Michael M. Engelgau

Prevalence of Diabetes and Impaired Fasting Glucose in Adults in the U.S. Population National Health and Nutrition Examination Survey 1999–2002

OBJECTIVE—The purpose of this study was to examine the prevalences of diagnosed and undiagnosed diabetes, and impaired fasting glucose (IFG) in U.S. adults during 1999–2002, and compare prevalences to those in 1988–1994. RESEARCH DESIGN AND METHODS—The National Health and Nutrition Examination Survey (NHANES) contains a probability sample of adults aged ≥20 years. In the NHANES 1999–2002, 4,761 adults were classified on glycemic status using standard criteria, based on an interview for diagnosed diabetes and fasting plasma glucose measured in a subsample. RESULTS—The crude prevalence of total diabetes in 1999–2002 was 9.3% (19.3 million, 2002 U.S. population), consisting of 6.5% diagnosed and 2.8% undiagnosed. An additional 26.0% had IFG, totaling 35.3% (73.3 million) with either diabetes or IFG. The prevalence of total diabetes rose with age, reaching 21.6% for those aged ≥65 years. The prevalence of diagnosed diabetes was twice as high in non-Hispanic blacks and Mexican Americans compared with non-Hispanic whites (both P < 0.00001), whereas the prevalence of undiagnosed diabetes was similar by race/ethnicity, adjusted for age and sex. The prevalence of diagnosed diabetes was similar by sex, but prevalences of undiagnosed diabetes and IFG were significantly higher in men. The crude prevalence of diagnosed diabetes rose significantly from 5.1% in 1988–1994 to 6.5% in 1999–2002, but the crude prevalences were stable for undiagnosed diabetes (from 2.7 to 2.8%) and IFG (from 24.7 to 26.0%). Results were similar after adjustment for age and sex. CONCLUSIONS—Although the prevalence of diagnosed diabetes has increased significantly over the last decade, the prevalences of undiagnosed diabetes and IFG have remained relatively stable. Minority groups remain disproportionately affected.

Priority actions for the non-communicable disease crisis

The UN High-Level Meeting on Non-Communicable Diseases (NCDs) in September, 2011, is an unprecedented opportunity to create a sustained global movement against premature death and preventable morbidity and disability from NCDs, mainly heart disease, stroke, cancer, diabetes, and chronic respiratory disease. The increasing global crisis in NCDs is a barrier to development goals including poverty reduction, health equity, economic stability, and human security. The Lancet NCD Action Group and the NCD Alliance propose five overarching priority actions for the response to the crisis--leadership, prevention, treatment, international cooperation, and monitoring and accountability--and the delivery of five priority interventions--tobacco control, salt reduction, improved diets and physical activity, reduction in hazardous alcohol intake, and essential drugs and technologies. The priority interventions were chosen for their health effects, cost-effectiveness, low costs of implementation, and political and financial feasibility. The most urgent and immediate priority is tobacco control. We propose as a goal for 2040, a world essentially free from tobacco where less than 5% of people use tobacco. Implementation of the priority interventions, at an estimated global commitment of about US

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

9 billion per year, will bring enormous benefits to social and economic development and to the health sector. If widely adopted, these interventions will achieve the global goal of reducing NCD death rates by 2% per year, averting tens of millions of premature deaths in this decade.

Improvements in diabetes processes of care and intermediate outcomes: United States, 1988-2002.

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

The Cost-Effectiveness of Lifestyle Modification or Metformin in Preventing Type 2 Diabetes in Adults with Impaired Glucose Tolerance

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

The Evolving Diabetes Burden in the United States

Context As the target of many quality improvement programs, positive change in diabetes care is a good marker for progress toward better health care. Content The authors analyzed measures of diabetes care from national population-based surveys that were conducted between 1988 and 2002. Improvements occurred in the proportion of patients with hemoglobin A1c between 6% and 8%, low-density lipoprotein (LDL) cholesterol levels less than 3.4 mmol/L (<130 mg/dL), annual influenza vaccination, and aspirin use. Blood pressure did not change. Substantial proportions of patients still had poor control of LDL cholesterol levels, glycemia, and blood pressure. Implications Despite some progress, population-based measurements show that care for many Americans with diabetes falls far short of targets. The Editors Diabetes currently affects 20.8 million people in the United States (1), and that number is projected to reach 39 million by the year 2050 (2). If current trends continue, 1 in 3 Americans will develop diabetes sometime in his or her lifetime, and those with diabetes will lose, on average, 10 to 15 life-years (3). In 2002, diabetes cost the nation an estimated

The effectiveness of disease and case management for people with diabetes: A systematic review

132 billion in direct and indirect costs (4). There is, however, a growing array of effective and cost-effective treatments to help prevent or delay diabetes complications and also diabetes itself (5-17). Diabetes care has been suboptimal and varied in the United States (18-21). The National Diabetes Quality Improvement Project, founded in 1997, developed a comprehensive set of measures of diabetes quality of care (22). These measures have been incorporated into the Health Plan Employer Data and Information Set, the American Diabetes Association Provider Recognition Program, the American Medical Association Diabetes Measures Group, the Veterans Administration performance monitoring program, and other activities. The Diabetes Quality Improvement Project partners now continue their work as a coalition of 13 influential private and public national organizations called the National Diabetes Quality Improvement Alliance. The Alliance develops, maintains, and promotes the use of an updated standardized measurement set (the Alliance measures) for quality of diabetes care (23). We previously established a national benchmark for diabetes quality of care in the United States for the years 1988 to 1995 by using the standard measurements recommended by the Diabetes Quality Improvement Project (18). On the basis of nationally representative data collected in 1999 to 2002, we report the changes in the quality of diabetes care from the 1990s to 2000s by using the standardized Alliance measures for both time periods. Methods Surveys We used data from 2 federally funded, nationally representative surveys: the National Health and Nutrition Examination Survey, 19881994 (NHANES III) and 19992002 (NHANES 19992002), and the Behavioral Risk Factor Surveillance System, 1995 (BRFSS 1995) and 2002 (BRFSS 2002). As previously explained (18), we used both BRFSS and NHANES to obtain data on all the process and intermediate outcome measures needed for the analysis. In our report, we refer to NHANES III and BRFSS 1995 as baseline surveys and NHANES 19992002 and BRFSS 2002 as recent surveys. We analyzed data from each survey separately. Table 1 presents the indicators used and their respective data source. Table 1. National Diabetes Quality Improvement Alliance and Additional Indicators of Diabetes Processes and Outcomes of Care National Health and Nutrition Examination Survey The NHANES consists of nationally representative samples of the U.S. civilian, noninstitutionalized population. Samples were obtained by using a stratified multistage probability design with planned oversampling of older and minority groups. Household interviews were conducted to ascertain sociodemographic characteristics and medical and family history. After the household interview, clinical examinations were conducted at a mobile examination center. Detailed descriptions of the design and data collection of each survey have been published elsewhere (24-27). Data from NHANES were self-reported (demographic characteristics and clinical variables) or were obtained during the clinical examination (hemoglobin A1c, cholesterol level, triglycerides level, and blood pressure level). Hemoglobin A1c measurements were standardized to the Diabetes Control and Complications Trial. Cholesterol levels were standardized by using the criteria established by the Centers for Disease Control and Prevention and the National Heart, Lung, and Blood Institute Lipid Standardization Program II. For persons who fasted for more than 8 hours and had triglyceride levels less than 4.5 mmol/L (<400 mg/dL), the Friedewald equation was applied to calculate low-density lipoprotein (LDL) cholesterol level. We log-transformed triglyceride levels because data were not normally distributed. We used the average of each persons blood pressure readings that were taken in the seated position during the clinical examination. Because we did not have data on annual testing for microalbuminuria, we assessed the absence of microalbuminuria, defined as albumin-to-creatinine ratio greater than 30 g/mg in spot urine collection (28). We analyzed the data for all indicators regardless of respective treatment status. Behavioral Risk Factor Surveillance System The BRFSS is an ongoing random-digit telephone survey of the noninstitutionalized U.S. adult population in each of the 50 states and the District of Columbia. Detailed descriptions of the design and data collection of the BRFSS have been published elsewhere (29). We used the diabetes-specific module that contains questions on clinical and preventive care practices to collect information from the participants with diabetes. Participants We included adults 18 to 75 years of age who reported a previous diagnosis of diabetes by a health care professional. We excluded women with gestational diabetes. We analyzed data from 1024 participants in NHANES III and 750 participants in NHANES 19992002 who selfreported a diagnosis of diabetes and who completed the clinical examination. We analyzed data from 3065 persons in BRFSS 1995 and 13078 persons in BRFSS 2002 who identified themselves as having diabetes. Participants reporting diabetes in all surveys were similar in age, sex, education, smoking, and insurance status at each point of time. Among participants of the recent surveys compared with those of the baseline surveys, the proportion of women and non-Hispanic white persons was lower and the proportion of participants with more than a high school education and an annual household income of

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

20000 or more was higher (Table 2). The proportion of people with diabetes who use insulin was also lower in the recent surveys but was statistically significant only in BRFSS 2002. Table 2. Characteristics of Participants 18 to 75 Years of Age with Self-Reported Diabetes in the National Health and Nutrition Examination Survey, 19881994 and 19992002, and Behavioral Risk Factors Surveillance System, 1995 and 2002 Performance Measurement Set We assessed the quality of diabetes care by using the Alliance measurement set (22) (Table 1). We used the Alliance measures of diabetes care wherever data were available, and we also examined additional measures that may be indicators of quality care in the future: pneumococcal vaccination, diabetes education, annual dental examination, and self-monitoring of blood glucose level. The BRFSS did not have a question about smoking counseling. We, therefore, used the proportion of smokers who tried to quit smoking. Questions about aspirin use were asked only every other year, so we used data from BRFSS 1996 for this variable. Statistical Analysis We conducted statistical analyses by using SAS for Windows software, version 7.0 (SAS Institute, Inc., Cary, North Carolina), for data management. We used SUDAAN software (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates and SEs based on sampling weights to produce national estimates accounting for the complex survey design. We used Taylor series linearization for variance estimation. We computed the percentage of respondents who reported receipt of each measure. We examined the diabetes care measures by age, sex, race or ethnicity, education, insulin use, and health insurance status because our previous analysis had variations by these factors (18). However, insulin users were not asked to fast; hence, we did not examine LDL levels by insulin use. We used multiple logistic regression and predictive margins to estimate the probability of receiving or meeting the care measure after controlling for all known potential confounders. Predictive margins are a type of direct standardization, where the predicted values from the logistic regression models are averaged over the covariate distribution in the population (30). This statistic has several advantages over the odds ratio: It is not influenced if the outcome is not rare; a comparison group is not required; and it provides a measure of absolute difference rather than relative difference. We included an interaction term between time and each measure in the models to allow estimation of the probability for each period. To assess the difference in the percentage change between the 2 comparison groups, we tested the interaction term of each demographic characteristic and clinical variable (age, sex, race or ethnicity, education, insulin use, and health insurance status) with time. Role of the Funding Source No funding was received for this study. Results Half of the quality care measures that we analyzed improved between the baseline and recent surveys, and the only measure that worsened was the proportion of participants with hemoglobin A1c < 6%. We observed absolute increases for LDL levels less than 3.4 mmol/L (<130 mg/dL) (22 percentage points), annual lipid profil

Comparison of Fasting and 2-Hour Glucose and HbA1c Levels for Diagnosing Diabetes: Diagnostic criteria and performance revisited

Context The Diabetes Prevention Program (DPP) showed that lifestyle changes or metformin effectively decreased the development of type 2 diabetes in adults with impaired glucose tolerance. The economics of these interventions is important to policymakers. Contribution This cost-effectiveness model estimates that the DPP life-style intervention would cost society about

Cardiovascular mortality, all-cause mortality, and diabetes incidence after lifestyle intervention for people with impaired glucose tolerance in the Da Qing Diabetes Prevention Study: a 23-year follow-up study

8800 and metformin would cost about