OBJECTIVE -To develop and validate a comprehensive computer simulation model to assess the impact of screening, prevention, and treatment strategies on type 2 diabetes and its complications, comorbidities, quality of life, and cost.RESEARCH DESIGN AND METHODS -The incidence of type 2 diabetes and its complications and comorbidities were derived from population-based epidemiologic studies and randomized, controlled clinical trials. Health utility scores were derived for patients with type 2 diabetes using the Quality of Well Being-Self-Administered. Direct medical costs were derived for managed care patients with type 2 diabetes using paid insurance claims. Monte Carlo techniques were used to implement a semi-Markov model. Performance of the model was assessed using baseline and 4-and 10-year follow-up data from the older-onset diabetic population studied in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR).RESULTS -Applying the model to the baseline WESDR population with type 2 diabetes, we predicted mortality to be 51% at 10 years. The prevalences of stroke and myocardial infarction were predicted to be 18 and 19% at 10 years. The prevalences of nonproliferative diabetic retinopathy, proliferative retinopathy, and macular edema were predicted to be 45, 16, and 18%, respectively; the prevalences of microalbuminuria, proteinuria, and end-stage renal disease were predicted to be 19, 39, and 3%, respectively; and the prevalences of clinical neuropathy and amputation were predicted to be 52 and 5%, respectively, at 10 years. Over 10 years, average undiscounted total direct medical costs were estimated to be $53,000 per person. Among survivors, the average utility score was estimated to be 0.56 at 10 years.CONCLUSIONS -Our computer simulation model accurately predicted survival and the cardiovascular, microvascular, and neuropathic complications observed in the WESDR cohort with type 2 diabetes over 10 years. The model can be used to predict the progression of diabetes and its complications, comorbidities, quality of life, and cost and to assess the relative effectiveness, cost-effectiveness, and cost-utility of alternative strategies for the prevention and treatment of type 2 diabetes. Diabetes Care 28:2856 -2863, 2005T ype 2 diabetes is associated with long-term complications that ultimately cause more cases of adult blindness, renal failure, and amputation than any other disease in the U.S. (1). In addition, people with type 2 diabetes are at increased risk for stroke and myocardial infarction, and mortality rates for people with type 2 diabetes are about twice those for people without diabetes (2). Because of the high morbidity, mortality, and cost associated with type 2 diabetes, there has been great interest in identifying strategies for the prevention and treatment of type 2 diabetes and in assessing the impact of those strategies on survival, disease progression, complications, comorbidities, quality of life, and cost.To address these issues, we have developed a comprehensive model that synthe...

Objectives: The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes. Methods: Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups' replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R 2 ). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed. Results: Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed. Conclusions: Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.

Background: Insulin therapy is most effective if dosage titrations are done regularly and frequently, which is seldom possible for busy clinicians. The d-Nav® Insulin Guidance System was design to address the insulin titration gap in patients with type 2 diabetes. It relies on the d-Nav handheld device, which is used to measure glucose, determine the glucose patterns and automatically determine the appropriate next insulin dose. It closes the titration gap in a scalable way utilizing the support of dedicated health care professionals (HCP). This multicenter randomized controlled study tested whether the combination of the d-Nav system and HCP support (d-Nav+HCP-S) is superior to HCP support alone (HCP-S).Methods: 181 subjects using insulin with sub-optimally controlled type 2 diabetes were randomized 1:1 to either d-Nav+HCP-S or HCP-S alone. Both groups were contacted 7 times

OBJECTIVE -To evaluate the impact of systematic patient evaluation and patient and provider feedback on the processes and intermediate outcomes of diabetes care in Independent Practice Association model internal medicine practices.RESEARCH DESIGN AND METHODS -Nine practices providing care to managed care patients were randomly assigned as intervention or comparison sites. Intervention-site subjects had Annual Diabetes Assessment Program (ADAP) assessments (HbA 1c , blood pressure, lipids, smoking, retinal photos, urine microalbumin, and foot examination) at years 1 and 2. Comparison-site subjects had ADAP assessments at year 2. At Intervention sites, year 1 ADAP results were reviewed with subjects, mailed to providers, and incorporated into electronic medical records with guideline-generated suggestions for treatment and follow-up. Medical records were evaluated for both groups for the year before both the year 1 and year 2 ADAP assessments. Processes and intermediate outcomes were compared using linear and logistic mixed hierarchical models.RESULTS -Of 284 eligible subjects, 103 of 173 (60%) at the Intervention sites and 71 of 111 (64%) at the comparison sites participated; 83 of 103 (81%) of the intervention-site subjects returned for follow-up at year 2. Performance of the six recommended assessments improved in intervention-site subjects at year 2 compared with year 1 (5.8 vs. 4.3, P ϭ 0.0001) and compared with comparison-site subjects at year 2 (4.2, P ϭ 0.014). No significant changes were noted in intermediate outcomes. Shortcomings in self-management support, clinical information systems, and decision support contribute to suboptimal diabetes care in primary care (23). The Annual Diabetes Assessment Program (ADAP) was designed as a population-based program of evaluation and feedback to support diabetes clinical practice guidelines (24,25). During a 1-h focused encounter with nonphysician providers within the primary care setting, key diabetes and cardiovascular health parameters were measured and discussed with the patient by a certified diabetes educator. A tailored report with guidelinedriven recommendations for care (25) was then sent to the patient's primary care provider (PCP) and incorporated into the electronic medical record. We hypothesized that implementing the ADAP would improve processes and intermediate outcomes of diabetes care. CONCLUSIONSTo rigorously assess whether the ADAP would improve measures of diabetes care, we conducted a randomized, controlled clinical trial. RESEARCH DESIGN AND METHODS Clinical settingNine university-affiliated primary care internal medicine (IM) practices affiliated with a managed care organization (MCO) were paired by size and type and then randomly assigned as intervention or comparison sites. One unpaired practice was assigned as an intervention site. Most of the PCPs in the practices were working full-time and there were no residents in the practices. ParticipantsDiabetic members of the MCO who were aged Ն18 years and had been members for at least 1 year wer...

This research was motivated by a desire to model the progression of a chronic disease through various disease stages when data are not available to directly estimate all the transition parameters in the model. This is a common occurrence when time and expense make it unfeasible to follow a single cohort to estimate all the transition parameters. One difficulty of developing a model of chronic disease progression from such data is that the available studies often do not include the transitions of interest. For example, in our model of diabetic nephropathy, many clinical studies did not differentiate between patients without nephropathy and those who had microalbuminuria (a pre-clinical stage of nephropathy). Another difficulty was a lack of data to directly estimate parameters of interest. We consider models which can accommodate such difficulties. In this paper we consider the problem of estimating parameters of a discrete-time Markov process when longitudinal data describing the entire process are not available. First, we present a likelihood approach to estimate parameters of a discrete-time Markov model. Next, we use simulation to investigate the finite-sample behaviour of our approach. Finally, we present two examples: a model of diabetic nephropathy and a model of cardiovascular disease in diabetes.