K. M. Venkat Narayan

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

Diabetes--a common, growing, serious, costly, and potentially preventable public health problem.

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

A dynamic Markov model for forecasting diabetes prevalence in the United States through 2050

An estimated 135 million people worldwide had diagnosed diabetes in 1995, and this number is expected to rise to at least 300 million by 2025. The number of people with diabetes will increase by 42% (from 51 to 72 million) in industrialized countries between 1995 and 2025 and by 170% (from 84 to 228 million) in industrializing countries. Several potentially modifiable risk factors are related to diabetes, including insulin resistance, obesity, physical inactivity and dietary factors. Diabetes may be preventable in high-risk groups, but results of ongoing clinical trials are pending. Several efficacious and economically acceptable treatment strategies are currently available (control of glycemia, blood pressure, lipids; early detection and treatment of retinopathy, nephropathy, foot-disease; use of aspirin and ACE inhibitors) to reduce the burden of diabetes complications. Diabetes is a major public health problem and is emerging as a pandemic. While prevention of diabetes may become possible in the future, there is considerable potential now to better utilize existing treatments to reduce diabetes complications. Many countries could benefit from research aimed at better understanding the reasons why existing treatments are under-used and how this can be changed.

Ethnic Disparities: Control of Glycemia, Blood Pressure, and LDL Cholesterol Among US Adults with Type 2 Diabetes

This study develops forecasts of the number of people with diagnosed diabetes and diagnosed diabetes prevalence in the United States through the year 2050. A Markov modeling framework is used to generate forecasts by age, race and ethnicity, and sex. The model forecasts the number of individuals in each of three states (diagnosed with diabetes, not diagnosed with diabetes, and death) in each year using inputs of estimated diagnosed diabetes prevalence and incidence; the relative risk of mortality from diabetes compared with no diabetes; and U.S. Census Bureau estimates of current population, live births, net migration, and the mortality rate of the general population. The projected number of people with diagnosed diabetes rises from 12.0 million in 2000 to 39.0 million in 2050, implying an increase in diagnosed diabetes prevalence from 4.4% in 2000 to 9.7% in 2050.

Randomized clinical trial of lifestyle interventions in Pima Indians: a pilot study

OBJECTIVE: To examine ethnic disparities in the quality of diabetes care among adults with diabetes in the US through a systematic qualitative review. DATA SOURCES: Material published in the English language was searched from 1993 through June 2003 using PubMed, Web of Science, Cumulative Index to Nursing and Allied Health, the Cochrane Library, Combined Health Information Database, and Education Resources Information Center. STUDY SELECTION AND DATA EXTRACTION: Studies of patients with diabetes in which at least 50% of study participants were ethnic minorities and studies that made ethnic group comparisons were eligible. Research on individuals having prediabetes, those <18 years of age, or women with gestational diabetes were excluded. Reviewers used a reproducible search strategy. A standardized abstraction and grading of articles for publication source and content were used. Data on glycemia, blood pressure, and low-density lipoprotein cholesterol (LDL-C) were extracted in patients with diabetes. A total of 390 studies were reviewed, with 78 meeting inclusion criteria. DATA SYNTHESIS: Ethnic minorities had poorer outcomes of care than non-Hispanic whites. These disparities were most pronounced for glycemic control and least evident for LDL-C control. Most studies showed blood pressure to be poorly controlled among ethnic minorities. CONCLUSIONS: Control of risk factors for diabetes (glycemia, blood pressure, LDL-C) is challenging and requires routine assessment. These findings indicate that additional efforts are needed to promote diabetes quality of care among minority populations.

Homocysteine as a risk factor for nephropathy and retinopathy in Type 2 diabetes

A pilot trial was conducted to test adherence to specific lifestyle interventions among Pima Indians of Arizona, and to compare them for changes in risk factors for diabetes mellitus. Ninety‐five obese, normoglycaemic men and women, aged 25–54 years, were randomized to treatments named ‘Pima Action’ (Action) and ‘Pima Pride’ (Pride), which were tested for 12 months. Action involved structured activity and nutrition interventions, and Pride included unstructured activities emphasizing Pima history and culture. Adherence to interventions, changes in self‐reported activity and diet, and changes in weight, glucose concentrations, and other risk factors were assessed regularly. Thirty‐five eligible subjects who had declined randomization were also followed as an ‘observational’ group and 22 members of this group were examined once at a median of 25 months for changes in weight and glucose concentration. After 12 months of intervention, members of both intervention groups reported increased levels of physical activity (median: Action 7.3 h month−1, Pride 6.3 h month−1, p < 0.001 for each), and Pride members reported decreased starch intake (28 g, p = 0.008). Body mass index, systolic and diastolic blood pressures, weight, 2‐h glucose and 2‐h insulin had all increased in Action members (p < 0.003 for each), and waist circumference had decreased in Pride members (p = 0.05). Action members gained more weight than Pride members (2.5 kg vs 0.8 kg, p = 0.06), and had a greater increase in 2‐h glucose than Pride members (1.33 mM vs 0.03 mM, p = 0.007). Members of the observational group gained an average of 1.9 kg year−1 in weight and had an increase of 0.36 mM year−1 in 2‐h glucose. Sustaining adherence in behavioural interventions over a long term was challenging. Pimas may find a less direct, less structured, and more participative intervention more acceptable than a direct and highly structured approach.

Changes in Health State Utilities With Changes in Body Mass in the Diabetes Prevention Program

Aims/hypothesisThe aim of this study was to examine the relation between serum total homocysteine concentrations and microvascular complications in Pima Indians with Type 2 diabetes.MethodsHomocysteine concentrations were measured in frozen sera of 396 diabetic participants in a longitudinal study who were 40 years of age or older and who had attended one or more examinations between 1982 and1985. Retinopathy was assessed by fundoscopy and nephropathy by an albumin:creatinine ratio greater than 300xa0mg/g. The incidence rate ratio for a 5xa0µmol/l difference in homocysteine was calculated using proportional hazard regression.ResultsThe incidence of each complication was assessed in subjects without that complication at baseline and with more than one follow-up examination: 229 for nephropathy, 212 for retinopathy and 266 for proliferative retinopathy. There were 101 incident cases of nephropathy, 113 of retinopathy and 40 of proliferative retinopathy during a mean follow-up of 8.6, 7.5 and 8.9 years, respectively. Incidence of nephropathy was associated with homocysteine concentrations: IRR=1.42 (95% CI, 1.09–1.84, p=0.01); this remained statistically significant controlled for age, sex and duration of diabetes (p=0.03), but not when controlled for baseline renal function (p=0.4). Homocysteine concentrations were not associated with the incidence of any retinopathy IRR=1.14 (95%CI 0.89–1.46, p=0.3) but were associated with the incidence of proliferative retinopathy IRR=1.62 (95% CI 1.16–2.28, p=0.005); this association remained statistically significant when controlled for baseline renal function and diabetes duration (p=0.02).Conclusions/interpretationIncreased homocysteine concentrations are associated with an increased risk for incidence of nephropathy and proliferative retinopathy; the relation with incidence of nephropathy seems to be explained by an association with baseline albuminuria status concentrations, whereas the relation with incidence of proliferative retinopathy does not.

Addressing the Burden of Diabetes in the 21st Century: Better Care and Primary Prevention

Health utilities are measures of health‐related quality of life (HRQL) used in cost‐effectiveness research. We evaluated whether changes in body weight were associated with changes in health utilities in the Diabetes Prevention Program (DPP) and whether associations differed by treatment assignment (lifestyle intervention, metformin, placebo) or baseline obesity severity. We constructed physical (PCS‐36) and mental component summary (MCS‐36) subscales and short‐form‐6D (SF‐6D) health utility index for all DPP participants completing a baseline 36‐item short form (SF‐36) HRQL assessment (N = 3,064). We used linear regression to test associations between changes in body weight and changes in HRQL indicators, while adjusting for other demographic and behavioral variables. Overall differences in HRQL between treatment groups were highly statistically significant but clinically small after 1 year. In multivariable models, weight change was independently associated with change in SF‐6D score (increase of 0.007 for every 5 kg weight loss; P < 0.001), but treatment effects independent of weight loss were not. We found no significant interaction between baseline obesity severity and changes in SF‐6D with changes in body weight. However, increases in physical function (PCS‐36) with weight loss were greater in persons with higher baseline obesity severity. In summary, improvements in HRQL are associated with weight loss but not with other effects of obesity treatments that are unrelated to weight loss. Although improvements in the SF‐6D did not exceed commonly reported thresholds for a minimally important difference (0.04), these changes, if causal, could still have a significant impact on clinical cost‐effectiveness estimates if sustained over multiple years.

Is the Number of Documented Diabetes Process-of-Care Indicators Associated With Cardiometabolic Risk Factor Levels, Patient Satisfaction, or Self-Rated Quality of Diabetes Care? The Translating Research into Action for Diabetes (TRIAD) study

By the end of the 20th century, the worldwide diabetes pandemic had affected an estimated 151 million persons. Strategies to mitigate both the human and economic burden are urgently needed. Efficacious treatments are currently available but the quality of diabetes care being delivered is suboptimal in both developed and developing countries. Some progress to improve quality has been made thought national strategies. These efforts need two elements: translation research that will establish the methods needed to assure that clinical research findings are delivered effectively in every day practice settings; and development and implementation of quality improvement measures that will reliably track progress. New interventions that prevent diabetes among those at high risk also now hold much promise and need to be implemented.

Prevention of type 2 diabetes: data from recent trials.

OBJECTIVE—Simple process-of-care indicators are commonly recommended to assess and compare quality of diabetes care across health plans. We sought to determine whether variation in the number of simple diabetes processes of care across provider groups is associated with variation in other quality indicators, including cardiometabolic risk factor levels, patient satisfaction with care, or patient-rated quality of care. RESEARCH DESIGN AND METHODS—We used cross-sectional survey and chart audit data for 8,733 patients with diabetes who received care from 68 provider groups nested in 10 health plans that participated in the Translating Research Into Action for Diabetes study. Analyses using hierarchical regression models assessed associations of the mean number of seven simple process measures with each of the following: HbA1c (A1C), systolic blood pressure (SBP), HDL and LDL cholesterol levels, patient satisfaction with care, and patient-rated quality of care. RESULTS—After adjusting for case-mix differences across groups and plans, an average of one additional documented process of care for each patient in a group or plan was associated with significantly lower mean LDL cholesterol levels (−4.51 mg/dl [95% CI 1.46–7.58]) but not with A1C, SBP, or HDL cholesterol levels. The number of care processes documented was associated with patient satisfaction measures and self-rated quality of diabetes care. CONCLUSIONS—Variation in the number of simple process-of-care indicators across provider groups or health plans is associated with differences in patient-centered measures of quality, but assessment of the quality of cardiometabolic risk factor control will require more advanced clinical performance indicators.