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Objective: Among individuals with diabetes, a comparison of HbA(1c) (A1C) levels between African Americans and non-Hispanic whites was evaluated. Data sources included PubMed, Web of Science, the Cumulative Index to Nursing and Allied Health, the Cochrane Library, the Combined Health Information Database, and the Education Resources Information Center.Research Design and Methods: We executed a search for articles published between 1993 and 2005. Data on sample size, age, sex, A1C, geographical location, and study design were extracted. Cross-sectional data and baseline data from clinical trials and cohort studies for African Americans and non-Hispanic whites with diabetes were included. Diabetic subjects aged <18 years and those with pre-diabetes or gestational diabetes were excluded. We conducted a meta-analysis to estimate the difference in the mean values of A1C for African Americans and non-Hispanic whites.Results: A total of 391 studies were reviewed, of which 78 contained A1C data. Eleven had data on A1C for African Americans and non-Hispanic whites and met selection criteria. A meta-analysis revealed the standard effect to be 0.31 (95% CI 0.39-0.25). This standard effect correlates to an A1C difference between groups of approximately 0.65%, indicating a higher A1C across studies for African Americans. Grouping studies by study type (cross-sectional or cohort), method of data collection for A1C (chart review or blood draw), and insurance status (managed care or nonmanaged care) showed similar results.Conclusions: The higher A1C observed in this meta-analysis among African Americans compared with non-Hispanic whites may contribute to disparity in diabetes morbidity and mortality in this population. [ABSTRACT FROM AUTHOR]

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: HbA(1c) (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. [ABSTRACT FROM AUTHOR]

OBJECTIVE: We examined factors associated with screening for albuminuria and initiation of ACE inhibitor or angiotensin receptor blocker (ARB) treatment in diabetic patients. RESEARCH DESIGN AND METHODS: We conducted surveys and medical record reviews for 5,378 patients participating in a study of diabetes care in managed care at baseline (2000-2001) and follow-up (2002-2003). Factors associated with testing for albuminuria were examined in cross-sectional analysis at baseline. Factors associated with initiating ACE inhibitor/ARB therapy were determined prospectively. RESULTS: At baseline, 52% of patients not receiving ACE inhibitor/ARB therapy and without known diabetic kidney disease (DKD) were screened for albuminuria. Patients > or =65 years of age, those with higher HbA(1c), those with cardiovascular disease (CVD), and those without hyperlipidemia were less likely to be screened. Of the patients with positive screening tests, 47% began ACE inhibitor/ARB therapy. Initiation of therapy was associated with positive screening test results, BMI > or =25 kg/m(2), treatment with insulin or oral antidiabetic agents, peripheral neuropathy, systolic blood pressure > or =140 mmHg, and CVD. Of the patients receiving ACE inhibitor/ARB therapy or with known DKD, 63% were tested for albuminuria. CONCLUSIONS: Screening for albuminuria was inadequate, especially in older patients or those with competing medical concerns. The value of screening could be increased if more patients with positive screening tests initiated ACE inhibitor/ARB therapy. The efficiency of screening could be improved by limiting screening to diabetic patients not receiving ACE inhibitor/ARB therapy and without known DKD. [ABSTRACT FROM AUTHOR]

Objective: Opportunistic screening for undiagnosed type 2 diabetes and pre-diabetes (either impaired glucose tolerance or impaired fasting glucose) is recommended by the American Diabetes Association. The aim of this study was to determine efficient cutoff points for three screening tests for detecting undiagnosed diabetes alone or both undiagnosed diabetes and pre-diabetes.Research Design and Methods: We estimated the number of individuals with undiagnosed diabetes alone or with both undiagnosed diabetes and pre-diabetes that could be detected by using different cutoff points for each screening test as the product of the prevalence of each condition, the sensitivity of the tests at each cutoff point for identifying each condition, and the number of individuals who would be eligible for screening in the U.S. We estimated the total cost of opportunistic screening by multiplying the cost for screening one person by the number of individuals screened.Results: The most efficient cutoff points for both detecting pre-diabetes and undiagnosed diabetes (100 mg/dl for the fasting plasma glucose test, 5.0% for the HbA(1c) test, and 100 mg/dl for the random capillary blood glucose test) were less than those for detecting undiagnosed diabetes alone (110 mg/dl for the fasting plasma glucose test, 5.7% for the HbA(1c) test, and 120 mg/dl for the random capillary blood glucose test).Conclusions: A lower cutoff value should be used when screening for pre-diabetes and undiagnosed diabetes together than when screening for undiagnosed diabetes alone. [ABSTRACT FROM AUTHOR]

Objective: To examine the association between the organizational model and diabetes processes of care.Research Design and Methods: We used data from the Translating Research into Action for Diabetes (TRIAD), a multicenter study of diabetes care in managed care, including 8354 patients with diabetes. We identified five model types: for-profit group/network, for-profit independent practice association (IPA), nonprofit group/network, nonprofit IPA, and nonprofit group/staff. Process measures included retinal, renal, foot, lipid, and HbA(1c) testing; aspirin recommendations; influenza vaccination; and a sum of these seven processes of care over 1 year. Hierarchical regression models were constructed for each process measure and accounted for clustering at the health plan and provider group levels and adjusted for participant age, sex, race, ethnicity, diabetes treatment and duration, education, income, health status, and survey language.Results: Participant membership in the model types ranged from 9% in nonprofit IPA models to 38% in nonprofit group/staff models. Over 75% of participants received most of the processes of care, regardless of model type. However, among for-profit plans, group/network models provided on average more processes of care than IPA models (5.5 vs. 4.7, P < 0.0001), and group/network models generally increased the probability of receiving a process by >or=10 percentage points. Among nonprofit plans, no effect of model type was found.Conclusions: Among for-profit plans, group/network models provided better diabetes processes of care than IPA models. Although reasons are speculative, this may be due to the clinical infrastructure available in group models that is not available in IPA models. [ABSTRACT FROM AUTHOR]

Objective: To examine the influence of obesity and prevention of weight gain on the incidence of type 2 diabetes.Research Design and Methods: We examined participants in the San Antonio Heart Study, a prospective population-based study of Mexican Americans and non-Hispanic whites residing in San Antonio, Texas. BMI was stratified into four categories: normal (<25 kg/m(2)), overweight (> or =25 kg/m(2) and <30 kg/m(2)), obese (> or =30 kg/m(2) and <35 kg/m(2)), and very obese (> or =35 kg/m(2)). The number and proportion of incident cases prevented by targeting each BMI category were estimated. In addition, we calculated the decrease in risk of developing type 2 diabetes associated with weight gain prevention across both the BMI and age spectra.Results: Preventing normal individuals from becoming overweight would result in the greatest reduction in incidence of type 2 diabetes. This would result in a 62 and 74% reduction in the incidence of type 2 diabetes in Mexican Americans and non-Hispanic whites, respectively. Preventing the entire population from gaining, on average, 1 BMI unit would result in a reduction in incidence of type 2 diabetes of 12.4 and 13.0% in Mexican Americans and non-Hispanic whites, respectively.Conclusions: The majority of cases of type 2 diabetes were in individuals who were overweight or mildly obese with a family history of type 2 diabetes. Public health resources should be directed toward the prevention of weight gain among normal and overweight individuals in order to prevent the maximum number of cases of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Objective: To systematically review the effectiveness of self-management training in type 2 diabetes.Research Design and Methods: MEDLINE, Educational Resources Information Center (ERIC), and Nursing and Allied Health databases were searched for English-language articles published between 1980 and 1999. Studies were original articles reporting the results of randomized controlled trials of the effectiveness of self-management training in people with type 2 diabetes. Relevant data on study design, population demographics, interventions, outcomes, methodological quality, and external validity were tabulated. Interventions were categorized based on educational focus (information, lifestyle behaviors, mechanical skills, and coping skills), and outcomes were classified as knowledge, attitudes, and self-care skills; lifestyle behaviors, psychological outcomes, and quality of life; glycemic control; cardiovascular disease risk factors; and economic measures and health service utilization.Results: A total of 72 studies described in 84 articles were identified for this review. Positive effects of self-management training on knowledge, frequency and accuracy of self-monitoring of blood glucose, self-reported dietary habits, and glycemic control were demonstrated in studies with short follow-up (<6 months). Effects of interventions on lipids, physical activity, weight, and blood pressure were variable. With longer follow-up, interventions that used regular reinforcement throughout follow-up were sometimes effective in improving glycemic control. Educational interventions that involved patient collaboration may be more effective than didactic interventions in improving glycemic control, weight, and lipid profiles. No studies demonstrated the effectiveness of self-management training on cardiovascular disease-related events or mortality; no economic analyses included indirect costs; few studies examined health-care utilization. Performance, selection, attrition, and detection bias were common in studies reviewed, and external generalizability was often limited.Conclusions: Evidence supports the effectiveness of self-management training in type 2 diabetes, particularly in the short term. Further research is needed to assess the effectiveness of self-management interventions on sustained glycemic control, cardiovascular disease risk factors, and ultimately, microvascular and cardiovascular disease and quality of life. [ABSTRACT FROM AUTHOR]

Objective: Age, BMI, and race/ethnicity are used in National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and American Diabetes Association (ADA) guidelines to prompt screening for pre-diabetes and diabetes, but cutoffs have not been evaluated rigorously.Research Design and Methods: Random plasma glucose (RPG) was measured and 75-g oral glucose tolerance tests were performed in 1,139 individuals without known diabetes. Screening performance was assessed by logistic regression and area under the receiver operating characteristic curve (AROC).Results: NIDDK/ADA indicators age >45 years and BMI >25 kg/m(2) provided significant detection of both diabetes and dysglycemia (both AROCs 0.63), but screening was better with continuous-variable models of age, BMI, and race and better still with models of age, BMI, race, sex, and family history (AROC 0.78 and 0.72). However, screening was even better with RPG alone (AROCs 0.81 and 0.72). RPG >125 mg/dl could be used to prompt further evaluation with an OGTT.Conclusions: Use of age, BMI, and race/ethnicity in guidelines for screening to detect diabetes and pre-diabetes may be less important than evaluation of RPG. RPG should be investigated further as a convenient, inexpensive screen with good predictive utility. [ABSTRACT FROM AUTHOR]

Objective: To describe the natural history of diabetes in Indians., Research Design and Methods: Data are from participants older than 20 years in the Centre for Cardiometabolic Risk Reduction in South Asia longitudinal study. Glycemic states were defined per American Diabetes Association criteria. Markov models were used to estimate annual transition probabilities and sojourn time through states., Results: Among 2,714 diabetes-free participants, 641 had isolated impaired fasting glucose (iIFG), and 341 had impaired glucose tolerance (IGT). The annual transition to diabetes for those with IGT was 13.9% (95% CI 12.0, 15.9) versus 8.6% (7.3, 9.8) for iIFG. In the normoglycemia ↔ iIFG → diabetes model, mean sojourn time in normoglycemia was 40.3 (34.6, 48.2) years, and sojourn time in iIFG was 9.7 (8.4, 11.4) years. For the normoglycemia ↔ IGT → diabetes model, mean sojourn time in normoglycemia was 34.5 (29.5, 40.8) years, and sojourn time in IGT was 6.1 (5.3, 7.1) years., Conclusions: Individuals reside in normoglycemia for 35-40 years; however, progression from prediabetes to diabetes is rapid., (© 2024 by the American Diabetes Association.)

Objective: To examine whether the effect of conventional lifestyle interventions on type 2 diabetes incidence differs by glucose-defined prediabetes phenotype., Research Design and Methods: We searched multiple databases until 1 April 2023 for randomized controlled trials that recruited people with isolated impaired fasting glucose (i-IFG), isolated impaired glucose tolerance (i-IGT), and impaired fasting glucose plus impaired glucose tolerance (IFG+IGT). Individual participant data were pooled from relevant trials and analyzed through random-effects models with use of the within-trial interactions approach., Results: Four trials with 2,794 participants (mean age 53.0 years, 60.7% men) were included: 1,240 (44.4%), 796 (28.5%), and 758 (27.1%) had i-IFG, i-IGT, and IFG+IGT, respectively. After a median of 2.5 years, the pooled hazard ratio for diabetes incidence in i-IFG was 0.97 (95% CI 0.66, 1.44), i-IGT 0.65 (0.44, 0.96), and IFG+IGT 0.51 (0.38, 0.68; Pinteraction = 0.01)., Conclusions: Conventional lifestyle interventions reduced diabetes incidence in people with IGT (with or without IFG) but not in those with i-IFG., (© 2023 by the American Diabetes Association.)

Objective: To assess the cost-effectiveness of collaborative versus usual care in adults with poorly controlled type 2 diabetes and depression in India., Research Design and Methods: We performed a within-trial cost-effectiveness analysis of a 24-month parallel, open-label, pragmatic randomized clinical trial at four urban clinics in India from multipayer and societal perspectives. The trial randomly assigned 404 patients with poorly controlled type 2 diabetes (HbA1c ≥8.0%, systolic blood pressure ≥140 mmHg, or LDL cholesterol ≥130 mg/dL) and depressive symptoms (9-item Patient Health Questionnaire score ≥10) to collaborative care (support from nonphysician care coordinators, electronic registers, and specialist-supported case review) for 12 months, followed by 12 months of usual care or 24 months of usual care. We calculated incremental cost-effectiveness ratios (ICERs) in Indian rupees (INR) and international dollars (Int'l-$) and the probability of cost-effectiveness using quality-adjusted life-years (QALYs) and depression-free days (DFDs)., Results: From a multipayer perspective, collaborative care costed an additional INR309,558 (Int'l-$15,344) per QALY and an additional INR290.2 (Int'l-$14.4) per DFD gained compared with usual care. The probability of cost-effectiveness was 56.4% using a willingness to pay of INR336,000 (Int'l-$16,654) per QALY (approximately three times per-capita gross domestic product). The willingness to pay per DFD to achieve a probability of cost-effectiveness >95% was INR401.6 (Int'l-$19.9). From a societal perspective, cost-effectiveness was marginally lower. In sensitivity analyses, integrating collaborative care in clinical workflows reduced incremental costs by ∼47% (ICER 162,689 per QALY, cost-effectiveness probability 89.4%), but cost-effectiveness decreased when adjusting for baseline values., Conclusions: Collaborative care for patients with type 2 diabetes and depression in urban India can be cost-effective, especially when integrated in clinical workflows. Long-term cost-effectiveness might be more favorable. Scalability across lower- and middle-income country settings depends on heterogeneous contextual factors., (© 2022 by the American Diabetes Association.)

Objective: To analyze national and state-specific trends in diabetes-related hospital admissions and determine whether disparities in rates of admission exist between demographic groups and geographically dispersed states., Research Design and Methods: We conducted serial cross-sectional analyses of the National Inpatient Sample (2008, 2011, 2014, and 2016) and State Inpatient Databases for Arizona, Florida, Kentucky, Iowa, Maryland, Nebraska, New Jersey, New York, North Carolina, Utah, and Vermont for 2008, 2011, 2014, and 2016/2017 among adult patients with type 1 and type 2 diabetes-related ICD codes (ICD-9 [250.XX] or ICD-10 [E10.XXX, E11.XXX, and E13.XXX]. We measured hospitalization rates for people with diabetes (all-cause hospitalizations) and for admissions with a primary diagnosis of diabetes or diabetes-related complications (diabetes-specific hospitalizations) per 10,000 people per year., Results: Nationally, all-cause and diabetes-specific hospitalizations declined by 3.1% (95% CI -5.5, -0.7) and 19.1% (95% CI -21.6, -16.6), respectively, over 2008 to 2016. The analysis of individual states showed that diabetes-specific admissions in individuals ≥65 years old declined during this time (16.3-48.8% decrease) but increased among patients 18-29 years old (10.5-81.5% increase) and that rural diabetes-specific admissions decreased in just over half of the included states (15.2-69.2% decrease). There were no differences in changes in admission rates among different racial/ethnic groups., Conclusions: Overall, rates of diabetes-related hospitalizations decreased over 2008 to 2016/2017, but there were large state-level differences across subgroups of patients. The rise in diabetes hospitalizations among young adults is a cause for concern. These state- and subpopulation-level differences highlight the need for state-level policies and interventions to address disparities in diabetes health care use., (© 2022 by the American Diabetes Association.)

Objective: Many patients with hyperglycemic crises present with combined features of diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS). The implications of concomitant acidosis and hyperosmolality are not well known. We investigated hospital outcomes in patients with isolated or combined hyperglycemic crises., Research Design and Methods: We analyzed admissions data listing DKA or HHS at two academic hospitals. We determined 1 ) the frequency distributions of HHS, DKA, and combined DKA-HHS (DKA criteria plus elevated effective osmolality); 2 ) the relationship of markers of severity of illness and clinical comorbidities with 30-day all-cause mortality; and 3 ) the relationship of hospital complications associated with insulin therapy (hypoglycemia and hypokalemia) with mortality., Results: There were 1,211 patients who had a first admission with confirmed hyperglycemic crises criteria, 465 (38%) who had isolated DKA, 421 (35%) who had isolated HHS, and 325 (27%) who had combined features of DKA-HHS. After adjustment for age, sex, BMI, race, and Charlson Comorbidity Index score, subjects with combined DKA-HHS had higher in-hospital mortality compared with subjects with isolated hyperglycemic crises (adjusted odds ratio [aOR] 2.7; 95% CI 1.4, 4.9; P = 0.0019). In all groups, hypoglycemia (<40 mg/dL) during treatment was associated with a 4.8-fold increase in mortality (aOR 4.8; 95% CI 1.4, 16.8). Hypokalemia ≤3.5 mEq/L was frequent (55%). Severe hypokalemia (≤2.5 mEq/L) was associated with increased inpatient mortality (aOR 4.9; 95% CI 1.3, 18.8; P = 0.02)., Conclusions: Combined DKA-HHS is associated with higher mortality compared with isolated DKA or HHS. Severe hypokalemia and severe hypoglycemia are associated with higher hospital mortality in patients with hyperglycemic crises., (© 2019 by the American Diabetes Association.)

Objective: Understanding the real-world impacts of lifestyle modification (LSM) for diabetes prevention is imperative to inform resource allocation. The purpose of this study was to synthetize global evidence on the impact of LSM strategies on diabetes incidence and risk factors in one parsimonious model., Research Design and Methods: PubMed, Embase, Cochrane Library, and ClinicalTrials.gov were searched for studies published between January 1990 and April 2015. Effectiveness/translation studies of any design testing LSM strategies, targeting high-risk populations (with prediabetes or diabetes risk factors), and reporting diabetes incidence, weight, or glucose outcomes were included. We extracted number of diabetes cases/incidence rates and mean changes in weight (kg), fasting blood glucose (FBG, mmol/L), 2-h postload glucose (mmol/L), and hemoglobin A 1c (%). Pairwise random-effects and frequentist random-effects network meta-analyses were used to obtain pooled effects., Results: Sixty-three studies were pooled in the meta-analysis ( n = 17,272, mean age 49.7 years, 28.8% male, 60.8% white/European). In analyses restricted to controlled studies ( n = 7), diabetes cumulative incidence was 9% among intervention participants and 12% among control participants (absolute risk reduction 3%; relative risk 0.71 [95% CI 0.58, 0.88]). In analyses combining controlled and uncontrolled studies ( n = 14), participants receiving group education by health care professionals had 33% lower diabetes odds than control participants (odds ratio 0.67 [0.49, 0.92]). Intervention participants lost 1.5 kg more weight [-2.2, -0.8] and achieved a 0.09 mmol/L greater FBG decrease [-0.15, -0.03] than control participants. Every additional kilogram lost by participants was associated with 43% lower diabetes odds (β = 0.57 [0.41, 0.78])., Conclusions: Real-world LSM strategies can reduce diabetes risk, even with small weight reductions., (© 2018 by the American Diabetes Association.)