V. Foos

The CORE Diabetes Model: Projecting Long-term Clinical Outcomes, Costs and Cost- effectiveness of Interventions in Diabetes Mellitus (Types 1 and 2) to Support Clinical and Reimbursement Decision-making

SUMMARY Objectives: We have developed an Internet-based, interactive computer model to determine the longterm health outcomes and economic consequences of implementing different treatment policies or interventions in type 1 and type 2 diabetes mellitus. The model projects outcomes for populations, taking into account baseline cohort characteristics and past history of complications, current and future diabetes management and concomitant medications, screening strategies and changes in physiological parameters over time. The development of complications, life expectancy, quality-adjusted life expectancy and total costs within populations can be calculated. Methods: The model is based on a series of sub-models that simulate important complications of diabetes (cardiovascular disease, eye disease, hypoglycaemia, nephropathy, neuropathy, foot ulcer, amputation, stroke, ketoacidosis, lactic acidosis and mortality). Each sub-model is a Markov model using Monte Carlo simulation incorporating time, state, time-in state, and diabetes type-dependent probabilities derived from published sources. Analyses can be performed on cohorts with type 1 or type 2 diabetes. Cohorts, defined in terms of age, gender, baseline risk factors and pre-existing complications, can be modified or new cohorts defined by the user. Economic and clinical data in the model can be edited, thus ensuring adaptability by allowing the inclusion of new data as they become available; creation of country- or provider-specific versions of the model; and allowing the investigation of new hypotheses. Conclusions: The CORE Diabetes Model allows the calculation of long-term outcomes, based on the best data currently available. Diabetes management strategies can be compared in different patient populations in a variety of realistic clinical settings, allowing the identification of efficient diabetes management strategies.

Validation of the IMS CORE Diabetes Model.

BACKGROUND The IMS CORE Diabetes Model (CDM) is a widely published and validated simulation model applied in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) analyses. Validation to external studies is an important part of demonstrating model credibility. OBJECTIVE Because the CDM is widely used to estimate long-term clinical outcomes in diabetes patients, the objective of this analysis was to validate the CDM to contemporary outcomes studies, including those with long-term follow-up periods. METHODS A total of 112 validation simulations were performed, stratified by study follow-up duration. For long-term results (≥15-year follow-up), simulation cohorts representing baseline Diabetes Control and Complications Trial (DCCT) and United Kingdom Prospective Diabetes Study (UKPDS) cohorts were generated and intensive and conventional treatment arms were defined in the CDM. Predicted versus observed macrovascular and microvascular complications and all-cause mortality were assessed using the coefficient of determination (R(2)) goodness-of-fit measure. RESULTS Across all validation studies, the CDM simulations produced an R(2) statistic of 0.90. For validation studies with a follow-up duration of less than 15 years, R(2) values of 0.90 and 0.88 were achieved for T1DM and T2DM respectively. In T1DM, validating against 30-year outcomes data (DCCT) resulted in an R(2) of 0.72. In T2DM, validating against 20-year outcomes data (UKPDS) resulted in an R(2) of 0.92. CONCLUSIONS This analysis supports the CDM as a credible tool for predicting the absolute number of clinical events in DCCT- and UKPDS-like populations. With increasing incidence of diabetes worldwide, the CDM is particularly important for health care decision makers, for whom the robust evaluation of health care policies is essential.

Economic impact of severe and non-severe hypoglycemia in patients with Type 1 and Type 2 diabetes in the United States

Abstract Objective: To identify the direct and indirect costs of hypoglycemia in patients with Type 1 or Type 2 diabetes mellitus (DM) in the US setting. Methods: A literature review was conducted to identify and review studies that reported data on the economic burden of hypoglycemia and the related medical resource consumption or productivity loss related to hypoglycemia in patients with Type 1 or Type 2 DM. Relevant information was collated in an economic model to assess the direct and indirect costs following severe and non-severe hypoglycemic events in Type 1 and Type 2 DM. Results: Detailed evidence of the medical cost burden of hypoglycemic events was identified from 14 studies. For both Type 1 and Type 2 DM, episodes requiring assistance from a healthcare practitioner were identified as particularly costly and amounted to

Cost-effectiveness of basal insulin from a US health system perspective: comparative analyses of detemir, glargine, and NPH.

1161 per episode (direct costs) compared with episode costs of

Self-monitoring of blood glucose (SMBG) in patients with type 2 diabetes on oral anti-diabetes drugs: cost-effectiveness in France, Germany, Italy, and Spain

66 and

PROactive 06: cost-effectiveness of pioglitazone in Type 2 diabetes in the UK

11 for events requiring third-party (non-medical) assistance and events managed by self-treatment, respectively. Indirect costs associated with severe hypoglycemia requiring non-medical assistance, severe hypoglycemia requiring medical assistance, and non-severe hypoglycemia were predicted to be

Insulin degludec early clinical experience: does the promise from the clinical trials translate into clinical practice—a case-based evaluation

242,

Long-Term Cost-Effectiveness of Pioglitazone versus Placebo in Addition to Existing Diabetes Treatment: A US Analysis Based on PROactive

160, and

Biphasic insulin aspart 70/30 vs. insulin glargine in insulin naïve type 2 diabetes patients: modelling the long-term health economic implications in a Swedish setting

11 for patients with Type 1 diabetes and

Computer modeling of diabetes and its transparency: A report on the Eighth Mount Hood Challenge

579,