Abstract
Introduction
Blacks, Hispanics, and Asians are disproportionately affected by diabetes. We assessed the state of racial/ethnic disparities in diabetes quality of care in the U.S.
Methods
We analyzed cross-sectional data of adults diagnosed with Type 2 diabetes in the nationally-representative 2013 Medical Expenditure Panel Survey. Differences in adherence to five diabetes quality of care recommendations (HbA1c twice yearly; yearly foot exam, dilated eye exam, blood cholesterol test and flu vaccination) were examined by race/ethnicity while controlling for three social determinants of health (health insurance status, poverty, and education) and other demographic variables.
Results
Among adults with diabetes in the U.S., 74.9% received two or more HbA1c tests, 69.0% had a foot exam, 64.9% had an eye exam, 85.4% had a cholesterol test, and 65.1% received flu vaccination in 2013. Compared to Whites, all were lower for Hispanics; HbA1c tests, eye exam, and flu vaccination were lower for Blacks; HbA1c tests, foot exam, and eye exam were lower for Asians. In adjusted models, the only remaining disparities in quality of care indicators were: HbA1c tests for Hispanics (AOR 0.67, CI=0.47 - 0.97), Blacks (AOR 0.59, CI=0.40 - 0.88), and Asians (AOR 0.47, CI=0.42 - 0.99); foot exams for Hispanics (AOR 0.65, CI=0.47 - 0.90) and flu vaccination for Blacks (AOR 0.68, CI= 0.49 - 0.93).
Conclusion
Lack of insurance coverage and education explained some of the racial/ethnic disparities observed in diabetes quality of care. Improving quality of diabetes care could help reduce rates of diabetes complications, healthcare costs and mortality.
Keywords: Diabetes, disparities, quality of care
INTRODUCTION
Type 2 diabetes mellitus is a continuously growing public health problem in the United States (U.S.). In 2012, 21 million people were diagnosed with diabetes, plus an additional 8 million with undiagnosed diabetes, contributing to an estimated $176 billion in direct medical costs and $69 billion in indirect costs.[1] This chronic disease disproportionally affects racial and ethnic minorities, with prevalence in 2012 for Hispanics at 12.8%, Blacks 13.2%, and Asians 9.0%, compared to 7.6% among Whites.[1] If not properly managed, Type 2 diabetes can lead to serious complications such as kidney failure, retinopathy, stroke, neuropathy, lower limb amputations, and premature death.[2] The prevalence of diabetes kidney complications among adult patients with Type 2 diabetes in the U.S. was estimated to be 15% in 2012[3] and 28.5% for diabetes retinopathy complications in 2010.[4] Hispanics, Blacks, and Native Americans have a 50-100% greater burden of diabetes complications and mortality than Whites.[5,6] Social determinants of health, such as educational attainment, access to medical care, and income have been associated with these disparities in diabetes-related morbidity and mortality.[7–12]
Quality of care refers to the extent to which recommended standards of care are implemented by healthcare providers.[13] Lower quality of care increases risk for diabetes complications and mortality.[14] Due to the complex nature of diabetes treatment and constantly emerging research evidence, the American Diabetes Association (ADA) periodically updates the standards for diabetes quality of care in the U.S.[15] The guidelines include the frequency that physicians should perform the hemoglobin A1c (HbA1c) test, foot exam, dilated eye exam, blood cholesterol test, and flu vaccine in adult patients with Type 2 diabetes.[16]
Some previous studies have examined racial/ethnic disparities in adherence to these diabetes quality of care recommendations.[17] However, very few studies have used nationally-representative data, with the most recent study using data from 2010, and results have been inconsistent.[18–20] Further research is needed to examine the extent to which social determinants of health, such as health care access and socioeconomic status explain racial/ethnic disparities in diabetes quality of care.[21] This study examined this question among adults with Type 2 diabetes in the U.S. by conducting secondary analysis of nationally representative survey data from the most recent available year (2013) of the Medical Expenditure Panel Survey (MEPS).
METHODS
Study Design and Data
We used a quantitative, cross-sectional, and observational design to conduct secondary analysis of 2013 MEPS data. The MEPS is a set of surveys of households and individuals, their healthcare providers, and their employers. MEPS has been sponsored and implemented by the Agency for Healthcare Research and Quality (AHRQ) and the National Center for Health Statistics (NCHS) since 1996. Each year a subsample of approximately 15,000 households that took part in the previous year's National Health Interview Survey is drawn to serve as the sample for the MEPS Household Component (MEPS-HC), a nationally representative samples of the civilian non-institutionalized population in the U.S., with an oversampling of Black, Asian, and Hispanic individuals.[22] As a panel survey, participants in each sample are interviewed five times over a period of two calendar years. As part of the MEPS-HC, the Diabetes Care Survey (DCS) is collected during Rounds 3 and 5 each year on adult participants (ages 18 and older) who reported physician-diagnosed diabetes. For each calendar year, the data for the two panels are consolidated and made available on the MEPS website as full-year public use data files, which are de-identified.[22]
Due to a lag in collecting and preparing the data for use by researchers, the most recent year of MEPS-HC data currently available is 2013, which had an 88.1% response rate for the DCS in adults with diabetes.[22] The full 2013 MEPS-HC dataset contained 35,086 participants. For this study, a subset of the 2013 MEPS-HC full-year sample was used based on the following criteria: adult (ages 18 and older), diagnosed with diabetes by a physician, and responded to the MEPS DCS special questionnaire. From the full dataset, 2,172 participants met the criteria for inclusion. Respondents who reported a race/ethnicity other than White, Black, Hispanic, Asian or multiple races were excluded from the analysis due to small numbers and the diverse composition of this category. Thus, the final analytic dataset included 2,107 respondents.
Measures
Diabetes Quality of Care Indicators
Five variables drawn from the DCS questionnaire were used as diabetes quality of care indicators, coded as dichotomous variables to indicate adherent or non-adherent to the following five recommended services during the past year per the ADA guidelines during 2013. [16] (1) HbA1c testing: respondents were asked how many times their doctor checked their HbA1c, and we recoded two or more HbA1c blood tests per year as adherent. Respondents were asked if they received each of the following services in 2013; if yes, it was considered adherent: (2) Foot exam: one annual foot exam, (3) Dilated eye exam: one annual dilated eye exam, (4) Blood cholesterol test: at least one blood cholesterol test per year, and (5) Flu vaccine: one annual flu vaccination.
Race/ethnicity
A categorical variable was used with the categories of 1=White non-Hispanic, 2= Hispanic, 3=Black non-Hispanic, and 4=Asian non-Hispanic.
Poverty Status
The MEPS dataset included a variable for family income as a percentage of federal poverty level (depending on family size) with the following categories coded as: 1=poor (below poverty level), 2=near poor (100% to less than 125% of poverty level), 3=low income (125% to less than 200% of poverty level), 4=middle income (200% to less than 400% of poverty level), and 5=high income (400% of poverty level or greater).
Healthcare Access
Health insurance coverage was used to measure access to healthcare. A categorical variable was coded with the values of 1=private insurance (e.g., employer, individual, military, veterans), 2=public insurance only (e.g., Medicaid, Medicare), and 3=uninsured.
Demographic Variables
The respondent's age as of 12/31/2013 was recoded into variables with age categories coded as 1=18 to 24, 2=25 to 34, 3=35-44, 4=45 to 54, 5=55 to 64, 6=65 to 74, and 7=75 to 85. Sex was represented as dichotomous variable with the values of 1=male and 0=female. Marital status as of 12/31/13 was coded as a dichotomous variable with the values of 1=married and 0=not married. Education was recoded as a variable with the values of 1= Less than high school, 2=High school/some college, and 3= Bachelor's degree, and 4= post graduate degree.
Statistical Analysis
The data were analyzed using IBM SPSS Statistics 24.0 software[23] using procedures for complex surveys that account for the stratified sample design of the MEPS and the provided DCS sample weight. Bivariate cross tabulations with 95% confidence intervals (CI) were used to compare the frequencies for the five diabetes quality of care indicators across racial /ethnic groups. Binomial logistic regression was performed for each of the diabetes quality of care indicators to estimate the differences by race/ethnicity while adjusting for insurance coverage, poverty level, years of education, age, sex, marital status. A level of alpha<0.05 was used to determine statistical significance.
RESULTS
Table 1 presents weighted sample characteristics. The weighted racial/ethnic composition of the sample was 16.3% Hispanics, 15.2% Blacks, 5.7% Asians, and 62.8% Whites. Over half respondents (57.3%) had private health insurance, while 34.7% had public insurance and 8.0% were uninsured. Nearly one-fifth of respondents were classified as poor or near poor. Over a quarter had less than a high school degree and nearly one-fifth had a bachelor's degree or higher. Most respondents were age 45 or older, including 43.1% age 65 or older. The sample was evenly divided by sex, and just over half of participants were married.
Table 1.
Sample Characteristics of Adults with Diabetes
| Variable | N | Weighted %a |
|---|---|---|
| Race/Ethnicity | ||
| White non-Hispanic | 773 | 62.8 |
| Hispanic | 614 | 16.3 |
| Black non-Hispanic | 559 | 15.2 |
| Asian non-Hispanic | 161 | 5.7 |
| Insurance Coverage | ||
| Private | 989 | 57.3 |
| Public | 890 | 34.7 |
| Uninsured | 228 | 8.0 |
| Poverty Status | ||
| Poor | 476 | 14.0 |
| Near poor | 155 | 5.8 |
| Low income | 384 | 17.6 |
| Middle income | 601 | 30.1 |
| High income | 491 | 32.5 |
| Education | ||
| Less than high school | 742 | 26.3 |
| High school / some college | 1,036 | 54.3 |
| Bachelor's degree | 193 | 11.7 |
| Post graduate degree | 111 | 7.6 |
| Age | ||
| 18-24 | 25 | 0.8 |
| 25-34 | 70 | 3.0 |
| 35-44 | 213 | 8.4 |
| 45-54 | 427 | 17.9 |
| 55-64 | 576 | 26.9 |
| 65-74 | 483 | 26.0 |
| 75-85 | 309 | 17.1 |
| Sex | ||
| Female | 1,142 | 50.2 |
| Male | 965 | 49.8 |
| Marital Status | ||
| Married | 1,106 | 56.7 |
| Not married | 1,001 | 43.3 |
Percentages are weighted and adjusted for complex sample design
Table 2 lists weighted percentages for the diabetes quality indicators by race/ethnicity in 2013. Among the quality of care outcomes, the most prevalent was receiving a blood cholesterol test (85.4%), and the least prevalent was receiving a dilated eye exam (64.9%).
Table 2.
Weighted Percentages of Respondents Receiving Diabetes Quality Indicators by Race/Ethnicity
| Race/Ethnicity | ≥2 HbA1c tests | Foot exam | Dilated eye exam | Blood cholesterol test | Flu vaccine |
|---|---|---|---|---|---|
| White non-Hispanic | 79.5 (75.9-82.7) | 72.8 (69.3-76.0) | 69.3 (65.5-72.9) | 87.8 (85.4-89.8) | 68.7 (65.0-72.1) |
| Hispanic | 63.4 (57.6-68.9) | 56.9 (51.8-61.9) | 53.1 (48.1-58.0) | 77.5 (72.6-81.8) | 59.2 (53.4-64.8) |
| Black non- Hispanic | 67.7 (61.5-73.3) | 69.1 (64.7-73.1) | 60.6 (56.1-65.0) | 84.2 (80.5-87.3) | 55.7 (50.8-60.5) |
| Asian non-Hispanic | 68.5 (55.4-79.2) | 61.0 (51.9-69.4) | 59.9 (51.3-68.0) | 84.1 (76.8-89.4) | 67.6 (57.7-76.2) |
| Total | 74.9 (72.0-77.6) | 69.0 (66.4-71.4) | 64.9 (62.2-67.5) | 85.4 (83.5-87.1) | 65.1 (62.4-67.7) |
aPercentages are weighted and adjusted for complex sample design
Table 3 presents the results of the logistic regression analyses for the five diabetes quality indicators to estimate differences by race/ethnicity before and after adjusting for insurance, poverty, education, and other confounders. In the unadjusted models, compared to Whites: Hispanics were lower on all quality indicators; Blacks were lower on HbA1c tests, eye exam, and flu vaccine; and Asians were lower on HbA1c tests, foot exam and eye exam.
Table 3.
Binomial Logistic Regressions for Each Diabetes Quality Indicator
|
Dependent Variables |
|||||
|---|---|---|---|---|---|
| 2+ HbA1c Tests | Foot Exam | Dilated Eye Exam | Cholesterol Test | Flu Vaccine | |
| Unadjusted Models | Crude ORa (95% CI) | Crude ORa (95% CI) | Crude ORa (95% CI) | Crude ORa (95% CI) | Crude ORa (95% CI) |
| Race/Ethnicity | |||||
| White non-Hispanic | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Hispanic | 0.48 (0.33 – 0.61) | 0.49 (0.39 - 0.63) | 0.50 (0.38 - 0.66) | 0.48 (0.35 - 0.66) | 0.66 (0.50 - 0.88) |
| Black non-Hispanic | 0.54 (0.39 – 0.75) | 0.84 (0.64 – 1.09) | 0.68 (0.53 - 0.88) | 0.74 (0.53 – 1.03) | 0.57 (0.44 - 0.74) |
| Asian non-Hispanic | 0.56 (0.31 – 0.99) | .557 (.371 - .835) | 0.66 (0.45 - 0.97) | 0.74 (0.45 – 1.21) | 0.95 (0.60 – 1.52) |
| Adjusted Models | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) | Adjusted OR (95% CI) |
| Race/Ethnicity | |||||
| White non-Hispanic | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Hispanic | 0.67 (0.47 - 0.97) | 0.65 (0.47 – 0.90) | 0.98 (0.71 - 1.36) | 0.94 (0.61 - 1.46) | 1.01 (0.72 - 1.43) |
| Black non-Hispanic | 0.59 (0.40 - 0.88) | 0.87 (0.64 - 1.19) | 0.88 (0.67 - 1.17) | 0.94 (0.61 - 1.45) | 0.68 (0.49 - 0.93) |
| Asian non-Hispanic | 0.47 (0.22 - 0.99) | 0.67 (0.42 - 1.06) | 0.63 (0.39 - 1.00) | 0.72 (.403 - 1.089) | 1.00 (0.58 - 1.73) |
| Insurance Coverage | |||||
| Private (ref) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Public | 0.92 (0.54 - 1.57) | 1.32 (0.93 - 1.85) | 0.74 (0.53 - 1.04) | 0.81 (0.51 - 1.29) | 0.82 (0.59 - 1.14) |
| Uninsured | 0.29 (0.16 - 0.55) | 0.45 (0.28 - 0.71) | 0.24 (0.16 - 0.36) | 0.22 (0.12 - 0.41) | 0.36 (0.23 - 0.57) |
| Poverty Status | |||||
| High income | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Poor | 0.77 (0.42 - 1.41) | 0.95 (0.56 - 1.60) | 0.89 (0.56 - 1.39) | 0.90 (0.48 – 1.67) | 1.32 (0.89 - 1.96) |
| Near poor | 0.69 (0.25 – 1.88) | 1.05 (0.53 – 1.60) | 1.02 (0.57 – 1.83) | 0.75 (0.31 – 1.79) | 0.98 (0.55 – 1.73) |
| Low income | 1.01 (0.65 – 1.70) | 0.99 (0.61 – 1.60) | 1.01 (0.66 – 1.83) | 0.98 (0.53 – 1.79) | 1.07 (0.72 – 1.59) |
| Middle income | 1.05 (0.66 – 1.70) | 0.85 (0.57 – 1.25) | 0.93 (0.62 – 1.39) | 1.15 (0.68 – 1.95) | 1.24 (0.86 – 1.78) |
| Education | |||||
| Less than high school | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| High school/some college | 0.95 (0.66 - 1.37) | 1.40 (1.01 - 1.94) | 1.64 (1.15 - 2.35) | 1.25 (0.86 - 1.82) | 1.35 (1.00 - 1.83) |
| Bachelor's degree | 1.02 (0.53 - 1.96) | 1.77 (1.04 - 3.01) | 3.00 (1.64 - 5.50) | 2.75 (1.30- 5.85) | 1.25 (0.74 - 2.10) |
| Post graduate degree | 1.42 (0.63 – 3.19) | 1.48 (0.79 – 2.77) | 1.87 (0.90 – 3.88) | 1.97 (0.87 – 4.48) | 1.96 (1.12 – 3.43) |
| Age | |||||
| 18-24 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| 25-34 | 0.09 (0.02 – (0.48) | 0.52 (0.16 - | 0.93 (0.33 – 2.62) | 1.92 (0.42 – 8.80) | 0.59 (0.20 – 1.77) |
| 35-44 | 0.09 (0.02 – 0.40) | 0.70 (0.23 – 2.09) | 0.39 (0.14 – 1.10) | 4.10 (1.06 – 15.84) | 0.55 (0.18 – 1.62) |
| 45-54 | 0.23 (0.05 – 1.06) | 1.01 (0.37 – 2.71) | 0.54 (0.22 – 1.31) | 4.38 (1.24 – 15.49) | 0.67 (0.25 – 1.81) |
| 55-64 | 0.21 (0.05 – 1.01) | 0.89 (0.31 – 2.58) | 0.65 (0.24 – 1.76) | 7.36 (2.01 – 26.98) | 0.95 (0.35 – 2.58) |
| 65-74 | 0.27 (0.06 – 1.26) | 1.17 (0.41 – 3.36) | 0.79 (0.31 – 2.03) | 6.37 (1.76 – 23.07) | 1.51 (0.54 – 4.25) |
| 75-85 | 0.17 (0.4 – 0.75) | 1.01 (0.41 – 3.36) | 1.37 (0.53 – 3.51) | 7.30 (2.01 – 26.62) | 2.21 (0.78 – 6.26) |
| Sex | |||||
| Male | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Female | 1.14 (0.82 – 1.58) | 0.95 (0.72 – 1.26) | 1.14 (0.87 – 1.47) | 1.03 (0.76 – 1.41) | 1.01 (0.77- 1.32) |
| Marital Status | |||||
| Not married | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Married | 1.57 (1.12 – 2.21) | 1.04 (0.80 – 1.36) | 1.26 (0.96 – 1.65) | 1.12 (0.79 – 1.61) | 0.83 (0.60 – 1.14) |
OR=Odds Ratio
bModels included race/ethnicity, insurance coverage, poverty status, years of education, age, sex, and marital status.
In the adjusted models, the following were no longer significantly different from Whites: eye exam, cholesterol test, and flu vaccine for Hispanics; eye exam for Blacks; and foot exam and eye exam for Asians. Disparities persisted in receipt of at least two HbA1c tests in the past year existed for all three racial/ethnic minority groups compared to Whites (Hispanics: AOR 0.67, 95% CI=0.47-0.97; Blacks: AOR 0.59, 95% CI=0.40-0.88; Asians: AOR 0.47, 95% CI=0.22-0.99). Hispanics were 35.0% less likely (AOR 0.65, 95% CI=0.47-0.90) than Whites to obtain an annual foot exam in the adjusted model. Black respondents were 32.0% less likely (AOR 0.68, 95% CI=0.49-0.93) to receive the flu vaccine compared to Whites in the adjusted model.
Uninsured adults with diabetes were significantly less likely to receive all five of the recommended services compared to those with private insurance, while adjusting for the other variables. Poverty status was not significantly associated with any of the diabetes quality of care indicators while adjusting for the other variables in the model.
Respondents with a high school degree and/or some college education were more likely to have a foot exam, a dilated eye exam, and a flu vaccine than with those with less than high school education, but did not differ on the other quality of care indicators while adjusting for the other variables. Individuals with a bachelor's degree were 77% more likely to obtain a foot exam, three times more likely to receive an eye exam, and nearly three times more likely to receive a blood cholesterol test than those with less than high school education, with no difference for HbA1c or flu vaccine. Respondents with a post graduate degree were more likely to obtain the flu vaccine compared to those with less than high school education but did not differ from the other quality of care indicators.
Respondents ages 25 to 44 and 75 to 85 were significantly less likely to receive two HbA1c tests compared to those ages 18 to 24 years while adjusting for the other variables. Patients ages 35 to 85 were between 4.1 and 7.4 times more likely to receive an annual blood cholesterol test than 18 to 24 year olds in the adjusted model. There were no significant differences in the quality of care indicators by sex and marital status while adjusting for the other variables.
DISCUSSION
According to our analysis of the 2013 MEPS data, 74.9% of adults with diabetes in the U.S. received two or more HbA1c tests in the past year, which surpassed the Healthy People 2020[24] national goal of 71.1%. The quality indicators for dilated eye exam (64.7%) and blood cholesterol test (85.4%) also exceed the Healthy People 2020 goals of 58.7% and 85.3%, respectively. However, only 69.0% had a foot exam the past year, which did not meet the goal of 74.8%. Healthy People 2020 did not establish a goal for annual flu vaccination specifically among adults with diabetes, but the overall goal for adults is 90%, showing that adults with diabetes did not meet this goal with 65.1% flu vaccination coverage in the 2013 MEPS data. Overall, between 14.6% and 35.1% of adults with diabetes in the U.S. did not receive one of these recommended services for diabetes care.
In our analyses for 2013, the unadjusted frequencies for all five quality of care indicators were lower for Hispanics compared to Whites. Blacks were lower than Whites for HbA1c tests, eye exam, and flu vaccination. Asians were lower on HbA1c tests, foot exam and eye exam. The most recent relevant study analyzing 2010 MEPS data measured the HbA1c quality of care indicator as receiving one or more test in the past year, which did not align with the ADA guideline of at least twice a year; thus, it is not directly comparable with our study.[20] Another study using 2008 MEPS data reported that Hispanics and Blacks adhered to the recommendation of two or more HbA1c tests less often than Whites (63% and 69% compared to 77%), with 74% for the total sample.[19] A study using 2005-2007 MEPS data found that an average of 83.2% of respondents reported receiving two or more HbA1c tests in the past year, including 83.0% for Whites, 77.8% for Hispanics, 84.5% for Blacks, and 83.3% for Asians.[18] Thus, our finding of 74.9% in 2013 was very similar to the 2008 study, which both suggest a possible decline in this quality of care indicator overall and for all of the racial/ethnic groups since 2005-2007.[18]
For the annual foot exam, the 2010 MEPS study found 68.6% adherence in the overall sample and significantly lower adherence for Hispanics (63.1%) and Blacks (64.8%) versus Whites.[20] This study did not examine Asians separately. Our findings were similar for the overall sample (69.0%) but suggested a potential decline in adherence among Hispanics to 56.9% and a potential improvement among Blacks to 69.1%. Adherence to the annual dilated eye exam was estimated at 64.7% overall, 54.8% for Hispanics, and 63.2% for Blacks in the 2010 MEPS study.[20] We found the same prevalence overall and slightly lower percentages for Blacks and Hispanics in 2013. In 2010, the annual blood cholesterol test was received by 83.9% overall, 78.8% of Hispanics, and 79.7% of Blacks.[20] In our analyses for 2013, use of the cholesterol test was similar overall and for Hispanics, and appeared to have increased to 84.2% for Blacks. None of these previous studies using MEPS data examined adherence to the flu vaccine recommendation for adults with diabetes.
In our analyses, some of the disparities observed in the unadjusted frequencies disappeared after adjusting for other factors – specifically eye exams for all groups; cholesterol test and flu vaccine for Hispanics; and foot exams for Asians. These factors did not explain any of the racial/ethnic disparities in HbA1c tests. The significant confounders were insurance for all models, education for all except HbA1c tests, and age for HbA1c tests and cholesterol tests.
Previous studies that examined racial/ethnic disparities in diabetes quality of care indicators in older years of MEPS data found inconsistent results in analyses that adjusted for insurance, income, education and other factors. In the 2008 MEPS study, the disparity in HbA1c adherence for Blacks disappeared after controlling for insurance and income but the disparity remained for Hispanics.[19] The 2010 MEPS study found that disparities among Hispanics and Blacks for foot exam remained after controlling for insurance, income, and education, and did not find disparities for eye exams or blood cholesterol tests before or after adjusting.[20] Expanding healthcare coverage, increasing educational attainment, and reducing the gap that exists between the generation of new scientific evidence and the implementation of evidence-based practice for diabetes, such that clinicians do not always comply with recommended diabetes care guideline .would likely help to reduce diabetes disparities.
The primary limitation of the study was the cross-sectional nature of the survey. Cross-sectional data cannot infer causality, only descriptions of variables and associations between variables. Another limitation was the self-reported nature of the questionnaire items for diabetes diagnosis, receiving diabetes-related care. Self-reported data can be biased because participants may be influenced by social desirability. Furthermore, these data can be less reliable than objectively measured data, because answers to the questions depends on participant's memory and an accurate understanding of the procedures done at the physician's office. Finally, the models may not examine the possibility of bias in intensity of care for racial/ethnic minorities, including referrals to specialists, and other potential cofounding variables that could also influence quality of care. Future research should explore additional factors.
Strengths of the study include the nationally-representative sample and the use of sample weights to enable generalizing to the population of adults with Type 2 diabetes in the U.S. In addition, this study examined all five of the diabetes quality of care indicators measured in MEPS and four separate racial/ethnic groups, compared to previous studies that did not include cholesterol tests and flu vaccination or Asians as a separate group.
This study contributes valuable information to the scientific literature about the current state of quality of diabetes care in the U.S. With the growing prevalence of diabetes and diabetic complications in the U.S., the need to improve diabetes quality of care becomes increasingly urgent. The findings can inform quality improvement efforts aimed at enhancing the implementation of ADA standards of care in various medical settings and for all adult patients with diabetes. Physicians and other healthcare providers need to receive continuing education about periodic changes in the diabetes standards of care and strategies for implementing them in practice. In addition, continued efforts to expand clinical support systems could help ensure providers implement evidence-based practice for diabetes care. Finally, patients with diabetes need to be engaged in educational programs that are culturally and linguistically appropriate to inform them about which services they should receive and how frequently they should receive these services as part of their treatment. A better management of Type 2 diabetes requires having well-informed patients to ask for appropriate quality of care to their doctors. Furthermore, continued efforts to expand health insurance coverage would help to reduce barriers for patients with diabetes to access quality care. Improvements in quality of diabetes care can contribute to reducing rates of diabetes complications, as well as diabetes-associated healthcare costs and diabetes mortality.
Acknowledgments
Research supported by Vanderbilt Institute for Clinical and Translational Research (VICTR), grant number UL1-TR000445
Footnotes
Conflicts of interest: Juan R. Canedo, Stephania Miller-Hughes, Mary K. Fadden, David Schlundt, Maureen Sanderson declare that they have no conflicts of interest.
Ethical approval: For this type of study formal consent is not required.
Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.
REFERENCES
- 1.Center for Disease Control and Prevention . National Diabetes Statistics Report: Estimates of Diabetes and its Burden in the United States, 2014 [Internet] U.S. Department of Health and Human Services; GA: 2014. pp. 1–12. Available from: https://www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf. [Google Scholar]
- 2.Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol. Rev. 2013;93:137–88. doi: 10.1152/physrev.00045.2011. [DOI] [PubMed] [Google Scholar]
- 3.Collins AJ, Foley RN, Chavers B, Gilbertson D, Herzog C, Johansen K, et al. US Renal Data System 2011 Annual Data Report. Am. J. Kidney Dis. 2012;59:A7. doi: 10.1053/j.ajkd.2011.11.015. [DOI] [PubMed] [Google Scholar]
- 4.Zhang X, Saaddine JB, Chou C-F, Cotch MF, Cheng YJ, Geiss LS, et al. Prevalence of Diabetic Retinopathy in the United States, 2005–2008. JAMA J. Am. Med. Assoc. 2010;304:649–56. doi: 10.1001/jama.2010.1111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chow EA, Foster H, Gonzalez V, McIver L. The Disparate Impact of Diabetes on Racial/Ethnic Minority Populations. Clin. Diabetes. 2012;30:130–3. [Google Scholar]
- 6.Vart P, Gansevoort RT, Coresh J, Reijneveld SA, Bültmann U. Socioeconomic Measures and CKD in the United States and The Netherlands. Clin. J. Am. Soc. Nephrol. CJASN. 2013;8:1685–93. doi: 10.2215/CJN.12521212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Nwanyanwu KH, Newman-Casey P-A, Gardner TW, Lim JI. Beyond HbA1c: Environmental Risk Factors for Diabetic Retinopathy. [2016 Sep 19];J. Clin. Exp. Ophthalmol. [Internet] 2015 doi: 10.4172/2155-9570.1000405. 6. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785841/ [DOI] [PMC free article] [PubMed]
- 8.Clark ML, Utz SW. Social determinants of type 2 diabetes and health in the United States. World J. Diabetes. 2014;5:296–304. doi: 10.4239/wjd.v5.i3.296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Zheng H, George LK. Rising U.S. income inequality and the changing gradient of socioeconomic status on physical functioning and activity limitations, 1984–2007. Soc. Sci. Med. 2012;75:2170–82. doi: 10.1016/j.socscimed.2012.08.014. [DOI] [PubMed] [Google Scholar]
- 10.Montez JK, Zajacova A. Explaining the Widening Education Gap in Mortality among U.S. White Women. J. Health Soc. Behav. 2013;54:166–82. doi: 10.1177/0022146513481230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Pampel FC, Krueger PM, Denney JT. Socioeconomic Disparities in Health Behaviors. Annu. Rev. Sociol. 2010;36:349–70. doi: 10.1146/annurev.soc.012809.102529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.O'Connor PJ, Bodkin NL, Fradkin J, Glasgow RE, Greenfield S, Gregg E, et al. Diabetes Performance Measures: Current Status and Future Directions. Diabetes Care. 2011;34:1651–9. doi: 10.2337/dc11-0735. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Cooperberg MR, Birkmeyer JD, Litwin MS. Defining high quality health care. Urol. Oncol. 2009;27:411–6. doi: 10.1016/j.urolonc.2009.01.015. [DOI] [PubMed] [Google Scholar]
- 14.Gregg EW, Li Y, Wang J, Burrows NR, Ali MK, Rolka D, et al. Changes in diabetes-related complications in the United States, 1990-2010. N. Engl. J. Med. 2014;370:1514–23. doi: 10.1056/NEJMoa1310799. [DOI] [PubMed] [Google Scholar]
- 15.American Diabetes Association Standards of Medical Care in Diabetes. 2016;39:S1–2. [Google Scholar]
- 16.American Diabetes Association Standards of medical care in diabetes--2013. Diabetes Care. 2013;36(Suppl 1):S11–66. doi: 10.2337/dc13-S011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Laiteerapong N, Fairchild PC, Chou C-H, Chin MH, Huang ES. Revisiting Disparities in Quality of Care Among US Adults With Diabetes in the Era of Individualized Care, NHANES 2007-2010. Med. Care. 2015;53:25–31. doi: 10.1097/MLR.0000000000000255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Richard P, Alexandre PK, Lara A, Akamigbo AB. Racial and Ethnic Disparities in the Quality of Diabetes Care in a Nationally Representative Sample. Prev. Chronic. Dis. [Internet] 2011:8. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3221581/ [PMC free article] [PubMed]
- 19.Pu J, Chewning B. Racial difference in diabetes preventive care. Res. Soc. Adm. Pharm. RSAP. 2013;9:790–6. doi: 10.1016/j.sapharm.2012.11.005. [DOI] [PubMed] [Google Scholar]
- 20.Hu R, Shi L, Rane S, Zhu J, Chen C-C. Insurance, racial/ethnic, SES-related disparities in quality of care among US adults with diabetes. J. Immigr. Minor. Health Cent. Minor. Public Health. 2014;16:565–75. doi: 10.1007/s10903-013-9966-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Walker R, Strom W, Egede L. Influence of Race, Ethnicity and Social Determinants of Health on Diabetes Outcomes. Am J Med Sci. 2016;35:366–73. doi: 10.1016/j.amjms.2016.01.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.MEPS HC-163 2013 Full Year Consolidated Data File [Internet] Agency for Healthcare Research and Quality; Rockville, MD: 2015. pp. 1–185. Available from: https://meps.ahrq.gov/data_stats/download_data/pufs/h163/h163doc.pdf. [Google Scholar]
- 23.IBM SPSS Statistics Version 24.0. IBM Corporation; Armonk, NY: 2016. [Google Scholar]
- 24.Healthy People 2020: Diabetes [Internet] Office of Disease Prevention and Health Promotion; 2016. [2016 Oct 5]. Available from: https://www.healthypeople.gov/2020/topics-objectives/topic/diabetes/objectives. [Google Scholar]