INTRODUCTION: To examine the long-term health and economic impact of a lifestyle diabetes prevention program in people with high risk of developing type 2 diabetes in Germany. RESEARCH DESIGN AND METHODS: We assessed the lifetime cost-effectiveness of a 2-year pragmatic lifestyle program for preventing type 2 diabetes targeting German adults aged 35-54 and 55-74 years old with hemoglobin A1c (HbA1c) from 6.0% to 6.4%. We used the Centers for Disease Control and Prevention RTI Diabetes Cost-Effectiveness Model to run a simulation on the program effectiveness. We estimated incremental health benefits in quality-adjusted life years (QALYs) and costs using an established simulation model adapted to the German context, from a healthcare system and societal perspective. The cost-effectiveness of the program was measured by incremental cost-effectiveness ratios (ICERs) in cost per QALY. We projected the number of type 2 diabetes cases prevented by participation rate if the program was implemented nationwide. RESULTS: The lifestyle program would result to more QALYs and higher costs. The lifetime ICERs were 14 690€ (35-54 years old) and 14 372€ (55-74 years old) from a healthcare system perspective and cost saving (ICER=-3805€) and cost-effective (ICER=4579€), respectively, from a societal perspective. A total of 10 527 diabetes cases would be prevented over lifetime if the program was offered to all eligible people nationwide and 25% of those would participate in the program. CONCLUSIONS: Implementing the lifestyle intervention for people with HbA1c from 6.0% to 6.4% could be a cost-effective at standard willingness to pay level strategy for type 2 diabetes prevention. The intervention in the younger cohort could be cost saving from a societal perspective. The successful implementation of a lifestyle-based diabetes prevention program could be an important component of a successful National Diabetes Strategy in Germany.

OBJECTIVE: To develop a microsimulation model to estimate the health effects, costs, and cost-effectiveness of public health and clinical interventions for preventing/managing type 2 diabetes. METHODS: We combined newly developed equations for complications, mortality, risk factor progression, patient utility, and cost-all based on U.S. studies-in a microsimulation model. We performed internal and external validation of the model. To demonstrate the model's utility, we predicted remaining life-years, quality-adjusted life-years (QALYs), and lifetime medical cost for a representative cohort of 10,000 U.S. adults with type 2 diabetes. We then estimated the cost-effectiveness of reducing HbA1c from 9% to 7% among adults with type 2 diabetes, using low-cost, generic, oral medications. RESULTS: The model performed well in internal validation; the average absolute difference between simulated and observed incidence for 17 complications was less than 8%. In external validation, the model was better at predicting outcomes in clinical trials than in observational studies. The cohort of U.S. adults with type 2 diabetes was projected to have an average of 19.95 remaining life-years (from mean age 61), incur $187,729 in discounted medical costs, and accrue 8.79 discounted QALYs. The intervention to reduce HbA1c increased medical costs by $1,256 and QALYs by 0.39, yielding an incremental cost-effectiveness ratio of $9,103 per QALY. CONCLUSIONS: Using equations exclusively derived from U.S. studies, this new microsimulation model achieves good prediction accuracy in U.S. POPULATIONS: The model can be used to estimate the long-term health impact, costs, and cost-effectiveness of interventions for type 2 diabetes in the United States.

AIMS: Cost-effectiveness (CE) of lifestyle change programs (LCP) for type 2 diabetes (T2D) prevention is influenced by a participant's risk. We identified the risk threshold of developing T2D in the intervention population that was cost-effective for three formats of the LCP: delivered in-person individually or in groups, or delivered virtually. We compared the cost-effectiveness across program formats when there were more than one cost-effective formats. METHODS: Using the CDC-RTI T2D diabetes CE simulation model, we estimated CEs associated with three program formats in 8 population groups with an annual T2D incidence of 1% to 8%. We generated a nationally representative simulation population for each risk level using the 2011-2016 National Health and Nutrition Examination Survey data. We used an incremental cost-effectiveness ratio (ICER), cost per quality-adjusted life year (QALY) gained in 25-years, to measure the CEs of the programs. We took a health care system perspective RESULTS: To achieve an ICER of $50,000/QALY or lower, the annual T2D incidence of the program participant needed to be ≥5% for the in-person individual program, ≥4% for the digital individual program, and ≥3% for the in-person group program. For those with T2D risk of ≥4%, the in-person group program always dominated the digital individual program. The in-person individual program was cost-effective compared with the in-person group program only among persons with T2D risk of ≥8%. CONCLUSIONS: Our findings could assist decision-makers in selecting the most appropriate target population for different formats of lifestyle intervention programs to prevent T2D.

More than 30 million people in the U.S. have diabetes, and approximately 7.4 million (30% of those with diabetes) regularly use one or more insulin formulations. For those who rely on it, i.e., all patients with type 1 diabetes and many patients with type 2 diabetes, insulin is a lifesaving medication. Between 2007 and 2016, the average annual total Medicare Part D payment on insulin per person increased by 358%, which resulted in an 81% increase for the out-of-pocket (OOP) payment (1). The consequences of unaffordability for insulin can be severe and costly. High OOP payments may force individuals to choose between purchasing their medication and paying for other necessities. To date, there are limited data on how total insulin payment and the corresponding OOP payment changed in the commercially insured population within the same period. The objective of this study is to delineate total and patients’ OOP payment trends for insulin among privately insured U.S. adults.

OBJECTIVE: To estimate trends in total payment and patients' out-of-pocket (OOP) payments of noninsulin glucose-lowering drugs by class from 2005 to 2018. RESEARCH DESIGN AND METHODS: We analyzed data for 53 million prescriptions from adults aged >18 years with type 2 diabetes under fee-for-service plans from the 2005-2018 IBM MarketScan Commercial Databases. The total payment was measured as the amount that the pharmacy received, and the OOP payment was the sum of copay, coinsurance, and deductible paid by the beneficiaries. We applied a joinpoint regression to evaluate nonlinear trends in cost between 2005 and 2018. We further conducted a decomposition analysis to explore the drivers for total payment change. RESULTS: Total annual payments for older drug classes, including metformin, sulfonylurea, meglitinide, α-glucosidase inhibitors, and thiazolidinedione, have declined during 2005-2018, ranging from -$271 (-53.8%) (USD) for metformin to -$2,406 (-92.2%) for thiazolidinedione. OOP payments for these drug classes also reduced. In the same period, the total annual payments for the newer drug classes, including dipeptidyl peptidase-4 inhibitors, glucagon-like peptide 1 receptor agonists, and sodium-glucose cotransporter 2 inhibitors, have increased by $2,181 (88.4%), $3,721 (77.6%), and $1,374 (37.0%), respectively. OOP payment for these newer classes remained relatively unchanged. Our study findings indicate that switching toward the newer classes for noninsulin glucose-lowering drugs was the main driver that explained the total payment increase. CONCLUSIONS: Average annual payments and OOP payment for noninsulin glucose-lowering drugs have increased significantly from 2005 to 2018. The uptake of newer drug classes was the main driver.

AIMS: To estimate the prevalence and medical expenditures of diabetes-related complications (DRCs) among adult Medicaid enrollees with diabetes. METHODS: We estimated the prevalence and medical expenditures for 12 diabetes-related complications by Medicaid eligibility category (disability-based vs. non-disability-based) in eight states. We used generalized linear models with log link and gamma distribution to estimate the total per-person annual medical expenditures for DRCs, controlling for demographics, and other comorbidities. RESULTS: Among non-disability-based enrollees (NDBEs), 40.1% (in California) to 47.5% (in Oklahoma) had one or more DRCs, compared to 53.6% (in Alabama) to 64.8% (in Florida) among disability-based enrollees (DBEs). The most prevalent complication was neuropathy (16.1%-27.1% for NDBEs; 20.2%-30.4% for DBEs). Lower extremity amputation (<1% for both eligibilities) was the least prevalent complication. The costliest per-person complication was dialysis (per-person excess annual expenditure of $22,481-$41,298 for NDBEs; $23,569-$51,470 for DBEs in 2012 USD). Combining prevalence and per-person excess expenditures, the three costliest complications were nephropathy, heart failure, and ischemic heart disease (IHD) for DBEs, compared to neuropathy, nephropathy, and IHD for NDBEs. CONCLUSIONS: Our study provides data that can be used for assessing the health care resources needed for managing DRCs and evaluating cost-effectiveness of interventions to prevent and management DRCs.

OBJECTIVE: To estimate the health utility impact of diabetes-related complications in a large, longitudinal U.S. sample of people with type 2 diabetes. RESEARCH DESIGN AND METHODS: We combined Health Utilities Index Mark 3 data on patients with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Look AHEAD (Action for Health in Diabetes) trials and their follow-on studies. Complications were classified as events if they occurred in the year preceding the utility measurement; otherwise, they were classified as a history of the complication. We estimated utility decrements associated with complications using a fixed-effects regression model. RESULTS: Our sample included 15,252 persons with an average follow-up of 8.2 years and a total of 128,873 person-visit observations. The largest, statistically significant (P < 0.05) health utility decrements were for stroke (event, -0.109; history, -0.051), amputation (event, -0.092; history, -0.150), congestive heart failure (event, -0.051; history, -0.041), dialysis (event, -0.039), estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m(2) (event, -0.043; history, -0.025), angina (history, -0.028), and myocardial infarction (MI) (event, -0.028). There were smaller effects for laser photocoagulation and eGFR <60 mL/min/1.73 m(2). Decrements for dialysis history, angina event, MI history, revascularization event, revascularization history, laser photocoagulation event, and hypoglycemia were not significant (P ≥ 0.05). CONCLUSIONS: With use of a large study sample and a longitudinal design, our estimated health utility scores are expected to be largely unbiased. Estimates can be used to describe the health utility impact of diabetes complications, improve cost-effectiveness models, and inform diabetes policies.

Objectives: This study aims to estimate the national impact and cost-effectiveness of the 2018 American College of Physicians (ACP) guidance statements compared to the status quo. Methods: Survey data from the 2011-2016 National Health and Nutrition Examination were used to generate a national representative sample of individuals with diagnosed type 2 diabetes in the United States. Individuals with A1c <6.5% on antidiabetic medications are recommended to deintensify their A1c level to 7.0% to 8.0% (group 1); individuals with A1c 6.5% to 8.0% and a life expectancy of <10 years are recommended to deintensify their A1c level >8.0% (group 2); and individuals with A1c >8.0% and a life expectancy of >10 years are recommended to intensify their A1c level to 7.0% to 8.0% (group 3). We used a Markov-based simulation model to evaluate the lifetime cost-effectiveness of following the ACP recommended A1c level. Results: 14.41 million (58.1%) persons with diagnosed type 2 diabetes would be affected by the new guidance statements. Treatment deintensification would lead to a saving of $363 600 per quality-adjusted life-year (QALY) lost for group 1 and a saving of $118 300 per QALY lost for group 2. Intensifying treatment for group 3 would lead to an additional cost of $44 600 per QALY gain. Nationally, the implementation of the guidance would add 3.2 million life-years and 1.1 million QALYs and reduce healthcare costs by $47.7 billion compared to the status quo. Conclusions: Implementing the new ACP guidance statements would affect a large number of persons with type 2 diabetes nationally. The new guidance is cost-effective.

AIMS: To estimate the cost of diabetes complications in the United States (U.S.). METHODS: We constructed longitudinal panel data using one of the largest claims databases in the U.S. for privately insured Type 1 (T1DM) and type 2 (T2DM) diabetes patients with a follow-up time of one to ten years. Complication costs were estimated both in years of the first occurrence and in subsequent years, using individual fixed-effects models. All costs were in 2016 dollars. RESULTS: 47,166 people with T1DM and 608,237 with T2DM were included in our study. Aside from organ transplants, which were rare, the estimated average costs for the top three most costly conditions in the first vs. subsequent years were: end stage renal disease ($73,534 vs. $97,431 for T1DM; $94,231 vs. $98,981 for T2DM), congestive heart failure ($41,681 vs. $14,855 for T1DM; $31,202 vs. $7062 for T2DM), and myocardial infarction ($40,899 vs. $9496 for T1DM; $45,251 vs. $8572 for T2DM). For both diabetes types, retinopathy and neuropathy tend to have the lowest cost estimates. CONCLUSIONS: Our study provides the latest and most comprehensive cost estimates for a broad set of diabetes complications needed to evaluate the long-term cost-effectiveness of interventions for preventing and managing diabetes.

BACKGROUND: Lifestyle change interventions (LCI) for prevention of type 2 diabetes are covered by Medicare, but rarely by US Medicaid programs that constitute the largest public payer system in the USA. We estimate the long-term health and economic implications of implementing LCIs in state Medicaid programs. METHODS: We compared LCIs modeled after the intervention of the Diabetes Prevention Program versus routine care advice using a decision analytic simulation model and best available data from representative surveys, cohort studies, Medicaid claims data, and the published literature. Target population were non-disability-based adult Medicaid beneficiaries aged 19-64 years at high risk for type 2 diabetes (BMI >/=25 kg/m(2) and HbA1c >/= 5.7% or fasting plasma glucose >/= 110 mg/dl) from eight study states (Alabama, California, Connecticut, Florida, Iowa, Illinois, New York, Oklahoma) that represent around 50% of the US Medicaid population. Incremental cost-effectiveness ratios (ICERs) measured in cost per quality-adjusted life years (QALYs) gained, and population cost and health impact were modeled from a healthcare system perspective and a narrow Medicaid perspective. RESULTS: In the eight selected study states, 1.9 million or 18% of non-disability-based adult Medicaid beneficiaries would belong to the eligible high-risk target population - 66% of them Hispanics or non-Hispanic black. In the base-case analysis, the aggregated 5- and 10-year ICERs are US$226 k/QALY and US$34 k/QALY; over 25 years, the intervention dominates routine care. The 5-, 10-, and 25-year probabilities that the ICERs are below US$50 k (US$100 k)/QALY are 6% (15%), 59% (82%) and 96% (100%). From a healthcare system perspective, initial program investments of US$800 per person would be offset after 13 years and translate to US$548 of savings after 25 years. With a 20% LCI uptake in eligible beneficiaries, this would translate to upfront costs of US$300 million, prevent 260 thousand years of diabetes and save US$205 million over a 25-year time horizon. Cost savings from a narrow Medicaid perspective would be much smaller. Minorities and low-income groups would over-proportionally benefit from LCIs in Medicaid, but the impact on population health and health equity would be marginal. CONCLUSIONS: In the long-term, investments in LCIs for Medicaid beneficiaries are likely to improve health and to decrease healthcare expenditures. However, population health and health equity impact would be low and healthcare expenditure savings from a narrow Medicaid perspective would be much smaller than from a healthcare system perspective.

AIMS: Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA. METHODS: We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group). RESULTS: In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs >/= 5% were below $50,000/QALY gained. CONCLUSIONS: Intervening in those with >/= 7% diabetes risk based on the GDRS or >/= 9% on the ARIC 2009 score would be cost-effective. A risk score threshold >/= 5% together with elevated FPG would also allow targeting interventions cost-effectively.

PURPOSE: The measurement and estimation of diabetes in populations guides resource allocation, health priorities, and can influence practice and future research. To provide a critical reflection on current diabetes surveillance, we provide in-depth discussion about how upstream determinants, prevalence, incidence, and downstream impacts of diabetes are measured in the USA, and the challenges in obtaining valid, accurate, and precise estimates. FINDINGS: Current estimates of the burden of diabetes risk are obtained through national surveys, health systems data, registries, and administrative data. Several methodological nuances influence accurate estimates of the population-level burden of diabetes, including biases in selection and response rates, representation of population subgroups, accuracy of reporting of diabetes status, variation in biochemical testing, and definitions of diabetes used by investigators. Technological innovations and analytical approaches (e.g., data linkage to outcomes data like the National Death Index) may help address some, but not all, of these concerns, and additional methodological advances and validation are still needed. Current surveillance efforts are imperfect, but measures consistently collected and analyzed over several decades enable useful comparisons over time. In addition, we proposed that focused subsampling, use of technology, data linkages, and innovative sensitivity analyses can substantially advance population-level estimation.