Theodore J. Thompson

The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study.

BACKGROUND Intensive lifestyle interventions can reduce the incidence of type 2 diabetes in people with impaired glucose tolerance, but how long these benefits extend beyond the period of active intervention, and whether such interventions reduce the risk of cardiovascular disease (CVD) and mortality, is unclear. We aimed to assess whether intensive lifestyle interventions have a long-term effect on the risk of diabetes, diabetes-related macrovascular and microvascular complications, and mortality. METHODS In 1986, 577 adults with impaired glucose tolerance from 33 clinics in China were randomly assigned to either the control group or to one of three lifestyle intervention groups (diet, exercise, or diet plus exercise). Active intervention took place over 6 years until 1992. In 2006, study participants were followed-up to assess the long-term effect of the interventions. The primary outcomes were diabetes incidence, CVD incidence and mortality, and all-cause mortality. FINDINGS Compared with control participants, those in the combined lifestyle intervention groups had a 51% lower incidence of diabetes (hazard rate ratio [HRR] 0.49; 95% CI 0.33-0.73) during the active intervention period and a 43% lower incidence (0.57; 0.41-0.81) over the 20 year period, controlled for age and clustering by clinic. The average annual incidence of diabetes was 7% for intervention participants versus 11% in control participants, with 20-year cumulative incidence of 80% in the intervention groups and 93% in the control group. Participants in the intervention group spent an average of 3.6 fewer years with diabetes than those in the control group. There was no significant difference between the intervention and control groups in the rate of first CVD events (HRR 0.98; 95% CI 0.71-1.37), CVD mortality (0.83; 0.48-1.40), and all-cause mortality (0.96; 0.65-1.41), but our study had limited statistical power to detect differences for these outcomes. INTERPRETATION Group-based lifestyle interventions over 6 years can prevent or delay diabetes for up to 14 years after the active intervention. However, whether lifestyle intervention also leads to reduced CVD and mortality remains unclear.

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

Impact of Recent Increase in Incidence on Future Diabetes Burden: U.S., 2005–2050

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

Body weight and obesity in adults and self-reported abuse in childhood

In an earlier study, we had forecasted 39 million with diagnosed diabetes in 2050 in the U.S. (1,2). However, since then, national diabetes incidence increased (3) and the relative risk of death among people with diabetes declined (4,5). These changes will impact future forecasts. Incorporating these changes, we now project 48.3 million people with diagnosed diabetes in the U.S. in 2050. We also present age-, sex-, and race/ethnicity-specific forecasts, with Bayesian CIs, of the number of people with diagnosed diabetes through 2050. We used a discrete-time (1-year intervals), incidence-based Markov model with three states (no diagnosed diabetes, diagnosed diabetes, and death) (1). In each cycle of the model, projections are developed for 808 population subgroups defined by age, sex, and race/ethnicity. We estimated the age-, sex-, and race/ethnicity-specific prevalence and incidence of diabetes from the U.S. National Health Interview Survey (6–9) and modeled data for 1984–2004 to improve the precision of 2004 estimates. Models were fit using Bayesian methods with improper flat priors applied to logistic regression. We assessed adequacy of model fit using posterior predictive P values (10). Estimated prevalence of diagnosed diabetes for 2000 and 2004 were 4.35 and 5.37%, respectively, and estimated incidence were 0.42 and 0.53% per year, respectively. The age-, sex-, and race/ethnicity-specific 2004 prevalence estimates were combined with U.S. population data for 2004 (11 …

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

BACKGROUND: Little is known about childhood factors and adult obesity. A previous study found a strong association between childhood neglect and obesity in young adults.OBJECTIVE: To estimate associations between self-reported abuse in childhood (sexual, verbal, fear of physical abuse and physical) adult body weight, and risk of obesity.DESIGN: Retrospective cohort study with surveys during 1995–1997.PATIENTS: A total of 13 177 members of California health maintenance organization aged 19–92 y.MEASUREMENTS: Body weight measured during clinical examination, followed by mailed survey to recall experiences during first 18 y of life. Estimates adjusted for adult demographic factors and health practices, and characteristics of the childhood household.RESULTS: Some 66% of participants reported one or more type of abuse. Physical abuse and verbal abuse were most strongly associated with body weight and obesity. Compared with no physical abuse (55%), being ‘often hit and injured’ (2.5%) had a 4.0 kg (95% confidence interval: 2.4–5.6 kg) higher weight and a 1.4 (1.2–1.6) relative risk (RR) of body mass index (BMI)≥30. Compared with no verbal abuse (53%), being ‘often verbally abused’ (9.5%) had an RR of 1.9 (1.3–2.7) for BMI≥40. The abuse associations were not mutually independent, however, because the abuse types strongly co-occurred. Obesity risk increased with number and severity of each type of abuse. The population attributable fraction for ‘any mention’ of abuse (67%) was 8% (3.4–12.3%) for BMI≥30 and 17.3% (−1.0–32.4%) for BMI≥40.CONCLUSIONS: Abuse in childhood is associated with adult obesity. If causal, preventing child abuse may modestly decrease adult obesity. Treatment of obese adults abused as children may benefit from identification of mechanisms that lead to maintenance of adult obesity.

Prevalence and Incidence Trends for Diagnosed Diabetes Among Adults Aged 20 to 79 Years, United States, 1980-2012

OBJECTIVE Nearly two decades ago, the National Diabetes Data Group (NDDG) and the World Health Organization (WHO) Expert Committee on Diabetes Mellitus published diagnostic criteria for diabetes. We undertook this study to compare the performance of three glycemic measures for diagnosing diabetes and to evaluate the performance of the WHO criteria. RESEARCH DESIGN AND METHODS In a cross-sectional population-based sample of 1,018 Egyptians ≥ 20 years of age, fasting and 2-h glucose and HbA1c levels were measured, and diabetic retinopathy was assessed by retinal photograph. Evidence for bimodal distributions was examined for each glycemic measure by fitting models for the mixture of two distributions using maximum likelihood estimates. Sensitivity and specificity for cutpoints of each glycemic measure were calculated by defining the true diabetes state (gold standard) as 1) the upper (diabetic) component of the fitted bimodal distribution for each glycemic measure, and 2) the presence of diabetic retinopathy. Receiver operating characteristic (ROC) curves were constructed to determine the performance of the glycemic measures in detecting diabetes as defined by diabetic retinopathy. RESULTS In the total population, the point of intersection of the lower and upper components that minimized misclassification for the fasting and 2-h glucose and HbA1c were 7.2 mmol/l (129 mg/dl), 11.5 mmol/l (207 mg/dl), and 6.7%, respectively. When diabetic retinopathy was used to define diabetes, ROC curve analyses found that fasting and 2-h glucose values were superior to HbA1c (P < 0.01). The performance of a fasting glucose of 7.8 mmol/l (140 mg/dl) was similar to a 2-h glucose of 12.2–12.8 mmol/l (220–230 mg/dl), and the performance of a 11.1 mmol/l (200 mg/dl) 2-h glucose was similar to a fasting glucose of 6.9–7.2 mmol/l (125–130 mg/dl). CONCLUSIONS Optimal cutpoints for defining diabetes differ according to how diabetes itself is defined. When diabetes is defined as the upper component of the bimodal population distribution, a fasting glucose level somewhat lower than the current WHO cutpoint and a 2-h glucose level somewhat higher than the current WHO cutpoint minimized misclassification. When diabetic retinopathy defines diabetes, we found that the current fasting diagnostic criterion favors specificity and the current 2-h criterion favors sensitivity. These results should prove valuable for defining the optimal tests and cutpoint values for diagnosing diabetes.

Intentional Weight Loss and Death in Overweight and Obese U.S. Adults 35 Years of Age and Older

IMPORTANCE Although the prevalence and incidence of diabetes have increased in the United States in recent decades, no studies have systematically examined long-term, national trends in the prevalence and incidence of diagnosed diabetes. OBJECTIVE To examine long-term trends in the prevalence and incidence of diagnosed diabetes to determine whether there have been periods of acceleration or deceleration in rates. DESIGN, SETTING, AND PARTICIPANTS We analyzed 1980-2012 data for 664,969 adults aged 20 to 79 years from the National Health Interview Survey (NHIS) to estimate incidence and prevalence rates for the overall civilian, noninstitutionalized, US population and by demographic subgroups (age group, sex, race/ethnicity, and educational level). MAIN OUTCOMES AND MEASURES The annual percentage change (APC) in rates of the prevalence and incidence of diagnosed diabetes (type 1 and type 2 combined). RESULTS The APC for age-adjusted prevalence and incidence of diagnosed diabetes did not change significantly during the 1980s (for prevalence, 0.2% [95% CI, -0.9% to 1.4%], P = .69; for incidence, -0.1% [95% CI, -2.5% to 2.4%], P = .93), but each increased sharply during 1990-2008 (for prevalence, 4.5% [95% CI, 4.1% to 4.9%], P < .001; for incidence, 4.7% [95% CI, 3.8% to 5.6%], P < .001) before leveling off with no significant change during 2008-2012 (for prevalence, 0.6% [95% CI, -1.9% to 3.0%], P = .64; for incidence, -5.4% [95% CI, -11.3% to 0.9%], P = .09). The prevalence per 100 persons was 3.5 (95% CI, 3.2 to 3.9) in 1990, 7.9 (95% CI, 7.4 to 8.3) in 2008, and 8.3 (95% CI, 7.9 to 8.7) in 2012. The incidence per 1000 persons was 3.2 (95% CI, 2.2 to 4.1) in 1990, 8.8 (95% CI, 7.4 to 10.3) in 2008, and 7.1 (95% CI, 6.1 to 8.2) in 2012. Trends in many demographic subpopulations were similar to these overall trends. However, incidence rates among non-Hispanic black and Hispanic adults continued to increase (for interaction, P = .03 for non-Hispanic black adults and P = .01 for Hispanic adults) at rates significantly greater than for non-Hispanic white adults. In addition, the rate of increase in prevalence was higher for adults who had a high school education or less compared with those who had more than a high school education (for interaction, P = .006 for <high school and P < .001 for high school). CONCLUSIONS AND RELEVANCE Analyses of nationally representative data from 1980 to 2012 suggest a doubling of the incidence and prevalence of diabetes during 1990-2008, and a plateauing between 2008 and 2012. However, there appear to be continued increases in the prevalence or incidence of diabetes among subgroups, including non-Hispanic black and Hispanic subpopulations and those with a high school education or less.

Projections of type 1 and type 2 diabetes burden in the U.S. population aged <20 years through 2050: dynamic modeling of incidence, mortality, and population growth.

Context Although being overweight is associated with many adverse health outcomes, observational studies of weight loss show associations between weight loss and increased mortality. This may be because these studies did not distinguish between intentional and unintentional weight loss. Contribution In a national survey of 6391 U.S. adults, people who were trying to lose weight had decreased mortality whether they lost weight or not. Lowest mortality was associated with modest intentional weight loss. People who lost weight unintentionally had increased mortality. Implications Weight loss has adverse associations with mortality only if it is unintentional. Trying to lose weight may have benefit even if people do not actually lose weight. The Editors Despite the many health risks associated with being overweight (1, 2), the effect of weight loss on long-term health and longevity remains controversial (3-5). Randomized clinical studies in overweight persons have shown that weight loss leads to short-term improvements in physiologic risk factors (6) and can prevent or delay hypertension and diabetes (7-9). However, only observational studies have examined the effect of weight loss on mortality rates (3-5) and have typically found weight loss to be associated with increased rather than decreased mortality (3-5, 10, 11). Most observational studies examining weight change and subsequent mortality rates have not assessed weight loss intention. The few studies that have tried to differentiate between the effects of intentional and unintentional weight loss have yielded mixed results (12-18). The Cancer Prevention Study I and the Malm Prevention Study found that intentional weight loss was associated with reduced mortality rates among persons with diabetes (17), impaired glucose tolerance (18), and other health conditions (12, 16). Other studies, however, have found no effect of intentional weight loss on mortality rates (12-16). Another concern is that self-identified intention to lose weight may indicate a healthy lifestyle or better access to health care rather than a biologically protective effect of weight loss. In 1989, part of the National Health Interview Survey, a nationally representative sample of the U.S. population (19), examined intent to lose weight and self-reported weight change. Death among members of this sample was followed through 1997 (20). Using these data, we estimated the association of weight change and intention to lose weight with all-cause mortality among overweight and obese U.S. adults. Methods Study Design and Study Sample The National Health Interview Survey is an ongoing nationwide survey of health status, conditions, and behaviors among the U.S. noninstitutionalized population (19, 20). The survey uses a multistage, probability-sampling strategy to select approximately 45 000 households and 120 000 persons yearly. Data are weighted to match the age, sex, and ethnicity distributions of the U.S. population and to account for survey nonresponse. In this study, we used data from a supplemental survey conducted in 1989 that assessed intentional weight loss in a random subsample of 20 847 adults older than 18 years of age (19). Sufficient data were available to link 20 439 respondents (98%) to the National Death Index, providing follow-up of vital status through December 1997 (up to 9 years). At this time, all survivors were censored (20). The National Death Index is a computerized database with standard identifying information about virtually all deaths in the United States and has been shown to have a high rate of ascertainment (21). We used an algorithm provided by the National Center for Health Statistics to determine which matches should be classified as deaths (20). Of the 20 439 persons for whom we had both survey and vital status information, we excluded 11 642 whose body mass index (BMI) was less than 25 kg/m2 before weight loss, since weight loss is not typically indicated for such persons. We also excluded 2328 persons younger than 35 years of age because the mortality rate in this group is extremely low (1.5 deaths per 1000 persons per year compared with 15.4 deaths per 1000 persons per year for those >35 years of age; hazard rate ratio [HRR], 0.10 [95% CI, 0.06 to 0.15]). Finally, we excluded 78 persons with missing data on weight loss or other covariates, leaving 6391 overweight and obese persons for the analyses. Measurements Interviewers determined age, ethnicity, sex, smoking status, self-rated health (on a five-point scale from excellent to poor), hospitalizations in the past year, physician visits, days spent in bed during the past year, and chronic and acute conditions that caused hospitalizations or days spent in bed. Participants were also asked whether they were limited in any activities or work because of an impairment or health problem; if they answered yes, they were asked to report the primary and secondary limiting health conditions that led to the impairment. Self-reported height and weight were used to compute BMI. To assess intentional weight loss, participants were asked, Have you tried to lose weight in the past year?; Is your weight now more, less, or about the same as a year ago?; and In the past year, about how much have you gained or lost? Statistical Analyses Chi-square and analysis of variance tests were used to compare study covariates at baseline across weight loss intent and weight change groups. We used Cox proportional-hazards regression analyses to determine the mortality HRR associated with weight loss intention and weight change while adjusting for potentially confounding variables. The HRR is the ratio of two rates of disease or mortality occurrence. It is a relative measure of how rapidly cases of disease or death occur in a group with the risk factor compared with the group without the risk factor. We found no violation of the statistical assumptions underlying the proportional hazards regression in graphical or statistical examinations. Similarly, we found no problematic influential data points or multicollinearity. We also computed predictive margins to estimate the multivariate-adjusted 8-year cumulative hazard rate (equivalent to the mean follow-up) for each weight loss intent and weight change group (22). Predictive margins are a type of direct standardization in which predicted values from the Cox proportional-hazards regression models are averaged over the covariate distribution in the sample. Because we found a statistically significant interaction between weight loss intent and weight change, we stratified weight change according to whether persons reported trying to lose weight. Multivariate models controlled for age, ethnicity, sex, smoking, education, initial BMI, measures of health status (self-rated health and days spent in bed during the past year), diabetes (none, noninsulin treated, insulin treated), cardiovascular disease or cancer cited as a cause of functional limitation, number of acute and chronic conditions, and measures of health care utilization (hospitalizations and physician visits during the past year). We also evaluated models that excluded smokers, since smokers are at increased risk for death and may be more likely to lose weight. We tested for interactions between weight loss intent and weight loss group and age (35 to 64 years vs. 65 years), sex, and BMI (<30 kg/m2 vs. 30 kg/m2) to determine the association with mortality. We computed poststratification weights to account for the 78 missing participants and conducted analyses using SUDAAN, version 7.5.4a (Research Triangle Institute, Research Triangle Park, North Carolina), to make the study estimates statistically representative of the U.S. noninstitutionalized population of overweight and obese adults 35 years of age and older. SUDAAN uses Taylor series linearization to estimate variances, allowing analyses to account for the unequal weighting, stratification, and clustering. Role of the Funding Source The authors analyzed the data and wrote this article as employees of the U.S. Centers for Disease Control and Prevention. The Centers for Disease Control and Prevention was therefore involved in the analysis and interpretation of the data and in the decision to submit the paper for publication. Results Fifty-eight percent, 12%, and 30% of participants reported no weight change, weight gain, and weight loss, respectively (Table 1). Compared with persons reporting stable weight, those who reported gaining or losing weight were more likely to be women, to report worse overall health and more days in bed, and to have had functional limitations, physician visits, and hospitalizations during the previous year. Persons who lost weight also had higher baseline BMIs and were more likely to be smokers, to have diabetes, and to have been hospitalized during the previous year than persons with stable weight or weight gain. Table 1. Characteristics of 6391 Overweight Persons, according to Weight Loss Intention and Reported Weight Change Fifty-eight percent of the study participants reported trying to lose weight during the previous year (Table 1). Persons who were trying to lose weight reported more weight loss than those who were not trying to lose weight (P < 0.001), but the magnitude of this difference was small (median weight change, 0.4 kg vs. 0.3 kg). Compared with persons who were not trying to lose weight, those who reported attempted weight loss were more likely to be women and to have diabetes, were younger, were more likely to be of white ethnicity and nonsmokers, had higher BMIs, and were less likely to report fair or poor health. Persons attempting weight loss also reported more physician visits and more days in bed during the previous year than those who were not trying to lose weight. During 9 years of follow-up (mean, 8.0 years), 892 participants died (age-adjusted rate, 17.8 deaths per 1000 person-years). Compared with persons with no weight change, pers

A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes

OBJECTIVE To forecast the number of U.S. individuals aged <20 years with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) through 2050, accounting for changing demography and diabetes incidence. RESEARCH DESIGN AND METHODS We used Markov modeling framework to generate yearly forecasts of the number of individuals in each of three states (diabetes, no diabetes, and death). We used 2001 prevalence and 2002 incidence of T1DM and T2DM from the SEARCH for Diabetes in Youth study and U.S. Census Bureau population demographic projections. Two scenarios were considered for T1DM and T2DM incidence: 1) constant incidence over time; 2) for T1DM yearly percentage increases of 3.5, 2.2, 1.8, and 2.1% by age-groups 0–4 years, 5–9 years, 10–14 years, and 15–19 years, respectively, and for T2DM a yearly 2.3% increase across all ages. RESULTS Under scenario 1, the projected number of youth with T1DM rises from 166,018 to 203,382 and with T2DM from 20,203 to 30,111, respectively, in 2010 and 2050. Under scenario 2, the number of youth with T1DM nearly triples from 179,388 in 2010 to 587,488 in 2050 (prevalence 2.13/1,000 and 5.20/1,000 [+144% increase]), with the greatest increase in youth of minority racial/ethnic groups. The number of youth with T2DM almost quadruples from 22,820 in 2010 to 84,131 in 2050; prevalence increases from 0.27/1,000 to 0.75/1,000 (+178% increase). CONCLUSIONS A linear increase in diabetes incidence could result in a substantial increase in the number of youth with T1DM and T2DM over the next 40 years, especially those of minority race/ethnicity.

The Association between Quality of Care and the Intensity of Diabetes Disease Management Programs

OBJECTIVE To develop a simple questionnaire to prospectively identify individuals at increased risk for undiagnosed diabetes. RESEARCH DESIGN AND METHODS People with newly diagnosed diabetes (n = 164) identified in the Second National Health and Nutrition Examination Survey and those with neither newly diagnosed diabetes nor a history of physician-diagnosed diabetes (n = 3,220) were studied. Major historical risk factors for undiagnosed non-insulin-dependent diabetes were defined, and classification trees were developed to identify people at higher risk for previously undiagnosed diabetes. The sensitivity, specificity, and predictive value of the classification trees were described and compared with those of an existing questionnaire. RESULTS The selected classification tree incorporated age, sex, history of delivery of a macrosomic infant, obesity, sedentary lifestyle, and family history of diabetes. In a representative sample of the U.S. population, the sensitivity of the tree was 79%, the specificity was 65%, and the predictive value positive was 10%. CONCLUSIONS This classification tree performed significantly better than an existing questionnaire and should serve as a simple, noninvasive, and potentially cost-effective tool for diagnosing diabetes in the U.S.