William H Herman

University of Michigan, Ann Arbor, Michigan, United States

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Publications (291)1741.17 Total impact

  • Keiko Asao, Laura N. McEwen, Joyce M. Lee, William H. Herman
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    ABSTRACT: To estimate and evaluate the sensitivity and specificity of providers' diagnosis codes and medication lists to identify outpatient visits by patients with diabetes. We used data from the 2006 to 2010 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. We assessed the sensitivity and specificity of providers' diagnoses and medication lists to identify patients with diabetes, using the checkbox for diabetes as the gold standard. We then examined differences in sensitivity by patients' characteristics using multivariate logistic regression models. The checkbox identified 12,647 outpatient visits by adults with diabetes among the 70,352 visits used for this analysis. The sensitivity and specificity of providers' diagnoses or listed diabetes medications were 72.3% (95% CI: 70.8% to 73.8%) and 99.2% (99.1% to 99.4%), respectively. Diabetic patients ≥75years of age, women, non-Hispanics, and those with private insurance or Medicare were more likely to be missed by providers' diagnoses and medication lists. Diabetic patients who had more diagnosis codes and medications recorded, had glucose or hemoglobin A1c measured, or made office- rather than hospital-outpatient visits were less likely to be missed. Providers' diagnosis codes and medication lists fail to identify approximately one quarter of outpatient visits by patients with diabetes. Copyright © 2015. Published by Elsevier Inc.
    Journal of diabetes and its complications 04/2015; DOI:10.1016/j.jdiacomp.2015.03.019 · 1.93 Impact Factor
  • William H Herman, William T Cefalu
    Diabetes care 03/2015; DOI:10.2337/dc15-0348 · 8.57 Impact Factor
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    ABSTRACT: Context: Gestational diabetes (GDM) confers a high risk of type 2 diabetes. In the Diabetes Prevention Program (DPP), intensive lifestyle (ILS) and metformin prevented or delayed diabetes in women with a history of GDM. Objective: The objective of the study was to evaluate the impact of ILS and metformin intervention over 10 years in women with and without a history of GDM in the DPP/Diabetes Prevention Program Outcomes Study. Design: This was a randomized controlled clinical trial with an observational follow-up. Setting: The study was conducted at 27 clinical centers. Participants: Three hundred fifty women with a history of GDM and 1416 women with previous live births but no history of GDM participated in the study. The participants had an elevated body mass index and fasting glucose and impaired glucose tolerance at study entry. Interventions: Interventions included placebo, ILS, or metformin. Outcomes Measure: Outcomes measure was diabetes mellitus. Results: Over 10 years, women with a history of GDM assigned to placebo had a 48% higher risk of developing diabetes compared with women without a history of GDM. In women with a history of GDM, ILS and metformin reduced progression to diabetes compared with placebo by 35% and 40%, respectively. Among women without a history of GDM, ILS reduced the progression to diabetes by 30%, and metformin did not reduce the progression to diabetes. Conclusions: Women with a history of GDM are at an increased risk of developing diabetes. In women with a history of GDM in the DPP/Diabetes Prevention Program Outcomes Study, both lifestyle and metformin were highly effective in reducing progression to diabetes during a 10-year follow-up period. Among women without a history of GDM, lifestyle but not metformin reduced progression to diabetes.
    Journal of Clinical Endocrinology &amp Metabolism 02/2015; DOI:10.1210/jc.2014-3761 · 6.31 Impact Factor
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    ABSTRACT: Most Americans see dentists at least once a year. Chair-side screening and referral may improve diagnosis of prediabetes and diabetes. In this study, we developed a multivariate model to screen for dysglycemia (prediabetes and diabetes defined as HbA1c ≥5.7 percent) using information readily available to dentists and assessed the prevalence of dysglycemia in general dental practices. We recruited 1,033 adults ≥30 years of age without histories of diabetes from 13 general dental practices. A sample of 181 participants selected on the basis of random capillary glucose levels and periodontal status underwent definitive diagnostic testing with hemoglobin A1c. Logistic models were fit to identify risk factors for dysglycemia, and sample weights were applied to estimate the prevalence of dysglycemia in the population ≥30 years of age. Individuals at high risk for dysglycemia could be identified using a questionnaire that assessed sex, history of hypertension, history of dyslipidemia, history of lost teeth, and either self-reported body mass index ≥35 kg/m(2) (severe obesity) or random capillary glucose ≥110 mg/dl. We estimate that 30 percent of patients ≥30 years of age seen in these general dental practices had dysglycemia. There is a substantial burden of dysglycemia in patients seen in general dental practices. Simple chair-side screening for dysglycemia that includes or does not include fingerstick random capillary glucose testing can be used to rapidly identify high-risk patients. Further studies are needed to demonstrate the acceptability, feasibility, effectiveness, and cost-effectiveness of chair-side screening. © 2015 American Association of Public Health Dentistry.
    Journal of Public Health Dentistry 02/2015; DOI:10.1111/jphd.12082 · 1.64 Impact Factor
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    ABSTRACT: Adults with diabetes typically take multiple medications for hyperglycemia, diabetes-associated conditions, and other comorbidities. Medication adherence is associated with improved outcomes, including reduced health care costs, hospitalization, and mortality. We conducted a retrospective analysis of a large pharmacy claims database to examine patient, medication, and prescriber factors associated with adherence to antidiabetic medications. We extracted data on a cohort of >200,000 patients who were treated for diabetes with noninsulin medications in the second half of 2010 and had continuous prescription benefits eligibility through 2011. Adherence was defined as a medication possession ratio ≥0.8. We used a modified adherence measure that accounted for switching therapies. Logistic regression analysis was performed to determine factors independently associated with adherence. Sixty-nine percent of patients were adherent. Adherence was independently associated with older age, male sex, higher education, higher income, use of mail order versus retail pharmacies, primary care versus nonendocrinology specialist prescribers, higher daily total pill burden, and lower out-of-pocket costs. Patients who were new to diabetes therapy were significantly less likely to be adherent. Several demographic, clinical, and potentially modifiable system-level factors were associated with adherence to antidiabetic medications. Patients typically perceived to be healthy (those who are younger, new to diabetes, and on few other medications) may be at risk for nonadherence. For all patients, efforts to reduce out-of-pocket costs and encourage use of mail order pharmacies may result in higher adherence. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
    Diabetes Care 01/2015; DOI:10.2337/dc14-2098 · 8.57 Impact Factor
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    ABSTRACT: Type 1 diabetes has been associated with an elevated relative risk (RR) of mortality compared to the general population. To review published studies on the RR of mortality of Type 1 diabetes patients compared to the general population, we conducted a meta-analysis and examined the temporal changes in the RR of mortality over time. Systematic review of studies reporting RR of mortality for Type 1 diabetes compared to the general population. We conducted meta-analyses using a DerSimonian and Laird random effects model to obtain the average effect and the distribution of RR estimates. Sub-group meta-analyses and multivariate meta-regression analysis was performed to examine heterogeneity. Summary RR with 95% CIs was calculated using a random-effects model. 26 studies with a total of 88 subpopulations were included in the meta-analysis and overall RR of mortality was 3.82 (95% CI 3.41, 3.4.29) compared to the general population. Observations using data prior to 1971 had a much larger estimated RR (5.80 (95% CI 4.20, 8.01)) when compared to: data between; 1971 and 1980 (5.06 (95% CI 3.44, 7.45)); 1981-90 (3.59 (95% CI 3.15, 4.09)); and those after 1990 (3.11 (95% CI 2.47, 3.91)); suggesting mortality of Type 1 diabetes patients when compared to the general population have been improving over time. Similarly, females (4.54 (95% CI 3.79-5.45)) had a larger RR estimate when compared to males (3.25 (95% CI 2.82-3.73) and the meta-regression found evidence for temporal trends and sex (p<0.01) accounting for heterogeneity between studies. Type 1 diabetes patients' mortality has declined at a faster rate than the general population. However, the largest relative improvements have occurred prior to 1990. Emphasis on intensive blood glucose control alongside blood pressure control and statin therapy may translate into further reductions in mortality in coming years.
    PLoS ONE 11/2014; 9(11):e113635. DOI:10.1371/journal.pone.0113635 · 3.53 Impact Factor
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    ABSTRACT: Objective To describe patient and provider characteristics associated with outpatient revisit frequency and to examine the associations between the revisit frequency and the processes and intermediate outcomes of diabetes care. Research Design and Methods We analyzed data from Translating Research Into Action for Diabetes (TRIAD), a prospective, multicenter, observational study of diabetes care in managed care. Results Our analysis included 6,040 eligible adult participants with type 2 diabetes (42.6% ≥ 65 years of age, 54.1% female) whose primary care providers were the main provider of the participants’ diabetes care. The median (interquartile range) revisit frequency was 4.0 (3.7, 6.0) visits per year. Being female, having lower education, lower income, more complex diabetes treatment, cardiovascular disease, higher Charlson comorbidity index, and impaired mobility were associated with higher revisit frequency. The proportion of participants who had annual assessments of HbA1c and LDL-cholesterol, foot examinations, advised or documented aspirin use, and influenza immunizations were higher for those with higher revisit frequency. The proportion of participants who met HbA1c (< 9.5%) and LDL-cholesterol (< 130 mg/dL) treatment goals was higher for those with a higher revisit frequency. The predicted probabilities of achieving more aggressive goals, HbA1c < 8.5%, LDL-cholesterol < 100 mg/dL, and blood pressure < 130/85 or even < 140/90 mmHg were not associated with higher revisit frequency. Conclusions Revisit frequency was highly variable and was associated with both sociodemographic characteristics and disease severity. A higher revisit frequency was associated with better processes of diabetes care, but the association with intermediate outcomes was less clear.
    Journal of Diabetes and its Complications 11/2014; 28(6). DOI:10.1016/j.jdiacomp.2014.06.006 · 1.93 Impact Factor
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    ABSTRACT: Preconception care for women with diabetes can reduce the occurrence of adverse birth outcomes. We aimed to estimate the preconception care (PCC)-preventable health and cost burden of adverse birth outcomes associated with diagnosed and undiagnosed pregestational diabetes mellitus (PGDM) in the United States. Among women of reproductive age (15-44 years), we estimated age- and race/ethnicity-specific prevalence of diagnosed and undiagnosed diabetes. We applied age and race/ethnicity-specific pregnancy rates, estimates of the risk reduction from PCC for 3 adverse birth outcomes (preterm birth, major birth defects, and perinatal mortality), and lifetime medical and lost productivity costs for children with those outcomes. Using a probabilistic model, we estimated the reduction in adverse birth outcomes and costs associated with universal PCC compared with no PCC among women with PGDM. We did not assess maternal outcomes and associated costs. We estimated 2.2% of US births are to women with PGDM. Among women with diagnosed diabetes, universal PCC might avert 8397 (90% prediction interval [PI], 5252-11,449) preterm deliveries, 3725 (90% PI, 3259-4126) birth defects, and 1872 (90% PI, 1239-2415) perinatal deaths annually. Associated discounted lifetime costs averted for the affected cohort of children could be as high as $4.3 billion (90% PI, 3.4-5.1 billion) (2012 US dollars). PCC among women with undiagnosed diabetes could yield an additional $1.2 billion (90% PI, 951 million-1.4 billion) in averted cost. Results suggest a substantial health and cost burden associated with PGDM that could be prevented by universal PCC, which might offset the cost of providing such care. Copyright © 2014 Elsevier Inc. All rights reserved.
    American Journal of Obstetrics and Gynecology 10/2014; DOI:10.1016/j.ajog.2014.09.009 · 3.97 Impact Factor
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    ABSTRACT: OBJECTIVE Glycated hemoglobin (HbA(1c)), a standard measure of chronic glycemia for managing diabetes, has been proposed to diagnose diabetes and identify people at risk. The Diabetes Prevention Program (DPP) was a 3.2-year randomized clinical trial of preventing type 2 diabetes with a 10-year follow-up study, the DPP Outcomes Study (DPPOS). We evaluated baseline HbA(1c) as a predictor of diabetes and determined the effects of treatments on diabetes defined by an HbA(1c) >= 6.5% (48 mmol/mol). RESEARCH DESIGN AND METHODS We randomized 3,234 nondiabetic adults at high risk of diabetes to placebo, metformin, or intensive lifestyle intervention and followed them for the development of diabetes as diagnosed by fasting plasma glucose (FPG) and 2-h postload glucose (2hPG) concentrations (1997 American Diabetes Association [ADA] criteria). HbA(1c) was measured but not used for study eligibility or outcomes. We now evaluate treatment effects in the 2,765 participants who did not have diabetes at baseline according to FPG, 2hPG, or HbA(1c) (2010 ADA criteria). RESULTS Baseline HbA(1c) predicted incident diabetes in all treatment groups. Diabetes incidence defined by HbA(1c) >= 6.5% was reduced by 44% by metformin and 49% by lifestyle during the DPP and by 38% bymetformin and 29% by lifestyle throughout follow-up. Unlike the primary DPP and DPPOS findings based on glucose criteria, metformin and lifestyle were similarly effective in preventing diabetes defined by HbA(1c). CONCLUSIONS HbA(1c) predicted incident diabetes. In contrast to the superiority of the lifestyle intervention on glucose-defined diabetes, metformin and lifestyle interventions had similar effects in preventing HbA(1c)-defined diabetes. The long-term implications for other health outcomes remain to be determined.
    Diabetes Care 10/2014; 38(1). DOI:10.2337/dc14-0886 · 8.57 Impact Factor
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    ABSTRACT: Previous studies have demonstrated lower prostate-specific antigen (PSA) concentrations in men with type 2 diabetes (T2DM), paralleling the reported lower prevalence of prostate cancer among diabetic men. Data on PSA in men with type 1 diabetes (T1DM), in whom insulin and obesity profiles differ from those in T2DM, are lacking. The objective of this study was to examine the relationship between long-term glycemic control and PSA in men with T1DM.
    The Journal of Urology 09/2014; 187(4):e33. DOI:10.1016/j.juro.2012.02.124 · 3.75 Impact Factor
  • William H Herman
    Diabetes Care 09/2014; 37(9):2424-6. DOI:10.2337/dc14-1232 · 8.57 Impact Factor
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    ABSTRACT: Objective To evaluate the performance of three alternative methods to identify diabetes in patients visiting Emergency Departments (EDs), and to describe the characteristics of patients with diabetes who are not identified when the alternative methods are used. Research Design and Methods We used data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) 2009 and 2010. We assessed the sensitivity and specificity of using providers’ diagnoses and diabetes medications (both excluding and including biguanides) to identify diabetes compared to using the checkbox for diabetes as the gold standard. We examined the characteristics of patients whose diabetes was missed using multivariate Poisson regression models. Results The checkbox identified 5,567 ED visits by adult patients with diabetes. Compared to the checkbox, the sensitivity was 12.5% for providers’ diagnoses alone, 20.5% for providers’ diagnoses and diabetes medications excluding biguanides, and 21.5% for providers’ diagnoses and diabetes medications including biguanides. The specificity of all three of the alternative methods was > 99%. Older patients were more likely to have diabetes not identified. Patients with self-payment, those who had glucose measured or received IV fluids in the ED, and those with more diagnosis codes and medications, were more likely to have diabetes identified. Conclusions NHAMCS’s providers’ diagnosis codes and medication lists do not identify the majority of patients with diabetes visiting EDs. The newly introduced checkbox is helpful in measuring ED resource utilization by patients with diabetes.
    Journal of diabetes and its complications 09/2014; 28(5). DOI:10.1016/j.jdiacomp.2014.02.005 · 1.93 Impact Factor
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    ABSTRACT: OBJECTIVE To examine whether previous severe hypoglycemic events were associated with the risk of all-cause mortality after major cardiovascular events (myocardial infarction [MI] or stroke) in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS This study is based on data from the Swedish National Diabetes Register linked to patient-level hospital records, prescription data, and death records. We selected patients with type 1 diabetes who visited a clinic during 2002-2010 and experienced a major cardiovascular complication after their clinic visit. We estimated a two-part model for all-cause mortality after a major cardiovascular event: logistic regression for death within the first month and a Cox proportional hazards model conditional on 1-month survival. At age 60 years, 5-year cumulative mortality risk was estimated from the models for patients with and without prior diabetes complications. RESULTS A total of 1,839 patients experienced major cardiovascular events, of whom 403 had previously experienced severe hypoglycemic events and 703 died within our study period. A prior hypoglycemic event was associated with a significant increase in mortality after a cardiovascular event, with hazard ratios estimated at 1.79 (95% CI 1.37-2.35) within the first month and 1.25 (95% CI 1.02-1.53) after 1 month. Patients with prior hypoglycemia had an estimated 5-year cumulative mortality risk of 52.4% (95% CI 45.3-59.5) and 39.8% (95% CI 33.4-46.3) for MI and stroke, respectively. CONCLUSIONS Wehave found evidence that patientswith type 1 diabetes in Sweden with prior severe hypoglycemic events have increased risk of mortality after a cardiovascular event.
    Diabetes Care 08/2014; 37(11). DOI:10.2337/dc14-0405 · 8.57 Impact Factor
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    ABSTRACT: BACKGROUND: High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four cardiometabolic risk factors for all countries and regions from 1980 to 2010. METHODS: We used data for exposure to risk factors by country, age group, and sex from pooled analyses of population-based health surveys. We obtained relative risks for the effects of risk factors on cause-specific mortality from meta-analyses of large prospective studies. We calculated the population attributable fractions for each risk factor alone, and for the combination of all risk factors, accounting for multicausality and for mediation of the effects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specific population attributable fractions by the number of disease-specific deaths. We obtained cause-specific mortality from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all the inputs to the final estimates. FINDINGS: In 2010, high blood pressure was the leading risk factor for deaths due to cardiovascular diseases, chronic kidney disease, and diabetes in every region, causing more than 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths, and high cholesterol for more than 10%. After accounting for multicausality, 63% (10·8 million deaths, 95% CI 10·1-11·5) of deaths from these diseases in 2010 were attributable to the combined effect of these four metabolic risk factors, compared with 67% (7·1 million deaths, 6·6-7·6) in 1980. The mortality burden of high BMI and glucose nearly doubled from 1980 to 2010. At the country level, age-standardised death rates from these diseases attributable to the combined effects of these four risk factors surpassed 925 deaths per 100 000 for men in Belarus, Kazakhstan, and Mongolia, but were less than 130 deaths per 100 000 for women and less than 200 for men in some high-income countries including Australia, Canada, France, Japan, the Netherlands, Singapore, South Korea, and Spain. INTERPRETATION: The salient features of the cardiometabolic disease and risk factor epidemic at the beginning of the 21st century are high blood pressure and an increasing effect of obesity and diabetes. The mortality burden of cardiometabolic risk factors has shifted from high-income to low-income and middle-income countries. Lowering cardiometabolic risks through dietary, behavioural, and pharmacological interventions should be a part of the global response to non-communicable diseases.
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    ABSTRACT: Context: Previous studies have demonstrated lower testosterone concentrations in men with type 2 diabetes mellitus. Data in men with type 1 diabetes mellitus (T1DM) are limited. Objective: To determine prevalence of low testosterone in men with T1DM and identify predisposing factors. Design, Setting, and Participants: This was a cross-sectional study of men with T1DM participating in UroEDIC (n=641), an ancillary study of urologic complications in the Epidemiology of Diabetes Interventions and Complications (EDIC). Main Outcome Measures: Total serum testosterone levels were measured using mass spectrometry and sex hormone binding globulin (SHBG) levels were measured using sandwich immunoassay on samples from EDIC year 17/18. Calculated free testosterone (cFT) was determined using an algorithm incorporating binding constants for albumin and SHBG. Low testosterone was defined as total testosterone <300 mg/dL. Multivariate regression models were used to compare age, body mass index, factors related to diabetes treatment and control, and diabetic complications with testosterone levels. Results: Mean age was 51 years. Sixty-one men (9.5%) had T <300 mg/dL. Decreased testosterone was significantly associated with obesity (p<0.01), older age (p<0.01) and decreased sex hormone-binding globulin (p<0.001). Insulin dose was inversely associated with cFT (p=0.02). Hypertension retained a significant adjusted association with lower testosterone (p=0.05). There was no observed significant relationship between lower testosterone and nephropathy, peripheral neuropathy, and autonomic neuropathy measures. Conclusion: The men with T1DM in the EDIC cohort do not appear to have a high prevalence of androgen deficiency. Risk factors associated with low testosterone levels in this population are similar to the general population.
    Journal of Clinical Endocrinology &amp Metabolism 07/2014; 99(9):jc20141317. DOI:10.1210/jc.2014-1317 · 6.31 Impact Factor
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    ABSTRACT: Background Current approaches to the management of type 2 diabetes focus on the early initiation of novel pharmacologic therapies and bariatric surgery. Objective The purpose of this study was to revisit the use of intensive, outpatient, behavioral weight management programs for the management of type 2 diabetes. Design Prospective observational study of 66 patients with type 2 diabetes and BMI ≥ 32 kg/m2 who enrolled in a program designed to produce 15% weight reduction over 12 weeks using total meal replacement and low- to moderate-intensity physical activity. Results Patients were 53 ± 7 years of age (mean ± SD) and 53% were men. After 12 weeks, BMI fell from 40.1 ± 6.6 to 35.1 ± 6.5 kg/m2. HbA1c fell from 7.4 ± 1.3% to 6.5 ± 1.2% (57.4 ± 12.3 to 47.7 ± 12.9 mmol/mol) in patients with established diabetes: 76% of patients with established diabetes and 100% of patients with newly diagnosed diabetes achieved HbA1c < 7.0% (53.0 mmol/mol). Improvement in HbA1c over 12 weeks was associated with higher baseline HbA1c and greater reduction in BMI. Conclusions An intensive, outpatient, behavioral weight management program significantly improved HbA1c in patients with type 2 diabetes over 12 weeks. The use of such programs should be encouraged among obese patients with type 2 diabetes.
    Journal of Diabetes and its Complications 07/2014; 28(4). DOI:10.1016/j.jdiacomp.2014.03.014 · 1.93 Impact Factor
  • Robert M Cohen, William H Herman
    04/2014; 2(4):264-5. DOI:10.1016/S2213-8587(14)70003-8
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    ABSTRACT: The number of people with diabetes worldwide has more than doubled during the past 20 years. One of the most worrying features of this rapid increase is the emergence of type 2 diabetes in children, adolescents, and young adults. Although the role of traditional risk factors for type 2 diabetes (eg, genetic, lifestyle, and behavioural risk factors) has been given attention, recent research has focused on identifying the contributions of epigenetic mechanisms and the effect of the intrauterine environment. Epidemiological data predict an inexorable and unsustainable increase in global health expenditure attributable to diabetes, so disease prevention should be given high priority. An integrated approach is needed to prevent type 2 diabetes, taking into account its many origins and heterogeneity. Thus, research needs to be directed at improved understanding of the potential role of determinants such as the maternal environment and other early life factors, as well as changing trends in global demography, to help shape disease prevention programmes.
    01/2014; 2(1):56-64. DOI:10.1016/S2213-8587(13)70112-8
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    ABSTRACT: To evaluate the impact of a managed care obesity intervention that requires enrollment in an intensive medical weight management program, a commercial weight loss program, or a commercial pedometer-based walking program to maintain enhanced benefits. Prospective observational study involving 1,138 adults with BMI ≥ 32 kg m(-2) with one or more comorbidities or BMI ≥ 35 kg m(-2) enrolled in a commercial, independent practice association-model health maintenance organization. Body mass index, blood pressure, lipids, HbA1c or fasting glucose, and per-member per-month costs were assessed 1 year before and 1 year after program implementation. Program uptake (90%) and 1 year adherence (79%) were excellent. Enrollees in all three programs exhibited improved clinical outcomes and reduced rates of increase in direct medical costs compared to members who did not enroll in any program. A managed care obesity intervention that offered financial incentives for participation and a variety of programs was associated with excellent program uptake and adherence, improvements in cardiovascular risk factors, and a lower rate of increase in direct medical costs over 1 year.
    Obesity 11/2013; 21(11). DOI:10.1002/oby.20597 · 4.39 Impact Factor
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    ABSTRACT: To assess the impact of weight loss on health-related quality-of-life (HRQL), to describe the factors associated with improvements in HRQL after weight loss, and to assess the relationship between obesity as assessed by body mass index (BMI) and HRQL before and after weight loss. We studied 188 obese patients with BMI ≥ 32 kg/m(2) with one or more comorbidities or ≥35 kg/m(2). All patients had baseline and follow-up assessments of BMI and HRQL using the EuroQol (EQ-5D) and its visual analog scale (VAS) before and after 6 months of medical weight loss that employed very low-calorie diets, physical activity, and intensive behavioral counseling. At baseline, age was 50 ± 8 years (mean ± SD), BMI was 40. 0 ± 5.0 kg/m(2), EQ-5D-derived health utility score was 0.85 ± 0.13, and VAS-reported quality-of-life was 0.67 ± 0.18. At 6-month follow-up, BMI decreased by 7.0 ± 3.2 kg/m(2), EQ-5D increased by 0.06 [interquartile range (IQR) 0.06-0.17], and VAS increased by 0.14 (IQR 0.04-0.23). In multivariate analyses, improvement in EQ-5D and VAS were associated with lower baseline BMI, greater reduction in BMI at follow-up, fewer baseline comorbidities, and lower baseline HRQL. For any given BMI category, EQ-5D and VAS tended to be higher at follow-up than at baseline. Measured improvements in HRQL between baseline and follow-up were greater than predicted by the reduction in BMI at follow-up. If investigators use cross-sectional data to estimate changes in HRQL as a function of BMI, they will underestimate the improvement in HRQL associated with weight loss and underestimate the cost-utility of interventions for obesity treatment.
    Quality of Life Research 10/2013; 23(4). DOI:10.1007/s11136-013-0557-8 · 2.86 Impact Factor

Publication Stats

14k Citations
1,741.17 Total Impact Points


  • 1991–2015
    • University of Michigan
      • • Department of Internal Medicine
      • • Medical School
      • • Division of Metabolism, Endocrinology & Diabetes
      Ann Arbor, Michigan, United States
  • 1990–2015
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States
  • 2013
    • George Washington University
      • Biostatistics Center
      Washington, Washington, D.C., United States
  • 2012–2013
    • Baker IDI Heart and Diabetes Institute
      • Clinical Diabetes and Epidemiology Research Group
      Melbourne, Victoria, Australia
    • University of California, San Francisco
      • Department of Obstetrics, Gynecology and Reproductive Sciences
      San Francisco, CA, United States
  • 1994–2013
    • Centers for Disease Control and Prevention
      • • Division of Diabetes Translation
      • • National Center for Chronic Disease Prevention and Health Promotion
      Atlanta, MI, United States
    • University of Chicago
      Chicago, Illinois, United States
  • 2011
    • Park Nicollet Health Services
      Minneapolis, Minnesota, United States
    • University of North Carolina at Chapel Hill
      North Carolina, United States
  • 2003–2011
    • Wayne State University
      • Department of Pharmacy Practice
      Detroit, MI, United States
    • MDCH Michigan Department of Community Health
      Michigan Center, Michigan, United States
    • Brigham and Women's Hospital
      Boston, Massachusetts, United States
    • University of California, Los Angeles
      • Department of Emergency Medicine
      Los Ángeles, California, United States
  • 2010
    • Carnegie Mellon University
      Pittsburgh, Pennsylvania, United States
    • University of Pittsburgh
      Pittsburgh, Pennsylvania, United States
  • 2008–2009
    • Kaiser Permanente
      Oakland, California, United States
    • RTI International
      Durham, North Carolina, United States
  • 2007
    • California State University, Los Angeles
      Los Ángeles, California, United States
  • 2003–2007
    • Indiana University-Purdue University Indianapolis
      Indianapolis, Indiana, United States
  • 2006
    • Indiana University Bloomington
      Bloomington, Indiana, United States
    • Oakland University
      • School of Nursing
      Rochester, MI, United States
  • 2005–2006
    • University of Florida
      • Department of Medicine
      Gainesville, FL, United States
    • University of Wisconsin–Madison
      Madison, Wisconsin, United States
  • 2003–2004
    • University of California, San Diego
      • Division of Endocrinology & Metabolism
      San Diego, California, United States
  • 2000
    • Hillsdale College
      Hillsdale, New Jersey, United States
  • 1999
    • University of Kuopio
      Kuopio, Northern Savo, Finland
  • 1997
    • Massachusetts Department of Public Health
      Boston, Massachusetts, United States