Improvements in Diabetes Processes of Care and Intermediate Outcomes: United States, 1988–2002 FREE

—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 $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.

We used data from 2 federally funded, nationally representative surveys: the National Health and Nutrition Examination Survey, 1988–1994 (NHANES III) and 1999–2002 (NHANES 1999–2002), 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 1999–2002 and BRFSS 2002 as “recent surveys.” We analyzed data from each survey separately. Table 1 presents the indicators used and their respective data source.

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 A_1c, cholesterol level, triglycerides level, and blood pressure level). Hemoglobin A_1c 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 person's 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 µg/mg in spot urine collection (28). We analyzed the data for all indicators regardless of respective treatment status.

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.

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 1999–2002 who selfreported a diagnosis of diabetes and who completed the clinical examination. We analyzed data from 3065 persons in BRFSS 1995 and 13 078 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 $20 000 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.

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.

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.

No funding was received for this study.

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 A_1c < 6%. We observed absolute increases for LDL levels less than 3.4 mmol/L (<130 mg/dL) (22 percentage points), annual lipid profile (8 percentage points), eye examination (5 percentage points), foot examination (4 percentage points), influenza vaccination (7 percentage points), and aspirin use (13 percentage points) (Table 3). Changes in proportion of persons who have hemoglobin A_1c greater than 9%, who are smokers, and who have blood pressure less than 140/90 mm Hg were not statistically significant. Although the prevalence of smokers showed little change, the percentage of smokers who tried to stop smoking increased by 19 percentage points. Of the other indicators, the proportion of people who received pneumococcal vaccine increased by 16 percentage points and those who self-monitored blood glucose level at least once daily increased by 17 percentage points, but annual dental examination did not change (Table 3). In the recent surveys, about 55% of participants reported having diabetes education. These data were not available in the baseline surveys.

While the overall mean hemoglobin A_1c levels did not change from baseline to recent surveys, we found that the proportion of people with diabetes with hemoglobin A_1c of 6% to 8% increased from 34.2% to 47.0% (Table 4). Moreover, only 42.3% of adults in NHANES 1999–2002 had hemoglobin A_1c levels at the American Diabetes Association goal of less than 7%, and 14% had hemoglobin A_1c 10% or more. The proportion of people with hemoglobin A_1c less than 6% decreased significantly between the 2 timelines (23.4% vs. 16.4%).

The mean LDL cholesterol level decreased from 3.6 mmol/L (138 mg/dL) at baseline to 3.1 mmol/L (119 mg/dL) in the recent surveys. About 42% of participants in the baseline surveys and about 64% of participants in the recent surveys had LDL cholesterol levels less than 3.4 mmol/L (<130 mg/dL). The distribution of LDL cholesterol level has, thus, shifted to the left. The other lipid distributions (total cholesterol, triglycerides, and high-density lipoprotein levels) had also shifted slightly to the left, with higher percentages of persons meeting the recommended levels (Table 4).

The mean systolic blood pressure did not change between the 2 timelines. In fact, we found no change in the percentage of participants with controlled blood pressure (blood pressure < 140/90 mm Hg) from the baseline surveys (67.6%) to the recent surveys (68.0%). Approximately 20% of participants had a systolic blood pressure of 140 mm Hg to 159 mm Hg and about 7% of participants had a systolic blood pressure of 160 mm Hg to 179 mm Hg in the baseline and recent surveys.

Table 5 shows the changes among the demographic groups. In general, most comparison groups improved between baseline and follow-up time. However, the percentage change differed between demographic and clinical characteristic subgroups. For example, the proportion of men with flu vaccine and aspirin use improved statistically significantly more than that of women. Those with less than a high school education had better improvement in flu vaccination but not in aspirin use compared with those with more than a high school education. People with health insurance improved aspirin use more than those without health insurance. We did not observe any variation in rate of improvement among the different age groups and among the different race or ethnic groups.

On the basis of nationally representative data, we have found encouraging improvements over the past decade in several processes and some intermediate outcomes of diabetes quality of care in the United States. Although the level of care continues to fall short of what is recommended, annual lipid testing, dilated eye and foot examinations, self-monitoring of blood glucose level, and adoption of aspirin use and pneumococcal and influenza vaccinations have sizeably improved. Impressive improvements in lipid control and some improvement in glycemic control have occurred, but blood pressure control has not improved.

Our findings are consistent with the results reported in specific populations and health care systems, such as the Veterans Health Administration (31), Indian Health Service (32), managed care organizations (33), and Medicare (34). Some of the most impressive improvements in diabetes and other chronic disease quality of care have happened in the Veterans Health Administration, where quality of diabetes care seems to be better than that of similar commercial managed care (35 - 36).

Recent reports, using national data, also showed improvement in cardiovascular disease risk factors among people with diabetes between NHANES III and NHANES 1999–2000 (37 - 38). Our analysis is unique because we used more comprehensive national data; assessed changes over a decade; and most important, used a standardized set developed by the Alliance of quality-of-care processes and intermediate outcome measures. We have also included additional futuristic measures of quality of care.

Major randomized, controlled trials suggest that much of the burden of diabetes is amenable to clinical and public health interventions (5 - 14). Results from major clinical trials in the period between the baseline and recent surveys confirm the benefits of glycemic control, blood pressure control, and lipid control. Microvascular complications would be reduced by 30% by reducing hemoglobin A_1c by 1% (6 - 7). A 10-mm reduction in blood pressure would reduce macrovascular and microvascular complications and the risk for death by 35% (7 - 8). Good lipid control can reduce the risk for coronary heart disease by 25% to 55% and for death by 43% (9). Thus, the improvements in the quality of care, while not yet ideal or complete, should translate into a substantial effect on the health status of people with diabetes. Of some concern is the lack of improvement in blood pressure control, which has been shown to be a highly cost-effective treatment in the United States (39).

In our earlier report, we included some important measures to the original Diabetes Quality Improvement Project measurement set (18). Several of these measures have been adopted by the new modified Alliance measures, and others are being assessed for inclusion. The American Diabetes Association and the American Association of Clinical Endocrinologists recommend a thorough assessment of tobacco use and the implementation of smoking cessation guidelines in the management of diabetes. They also recommend aspirin use for the secondary prevention of vascular events and as primary prevention for high-risk groups with diabetes (40 - 42). The Alliance included smoking cessation and aspirin use in 2003 as new quality improvement and public reporting measures because of these recommendations (23).

As science advances, several more new measures may be considered for inclusion in future versions of the Alliance measurement set. Several reports have demonstrated the association between diabetes and periodontal disease and missing teeth (43 - 44). Conversely, these oral health problems can complicate overall diabetes management and increase the risk for poor glycemic control (45). We did not find any change in the proportion of people with diabetes who had had an annual dental examination between the baseline and recent surveys. Furthermore, clinical trials from middle-age and older adults with diabetes suggest that diabetes management education can improve glycemic control (46). Consequently, diabetes management education may be a candidate measure for a future Alliance measurement set. Some early evidence suggest that lowering triglyceride levels in people with diabetes may reduce cardiovascular disease (47), and other trials testing this hypothesis are under way (48). We found modest improvements in triglyceride levels, which may be related to improvements in glycemic control. With strong evidence that diabetes may be prevented or delayed among high-risk people (5), future measures of diabetes quality of care might include indicators of delivery of prevention (for example, weight management and physical activity promotion) for people with impaired glucose tolerance or impaired fasting glucose (pre-diabetes).

Although the improvements in implementing processes of care have been impressive, the changes in intermediate outcomes (for example, blood pressure or hemoglobin A_1c) are less so. These findings are consistent with those of other recent reports (18,34,49). These data indicate the need for continued efforts to improve intermediate outcomes and have several implications for the future focus of quality improvement. For example, is it time to develop the next generation of diabetes quality-of-care indicators that reflect intensity of treatment (for example, proportion of people receiving various doses of statins, proportion of people receiving 1 or more antihypertensive drugs or hypoglycemic treatments, and proportion of people receiving angiotensin-converting enzyme inhibitors or angiotesin-receptor blockers)? Should quality-of-care indicators also specifically target people achieving poor intermediate outcomes (for example, poor control of glycemia, lipid levels, or blood pressure)? Is it enough to measure the use of dilated eye examination, or should the next generation of quality indicators also assess the proportion of treatable diabetic eye diseases that are detected, followed up, and treated (50)?

Our study is subject to the limitations inherent in any self-reported data, such as recall bias. However, the self-reported diabetes question and most other BRFSS questions have been validated (51 - 53). Many studies have shown that the percentages of a performance measure obtained from member surveys are almost always higher, sometimes substantially higher, than the percentages based on medical record abstract or administrative data (54). Yet, the percentages of almost all the measures were suboptimal, and we do not believe that such bias would differentially affect our assessment of secular trends. The BRFSS sample size is large, and some of the statistically significant differences may purely reflect this. Conversely, the smaller sample size of the NHANES may explain some of clinically significant differences that were not statistically significant. Finally, we did not have the information needed to assess all of the Alliance measures (for example, urine protein screening), although we have measured changes in the prevalence of microalbuminuria.

Our data suggest that certain subgroups in the population, such as people with less education and noninsulin users, continue to experience lower rates of process of care and intermediate outcomes. Moreover, the availability of health insurance is also a strong factor associated with better processes and outcome measures. We hope that the positive changes in quality improvement follow the sigmoid pattern of any diffusion of innovation, and with time and effort, the beneficial improvement will spread to the more resistant groups (55). We should identify and target those people who have not yet benefited from these improvements.

Overall, quality of care for people with diabetes has improved in the past 10 years, but important opportunities remain for further improvement. Currently, 1 in 5 people with diabetes (2.2 million people) has poor glycemic control (hemoglobin A_1c > 9%), 2 in 5 people with diabetes (3.6 million people) have poor LDL cholesterol level control (LDL cholesterol level ≥ 3.4 mmol/L [≥130 mg/dL]), 1 in 3 people with diabetes (3.5 million people) has poor blood pressure control (≥140/90 mm Hg), and 1 in 3 people with diabetes has not received annual eye (3.2 million people) or foot examinations (3.1 million people).

The change in the demographic composition of the U.S. population and the increasing diabetes prevalence will mean many more people with diabetes in the future (2). Ensuring access to and delivering high-quality care for all people with diabetes should be a national priority. Understanding how to better implement known and existing diabetes care interventions within finite resources may be the key.