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 $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