46.06) 79.67 (40.25) 81.64 (38.71)
Part of a chain 58.24 (49.32) 60.02 (48.98) 49.74 (50.00)
% Medicaid primary payer 53.19 (22.78) 62.38 (21.79) 60.07
(21.53)

Rehospitalization rate 17.07 (4.66) 19.07 (4.51) 18.11 (4.40)
CMS nursing home star rating 3.54 (1.30) 3.26 (1.33) 3.52
(1.32)
Note. Authors’ analysis 2014 of the Minimum Data Set, the
Master Beneficiary Summary File,
the Certification and Survey Provider Enhanced Reporting
system, and Long-Term Care: Facts
on Care in the US. Differences across groups are jointly
significant at the p < .001 level. CMS
= Centers for Medicare & Medicaid Services.
with ADRD were admitted, and 3.3 for nursing homes where
African
Americans were admitted.
Discussion
In this national study of rates of ADRD among Hispanic,
African American,
and White Medicare beneficiaries who were newly admitted to
nursing
homes, we found that a higher share of Hispanic residents had a
diagnosis of
ADRD compared with African Americans and Whites in 2014;
Hispanics
were about two percentage points more likely to be admitted
with an ADRD
diagnosis than Whites or African Americans. In addition, we
found dispari-
ties in the facility characteristics and quality of nursing homes
to which

Hispanics and African Americans were admitted compared with
Whites.
Nursing homes where Whites were admitted had on average one
to two per-
centage points lower 30-day rehospitalization rates than
facilities to which
Hispanics and African Americans were admitted. Hispanic
residents were up
to nine percentage points less likely to go to facilities with
Alzheimer’s units
compared with White residents. Another important finding in
this study is the
large concentration of racial and ethnic groups to which
residents were admit-
ted. A large fraction of Hispanic residents with ADRD went to
nursing homes
with a high percentage of Hispanic residents. Similarly, African
American
residents with ADRD were admitted to facilities with a greater
proportion of
African Americans.
In the present study, we found that Hispanics and African
American resi-
dents were more likely to be cognitively and physically
impaired as com-
pared with Whites. Our findings may be explained by disease
severity and
caregiver characteristics (Gaugler et al., 2009; Yaffe et al.,
2002). This sug-
gests that minorities may be admitted to a nursing home at a
stage further in
their ADRD disease progression. Previous studies have
mentioned that
African Americans and Hispanics have higher incidence of
ADRD than

Whites (Mayeda et al., 2014), and at the same time, a higher
proportion of
them may not receive an immediate diagnosis (Fitten, Ortiz, &
Pontón,
2001; Schrauf & Iris, 2012; Wilkins et al., 2007). A delayed
diagnosis may
influence patient access to support and services, as well as
family and care-
giver planning (Shih et al., 2014). Others have found that being
older and
White increases the risk of nursing home placement among
people with
ADRD (Andel, Hyer, & Slack, 2007), and that up to 70% of
African
American Medicare beneficiaries may delay institutionalization
(Gaugler,
Leach, Clay, & Newcomer, 2004). Similarly, Hispanic families
are less
likely than Whites to use nursing home and other formal care
services; yet,
institutionalized care is now becoming more common than
before among
these families (Feng et al., 2011). Additional work among
nursing home
placement for minorities with ADRD is needed to understand
the mecha-
nisms behind these differences and whether these differences
can be attrib-
utable to variability in the availability of informal caregivers,
cultural,
patient, and family preferences or access to services.

These findings also highlight the need to expand the literature
regarding
delirium prevalence in nursing home settings among ADRD
patients. A prior
study done of older patients in post acute care settings did not
find significant
differences in the rates of delirium among race and ethnic
groups (Marcantonio
et al., 2005). Yet, our results show that African Americans and
Hispanics with
ADRD have lower rates of delirium by two to four percentage
points, respec-
tively, despite having higher rates of severe cognitive
impairment. Although
dementia and delirium are associated, causation is still unclear
(Richardson
et al., 2017). Delirium may occur due to a complex
interrelationship among
different factors, including higher rates of cognitive decline
(Inouye,
Westendorp, & Saczynski, 2014), which needs to be further
examined in this
group. Other studies have also found that delirium increases the
risk of insti-
tutionalization, poor health outcomes, and mortality (Kosar et
al., 2017;
Witlox et al., 2010); yet, we found that Hispanics with ADRD
were two per-
centage points more likely to become long-stay residents than
African
Americans or Whites with ADRD.
The present study results are consistent with previous work
regarding
racial disparities in quality among nursing home residents

(Fennell, Feng,
Clark, & Mor, 2010; Gaugler et al., 2004; Mor, Zinn, Angelelli,
Teno, &
Miller, 2004; Shippee et al., 2015). These two groups are also
less likely to go
to nursing homes where residents with ADRD receive special
dementia care
(i.e., being in a nursing home with a dementia special care unit),
which has
been documented to provide better quality of life for people
with ADRD in
different domains (Cadigan, Grabowski, Givens, & Mitchell,
2012; Luo,
Fang, Liao, Elliott, & Zhang, 2010). Existing literature on this
topic suggests
that Whites with ADRD are more likely to receive such
specialized treat-
ment. This is partially because Whites are more likely to go to
facilities with
a better payer mix and that are not-for-profit (Sengupta, Decker,
Harris-
Kojetin, & Jones, 2012). These facilities tend to be linked
primarily to special
care unit nursing homes (Buchanan, Choi, Wang, Ju, & Graber,
2005;
Congress of the United States, Office of Technology
Assessment, 1992). This
is consistent with our results as we observe that African
Americans and
Hispanics with ADRD are more likely to go to facilities that are
for-profit and
highly dependent on Medicaid.
In addition, we found that African Americans and Hispanics are
still more
likely to go to facilities serving a higher proportion of non-

White residents.
This is relevant as racial segregation has long been associated
with quality
outcomes in long-term care settings (Davis, Weech-Maldonado,
Lapane, &
Laberge, 2014; Li et al., 2015; Mor et al., 2004). Although,
these patients
may be exhibiting race- or distance-based preferences (Rahman
& Foster,
2015), and may be actively choosing nursing homes that have
patients with
similar background or homes that are also located near their
communities or
family, it could potentially mean that these lower quality
nursing homes are
located within more segregated neighborhoods (Shippee,
Henning-Smith,
Rhee, Held, & Kane, 2016). Future research should investigate
consumer
choice of nursing homes among minority groups with ADRD
and the extent
to which sociodemographic and geographic characteristics
influence access,
quality of care, and patient outcomes.
Our results also show that a higher percentage of minorities
with ADRD
in nursing home are enrolled in Medicare Advantage, at least
among those
who are newly admitted. Yet, there are no national studies of
beneficiaries

with ADRD in the Medicare advantage program, despite the
evidence that
shows that racial and ethnic minority groups are more likely to
enroll in man-
aged care (Weinick, Haviland, Hambarsoomian, & Elliott,
2014). However,
Medicare Advantage may promote disenrollment among patients
with ADRD
when complex health care needs and high costs are …
Black History Month Focus and Health Disparities in the
African American Community
Learning Outcomes
The purpose of this clinical assignment is to highlight some of
the healthcare issues of African Americans in celebration of
Black History Month. This assignment will also address the
fact that disparities in health care are strong social determinants
of health.
Specifically, students will create a brief document in which they
will demonstrate their ability to:
· Identify major health conditions that impact the African
American (AA) communities in Chicago.
· Compare and contrast epidemiological data that showcases the
differences among different racial and ethnic groups within the
city of Chicago and at the state level. Students will also
interpret the data about the impact of the CoVID pandemic on
the AA population.
· Create a document /pamphlet for teaching about sickle cell
disease in the AA community
· Realize that many health-care policies affect African
Americans in a negative way
· Appreciate that many African Americans have influenced
Public Health in positive ways
· Appreciate that the Resurrection University Library has a
variety of educational texts and data bases related to African

American history and healthcare in the U.S.
Please answer the following in a brief 2-page paper. Place the
questions in BOLD type
1. “The empirical evidence that race and ethnicity influence
physicians to make harmful distinctions in how they treat and
interact with White patients versus patients of color is
overwhelming.” (Matthew, D. in Just Medicine, A Cure for
Racial Inequality in American Health care (2015). NY: New
York University Press)
a. What do you think this statement means?
b. Have you had any experience that you would discuss or
share?
2. Panelists who presented in the “Healthy Chicago 2025 video”
noted that there is a significant lifespan difference between
African Americans living in Chicago as compared to Caucasians
in Chicago.
a. What is the average lifespan of African Americans in the
community that you analyzed for Community /Windshield
Project? Compare this evidence to other communities assigned
to your teams.
b. If we focus on the health condition of sickle cell disease in
the AA population, what public health education/promotion is
done in the community that you are aware of? As an example of
a primary intervention, create a health ed document
/pamphlet/fact sheet that could be used in a community forum
on sickle cell disease.

3. Researchers noted below have emphasized that racial
disparities exist among AA nursing home residents. For
example, AA patients are often inappropriately diagnosed with
dementia and physical restraint is also deployed with more
frequency. Please read the following articles and list three
major findings from each:
Rivera –Hernandez, M; Kumar, A.; Epstein-Lubow, G &
Thomas, K.S. (2019). Disparities in nursing home use and
quality among African American, Hispanic, and White Medicare
residents with Alzheimer’s disease and related dementias.
Journal of Aging and Health, 31(7), 1259-1277.
doi.org/10.1177/0898264318767778.
Shippee, T.P.; Henning-Smith, C., & Rhee, T.G. et.al. (2016).
Racial Differences in Minnesota nursing home residents’ quality
of life: The importance of looking beyond individual predictors.
Journal of Aging and Health, 28(2), 199-224.
doi.org/10.1177/0898264315589576.
Following the virtual class presentation with the Res U library
staff:
1. Write a paragraph about something new that you learned
from the presentation.
2. Who are some African American heroes in the medical field?
3. What are some resources that you learned about for yourself
and the families that you will be working with? Please
document some of the resources below.

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