Gloria L. Beckles

A diabetes report card for the United States: Quality of care in the 1990s

Context There are no recent national evaluations of diabetes care in the United States. Contribution Using data from two national surveys, this study documents a substantial gap between the recommended and actual care of diabetes in the United States between 1988 and 1995. Many participants had hemoglobin A1c levels greater than 9.5% (18.0%), poorly controlled blood pressure (34.3%), and elevated cholesterol levels (58.0%). Implications As a nation, the United States is falling short in caring for patients with diabetes. A periodic national report card may help us to gauge the success of future efforts to improve. The Editors Diabetes, a typical example of chronic disease, currently affects 16 million people in the United States, causes considerable morbidity and mortality, and costs the nation almost

Population-based assessment of the level of care among adults with diabetes in the U.S.

100 billion per year (1, 2). Fortunately, several efficacious treatment strategies to prevent or delay diabetes complications have emerged during the past decade, including control of glycemia, lipids, and hypertension; early detection and treatment of diabetic retinopathy, nephropathy, and foot disease; therapy with aspirin and angiotensin-converting enzyme inhibitors; and influenza and pneumococcal vaccines (3-12). Many of these treatments are cost-effective (13-17). However, their implementation in the United States remains suboptimal and varied (18-22). There is considerable pressure on U.S. health care systems to improve this situation and to deliver high-quality care while controlling costs. Influential bodies, such as the Institute of Medicine, have recently emphasized the need for strategies to improve the current quality of all medical care in the United States (23). Federal agencies are also increasingly responding with initiatives to address quality of care (24, 25). One powerful quality initiative in the United States is the Diabetes Quality Improvement Project (DQIP), which is designed to influence the care of millions of U.S. patients with diabetes (26). The standard measures of quality of diabetes care proposed by DQIP were incorporated into the National Committee for Quality Assurance (NCQA) Health Plan Employer and Data Information Set. In contrast to guidelines or clinical goals for individual care, DQIP measures are designed to assess the performance of health care systems for a population, and they offer a wayto make comparisons across health care systems. Several public and private health care systems, including the Indian Health Service (27), the Veterans Administration, the U.S. Department of Defense, and numerous managed care organizations, have adopted these indicators. Currently, there is no national reference for assessing the quality of diabetes care using a standard set of measures. Such a reference could help health care systems using DQIP measures to compare their own performance against population norms rather than the norms of other health systems. This reference could be a benchmark for assessing population changes in quality of diabetes care after implementation of national quality improvement initiatives recommended by the Institute of Medicine and NCQA. Furthermore, the use of a standard set of measures to assess the quality of diabetes care at a national level can facilitate international comparison. For this report, we used U.S. national data to provide a reference and benchmark of the quality of diabetes care as measured by the DQIP indicators. Methods Surveys We used two federally funded, nationally representative population-based surveys, the Third U.S. National Health and Nutrition Examination Survey (NHANES III) (19881994) and the Behavioral Risk Factors Surveillance System (BRFSS) (1995). Data from these two surveys were analyzed separately. Some measures came exclusively from NHANES III, and others came from BRFSS. We included adults 18 to 75 years of age who reported receiving a previous diagnosis of diabetes from a physician. Women with gestational diabetes were excluded. The two survey groups were similar in demographic characteristics, household income, health insurance coverage, and prevalence of diabetes. In NHANES III compared with BRFSS, more persons did not have a high school education (41.5% vs. 27.1%) and fewer persons used insulin (30.9% vs. 39.8%) (Table 1). Table 1. Characteristics of Persons with Self-Reported Diabetes in the Third U.S. National Health and Nutrition Examination Survey (19881994) and the Behavioral Risk Factors Surveillance System1995 The methods for NHANES III are described elsewhere (28). Briefly, the NHANES III survey used a nationally representative sample of the civilian noninstitutionalized population obtained through a complex multistage cluster sampling design, with oversampling of non-Hispanic black persons, Mexican-American persons, and elderly persons. During a household interview, data were collected on sociodemographic characteristics and medical and family history. Within 4 weeks, this was followed by a clinical examination at a mobile examination center. The procedures for blood collection and processing have also been described elsewhere (29, 30). Low-density lipoprotein (LDL) cholesterol level was calculated by using the Friedwald equation for persons who fasted more than 8 hours (n = 302). Data from NHANES III were self-reported (demographic and clinical variables) or were obtained during clinical examination (hemoglobin A1c level, cholesterol level, triglyceride level, and blood pressure). The survey included 16 705 participants 18 to 75 years of age, 1026 of whom reported receiving a diagnosis of diabetes from a physician before the survey. The BRFSS is an annual random-digit telephone survey of state populationbased samples of the civilian noninstitutionalized population of adults ( 18 years of age) in each of the 50 states and the District of Columbia. Its purpose, methods, and data analyses have been described in detail elsewhere (31). A diabetes-specific BRFSS module was used to collect data on clinical characteristics and preventive care practices from respondents with diabetes. Data from BRFSS were self-reported (dilated eye examination, lipid test, foot examination, and demographic and clinical variables). The survey included 103 929 participants 18 to 75 years of age, 3059 of whom completed the diabetes module and reported receiving a diagnosis of diabetes from a physician. We used the 1995 BRFSS so that the time frame would correspond with NHANES III. Quality-of-Care Measures We assessed the quality of diabetes care using indicators from the DQIP measurement set for which data were available. The DQIP began under the sponsorship of a coalition that included the American Diabetes Association, the Foundation for Accountability, the Health Care Financing Administration, and NCQA. The American Academy of Physicians, the American College of Physicians, the Veterans Administration, and the Centers for Disease Control and Prevention later joined the coalition. A committee of experts in diabetes care was responsible for developing the DQIP measure set. The DQIP classified the proposed indicators into three categories: accountability indicators, quality improvement indicators, and indicators under field-testing. The accountability measures, which are well grounded in evidence, have received consensus support from the scientific and medical community and have been extensively field-tested in a variety of health care settings. These measures are used to compare health systems and plans or providers. The quality improvement measures are not considered appropriate for comparing systems and plans or providers but were recommended by the NCQA for assessing internal performance. Table 2 shows all of the analyzed quality indicators and their data sources. The DQIP measures are a combination of process and outcome measures. In this paper, we use DQIP terminology and DQIP accountability and quality improvement measures. Table 2. Diabetes Quality Improvement Project Indicators and Related Data Sources Statistical Analysis All analyses were conducted by using SAS software (SAS Institute, Inc., Cary, North Carolina) for data management and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina) to account for the complex sampling scheme, the unequal probability of selection, the oversampling of certain demographic groups, and nonresponse factors (32, 33). Level of care was estimated by the percentage, with corresponding 95% CIs, of respondents who reported each preventive care practice. We estimated prevalence by groups of sex, age, ethnicity, education, health insurance, type of treatment, and duration of diabetes. Multiple logistic regression and computation of predictive margins were used to estimate the probability of receiving DQIP indicators when we controlled for all other independent variables. Predictive margins are a type of direct standardization in which the predicted values from the logistic regression models are averaged over the covariate distribution in the population (34). Predictive margins and their standard errors from logistic regression models are provided by the current version of SUDAAN (33). Taylor linearization is used in SUDAAN for calculating these standard errors. Odds ratios are usually used to display the result from logistic regression models. However, predictive margins are easier to interpret than odds ratios, and they do not require designating one of the groups as the referent group. Results Diabetes Quality Improvement Project Accountability and Quality Improvement Measures Among adults 18 to 75 years of age with diabetes, 28.8% had had hemoglobin A1c levels tested in the past year, and 18.0% had a hemoglobin A1c level greater than 9.5%. Biannual lipid testing was done in 85.3% of participants, but only 42.0% had an LDL cholesterol level less than 3.4 mmol/L (<130 mg/dL). Only 65.7% had blood pressure less than 140/90 mm Hg, 63.3% had an annual dilated eye examination, an

Association Between Iron Deficiency and A1C Levels Among Adults Without Diabetes in the National Health and Nutrition Examination Survey, 1999–2006

OBJECTIVE To estimate the levels of use of preventive care and to identify correlates of such care among people with diabetes in the U.S. RESEARCH DESIGN AND METHODS A cross-sectional study was conducted using a sample of 2,118 adults, age ≥18 years, with self-reported diabetes in 22 states that participated in the 1994 Behavioral Risk Factor Surveillance System. Most subjects were age ≥45 years (83%), women (51%), and white (75%) and were diagnosed at ages ≥30 years (83%), had type 2 diabetes (89%), and were not using insulin (66%). RESULTS Among all people with diabetes, 78% practiced self-monitoring of blood glucose, and 25% were aware of the term “glycosylated hemoglobin” or “hemoglobin A one C” (HbA1c). In the last year, 72% of the subjects visited a health care provider for diabetes care at least once, 61% had their feet inspected at least once, and 61% received a dilated eye examination. Controlled for age and sex, the odds ratios (ORs) for insulin use were for self-monitoring (OR [95% CI]; 4.0 [2.6–6.1]); having heard of HbA1c or receipt of a dilated eye examination (1.9 [1.4–2.5]); at least one visit to a provider (3.4 [1.9–7.2]); and feet inspected at least once (2.1 [1.5–2.9]). In addition, people <45 years, those who did not complete high school, and those without insurance coverage were high-risk groups for underuse of preventive care. Only 3% of insulin users and 1% of nonusers met all five of the American Diabetes Association standards in the previous year. CONCLUSIONS Underuse of recommended preventive care practices is common among people with diabetes.

Family history of diabetes, awareness of risk factors, and health behaviors among African Americans

OBJECTIVE Iron deficiency has been reported to elevate A1C levels apart from glycemia. We examined the influence of iron deficiency on A1C distribution among adults without diabetes. RESEARCH DESIGN AND METHODS Participants included adults without self-reported diabetes or chronic kidney disease in the National Health and Nutrition Examination Survey 1999–2006 who were aged ≥18 years of age and had complete blood counts, iron studies, and A1C levels. Iron deficiency was defined as at least two abnormalities including free erythrocyte protoporphyrin >70 μg/dl erythrocytes, transferrin saturation <16%, or serum ferritin ≤15 μg/l. Anemia was defined as hemoglobin <13.5 g/dl in men and <12.0 g/dl in women. RESULTS Among women (n = 6,666), 13.7% had iron deficiency and 4.0% had iron deficiency anemia. Whereas 316 women with iron deficiency had A1C ≥5.5%, only 32 women with iron deficiency had A1C ≥6.5%. Among men (n = 3,869), only 13 had iron deficiency and A1C ≥5.5%, and only 1 had iron deficiency and A1C ≥6.5%. Among women, iron deficiency was associated with a greater odds of A1C ≥5.5% (odds ratio 1.39 [95% CI 1.11–1.73]) after adjustment for age, race/ethnicity, and waist circumference but not with a greater odds of A1C ≥6.5% (0.79 [0.33–1.85]). CONCLUSIONS Iron deficiency is common among women and is associated with shifts in A1C distribution from <5.5 to ≥5.5%. Further research is needed to examine whether iron deficiency is associated with shifts at higher A1C levels.

Health behaviors and quality of care among Latinos with diabetes in managed care.

OBJECTIVES We examined the role of family history of diabetes in awareness of diabetes risk factors and engaging in health behaviors. METHODS We conducted a cross-sectional analysis of 1122 African American adults without diabetes who were participants in Project DIRECT (Diabetes Interventions Reaching and Educating Communities Together). RESULTS After adjustment for age, gender, income, education, body mass index, and perceived health status, African Americans with a family history of diabetes were more aware than those without such a history of several diabetes risk factors: having a family member with the disease (relative risk [RR]=1.09; 95% confidence interval [CI]=1.03, 1.15), being overweight (RR=1.12; 95% CI=1.05, 1.18), not exercising (RR=1.17; 95% CI=1.07, 1.27), and consuming energy-dense foods (RR=1.10; 95% CI=1.00, 1.17). Also, they were more likely to consume 5 or more servings of fruits and vegetables per day (RR=1.31; 95% CI=1.02, 1.66) and to have been screened for diabetes (RR=1.21; 95% CI=1.12, 1.29). CONCLUSIONS African Americans with a family history of diabetes were more aware of diabetes risk factors and more likely to engage in certain health behaviors than were African Americans without a family history of the disease.

Aging, diabetes, and the public health system in the United States.

OBJECTIVES We evaluated whether ethnicity and language are associated with diabetes care for Latinos in managed care. METHODS Using data from 4685 individuals in the Translating Research Into Action for Diabetes (TRIAD) Study, a multicenter study of diabetes care in managed care, we constructed multivariate regression models to compare health behaviors, processes of care, and intermediate outcomes for Whites and English- and Spanish-speaking Latinos. RESULTS Latinos had lower rates of self-monitoring of blood glucose and worse glycemic control than did Whites, higher rates of foot self-care and dilated-eye examinations, and comparable rates of other processes and intermediate outcomes of care. CONCLUSIONS Although self-management and quality of care are comparable for Latinos and Whites with diabetes, important ethnic disparities persist in the managed care settings studied.

Socioeconomic Status and Mortality: Contribution of health care access and psychological distress among U.S. adults with diagnosed diabetes

Diabetes (diagnosed or undiagnosed) affects 10.9 million US adults aged 65 years and older. Almost 8 in 10 have some form of dysglycemia, according to tests for fasting glucose or hemoglobin A1c. Among this age group, diagnosed diabetes is projected to reach 26.7 million by 2050, or 55% of all diabetes cases. In 2007, older adults accounted for

The missed patient with diabetes: how access to health care affects the detection of diabetes.

64.8 billion (56%) of direct diabetes medical costs,

Perception of Neighborhood Problems, Health Behaviors, and Diabetes Outcomes Among Adults With Diabetes in Managed Care: The Translating Research Into Action for Diabetes (TRIAD) Study

41.1 billion for institutional care alone. Complications, comorbid conditions, and geriatric syndromes affect diabetes care, and medical guidelines for treating older adults with diabetes are limited. Broad public health programs help, but effective, targeted interventions and expanded surveillance and research and better policies are needed to address the rapidly growing diabetes burden among older adults.

Agreement between self-reports and medical records was only fair in a cross-sectional study of performance of annual eye examinations among adults with diabetes in managed care

OBJECTIVE Although several studies have examined the association between socioeconomic status (SES) and mortality in the general population, few have investigated this relationship among people with diabetes. This study sought to determine how risk of mortality associated with measures of SES among adults with diagnosed diabetes is mitigated by association with demographics, comorbidities, diabetes treatment, psychological distress, or health care access and utilization. RESEARCH DESIGN AND METHODS The study included 6,177 adults aged 25 years or older with diagnosed diabetes who participated in the National Health Interview Surveys (1997–2003) linked to mortality data (follow-up through 2006). SES was measured by education attained, financial wealth (either stocks/dividends or home ownership), and income-to-poverty ratio. RESULTS In unadjusted analysis, risk of death was significantly greater for people with lower levels of education and income-to-poverty ratio than for those at the highest levels. After adjusting for demographics, comorbidities, diabetes treatment and duration, health care access, and psychological distress variables, the association with greater risk of death remained significant only for people with the lowest level of education (relative hazard 1.52 [95% CI 1.04–2.23]). After multivariate adjustment, the risk of death was significantly greater for people without certain measures of financial wealth (e.g., stocks, home ownership) (1.56 [1.07–2.27]) than for those with them. CONCLUSIONS The findings suggest that after adjustments for demographics, health care access, and psychological distress, the level of education attained and financial wealth remain strong predictors of mortality risk among adults with diabetes.