Open Access

Insurance-related disparities in primary care quality among U.S. Type 2 diabetes patients

International Journal for Equity in HealthThe official journal of the International Society for Equity in Health201615:124

https://doi.org/10.1186/s12939-016-0413-x

Received: 3 May 2016

Accepted: 28 July 2016

Published: 2 August 2016

Abstract

Background

This study explored insurance-related disparities in primary care quality among Americans with type 2 diabetes.

Methods

Data came from the household component of the 2012 Medical Expenditure Panel Survey (MEPS). Analysis focused on adult subjects with type 2 diabetes. Logistic regressions were performed to investigate the associations between insurance status and primary care attributes related to first contact, longitudinality, comprehensiveness, and coordination, while controlling for confounding factors.

Results

Preliminary findings revealed differences among three insurance groups in the first contact domain of primary care quality. After controlling for confounding factors, these differences were no longer apparent, with all insurance groups reporting similar primary care quality according to the four domains of interest in the study. There were significant differences in socioeconomic status among different insurance groups.

Conclusion

This study reveals equitable primary care quality for diabetes patients despite their health insurance status. In addition to insurance-related differences, the other socioeconomic stratification factors are assumed to be the root cause of disparities in care. This research emphasizes the crucial role that primary care plays in the accessibility and quality of care for chronically ill patients. Policy makers should continue their commitment to reduce gaps in insurance coverage and improve access as well as quality of diabetic care.

Keywords

Insurance-related disparities Primary care quality Diabetes

Background

Diabetes is one of the leading causes of deaths worldwide. According to the World Health Organization (WHO), around 1.5 million people worldwide died due to diabetes in 2012 [1]. In 2000, the prevalence of diabetes was about 171 million worldwide, and the WHO estimates that by 2030, the prevalence will rise to 366 million individuals [2]. In 2012, 9.3 % of the U.S. population had diabetes [3]. Diabetes is among the ten most expensive medical conditions in the U.S. [4]. The estimated diabetes costs in the U.S. in 2012 was $245 billion [3]. Diabetes is also associated with many health complications if preventive care and proper treatment is not received, including renal disease, non-traumatic lower limb amputations, blindness, and increased risk for cardiovascular disease and stroke [5].

Timely access to primary care and proper adherence to clinical treatment for diabetes can reduce the risk of health complications and improve long-term health outcomes for diabetes patients. Evidence suggests that insurance coverage can greatly improve diabetes patients’ access to care, having an impact on quality of care as well as health outcomes, especially when gaps and disparities are addressed [46].

Studies have shown a significant association between diabetes quality of care and insurance coverage. A study comparing the quality of diabetes care by insurance type in federally funded community health centers in the United States gives evidence of insurance-related disparities [4]. The results showed that those without insurance were least likely to meet the quality of care measures and that those with Medicaid had quality of care similar to those with no insurance [4]. The finding of lower quality and access to care for uninsured patients is supported by another study by Hu et al. This study noted that participants with private insurance or Medicare and Medicaid coverage were more likely to receive quality diabetes care than uninsured individuals [5]. A study by Booth et al. showed the universal drug coverage can help improve outcomes for diabetes patients of lower socioeconomic status [6].

While previous literature has uncovered insurance-related disparities in diabetes care and health outcomes, little exploration has been conducted on the relationship between insurance status and primary care quality among diabetic patients. This is important, as primary care has been proven to be effective in the management of diabetes [79]. The purpose of this study is to explore insurance-related differences in primary care quality – particularly, the cardinal attributes of first contact, longitudinality, comprehensiveness, and coordination [10] - among Americans with type 2 diabetes. First contact care means that care is first sought from the primary care provider when a new health or medical need arises. Longitudinality refers to the longitudinal use of a regular source of care over time, regardless of the presence or absence of disease or injury. Comprehensiveness refers to the availability of a wide range of services in primary care and their appropriate provision across the entire spectrum of types of needs for all but the most uncommon problems in the population by a primary care provider. Coordinated care is the linking of healthcare visits and services so that patients receive appropriate care for all their health problems, physical as well as mental [10, 11].

The unique contribution of this study lies in its enhanced generalizability by using a nationally-representative sample, up-to-date information on the topic, as well as empirical evidence for tracking the impact of the Affordable Care Act (ACA) on primary care system and the benefit for chronically ill patients.

Methods

Data

Data from the household component of the 2012 Medical Expenditure Panel Survey (MEPS) was used for this study. MEPS is a nationally representative survey of the US noninstitutionalized civilian population, composed of survey data of families and individuals, their medical providers, and employers. The annual data files are released with one common variance structure, which reflects the complex sample design of the MEPS. MEPS is supported by the Agency for Healthcare Research and Quality (AHRQ) [12]. The dataset used was the most currently released version at the time this study was conducted. The 2012 MEPS contained a total of 38,974 observations; the current study included respondents aged 18 and over who reported being told by a clinician that they had diabetes. We excluded respondents who had missing value for insurance status.

Measures

The household component of MEPS collects detailed data on demographic characteristics, health conditions, health status, use of medical care services, charges and payments, access to primary care, satisfaction with care, health insurance coverage, income, and employment [12]. In this study, we selected measures of primary care attributes (dependent variables), types of health insurance (independent variable), and individual characteristics (covariates).

Following previous work conducted on primary care quality [10, 13, 14], we examined four cardinal attributes of primary care – first contact, longitudinality, comprehensiveness, and coordination – as dependent variables of interest. We selected eight measures from MEPS related to first contact attribute, which were having a usual source of care (USC) (yes, no); provider type of USC (facility, person/person in facility); provider specialty of USC (primary care, other); USC location (office, hospital); difficulty contacting USC by phone (not very difficult, very difficult); USC office hours on nights/weekends (yes, no); time to get to USC (<=30 min, >30 min); and difficulty getting to USC (not difficult, difficult). In terms of longitudinality, we used one measure of USC provider listening to patients (yes, no). For the attribute of comprehensiveness, we selected one question: going to USC for preventive health care (yes, no). Finally, two measures were selected for measuring the coordination, which were provider asking about other treatments (yes, no) and patient going to USC for referrals (yes, no).

We used Aday and Andersen’s access-to-care framework to select individual covariates that are potentially related to the primary care experience. Predisposing factors included: age (18–45, 46–64, above 64); sex (male, female); race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Non-Hispanic Asian, Others); health insurance (public, private, no insurance); education (no degree, high school diploma, bachelor and higher degree, other); employment status (not employed, employed); income (<$20,000, $20,000–39,999, > = $40,000); and marital status (married, not married). Enabling factors included: metropolitan statistical area (MSA) (yes, no) and census region (northwest, Midwest, south, west). Need factors were: perceived health status (excellent/very good/good, fair/poor); perceived mental health status (excellent/very good/good, fair/poor); help with activities of daily living (ADL help screener) (yes, no); and help with instrumental activities of daily living (IADL help screener) (yes, no).

Analysis

We performed all the data analyses by using Stata/SE 14.0. All the analyses accounted for both the design effect and the sampling weights by using svy command. Bivariate comparisons were performed between an individual’s insurance type and primary care measures related to first contact, longitudinality, comprehensiveness, and coordination. Chi-square tests were performed to determine whether there were differences between insurance groups in primary care quality. Logistic regressions were used to examine the association between insurance types and primary care measures, while controlling for individual covariates. We also performed bivariate comparison to show the variations in socioeconomic status (education, employment status and income) among types of insurance. We used standard errors, p-values, odds ratios, and 95 % confidence intervals to interpret statistical significance and effect size.

Results

In 2012, it was estimated that more than 21.8 million Americans had type 2 diabetes. The majority of those were between the ages of 46 and 64 (44 %), and 65 and older (43 %). In terms of race/ethnicity, 60 % were non-Hispanic white, 17 % were Hispanic, 15 % were black, 5 % were Asian, and 3 % were others. Only 8 % were uninsured. 58 % were covered by private health insurance and 34 % were covered by public health insurance. Individuals with a high school diploma, the unemployed, and those with incomes below $20,000 accounted for over half of those with diabetes. About 41 % of diabetes cases were from the southern census region and 83 % were from urban areas. Table 1 shows additional information about the study population.
Table 1

Demographic and primary care characteristics for adults with diabetes

Personal Characteristics

Frequency

Weighted Frequency

Weighted %

Standard Error (SE)

Predisposing factors

Age in years***

 18–45

403

2,924,379

13.41

0.86

 46–64

1,164

9,561,753

43.84

1.5

 Above 64

1,049

9,325,508

42.75

1.5

Sex**

 Male

1,230

11,156,923

51.15

1.2

 Female

1,387

10,656,858

48.85

1.2

Race/Ethnicity***

 Non-Hispanic White

953

13,159,194

60.33

1.7

 Non-Hispanic Black

683

3,368,274

15.44

1.1

 Hispanic

743

3,592,967

16.47

1.4

 Non-Hispanic Asian

171

1,012,791

4.64

0.55

 Others

67

680,554

3.12

0.62

Health insurance***

 Private

1,260

12,567,988

57.61

1.5

 Public

1,041

7,514,538

34.45

1.4

 No insurance

316

1,731,254

7.94

0.65

Education***

 No Degree

373

2,135,699

20.92

1.3

 High School Diploma

655

5,823,750

57.04

1.8

 Bachelor and Higher Degree

193

1,686,156

16.51

1.3

 Other

63

565,057

5.53

0.88

Employment status***

 Not employed

1,566

12,807,108

58.86

1.4

 Employed

1,046

8,952,016

41.14

1.4

Income***

 

*

 

  < $20,000

1,500

10,985,633

50.39

1.4

 $20,000–39,999

612

5,322,165

24.41

1

  > = $40,000

504

5,493,176

25.2

1.3

Marital***

 No

1,245

9,458,394

43.36

1.2

 Yes

1,372

12,355,387

56.64

1.2

Enabling factors

MSA**

 

***

 

 No

387

3,870,681

17.75

1.6

 Yes

2,229

17,940,960

82.25

1.6

Census region**

 

***

 

 Northeast

424

3,855,015

17.67

1.3

 Midwest

449

4,705,831

21.57

1.3

 South

1,109

8,893,429

40.77

1.4

 West

634

4,357,367

19.98

1.1

Need factors

Perceived health status ***

 

***

 

 Excellent/VG/Good

1,656

14,350,075

65.78

1.2

 Fair/Poor

961

7,463,705

34.22

1.2

Perceived mental health status ***

 

***

 

 Excellent/VG/Good

2,203

18,510,339

84.86

0.93

 Fair/Poor

414

3,303,441

15.14

0.93

ADL help screener ***

 

***

 

 No

2,457

20,608,010

94.47

0.64

 Yes

160

1,205,770

5.53

0.64

IADL help screener ***

 

***

 

 No

2,362

19,750,771

90.54

0.72

 Yes

255

2,063,009

9.46

0.72

Primary Care Attribute

        First Contact

    

Have USC ***

    

 No

253

1,676,213

7.81

0.75

 Yes

2,316

19,799,721

92.19

0.75

Provider type of USC ***

    

 Facility

1,140

8,879,718

44.85

1.71

 Person/Person in facility

1,176

10,920,003

55.15

1.71

Provider specialty of USC

    

 Primary care

1,066

9,727,524

89.08

1.52

 Other

110

1,192,479

10.92

1.52

USC location

    

 Office

1,664

15,321,478

77.48

1.44

 Hospital

648

4,453,434

22.52

1.44

Difficulty in contacting USC by phone ***

    

 Not very difficult

2,096

17,845,798

93.95

0.71

 Very difficult

139

1,148,599

6.05

0.71

USC has office hours nights/weekends ***

    

 No

1,412

12,252,897

68.91

1.44

 Yes

670

5,528,129

31.09

1.44

How long it takes get to USC ***

    

  ≤ 30 min

1,974

17,095,937

86.43

0.96

  > 30 min

338

2,684,740

13.57

0.96

How difficult is it get to USC

    

 Difficulty

2,280

19,579,459

99.03

0.23

 Not difficult

31

192,250

0.97

0.23

Longitudinality

USC provider listens

    

 No

28

151,190

0.81

0.19

 Yes

2,139

18,552,573

99.19

0.19

Comprehensiveness

Goes to USC for preventive health care ***

    

 No

24

155,285

0.79

0.20

 Yes

2,288

19,605,865

99.21

0.20

Coordination

Provider asks about other treatments

    

 No

379

3,188,383

16.61

1.08

 Yes

1,875

16,007,106

83.39

1.08

Goes to USC for referrals

    

 No

35

369,907

1.87

0.40

 Yes

2,277

19,416,969

98.13

0.40

*p < 0.05, **p < 0.01, ***p < 0.001

When looking at first contact attributes of primary care among the study population by three insurance types, 69 % of uninsured reported having a usual source of care, compared to 94 % of privately-insured and 94 % of publicly-insured (p < .001). The uninsured overwhelmingly reported a facility to be their usual source of care (62 %) compared to people under private health insurance coverage (44 %) and under public insurance coverage (43 %) (p < .01). Hospitals accounted for 31 % of USC locations among uninsured, 26 % among publicly-insured, and only 20 % among privately-insured (p < .01). About 1 % of privately-insured, 2 % of publicly-insured and 1 % of uninsured reported not difficult in getting to USC (p < .01). When looking at the other measures in first contact as well as the measures regarding the longitudinality, comprehensiveness and coordination attributes of primary care, no additional significant differences were found. Additional findings are presented in Table 2.
Table 2

Primary care characteristics for adults with diabetes, by insurance status

 

Insurance

 

Private, % (SE)

Public, % (SE)

Uninsured, % (SE)

Primary Care Attribute

Freq

Weighted Frequency

Weighted %

SE

Freq

Weighted Frequency

Weighted %

SE

Freq

Weighted Frequency

Weighted %

SE

   First Contact

Have USC ***

            

 No

86

702,336

5.65

0.9

68

457,288

6.21

1.1

99

516,590

30.84

3.8

 Yes

1,159

11,732,932

94.35

0.9

946

6,908,250

93.79

1.1

211

1,158,539

69.16

3.8

Provider type of USC **

            

 Facility

542

5,184,360

44.19

2.2

454

2,982,421

43.17

2.2

144

712,937

61.54

4.7

 Person/Person in facility

617

6,548,572

55.81

2.2

492

3,925,828

56.83

2.2

67

445,603

38.46

4.7

Provider specialty of USC

            

 Primary care

562

5,821,805

88.9

2.1

446

3,512,108

89.46

2

58

393,611

88.33

4.5

 Other

55

726,767

11.1

2.1

46

413,720

10.54

2

9

51,992

11.67

4.5

USC location **

          

 Office

875

9,386,435

80.07

1.8

656

5,139,253

74.48

2

133

795,790

69.09

4.4

 Hospital

282

2,336,115

19.93

1.8

289

1,761,217

25.52

2

77

356,102

30.91

4.4

Difficulty in contacting USC by phone

            

 Not very difficult

1,060

10,580,584

94.5

1

861

6,263,976

93.5

1.1

175

1,001,238

91.13

2.1

 Very difficult

55

615,910

5.5

1

58

435,224

6.5

1.1

26

97,466

8.87

2.1

USC has office hours nights/weekends

            

 No

679

7,168,320

67.41

2

603

4,379,029

71.62

2.1

130

705,548

68.31

4.4

 Yes

373

3,465,310

32.59

2

236

1,735,547

28.38

2.1

61

327,272

31.69

4.4

How long it takes get to USC

            

  ≤ 30 min

1,014

10,313,811

87.9

1.4

792

5,835,272

84.49

1.4

168

946,853

82.94

3.1

  > 30 min

145

1,419,120

12.1

1.4

153

1,070,859

15.51

1.4

40

194,761

17.06

3.1

How difficult is it get to USC **

            

 Difficulty

1,152

11,671,618

99.49

0.24

923

6,778,351

98.25

0.43

205

1,129,490

98.94

0.69

 Not difficult

6

59,276

0.51

0.24

22

120,850

1.75

0.43

3

12,124

1.06

0.69

   Longitudinality

USC provider listens

            

 No

9

65,133

0.6

0.26

16

76,154

1.16

0.3

3

9,903

0.85

0.51

 Yes

1,057

10,880,003

99.4

0.26

889

6,511,449

98.84

0.3

193

1,161,121

99.15

0.51

   Comprehensiveness

Goes to USC for preventive health care

            

 No

8

61,016

0.52

0.21

11

79,061

1.15

0.42

5

15,208

1.32

0.69

 Yes

1,150

11,652,671

99.48

0.21

934

6,820,140

98.85

0.42

204

1,133,054

98.68

0.69

   Coordination

Provider asks about other treatments

            

No

183

1,833,937

16.09

1.5

154

1,155,387

17.3

1.6

42

199,060

17.83

3.3

Yes

947

9,566,148

83.91

1.5

768

5,523,532

82.7

1.6

160

917,426

82.17

3.3

Goes to USC for referrals

            

No

18

194,748

1.66

0.5

13

161,616

2.34

0.81

4

13,543

1.18

0.62

Yes

1,141

11,538,183

98.34

0.5

933

6,746,634

97.66

0.81

203

1,132,151

98.82

0.62

*p < 0.05, **p < 0.01, ***p < 0.001

After controlling for individual’s predisposing, enabling, and needs factors, including race/ethnicity, insurance, age, gender, employment status, education, marital status, income, health status, mental health status, having an ADL or IADL screener, MSA and region, the differences found in Table 2 were no longer significant. Table 3 shows the results of logistic regressions associating health insurance status with primary care quality according to the four domains of primary care. Model 1 shows the unadjusted odds ratios expressed as the odds of each primary care attribute among each health insurance group compared with privately-insured. Similar to the findings from Table 2, the uninsured were less likely to have USC compared with people under private insurance coverage (OR = 0.134, P < .001). The uninsured were more likely to report a facility to be their usual source of care (OR = 2.021, P < .01) and were less likely to report office as their USC locations than privately-insured (OR = 0.556, P < .001). The publicly-insured were also less likely to report an office as their USC location than privately-insured (OR = 0.726, P < .05). The publicly-insured were 3.511 times more likely to have difficulties in getting to their USC than privately-insured.
Table 3

Logistic regressions: primary care characteristics for adults with diabetes, insurance status

 

Odds Ratio (95 % CI)

 

Model 1

Model 2

Primary Care Attribute

Public vs. Private

Uninsured vs Private

Public vs. Private

Uninsured vs Private

Have USC

    

 Yes

0.904 (0.557 1.467)

0.134 *** (0.081 0.222)

0.831 (0.484 1.428)

0.186 *** (0.103 0.337)

 No

1

1

1

1

Provider type of USC

    

 Facility

0.960 (0.774 1.189)

2.021 ** (1.317 3.100)

0.794 (0.598 1.055)

1.352 (0.863 2.118)

 Person/Person in facility

1

1

1

1

Provider specialty of USC

    

 Primary care

1.060 (0.614 1.828)

0.945 (0.373 2.397)

1.072 (0.550 2.087)

1.056 (0.373 2.989)

 Other

1

1

1

1

USC location

    

 Office

0.726 * (0.552 0.956)

0.556 ** (0.366 0.845)

0.893 (0.628 1.270)

0.797 (0.506 1.254)

 Hospital

1

1

1

1

Difficulty in contacting USC by phone

    

 Very difficult

1.194 (0.683 2.085)

1.672 (0.896 3.120)

0.875 (0.420 1.821)

1.234 (0.652 2.337)

 Not very difficult

1

1

1

1

USC has office hours nights/weekends

    

 Yes

0.820 (0.622 1.080)

0.960 (0.613 1.501)

0.915 (0.656 1.276)

0.936 (0.576 1.521)

 No

1

1

1

1

How long it takes get to USC

    

  ≤ 30 min

0.750 (0.524 1.073)

0.669 (0.402 1.113)

0.914 (0.598 1.396)

0.867 (0.491 1.530)

  > 30 min

1

1

1

1

How difficult is it get to USC

    

 Difficulty

3.511 ** (1.369 9.006)

2.113 (0.367 2.160)

1.011 (0.301 3.392)

0.905 (0.111 7.392)

 Not difficult

1

1

1

1

Longitudinality

USC provider listens

    

 Yes

0.512 (0.187 1.398)

0.702 (0.159 3.102)

0.742 (0.235 2.341)

1.926 (0.416 8.927)

 No

1

1

1

1

Comprehensiveness

Goes to USC for preventive health care

    

 Yes

0.452 (0.149 1.372)

0.390 (0.104 1.460)

0.730 (0.191 2.790)

0.478 (0.107 2.136)

 No

1

1

1

1

Coordination

Provider asks about other treatments

    

 Yes

0.917 (0.679 1.238)

0.884 (0.541 1.443)

0.925 (0.646 1.325)

0.887 (0.525 1.497)

 No

1

1

1

1

Goes to USC for referrals

    

 Yes

0.705 (0.278 1.784)

1.411 (0.407 4.888)

0.543 (0.149 1.974)

0.932 (0.233 3.735)

 No

1

1

1

1

Notes: Model 2 adjusted for race/ethnicity, insurance, age, gender, employment status, education, marital, income, health status, mental health status, ADL screener, IADL screener, MSA, and region

*p < 0.05, **p < 0.01, ***p < 0.001

Model 2 shows the results of multivariate logistic regressions. Odds ratios have been adjusted for individuals’ covariates that are potentially related to the primary care experience. After accounting for the individuals’ predisposing, enabling and need factors, the significant differences in primary care quality, which were found in Model 1, were no longer apparent. More specifically, only one insurance group, the uninsured, was still associated with lower odds in having USC (OR = 0.186, P < .001). No negative associations were found between privately-insured and primary care quality. The significant associations found in Model 1, between uninsured and higher odds of reporting a facility as their USC provider, and between uninsured and lower odds of reporting an office as their USC location, were no longer statistically significant after controlling for the confounding factors. In terms of the longitudinality, comprehensiveness and coordination attributes, there was no statistically significant association found between insurance types and primary care quality.

Table 4 shows the variations in socioeconomic status (education, employment status and income) among three types of insurance. Sixty-two percent of privately-insured reported having a high school diploma, compared to 50 % of publicly-insured and 50 % of uninsured (p < .001). Most of the publicly-insured were unemployed (89 %) compared to people under private health insurance coverage (43 %) or uninsured (42 %) (p < .001). Thirty-three percent of the privately-insured reported their income level as below $20,000, compared to 77 % of the publicly-insured and 64 % of the uninsured (p < .001).
Table 4

Socioeconomic status (SES) for adults with diabetes, by insurance status

 

Private

Public

Uninsured

SES

Weighted %

SE

Weighted %

SE

Weighted %

SE

Education ***

 No Degree

9.35

1.28

36.92

2.73

35.34

4.76

 High School Diploma

62.31

2.43

49.86

3.05

49.94

5.46

 Bachelor and Higher Degree

21.17

1.96

9.85

1.63

11.26

3.07

 Other

7.17

1.28

3.37

1.13

3.47

1.65

Employment status ***

 Not employed

43.28

2.03

88.73

1.20

41.93

3.81

 Employed

56.72

2.03

11.27

1.20

58.07

3.81

Income ***

  < $20,000

32.83

1.7

76.68

2.04

64.40

4.21

 $20,000–39,999

29.30

1.46

16.73

1.61

21.98

3.26

  > = $40,000

37.88

1.80

6.59

1.19

13.62

3.36

*p < 0.05, **p < 0.01, ***p < 0.001

Discussion

The study used nationally-representative MEPS data to explore the presence of disparities in quality of primary care, and to build on past research investigating whether insurance differences in quality of care persist in an effort to eliminate health disparities over the years. The unadjusted results revealed differences in primary care quality among different insurance groups across measures in the first contact attribute. After accounting for the individuals’ predisposing, enabling and need factors, the significant differences were no longer apparent. Our study suggests that equitable primary care quality was received by diabetes patients despite their health insurance status and implies the crucial role that primary care plays in providing a more equitable level of care for patients with chronic disease.

Previous studies suggests that there were insurance-related disparities in access to primary care, the medical management of chronic illness, health care qualities and health outcomes [1517]. However, with ACA providing a solid foundation for expansions in health insurance coverage and strengthening the U.S. primary care system [18], health care disparities have been narrowed among groups with different insurance statuses. The ACA spurred major expansions in health insurance coverage, with some of the biggest gains from the federally operated marketplace and in states that expanded eligibility for their Medicaid programs. About 6.7 million people enrolled in health plans sold through the ACA’s marketplaces in 2014 [19]. Nearly 10 million people have newly enrolled in Medicaid since October 2013 [20]. Moreover, multiple provisions were included in the ACA for improving primary care, such as the support for innovation in primary care delivery, increasing Medicaid and Medicare payments to primary care providers, and investing in primary care workforce development. For chronic disease patients, the ACA advances the “medical home” concept for Medicaid patients with chronic conditions. Starting in 2011, millions of Medicaid patients with chronic conditions could have a health home to help them manage their condition. Such reforms in health insurance coverage and primary care provide a solid foundation for strengthening the U.S. primary care system, and have a positive impact on patient’s care, especially for chronically ill patients [18]. Policy makers should continue their commitment to target vulnerable groups, such as elderly, poor, and/or medically underserved populations, and reduce gaps in insurance coverage in further and therefore ultimately improving access and quality of care.

This study has several limitations. First, it is difficult to make causal inferences due to the secondary nature of the dataset. Second, MEPS data on primary care relies on household respondents’ self-report, which is subject to recall bias. Third, the study only included measures regarding primary care experience reported by the patients, rather than their health outcomes. Further studies may include more health outcomes measures, such as clinical performance indicators, to evaluate the impact of health insurance on diabetic care outcomes. Fourth, our results showed there were significant differences in socioeconomic status among major types of health insurance. The analysis could be improved to present analyses that characterize the degree to which each type of SES explained the insurance-quality associations in the unadjusted models, such as by using a hierarchical modeling approach. Lastly, our primary care measures were operationalized from MEPS rather than the investigator-initiated, which preclude the examination of all the major measures of primary care, especially with regard to measures for the logitudinality and comprehensiveness attributes.

Despite these shortcomings, this study demonstrates important findings to the field and could contribute to improving primary care for diabetic patients. This study reveals equitable primary care quality for diabetes patients despite their health insurance status. In addition to insurance-related differences, other socioeconomic stratification factors, such as the inequality income, education, and occupation, are assumed to be the root cause of disparities in care and population health [21]. Future efforts are needed to investigate both insurance-related and SES-based disparities in healthcare, to identify the major mediators of differences in quality of care. The bulk of the evidence suggests that equitable primary care eliminates racial and ethnic disparities [2225]. Next steps and future directions should be undertaken to examine the role of primary care in improvements in the management of chronic diseases by reducing both insurance and SES-based disparities. In conclusion, the causes of disparities in diabetes care are complex and include societal issues such as lower SES status and poor access to health care. The affordable care act has improved accessibility and affordability of health care. To further improve the quality and equity of primary care for diabetes patients, a number of policy changes could potentially make a positive contribution, such as encouraging new models of care for pre-diabetes and diabetes patients, and raising reimbursement levels for primary care providers who deliver evidence-based diabetes prevention and care.

Conclusions

This study reveals equitable primary care quality for diabetes patients despite their health insurance status. In addition to insurance-related differences, the other socioeconomic stratification factors are assumed to be the root cause of disparities in care. This research emphasizes the crucial role that primary care plays in the accessibility and quality of care for chronically ill patients. Policy makers should continue their commitment to reduce gaps in insurance coverage and improve access as well as quality of diabetic care.

Abbreviations

ACA, Affordable Care Act; ADL, Activities of daily living; AHRQ, Agency for Healthcare Research and Quality; IADL, Instrumental activities of daily living; MEPS, Medical Expenditure Panel Survey; MSA, Metropolitan statistical area; USC, Usual source of care; WHO, World Health Organization

Declarations

Acknowledgements

Not applicable.

Funding

The study was sponsored by the Johns Hopkins Primary Care Policy Center.

Availability of data and material

The data that support the findings of this study are available from Medical Expenditure Panel Survey, https://meps.ahrq.gov/mepsweb/.

Authors’ contributions

D-CL and LS conceptualized the study; HL provided the analyses; all authors drafted and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Johns Hopkins Primary Care Policy Center
(2)
Department of Information Management, Da-Yeh University
(3)
Johns Hopkins Bloomberg School of Public Health

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Copyright

© The Author(s). 2016