Advertisement

Value of Lifestyle Intervention to Prevent Diabetes and Sequelae

Open AccessPublished:December 09, 2014DOI:https://doi.org/10.1016/j.amepre.2014.10.003

      Background

      The Community Preventive Services Task Force recommends combined diet and physical activity promotion programs for people at increased risk of type 2 diabetes, as evidence continues to show that intensive lifestyle interventions are effective for overweight individuals with prediabetes.

      Purpose

      To illustrate the potential clinical and economic benefits of treating prediabetes with lifestyle intervention to prevent or delay onset of type 2 diabetes and sequelae.

      Methods

      This 2014 analysis used a Markov model to simulate disease onset, medical expenditures, economic outcomes, mortality, and quality of life for a nationally representative sample with prediabetes from the 2003−2010 National Health and Nutrition Examination Survey. Modeled scenarios used 10-year follow-up results from the lifestyle arm of the Diabetes Prevention Program and Outcomes Study versus simulated natural history of disease.

      Results

      Over 10 years, estimated average cumulative gross economic benefits of treating patients who met diabetes screening criteria recommended by the ADA ($26,800) or USPSTF ($24,700) exceeded average benefits from treating the entire prediabetes population ($17,800). Estimated cumulative, gross medical savings for these three populations averaged $10,400, $11,200, and $6,300, respectively. Published estimates suggest that opportunistic screening for prediabetes is inexpensive, and lifestyle intervention similar to the Diabetes Prevention Program can be achieved for ≤$2,300 over 10 years.

      Conclusions

      Lifestyle intervention among people with prediabetes produces long-term societal benefits that exceed anticipated intervention costs, especially among prediabetes patients that meet the ADA and USPSTF screening guidelines.

      Introduction

      Approximately 86 million people in the U.S. have prediabetes, a condition where blood glucose levels are elevated but remain below the diabetic threshold. These individuals are at high risk for developing type 2 diabetes, heart disease, and stroke, with estimated annual diabetes incidence rates ranging from 1% to 2%
      • Nichols G.A.
      • Hillier T.A.
      • Brown J.B.
      Progression from newly acquired impaired fasting glusose to type 2 diabetes.
      • Gillies C.L.
      • Lambert P.C.
      • Abrams K.R.
      • et al.
      Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis.
      for the entire prediabetes population to 5% to 7%
      • Nichols G.A.
      • Hillier T.A.
      • Brown J.B.
      Progression from newly acquired impaired fasting glusose to type 2 diabetes.
      • Rasmussen S.S.
      • Glumer C.
      • Sandbaek A.
      • Lauritzen T.
      • Borch-Johnsen K.
      Progression from impaired fasting glucose and impaired glucose tolerance to diabetes in a high-risk screening programme in general practice: the ADDITION Study, Denmark.
      • Rasmussen S.S.
      • Glumer C.
      • Sandbaek A.
      • Lauritzen T.
      • Borch-Johnsen K.
      Determinants of progression from impaired fasting glucose and impaired glucose tolerance to diabetes in a high-risk screened population: 3 year follow-up in the ADDITION study, Denmark.
      among high-risk populations.
      Clinical trials demonstrate that counseling and treatment can prevent diabetes or delay onset among high-risk populations.
      Diabetes Prevention Program Research Group
      Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
      • Knowler W.C.
      • Fowler S.E.
      • Hamman R.F.
      • et al.
      10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study.
      • Gillies C.L.
      • Abrams K.R.
      • Lambert P.C.
      • et al.
      Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis.
      • Albright A.L.
      • Gregg E.W.
      Preventing type 2 diabetes in communities across the U.S.: the National Diabetes Prevention Program.
      Lifestyle intervention in the Diabetes Prevention Program and Outcomes Study (DPPOS) resulted in reduced body weight and hemoglobin A1c (HbA1c) levels that persisted through 10-year follow-up.
      • Knowler W.C.
      • Fowler S.E.
      • Hamman R.F.
      • et al.
      10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study.
      Screening for diabetes and prediabetes and subsequent diagnosis are precursors to receiving counseling and treatment. The American Diabetes Association (ADA) recommends screening overweight, asymptomatic adults with additional risk factors, and triennial screening for adults aged ≥45 years without risk factors.
      American Diabetes Association
      Standards of medical care in diabetes−2014.
      The U.S. Preventive Services Task Force (USPSTF) 2008 guidelines recommend screening asymptomatic adults with sustained hypertension, though draft new screening guidelines from USPSTF are similar to the ADA guidelines.

      U.S. Preventive Services Task Force. Screening for Type 2 Diabetes Mellitus in Adults, Topic Page. August 1, 2013. http://www.uspreventiveservicestaskforce.org/Page/Document/ClinicalSummaryFinal/diabetes-mellitus-type-2-in-adults-screening.

      ADA recommends screening for prediabetes; USPSTF (2008 guidelines) does not, citing limited evidence on long-term benefits. Limited information exists on screening cost effectiveness, although one study concluded that prediabetes and diabetes screening and subsequent intervention appear cost effective.
      • Gillies C.L.
      • Lambert P.C.
      • Abrams K.R.
      • et al.
      Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis.
      This study estimates the potential long-term health and economic benefits of lifestyle intervention among the total U.S. prediabetes population and subsets meeting ADA and USPSTF screening criteria.

      Methods

      This 2014 analysis used a Markov-based microsimulation model similar to those previously used to study health outcomes.
      • Dall T.M.
      • Zhang Y.
      • Zhang S.
      • et al.
      Weight loss and lifetime medical expenditures: a case study with TRICARE prime beneficiaries.
      • Yang W.
      • Dall T.M.
      • Zhang Y.
      • et al.
      Simulation of quitting smoking in the military shows higher lifetime medical spending more than offset by productivity gains.
      The approach simulated interactions between demographics; smoking status; biometrics—BMI, systolic (SBP) and diastolic blood pressure (DBP), total cholesterol, high-density lipoprotein cholesterol, and HbA1c levels; incidence of disease and adverse health events; and mortality (Figure 1). Health outcomes impacted annual medical costs, productivity (employment status, missed work days, income, and disability payments), and quality of life. Modeled scenarios used 10-year follow-up results from the lifestyle arm of the DPPOS versus simulated natural disease history.
      Diabetes Prevention Program Research Group
      The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS.
      Figure thumbnail gr1
      Figure 1Disease Prevention Microsimulation Model Overview.
      Note: Arrows indicate linkages in the model. Risk factors (especially aging) affect annual change in biometrics. Risk factors (including biometrics) are used to model disease incidence. Presence of disease, and other factors such as demographics, affects mortality risk, medical expenditures, and the economic outcomes modeled. See (available online) for more detail on the model.
      CHF, congestive heart failure; CKD, chronic kidney disease; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; IHD, ischemic heart disease; LVH, left ventricular hypertrophy; PVD, peripheral vascular disease; QALY, quality-adjusted life year; SBP, systolic blood pressure.
      The analyzed population, statistical analysis, and predictive equations are briefly described in the following sections. The Appendix (available online) provides additional detail on model design, data, assumptions, methods, and validation, following guidelines published by the International Society for Pharmacoeconomics and Outcomes Research and Society for Medical Decision Making.
      • Caro J.J.
      • Briggs A.H.
      • Siebert U.
      • Kuntz K.M.
      Modeling good research practices—overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1.

      Study Population

      The National Health and Nutrition Examination Survey (NHANES) is a nationally representative sample of the noninstitutionalized population.

      National Center for Health Statistics. Continous NHANES Web Tutorial. 2013. http://www.cdc.gov/nchs/tutorials/NHANES/index_continuous.htm.

      The combined 2003−2010 NHANES contains 3,700 adults with prediabetes defined by 6.4%≥HbA1c≥5.7%.
      American Diabetes Association
      Standards of medical care in diabetes−2014.
      Although other diagnostic tests (fasting plasma glucose [FPG] and 2-hour oral glucose tolerance test [OGTT]) can be used to determine prediabetes status, HbA1c was used to model diabetes onset and is a risk factor in published comorbidity prediction equations. Sensitivity analyses used both HbA1c and FPG to identify the prediabetes population and model diabetes onset.
      Individual profiles contained demographics (age, sex, race, and Hispanic ethnicity), biometrics, current smoking status, and presence of recognized risk factors and complications of diabetes—hypertension, ischemic heart disease, congestive heart failure, stroke, heart attack, renal failure, amputation, and blindness.
      • Newton K.M.
      • Wagner E.H.
      • Ramsey S.D.
      • et al.
      The use of automated data to identify complications and comorbidities of diabetes: a validation study.
      CDC
      National diabetes statistics report, 2014.
      • Clarke P.M.
      • Gray A.M.
      • Briggs A.
      • et al.
      A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
      This profile created a starting point for simulating future health outcomes.
      Intervention and nonintervention scenarios were simulated for three prediabetes populations: (1) all adults (n=3,700); (2) adults satisfying ADA screening criteria (n=2,887)
      American Diabetes Association
      Standards of medical care in diabetes−2014.
      ; and (3) adults satisfying USPSTF (2008 guidelines) screening criteria (n=1,621).

      U.S. Preventive Services Task Force. Screening for Type 2 Diabetes Mellitus in Adults, Topic Page. August 1, 2013. http://www.uspreventiveservicestaskforce.org/Page/Document/ClinicalSummaryFinal/diabetes-mellitus-type-2-in-adults-screening.

      Each simulation used 100,000 observations where sample weights determined the probability a person was drawn from the NHANES sample.

      Simulating Health Outcomes, Mortality, and Quality of Life

      Each person’s current characteristics were used to predict next year’s health outcomes, with this process repeated through 10 years or death. The intervention scenario assumed average HbA1c and body weight declines observed during the DPPOS 10-year follow-up; the nonintervention scenario assumed the natural history of disease under current standards of care.
      Diabetes Prevention Program Research Group
      The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS.
      Model parameters and prediction equations came from the United Kingdom Prospective Diabetes Study (UKPDS),
      • Clarke P.M.
      • Gray A.M.
      • Briggs A.
      • et al.
      A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
      • Kannel W.B.
      • Wolf P.A.
      • Benjamin E.J.
      • Levy D.
      Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates.
      • Nichols G.A.
      • Reinier K.
      • Chugh S.S.
      Independent contribution of diabetes to increased prevalence and incidence of atrial fibrillation.
      • Anderson K.M.
      • Odell P.M.
      • Wilson P.W.
      • Kannel W.B.
      Cardiovascular disease risk profiles.
      • Hippisley-Cox J.
      • Coupland C.
      Predicting the risk of chronic kidney disease in men and women in England and Wales: prospective derivation and external validation of the QKidney-Scores.
      Framingham Heart Study,
      • Wilson P.W.
      • Anderson K.M.
      • Harri T.
      • Kannel W.B.
      • Castelli W.P.
      Determinants of change in total cholesterol and HDL-C with age: the Framingham Study.
      • Wilson P.W.
      • Bozeman S.R.
      • Burton T.M.
      • et al.
      Prediction of first events of coronary heart disease and stroke with consideration of adiposity.
      • Ho K.K.
      • Anderson K.M.
      • Kannel W.B.
      • Grossman W.
      • Levy D.
      Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
      USDHHS
      The Framingham Study: an epidemiological intervention of cardiovascular diseases: section 34: some risk factors related to the annual incidence of cardiovascular disease and death using pooled repeated biennial measurements: Framingham Heart Study, 30 year followup.
      • D’Agostino R.B.
      • Vasan R.S.
      • Pencina M.J.
      • et al.
      General cardiovascular risk profile for use in primary care the Framingham Heart Study.
      and published trials and observational studies. Original research with NHANES filled in data gaps (Appendix, available online).
      Illustrating interdependence of clinical and disease outcomes, the following sequence modeled annual change in risk factors and outcomes:
      • 1.
        Body weight changed with age. The rate of change reflected the average difference in BMI between subsequent ages in a cross-sectional analysis of NHANES data, calculated separately by sex and body weight category (BMI<25, 25≤BMI<30, BMI≥30). Validation found patterns similar to published findings using longitudinal data.
        • Sheehan T.J.
        • DuBrava S.
        • DeChello L.M.
        • Fang Z.
        Rates of weight change for black and white Americans over a twenty year period.
      • 2.
        For people experiencing diabetes onset, rates of change in SBP, HbA1c, and cholesterol were predicted using demographics and BMI change with equations from the UKPDS Outcomes Model.
        • Clarke P.M.
        • Gray A.M.
        • Briggs A.
        • et al.
        A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
        For people with prediabetes, annual changes in SBP, DBP, total cholesterol, and high-density lipoprotein cholesterol were modeled based on age and change in BMI. Annual change in HbA1c was modeled based on age, BMI change, and total cholesterol. The equations combined analysis of NHANES and parameters from the literature.
        • Wilson P.W.
        • Anderson K.M.
        • Harri T.
        • Kannel W.B.
        • Castelli W.P.
        Determinants of change in total cholesterol and HDL-C with age: the Framingham Study.
        • Neter J.E.
        • Stam B.E.
        • Kok F.J.
        • Grobbee D.E.
        • Geleijnse J.M.
        Influence of weight reduction on blood pressure a meta-analysis of randomized controlled trials.
        • Heianza Y.
        • Arase Y.
        • Fujihara K.
        • et al.
        Longitudinal trajectories of HbA1c and fasting plasma glucose levels during the development of type 2 diabetes the Toranomon Hospital Health Management Center Study 7 (TOPICS 7).
        A meta-analysis of clinical trials found that a 1-kg loss in excess body weight is associated with a 1.05-mmHg reduction in SBP.
        • Neter J.E.
        • Stam B.E.
        • Kok F.J.
        • Grobbee D.E.
        • Geleijnse J.M.
        Influence of weight reduction on blood pressure a meta-analysis of randomized controlled trials.
        To estimate age-associated SBP changes, ordinary least squares regression with NHANES data (separately for men and women) used SBP as the dependent variable, age and age squared as explanatory variables, and BMI as a control variable.
      • 3.
        Onset of diabetes and hypertension were modeled from HbA1c
        American Diabetes Association
        Diagnosis and classification of diabetes mellitus.
        and SBP levels,
        National Heart, Lung, and Blood Institute
        Obesity initiative: the practical guide: identification, evaluation, and treatment of overweight and obesity in adults.
        respectively, using clinical guidelines.
      • 4.
        Equations to predict incidence of atrial fibrillation
        • Kannel W.B.
        • Wolf P.A.
        • Benjamin E.J.
        • Levy D.
        Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates.
        • Nichols G.A.
        • Reinier K.
        • Chugh S.S.
        Independent contribution of diabetes to increased prevalence and incidence of atrial fibrillation.
        ; left ventricular hypertrophy
        • de Simone G.
        • Devereux R.B.
        • Roman M.J.
        • Alderman M.H.
        • Laragh J.H.
        Relation of obesity and gender to left ventricular hypertrophy in normotensive and hypertensive adults.
        ; ischemic heart disease (IHD), myocardial infarction (MI), congestive heart failure (CHF), and stroke
        • Clarke P.M.
        • Gray A.M.
        • Briggs A.
        • et al.
        A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
        • Anderson K.M.
        • Odell P.M.
        • Wilson P.W.
        • Kannel W.B.
        Cardiovascular disease risk profiles.
        • Wilson P.W.
        • Bozeman S.R.
        • Burton T.M.
        • et al.
        Prediction of first events of coronary heart disease and stroke with consideration of adiposity.
        ; chronic kidney disease (CKD)
        • Hippisley-Cox J.
        • Coupland C.
        Predicting the risk of chronic kidney disease in men and women in England and Wales: prospective derivation and external validation of the QKidney-Scores.
        ; and peripheral vascular disease
        • Hooi J.D.
        • Kester A.D.
        • Stoffers H.E.
        • et al.
        Incidence of and risk factors for asymptomatic peripheral arterial occlusive disease: a longitudinal study.
        came from the literature for the prediabetes population. For the diabetes population, the equations for many of these conditions and amputation and blindness came from the UKPDS Outcomes Model.
        • Clarke P.M.
        • Gray A.M.
        • Briggs A.
        • et al.
        A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
      • 5.
        Annual, event-based mortality rates for diabetes,
        • Clarke P.M.
        • Gray A.M.
        • Briggs A.
        • et al.
        A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
        IHD,
        • Anderson K.M.
        • Odell P.M.
        • Wilson P.W.
        • Kannel W.B.
        Cardiovascular disease risk profiles.
        CHF,
        • Schaufelberger M.
        • Swedberg K.
        • Koster M.
        • Rosen M.
        • Rosengren A.
        Decreasing one-year mortality and hospitalization rates for heart failure in Sweden Data from the Swedish Hospital Discharge Registry 1988 to 2000.
        MI,
        Socialstyrelsen
        Swedish Health and Welfare Statistical Databases: AMI statistics.
        stroke,
        • Vemmos K.N.
        • Bots M.L.
        • Tsibouris P.K.
        • et al.
        Prognosis of stroke in the south of Greece: 1 year mortality, functional outcome and its determinants: the Arcadia Stroke Registry.
        renal failure,
        • Hippisley-Cox J.
        • Coupland C.
        Predicting the risk of chronic kidney disease in men and women in England and Wales: prospective derivation and external validation of the QKidney-Scores.
        and CKD
        • Tonelli M.
        • Wiebe N.
        • Culleton B.
        • et al.
        Chronic kidney disease and mortality risk: a systematic review.
        came from published equations and reflect mortality risk associated with demographics, biometrics, smoking, and disease presence. All-cause mortality rates were adjusted to remove cause-specific mortality modeled separately.

        CDC, National Center for Health Statistics. Underlying cause of death 1999−2010 on CDC WONDER Online Database. Released 2012. http://wonder.cdc.gov.

      • 6.
        Estimates of reduced quality of life associated with obesity, amputation, and renal failure were based on people with type 2 diabetes, whereas estimates for other conditions were based on a nationally representative sample of adults.
        • Zhang P.
        • Brown M.B.
        • Bilik D.
        • et al.
        Health utility scores for people with type 2 diabetes in U.S. managed care health plans results from Translating Research Into Action for Diabetes (TRIAD).
        • Sullivan P.W.
        • Lawrence W.F.
        • Ghushchyan V.
        A national catalog of preference-based scores for chronic conditions in the United States.

      Simulating Medical Expenditures and Economic Outcomes

      All monetary estimates are in 2013 U.S. dollars and reflect present values using a 3% discount rate. The relationship between annual medical expenditures and patient characteristics was estimated using a generalized linear model with gamma distribution and log link, analyzing data from the 2006−2010 files (n=165,913) of the Medical Expenditure Panel Survey (MEPS). Explanatory variables were age group, sex, race, Hispanic ethnicity, insurance status, body weight (overweight, obese), presence of modeled diseases, and interaction terms for diabetes and modeled diseases (regression results shown in Appendix Exhibit 27, available online). Estimates based on MEPS reflect average annual costs for those living. End of life costs were based on published estimates.
      • Riley G.F.
      • Lubitz J.D.
      Long-term trends in Medicare payments in the last year of life.
      Estimated relationships between disease presence and economic outcomes came from regression analysis of the linked 2008−2010 MEPS and National Health Interview Survey files. Explanatory variables were the same as those described here. Economic outcomes analyzed for the entire adult population in the linked files were employed status (n=25,296) and receiving Supplemental Security Income for disability (n=26,080)—both estimated with logistic regression. Annual missed workdays were analyzed for the employed population (n=18,699) using negative binomial regression. Ordinary least squares regression with MEPS data (n=165,913) modeled household income (regression results presented in Appendix Exhibit 28, available online).

      Results

      The simulated sample is nationally representative of the prediabetic population identifiable using HbA1c. In the initial simulation year, 51% were women, and mean ages were 54 years for men and 58 years for women (Table 1). Those meeting USPSTF (2008 guidelines) criteria were older, with higher BMI, and had greater prevalence of cardiovascular disease relative to those meeting ADA criteria and the total prediabetic population.
      Table 1Prediabetes Population Starting Year Characteristics
      All prediabetesADA identifiedUSPSTF (2008) identified
      MenWomenMenWomenMenWomen
      %49.250.849.750.346.153.9
      Mean
       Age, y53.958.155.058.959.363.4
       BMI30.030.131.031.531.330.9
       Systolic blood pressure, mmHg128.1129.7128.3130.6132.6136.8
       Diastolic blood pressure, mmHg73.669.773.869.874.169.6
       Cholesterol ratio4.74.04.84.14.64.0
       HbA1c, %5.95.95.95.95.95.9
      Disease prevalence, %
       Congestive heart failure3.42.34.32.76.34.3
       History of myocardial infarction7.32.99.23.312.14.9
       History of stroke3.34.43.74.76.57.5
       Hypertension50.755.140.838.9100.0100.0
       Ischemic heart disease11.26.614.07.919.211.8
       Obesity43.144.348.347.951.147.4
      Note: Several risk factors and health states in the model are not available in the NHANES data used to create the starting year health profile. These conditions are modeled based on other patient characteristics—with some conditions modeled only among the population with simulated diabetes onset. Modeled conditions include amputation, atrial fibrillation, diabetic retinopathy, left ventricular hypertrophy, peripheral vascular disease, and renal failure (to include chronic kidney disease and end stage renal disease).
      ADA, American Diabetes Association; HbA1c, hemoglobin A1c; NHANES, National Health and Nutrition Examination Survey; USPSTF, U.S. Preventive Services Task Force.
      Under the nonintervention scenario, nearly one third of the prediabetes population (32.5%) developed diabetes within 10 years (Table 2). Mortality reached 33.9%, and diabetes prevalence among those surviving at year 10 reached 36.4%. Over 10 years, 15.7% developed CKD, 13.8% developed CHF, and 10.7% developed IHD. The present value of gross medical expenditures over 10 years averaged $73,900 per person ($90,200 per person living at year 10).
      Table 2Cumulative Outcomes for Nationally Representative Sample of 100,000 Adults with Prediabetes (No Intervention Scenario)
      2 Years5 Years10 Years
      New disease cases, n
       Diabetes7,10016,90032,500
       Ischemic heart disease1,9005,10010,700
       Congestive heart failure2,6006,70013,800
       Stroke1,7004,5009,300
       Heart attack1,0002,7005,900
       Renal failure3,4008,30015,700
       Amputation1050140
       Blindness503401,200
      Medical expenditures ($ millions)1,5213,8697,389
       Medical expenditures/person still living15,70042,20090,200
      Nonmedical economic outcomes ($ millions)8,76319,48931,909
       Household income ($ millions)9,13720,34633,372
       Years of employment104,850243,210421,810
       Absenteeism (missed work days)1,555,0003,630,0006,365,000
        Absenteeism productivity loss ($ millions)2896491,078
       Supplemental Security Income ($ millions)85207385
      Mortality5,00014,80033,900
       Years of life193,200459,400828,100
      Quality-adjusted life years150,430353,960628,450
      Note: All dollar figures are present values in 2013 dollars, using a 3% discount rate. Numbers might not sum to totals because of rounding.
      Over 10 years, intervention reduced diabetes onset by 41%, CHF by 33%, IHD by 22%, and mortality by 20% (Table 3). Cumulative medical expenditures per person were $6,300 (9%) lower. Average nonmedical benefits were $11,500 higher—primarily from increased employment and household income. Absenteeism per worker declined, but higher employment increased total missed workdays. The present value of gross economic benefits of intervention averaged $17,800 ($16,100 using a 5% discount rate, $21,000 using a 0% discount rate).
      Table 3Cumulative Benefits of Lifestyle Intervention for Nationally Representative Sample of 100,000 Adults with Prediabetes
      Cumulative impactImpact at year 10, %
      2 Years5 Years10 Years
      New disease cases, n
       Diabetes(2,800)(5,400)(13,300)(41)
       Ischemic heart disease(280)(1,000)(2,300)(22)
       Congestive heart failure(440)(1,700)(4,500)(33)
       Stroke(380)(1,400)(3,400)(36)
       Heart attack(180)(800)(2,100)(35)
       Renal failure(60)(130)00
       Amputation(10)(30)(90)(63)
       Blindness(20)(140)(510)(40)
      Medical expenditures ($ millions)(69)(256)(630)(9)
      Nonmedical economic outcomes ($ millions)1003491,1544
       Household income ($ millions)1063541,1453
       Years of employment3702,73012,6103
       Absenteeism (missed work days)(29,000)(28,000)67,0001
        Absenteeism productivity loss ($ millions)(5)(5)91
       Supplemental Security Income ($ millions)(0.4)(0.3)(0.5)0
      Mortality(410)(2,200)(6,800)(20)
       Years of life6005,50031,3004
      Total economic benefits ($ millions)1696041,784
      Quality adjusted life years2,5309,12034,7206
      Note: These numbers reflect a representative sample of 100,000 adults with prediabetes who participate in a lifestyle intervention program that achieves 10-year results similar to the Diabetes Prevention Program Outcomes Study. All dollar figures are present values in 2013 dollars, using a 3% discount rate. Numbers might not sum to totals because of rounding. Numbers in parentheses reflect decreases relative to the nonintervention scenario.
      Among the prediabetes population, approximately 43% had undetected prediabetes but met ADA criteria for diabetes screening, and 30% had undetected prediabetes but met USPSTF criteria in 2010.
      • Dall T.M.
      • Narayan K.M.
      • Gillespie K.B.
      • et al.
      Detecting type 2 diabetes and prediabetes among asymptomatic adults in the United States: modeling American Diabetes Association versus US Preventive Services Task Force diabetes screening guidelines.
      Applied to the 2012 estimate of 86 million with prediabetes, this suggests that potentially 26−37 million case patients in 2012 could have been detected and enrolled in treatment. Without intervention, simulated diabetes onset over 10 years was 46.4% for the ADA-identified population and 44.3% for the USPSTF-identified population. Intervention reduced average, 10-year cumulative medical costs for the ADA-identified population by $10,400 ($12,000−$9,500 using a 0%−5% discount rate) and for the USPSTF-identified population by $11,200 ($12,800−$10,200) (Table 4).
      Table 4Potential Cumulative 10-year Benefits if U.S. Population with Prediabetes Achieved DPPOS Lifestyle Intervention Results
      Total prediabetes populationMeet ADA screening criteriaMeet USPSTF (2008) screening criteria
      Estimated national cases with prediabetes in 2010 (millions)863726
      Disease cases prevented, n
       Diabetes(11,440,000)(9,480,000)(6,190,000)
       Ischemic heart disease(1,980,000)(1,000,000)(890,000)
       Congestive heart failure(3,870,000)(1,890,000)(1,460,000)
      Disease incidence prevented, n
       Stroke(2,920,000)(1,330,000)(1,070,000)
       Heart attack(1,810,000)(890,000)(650,000)
       Renal failure0(320,000)(230,000)
       Amputation(80,000)(70,000)(50,000)
       Blindness(440,000)(410,000)(340,000)
      Total U.S. medical expenditures ($ billions)(539)(384)(292)
       Medical expenditures per person, $
      • (6,300)
      • (7,300)
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • (10,400)
      • (12,000)
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • (11,200)
      • (12,800)
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
       Total U.S. non-medical benefits ($ billions)992607353
       Nonmedical benefits per person, $
      • 11,500
      • 13,700
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • 16,400
      • 19,300
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • 13,500
      • 15,900
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
       Household income ($ billions)985603349
       Years of employment (millions)10.87.04.2
       Absenteeism (millions missed work days)586444
       Productivity ($ billions)8.09.76.8
       Cost of Supplemental Security Income ($ billions)(0.4)(5.9)(3.5)
      Mortality (millions)(5.8)(3.0)(2.5)
       Years of life (millions)26.913.511.7
      Total economic benefits ($ billions)1,531991644
       Total economic benefits per person, $
      • 17,800
      • 21,000
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • 26,800
      • 31,300
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • 24,700
      • 28,700
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      Quality-adjusted life years ($ millions)
      • 25.1
      • 29.9
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • 14.5
      • 17.1
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      • 10.6
      • 12.5
        Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      Note: All dollar and quality-adjusted life year figures are present values in 2013 dollars and use a 3% discount rate.
      ADA, American Diabetes Association; DPPOS, Diabetes Prevention Program Outcomes Study; USPSTF, U.S. Preventive Services Task Force.
      a Indicates undiscounted estimates. Numbers might not sum to totals because of rounding.
      If estimated benefits were scaled to the entire prediabetes population, these findings suggest that, over 10 years, the nation could potentially prevent more than 11.4 million cases of diabetes, avoid $539 billion in medical costs, create $992 billion in nonmedical benefits (largely through 11 million additional years of employment), and gain nearly 30 million quality-adjusted life years. Among the 37 million people with undiagnosed prediabetes meeting ADA screening guidelines, intervention could prevent 9.5 million diabetes cases with economic benefits of $991 billion.
      A formal cost effectiveness analysis was outside the scope of this study, but others have reported costs for screening and treatment.
      • Vojta D.
      • Koehler T.B.
      • Longjohn M.
      • Lever J.A.
      • Caputo N.F.
      A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program.
      • Lawlor M.S.
      • Blackwell C.S.
      • Isom S.P.
      • et al.
      Cost of a group translation of the Diabetes Prevention Program: healthy living partnerships to prevent diabetes.
      • Hernan W.H.
      • Brandle M.
      • Zhang P.
      • et al.
      Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.
      The Healthy Living Partnerships to Prevent Diabetes (HELP PD) trial, which adapted the DPP approach to community-based settings, reported costs of $7.50 (2013 dollars) per test and 2.5 people screened for each identified prediabetes case.
      • Lawlor M.S.
      • Blackwell C.S.
      • Isom S.P.
      • et al.
      Cost of a group translation of the Diabetes Prevention Program: healthy living partnerships to prevent diabetes.
      Opportunistic screening for prediabetes is relatively inexpensive per person, although follow-up visits for confirmation and testing would increase detection costs.
      DPP lifestyle intervention cost $3,770 (2013 dollars) per participant.
      • Hernan W.H.
      • Brandle M.
      • Zhang P.
      • et al.
      Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.
      Subsequent lifestyle interventions have achieved similar outcomes at lower treatment cost through a different mix of medical and allied health professionals, use of electronic media, and a less-individualized approach. HELP PD costs were $850 per participant over 2 years, including the costs for counselor time, distribution of materials, a monthly newsletter, and reminder calls and e-mails. Cost was $568 during the intensive phase (Months 1−6), and $282 during the maintenance phase (Months 7−24). Over 10 years (with a 3% discount rate and attrition due to mortality), the cost would be $2,300 per participant. The YMCA’s Diabetes Prevention Program achieved short-term patient weight loss results similar to HELP PD and the original DPP study, but at service-delivery costs of about $400 per person completing the 12-month program.
      • Vojta D.
      • Koehler T.B.
      • Longjohn M.
      • Lever J.A.
      • Caputo N.F.
      A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program.

      Discussion

      Lifestyle intervention among adults with prediabetes can reduce body weight, BP, and glycemic levels,
      Diabetes Prevention Program Research Group
      Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
      • Vojta D.
      • Koehler T.B.
      • Longjohn M.
      • Lever J.A.
      • Caputo N.F.
      A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program.
      • Hernan W.H.
      • Brandle M.
      • Zhang P.
      • et al.
      Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.
      although clinical trials of treatment for prediabetes and early diabetes stages have produced disappointing results regarding impact on long-term complications.
      • Rasmussen S.S.
      • Glumer C.
      • Sandbaek A.
      • Lauritzen T.
      • Borch-Johnsen K.
      Determinants of progression from impaired fasting glucose and impaired glucose tolerance to diabetes in a high-risk screened population: 3 year follow-up in the ADDITION study, Denmark.
      • Alibasic E.
      • Ramic E.
      • Alic A.
      Prevention of diabetes in family medicine.
      Evidence suggests that lifestyle interventions implemented over a short period can still have long-lasting, beneficial, carryover effects on type 2 diabetes incidence.
      • Tuomilehto J.
      • Schwarz P.
      • Lindstrom J.
      Long-term benefits from lifestyle interventions for type 2 diabetes prevention: time to expand the efforts.
      Long-term health benefits are considered the true measure of diabetes screening program cost effectiveness.
      • Zhang P.
      • Engelgau M.M.
      • Valdez R.
      • et al.
      Costs of screening for pre-diabetes among US adults: a comparison of different screening strategies.
      Although other studies have reported the clinical benefits of lifestyle intervention, the primary contribution of this study is translating improvement in body weight and glycemic levels into estimates of long-term economic outcomes. There are three key implications of this study.
      First, the simulated economic benefits of treating prediabetes via lifestyle intervention appear to far outweigh intervention costs over the analyzed 10-year period, with higher simulated benefits among the prediabetes population meeting ADA and USPSTF screening guidelines. Published estimates of opportunistic screening costs ($18.50) and intervention costs (about $2,300) are well below the simulated medical savings ($6,300−$11,200) and total societal benefits ($17,800−$26,800) per participant.
      Second, the prediabetes population meeting ADA screening guidelines is younger and healthier than the population meeting USPSTF (2008) guidelines. Over 10 years, average medical savings from intervention were greater among the USPSTF population, but societal economic benefits were greater among the ADA population. In 2010, approximately 14.9 million people with undetected prediabetes met ADA, but not USPSTF, diabetes screening criteria, while 5.4 million met USPSTF, but not ADA, criteria.
      • Dall T.M.
      • Narayan K.M.
      • Gillespie K.B.
      • et al.
      Detecting type 2 diabetes and prediabetes among asymptomatic adults in the United States: modeling American Diabetes Association versus US Preventive Services Task Force diabetes screening guidelines.
      Preliminary analysis of USPSTF’s new draft screening guidelines suggest that the population detected with prediabetes is slightly larger than the population detected under the ADA guidelines, but that average benefits from a DPP-type lifestyle intervention would be similar to results achieved for an ADA-identified population.
      Third, the cumulative benefits of intervention continued to grow over time. Among the ADA-identified population, average gross economic benefits were estimated to be $3,070, $10,500, and $26,800 within 2, 5, and 10 years, respectively.
      Screening and detection are precursors to receiving counseling and treatment. USPSTF’s lack of recommendation on screening for prediabetes in their 2008 guidelines stems from the paucity of published data on the value of screening and treatment among the general diabetic population.
      • Norris S.L.
      • Kansagara D.
      • Bougatsos C.
      • Fu R.
      Screening adults for type 2 diabetes: a review of the evidence for the US Preventive Services Task Force.
      Limited published evidence stems in part from the challenges in using clinical trials as the main source of evidence supporting interventions.
      • English R.
      • Lebovitz Y.
      • Griffin R.
      Transforming clinical research in the United States: challenges and opportunities: workshop summary.
      Although clinical trials are the gold standard of evidence-based medicine, they often are of insufficient size and duration to quantify outcomes that take years to manifest. In this context, simulation modeling has increased in importance to inform health policy decisions.
      • Kahn R.
      • Alperin P.
      • Eddy D.
      • et al.
      Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis.
      • Palmer A.J.
      • Clarke P.
      • Gray A.
      • et al.
      Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting.
      • Stevens R.J.
      • Kothari V.
      • Adler A.I.
      • Stratton I.M.
      • Holman R.R.
      The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56).
      • Hoerger T.J.
      • Segel J.E.
      • Zhang P.
      • Sorensen S.W.
      Validation of the CDC-RTI diabetes cost-effectiveness model. RTI Press Publication No. MR-0013-090.
      • Palmer A.J.
      • Roze S.p.
      • Valentine W.J.
      • et al.
      The CORE Diabetes Model: projecting long-term clinical outcomes, costs and costeffectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making.
      Yudkin and Montori
      • Yudkin J.S.
      • Montori V.M.
      The epidemic of pre-diabetes: the medicine and the politics.
      argue that labeling and treating people with prediabetes are associated with huge social and economic burden. Our work suggests that relatively inexpensive diabetes lifestyle treatment programs can reduce the social and economic burden among this population.

      Study Strengths and Limitations

      This study used a microsimulation model that incorporates estimates from clinical trials and other sources to track the pathways between a person’s characteristics, biometrics, disease risk, onset, mortality, medical expenditures, and workforce participation. Simulation allows for better understanding of the pathways by which reduction in body weight and glycemic levels attributed to lifestyle intervention can prevent or delay onset of diabetes and sequelae. Simulation also allowed comparisons across different populations.
      One limitation is the lack of a single longitudinal data source covering a sufficient time period and of sufficient size to quantify disease onset and the relationship between other patient characteristics. Therefore, data were used from multiple sources, including UKPDS data collected from a population outside the U.S. A second limitation is the use of cross-sectional data for estimating change in BMI and BP associated with aging. Validation activities reported that the model’s predictive equations produced outcomes consistent with aggregate published estimates based on longitudinal data. A third limitation is that some predictive equations for the prediabetic population are based on analyses of a nondiabetic population, which likely understates disease incidence under both the intervention and nonintervention scenarios.
      A fourth limitation is that some older data sources were used (e.g., Framingham and UKPDS), and standards of care such as statin use have evolved over time. Data from the Look Action for Health in Diabetes (AHEAD) trial and other studies report that statin use has increased over time and is associated with decreased risk of adverse cardiovascular disease (CVD) events, and that after controlling for cholesterol levels, the impact of body weight loss on CVD outcomes largely disappears.
      • Wing R.R.
      • Bolin P.
      • Brancati F.L.
      • et al.
      Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes.
      • Wing R.R.
      Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial.
      • Nylen E.S.
      • Faselis C.
      • Kheirbek R.
      • et al.
      Statins modulate the mortality risk associated with obesity and cardiorespiratory fitness in diabetics.
      The model disease risk equations reflect that absolute probability risk for CVD and other adverse events are generally lower today compared to earlier years.
      A fifth limitation is using HbA1c to identify the simulated population and model diabetes onset. Although HbA1c is commonly used for diagnosis, trials often use OGTT owing to sensitivity concerns. Prediabetic adults identified by HbA1c, compared to OGTT, are more likely to be non-Hispanic but have similar age and sex distribution.
      • James C.
      • Bullard K.M.
      • Rolka D.B.
      • et al.
      Implications of alternative definitions of prediabetes for prevalence in U.S. adults.
      Prediabetic adults identified by HbA1c, FPG, and OGTT also differ in risk for diabetes and cardiovascular disease—although the association between test type and risk may be partially explained by the other patient characteristics captured in our model.
      American Diabetes Association
      Standards of medical care in diabetes−2014.
      Exploratory analysis using HbA1c and FPG, but not OGTT because of data limitations, suggests that using HbA1c and FPG, rather than HbA1c alone, to identify the simulation population and model diabetes onset increased estimated medical savings from intervention by 4%.
      Excess body weight increases risk for various types of cancer, musculoskeletal problems, respiratory problems, and other health issues omitted from this analysis.
      • Guh D.P.
      • Zhang W.
      • Bansback N.
      • et al.
      The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis.
      The estimated benefits of weight loss from lifestyle intervention are conservative with respect to these omitted conditions.
      Despite these limitations, validation activities suggest a robust model. Simulated annual transition rates to diabetes absence intervention (5.3%−7.6%) for the population meeting ADA and USPSTF screening criteria are consistent with rates (5%−10%) reported elsewhere.
      • Gerstein H.C.
      • Santaguida P.
      • Raina P.
      • et al.
      Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies.
      Validation activities found that predicted incidence of cardiovascular events matched well against recent published data.
      Sensitivity analysis suggests medical expenditures are most sensitive to HbA1c parameters and assumptions, whereas mortality is most sensitive to CHF assumptions. Excluding HbA1c, varying key modeled parameters by 50% in either direction resulted in maximum deviations from baseline estimates of 4.5%, 10.9%, and 18.8%, respectively, for cumulative 10-year diabetes incidence, medical expenditures, and mortality. This suggests that model results are robust to changes in input assumptions and parameters.

      Conclusions

      Ninety percent of people with prediabetes are undiagnosed, underscoring the need for a paradigm shift in screening.
      • Geiss L.S.
      • James C.
      • Gregg E.W.
      • et al.
      Diabetes risk reduction behaviors among U.S. adults with prediabetes.
      • Narayan K.M.
      • Echouffo-Tcheugui J.B.
      • Mohan V.
      • Ali M.K.
      Analysis & commentary: global prevention and control of type 2 diabetes will require paradigm shifts in policies within and among countries.
      This study highlights the value of treating people with prediabetes. Total simulated economic benefits of lifestyle intervention averaged $26,800 and $24,700, respectively, for people with prediabetes meeting ADA and 2008 USPSTF screening criteria. Published studies suggest that such interventions could be achieved with investments of ≤$2,300.
      • Vojta D.
      • Koehler T.B.
      • Longjohn M.
      • Lever J.A.
      • Caputo N.F.
      A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program.
      • Lawlor M.S.
      • Blackwell C.S.
      • Isom S.P.
      • et al.
      Cost of a group translation of the Diabetes Prevention Program: healthy living partnerships to prevent diabetes.
      • Hernan W.H.
      • Brandle M.
      • Zhang P.
      • et al.
      Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.
      Among the estimated 37 million people with undetected prediabetes meeting ADA screening criteria, intervention could potentially prevent 9.5 million diabetes cases with gross national economic benefits of $911 billion over 10 years. By contrast, among 26 million people with undetected prediabetes meeting USPSTF screening criteria, intervention could prevent 6.2 million diabetes cases over 10 years with national economic benefits of $644 billion. These findings illustrate the potential large economic benefits of overall screening and treatment for prediabetes.

      Acknowledgments

      Appreciation is expressed to Karin Gillespie, Jerry Franz, Laura Lawlor, Dr. Fannie Smith, and Dr. William Quick who commented on earlier versions of this paper, and to Ted Agres who provided editorial support.
      Funding for this study was provided by Novo Nordisk Inc. NW is an employee of Novo Nordisk Inc. The other study authors provide paid consulting services to the study sponsor for this and other research. The study sponsor approved the study design developed by TMD, MVS, and APS. Data collection and analysis was conducted solely by TMD, MVS, and APS. All study authors contributed to interpretation of study findings. The manuscript was prepared by TMD and MVS, with all authors reviewing and commenting on drafts. Medical and policy experts employed by the sponsor or paid consultants to the sponsor reviewed early drafts of the paper and provided feedback on study presentation.
      TMD, MVS, APS, MO, and KMVN provide paid consulting services to pharmaceutical companies, federal and state government agencies, trade and professional associations, and other for-profit and non-profit organizations. NW is an employee of Novo Nordisk Inc. No other financial disclosures were reported by the authors of this paper.

      Appendix A. Supplementary Materials

      References

      1. CDC. National diabetes fact sheet. 2011. http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf

        • Nichols G.A.
        • Hillier T.A.
        • Brown J.B.
        Progression from newly acquired impaired fasting glusose to type 2 diabetes.
        Diabetes Care. 2007; 30: 228-233
        • Gillies C.L.
        • Lambert P.C.
        • Abrams K.R.
        • et al.
        Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis.
        BMJ. 2008; 336: 1180-1185
        • Rasmussen S.S.
        • Glumer C.
        • Sandbaek A.
        • Lauritzen T.
        • Borch-Johnsen K.
        Progression from impaired fasting glucose and impaired glucose tolerance to diabetes in a high-risk screening programme in general practice: the ADDITION Study, Denmark.
        Diabetologia. 2007; 50: 293-297
        • Rasmussen S.S.
        • Glumer C.
        • Sandbaek A.
        • Lauritzen T.
        • Borch-Johnsen K.
        Determinants of progression from impaired fasting glucose and impaired glucose tolerance to diabetes in a high-risk screened population: 3 year follow-up in the ADDITION study, Denmark.
        Diabetologia. 2008; 51: 249-257
        • Diabetes Prevention Program Research Group
        Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
        N Engl J Med. 2002; 346: 393-403
        • Knowler W.C.
        • Fowler S.E.
        • Hamman R.F.
        • et al.
        10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study.
        Lancet. 2009; 374: 1677-1686
        • Gillies C.L.
        • Abrams K.R.
        • Lambert P.C.
        • et al.
        Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis.
        BMJ. 2007; 334: 299
        • Albright A.L.
        • Gregg E.W.
        Preventing type 2 diabetes in communities across the U.S.: the National Diabetes Prevention Program.
        Am J Prev Med. 2013; 44: S346-S351
        • American Diabetes Association
        Standards of medical care in diabetes−2014.
        Diabetes Care. 2014; 37: S14-S80
      2. U.S. Preventive Services Task Force. Screening for Type 2 Diabetes Mellitus in Adults, Topic Page. August 1, 2013. http://www.uspreventiveservicestaskforce.org/Page/Document/ClinicalSummaryFinal/diabetes-mellitus-type-2-in-adults-screening.

        • Dall T.M.
        • Zhang Y.
        • Zhang S.
        • et al.
        Weight loss and lifetime medical expenditures: a case study with TRICARE prime beneficiaries.
        Am J Prev Med. 2011; 40: 338-344
        • Yang W.
        • Dall T.M.
        • Zhang Y.
        • et al.
        Simulation of quitting smoking in the military shows higher lifetime medical spending more than offset by productivity gains.
        Health Aff (Millwood). 2012; 31: 2717-2726
        • Diabetes Prevention Program Research Group
        The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS.
        Diabetes Care. 2012; 35: 723-730
        • Caro J.J.
        • Briggs A.H.
        • Siebert U.
        • Kuntz K.M.
        Modeling good research practices—overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1.
        Value Health. 2012; 15: 796-803
      3. National Center for Health Statistics. Continous NHANES Web Tutorial. 2013. http://www.cdc.gov/nchs/tutorials/NHANES/index_continuous.htm.

        • Newton K.M.
        • Wagner E.H.
        • Ramsey S.D.
        • et al.
        The use of automated data to identify complications and comorbidities of diabetes: a validation study.
        J Clin Epidemiol. 1999; 52: 199-207
        • CDC
        National diabetes statistics report, 2014.
        CDC, Atlanta GA2014
        • Clarke P.M.
        • Gray A.M.
        • Briggs A.
        • et al.
        A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
        Diabetologia. 2004; 47: 1747-1759
        • Kannel W.B.
        • Wolf P.A.
        • Benjamin E.J.
        • Levy D.
        Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates.
        Am J Cardiol. 1998; 82: 2-9N
        • Nichols G.A.
        • Reinier K.
        • Chugh S.S.
        Independent contribution of diabetes to increased prevalence and incidence of atrial fibrillation.
        Diabetes Care. 2009; 32: 1851-1856
        • Anderson K.M.
        • Odell P.M.
        • Wilson P.W.
        • Kannel W.B.
        Cardiovascular disease risk profiles.
        Am Heart J. 1991; 121: 293-298
        • Hippisley-Cox J.
        • Coupland C.
        Predicting the risk of chronic kidney disease in men and women in England and Wales: prospective derivation and external validation of the QKidney-Scores.
        BMC Fam Pract. 2010; 11: 49
        • Wilson P.W.
        • Anderson K.M.
        • Harri T.
        • Kannel W.B.
        • Castelli W.P.
        Determinants of change in total cholesterol and HDL-C with age: the Framingham Study.
        J Gerontol. 1994; 49: M252-M257
        • Wilson P.W.
        • Bozeman S.R.
        • Burton T.M.
        • et al.
        Prediction of first events of coronary heart disease and stroke with consideration of adiposity.
        Circulation. 2008; 118: 124-130
        • Ho K.K.
        • Anderson K.M.
        • Kannel W.B.
        • Grossman W.
        • Levy D.
        Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
        Circulation. 1993; 88: 107-115
        • USDHHS
        The Framingham Study: an epidemiological intervention of cardiovascular diseases: section 34: some risk factors related to the annual incidence of cardiovascular disease and death using pooled repeated biennial measurements: Framingham Heart Study, 30 year followup.
        USDHHS, Washington DCApril 13, 2009
        • D’Agostino R.B.
        • Vasan R.S.
        • Pencina M.J.
        • et al.
        General cardiovascular risk profile for use in primary care the Framingham Heart Study.
        Circulation. 2008; 117: 743-753
        • Sheehan T.J.
        • DuBrava S.
        • DeChello L.M.
        • Fang Z.
        Rates of weight change for black and white Americans over a twenty year period.
        Int J Obesity. 2003; 27: 498-504
        • Neter J.E.
        • Stam B.E.
        • Kok F.J.
        • Grobbee D.E.
        • Geleijnse J.M.
        Influence of weight reduction on blood pressure a meta-analysis of randomized controlled trials.
        Hypertension. 2003; 42: 878-884
        • Heianza Y.
        • Arase Y.
        • Fujihara K.
        • et al.
        Longitudinal trajectories of HbA1c and fasting plasma glucose levels during the development of type 2 diabetes the Toranomon Hospital Health Management Center Study 7 (TOPICS 7).
        Diabetes Care. 2012; 35: 1050-1052
        • American Diabetes Association
        Diagnosis and classification of diabetes mellitus.
        Diabetes Care. 2010; 33: S62-S69
        • National Heart, Lung, and Blood Institute
        Obesity initiative: the practical guide: identification, evaluation, and treatment of overweight and obesity in adults.
        National Heart, Lung, and Blood Institute, Washington DCOctober 1, 2000
        • de Simone G.
        • Devereux R.B.
        • Roman M.J.
        • Alderman M.H.
        • Laragh J.H.
        Relation of obesity and gender to left ventricular hypertrophy in normotensive and hypertensive adults.
        Hypertension. 1994; 23: 600-606
        • Hooi J.D.
        • Kester A.D.
        • Stoffers H.E.
        • et al.
        Incidence of and risk factors for asymptomatic peripheral arterial occlusive disease: a longitudinal study.
        Am J Epidemiol. 2001; 153: 666-672
        • Schaufelberger M.
        • Swedberg K.
        • Koster M.
        • Rosen M.
        • Rosengren A.
        Decreasing one-year mortality and hospitalization rates for heart failure in Sweden Data from the Swedish Hospital Discharge Registry 1988 to 2000.
        Eur Heart J. 2004; 25: 300-307
        • Socialstyrelsen
        Swedish Health and Welfare Statistical Databases: AMI statistics.
        Socialstyrelsen. 2013; (http://www.socialstyrelsen.se/statistics)
        • Vemmos K.N.
        • Bots M.L.
        • Tsibouris P.K.
        • et al.
        Prognosis of stroke in the south of Greece: 1 year mortality, functional outcome and its determinants: the Arcadia Stroke Registry.
        J Neurol Neurosurg Psychiatry. 2000; 69: 595-600
        • Tonelli M.
        • Wiebe N.
        • Culleton B.
        • et al.
        Chronic kidney disease and mortality risk: a systematic review.
        J Am Soc Nephrol. 2006; 17: 2034-2047
      4. CDC, National Center for Health Statistics. Underlying cause of death 1999−2010 on CDC WONDER Online Database. Released 2012. http://wonder.cdc.gov.

        • Zhang P.
        • Brown M.B.
        • Bilik D.
        • et al.
        Health utility scores for people with type 2 diabetes in U.S. managed care health plans results from Translating Research Into Action for Diabetes (TRIAD).
        Diabetes Care. 2012; 35: 2250-2256
        • Sullivan P.W.
        • Lawrence W.F.
        • Ghushchyan V.
        A national catalog of preference-based scores for chronic conditions in the United States.
        Med Care. 2005; 43: 736-749
        • Riley G.F.
        • Lubitz J.D.
        Long-term trends in Medicare payments in the last year of life.
        Health Serv Res. 2010; 45: 565-576
        • Dall T.M.
        • Narayan K.M.
        • Gillespie K.B.
        • et al.
        Detecting type 2 diabetes and prediabetes among asymptomatic adults in the United States: modeling American Diabetes Association versus US Preventive Services Task Force diabetes screening guidelines.
        Popul Health Metr. 2014; 12: 12
        • Vojta D.
        • Koehler T.B.
        • Longjohn M.
        • Lever J.A.
        • Caputo N.F.
        A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program.
        Am J Prev Med. 2013; 44: S301-S306
        • Lawlor M.S.
        • Blackwell C.S.
        • Isom S.P.
        • et al.
        Cost of a group translation of the Diabetes Prevention Program: healthy living partnerships to prevent diabetes.
        Am J Prev Med. 2013; 44: S381-S389
        • Hernan W.H.
        • Brandle M.
        • Zhang P.
        • et al.
        Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.
        Diabetes Care. 2003; 26: 36-47
        • Alibasic E.
        • Ramic E.
        • Alic A.
        Prevention of diabetes in family medicine.
        Mater Sociomed. 2013; 25: 80-82
        • Tuomilehto J.
        • Schwarz P.
        • Lindstrom J.
        Long-term benefits from lifestyle interventions for type 2 diabetes prevention: time to expand the efforts.
        Diabetes Care. 2011; 34: S210-S214
        • Zhang P.
        • Engelgau M.M.
        • Valdez R.
        • et al.
        Costs of screening for pre-diabetes among US adults: a comparison of different screening strategies.
        Diabetes Care. 2003; 26: 2536-2542
        • Norris S.L.
        • Kansagara D.
        • Bougatsos C.
        • Fu R.
        Screening adults for type 2 diabetes: a review of the evidence for the US Preventive Services Task Force.
        Ann Intern Med. 2008; 148: 855-868
        • English R.
        • Lebovitz Y.
        • Griffin R.
        Transforming clinical research in the United States: challenges and opportunities: workshop summary.
        The National Academies Press, Washington DC2010
        • Kahn R.
        • Alperin P.
        • Eddy D.
        • et al.
        Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis.
        Lancet. 2010; 375: 1365-1374
        • Palmer A.J.
        • Clarke P.
        • Gray A.
        • et al.
        Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting.
        Value Health. 2013; 16: 670-685
        • Stevens R.J.
        • Kothari V.
        • Adler A.I.
        • Stratton I.M.
        • Holman R.R.
        The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56).
        Clin Sci (Lond). 2001; 101: 671-679
        • Hoerger T.J.
        • Segel J.E.
        • Zhang P.
        • Sorensen S.W.
        Validation of the CDC-RTI diabetes cost-effectiveness model. RTI Press Publication No. MR-0013-090.
        RTI International, Research Triangle Park NC2009
        • Palmer A.J.
        • Roze S.p.
        • Valentine W.J.
        • et al.
        The CORE Diabetes Model: projecting long-term clinical outcomes, costs and costeffectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making.
        Curr Med Res Opin. 2004; 20: S5-S26
        • Yudkin J.S.
        • Montori V.M.
        The epidemic of pre-diabetes: the medicine and the politics.
        BMJ. 2014; 349: g4485
        • Wing R.R.
        • Bolin P.
        • Brancati F.L.
        • et al.
        Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes.
        N Engl J Med. 2013; 369: 145-154
        • Wing R.R.
        Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial.
        Arch Intern Med. 2010; 170: 1566-1575
        • Nylen E.S.
        • Faselis C.
        • Kheirbek R.
        • et al.
        Statins modulate the mortality risk associated with obesity and cardiorespiratory fitness in diabetics.
        J Clin Endocrinol Metab. 2013; 98: 3394-3401
        • James C.
        • Bullard K.M.
        • Rolka D.B.
        • et al.
        Implications of alternative definitions of prediabetes for prevalence in U.S. adults.
        Diabetes Care. 2011; 34: 387-391
        • Guh D.P.
        • Zhang W.
        • Bansback N.
        • et al.
        The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis.
        BMC Public Health. 2009; 9: 88
        • Gerstein H.C.
        • Santaguida P.
        • Raina P.
        • et al.
        Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies.
        Diabetes Res Clin Pract. 2007; 78: 305-312
        • Geiss L.S.
        • James C.
        • Gregg E.W.
        • et al.
        Diabetes risk reduction behaviors among U.S. adults with prediabetes.
        Am J Prev Med. 2010; 38: 403-409
        • Narayan K.M.
        • Echouffo-Tcheugui J.B.
        • Mohan V.
        • Ali M.K.
        Analysis & commentary: global prevention and control of type 2 diabetes will require paradigm shifts in policies within and among countries.
        Health Aff (Millwood). 2012; 31: 84-92