Abstract
Background
Type 2 diabetes mellitus (T2DM) is the most common form of diabetes. Risk factors for its development include older age, obesity, family history of diabetes, history of gestational diabetes, impaired glucose metabolism, physical inactivity, and race/ethnicity.
Objective
The purpose of this study was to characterize T2DM burden, from a patient perspective, with respect to age and race/ethnicity.
Methods
Adults aged ≥18 years with T2DM from a large, Internet-based, nationwide survey were retrospectively analyzed. Demographic and clinical characteristics (glycemic control, body mass index [BMI], comorbidities, and diabetes-related complications), hypoglycemic episodes, and medication adherence were used to assess diabetes burden. Degree of burden was compared across age (18–64, 65–74, and ≥75 years) and racial/ethnic (white, African American, Hispanic, Asian, and American Indian) groups.
Results
An apparent association was found between glycemic control and medication adherence. Hispanics had the lowest percentage of participants with a hemoglobin A1c (HbA1c) level <7.0% (24.4%) and the highest percentage of those not knowing their HbA1c levels (55.4%) but also had the poorest medication adherence among racial/ethnic groups. Conversely, American Indians and whites had the best glycemic control, HbA1c knowledge, and medication adherence. The 18- to 64-year age group had the poorest glycemic control (28.8%), the most with unknown HbA1c levels (46.3%), and the poorest medication adherence of the age groups. Mean BMIs were high (>30 mg/kg2) for all racial/ethnic groups other than the Asian group (28.9 mg/kg2). Approximately 71% of Asians were obese or overweight compared with ≥90% in the other racial/ethnic groups. Mean BMIs decreased with increasing age group (34.5, 32.6, and 29.8 kg/m2 for the age groups of 18–64, 65–74, and ≥75 years, respectively). Regarding diabetes-related comorbidities, the Asian group had the lowest percentages of those with hypertension (39.1%) and hypercholesterolemia (46.6%). The Asian group had the lowest mean Charlson Comorbidity Index (CCI) score (score of 1.4); the American Indian group had the highest CCI score (score of 1.8). Of the age groups, the 65- to 74-year group had the highest percentages of those with hypertension (69.0%) and hypercholesterolemia (67.4%). The mean CCI scores in the 65- to 74-year and ≥75-year age groups (scores of 1.8 for both) were significantly higher than in the 18- to 64-year age group. The Asian group had the lowest percentage of participants reporting hypoglycemia (37.3%). The 18- to 64-year age group had the highest percentage of participants reporting hypoglycemia (52.7%). Limitations of this study include selection bias (Internet-based survey), recall bias, missing values, and descriptive analyses without adjustment for multiplicity.
Conclusion
There are many factors that contribute to diabetes burden and the complexity of diabetes management. The results of this study provide insight from a patient perspective regarding how these factors vary across age and race/ethnicity to aid in the individualization of diabetes treatment.
Key words
Introduction
Diabetes is an insidious public health problem. The prevalence of diabetes in the United States has nearly tripled in the past couple of decades. From 1990 to 2010, the prevalence of diabetes in those aged ≥18 years increased from 6.6 million in 1990 to 20.7 million in 2010.
1
Type 2 diabetes mellitus (T2DM), also known as non–insulin-dependent diabetes mellitus or adult-onset diabetes, is the most common form of diabetes, affecting approximately 90% to 95% of all patients diagnosed as having diabetes. Risk factors for its development include older age, obesity, family history of diabetes, history of gestational diabetes, impaired glucose metabolism, physical inactivity, and race/ethnicity.Centers for Disease Control and Prevention. Diabetes Data & Trends. Incidence and Age at Diagnosis. 2012. http://www.cdc.gov/diabetes/statistics/incidence/fig1.htm. Accessed March 13, 2013.
2
, Centers for Disease Control and Prevention. Diabetes Public Health Resource. 2011. http://www.cdc.gov/diabetes/pubs/general11.htm#what. Accessed March 13, 2013.
3
, Centers for Disease Control and Prevention. Diabetes Data & Trends. Risk Factors for Complications. 2012. http://www.cdc.gov/diabetes/statistics/risk_factors_national.htm. Accessed March 13, 2013.
4
Age is a significant driver of the diabetes epidemic, with >25% of the US population aged ≥65 years having diabetes. The prevalence of diabetes is expected to double in the next 20 years, in part because of the aging of the population.International Diabetes Foundation. Risk Factors for Diabetes. 2005. http://www.cvd.idf.org/Diabetes/Risk_Factors_for_Diabetes/index.html. Accessed March 14, 2013.
5
Certain races/ethnicities have a particularly high risk of developing T2DM. The age-adjusted prevalence rates of T2DM in adults aged ≥20 years in 2010 were 16.1%, 12.6%, 11.8%, and 8.4% among American Indian and Alaska Natives, African Americans, Hispanics, and Asian Americans, respectively.6
Being overweight or obese is considered the principal modifiable risk factor.Centers for Disease Control and Prevention. National Diabetes Fact Sheet, 2011. http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. Accessed March 13, 2013.
4
In 2010, 84.7% of adults aged ≥18 years with diagnosed diabetes were overweight or obese.International Diabetes Foundation. Risk Factors for Diabetes. 2005. http://www.cvd.idf.org/Diabetes/Risk_Factors_for_Diabetes/index.html. Accessed March 14, 2013.
7
There are disproportionate increases in the prevalence rates of T2DM among African Americans and Hispanics in overweight adults aged 20 to 74 years; however, the differences become minimal in obese and severely obese adults with T2DM.Centers for Disease Control and Prevention. Diabetes Data & Trends. Age-Adjusted Percentage of Adults Aged 18 Years or Older with Diagnosed Diabetes Who Were Overweight, United States, 1994–2010. 2012. http://www.cdc.gov/diabetes/statistics/comp/fig7_overweight.htm. Accessed March 22, 2013.
8
Although there is evidence that factors such as age, race/ethnicity, and obesity influence the risk of developing T2DM, fewer studies have examined how these factors influence the burden (eg, glycemic control, comorbidities, diabetes-related complications, risk of mortality, medication adherence, and hypoglycemia) in those who have T2DM. There is some evidence that age and race/ethnicity affect the development of diabetes-related complications and mortality. The prevalence rates of cardiovascular disease (heart disease or stroke), hospitalization for cardiovascular disease, end-stage renal disease (ESRD), and hospitalization for lower-extremity amputation have been found to increase with increasing age.
9
Rates of diabetic complications are higher in racial/ethnic minority populations in the United States. Compared with non-Hispanic whites, African Americans have higher prevalence rates of visual impairment, ESRD, hospital discharges, and mortality; Hispanics have higher rates of ESRD and mortality; and American Indians have a higher mortality rate.Centers for Disease Control and Prevention. Diabetes Data & Trends. Diabetes Complications. 2012. http://www.cdc.gov/diabetes/statistics/complications_national.htm. Accessed March 22, 2013.
10
A contributing factor may be the development of diabetes at younger ages in minorities, increasing the risk of developing complications at a younger age. Obesity complicates T2DM management by increasing insulin resistance and blood glucose concentrations. Being overweight or obese is an independent risk factor for coronary heart disease and cardiovascular disease in patients with T2DM. Intentional weight loss has been associated with improved insulin action, decreased fasting blood glucose concentrations, and reduced need for diabetes medication. Weight loss has also been found to decrease cardiovascular risk by decreasing blood pressure, improving serum lipid concentrations, and reducing serum markers of inflammation.Office of Minority Health. Diabetes data/statistics. 2012. http://minorityhealth.hhs.gov/templates/browse.aspx?lvl=3&lvlid=62. Accessed May 27, 2013.
11
, 12
The complexity of diabetes and diabetes management has caused an evolution in diabetes management guidelines. Recognizing this complexity, medical treatment guidelines have been broad, focusing on not only evidence-based glycemic control targets but also the management of comorbidities. The American Diabetes Association (ADA) continues to recommend a general hemoglobin A1c (HbA1c) goal of <7.0% and the potential for additional benefits with more intensive control in selected patients, but they have recently emphasized the individualization of treatment based on factors such as the duration of diabetes, comorbid conditions, known cardiovascular disease or advanced microvascular complications, hypoglycemia unawareness, and other individual patient considerations.
13
However, the ADA guidelines have minimal consideration for management in elderly patients and even less consideration for race/ethnicity. The compounding of health conditions in older patients results in higher medical complexity. Recognizing this, several diabetes organizations have recently released management guidelines that focus on the elderly population, such as the ADA and American Geriatric Society consensus report on diabetes in older adults, the South Asian consensus guidelines for the management of hyperglycemia in geriatric patients with diabetes mellitus, and the International Association of Gerontology and Geriatrics, European Diabetes Working Party for Older People, and International Task Force of Experts in Diabetes position statement on diabetes in older people.5
, 14
, 15
All the guidelines agree that glycemic goals should be tailored individually for older patients based on their age, comorbid conditions, hypoglycemia risk, and projected life expectancy, with additional considerations regarding frailty and cognitive decline. However, these recommendations are based on general treatment guidelines that have been extrapolated to elderly populations based on the aforementioned considerations. A consensus report that was jointly authored by the ADA and the American Geriatrics Society acknowledged the lack of data regarding the health effects of glycemic control in patients aged ≥75 years and recommended more research to determine race/ethnic disparities among older adults.- Sinclair A.
- Morley J.E.
- Rodriguez-Manas L.
- et al.
Diabetes mellitus in older people: position statement on behalf of the International Association of Gerontology and Geriatrics (IAGG), European Diabetes Working Party for Older People (EDWPOP), and International Task Force of Experts in Diabetes.
J Am Med Dir Assoc. 2012; 13: 497-502
5
Medication adherence is an important modifiable factor when considering the medical complexity of diabetes, particularly in elderly patients. Medication adherence has been found to predict long-term glycemic control.
16
However, as the population gets older, increases in comorbidities will result in an increase in polypharmacy. Polypharmacy has been found to decrease adherence over time in elderly patients.17
Medication adherence may also vary across racial/ethnic groups. Multiple studies have found that African Americans and Hispanics have lower oral antidiabetic medication adherence rates compared with whites.18
, 19
, 20
, 21
, 22
, 23
, 24
, 25
However, the association with race/ethnicity is not fully understood because a systematic review concluded that little is known about the association between ethnicity and medication adherence due to insufficient evidence.26
- Peeters B.
- Van Tongelen I.
- Boussery K.
- Mehuys E.
- Remon J.P.
- Willems S.
Factors associated with medication adherence to oral hypoglycaemic agents in different ethnic groups suffering from type 2 diabetes: a systematic literature review and suggestions for further research.
Diabet Med. 2011; 28: 262-275
The effect of factors such as treatment adherence and weight on T2DM burden across age and racial/ethnic groups is not fully understood. The purpose of this study was to characterize T2DM patients and understand how T2DM burden may differ across age and race/ethnicity groups with respect to comorbidities, diabetes-related complications, body mass index (BMI), and medication adherence.
Patients and Methods
Survey Design
Data for this descriptive analysis were taken from the 2012 US National Health and Wellness Survey (NHWS). The NHWS is a cross-sectional, self-administered, web-based survey administered to a sample of adults (aged ≥18 years) identified through a web-based consumer panel.
The survey encompasses 100 various conditions. The diabetes condition series consisted of 41 questions. The weight loss condition series consisted of 24 questions. Survey data for this analysis were collected during the first 3 quarters of 2012. Data from the Current Population Survey of the US Census were used to identify the relative proportions of age, sex, and racial/ethnic groups in the United States. The results of the analysis were stratified, then weighted and projected using known population incidences for key groups to reflect the demographic composition of the US Census population.
Participant Selection
Participants were recruited through opt-in e-mails, coregistration with other panels, e-newsletter campaigns, and online banner placements. The total sample size of the NHWS consisted of 71,157 adults. Participants were included in this analysis if they were adults aged ≥18 years who reported a diagnosis of T2DM, both treated and untreated.
Participant Characterization
Respondents were categorized into 1 of 3 age groups (18–64, 65–74, and ≥75 years) and 1 of 5 race/ethnicity groups (white, African American, Hispanic, Asian American, and American Indian). The degree of blood glucose control, distribution across weight categories, prevalence of selected comorbidities and diabetes-related complications, hypoglycemic episodes, mortality risk, and medication adherence were determined for each demographic category. Participants were considered in glycemic control if their self-reported HbA1c levels were <7.0%. Participants were categorized according to BMI as obese (≥30 kg/m2), overweight (25 to <30 kg/m2), normal weight (≥19 to <25 kg/m2), or underweight (<19 kg/m2). Comorbidities were self-reported. For all respondents, the comorbidities of special interest were hypertension, hypercholesterolemia, and moderate-severe kidney disease. Diabetes-related complications were also self-reported and included macular edema or retinopathy, kidney disease, foot or leg ulcer, neuropathic pain, and diabetes-related end-organ damage. Hypoglycemic episodes were grouped as those that were experienced any time in the past (ever hypoglycemia) or in the previous 3 months (recent hypoglycemia).
Mortality Risk
Mortality risk based on comorbid conditions was calculated using the Charlson Comorbidity Index (CCI) as another indicator of disease burden. Increasing CCI score has been reported to correlate with increased risk of mortality in T2DM patients.
27
, 28
Adherence Characterization
Adherence to antidiabetic medications was determined using the Morisky Medication Adherence Scale (MMAS). The MMAS is a structured, self-reported measure, with higher scores representing higher medication adherence. The 8-item MMAS (MMAS-8) has been validated in hypertensive patients taking antihypertensive medications.
29
The Malaysian version of the MMAS-8 has been validated in patients with T2DM.30
Studies have found that higher scores in the 4-item MMAS (MMAS-4) and the MMAS-8 are significantly associated with lower HbA1c levels.16
, 30
, 31
In this analysis, the 8-item questionnaire was truncated to 7 items, eliminating the question, “Do you sometimes forget to take your pills?” The question was removed to prevent confusion for those participants taking insulin and other injectable medications to preserve their inclusion in the study. From a reliability assessment, the developer of the scale assessed the internal consistency of the 7-item scale, and it did not significantly differ for reliability; removal of the question decreases the internal consistency (Cronbach α) of the MMAS from 0.680 to 0.78.29
32
A MMAS score of 7 is categorized as high adherence, 5 to 6 is medium adherence, and <5 is low adherence.Statistical Analysis
Statistical analyses were conducted with IBM SPSS Data Collection Quantum software, version 5.8.1 (SPSS Inc, Chicago, Illinois). Group comparisons were performed using t tests or ANOVA for continuous variables and χ2 analysis for categorical variables.
Results
A total of 7239 survey participants aged ≥18 years reported a diagnosis of T2DM (Table I). The mean age was 59.9 years, and approximately 59.5% were male. Most were white (76.2%), followed by African American (11.4%), Hispanic (6.9%), Asian (2.2%), and American Indian (1.6%).
Data are presented as number (percentage) of survey participants unless otherwise indicated.
Table IDemographic characteristics of the survey participants (aged ≥18 years) with T2DM.
| Characteristic | No. (%) of participants (N = 7239) |
|---|---|
| Age mean (SD), y | 59.9 (12.2) |
| Sex | |
| Male | 4306 (59.5) |
| Female | 2933 (40.5) |
| Race/ethnicity | |
| White | 5517 (76.2) |
| African American | 828 (11.4) |
| Hispanic | 500 (6.9) |
| Asian | 161 (2.2) |
| American Indian | 114 (1.6) |
T2DM = type 2 diabetes mellitus.
Almost a quarter (23.0%) of participants self-reported poor glycemic control, and 41.7% did not know their HbA1c levels (Table II). Most participants (90.4%) were overweight or obese, with an overall mean BMI of 33.5 kg/m2. Approximately 60% of participants reported having had high cholesterol levels or hypertension. The mean CCI score was 1.7.
Data are presented as number (percentage) of survey participants unless otherwise indicated.
Table IIClinical characteristics of the survey participants with T2DM.
| Characteristic | No. (%) of Participants (N = 7239) |
|---|---|
| HbA1c | |
| <7.0% | 2561 (35.4) |
| ≥7.0% | 1663 (23.0) |
| Unknown | 3015 (41.7) |
| BMI (kg/m2) | |
| Obese (≥30) | 4452 (63.0) |
| Overweight (25 to <30) | 1937 (27.4) |
| Normal weight (≥19 to <25) | 655 (9.3) |
| Underweight (<19) | 22 (0.3) |
| Mean (SD) | 33.5 (7.7) |
| Comorbidities | |
| Ever experienced high cholesterol levels | 4450 (61.5) |
| Ever experienced hypertension | 4515 (62.4) |
| Ever experienced moderate-severe kidney disease | 144 (2.0) |
| Diabetes-related complications | |
| Macular edema or diabetic retinopathy | 323 (4.5) |
| Kidney disease | 320 (4.4) |
| Foot or leg ulcer | 274 (4.8) |
| Neuropathic pain | 1463 (20.2) |
| None | 5374 (74.2) |
| Charlson Comorbidity Index score | |
| 1 | 4747 (65.6) |
| 2 | 1238 (17.1) |
| 3 | 730 (10.1) |
| ≥4 | 524 (7.2) |
| Mean (SD) | 1.7 (1.3) |
| Antihyperglycemic agents | |
| Oral | 4109 (56.8) |
| Insulin only | 547 (7.6) |
| Insulin and oral | 1075 (14.9) |
| Other injectables (GLP-1 agonists) | 313 (4.3) |
| Untreated | 1174 (16.2) |
| Hypoglycemia episodes | |
| Ever | 3651 (50.4) |
| Recent | 1898 (26.2) |
| Medication adherence (MMAS score) | |
| No. | 6244 |
| High (score, 7) | 2932 (47.0) |
| Medium (score, 5–6) | 2464 (39.5) |
| Low (score, <5) | 848 (13.6) |
BMI = body mass index; GLP-1 = glucagon-like peptide 1; HbA1c = hemoglobin A1c; MMAS = Morisky Medication Adherence Scale; T2DM = type 2 diabetes mellitus.
Table III, Table IV summarize the characteristics and diabetes burden by racial/ethnic group and age group, respectively. There were more males with T2DM in the white, Hispanic, and Asian groups (62.5%, 54.6%, and 66.5%, respectively) but more females in the African American group (57.0%). The proportion of men and women in the American Indian group was about the same. The white group had the highest mean age (61.9 years), and the Asian group had the lowest mean age (49.3 years).
Data are presented as number (percentage) of survey participants unless otherwise indicated.
Table IIIClinical characteristics and diabetes burden by age group.
| Characteristic | No. (%) of Survey Participants by age | ||
|---|---|---|---|
| 18–64 years (n = 4151) | 65–74 years (n = 2506) | ≥75 years (n = 582) | |
| Sex | |||
| Male | 2446 (58.9) | 1499 (59.8) | 361 (62.0) |
| Female | 1705 (41.1) | 1007 (40.2) | 221 (38.0) |
| HbA1c | |||
| <7.0% | 1195 (28.8) | 1113 (44.4) | 253 (43.5) |
| ≥7.0% | 1034 (24.9) | 519 (20.7) | 110 (18.9) |
| Unknown | 1922 (46.3) | 874 (34.9) | 219 (37.6) |
| BMI (kg/m2) | |||
| No. | 4038 | 2452 | 576 |
| Obese (≥30) | 2735 (67.7) | 1480 (60.4) | 237 (41.2) |
| Overweight (25 to <30) | 986 (24.4) | 717 (29.2) | 234 (40.6) |
| Normal weight (≥19 to <25) | 301 (7.5) | 250 (10.2) | 104 (18.1) |
| Underweight (<19) | 16 (0.4) | 5 (0.2) | 1 (0.2) |
| Mean (SD) | 34.5 (8.3) | 32.6 (6.8) | 29.8 (5.7) |
| Comorbidities | |||
| Hypertension | 2414 (58.2) | 1729 (69.0) | 372 (63.9) |
| High cholesterol level | 2395 (57.7) | 1688 (67.4) | 367 (63.1) |
| Moderate-severe kidney disease | 61 (1.5) | 63 (2.5) | 20 (3.4) |
| Diabetes-related complications | |||
| Macular edema or retinopathy | 178 (4.3) | 108 (4.3) | 37 (6.4) |
| Kidney disease | 153 (3.7) | 132 (5.3) | 35 (6.0) |
| Foot or leg ulcer | 179 (4.3) | 75 (3.0) | 20 (3.4) |
| Neuropathic pain | 825 (19.9) | 514 (20.5) | 124 (21.3) |
| End-organ damage | 47 (1.1) | 28 (1.1) | 4 (0.7) |
| None | 3096 (74.6) | 1870 (74.6) | 408 (70.1) |
| Charlson Comorbidity Index score | |||
| 1 | 2904 (70.0) | 1505 (60.1) | 338 (58.1) |
| 2 | 627 (15.1) | 501 (20.0) | 110 (18.9) |
| 3 | 372 (9.0) | 271 (10.8) | 87 (15.0) |
| ≥4 | 248 (6.0) | 229 (9.1) | 47 (8.1) |
| Mean (SD) | 1.6 (1.3) | 1.8 (1.3) | 1.8 (1.2) |
| Hypoglycemic episodes | |||
| Ever | 2188 (52.7) | 1206 (48.1) | 257 (44.2) |
| Recent | 1182 (28.5) | 594 (23.7) | 122 (21.0) |
| Medication adherence (MMAS score) | |||
| No. | 3536 | 2221 | 487 |
| High (score, 7) | 1374 (38.9) | 1259 (56.7) | 299 (61.4) |
| Medium (score, 5–6) | 1488 (42.1) | 807 (36.3) | 169 (34.7) |
| Low (score, <5) | 674 (19.1) | 155 (7.0) | 19 (3.9) |
BMI = body mass index; HbA1c = hemoglobin A1c; MMAS = Morisky Medication Adherence Scale.
† P < 0.05 vs the 18- to 64-year age group.
§ P < 0.05 vs the 65- to 74-year age group.
‡ P < 0.05 vs the ≥75-year age group.
Table IVClinical characteristics and diabetes burden by race/ethnicity.
| Characteristic | No. (%) of Survey Participants | ||||
|---|---|---|---|---|---|
| White (n = 5517) | African American (n = 828) | Hispanic (n = 500) | Asian (n = 161) | American Indian (n = 114) | |
| Sex | |||||
| Male | 3446 (62.5) | 356 (43.0) | 273 (54.6) | 107 (66.5) | 58 (50.9) |
| Female | 2071 (37.5) | 472 (57.0) | 227 (45.4) | 54 (33.5) | 56 (49.1) |
| Age, y | |||||
| >18 | 5517 (100.0) | 828 (100.0) | 500 (100.0) | 161 (100.0) | 114 (100.0) |
| 18–64 | 2783 (50.4) | 640 (77.3) | 421 (84.2) | 139 (86.3) | 88 (77.2) |
| 65–74 | 2193 (39.8) | 166 (20.1) | 71 (14.2) | 19 (11.8) | 21 (18.42) |
| ≥75 | 541 (9.8) | 22 (2.7) | 8 (1.6) | 3 (1.9) | 5 (4.4) |
| Mean (SD) | 61.9 (11.4) | 55.3 (11.8) | 50.7 (12.6) | 49.3 (13.0) | 55.8 (10.9) |
| HbA1c | |||||
| <7.0% | 2095 (38.0) | 216 (26.1) | 122 (24.4) | 49 (30.4) | 49 (43.0) |
| ≥7.0% | 1291 (23.4) | 173 (20.9) | 101 (20.2) | 44 (27.3) | 22 (19.3) |
| Unknown | 2131 (38.6) | 439 (53.0) | 277 (55.4) | 68 (42.2) | 43 (37.7) |
| BMI (kg/m2) | |||||
| No. | 5390 | 808 | 485 | 160 | 111 |
| Obese (≥30) | 3420 (63.5) | 512 (63.4) | 309 (63.7) | 58 (36.3) | 78 (70.3) |
| Overweight (25 to <30) | 1477 (27.4) | 224 (27.7) | 127 (26.2) | 56 (35.0) | 25 (22.5) |
| Normal weight (≥19 to <25) | 478 (8.9) | 72 (8.9) | 46 (9.5) | 42 (26.3) | 8 (7.2) |
| Underweight (<19) | 15 (0.3) | 0 (0.0) | 3 (0.6) | 4 (2.5) | 0 (0.0) |
| Mean (SD) | 33.4 (7.6) | 33.8 (8.1) | 34.0 (8.4) | 28.9 (6.4) | 35.7 (8.4) |
| Comorbidities | |||||
| Hypertension | 3528 (64.0) | 528 (63.8) | 252 (50.4) | 63 (39.1) | 68 (59.7) |
| High cholesterol | 3536 (64.1) | 432 (52.2) | 267 (53.4) | 75 (46.6) | 68 (59.7) |
| Moderate-severe kidney disease | 119 (2.2) | 8 (1.0) | 10 (2.0) | 2 (1.2) | 1 (0.9) |
| Diabetes-related complications | |||||
| Macular edema or retinopathy | 265 (4.8) | 22 (2.7) | 15 (3.0) | 6 (3.7) | 6 (5.3) |
| Kidney disease | 253 (4.6) | 24 (2.9) | 26 (5.2) | 7 (4.4) | 6 (5.3) |
| Foot/leg ulcer | 210 (3.8) | 26 (3.1) | 23 (4.6) | 9 (5.6) | 5 (4.4) |
| Neuropathic pain | 1192 (21.6) | 126 (15.2) | 85 (17.0) | 9 (5.6) | 28 (24.6) |
| End organ damage | 54 (1.0) | 10 (1.2) | 9 (1.8) | 2 (1.2) | 3 (2.6) |
| None | 4020 (72.9) | 664 (80.2) | 387 (77.4) | 134 (83.2) | 78 (68.4) |
| Charlson Comorbidity Index score | |||||
| 1 | 3516 (63.7) | 582 (70.3) | 374 (74.8) | 123 (76.4) | 76 (66.7) |
| 2 | 988 (17.9) | 137 (16.6) | 64 (12.8) | 21 (13.0) | 13 (11.4) |
| 3 | 583 (10.6) | 69 (8.3) | 36 (7.2) | 12 (7.5) | 12 (10.5) |
| ≥4 | 430 (7.8) | 40 (4.8) | 26 (5.2) | 5 (3.1) | 13 (11.4) |
| Mean (SD) | 1.7 (1.3) | 1.6 (1.2) | 1.5 (1.2) | 1.4 (0.8) | 1.8 (1.4) |
| Hypoglycemic episodes | |||||
| Ever | 2763 (50.1) | 416 (50.2) | 264 (52.8) | 60 (37.3) | 78 (68.4) |
| Recent | 1444 (26.2) | 192 (23.1) | 138 (27.6) | 35 (21.7) | 52 (45.6) |
| Medication adherence | |||||
| No. | 4766 | 717 | 427 | 143 | 93 |
| High (score, 7) | 2396 (50.3) | 276 (38.5) | 136 (31.9) | 48 (33.6) | 42 (45.2) |
| Medium (score, 5–6) | 1853 (38.9) | 291 (40.6) | 180 (42.2) | 63 (44.1) | 36 (38.7) |
| Low (score, <5) | 517 (10.9) | 150 (20.9) | 111 (26.0) | 32 (22.4) | 15 (16.1) |
BMI = body mass index; HbA1c = hemoglobin A1c; MMAS = Morisky Medication Adherence Scale.
* Data are presented as number (percentage) of survey participants unless otherwise indicated.
† P < 0.05 vs the white group.
‡ P < 0.05 vs the African American group.
§ P < 0.05 vs the Hispanic group.
|| P < 0.05 vs the Asian group.
¶ P < 0.05 vs the American Indian group.
Glycemic Control
The African American and Hispanic groups had the lowest percentages of participants with HbA1c <7.0% (26.1% and 24.4%, respectively). The percentages were significantly lower than the white (38.0%) and American Indian (43.0%) groups (P < 0.001 vs both) but not the Asian group (30.4%; P > 0.12 vs both). The percentage reporting glycemic control (HbA1c <7%) for the American Indian group was significantly higher than the African American, Hispanic, and Asian groups (P < 0.04 for all) but not the white group (P = 0.276). In addition to having the lowest percentages of participants with blood glucose control, the African American and Hispanic groups also had the highest percentage of participants who did not know their HbA1c levels (53.0% and 55.4%, respectively; P < 0.05 for both groups vs the white, Asian, and American Indian groups).
Significantly fewer participants in the 18- to 64-year age group had HbA1c levels <7.0% compared with the 65- to 74-year and ≥75-year age groups (28.8%, 44.4%, and 43.5%, respectively; P < 0.001 for both). In addition, the 18- to 64-year age group had a significantly higher percentage of participants who did not know their HbA1c levels (46.3%, 34.9%, and 37.6%, respectively; P < 0.001 for both).
BMI
Mean BMIs were in the obese range (≥30 mg/kg2) for all racial/ethnic groups (33.4, 33.8, 34.0, and 35.7 kg/m2 for the white, African American, Hispanic, and American Indian groups, respectively) except for the Asian group (28.9 kg/m2), which had significantly lower BMIs than all other groups (P < 0.001 for all comparisons). The percentage of Asians who were obese or overweight was 71.3%, compared with 90.9%, 91.1%, 89.9%, and 92.8% of whites, African Americans, Hispanics, and American Indians, respectively (P < 0.05 for all). At least two-thirds of participants in the latter racial/ethnic groups were obese, whereas the Asian group had more even distribution among obese, overweight, and normal weight participants.
Mean BMIs were inversely related to age; the mean BMI in the 18- to 64-year age group (34.5 kg/m2) was significantly higher than in the 65- to 74-year age group (32.6 kg/m2), which was significantly higher than in the ≥75-year age group (29.8 kg/m2; P < 0.001 for all comparisons). The differences in mean BMI across age groups were mainly driven by the large percentage of participants who were obese (BMI ≥30 kg/m2) in the 18- to 64-year age group (67.7%), which decreased with increasing age (60.4% and 41.2% in the 65- to 74-year and ≥75-year age groups, respectively).
Diabetes-Related Comorbidities, Complications, and Disease Burden
More than half of all racial/ethnic groups experienced hypertension or high cholesterol levels, with the exception of the Asian group. The percentages of whites, African Americans, Hispanics, and American Indians who experienced hypertension were 64.0%, 63.8%, 50.4%, and 59.7%, respectively. The percentage of Asians was significantly lower than all other groups (39.1%; P < 0.001 for all comparisons). The percentages of whites, African Americans, Hispanics, and American Indians who experienced high cholesterol levels were 64.1%, 52.2%, 53.4%, and 59.7%, respectively. The percentage of Asians was lower than all other groups (46.6%; P < 0.05 vs white and American Indians). The rates of moderate-to-severe kidney disease were similar across racial/ethnic groups. For diabetes-related complications, the proportions were similar except for the significantly lower rate of neuropathic pain in the Asian group (5.6%) compared with the other racial/ethnic groups (21.6%, 15.2%, 17.0%, and 24.6% for the white, African American, Hispanic, and American Indian groups, respectively; P < 0.001 for all comparisons). The Asian group had the lowest mean CCI score (score of 1.4) compared with the other racial/ethnic groups (scores of 1.7, 1.6, 1.5, and 1.8 for the white, African American, Hispanic, and American Indian groups, respectively; P < 0.005 vs the White and American Indian groups).
Significantly more elderly participants (≥65 years) experienced hypertension (68.0%) or high cholesterol levels (66.5%) compared with participants 18 to 64 years of age (58.2% and 57.7%, respectively; P < 0.02 for both comparisons). A similar pattern was observed with moderate-to-severe kidney disease, occurring in 2.7% of elderly participants compared with 1.5% of participants 18 to 64 years of age (P < 0.002). There were no trends between age group and diabetes-related complications, with the exception of kidney disease. The incidence of kidney disease increased with increasing age group (3.7%, 5.3%, and 6.0% in the age groups of 18–64, 65–74, and ≥75 years, respectively), although the difference between the 65- to 74-year and ≥75-year age groups was not significant. The mean CCI scores in the 65- to 74-year (score of 1.8) and ≥75-year (score of 1.8) age groups were significantly higher than in the 18- to 64-year age group (score of 1.6; P < 0.001 for both). The percentage of participants with a CCI score of 1 decreased with increasing age, but the percentage of participants with CCI scores of ≥2 was significantly higher in the elderly age groups than in the 18- to 64-year age group (P < 0.05 for all comparisons).
Hypoglycemic Episodes
At least half of all racial/ethnic groups reported ever having experienced hypoglycemia, with the exception of the Asian group. The percentage of Asian participants reporting hypoglycemia (37.3%) was significantly lower than all other racial/ethnic groups (50.1%, 50.2%, 52.8%, and 68.4% for the white, African American, Hispanic, and American Indian groups, respectively; P < 0.003 for all comparisons). For recent hypoglycemia, the rates were not significantly different among the white, African American, Hispanic, and Asian groups (26.2%, 23.1%, 27.6%, and 21.7%, respectively) with the exception of the American Indian group, whose rate (45.6%) was significantly higher than all other racial/ethnic groups (P < 0.001 vs all other groups).
Self-reported hypoglycemia decreased with increasing age. Hypoglycemic episodes were reported in 52.7% of participants in the 18- to 64-year age group compared with 47.4% in the elderly age groups (P < 0.001). Of those, 54.0% and 48.9%, respectively, were episodes that occurred in the past 3 months (P = 0.003).
Medication Adherence
The Hispanic group had the lowest percentage of participants with high medication adherence (31.9%; P < 0.05 vs all groups except the Asian group) and the highest percentage of participants with low medication adherence (MMAS score of <5; 26.0%; P < 0.05 vs all groups except the Asian group). The white group had the highest percentage of participants with high medication adherence (50.3%; P < 0.001 vs all groups except the American Indian group) and the lowest percentage of participants with low medication adherence (10.9%; P < 0.001 vs all groups except the American Indian group).
Medication adherence was poorer in the 18- to 64-year age group compared with the elderly age groups. In the 18- to 64-year age group, 61.1% of participants had medium-to-low medication adherence compared with 42.5% of participants in the elderly age groups (P < 0.002).
Discussion
This large-scale, descriptive analysis identified differences in diabetes burden from a patient perspective across age and racial/ethnic groups in a representative US population. Of the racial/ethnic groups, American Indians had the best glycemic control (HbA1c <7.0%) and the lowest percentage of those not knowing their HbA1c levels. Hispanics had the poorest glycemic control and the highest percentage of those not knowing their HbA1c levels. American Indians had the highest mean BMIs and Asians had the lowest mean BMIs among the racial/ethnic groups. Whites had the highest incidences of hypertension and high cholesterol levels and Asians had the lowest. American Indians had the highest mean CCI scores and Asians had the lowest. American Indians had the highest percentage of participants ever reporting hypoglycemia, whereas Asians had the lowest. Whites had the best adherence, as determined by the highest percentage of those with high adherence scores and the lowest percentage of those with low adherence scores. Hispanics had the poorest adherence, as determined by the highest percentage of participants with low adherence scores and the lowest percentage of participants with high adherence scores.
Of the age groups, the 18- to 64-year group had the poorest glycemic control and the highest percentage of those not knowing their HbA1c levels. The 65- to 74-year age group had the best glycemic control and the lowest percentage of those not knowing their HbA1c levels. Mean BMI decreased with increasing age group. The 65- to 74-year age group had the highest incidences of hypertension and hypercholesterolemia, and the 18- to 64-year age group had the lowest. The 65- to 74-year and ≥75-year groups had equivalent mean CCI scores, which were higher than in the 18- to 64-year group. The percentage of participants ever reporting hypoglycemia decreased with increasing age group. Medication adherence improved with increasing age group.
On the basis of the overall results, there appeared to be an association between glycemic control and medication adherence observed among racial/ethnic and age groups. Notably, Hispanics had the poorest glycemic control and most unawareness of their HbA1c levels, as well as the poorest medication adherence. Conversely, whites and American Indians had the best glycemic control and the least unawareness of their HbA1c levels, as well as the best medication adherence. Interestingly, African Americans had better adherence than Hispanics but had similar glycemic control and unawareness of their HbA1c levels. Among age groups, the 18- to 64-year group had the poorest glycemic control and most unawareness of their HbA1c levels, as well as the lowest medication adherence.
Another notable observation was that Asians had the lowest overall mean BMIs, the lowest rates of hypertension and high cholesterol levels, the lowest mean CCI score, and the fewest reports of ever having hypoglycemia. However, Asians had the next poorest levels of glycemic control and medication adherence compared with Hispanics. Asians had the lowest mean CCI score, but this finding was not significant for Hispanics. Thus, potential advantages that Asians have, such as lower BMI and comorbidities, may be offset by poor glycemic control and medication adherence.
The association between better medication adherence and improved glycemic control has been described in the literature.
33
Older age has been associated with better medication adherence, but little is known about the association between race/ethnicity and medication adherence.26
The results of this analysis suggest that there is an association between medication adherence and glycemic control and that there are differences in both based on race/ethnicity and age. The results support the need for individualization and consideration of patient perspectives, especially race/ethnicity, in the management of T2DM. Although current treatment guidelines emphasize the individualization of treatment, more needs to be done to reduce the high rate of treatment failure, including a focus on race/ethnicity through cultural differences.- Peeters B.
- Van Tongelen I.
- Boussery K.
- Mehuys E.
- Remon J.P.
- Willems S.
Factors associated with medication adherence to oral hypoglycaemic agents in different ethnic groups suffering from type 2 diabetes: a systematic literature review and suggestions for further research.
Diabet Med. 2011; 28: 262-275
13
Individualization of treatment is difficult because there is little insight regarding the relative effectiveness of interventions among age and race/ethnicity subgroups because of a lack of clinical trial data or database analyses.34
This study provides an initial framework on which to base future studies.The first step in the individualization of therapy may be the identification of factors across race/ethnicity and age that contribute to poor medication adherence. This is a daunting task because socioeconomic, psychological, and cultural factors may explain differences that are attributed to race/ethnicity.
26
Effective clinical strategies to improve medication adherence may be those that reduce forgetting, such as automated reminders, regimen simplification, and regimen tailoring. Additional validated strategies include electronic monitoring and motivational interviewing. Simple questioning at routine diabetes assessments may also be effective when the goal is to achieve or maintain glycemic control.- Peeters B.
- Van Tongelen I.
- Boussery K.
- Mehuys E.
- Remon J.P.
- Willems S.
Factors associated with medication adherence to oral hypoglycaemic agents in different ethnic groups suffering from type 2 diabetes: a systematic literature review and suggestions for further research.
Diabet Med. 2011; 28: 262-275
16
These strategies, with cultural adaptations, could be incorporated into the diabetes management plan of populations that have the potential for poor medication adherence, such as Hispanics, Asians, and African Americans. Because younger age groups also tend to have poorer medication adherence, early education and implementation of these strategies with generational- and age-focused adaptations may be beneficial.This study is limited by its descriptive design and the fact that it was Internet based, which may not be fully representative of the T2DM population. There are also inherent limitations to survey studies, such as recall bias, missing values, and variability in the interpretation of questions. Because of limitations in how the data were collected, the analysis cannot examine whether persons who self-identify as mixed race differ in terms of burden. Because this analysis was descriptive, there was no adjustment for multiplicity. Some of the race/ethnicity groups were small, making it difficult to make firm generalizations. In addition, the data are essentially “snapshots” in time and are thus unable to characterize longitudinal trends in diabetes burden.
Conclusion
This survey analysis of T2DM patients provides insight into specific differences in diabetes burden across age and race/ethnicity from the patient perspective. In this analysis, Hispanics and younger participants had worse medication adherence and poorer glucose control compared with other race/ethnicities and older participants, respectively. Thus, there are distinctive patterns of diabetes burden across racial/ethnic and age groups that could direct more individualized diabetes preventive care and management practices. There is a need for further study and specific guideline development in these subgroups to reflect these differences. For instance, although elderly patients appear to be more aware of their health status and adherent to their treatment, their diabetes management is complicated by more comorbidities and complications than younger patients. In addition, more diabetes management with adaptations for race/ethnicity and age or generational considerations may need to be developed and applied. Given the renewed focus on patient engagement in care provision in the United States, understanding nuances from the patient perspective that are more likely to affect effective management of T2DM based on racial/ethnicity or age differences is an opportunity for improving patient-centric care. This information should encourage health care practitioners to not only tailor education to the individual but also provide topics to better engage minority populations in their health management.
Conflicts of Interest
Drs Lopez, Bailey, and Rupnow are employees of Janssen Pharmaceuticals, Inc. Ms Annunziata is an employee of Kantar Health. This research was funded by Janssen Scientific Affairs, LLC, Raritan, NJ.
Acknowledgments
Drs. Lopez, Bailey, and Rupnow were responsible for study design and analysis and interpretation of data. Ms. Annunziata and Kantar Health were responsible for the database and statistical analysis. The authors would like to acknowledge Dr. Kenneth Chiang and ECIR Medical Communications for writing and editorial support.
References
Centers for Disease Control and Prevention. Diabetes Data & Trends. Incidence and Age at Diagnosis. 2012. http://www.cdc.gov/diabetes/statistics/incidence/fig1.htm. Accessed March 13, 2013.
Centers for Disease Control and Prevention. Diabetes Public Health Resource. 2011. http://www.cdc.gov/diabetes/pubs/general11.htm#what. Accessed March 13, 2013.
Centers for Disease Control and Prevention. Diabetes Data & Trends. Risk Factors for Complications. 2012. http://www.cdc.gov/diabetes/statistics/risk_factors_national.htm. Accessed March 13, 2013.
International Diabetes Foundation. Risk Factors for Diabetes. 2005. http://www.cvd.idf.org/Diabetes/Risk_Factors_for_Diabetes/index.html. Accessed March 14, 2013.
- Diabetes in older adults.Diabetes Care. 2012; 35: 2650-2664
Centers for Disease Control and Prevention. National Diabetes Fact Sheet, 2011. http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. Accessed March 13, 2013.
Centers for Disease Control and Prevention. Diabetes Data & Trends. Age-Adjusted Percentage of Adults Aged 18 Years or Older with Diagnosed Diabetes Who Were Overweight, United States, 1994–2010. 2012. http://www.cdc.gov/diabetes/statistics/comp/fig7_overweight.htm. Accessed March 22, 2013.
- Changes in racial/ethnic disparities in the prevalence of type 2 diabetes by obesity level among U.S. adults.Ethn Health. 2009; 14: 439-457
Centers for Disease Control and Prevention. Diabetes Data & Trends. Diabetes Complications. 2012. http://www.cdc.gov/diabetes/statistics/complications_national.htm. Accessed March 22, 2013.
Office of Minority Health. Diabetes data/statistics. 2012. http://minorityhealth.hhs.gov/templates/browse.aspx?lvl=3&lvlid=62. Accessed May 27, 2013.
- Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies.Diabetes Care. 2004; 27: 2067-2073
- Patient and treatment perspectives: revisiting the link between type 2 diabetes, weight gain, and cardiovascular risk.Cleve Clin J Med. 2009; 76: S20-S27
- Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD).Diabetes Care. 2012; 35: 1364-1379
- Management of hyperglycemia in geriatric patients with diabetes mellitus: South Asian Consensus Guidelines.Ind J Endocrinol Metab. 2011; 15: 75-90
- Diabetes mellitus in older people: position statement on behalf of the International Association of Gerontology and Geriatrics (IAGG), European Diabetes Working Party for Older People (EDWPOP), and International Task Force of Experts in Diabetes.J Am Med Dir Assoc. 2012; 13: 497-502
- Longitudinal association between medication adherence and glycaemic control in type 2 diabetes.Diabet Med. 2013; 30: 338-344
- A retrospective database analysis of neuropathic pain and oral antidiabetic medication use and adherence among Texas adults with type 2 diabetes enrolled in Medicaid.Clin Ther. 2012; 34: 605-613
- Medication adherence and racial differences in A1C control.Diabetes Care. 2008; 31: 916-921
- Predictors of medication nonadherence among patients with diabetes in Medicare Part D programs: a retrospective cohort study.Clin Ther. 2009; 31: 2178-2188
- Beliefs about prescription medications among patients with diabetes: variation across racial groups and influences on cost-related medication underuse.J Health Care Poor Underserved. 2010; 21: 349-361
- Using quantile regression to investigate racial disparities in medication non-adherence.BMC Med Res Methodol. 2011; 11: 88
- Health literacy explains racial disparities in diabetes medication adherence.J Health Commun. 2011; 16: 268-278
- Predictors of medication adherence in an urban Latino community with healthcare disparities.J Immigr Minor Health. 2012; 14: 589-595
- The effects of financial pressures on adherence and glucose control among racial/ethnically diverse patients with diabetes.J Gen Intern Med. 2012; 27: 432-437
- Correlates of medication nonadherence among Latinos with type 2 diabetes.Diabetes Educ. 2012; 38: 552-561
- Factors associated with medication adherence to oral hypoglycaemic agents in different ethnic groups suffering from type 2 diabetes: a systematic literature review and suggestions for further research.Diabet Med. 2011; 28: 262-275
- Are comorbidity indices useful in predicting all-cause mortality in type 2 diabetic patients? comparison between Charlson index and disease count.Aging Clin Exp Res. 2007; 19: 492-496
- Predictors of mortality over 8 years in type 2 diabetic patients: Translating Research Into Action for Diabetes (TRIAD).Diabetes Care. 2012; 35: 1301-1309
- Predictive validity of a medication adherence measure in an outpatient setting.J Clin Hypertens (Greenwich). 2008; 10: 348-354
- The eight-item Morisky Medication Adherence Scale MMAS: translation and validation of the Malaysian version.Diabetes Res Clin Pract. 2010; 90: 216-221
- Medication adherence and associated hemoglobin A1c in type 2 diabetes.Ann Pharmacother. 2004; 38: 1357-1362
- Adherence and satisfaction with oral hypoglycemic medications: a pilot study in Palestine.Int J Clin Pharm. 2011; 33: 942-948
- A review of diabetes treatment adherence and the association with clinical and economic outcomes.Clin Ther. 2011; 33: 74-109
- Individualizing therapies in type 2 diabetes mellitus based on patient characteristics: what we know and what we need to know.J Clin Endocrinol Metab. 2010; 95: 1566-1574
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Published online: February 07, 2014
Accepted:
December 30,
2013
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