The global diabetes model: user friendly version 3.0

A New Type 2 Diabetes Microsimulation Model to Estimate Long-Term Health Outcomes, Costs, and Cost-Effectiveness

OBJECTIVES

This study aimed 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 US 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 US adults with type 2 diabetes. We then estimated the cost-effectiveness of reducing hemoglobin A1c 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 < 8%. In external validation, the model was better at predicting outcomes in clinical trials than in observational studies. The cohort of US 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 hemoglobin A1c increased medical costs by $1256 and QALYs by 0.39, yielding an incremental cost-effectiveness ratio of $9103 per QALY.

CONCLUSIONS

Using equations exclusively derived from US studies, this new microsimulation model achieves good prediction accuracy in US 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.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

AIM

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

METHODS

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

RESULTS

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

CONCLUSIONS

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Long-Term Cost-Effectiveness of Subcutaneous Once-Weekly Semaglutide Versus Polyethylene Glycol Loxenatide for Treatment of Type 2 Diabetes Mellitus in China

OBJECTIVE

This study aimed to evaluate the long-term cost-effectiveness of once-weekly subcutaneous semaglutide versus polyethylene glycol loxenatide (PEG-loxenatide) in patients with type 2 diabetes uncontrolled on metformin, from a Chinese healthcare systems perspective.

METHODS

The study applied the Swedish Institute of Health Economics Diabetes Cohort Model to evaluate the long-term clinical and economic outcomes of once-weekly treatment of semaglutide at 0.5 mg and 1.0 mg, respectively, versus PEG-loxenatide 0.2 mg, over a 40-year time horizon. Baseline cohort characteristics were collected from the SUSTAIN China trial. A network meta-analysis was conducted to obtain comparative treatment effects of once-weekly semaglutide and PEG-loxenatide based on two phase 3a clinical trials. Drug costs were sourced from the national bidding price of China. Outcomes were discounted at 5.0% per annum. One-way sensitivity analysis and probabilistic sensitivity analysis were conducted to assess the uncertainty of the base-case results.

RESULTS

When compared with PEG-loxenatide 0.2 mg, the projections of outcomes over the 40-year time horizon in patients with type 2 diabetes uncontrolled on metformin showed that treatment with once-weekly semaglutide 0.5 mg and 1.0 mg were associated with improved discounted life expectancy by 0.08 and 0.12 years, and improved discounted quality-adjusted life expectancy by 0.16 and 0.22 quality-adjusted life-years, respectively. Once-weekly semaglutide 0.5 mg and 1.0 mg were achieved at lifetime cost savings of 19,309 China Yuan (CNY) and 10,179 CNY, respectively. Sensitivity analyses verified the robustness of the results.

CONCLUSION

From the perspective of Chinese healthcare systems, treatment with once-weekly subcutaneous semaglutide represents a dominant option versus PEG-loxenatide for patients with type 2 diabetes uncontrolled on metformin.

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.

Long-Term Cost-Effectiveness Analysis of Once-Weekly Semaglutide versus Dulaglutide in Patients with Type 2 Diabetes with Inadequate Glycemic Control in China

INTRODUCTION

The objective of the current study was to assess the long-term cost-effectiveness of once-weekly semaglutide 0.5 mg and 1.0 mg versus dulaglutide 1.5 mg for the treatment of patients with type 2 diabetes uncontrolled on metformin in the Chinese setting.

METHODS

The Swedish Institute of Health Economics Diabetes Cohort Model (IHE-DCM) was used to evaluate the long-term health and economic outcomes of once-weekly semaglutide and dulaglutide. Analysis was conducted from the perspective of the Chinese healthcare systems over a time horizon of 40 years. Data on baseline cohort characteristics and treatment effects were sourced from the SUSTAIN 7 clinical trial. Costs included treatment costs and costs of complications. Projected health and economic outcomes were discounted at a rate of 5% annually. The robustness of the results was evaluated through one-way sensitivity analyses and probabilistic sensitivity analyses.

RESULTS

Compared with dulaglutide 1.5 mg, once-weekly semaglutide 0.5 mg and 1.0 mg were associated with improvements in discounted life expectancy of 0.04 and 0.10 years, respectively, and improvements in discounted quality-adjusted life expectancy of 0.08 and 0.19 quality-adjusted life years (QALYs), respectively. Clinical benefits were achieved at reduced costs, with lifetime cost savings of 8355 Chinese Yuan (CNY) with once-weekly semaglutide 0.5 mg and 11,553 CNY with once-weekly semaglutide 1.0 mg. Sensitivity analyses verified the robustness of the research results.

CONCLUSIONS

Once-weekly semaglutide was suggested to be dominant (more effective and less costly) versus dulaglutide 1.5 mg in patients with type 2 diabetes uncontrolled on metformin treatment in China.

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

INTRODUCTION

As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.

METHODS

This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.

RESULTS

The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.

CONCLUSION

Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.

Decision models in type 2 diabetes mellitus: A systematic review

AIMS

To reduce the burden of type 2 diabetes (T2DM), the disease decision model plays a vital role in supporting decision-making. Currently, there is no comprehensive summary and assessment of the existing decision models for T2DM. The objective of this review is to provide an overview of the characteristics and capabilities of published decision models for T2DM. We also discuss which models are suitable for different study demands.

MATERIALS AND METHODS

Four databases (PubMed, Web of Science, Embase, and the Cochrane Library) were electronically searched for papers published from inception to August 2020. Search terms were: "Diabetes-Mellitus, Type 2", "cost-utility", "quality-of-life", and "decision model". Reference lists of the included studies were manually searched. Two reviewers independently screened the titles and abstracts following the inclusion and exclusion criteria. If there was insufficient information to include or exclude a study, then a full-text version was sought. The extracted information included basic information, study details, population characteristics, basic modeling methodologies, model structure, and data inputs for the included applications, model outcomes, model validation, and uncertainty.

RESULTS

Fourteen unique decision models for T2DM were identified. Markov chains and risk equations were utilized by four and three models, respectively. Three models utilized both. Except for the Archimedes model, all other models (n = 13) implemented an annual cycle length. The time horizon of most models was flexible. Fourteen models had differences in the division of health states. Ten models emphasized macrovascular and microvascular complications. Six models included adverse events. Majority of the models (n = 11) were patient-level simulation models. Eleven models simulated annual changes in risk factors (body mass index, glycemia, HbA1c, blood pressure (systolic and/or diastolic), and lipids (total cholesterol and/or high-density lipoprotein)). All models reported the main data sources used to develop health states of complications. Most models (n = 11) could deal with the uncertainty of models, which were described in varying levels of detail in the primary studies. Eleven studies reported that one or more validation checks were performed.

CONCLUSIONS

The existing decision models for T2DM are heterogeneous in terms of the level of detail in the classification of health states. Thus, more attention should be focused on balancing the desired level of complexity against the required level of transparency in the development of T2DM decision models.

Development and Internal Validation of a Discrete Event Simulation Model of Diabetic Kidney Disease Using CREDENCE Trial Data

INTRODUCTION

The Canagliflozin and Renal Endpoints in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) study showed that compared with placebo, canagliflozin 100 mg significantly reduced the risk of major cardiovascular events and adverse renal outcomes in patients with diabetic kidney disease (DKD). We developed a simulation model that can be used to estimate the long-term health and economic consequences of DKD treatment interventions for patients matching the CREDENCE study population.

METHODS

The CREDENCE Economic Model of DKD (CREDEM-DKD) was developed using patient-level data from CREDENCE (which recruited patients with estimated glomerular filtration rate 30 to < 90 mL/min/1.73 m², urinary albumin to creatinine ratio > 300-5000 mg/g, and taking the maximum tolerated dose of a renin-angiotensin-aldosterone system inhibitor). Risk prediction equations were fit for start of maintenance dialysis, doubling of serum creatinine, hospitalization for heart failure, nonfatal myocardial infarction, nonfatal stroke, and all-cause mortality. A micro-simulation model was constructed using these risk equations combined with user-definable kidney transplant event risks. Internal validation was performed by loading the model to replicate the CREDENCE study and comparing predictions with trial Kaplan-Meier estimate curves. External validation was performed by loading the model to replicate a subgroup of the CANagliflozin cardioVascular Assessment Study (CANVAS) Program with patient characteristics that would have qualified for inclusion in CREDENCE.

RESULTS

Risk prediction equations generally fit well and exhibited good concordance, especially for the placebo arm. In the canagliflozin arm, modest underprediction was observed for myocardial infarction, along with overprediction of dialysis, doubling of serum creatinine, and all-cause mortality. Discrimination was strong (0.85) for the renal outcomes, but weaker for the macrovascular outcomes and all-cause mortality (0.60-0.68). The model performed well in internal and external validation exercises.

CONCLUSION

CREDEM-DKD is an important new tool in the evaluation of treatment interventions in the DKD population.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT02065791.

Economic Simulation Modeling in Type 2 Diabetes

PURPOSE OF REVIEW

This paper provides an overview of type 2 diabetes economic simulation modeling and reviews current topics of discussion and major challenges in the field.

RECENT FINDINGS

Important challenges in the field include increasing the generalizability of models and improving transparency in model reporting. To identify and address these issues, modeling groups have organized through the Mount Hood Diabetes Challenge meetings and developed tools (i.e., checklist, impact inventory) to standardize modeling methods and reporting of results. Accordingly, many newer diabetes models have begun utilizing these tools, allowing for improved comparability between diabetes models. In the last two decades, type 2 diabetes simulation models have improved considerably, due to the collaborative work performed through the Mount Hood Diabetes Challenge meetings. To continue to improve diabetes models, future work must focus on clarifying diabetes progression in racial/ethnic minorities and incorporating equity considerations into health economic analysis.

Economic Modelling of Chronic Kidney Disease: A Systematic Literature Review to Inform Conceptual Model Design

BACKGROUND

Chronic kidney disease (CKD) is a progressive condition that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality. As novel interventions become available, estimates of economic and clinical outcomes are needed to guide payer reimbursement decisions.

OBJECTIVE

The aim of the present study was to systematically review published economic models that simulated long-term outcomes of kidney disease to inform cost-effectiveness evaluations of CKD treatments.

METHODS

The review was conducted across four databases (MEDLINE, Embase, the Cochrane library and EconLit) and health technology assessment agency websites. Relevant information on each model was extracted. Transition and mortality rates were also extracted to assess the choice of model parameterisation on disease progression by simulating patient's time with end-stage renal disease (ESRD) and time to ESRD/death. The incorporation of cardiovascular disease in a population with CKD was qualitatively assessed across identified models.

RESULTS

The search identified 101 models that met the criteria for inclusion. Models were classified into CKD models (n = 13), diabetes models with nephropathy (n = 48), ESRD-only models (n = 33) and cardiovascular models with CKD components (n = 7). Typically, published models utilised frameworks based on either (estimated or measured) glomerular filtration rate (GFR) or albuminuria, in line with clinical guideline recommendations for the diagnosis and monitoring of CKD. Generally, two core structures were identified, either a microsimulation model involving albuminuria or a Markov model utilising CKD stages and a linear GFR decline (although further variations on these model structures were also identified). Analysis of parameter variability in CKD disease progression suggested that mean time to ESRD/death was relatively consistent across model types (CKD models 28.2 years; diabetes models with nephropathy 24.6 years). When evaluating time with ESRD, CKD models predicted extended ESRD survival over diabetes models with nephropathy (mean time with ESRD 8.0 vs. 3.8 years).

DISCUSSION

This review provides an overview of how CKD is typically modelled. While common frameworks were identified, model structure varied, and no single model type was used for the modelling of patients with CKD. In addition, many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes. However, the variability of individual patients' GFR and albuminuria trajectories perhaps provides rationale for a model structure designed around the prediction of individual patients' GFR trajectories. Frameworks of future CKD models should be informed and justified based on clinical rationale and availability of data to ensure validity of model results. In addition, further clinical and observational research is warranted to provide a better understanding of prognostic factors and data sources to improve economic modelling accuracy in CKD.

Cost Effectiveness of Once-Weekly Semaglutide Versus Once-Weekly Dulaglutide in the Treatment of Type 2 Diabetes in Canada

OBJECTIVE

The aim of this study was to assess the cost effectiveness of semaglutide versus dulaglutide, as an add-on to metformin monotherapy, for the treatment of type 2 diabetes (T2D), from a Canadian societal perspective.

METHODS

The Swedish Institute for Health Economics Cohort Model of T2D was used to assess the cost effectiveness of once-weekly semaglutide (0.5 or 1.0 mg) versus once-weekly dulaglutide (0.75 or 1.5 mg) over a 40-year time horizon. Using data from the SUSTAIN 7 trial, which demonstrated comparatively greater reductions in glycated hemoglobin (HbA1c), body mass index and systolic blood pressure with semaglutide, compared with dulaglutide, a deterministic base-case and scenario simulation were conducted. The robustness of the results was evaluated with probabilistic sensitivity analyses and 15 deterministic sensitivity analyses.

RESULTS

The base-case analysis indicated that semaglutide is a dominant treatment option, compared with dulaglutide. Semaglutide was associated with lower total costs (Canadian dollars [CAN$]) versus dulaglutide for both low-dose (CAN$113,287 vs. CAN$113,690; cost-saving: CAN$403) and high-dose (CAN$112,983 vs. CAN$113,695; cost-saving: CAN$711) comparisons. Semaglutide resulted in increased quality-adjusted life-years (QALYs) and QALY gains, compared with dulaglutide, for both low-dose (11.10 vs. 11.07 QALYs; + 0.04 QALYs) and high-dose (11.12 vs. 11.07 QALYs; + 0.05 QALYs) comparisons. The probabilistic sensitivity analysis showed that for 66-73% of iterations, semaglutide was either dominant or was considered cost effective at a willingness-to-pay threshold of CAN$50,000.

CONCLUSIONS

From a Canadian societal perspective, semaglutide may be a cost-effective treatment option versus dulaglutide in patients with T2D who are inadequately controlled on metformin monotherapy.

Cost-Effectiveness Analysis of Canagliflozin 300 mg Versus Dapagliflozin 10 mg Added to Metformin in Patients with Type 2 Diabetes in the United States

INTRODUCTION

Agents that inhibit sodium glucose co-transporter 2 (SGLT2), including canagliflozin and dapagliflozin, are approved in the United States for the treatment of adults with type 2 diabetes mellitus (T2DM). SGLT2 inhibition lowers blood glucose by increasing urinary glucose excretion, which leads to a mild osmotic diuresis and a net loss of calories that are associated with reductions in body weight and blood pressure. This analysis evaluated the cost-effectiveness of canagliflozin 300 mg versus dapagliflozin 10 mg in patients with T2DM inadequately controlled with metformin in the United States.

METHODS

A 30-year cost-effectiveness analysis was performed using the validated Economic and Health Outcomes Model of T2DM (ECHO-T2DM) from the perspective of the third-party health care system in the United States. Patient demographics, biomarker values, and treatment effects for the ECHO-T2DM model were sourced primarily from a network meta-analysis (NMA) that included studies of canagliflozin and dapagliflozin in patients with T2DM on background metformin. Costs were derived from sources specific to the United States. Outcomes and costs were discounted at 3%. Sensitivity analyses that varied key model parameters were conducted.

RESULTS

Canagliflozin 300 mg dominated dapagliflozin 10 mg as an add-on to metformin over 30 years, with an estimated cost offset of $13,991 and a quality-adjusted life-year gain of 0.08 versus dapagliflozin 10 mg. Results were driven by the better HbA1c lowering achieved with canagliflozin, which translated to less need for insulin rescue therapy. Findings from sensitivity analyses were consistent with the base case.

CONCLUSION

These results suggest that canagliflozin 300 mg is likely to provide better health outcomes at a lower overall cost than dapagliflozin 10 mg in patients with T2DM inadequately controlled with metformin from the perspective of the United States health care system.

FUNDING

Janssen Scientific Affairs, LLC and Janssen Global Services, LLC.

Cost-effectiveness of liraglutide versus lixisenatide as add-on therapies to basal insulin in type 2 diabetes

Background

We assessed the cost-effectiveness of the glucagon-like peptide 1 receptor agonists liraglutide 1.8 mg and lixisenatide 20 μg (both added to basal insulin) in patients with type 2 diabetes (T2D) in Sweden.

Methods

The Swedish Institute for Health Economics cohort model for T2D was used to compare liraglutide and lixisenatide (both added to basal insulin), with a societal perspective and with comparative treatment effects derived by indirect treatment comparison (ITC). Drug prices were 2016 values, and all other costs 2015 values. The cost-effectiveness of IDegLira (fixed-ratio combination of insulin degludec and liraglutide) versus lixisenatide plus basal insulin was also assessed, under different sets of assumptions.

Results

From the ITC, decreases in HbA1c were –1.32% and –0.43% with liraglutide and lixisenatide, respectively; decreases in BMI were –1.29 and –0.65 kg/m², respectively. An estimated 2348 cases of retinopathy, 265 of neuropathy and 991 of nephropathy would be avoided with liraglutide compared with lixisenatide in a cohort of 10,000 patients aged over 40 years. In the base-case analysis, total direct costs were higher with liraglutide than lixisenatide, but costs associated with complications were lower. The cost/quality-adjusted life-year (QALY) for liraglutide added to basal insulin was SEK30,802. Base-case findings were robust in sensitivity analyses, except when glycated haemoglobin (HbA1c) differences for liraglutide added to basal insulin were abolished, suggesting these benefits were driving the cost/QALY. With liraglutide 1.2 mg instead of liraglutide 1.8 mg (adjusted for efficacy and cost), liraglutide added to basal insulin was dominant over lixisenatide 20μg.IDegLira was dominant versus lixisenatide plus basal insulin when a defined daily dose was used in the model.

Conclusions

The costs/QALY for liraglutide, 1.8 or 1.2 mg, added to basal insulin, and for IDegLira (all compared with lixisenatide 20 μg added to basal insulin) were below the threshold considered low by Swedish authorities. In some scenarios, liraglutide and IDegLira were cost-saving.

Description of a New Predictive Modeling Approach That Correlates the Risk and Associated Cost of Well-Defined Diabetes-Related Complications With Changes in Glycated Hemoglobin (HbA1c)

The modeling approach described here is designed to support the development of spreadsheet-based simple predictive models. It is based on 3 pillars: association of the complications with HbA1c changes, incidence of the complications, and average cost per event of the complication. For each pillar, the goal of the analysis was (1) to find results for a large diversity of populations with a focus on countries/regions, diabetes type, age, diabetes duration, baseline HbA1c value, and gender; (2) to assess the range of incidences and associations previously reported. Unlike simple predictive models, which mostly are based on only 1 source of information for each of the pillars, we conducted a comprehensive, systematic literature review. Each source found was thoroughly reviewed and only sources meeting quality expectations were considered. The approach allows avoidance of unintended use of extreme data. The user can utilize (1) one of the found sources, (2) the found range as validation for the found figures, or (3) the average of all found publications for an expedited estimate. The modeling approach is intended for use in average insulin-treated diabetes populations in which the baseline HbA1c values are within an average range (6.5% to 11.5%); it is not intended for use in individuals or unique diabetes populations (eg, gestational diabetes). Because the modeling approach only considers diabetes-related complications that are positively associated with HbA1c decreases, the costs of negatively associated complications (eg, severe hypoglycemic events) must be calculated separately.

Validation of the Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM)

BACKGROUND

The Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM) was developed to address study questions pertaining to the cost-effectiveness of treatment alternatives in the care of patients with type 2 diabetes mellitus (T2DM). Naturally, the usefulness of a model is determined by the accuracy of its predictions. A previous version of ECHO-T2DM was validated against actual trial outcomes and the model predictions were generally accurate. However, there have been recent upgrades to the model, which modify model predictions and necessitate an update of the validation exercises.

OBJECTIVES

The objectives of this study were to extend the methods available for evaluating model validity, to conduct a formal model validation of ECHO-T2DM (version 2.3.0) in accordance with the principles espoused by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM), and secondarily to evaluate the relative accuracy of four sets of macrovascular risk equations included in ECHO-T2DM.

METHODS

We followed the ISPOR/SMDM guidelines on model validation, evaluating face validity, verification, cross-validation, and external validation. Model verification involved 297 'stress tests', in which specific model inputs were modified systematically to ascertain correct model implementation. Cross-validation consisted of a comparison between ECHO-T2DM predictions and those of the seminal National Institutes of Health model. In external validation, study characteristics were entered into ECHO-T2DM to replicate the clinical results of 12 studies (including 17 patient populations), and model predictions were compared to observed values using established statistical techniques as well as measures of average prediction error, separately for the four sets of macrovascular risk equations supported in ECHO-T2DM. Sub-group analyses were conducted for dependent vs. independent outcomes and for microvascular vs. macrovascular vs. mortality endpoints.

RESULTS

All stress tests were passed. ECHO-T2DM replicated the National Institutes of Health cost-effectiveness application with numerically similar results. In external validation of ECHO-T2DM, model predictions agreed well with observed clinical outcomes. For all sets of macrovascular risk equations, the results were close to the intercept and slope coefficients corresponding to a perfect match, resulting in high R ² and failure to reject concordance using an F test. The results were similar for sub-groups of dependent and independent validation, with some degree of under-prediction of macrovascular events.

CONCLUSION

ECHO-T2DM continues to match health outcomes in clinical trials in T2DM, with prediction accuracy similar to other leading models of T2DM.

Personalized support for chronic conditions. A novel approach for enhancing self-management and improving lifestyle

OBJECTIVE

Solutions for improving management of chronic conditions are under the attention of healthcare systems, due to the increasing prevalence caused by demographic change and better survival, and the relevant impact on healthcare expenditures. The objective of this study was to propose a comprehensive architecture of a mHealth system aimed at boosting the active and informed participation of patients in their care process, while at the same time overcoming the current technical and psychological/clinical issues highlighted by the existing literature.

METHODS

After having studied the current challenges outlined in the literature, both in terms of technological and human requirements, we focused our attention on some specific psychological aspects with a view to providing patients with a comprehensive and personalized solution. Our approach has been reinforced through the results of a preliminary assessment we conducted on 22 patients with chronic conditions. The main goal of such an assessment was to provide a preliminary understanding of their needs in a real context, both in terms of self-awareness and of their predisposition toward the use of IT solutions.

RESULTS

According to the specific needs and features, such as mindfulness and gamification, which were identified through the literature and the preliminary assessment, we designed a comprehensive open architecture able to provide a tailor-made solution linked to specific individuals' needs.

CONCLUSION

The present study represents the preliminary step towards the development of a solution aimed at enhancing patients' actual perception and encouraging self-management and self-awareness for a better lifestyle. Future work regards further identification of pathology-related needs and requirements through focus groups including all stakeholders in order to describe the architecture and functionality in greater detail.

TIME Impact - a new user-friendly tuberculosis (TB) model to inform TB policy decisions

Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria).TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response.The explicit aim of TIME Impact's user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general.TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact.

How Do Diabetes Models Measure Up? A Review of Diabetes Economic Models and ADA Guidelines

Introduction: Economic models and computer simulation models have been used for assessing short-term cost-effectiveness of interventions and modelling long-term outcomes and costs. Several guidelines and checklists have been published to improve the methods and reporting. This article presents an overview of published diabetes models with a focus on how well the models are described in relation to the considerations described by the American Diabetes Association (ADA) guidelines. Methods: Relevant electronic databases and National Institute for Health and Care Excellence (NICE) guidelines were searched in December 2012. Studies were included in the review if they estimated lifetime outcomes for patients with type 1 or type 2 diabetes. Only unique models, and only the original papers were included in the review. If additional information was reported in subsequent or paired articles, then additional citations were included. References and forward citations of relevant articles, including the previous systematic reviews were searched using a similar method to pearl growing. Four principal areas were included in the ADA guidance reporting for models: transparency, validation, uncertainty, and diabetes specific criteria. Results: A total of 19 models were included. Twelve models investigated type 2 diabetes, two developed type 1 models, two created separate models for type 1 and type 2, and three developed joint type 1 and type 2 models. Most models were developed in the United States, United Kingdom, Europe or Canada. Later models use data or methods from earlier models for development or validation. There are four main types of models: Markov-based cohort, Markov-based microsimulations, discrete-time microsimulations, and continuous time differential equations. All models were long-term diabetes models incorporating a wide range of compilations from various organ systems. In early diabetes modelling, before the ADA guidelines were published, most models did not include descriptions of all the diabetes specific components of the ADA guidelines but this improved significantly by 2004. Conclusion: A clear, descriptive short summary of the model was often lacking. Descriptions of model validation and uncertainty were the most poorly reported of the four main areas, but there exist conferences focussing specifically on the issue of validation. Interdependence between the complications was the least well incorporated or reported of the diabetes-specific criterion.

A Review of Emerging Technologies for the Management of Diabetes Mellitus

OBJECTIVE

High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making.

METHODS

A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels.

RESULTS

Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM.

CONCLUSION

Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care.

SIGNIFICANCE

The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

The Michigan Model for Coronary Heart Disease in Type 2 Diabetes: Development and Validation

OBJECTIVES

The aim of this study was to develop and validate a computer simulation model for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) that reflects current medical and surgical treatments.

RESEARCH DESIGN AND METHODS

We modified the structure of the CHD submodel in the Michigan Model for Diabetes to allow for revascularization procedures before and after first myocardial infarction, for repeat myocardial infarctions and repeat revascularization procedures, and for congestive heart failure. Transition probabilities that reflect the direct effects of medical and surgical therapies on outcomes were derived from the literature and calibrated to recently published population-based epidemiologic studies and randomized controlled clinical trials. Monte Carlo techniques were used to implement a discrete-state and discrete-time multistate microsimulation model. Performance of the model was assessed using internal and external validation. Simple regression analysis (simulated outcome=b(0)+b(1)×published outcome) was used to evaluate the validation results.

RESULTS

For the 21 outcomes in the six studies used for internal validation, R(2) was 0.99, and the slope of the regression line was 0.98. For the 16 outcomes in the five studies used for external validation, R(2) was 0.81, and the slope was 0.84.

CONCLUSIONS

Our new computer simulation model predicted the progression of CHD in patients with T2DM and will be incorporated into the Michigan Model for Diabetes to assess the cost-effectiveness of alternative strategies to prevent and treat T2DM.

Cost-Effectiveness of Canagliflozin versus Sitagliptin as Add-on to Metformin in Patients with Type 2 Diabetes Mellitus in Mexico

OBJECTIVE

To assess the cost-effectiveness of canagliflozin versus sitagliptin for the treatment of type 2 diabetes mellitus (T2DM) as an add-on to metformin in Mexico.

METHODS

A validated model (Economic and Health Outcomes [ECHO]-T2DM) was used to estimate the cost-effectiveness of canagliflozin 300 or 100 mg versus sitagliptin 100 mg in patients with T2DM inadequately controlled on metformin monotherapy. Data from a head-to-head, phase III clinical trial, including patients' baseline demographic characteristics, biomarker values, and treatment effects, were used to simulate outcomes and resource use over 20 years from the perspective of the Mexican health care system. Costs of complications and adverse events were tailored to the Mexican setting and discounted at 5%. Cost-effectiveness was assessed using willingness-to-pay thresholds equivalent to 1 times the gross domestic product per capita (locally perceived to be "very cost-effective") and 3 times the gross domestic product per capita (locally perceived to be "cost-effective") on the basis of recommendations of the Mexican government and the World Health Organization.

RESULTS

Owing primarily to better glycated hemoglobin (HbA_1c), body weight, and systolic blood pressure values, canagliflozin 300 and 100 mg were associated with an incremental benefit of 0.16 and 0.06 quality-adjusted life-years (QALYs) versus sitagliptin 100 mg, respectively, over 20 years. The mean differences in cost for canagliflozin 300 and 100 mg versus sitagliptin 100 mg were Mexican pesos (MXP) 1797 (US $134) and MXP 7262 (US $540), respectively, resulting in a cost per QALY gained of MXP 11,210 (US $834) and MXP 128,883 (US $9590), respectively. Both of these cost-effectiveness ratios are below the very cost-effective willingness-to-pay threshold in Mexico. The general finding that canagliflozin is cost-effective versus sitagliptin in Mexico was supported by sensitivity analyses.

CONCLUSION

In Mexico, both doses of canagliflozin are likely to be cost-effective versus sitagliptin in patients with T2DM who have inadequate glucose control on metformin, primarily because of better biomarker control and higher QALYs.

Cost-utility analysis of glucagon-like Peptide-1 agonists compared with dipeptidyl peptidase-4 inhibitors or neutral protamine hagedorn Basal insulin as add-on to metformin in type 2 diabetes in sweden

INTRODUCTION

This study aimed to assess the costs and benefits of three alternative second-line treatment strategies for Swedish patients with type 2 diabetes mellitus (T2DM) who fail to reach glycated hemoglobin (HbA1c) ≤ 7% with metformin treatment alone: glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 (DPP-4) inhibitors, and neutral protamine Hagedorn (NPH) insulin.

METHODS

A previously developed cohort model for T2DM was applied over a 35-year time horizon. Data on T2DM patients on metformin monotherapy with HbA1c > 7% were collected from the Swedish National Diabetes Register. Treatment effects were taken from published studies. Costs and effects were discounted at 3% per annum, and the analysis was conducted from a societal perspective. The robustness of the results was evaluated using one-way and probabilistic sensitivity analyses.

RESULTS

Treatment with GLP-1 agonists was associated with a discounted incremental benefit of 0.10 and 0.25 quality-adjusted life years (QALYs) and higher discounted costs of Swedish Krona (SEK) 34,865 and SEK 40,802 compared with DPP-4 inhibitors and NPH insulin, respectively. Assuming willingness-to-pay (WTP) of SEK 500,000 per QALY, treatment strategy with GLP-1 agonists was a cost-effective option with incremental cost-effectiveness ratios of SEK 353,172 and SEK 160,618 per QALY gained versus DPP-4 inhibitors and NPH insulin, respectively. The results were most sensitive to incidence rate of moderate/major hypoglycemia and disutilities associated with insulin treatment, body mass index (BMI), and hypoglycemia.

CONCLUSION

Assuming a WTP of SEK 500,000 per QALY, treatment strategy with GLP-1 agonists is a cost-effective strategy in comparison to DPP-4 inhibitors and NPH insulin among T2DM patients inadequately controlled with metformin alone in a Swedish setting.

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.

A user-friendly, open-source tool to project impact and cost of diagnostic tests for tuberculosis

Most models of infectious diseases, including tuberculosis (TB), do not provide results customized to local conditions. We created a dynamic transmission model to project TB incidence, TB mortality, multidrug-resistant (MDR) TB prevalence, and incremental costs over 5 years after scale-up of nine alternative diagnostic strategies. A corresponding web-based interface allows users to specify local costs and epidemiology. In settings with little capacity for up-front investment, same-day microscopy had the greatest impact on TB incidence and became cost-saving within 5 years if delivered at $10/test. With greater initial investment, population-level scale-up of Xpert MTB/RIF or microcolony-based culture often averted 10 times more TB cases than narrowly-targeted strategies, at minimal incremental long-term cost. Xpert for smear-positive TB had reasonable impact on MDR-TB incidence, but at substantial price and little impact on overall TB incidence and mortality. This user-friendly modeling framework improves decision-makers' ability to evaluate the local impact of TB diagnostic strategies.

DOI:http://dx.doi.org/10.7554/eLife.02565.001

Tuberculosis is an infectious bacterial disease caused predominantly by the microorganism Mycobacterium tuberculosis. Although the number of deaths from tuberculosis has been falling in recent years, the disease still kills more than 1 million people every year, mainly in developing countries. Tuberculosis can be treated with antibiotics, but the emergence of bacteria that are resistant to existing drugs is threatening efforts to eradicate the disease.

Preventing the spread of tuberculosis is heavily dependent on accurate diagnosis of individuals with the disease. This is challenging because the initial symptoms are often mild, usually just a cough, which means that someone can spread the disease to many others over a period of several months before the symptoms become worse—fever, night sweats, and weight loss—and they realize that they are sick. Multiple diagnostic strategies are available, from the relatively low-tech—examining sputum samples under a microscope to detect tuberculosis bacteria—to more sophisticated tests that can detect bacterial DNA and determine whether the bacteria are drug-resistant in less than 2 hr.

Choosing which diagnostic strategy to adopt can be challenging because the optimal solution in a region will depend on the specific local conditions. To overcome this problem, Dowdy et al. have developed a computer program that enables decision-makers to input four key parameters that describe the tuberculosis situation in their region, and to obtain 5-year projections of the rate of new infections, mortality, and total costs likely to result from adopting any of nine different diagnostic strategies.

The four parameters are the number of new cases of tuberculosis each year (incidence), the proportion of new cases that are multi-drug resistant, the proportion of the adult population that has HIV, and the local costs of various diagnostic techniques and treatments. Since the entire computer program is written in a freely available open-source programming language (Python), any user can tweak these parameters to provide a more precise fit to their own region. Alternatively, the standard version of the program can be run directly from a website without any need to interact with computer code.

This model is the first to enable local decision-makers to evaluate the impact of different diagnostic strategies for tuberculosis under the conditions specific to their region. The model predicts, for example, that in areas where there is little money available for up-front investment, same-day microscopy analysis of sputum samples and starting patients on treatment is the most cost-effective strategy for reducing the rate of new infections. Given the wide variation in conditions within even small geographical areas, this more flexible approach should lead to the more efficient use of resources and may, ultimately, help to reduce the spread of tuberculosis.

DOI:http://dx.doi.org/10.7554/eLife.02565.002

Cost-effectiveness of health-related lifestyle advice delivered by peer or lay advisors: synthesis of evidence from a systematic review

BACKGROUND

Development of new peer or lay health-related lifestyle advisor (HRLA) roles is one response to the need to enhance public engagement in, and improve cost-effectiveness of, health improvement interventions. This article synthesises evidence on the cost-effectiveness of HRLA interventions aimed at adults in developed countries, derived from the first systematic review of the effectiveness, cost-effectiveness, equity and acceptability of different types of HRLA role.

METHODS

The best available evidence on the cost-effectiveness of HRLA interventions was obtained using systematic searches of 20 electronic databases and key journals, as well as searches of the grey literature and the internet. Interventions were classified according to the primary health behaviour targeted and intervention costs were estimated where necessary. Lifetime health gains were estimated (in quality-adjusted life years, where possible), based on evidence of effectiveness of HRLAs in combination with published estimates of the lifetime health gains resulting from lifestyle changes, and assumptions over relapse. Incremental cost-effectiveness ratios are reported.

RESULTS

Evidence of the cost-effectiveness of HRLAs was identified from 24 trials included in the systematic review. The interventions were grouped into eight areas. We found little evidence of effectiveness of HRLAs for promotion of exercise/improved diets. Where HRLAs were effective cost-effectiveness varied considerably: Incremental Cost effectiveness Ratios were estimated at £6,000 for smoking cessation; £14,000 for a telephone based type 2 diabetes management; and £250,000 or greater for promotion of mammography attendance and for HIV prevention amongst drug users. We lacked sufficient evidence to estimate ICERs for breastfeeding promotion and mental health promotion, or to assess the impact of HRLAs on health inequalities.

CONCLUSIONS

Overall, there is limited evidence suggesting that HRLAs are cost-effective in terms of changing health-related knowledge, behaviours or health outcomes. The evidence that does exist indicates that HRLAs are only cost-effective when they target behaviours likely to have a large impact on overall health-related quality of life. Further development of HRLA interventions needs to target specific population health needs where potential exists for significant improvement, and include rigorous evaluation to ensure that HRLAs provide sufficient value for money.

Validation of economic and health outcomes simulation model of type 2 diabetes mellitus (ECHO-T2DM)

OBJECTIVE

This study constructed the Economic and Health Outcomes Model for type 2 diabetes mellitus (ECHO-T2DM), a long-term stochastic microsimulation model, to predict the costs and health outcomes in patients with T2DM. Naturally, the usefulness of the model depends upon its predictive accuracy. The objective of this work is to present results of a formal validation exercise of ECHO-T2DM.

METHODS

The validity of ECHO-T2DM was assessed using criteria recommended by the International Society for Pharmacoeconomics and Outcomes Research/Society for Medical Decision Making (ISPOR/SMDM). Specifically, the results of a number of clinical trials were predicted and compared with observed study end-points using a scatterplot and regression approach. An F-test of the best-fitting regression was added to assess whether it differs statistically from the identity (45°) line defining perfect predictions. In addition to testing the full model using all of the validation study data, tests were also performed of microvascular, macrovascular, and survival outcomes separately. The validation tests were also performed separately by type of data (used vs not used to construct the model, economic simulations, and treatment effects).

RESULTS

The intercept and slope coefficients of the best-fitting regression line between the predicted outcomes and corresponding trial end-points in the main analysis were -0.0011 and 1.067, respectively, and the R(2) was 0.95. A formal F-test of no difference between the fitted line and the identity line could not be rejected (p = 0.16). The high R(2) confirms that the data points are closely (and linearly) associated with the fitted regression line. Additional analyses identified that disagreement was highest for macrovascular end-points, for which the intercept and slope coefficients were 0.0095 and 1.225, respectively. The R(2) was 0.95 and the estimated intercept and slope coefficients were 0.017 and 1.048, respectively, for mortality, and the F-test was narrowly rejected (p = 0.04). The sub-set of microvascular end-points showed some tendency to over-predict (the slope coefficient was 1.095), although concordance between predictions and observed values could not be rejected (p = 0.16).

LIMITATIONS

Important study limitations include: (1) data availability limited one to tests based on end-of-study outcomes rather than time-varying outcomes during the studies analyzed; (2) complex inclusion and exclusion criteria in two studies were difficult to replicate; (3) some of the studies were older and reflect outdated treatment patterns; and (4) the authors were unable to identify published data on resource use and costs of T2DM suitable for testing the validity of the economic calculations.

CONCLUSIONS

Using conventional methods, ECHO-T2DM simulated the treatment, progression, and patient outcomes observed in important clinical trials with an accuracy consistent with other well-accepted models. Macrovascular outcomes were over-predicted, which is common in health-economic models of diabetes (and may be related to a general over-prediction of event rates in the United Kingdom Prospective Diabetes Study [UKPDS] Outcomes Model). Work is underway in ECHO-T2DM to incorporate new risk equations to improve model prediction.

Development of an economic evaluation of diagnostic strategies: the case of monogenic diabetes

OBJECTIVES

To describe the development process for defining an appropriate model structure for the economic evaluation of test-treatment strategies for patients with monogenic diabetes (caused by mutations in the GCK, HNF1A or HNF4A genes).

DESIGN

Experts were consulted to identify and define realistic test-treatment strategies and care pathways. A systematic assessment of published diabetes models was undertaken to inform the model structure.

SETTING

National Health Service in England and Wales.

PARTICIPANTS

Experts in monogenic diabetes whose collective expertise spans the length of the patient care pathway.

PRIMARY AND SECONDARY OUTCOMES

A defined model structure, including the test-treatment strategies, and the selection of a published diabetes model appropriate for the economic evaluation of strategies to identify patients with monogenic diabetes.

RESULTS

Five monogenic diabetes test-treatment strategies were defined: no testing of any kind, referral for genetic testing based on clinical features as noted by clinicians, referral for genetic testing based on the results of a clinical prediction model, referral for genetic testing based on the results of biochemical and immunological tests, referral for genetic testing for all patients with a diagnosis of diabetes under the age of 30 years. The systematic assessment of diabetes models identified the IMS CORE Diabetes Model (IMS CDM) as a good candidate for modelling the long-term outcomes and costs of the test-treatment strategies for monogenic diabetes. The short-term test-treatment events will be modelled using a decision tree which will feed into the IMS CDM.

CONCLUSIONS

Defining a model structure for any economic evaluation requires decisions to be made. Expert consultation and the explicit use of critical appraisal can inform these decisions. Although arbitrary choices have still been made, decision modelling allows investigation into such choices and the impact of assumptions that have to be made due to a lack of data.

Towards renewed health economic simulation of type 2 diabetes: risk equations for first and second cardiovascular events from Swedish register data

OBJECTIVE

Predicting the risk of future events is an essential part of health economic simulation models. In pursuit of this goal, the current study aims to predict the risk of developing first and second acute myocardial infarction, heart failure, non-acute ischaemic heart disease, and stroke after diagnosis in patients with type 2 diabetes, using data from the Swedish National Diabetes Register.

MATERIAL AND METHODS

Register data on 29,034 patients with type 2 diabetes were analysed over five years of follow up (baseline 2003). To develop and validate the risk equations, the sample was randomly divided into training (75%) and test (25%) subsamples. The Weibull proportional hazard model was used to estimate the coefficients of the risk equations, and these were validated in both the training and the test samples.

RESULTS

In total, 4,547 first and 2,418 second events were observed during the five years of follow up. Experiencing a first event substantially elevated the risk of subsequent events. There were heterogeneities in the effects of covariates within as well as between events; for example, while for females the hazard ratio of having a first acute myocardial infarction was 0.79 (0.70-0.90), the hazard ratio of a second was 1.21 (0.98-1.48). The hazards of second events decreased as the time since first events elapsed. The equations showed adequate calibration and discrimination (C statistics range: 0.70-0.84 in test samples).

CONCLUSION

The accuracy of health economic simulation models of type 2 diabetes can be improved by ensuring that they account for the heterogeneous effects of covariates on the risk of first and second cardiovascular events. Thus it is important to extend such models by including risk equations for second cardiovascular events.

Estimated incidence of cardiovascular complications related to type 2 diabetes in Mexico using the UKPDS outcome model and a population-based survey

BACKGROUND

To estimate the incidence of complications, life expectancy and diabetes related mortality in the Mexican diabetic population over the next two decades using data from a nation-wide, population based survey and the United Kingdom Prospective Diabetes Study (UKPDS) outcome model.

METHODS

The cohort included all patients with type 2 diabetes evaluated during the National Health and Nutrition Survey (ENSANut) 2006. ENSANut is a probabilistic multistage stratified survey whose aim was to measure the prevalence of chronic diseases. A total of 47,152 households were visited. Results are shown stratified by gender, time since diagnosis (> or ≤ to 10 years) and age at the time of diagnosis (> or ≤ 40 years).

RESULTS

The prevalence of diabetes in our cohort was 14.4%. The predicted 20 year-incidence for chronic complications per 1000 individuals are: ischemic heart disease 112, myocardial infarction 260, heart failure 113, stroke 101, and amputation 62. Furthermore, 539 per 1000 patients will have a diabetes-related premature death. The average life expectancy for the diabetic population is 10.9 years (95%CI 10.7-11.2); this decreases to 8.3 years after adjusting for quality of life (CI95% 8.1-8.5). Male sex and cases diagnosed after age 40 have the highest risk for developing at least one major complication during the next 20 years.

CONCLUSIONS

Based on the current clinical profile of Mexican patients with diabetes, the burden of disease related complications will be tremendous over the next two decades.

Economic models in type 2 diabetes

OBJECTIVE

To identify and critically appraise cost-effectiveness models developed to evaluate type 2 diabetes (T2D) treatments and to assess which types of treatment effects they capture.

RESEARCH DESIGN AND METHODS

A systematic search was performed in MEDLINE, EMBASE, Centre for Reviews and Dissemination databases at the University of York, and Health Economic Evaluation Database for the period to September 2008. The websites of Health Technology Assessment (HTA) bodies in different countries were also screened for relevant models. For each of the identified original models, details of the structure, data in- and outputs were extracted and the overall quality of the model in terms of the combination of structure, assumptions and data inputs were appraised using published criteria.

RESULTS

Seventy-eight articles and 41 HTAs reporting relevant economic evaluations were identified. There were ten models with multiple publications, and a further ten models with one associated publication. The critical review demonstrated that most had the same fundamental structure, used similar micro-simulation techniques and were based on the same key data sources. However, the process for identification of relevant data and their synthesis, and the selection of outcomes lacked transparency. The models differed according to the extent and type of interventions they evaluated and which diabetes complications and treatment-related adverse events were captured. For example, just one model incorporated changes in patient weight, despite the fact that weight gain can be a side-effect of some treatments, and weight loss a potential benefit of others.

CONCLUSIONS

Whilst many economic models exist in T2D, most share common features such as the model type. Identified shortcomings are lack of transparency in data identification and evidence synthesis as well as the selection of the modelled outcomes. Future models should aim to include all relevant treatment outcomes, whether these relate to effects on underlying diabetes and its complications or to short- or long-term side effects of treatment.

Chronic disease modeling and simulation software

Computers allow describing the progress of a disease using computerized models. These models allow aggregating expert and clinical information to allow researchers and decision makers to forecast disease progression. To make this forecast reliable, good models and therefore good modeling tools are required. This paper will describe a new computer tool designed for chronic disease modeling. The modeling capabilities of this tool were used to model the Michigan model for diabetes. The modeling approach and its advantages such as simplicity, availability, and transparency are discussed.

Development of a population-based microsimulation model of osteoarthritis in Canada

OBJECTIVES

The purpose of the study was to develop a population-based simulation model of osteoarthritis (OA) in Canada that can be used to quantify the future health and economic burden of OA under a range of scenarios for changes in the OA risk factors and treatments. In this article we describe the overall structure of the model, sources of data, derivation of key input parameters for the epidemiological component of the model, and preliminary validation studies.

DESIGN

We used the Population Health Model (POHEM) platform to develop a stochastic continuous-time microsimulation model of physician-diagnosed OA. Incidence rates were calibrated to agree with administrative data for the province of British Columbia, Canada. The effect of obesity on OA incidence and the impact of OA on health-related quality of life (HRQL) were modeled using Canadian national surveys.

RESULTS

Incidence rates of OA in the model increase approximately linearly with age in both sexes between the ages of 50 and 80 and plateau in the very old. In those aged 50+, the rates are substantially higher in women. At baseline, the prevalence of OA is 11.5%, 13.6% in women and 9.3% in men. The OA hazard ratios for obesity are 2.0 in women and 1.7 in men. The effect of OA diagnosis on HRQL, as measured by the Health Utilities Index Mark 3 (HUI3), is to reduce it by 0.10 in women and 0.14 in men.

CONCLUSIONS

We describe the development of the first population-based microsimulation model of OA. Strengths of this model include the use of large population databases to derive the key parameters and the application of modern microsimulation technology. Limitations of the model reflect the limitations of administrative and survey data and gaps in the epidemiological and HRQL literature.

A review of methods used in long-term cost-effectiveness models of diabetes mellitus treatment

Diabetes mellitus is a major healthcare concern from both a treatment and a funding perspective. Although decision makers frequently rely on models to evaluate the long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the methods used in long-term cost-effectiveness models of diabetes treatment. The following databases were searched up to April 2008 to identify published economic models evaluating treatments for diabetes mellitus: OVID MEDLINE, EMBASE and the Thomson's Biosis Previews, NHS EED via Wiley's Cochrane Library, and Wiley's HEED database. Identified articles were reviewed and grouped according to unique models. When a model was applied in different settings (e.g. country) or compared different treatment alternatives, only the original publication describing the model was included. In some cases, subsequent articles were included if they provided methodological advances from the original model. The following data were captured for each study: (i) study characteristics; (ii) model structure; (iii) long-term complications, data sources, methods reporting and model validity; (iv) utilities, data sources and methods reporting; (v) costs, data sources and methods reporting; (vi) model data requirements; and (vii) economic results including methods to deal with uncertainty. A total of 17 studies were identified, 12 of which allowed for the conduct of a cost-effectiveness analysis and a cost-utility analysis. Although most models were Markov-based microsimulations, models differed with respect to the number of diabetes-related complications included. The majority of the studies used a lifetime time horizon and a payer perspective. The DCCT for type 1 diabetes and the UKPDS for type 2 diabetes were the trial data sources most commonly cited for the efficacy data, although several non-randomized data sources were used. While the methods used to derive the efficacy data were commonly reported, less information was given regarding the derivation of the utilities or the costs. New interventions were generally deemed cost effective based on ten studies presenting results. Authors relied mostly on univariate sensitivity analyses to test the robustness of their models. Although diabetes is a complex disease, several models have been developed to predict the long-term costs and consequences associated with diabetes treatment. Combined with previous findings, this review supports the development of a reference case that could be used for any new diabetes models. Models could be enhanced if they had the capacity to deal with both first- and second-order uncertainty. Future research should continue to compare economic models for diabetes treatment in terms of clinical outcomes, costs and QALYs when applicable.

Impact of a natural disaster on diabetes: exacerbation of disparities and long-term consequences

OBJECTIVE

To examine the impact of Hurricane Katrina on the health of individuals with diabetes.

RESEARCH DESIGN AND METHODS

This was an observational study in 1,795 adults with an A1C measurement 6 months before and 6-16 months after Hurricane Katrina in three health care systems: private (Tulane University Hospital and Clinic [TUHC]), state (Medical Center of Louisiana at New Orleans [MCLNO]), and Veterans Affairs (VA). Glycemic control (A1C), blood pressure, and lipids before the hurricane were compared with the patients' first measurement thereafter. The CORE Diabetes Model was used to project life expectancy and health economic impact.

RESULTS

Mean predisaster A1C levels differed between MCLNO and VA patients (mean 7.7 vs. 7.3%, P < 0.001) and increased significantly among MCLNO patients to 8.3% (P < 0.001) but not among VA and TUHC patients. Mean systolic blood pressure increased in all three systems (130-137.6 mmHg for TUHC and 130.7-143.7 for VA, P < 0.001; 132-136 for MCLNO, P = 0.008). Mean LDL cholesterol increased in the VA (97.1-104.3 mg/dl) and TUHC patients (103.4-115.5; P < 0.001). Hurricane Katrina increased modeled direct, indirect, and total health care costs and also reduced life expectancy as well as quality-adjusted life expectancy, with the economic impact being quite substantial because of the large population size affected. We estimate a lifetime cost of USD $504 million for the adult population affected, with the largest economic impact seen among MCLNO patients.

CONCLUSIONS

A major disaster had a significant effect on diabetes management and exacerbated existing disparities. These effects may have a lasting impact on both health and economic implications.

Characterization and comparison of health-related utility in people with diabetes with various single and multiple vascular complications

AIMS

To characterize and compare health-related utility in a large cohort of patients treated in hospital with diabetes and with single and multiple comorbidities.

METHODS

The study was conducted in Cardiff and the Vale of Glamorgan, UK. Health-related utility was measured using the EQ5D(index), a standardized instrument for measuring health outcome. Patients from the Health Outcomes Data Repository (HODaR) were surveyed by postal questionnaire 6 weeks post discharge for in-patients and during clinics for patients attending as out-patients between January 2002 and July 2005. Patients with diabetes were identified by a previous history of in-patient admission with diabetes or as an out-patient with diabetes recorded as a coexisting diagnosis.

RESULTS

We identified 4502 patients with diabetes. Mean ages were 65.4 and 64.2 years for males and females, respectively. Of these, 2003 (45%) had no recorded vascular complication. Overall, the EQ5D(index) was 0.584 (sd 0.325) for males and 0.533 (sd 0.351) for females. For those without any vascular complications the mean EQ5D(index) was 0.735 (sd 0.288). In a general linear model, the presence of single and multiple complications had a detrimental impact on the EQ5D(index).

CONCLUSION

The results of this study provide an indication of the true impact of diabetes in terms of health-related utility. There was a decrease in the mean EQ5D(index) for those with vascular complications. Economic models of diabetes that have used additive or multiplicative methods to assess utility in individuals with several complications may be unreliable, and direct measurements, such as this, are recommended.

A critical review of mathematical models and data used in diabetology

The literature dealing with mathematical modelling for diabetes is abundant. During the last decades, a variety of models have been devoted to different aspects of diabetes, including glucose and insulin dynamics, management and complications prevention, cost and cost-effectiveness of strategies and epidemiology of diabetes in general. Several reviews are published regularly on mathematical models used for specific aspects of diabetes. In the present paper we propose a global overview of mathematical models dealing with many aspects of diabetes and using various tools. The review includes, side by side, models which are simple and/or comprehensive; deterministic and/or stochastic; continuous and/or discrete; using ordinary differential equations, partial differential equations, optimal control theory, integral equations, matrix analysis and computer algorithms.

Development and validation of the Economic Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE) model

BACKGROUND

The Economic Assessment of Glycemic control and Long-term Effects of diabetes (EAGLE) model was developed to provide a flexible and comprehensive tool for the simulation of the long-term effects of diabetes treatment and related costs in type 1 and type 2 diabetes.

METHODS

EAGLE simulations are based on risk equations, which were developed using published data from several large studies including the Diabetes Control and Complications Trial, the United Kingdom Prospective Diabetes Study, and the Wisconsin Epidemiological Study of Diabetic Retinopathy. Risk equations for the probability of complications (including hypoglycemia, retinopathy, macular edema, end-stage renal disease, neuropathy, diabetic foot syndrome, myocardial infarction, and stroke) were based on regression analyses, using linear, exponential, and quadratic regression formulae. Subsequent cost calculations are made from the simulated event rates. Internal validation of the EAGLE model was completed by comparing simulated event rates with the published event rates used as the basis for the model.

RESULTS

EAGLE provides microsimulations of virtual patient cohorts for type 1 and type 2 diabetes over n years in 1-year cycles. Complications include microvascular and macrovascular events and death, which are calculated over time as cumulative incidences. Glycosylated hemoglobin levels over time are simulated in relation to treatment regimen. Internal validation demonstrated that each mean event rate simulated by EAGLE overlapped with the published mean event (within a range of +/-10%).

CONCLUSIONS

The EAGLE model is an evidence-based, internally valid tool for the assessment of the long-term effects of diabetes treatment and related costs.

A model of long-term metabolic progression of type 2 diabetes mellitus for evaluating treatment strategies

AIM

To develop a novel metabolic computer model of the natural lifetime progression of type 2 diabetes that generates dynamic risk factor trajectories consistent with prespecified lifetime therapeutic strategies, in order to enhance the long-term economic and outcome modelling of type 2 diabetes and its complications.

METHODS

The main model drivers of progressive disease were changes in insulin sensitivity and islet beta-cell function derived from an analysis of follow-up results from the Belfast Diet Study. These were related to clinical measures through an adaptation of the homeostasis model assessment. Established causal relationships estimating body mass index, lipids and blood pressure from measures of glycaemia and plasma insulin were calibrated using Third National Health and Nutrition Examination Survey (USA) data, standardizing for age, sex, ethnicity and smoking. The effects of individual interventions were calibrated using published trial evidence, in line with the current understanding of the main modes of action of each agent.

RESULTS

A comparison of the effects of common therapies using the model showed both similarities and differences. Large improvements in glycaemic control from lifestyle modifications, further enhanced by oral glucose-lowering drugs or insulin, were reproduced. Projections comparing lifetime therapeutic strategies suggest that simple guidelines may not always be valid.

CONCLUSION

This novel mathematical model using evidence from the long-term natural history of type 2 diabetes is able to project the expected effects of various antihyperglycaemic therapies. Coupled with an economic model, this metabolic model may provide a mechanism for healthcare professionals and policymakers to evaluate different long-term strategies for the management of type 2 diabetes.

Lifetime health consequences and cost-effectiveness of rosiglitazone in combination with metformin for the treatment of type 2 diabetes in Spain

OBJECTIVE

To assess the lifetime diabetes health consequences and cost-effectiveness in Spain of rosiglitazone in combination with metformin for the treatment of type 2 diabetes in overweight and obese patients failing to maintain glycaemic control with metformin monotherapy compared with conventional care of metformin in combination with either sulfonylureas or bedtime insulin.

RESEARCH DESIGN AND METHODS

The Diabetes Decision Analysis of Cost--Type 2 was adapted for clinical practice and healthcare funding in Spain, and was calibrated with Spanish epidemiological, healthcare resource use and cost data, taking the perspective of the Spanish National Health System. The model simulates lifetime treatment histories, complications and consequences of type 2 diabetes, and associated health outcomes and costs for age and sex-matched cohorts of 1000 overweight and obese patients. The primary health outcome measures compared are glycaemic control, time to insulin, incidence and prevalence of coronary heart disease, stroke, clinical nephropathy, ulceration and amputation, and severe visual loss, and incremental life-years and quality-adjusted life-years (QALYs).

RESULTS

Rosiglitazone in combination with metformin produces better glycaemic control than conventional care of metformin in combination with either sulfonylureas or bedtime insulin in most patients, and extends the viability of combination therapy by between 6 and 13 years before requiring insulin. Rosiglitazone patients have a longer life expectancy, gaining between 106 and 175 additional life-years per 1000 patients, experience fewer episodes of coronary disease and clinical nephropathy, and live for longer periods free of complications. The improvements in morbidity and mortality are projected to yield between 134 and 238 additional QALY per 1000 patients over their lifetime. Discounted incremental cost-effectiveness ratios range from euro 9406 to euro 23,514 per QALY gained.

CONCLUSION

The model predicts that rosiglitazone in combination with metformin is a cost-effective intervention for the treatment of both overweight and obese patients with type 2 diabetes when compared with conventional care in Spain.

A discrete-state discrete-time model using indirect observation

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.

A computer simulation model of diabetes progression, quality of life, and cost

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 USD $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.

Cost-utility analyses of intensive blood glucose and tight blood pressure control in type 2 diabetes (UKPDS 72)

AIMS/HYPOTHESIS

This study estimated the economic efficiency (1) of intensive blood glucose control and tight blood pressure control in patients with type 2 diabetes who also had hypertension, and (2) of metformin therapy in type 2 diabetic patients who were overweight.

METHODS

We conducted cost-utility analysis based on patient-level data from a randomised clinical controlled trial involving 4,209 patients with newly diagnosed type 2 diabetes conducted in 23 hospital-based clinics in England, Scotland and Northern Ireland as part of the UK Prospective Diabetes Study (UKPDS). Three different policies were evaluated: intensive blood glucose control with sulphonylurea/insulin; intensive blood glucose control with metformin for overweight patients; and tight blood pressure control of hypertensive patients. Incremental cost : effectiveness ratios were calculated based on the net cost of healthcare resources associated with these policies and on effectiveness in terms of quality-adjusted life years gained, estimated over a lifetime from within-trial effects using the UKPDS Outcomes Model.

RESULTS

The incremental cost per quality-adjusted life years gained (in year 2004 UK prices) for intensive blood glucose control was 6,028 UK pounds, and for blood pressure control was 369 UK pounds. Metformin therapy was cost-saving and increased quality-adjusted life expectancy.

CONCLUSIONS/INTERPRETATION

Each of the three policies evaluated has a lower cost per quality-adjusted life year gained than that of many other accepted uses of healthcare resources. The results provide an economic rationale for ensuring that care of patients with type 2 diabetes corresponds at least to the levels of these interventions.

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)

AIMS/HYPOTHESIS

The aim of this study was to develop a simulation model for type 2 diabetes that can be used to estimate the likely occurrence of major diabetes-related complications over a lifetime, in order to calculate health economic outcomes such as quality-adjusted life expectancy.

METHODS

Equations for forecasting the occurrence of seven diabetes-related complications and death were estimated using data on 3642 patients from the United Kingdom Prospective Diabetes Study (UKPDS). After examining the internal validity, the UKPDS Outcomes Model was used to simulate the mean difference in expected quality-adjusted life years between the UKPDS regimens of intensive and conventional blood glucose control.

RESULTS

The model's forecasts fell within the 95% confidence interval for the occurrence of observed events during the UKPDS follow-up period. When the model was used to simulate event history over patients' lifetimes, those treated with a regimen of conventional glucose control could expect 16.35 undiscounted quality-adjusted life years, and those receiving treatment with intensive glucose control could expect 16.62 quality-adjusted life years, a difference of 0.27 (95% CI: -0.48 to 1.03).

CONCLUSIONS/INTERPRETATIONS

The UKPDS Outcomes Model is able to simulate event histories that closely match observed outcomes in the UKPDS and that can be extrapolated over patients' lifetimes. Its validity in estimating outcomes in other groups of patients, however, remains to be evaluated. The model allows simulation of a range of long-term outcomes, which should assist in informing future economic evaluations of interventions in type 2 diabetes.

Archimedes: a trial-validated model of diabetes

OBJECTIVE

To build a mathematical model of the anatomy, pathophysiology, tests, treatments, and outcomes pertaining to diabetes that could be applied to a wide variety of clinical and administrative problems and that could be validated.

RESEARCH DESIGN AND METHODS

We used an object-oriented approach, differential equations, and a construct we call "features." The level of detail and realism was determined by what clinicians considered important, by the need to distinguish clinically relevant variables, and by the level of detail used in the conduct of clinical trials.

RESULTS

The model includes the pertinent organ systems, more than 50 continuously interacting biological variables, and the major symptoms, tests, treatments, and outcomes. The level of detail corresponds to that found in general medical textbooks, patient charts, clinical practice guidelines, and designs of clinical trials. The model is continuous in time and represents biological variables continuously. As demonstrated in a companion article, the equations can simulate a variety of clinical trials and reproduce their results with good accuracy.

CONCLUSIONS

It is possible to build a mathematical model that replicates the pathophysiology of diabetes at a high level of biological and clinical detail and that can be tested by simulating clinical trials.

Determining the Value of Disease Management Programs

BACKGROUND

Increasing prevalence, rising costs, and persisting deficiencies in quality of care for chronic diseases pose economic and policy challenges to providers and purchasers. Disease management (DM) programs may address these challenges, but neither purchasers nor providers can assess their value. The potpourri of current quality indicators provides limited insight into the actual clinical benefit achieved. A conference sponsored by the Agency for Healthcare Research and Quality (AHRQ) and held in October 2002 explored new approaches to measuring and reporting the value of DM for diabetes mellitus.

RESULTS

Quantifying the value of DM requires measuring clinical benefit and net impact on health care costs for the entire population with diabetes. If quality is measured with indicators that are clearly linked to outcomes, clinical benefit can be estimated. Natural history models combine the expected benefits of improvements in multiple indicators to yield a single, composite measure, the quality-adjusted life-year. Such metrics could fairly express, in terms of survival and complications prevention, relatively disparate DM programs' benefits. Measuring and comparing health care costs requires data validation and appropriate case-mix adjustment. Comparing value across programs may provide more accurate assessments of performance, enhance quality improvement efforts within systems, and contribute generalizable knowledge on the utility of DM approaches.

CONCLUSIONS

Conference attendees recommended pilot projects to further explore use of natural history models for measuring and reporting the value of DM.