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Erschienen in: BMC Health Services Research 1/2018

Open Access 01.12.2018 | Research article

Patient-centered medical home care access among adults with chronic conditions: National Estimates from the medical expenditure panel survey

verfasst von: Ziyad S Almalki, Nedaa A Karami, Imtinan A Almsoudi, Roaa K Alhasoun, Alaa T Mahdi, Entesar A Alabsi, Saad M Alshahrani, Nourah D Alkhdhran, Tahani M Alotaib

Erschienen in: BMC Health Services Research | Ausgabe 1/2018

Abstract

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.

Background

In the United States (U.S.), chronic conditions are among major causes of disability, mortality, and high medical costs [14]. 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 [1318]. 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.

Methods

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 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 [2629].
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 professionals
 
Does the provider provide care for ongoing health problems
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 participant
 
Does the provider ask participant to help decide treatment choice
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
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 person-specific 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 poor/fair).

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).

Results

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 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
aSample size (N) is unweighted; Percentage weighted using weights provided with 2010–2015 MEPS
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 (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.
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; Weighted
N = 213,733,954)
(N = 16,443; Weighted
N = 171,600,510)
(N = 3960; Weighted
N = 42,133,444)
Predisposing
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
 
Enabling
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
 
 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 Conditions
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
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).
Table 4
Adjusted odds ratios of having access to PCMH care among adults with chronic conditions, 2010–2015a
Independent Variable
Has a PCMH
OR b
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
Sex
 Female
9926
2270
1.00
   
 Male
6517
1690
1.08
0.99
1.18
0.05
Race
 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
Enabling
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
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
aSample size (N) is unweighted; Percentage weighted using weights provided with 2010–2015 MEPS
bAdjusted Odds Ratio
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.

Discussion

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 U.S.
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 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 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 [4346]. 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 conditions.
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].

Conclusion

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 improve the overall health of Americans with chronic conditions.

Acknowledgments

The authors would like to thank the Saudi Association for Scientific Research (SASR) for providing logistical support throughout the duration of the project.

Funding

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, [https://​meps.​ahrq.​gov/​mepsweb/​data_​stats/​onsite_​datacenter.​jsp].
Not applicable.
Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Appendix

Table 5
Demographic data by state
 
2017 Population
Sex
Race
Male
Female
Hispanic
Not Hispanic
White
Black or African American
Asian
White
Black or African American
Asian
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
Northeast Region
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 Columbia
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
Louisiana
4,684,333
2,289,446
2,394,887
211,356
27,336
4703
2,807,713
1,545,237
101,469
Oklahoma
3,930,864
1,947,562
1,983,302
360,519
20,791
5623
2,782,296
349,881
111,591
Texas
28,304,596
14,061,793
14,242,803
10,654,967
287,901
90,702
12,255,269
3,556,780
1,521,035
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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Metadaten
Titel
Patient-centered medical home care access among adults with chronic conditions: National Estimates from the medical expenditure panel survey
verfasst von
Ziyad S Almalki
Nedaa A Karami
Imtinan A Almsoudi
Roaa K Alhasoun
Alaa T Mahdi
Entesar A Alabsi
Saad M Alshahrani
Nourah D Alkhdhran
Tahani M Alotaib
Publikationsdatum
01.12.2018
Verlag
BioMed Central
Erschienen in
BMC Health Services Research / Ausgabe 1/2018
Elektronische ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-018-3554-3

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