National Estimates from the medical expenditure panel survey

R E S EAR CH A R TIC L E Open Access
Patient-centered medical home care access
among adults with chronic conditions:
National Estimates from the medical
expenditure panel survey
Ziyad S Almalki1* , Nedaa A Karami2
, Imtinan A Almsoudi2
, Roaa K Alhasoun3
, Alaa T Mahdi4
, Entesar A Alabsi5
Saad M Alshahrani6
, Nourah D Alkhdhran7 and Tahani M Alotaib7
Background: The Patient-Centered Medical Home (PCMH) model is a coordinated-care model that has served as a
means to improve several chronic disease outcomes and reduce management costs. However, access to PCMH has
not been explored among adults suffering from chronic conditions in the United States. Therefore, the aim of this
study was to describe the changes in receiving PCMH among adults suffering from chronic conditions that occurred
from 2010 through 2015 and to identify predisposing, enabling, and need factors associated with receiving a PCMH.
Methods: A cross-sectional analysis was conducted for adults with chronic conditions, using data from the 2010–2015
Medical Expenditure Panel Surveys (MEPS). Most common chronic conditions in the United States were identified by using
the most recent data published by the Agency for Healthcare Research and Quality (AHRQ). The definition established by
the AHRQ was used as the basis to determine whether respondents had access to PCMH. Multivariate logistic regression
analyses were conducted to detect the association between the different variables and access to PCMH care.
Results: A total of 20,403 patients with chronic conditions were identified, representing 213.7 million U.S. lives.
Approximately 19.7% of the patients were categorized as the PCMH group at baseline who met all the PCMH criteria
defined in this paper. Overall, the percentage of adults with chronic conditions who received a PCMH decreased from
22.3% in 2010 to 17.8% in 2015. The multivariate analyses revealed that several subgroups, including individuals aged
66 and older, separated, insured by public insurance or uninsured, from low-income families, residing in the South or
the West, and with poor health, were less likely to have access to PCMH.
Conclusion: Our findings showed strong insufficiencies in access to a PCMH between 2010 and 2015, potentially
driven by many factors. Thus, more resources and efforts need to be devoted to reducing the barriers to PCMH care
which may improve the overall health of Americans with chronic conditions.
Keywords: PCMH, MEPS, Care access, Chronic conditions
* Correspondence: [email protected] 1
Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin
Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
( applies to the data made available in this article, unless otherwise stated.
Almalki et al. BMC Health Services Research (2018) 18:744
In the United States (U.S.), chronic conditions are
among major causes of disability, mortality, and high
medical costs [1–4]. It has been estimated that nearly
half (50.9%) of U.S. adults live with at least one
chronic condition, while 26% have two or more
chronic conditions [5]. These conditions are responsible for 46% of all deaths among the U.S. population
annually. Furthermore, the associated costs of these
conditions are enormous and compromise the health
of the U.S. [6] It was estimated that 86% of U.S.
health care expenditures are correlated with the treatment of chronic conditions [7].
With the growing number of chronic conditions
[8], the associated costs made by these conditions
will continue to threaten the entire federal budget.
Over the last three decades, several improvements
have been implemented into U.S. law, but they all
focused heavily on insurance reforms. These steps
will not be adequate unless they are coupled with
fundamental health care improvement efforts targeting the primary care practice [9]. To achieve this
goal, more attention has been paid to replace the
poorly coordinated, acute-focused, episodic primary
care practice with a care that is continuous, comprehensive, patient-centered, coordinated, and accessible, and that provides communication and shared
decision-making [10].
A recent, successful approach to improve the
chronic care management is the patient-centered
medical home (PCMH). The PCMH model is an
innovative primary care delivery system that has
served to improve the quality of care and to reduce
medical costs. PCMH rearranges how primary care
service is designed and delivered to the patients,
with the prime focus on patient needs and preferences [11, 12]. Over the past few years, with the
growing numbers of adults with chronic conditions,
many healthcare stakeholders in the U.S. have
adopted the PCMH to prevent or inhibit the progression of specific chronic conditions [12].
Several studies have demonstrated the ability of
PCMH application in improving the primary care
quality, safety, and efficiency across the U.S. Some
studies, for example, have suggested that receiving
PCMH care is associated with a decreased number of
hospitalizations and emergency room visits [13–18].
Others have also identified improvements in the
quality of health care after implementing PCMH care
[17, 19, 20].
Despite growing evidence in the literature that supports the effectiveness of the PCMH in improving
health care outcomes and reducing costs, the extent
of the PCMH’s adoption in treating Americans with
chronic conditions remains unknown. Therefore, the
objective of this study is to describe, at the national
level, the changes in receiving PCMH among adults
suffering from chronic conditions and to identify predisposing, enabling, and need factors associated with
accessing PCMH care.
Data source
We conducted an observational cross-sectional analysis of the 2010–2015 Medical Expenditure Panel
Survey (MEPS). MEPS has been conducted by the
Agency for Healthcare Research and Quality (AHRQ)
since 1996. MEPS is a nationally representative
population-based survey of health care utilization and
expenditures of the U.S. civilian noninstitutionalized
population. The MEPS utilizes an overlapping panel
design in which participant data are collected over a
series of five rounds of interviews spaced about five
months apart. The collected data include patient
demographics, access to health care, use of health services, health conditions, health status, and other data
as well. Information regarding the data and a description of its survey design have been published previously [21].
Study population
Individuals aged 18 years and older who were diagnosed with at least one of the most common chronic
conditions (i.e., hypertension, hyperlipidemia, mood
disorders, diabetes, anxiety disorders, upper respiratory conditions, arthritis, asthma, or coronary artery
disease) were identified. These conditions were considered to be chronic because they are long-lasting,
cause diminished physical and/or mental capacity, or
require long-term monitoring and medical interventions [22]. The prevalence of these conditions has
been confirmed by the most recent data published by
the Agency for Healthcare Research and Quality
(AHRQ) [23]. According to MEPS documentation, patients in each year may be used as independent observations since each year in MEPS data is intended
to be nationally representative [24].
Primary outcome
The primary outcome of our analysis was determining whether the individual was receiving care consistent with PCMH principles. PCMH care was
defined using the provider-related questionnaires in
MEPS. AHRQ’s definition classifying PCMH care
was used to determine whether respondents had a
PCMH [25]. The respondent was considered to be
receiving PCMH if the patient received comprehensive, patient-centered, and accessible care. Table 1
Almalki et al. BMC Health Services Research (2018) 18:744 Page 2 of 11
shows the survey items used to define PCMH features based on AHRQ’s criteria. Similar questions
had been used in high-quality research to detect access to PCMH care using the same data [26–29].
We determined that the care received by an individual was comprehensive care if the provider did all
of the following: 1) usually asked about any medications prescribed by other doctors; 2) provided care
for new health problems; 3) provided preventive
care; 4) offered referrals to other health professionals; and 5) provided care for ongoing health
problems. We considered the individual to have received patient-centered care if the provider 1)
showed respect for the medical, traditional, and alternative treatments other doctors may give; 2) explained all healthcare options to the individual; and
3) asked the individual to help decide on treatment.
We considered care to be accessible if the provider
1) was easy to contact by phone about a health
problem during regular office hours; 2) offered night
and weekend office hours; and 3) spoke the participant’s language or provided translation services. Participants with responses of don’t know, refused, or
not ascertained to any question were excluded from
the final dataset.
Independent variables
By using the Andersen Behavioral Model [30] in the
current analysis, we examined the effects of personspecific predisposing, enabling, and need factors on
having a PCMH. Predisposing factors investigated in
this study included age, sex, race, marital status, and
education years. Enabling factors consisted of health
insurance, employment status, family income, and
census region. (Appendix A contains a list of states
composing each region with demographic data.) [31]
Our assessments of health needs were based on
self-rated health status variables (good/excellent or
Data analysis
Descriptive statistics were used to characterize and
evaluate changes in annual percentage for individuals
who had PCMH over the six-year pooled dataset. The
number of those individuals and their weighted percentage were calculated. Rao–Scott chi-square (a design-adjusted Pearson chi-square test) [32] analyses were
performed to examine significant subgroup differences
across strata for the two groups (having PCMH and having no PCMH). Adjusted multiple logistic regression
analyses were then conducted to assess predictors associated with having a PCMH. In all analyses, we control for
age, sex, race, marital status, education years, health insurance type, employment status, family income, chronic conditions, and calendar year. The c-statistic was calculated for
each model to assess the model’s practical ability for correctly discriminating an individual outcome (PCMH/ No
PCMH). A model demonstrates a good discrimination
when the c-statistic is > 0.7 and outstanding when > 0.9.
To adjust for the complex multistage survey design and
nonresponse, the estimates that are calculated from the
data sample were multiplied by person-specific sampling
weights provided within the original datasets of MEPS. All
analyses were conducted with the use of SAS 9.4 software
(SAS, Cary, NC).
A total of 20,403 patients with chronic conditions
were identified, representing 213.7 million U.S. lives
between 2010 and 2015. Approximately 19.7% of the
patients were categorized as the PCMH group at
baseline who met all the PCMH criteria defined in
this study. The proportion of adults with chronic
conditions who received a PCMH decreased from
22.3% in 2010 to 17.8% in 2015. However, in 2012
there was an increase in the number to 23.31%
(Table 2).
Table 3 presents the results of the study population’s
descriptive characteristics. Individuals aged between 41
and 65 were most likely to report that they had at least
one chronic condition (49.5%). The overall sample was
predominantly female (57.1%), white (79.5%), married
Table 1 MEPS survey items used to define PCMH care
PCMH criteria Survey items used
Comprehensive care
Does the provider usually ask about medications and
treatments prescribed by other doctors
Does the provider provide care for new health problems
Does the provider provide preventive healthcare
Does the provider provide referrals to other health
Does the provider provide care for ongoing health
Patient-centered care
Does the provider show respect for the medical, traditional,
and alternative treatments other doctors may give
Does the provider explain all healthcare options to
Does the provider ask participant to help decide treatment
Accessible care
Is it difficult to contact the provider by phone about a
health problem during regular office hours
Does the provider offer night and weekend office hours
Does the provider speak the participant’s language or
provide translation services
Almalki et al. BMC Health Services Research (2018) 18:744 Page 3 of 11
(57.8%), educated beyond high school (59.6%), insured
by private insurance (70.1%), employed (58.1%), from a
family with a high level of income (42.1%), from the
southern U.S. geographical region (38.2%), and in excellent/good perceived health (79.7%). Hypertension, arthritis, and hyperlipidemia were the most prevalent
chronic conditions among the study sample, 47.4%,
44.9%, and 37.8%, respectively.
Compared to those who did not receive a PCMH,
those who received PCMH were more likely to be
younger, male individuals (44.7% vs. 42.4%), married
individuals (62.1% vs. 56.7%), employed (62.7% vs.
57.01%), from families with higher income levels
(47.6% vs. 40.8%), covered by private insurance (76.6%
vs. 68.5%), and in excellent/good perceived health status (85.03% vs. 78.4%). They were also more likely to
have achieved a higher level of education (had more
than 12 years of education, 62.6% vs. 58.9%), and less
likely to be from the southern U.S. geographical region (31.8% vs. 39.8%).
In Table 4, we found that the odds ratios (ORs) for
individuals 66 years and older of having access to
PCMH were 0.8 (confidence interval [CI]: 0.67–0.95).
Compared with married individuals, those who were
separated had significantly lower odds of having access to PCMH (OR = 0.78; CI: 0.67–0.91). Compared
with individuals who completed fewer than 12 years
of education, those who had more than 12 years of
education had significantly higher odds of having a
PCMH (OR = 1.25; CI:1.05–1.48).
The result shows that the most important driver of
having a PCMH was health insurance status. Compared with individuals covered by private insurance,
those with public insurance were 71% as likely to
have access to PCMH, while the uninsured were 73%
as likely to have access to PCMH. There was also a
significant difference in the employment status. Unemployed individuals were less likely to have access
to PCMH compared to employed individuals (OR =
0.83; CI: 0.74–0.93).
Significant differences in the family income were
observed in relation to having PCMH access.
Individuals who were living in a poor or
low-income family were about 33% less likely to
have a PCMH compared to those living with a family with a high income (OR = 0.67; CI: 0.57–0.78).
Individuals living in the South and West were the
most likely to not have access to PCMH compared
to individuals living in the Midwest (South: OR =
0.64; CI: 0.52–0.78; West: OR = 0.76; CI: 0.61–0.96).
The analyses also showed that individuals who
reported having fair or poor health were negatively
associated with having a PCMH compared to those
who reported excellent or good general health (OR
= 0.65; CI: 0.57–0.76). In this population, individuals
with the chronic conditions hyperlipidemia, mood
disorders, anxiety disorders, and arthritis were significantly associated with limited access to PCMH.
However, individuals diagnosed with upper respiratory conditions were positively associated with having access to a PCMH. The c-statistics associated
with these adjusted logistic models ranged between
0.71 and 0.86.
As the first national study to present the extent of
access to PCMH among adults with chronic conditions and to identify potential drivers for its trends,
this study attempts to address this gap in the literature. In this research, we examined the prevalence
of adult patients with chronic conditions who
accessed PCMH care over the six-year period in the
This study found only a small percentage of patients with chronic conditions had access to PCMH
care with a decreasing trend during the study period.
This may raise concerns as this vulnerable population
typically requires comprehensive and continuous care
by primary care providers to manage their chronic
physical problems, especially when the number and
complexity of care needs increase as the number of
chronic conditions a patient has increases [33]. In
terms of medical services, the average numbers of
Table 2 Annual changes in individuals with chronic conditionsa
Year N N, weighted, in million No PCMH, % (95% CI) PCMH, % (95% CI)
2010 1458 15.6 77.69 (74.73–80.64) 22.31 (19.35–25.26)
2011 2935 31.8 81.21 (78.91–83.51) 18.78 (16.48–21.26)
2012 3725 37.3 76.68 (74.42–78.94) 23.31 (21.05–25.57)
2013 3313 33.7 81.31 (79.05–83.57) 18.68 (16.42–20.94)
2014 3112 33.7 80.13 (77.91–82.35) 19.86 (17.64–22.08)
2015 5860 61.3 82.17 (80.37–83.97) 17.82 (16.02–19.62)
Abbreviations: CI, confidence interval
Sample size (N) is unweighted; Percentage weighted using weights provided with 2010–2015 MEPS
Almalki et al. BMC Health Services Research (2018) 18:744 Page 4 of 11
Table 3 Baseline characteristics of individuals with chronic conditions, by PCMH access
Characteristic Has a PCMH P
Total No Yes
N Weighted % N Weighted % N Weighted %
(N = 20,403;
N = 213,733,954)
(N = 16,443;
N = 171,600,510)
(N = 3960;
N = 42,133,444)
Age (Years) 0.001
19 to 40 5423 26.3 4299 25.9 1124 28.3
41 to 65 10,227 49.5 8213 49.2 2014 50.5
66 and older 4753 24.1 3931 24.8 822 21.2
Sex 0.012
Female 12,196 57.1 9926 57.6 2270 55.2
Male 8207 42.8 6517 42.4 1690 44.7
Race 0.8
Non-white 6834 20.4 5485 20.5 1349 20.3
White 13,569 79.5 10,958 79.5 2611 79.6
Marital Status <.0001
Married 10,810 57.8 8508 56.7 2302 62.1
Never Married 4272 18.4 3465 18.5 807 18.3
Separated 5321 23.6 4470 24.7 851 19.5
Education Years 0.001
< 12 Years 3505 14.1 2980 14.7 525 11.8
12 Years 4764 26.2 3833 26.3 931 25.5
> 12 Years 8876 59.6 6956 58.9 1920 62.6
Health Insurance <.0001
Any Private 12,422 70.1 9708 68.5 2714 76.6
Public Only 6301 23.7 5319 25.04 982 18.2
Uninsured 1680 6.2 1416 6.4 264 5.2
Employment Status <.0001
Employed 11,006 58.1 8656 57.01 2350 62.7
Not employed 9336 41.8 7734 42.9 1602 37.2
Family Income Categorical <.0001
High 6515 42.2 5001 40.8 1514 47.6
Middle 5913 28.4 4747 28.3 1166 28.8
Poor/ Low 7975 29.4 6695 30.8 1280 23.6
Census Region <.0001
Midwest 4073 21.8 3175 20.9 898 25.5
Northeast 3355 17.8 2538 16.7 817 21.9
South 7872 38.2 6583 39.8 1289 31.8
West 5103 22.2 4147 22.5 956 20.8
Healthcare Need
Self-Reported Health <.0001
Excellent/Good 15,144 79.7 1,1957 78.4 3187 85.03
Almalki et al. BMC Health Services Research (2018) 18:744 Page 5 of 11
ambulatory and emergency department visits, inpatient stays, and number of prescribed medications
were much higher among individuals who suffered
from two or more chronic conditions compared to
those with no chronic condition [34].
To better understand the characteristics and
drivers of that observed trend in this population, we
analyzed many factors and found several factors
were associated with access to PCMH. A change in
one of these factors can cause a change in the
PCMH trend. The older adults (66 and older) were
less likely than comparable younger adults [19 to
40] to have access to PCMH care. This finding is
consistent with what has been reported by prior
studies that older patients were less likely to have
PCMH access [35]. This can be explained by the dynamic health status of such individuals who often
use more than one healthcare provider with no one
provider responsible for all care. Older patients with
chronic conditions are usually heterogeneous in
terms of number and severity of chronic conditions,
health status, and risk of adverse events [36]. Thus,
policy leaders should promote access to PCMH care
among older patients with chronic conditions
because it may help coordinate their complex medical needs, which would improve quality and health
outcomes. This was confirmed in a prospective
before-and-after study among seniors receiving a
PCMH. That study reported that seniors who experienced PCMH care made fewer and less costly
emergency department visits and had fewer hospitalizations [37].
Our findings also revealed that marital status is an
important factor associated with access to PCMH.
Being separated had the effect of decreasing the
likelihood of having access to PCMH versus being
married. Similar to previously published studies, this
study showed that the separated patients were less
likely to receive PCMH care, although the number
was not significant [38]. Our findings showed a
positive association between a higher level of education and having access to PCMH care. A possible
explanation of this finding is that better-educated
individuals typically have a higher impact on changing their economic barriers to have full access to
PCMH care [39].
All enabling factors were significantly associated
with the probability of having PCMH access. Individuals with private insurance, employed, and living in
a high-income family were found to report better access to PCMH. These findings are consistent with
the literature in that access to PCMH is limited due
to financial barriers [40]. Therefore, policy makers
and health care providers should pay special attention to these barriers as they may negatively affect
health-related outcomes, and the effect is substantial,
especially among individuals with chronic diseases.
Our findings suggest that expanding health insurance
coverage is not an adequate approach to increase
access to such care, but policy makers should also
Table 3 Baseline characteristics of individuals with chronic conditions, by PCMH access (Continued)
Characteristic Has a PCMH P
Total No Yes
N Weighted % N Weighted % N Weighted %
(N = 20,403;
N = 213,733,954)
(N = 16,443;
N = 171,600,510)
(N = 3960;
N = 42,133,444)
Fair/Poor 4872 20.3 4157 21.6 715 14.9
Chronic Conditions
Hypertension 10,207 47.4 8350 48.1 1857 44.4 0.001
Hyperlipidemia 7732 37.8 6359 38.6 1373 34.5 0.0001
Mood Disorders 3902 20.4 3259 21.3 643 17.05 <.0001
Diabetes Mellitus 4474 19.1 3673 19.4 801 17.9 0.06
Anxiety Disorders 3589 19.4 2976 19.9 613 17.1 0.002
Upper Respiratory
7405 38.8 5888 38.03 1517 42.1 0.0005
Arthritis 9250 44.9 7682 46.3 1568 39.3 <.0001
Asthma 2557 12.2 2071 12.3 486 11.8 0.4
Coronary Artery Disease 2197 10.8 1787 11.05 410 9.8 0.04
PCMH indicates Patient-Centered Medical Home
Almalki et al. BMC Health Services Research (2018) 18:744 Page 6 of 11
Table 4 Adjusted odds ratios of having access to PCMH care among adults with chronic conditions, 2010–2015a
Independent Variable Has a PCMH ORb 95% CI P
No Yes
Predisposing N N
Age (Years)
19 to 40 4299 1124 1.00
41 to 65 8213 2014 0.93 0.82 1.06 0.3
66 and older 3931 822 0.80 0.67 0.95 0.01
Female 9926 2270 1.00
Male 6517 1690 1.08 0.99 1.18 0.05
Non-white 5485 1349 1.00
White 10,958 2611 1.003 0.88 1.13 0.9
Marital Status
Married 8508 2302 1.00
Never Married 3465 807 0.87 0.75 1.01 0.06
Separated 4470 851 0.78 0.67 0.91 0.001
Education Years
< 12 Years 2980 525 1.00
12 Years 3833 931 1.17 0.99 1.37 0.05
> 12 Years 6956 1920 1.25 1.05 1.48 0.01
Health Insurance
Any Private 9708 2714 1.00
Public Only 5319 982 0.71 0.63 0.81 <.0001
Uninsured 1416 264 0.73 0.59 0.91 0.005
Employment Status
Employed 8656 2350 1.00
Not employed 7734 1602 0.83 0.74 0.93 0.001
Family Income Categorical
High 5001 1514 1.00
Middle 4747 1166 0.89 0.77 1.03 0.1
Poor/ Low 6695 1280 0.67 0.57 0.78 <.0001
Census Region
Midwest 3175 898 1.00
Northeast 2538 817 1.11 0.89 1.39 0.3
South 6583 1289 0.64 0.52 0.78 <.0001
West 4147 956 0.76 0.61 0.96 0.02
Healthcare Need
Self-Reported Health
Excellent/Good 1,1957 3187 1.00
Fair/Poor 4157 715 0.65 0.56 0.76 <.0001
Almalki et al. BMC Health Services Research (2018) 18:744 Page 7 of 11
improve the provided public health insurance coverage for this population to have better access to
PCMH care [41].
Clearly, census region is also important. Individuals
who resided in the South or the West were less likely
to have access to PCMH. This is not surprising because of the considerable difference in socioeconomic
status of the majority of people who live in the South
or the West compared to those in other regions. For
example, a higher proportion of the population in the
South and the West are racially Hispanic and Black
[42]. There is evidence in many studies that these
groups tend to not seek care for their chronic conditions [43–46]. Furthermore, compared to those in
other regions, people in the South or the West are
more likely to be uninsured, hence, less likely to have
access to PCMH [47].
By looking closely at the chronic conditions, we
identified a lack of uniform access to PCMH care
across chronic conditions. We found that hyperlipidemia, mood disorders, anxiety disorders, and arthritis were significantly associated with limited
access to PCMH, yet patients with upper respiratory conditions had better access to the care. A
possible explanation is that upper respiratory conditions are minor and very common [48, 49]; thus,
patients often seek the primary care provider’s help
instead of the emergency department’s help, which
results in a lower cost in managing their
Despite the uniqueness of the information provided by MEPS on individuals’ socioeconomics, access to care, and others in the U.S., there are
limitations to the interpretation of the results of
this study. First, as noted above, MEPS data provide
information on the civilian, noninstitutionalized
population, and hence exclude individuals living in
institutions, such as individuals in nursing homes
and long-term care hospitals who live with broad
arrays of chronic conditions. Second, the definition
of PCMH used in this study was based on patient
responses, which might be subject to recall bias;
thus, our estimates may underrepresent actual
PCMH use. Despite the limitations, this study provides an important overview of the access to PCMH
in a nationally representative general population
sample of the U.S.
More effort is needed to facilitate access to
PCMH among those with chronic conditions. In the
PCMH care model, the primary care health professionals provide labor-intensive work behind the
scenes, and it should be compensated accordingly
because the total PCMH care fees ultimately
demanded by physicians exceed the avoided expense
for chronic conditions. This will increase access to
PCMH, improve the quality of care, and reduce the
overall cost associated with chronic conditions considerably [50, 51].
Despite general agreement about the importance of
PCMH, our findings showed strong deficiencies in
access to PCMH between 2010 and 2015 to be potentially driven by many factors. These findings
serve as a sign for more general problems with access to appropriate care. Moreover, reduced access
to comprehensive and continuous services such as
PCMH care may exacerbate chronic conditions,
leading to more emergency department visits and
hospitalizations that might have been preventable,
as was reported in the literature. Thus, more resources and efforts need to be devoted to reduce
barriers to PCMH care across the U.S., which may
Table 4 Adjusted odds ratios of having access to PCMH care among adults with chronic conditions, 2010–2015a (Continued)
Independent Variable Has a PCMH ORb 95% CI P
Chronic Conditions (Yes vs No)
Hypertension 8350 1857 0.90 0.80 1.01 0.09
Hyperlipidemia 6359 1373 0.88 0.79 0.98 0.02
Mood Disorders 3259 643 0.79 0.69 0.90 0.0006
Diabetes Mellitus 3673 801 0.95 0.83 1.07 0.4
Anxiety Disorders 2976 613 0.81 0.707 0.93 0.002
Upper Respiratory Conditions 5888 1517 1.14 1.01 1.28 0.02
Arthritis 7682 1568 0.78 0.70 0.87 <.0001
Asthma 2071 486 0.93 0.80 1.06 0.3
Coronary Artery Disease 1787 410 0.96 0.82 1.11 0.5
Abbreviations: PCMH indicates Patient-Centered Medical Home; CI, confidence interval
Sample size (N) is unweighted; Percentage weighted using weights provided with 2010–2015 MEPS b
Adjusted Odds Ratio
Almalki et al. BMC Health Services Research (2018) 18:744 Page 8 of 11
Table 5 Demographic data by state
Sex Race
Male Female Hispanic Not Hispanic
White Black or African
Asian White Black or African
United States 325,719,178 160,408,119 165,311,059 53,403,379 3673,214 1,081,490 203,948,942 43,738,256 21,101,628
56,470,581 27,530,306 28,940,275 6,670,850 1,413,848 130,784 37,714,017 6,915,133 4,206,459
Connecticut 3,588,184 1,751,800 1,836,384 494,988 79,472 7401 2,459,296 399,168 190,313
Maine 1,335,907 654,520 681,387 19,619 1833 672 1,267,954 27,024 22,099
Massachusetts 6,859,819 3,330,365 3,529,454 663,031 147,199 12,577 5,064,022 550,067 515,303
New Hampshire 1,342,795 665,009 677,786 43,686 5339 1011 1,235,192 24,697 43,679
Rhode Island 1,059,639 514,991 544,648 129,144 30,302 2578 787,314 75,632 43,896
Vermont 623,657 308,256 315,401 10,773 1080 315 590,084 11,433 14,181
New Jersey 9,005,644 4,396,574 4,609,070 1,583,995 232,080 26,086 5,074,996 1,231,086 952,219
New York 19,849,399 9,637,462 10,211,937 2,972,074 744,422 63,004 11,249,519 3,080,220 1,914,601
Pennsylvania 12,805,537 6,271,329 6,534,208 753,540 172,121 17,140 9,985,640 1,515,806 510,168
Midwest Region 68,179,351 33,659,324 34,520,027 4,907,673 328,391 75,567 52,871,947 7,828,966 2,621,209
Illinois 12,802,023 6,292,478 6,509,545 2,059,344 92,288 26,288 8,033,680 1,907,543 792,728
Indiana 6,666,818 3,287,095 3,379,723 424,866 31,395 6099 5,394,727 699,635 182,314
Michigan 9,962,311 4,903,752 5,058,559 448,997 45,859 7872 7,688,615 1,490,926 373,137
Ohio 11,658,609 5,713,100 5,945,509 380,535 56,605 7623 9,443,607 1,616,217 319,890
Wisconsin 5,795,483 2,882,738 2,912,745 360,587 25,733 5206 4,803,844 417,245 190,977
Iowa 3,145,711 1,564,733 1,580,978 174,674 8476 2622 2,745,459 143,876 94,566
Kansas 2,913,123 1,451,956 1,461,167 320,506 16,978 4476 2,278,889 204,687 105,079
Minnesota 5,576,606 2,776,846 2,799,760 266,704 20,460 6451 4,570,571 414,490 318,572
Missouri 6,113,532 3,002,236 3,111,296 232,440 19,122 4914 4,977,790 774,014 154,207
Nebraska 1920,076 958,131 961,945 189,923 8429 2832 1,549,724 109,839 58,318
North Dakota 755,393 387,299 368,094 23,519 1594 574 652,943 27,037 15,402
South Dakota 869,666 438,960 430,706 25,578 1452 610 732,098 23,457 16,019
South Region 123,658,624 60,616,528 63,042,096 20,466,319 1,205,243 240,734 72,437,426 24,796,491 5,027,316
Delaware 961,939 465,514 496,425 74,221 12,835 1245 617,848 223,603 44,712
District of
693,972 329,199 364,773 60,912 13,196 1737 267,319 325,427 35,717
Florida 20,984,400 10,256,819 10,727,581 4,998,757 346,858 46,802 11,635,713 3,457,022 716,287
Georgia 10,429,379 5,075,507 5,353,872 862,177 113,757 15,801 5,667,431 3,381,501 488,821
Maryland 6,052,177 2,934,154 3,118,023 514,832 81,314 13,744 3,199,793 1,884,099 454,595
North Carolina 10,273,419 5001,438 5,271,981 821,416 104,603 17,794 6,654,534 2,311,221 353,769
South Carolina 5,024,369 2,437,687 2,586,682 245,815 31,699 4933 3,277,257 1,397,097 103,733
Virginia 8,470,020 4,166,727 4,303,293 692,903 79,560 19,440 5,438,214 1,730,600 659,457
West Virginia 1,815,857 898,620 917,237 26,126 2588 609 1,703,491 80,375 19,632
Alabama 4,874,747 2,359,836 2,514,911 183,434 18,971 3211 3,264,132 1,329,710 86,055
Kentucky 4,454,189 2,194,318 2,259,871 145,839 13,639 2959 3,844,055 410,166 83,722
Mississippi 2,984,100 1,445,878 1,538,222 78,571 12,356 1799 1,721,204 1,136,985 39,692
Tennessee 6,715,984 3,275,966 3,440,018 324,771 28,280 6655 5,070,645 1,189,264 148,743
Arkansas 3,004,279 1,476,064 1,528,215 209,703 9559 2977 2,230,512 487,523 58,286
Almalki et al. BMC Health Services Research (2018) 18:744 Page 9 of 11
improve the overall health of Americans with
chronic conditions.
The authors would like to thank the Saudi Association for Scientific Research
(SASR) for providing logistical support throughout the duration of the project.
This research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors.
Availability of data and materials
The datasets generated and/or analyzed during the current study are
available in the AHRQ RDC, [
Authors’ contributions
ZA carried out the literature review, statistical analyses, manuscript drafting,
manuscript editing, and manuscript revision. NK and IA carried out the study
design, statistical analyses, and manuscript revision. RA and AM participated
in data collection, statistical analyses, and manuscript editing. NA and TA
participated in manuscript editing and manuscript revision. EA and SA
participated in study design and data collection, manuscript editing,
manuscript revision, and coordination. All authors read and approved the
final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Author details
Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin
Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia. 2
Department of Clinical
Pharmacy, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi
Arabia. 3
College of Pharmacy, Princess Nourah bint Abdulrahman University,
Riyadh, Saudi Arabia. 4
Department of Pharmaceutical Science, College of
Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia. 5
Department of
Clinical Pharmacy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia.
Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin
Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia. 7
College of Pharmacy,
Prince Sattam Bin Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia.
Received: 4 May 2018 Accepted: 21 September 2018
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Table 5 Demographic data by state (Continued)
Sex Race
Male Female Hispanic Not Hispanic
White Black or African
Asian White Black or African
Louisiana 4,684,333 2,289,446 2,394,887 211,356 27,336 4703 2,807,713 1,545,237 101,469
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West Region 77,410,622 38,601,961 38,808,661 21,358,537 725,732 634,405 40,925,552 4,197,666 9,246,644
Arizona 7,016,270 3,488,301 3,527,969 2,030,058 68,296 35,498 3,976,031 360,278 284,344
Colorado 5,607,154 2,822,333 2,784,821 1,107,360 43,409 19,603 3,944,067 273,910 230,929
Idaho 1,716,943 860,458 856,485 198,805 4338 3542 1,441,202 19,658 37,789
Montana 1,050,493 528,956 521,537 32,730 1333 995 930,784 10,369 13,960
Nevada 2,998,039 1,503,749 1,494,290 791,040 38,478 24,854 1,556,233 304,546 303,310
New Mexico 2,088,070 1,034,144 1,053,926 952,789 20,820 9381 811,077 48,485 41,353
Utah 3,101,833 1,561,688 1,540,145 399,778 13,416 7644 2,494,166 50,068 103,551
Wyoming 579,315 295,438 283,877 52,917 1579 795 496,212 9466 8176
Alaska 739,795 386,792 353,003 41,519 4643 2596 493,807 34,833 60,211
California 39,536,653 19,647,553 19,889,100 14,316,549 459,987 416,190 15,638,899 2,551,034 6,388,282
Hawaii 1,427,538 716,087 711,451 101,593 8043 68,273 516,294 42,935 747,347
Oregon 4,142,776 2,052,989 2,089,787 492,326 17,686 12,830 3,264,775 113,156 240,501
Washington 7405,743 3,703,473 3,702,270 841,073 43,704 32,204 5,362,005 378,928 786,891
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