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

The Impact of Unrelated Future Medical Costs on Economic Evaluation Outcomes for Different Models of Diabetes

OBJECTIVE

This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus.

METHODS

Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes.

RESULTS

The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations.

CONCLUSIONS

For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations.

Comparing the effectiveness and cost-effectiveness of alternative type 2 diabetes monitoring intervals in resource limited settings

Type 2 diabetes (T2D) represents a growing disease burden in South Africa. While glycated haemoglobin (HbA1c) testing is the gold standard for long-term blood glucose management, recommendations for HbA1c monitoring frequency are based on expert opinion. This study investigates the effectiveness and cost-effectiveness of alternative HbA1c monitoring intervals in the management of T2D. A Markov model with three health states (HbA1c <7%, HbA1c ≥ 7%, Dead) was used to estimate lifetime costs and quality-adjusted life years (QALYs) of alternative HbA1c monitoring intervals among patients with T2D, using a provider's perspective and a 3% discount rate. HbA1c monitoring strategies (three-monthly, four-monthly, six-monthly and annual tests) were evaluated with respect to the incremental cost-effectiveness ratio (ICER) assessing each comparator against a less costly, undominated alternative. The scope of costs included the direct medical costs of managing diabetes. Transition probabilities were obtained from routinely collected public sector HbA1c data, while health service utilization and health-related-quality-of-life (HRQoL) data were obtained from a local cluster randomized controlled trial. Other parameters were obtained from published studies. Robustness of findings was evaluated using one-way and probabilistic sensitivity analyses. A South African indicative cost-effectiveness threshold of USD2665 was adopted. Annual and lifetime costs of managing diabetes increased with HbA1c monitoring, while increased monitoring provides higher QALYs and life years. For the overall cohort, the ICER for six-monthly vs annual monitoring was cost-effective (USD23 22.37 per QALY gained), whereas the ICER of moving from six-monthly to three-monthly monitoring was not cost effective (USD6437.79 per QALY gained). The ICER for four-monthly vs six-monthly monitoring was extended dominated. The sensitivity analysis showed that the ICERs were most sensitive to health service utilization rates. While the factors influencing glycaemic control are multifactorial, six-monthly monitoring is potentially cost-effective while more frequent monitoring could further improve patient HrQoL.

A Systematic Review of Methodologies Used in Models of the Treatment of Diabetes Mellitus

BACKGROUND

Diabetes mellitus is a chronic and complex disease, increasing in prevalence and consequent health expenditure. Cost-effectiveness models with long time horizons are commonly used to perform economic evaluations of diabetes' treatments. As such, prediction accuracy and structural uncertainty are important features in cost-effectiveness models of chronic conditions.

OBJECTIVES

The aim of this systematic review is to identify and review published cost-effectiveness models of diabetes treatments developed between 2011 and 2022 regarding their methodological characteristics. Further, it also appraises the quality of the methods used, and discusses opportunities for further methodological research.

METHODS

A systematic literature review was conducted in MEDLINE and Embase to identify peer-reviewed papers reporting cost-effectiveness models of diabetes treatments, with time horizons of more than 5 years, published in English between 1 January 2011 and 31 of December 2022. Screening, full-text inclusion, data extraction, quality assessment and data synthesis using narrative synthesis were performed. The Philips checklist was used for quality assessment of the included studies. The study was registered in PROSPERO (CRD42021248999).

RESULTS

The literature search identified 30 studies presenting 29 unique cost-effectiveness models of type 1 and/or type 2 diabetes treatments. The review identified 26 type 2 diabetes mellitus (T2DM) models, 3 type 1 DM (T1DM) models and one model for both types of diabetes. Fifteen models were patient-level models, whereas 14 were at cohort level. Parameter uncertainty was assessed thoroughly in most of the models, whereas structural uncertainty was seldom addressed. All the models where validation was conducted performed well. The methodological quality of the models with respect to structure was high, whereas with respect to data modelling it was moderate.

CONCLUSIONS

Models developed in the past 12 years for health economic evaluations of diabetes treatments are of high-quality and make use of advanced methods. However, further developments are needed to improve the statistical modelling component of cost-effectiveness models and to provide better assessment of structural uncertainty.

A Systematic Review of Cost-Effectiveness Studies of Newer Non-Insulin Antidiabetic Drugs: Trends in Decision-Analytical Models for Modelling of Type 2 Diabetes Mellitus

BACKGROUND

We performed a systematic overview of the cost-effectiveness analyses (CEAs) comparing Non-insulin antidiabetic drugs (NIADs) with other NIADs for the treatment of type 2 diabetes mellitus (T2DM), using decision-analytical modelling (DAM), focusing on both the economic results and the underlying methodological choices.

METHODS

Eligible studies were CEAs using DAM to compare NIADs within the glucagon-like peptide-1 (GLP1) receptor agonists, sodium-glucose cotransporter-2 (SGLT2) inhibitors, or dipeptidyl peptidase-4 (DPP4) inhibitor classes with other NIADs within those classes for the treatment of T2DM. The PubMed, Embase and Econlit databases were searched from 1 January 2018 to 15 November 2022. Two reviewers screened the studies for relevance by titles and abstracts and then for eligibility via full-text screening, extracted the data from the full texts and appendices, and then stored the data in a spreadsheet.

RESULTS

The search yielded 890 records and 50 studies were eligible for inclusion. The studies were mainly based on a European setting (60%). Industry sponsorship was found in 82% of studies. The CORE diabetes model was used in 48% of the studies. GLP1 and SGLT2 products were the main comparators in 31 and 16 studies, respectively, while one study had DPP4 and two had no easily discernible main comparator. Direct comparison between SGLT2 and GLP1 occurred in 19 studies. At a class level, SGLT2 dominated GLP1 in six studies and was cost effective against GLP1 once as part of a treatment pathway. GLP1 was cost effective in nine studies and not cost effective against SGLT2 in three studies. At a product level, oral and injectable semaglutide, and empagliflozin, were cost effective against other within-class products. Injectable and oral semaglutide were more frequently found cost effective in these comparisons, with some conflicting results. Most of the modelled cohorts and treatment effects were sourced from randomised controlled trials. The following model assumptions varied depending on the class of the main comparator: choice of and reasoning behind risk equations, the time until the treatment switch, and how often the comparators were discontinued. Diabetes-related complications were emphasised on par with quality-adjusted life-years as model outputs. The main quality issues were regarding the description of alternatives, the perspective of analysis, the measurement of costs and consequences, and patient subgroups.

CONCLUSION

The included CEAs using DAMs have limitations that hinder their ability to inform decision makers on the cost-effective choice: lack of updated reasoning behind the choice of key model assumptions, over-reliance on risk equations based on older treatment practices, and sponsorship bias. The question of which NIAD is cost effective for the treatment of which T2DM patient is a pressing one and the answer remains unclear.

Pharmacoeconomic evaluation of insulin aspart and glargine in type 1 and 2 diabetes mellitus in Iran

PURPOSE

The higher costs of insulin analogs including short-acting insulin aspart (IAsp) and long-acting insulin glargine (IGla) have restricted their widespread uptake despite having improved pharmacokinetic and pharmacodynamic properties and patient convenience. This study aims to evaluate the cost-effectiveness of IAsp versus Regular Insulin (RI) and IGla versus NPH Insulin in type 1 and 2 diabetes from the perspective of the Iranian healthcare system.

METHODS

Clinical data including HbA1c levels, hypoglycemia, weight gain, and health-related quality of life were derived from the included systematic review and meta-analysis studies. Different methods of pharmacoeconomic evaluation were used for an annual time horizon. Utility decrements for diabetes-related complications were extracted from the literature. Direct medical costs were calculated in 2022 prices. A one-way sensitivity analysis was also performed.

RESULTS

In type 1 diabetes, IAsp was associated with more costs and effects in terms of reducing HbA1c compared with RI. An incremental cost of $83 was estimated to obtain an additional 1% reduction in HbA1c per patient per year. Similarly, an incremental cost of $16 was estimated for IGla compared with NPH. In type 2 diabetes, IAsp and RI were associated with equal efficacy and safety. For IGla versus NPH, the incremental cost-effectiveness ratio was calculated at $1975 per quality-adjusted life-year. The robustness of the result was confirmed through sensitivity analysis.

CONCLUSION

Insulin analogs, IAsp and IGla, are cost-effective for type 1 diabetes versus human insulins, RI and NPH. For type 2 diabetes, IAsp is not cost-effective when compared with RI. For IGla versus NPH, however, the incremental cost-effectiveness ratio seems to be within the accepted thresholds.

Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

BACKGROUND

Decision analytical models (DAMs) are used to develop an evidence base for impact and health economic evaluations, including evaluating interventions to improve diabetes care and health services-an increasingly important area in low- and middle-income countries (LMICs), where the disease burden is high, health systems are weak, and resources are constrained. This study examines how DAMs-in particular, Markov, system dynamic, agent-based, discrete event simulation, and hybrid models-have been applied to investigate non-pharmacological population-based (NP) interventions and how to advance their adoption in diabetes research in LMICs.

METHODS

We systematically searched peer-reviewed articles published in English from inception to 8th August 2022 in PubMed, Cochrane, and the reference list of reviewed articles. Articles were summarised and appraised based on publication details, model design and processes, modelled interventions, and model limitations using the Health Economic Evaluation Reporting Standards (CHEERs) checklist.

RESULTS

Twenty-three articles were fully screened, and 17 met the inclusion criteria of this qualitative review. The majority of the included studies were Markov cohort (7, 41%) and microsimulation models (7, 41%) simulating non-pharmacological population-based diabetes interventions among Asian sub-populations (9, 53%). Eleven (65%) of the reviewed studies evaluated the cost-effectiveness of interventions, reporting the evaluation perspective and the time horizon used to track cost and effect. Few studies (6,35%) reported how they validated models against local data.

CONCLUSIONS

Although DAMs have been increasingly applied in LMICs to evaluate interventions to control diabetes, there is a need to advance the use of DAMs to evaluate NP diabetes policy interventions in LMICs, particularly DAMs that use local research data. Moreover, the reporting of input data, calibration and validation that underlies DAMs of diabetes in LMICs needs to be more transparent and credible.

A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries

INTRODUCTION

The burden of type 2 diabetes is steadily increasing in low-and-middle-income countries, thereby posing a major threat from both a treatment, and funding standpoint. Although simulation modelling is generally relied upon for evaluating long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the characteristics and capabilities of available models used in low-and-middle-income countries. We review the use of computer simulation modelling for the management of type 2 diabetes in low-and-middle-income countries.

METHODS

A search for studies reporting computer simulation models of the natural history of individuals with type 2 diabetes and/or decision models to evaluate the impact of treatment strategies on these populations was conducted in PubMed. Data were extracted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and assessed using modelling checklists. Publications before the year 2000, from high-income countries, studies involving animals and analyses that did not use mathematical simulations were excluded. The full text of eligible articles was sourced and information about the intervention and population being modelled, type of modelling approach and the model structure was extracted.

RESULTS

Of the 79 articles suitable for full text review, 44 studies met the inclusion criteria. All were cost-effectiveness/utility studies with the majority being from the East Asia and Pacific region (n = 29). Of the included studies, 34 (77.3%) evaluated the cost-effectiveness of pharmacological interventions and approximately 75% of all included studies used HbA1c as one of the treatment effects of the intervention. 32 (73%) of the publications were microsimulation models, and 29 (66%) were state-transition models. Most of the studies utilised annual cycles (n = 29, 71%), and accounted for costs and outcomes over 20 years or more (n = 38, 86.4%).

CONCLUSIONS

While the use of simulation modelling in the management of type 2 diabetes has been steadily increasing in low-and-middle-income countries, there is an urgent need to invest in evaluating therapeutic and policy interventions related to type 2 diabetes in low-and-middle-income countries through simulation modelling, especially with local research data. Moreover, it is important to improve transparency and credibility in the reporting of input data underlying model-based economic analyses, and studies.

Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and Analysis

Background: Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. Methods: The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. Results: 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. Conclusion: The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.

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.

Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases

(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of the T2D Holistic Care model via estimating the probability of diabetes-related complications and the time-to-occurrence from a population-based database. (2) Methods: The model was based on the database of a Taiwan pay-for-performance reimbursement scheme for T2D between November 2002 and July 2017. A nonhomogeneous Markov model was applied to simulate multistate (7 main complications and death) transition probability after considering the sequential and repeated difficulties. (3) Results: The Markov model was constructed based on clinical care information from 163,452 patients with T2D, with a mean follow-up time of 5.5 years. After simulating a cohort of 100,000 hypothetical patients over a 10-year time horizon based on selected patient characteristics at baseline, a good predicted complication and mortality rates with a small range of absolute error (0.3–3.2%) were validated in the original cohort. Better and optimal predictabilities were further confirmed compared to the UKPDS Outcomes model and applied the model to other Asian populations, respectively. (4) Contribution: The study provides well-elucidated evidence to apply real-world data to the estimation of the occurrence and time point of major diabetes-related complications over a patient’s lifetime. Further applications in health decision science are encouraged.

Using polyphenols as a relevant therapy to diabetes and its complications, a review

Diabetes is currently a worldwide health concern. Hyperglycemia, hypertension, obesity, and oxidative stress are the major risk factors that inevitably lead to all the complications from diabetes. These complications severely impact the quality of life of patients, and they can be managed, reduced, or even reverted by several polyphenols, plant extracts and foods rich in these compounds. The goal of this review is to approach diabetes not as a single condition but rather an interconnected combination of risk factors and complications. This work shows that polyphenols have multi target action and effects and they have been systematically proven to be relevant in the reduction of each risk factor and improvement of associated complication.

Cost-effectiveness of intensive interventions compared to standard care in individuals with type 2 diabetes: A systematic review and critical appraisal of decision-analytic models

AIMS

The objective of this systematic review is to identify and assess the quality of published decision-analytic models evaluating the long-term cost-effectiveness of target-driven intensive interventions for single and multifactorial risk factor control compared to standard care in people with type 2 diabetes.

METHODS

We searched the electronic databases MEDLINE, the National Health Service Economic Evaluation Database, Web of Science and the Cochrane Library from inception to October 31, 2019. Articles were eligible for inclusion if the studies had used a decision-analytic model evaluating both the long-term costs and benefits associated with intensive interventions for risk factor control compared to standard care in people with type 2 diabetes. Data were extracted using a standardised form, while quality was assessed using the decision-analytic model-specific Philips-criteria.

RESULTS

Overall, nine articles (11 models) were identified, four models evaluated intensive glycaemic control, three evaluated intensive blood pressure control, two evaluated intensive lipid control, and two evaluated intensive multifactorial interventions. Six reported using discrete-time simulations modelling approach, whereas five reported using a Markov modelling framework. The majority, seven studies, reported that the intensive interventions were dominant or cost-effective, given the assumptions and analytical perspective taken. The methodological and reporting quality of the studies was generally weak, with only four studies fulfilling more than 50% of their applicable Philips-criteria.

CONCLUSIONS

This is the first systematic review of decision-analytic models of target-driven intensive interventions for single and multifactorial risk factor control in individuals with type 2 diabetes. Identified shortcomings are lack of transparency in data identification and evidence synthesis as well as for the selection of the modelling approaches. Future models should aim to include greater evaluation of the quality of the data sources used and the assessment of uncertainty in the model.

Evaluation of the Short-Term Cost-Effectiveness of IDegLira Versus Basal Insulin and Basal-Bolus Therapy in Patients with Type 2 Diabetes Based on Attainment of Clinically Relevant Treatment Targets

BACKGROUND

Effective glycemic control can reduce the risk of complications and their related costs in patients with type 2 diabetes (T2D). Many patients fail to reach hemoglobin A1c (HbA1c) ≤ 6.5% or < 7.0%, often due to adverse effects of treatment, such as hypoglycemia and weight gain. Glycemic targets should be individualized and consider multiple factors, including the risk of adverse events and the patient's characteristics and comorbid conditions.

OBJECTIVE

To compare the odds and annual cost of achieving treatment targets, which incorporate HbA1c targets of < 7.5%, < 8.0%, and ≤ 9.0%, with insulin degludec/liraglutide (IDegLira) versus basal insulin and basal-bolus therapy.

METHODS

This is a post hoc analysis of the DUAL V and DUAL VII 26-week trials, which randomized patients with T2D uncontrolled (HbA1c 7%-10%) on insulin glargine 100 units/mL (IGlar U100) and metformin to IDegLira or continued IGlar U100 titration (DUAL V) or IGlar U100 + insulin aspart (DUAL VII), all with metformin. Proportions of patients achieving HbA1c targets (< 7.5%, < 8.0%, and ≤ 9.0%) by the end of trial were assessed via 3 outcomes: alone, without either hypoglycemia or weight gain (double composite outcome), or without a combination of hypoglycemia and weight gain (triple composite outcome). The cost per patient achieving the triple composite outcome at each HbA1c target (< 7.5%, < 8.0%, and ≤ 9.0%) was calculated by dividing the annual cost of treatment by the proportion of patients achieving the target. This short-term (1-year) cost-effectiveness analysis was conducted from the perspective of a U.S. health care payer.

RESULTS

More patients achieved HbA1c < 7.5% (P < 0.0001) and < 8.0% (P = 0.0003), and a similar percentage achieved HbA1c ≤ 9.0% with IDegLira versus IGlar U100 (DUAL V). Similar proportions of patients achieved all 3 HbA1c targets with IDegLira compared with basal-bolus therapy (DUAL VII). The odds of achieving double or triple composite outcomes were significantly higher for IDegLira versus IGlar U100 or basal-bolus for all 3 HbA1c targets (P < 0.0001 in each case) in both trials. For each $1 spent on IDegLira, the equivalent annual costs per patient to achieve HbA1c targets of < 7.5%, < 8.0%, or ≤ 9.0% without hypoglycemia and without weight gain were $2.43, $2.10, and $2.05, respectively, for IGlar U100 and $6.33, $5.80, and $6.06, respectively, for basal-bolus therapy.

CONCLUSIONS

Based on data from DUAL V and DUAL VII, this analysis showed that a greater or similar proportion of patients with T2D reached HbA1c targets with IDegLira compared with IGlar U100/basal-bolus therapy. Odds of achieving double or triple composite outcomes of HbA1c reduction without hypoglycemia and/or without weight gain were greatest for IDegLira. Short-term cost analyses based on the triple composite outcomes suggest that IDegLira is a cost-effective treatment option in the United States compared with either uptitration of IGlar U100 or basal-bolus therapy.

DISCLOSURES

This study was supported by Novo Nordisk A/S. The analysis was based on the DUAL V (NCT01952145) and DUAL VII (NCT02420262) trials, which were funded and conducted by Novo Nordisk. This post hoc analysis was conceived and interpreted by the authors and drafted with medical writing support that was funded by Novo Nordisk. Novo Nordisk also reviewed the manuscript for medical accuracy. Hunt and Malkin are employees of Ossian Health Economics and Communications, which received consulting fees from Novo Nordisk during the conduct of this study and has received consulting fees from Novo Nordisk, unrelated to this study. Mocarski, Ranthe, and Schiffman are employees of Novo Nordisk and Novo Nordisk A/S. Cannon has received speaker fees/honoraria from Abbvie, Amgen, and Janssen; speaker fees from Novo Nordisk; and has stock ownership in Novo Nordisk. Bargiota has received speaker fees/honoraria from AstraZeneca, Eli Lilly, MSD, Novo Nordisk, Sanofi, Boehringer Ingelheim, and Novartis. Billings has received personal fees from Novo Nordisk, Sanofi, and Dexcom, unrelated to this study. Leiter reports grants and personal fees from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Sanofi, Servier, and GSK, unrelated to this study. Doshi has no relevant conflicts of interest to disclose. Parts of this study were presented as a poster at the AMCP Managed Care & Specialty Pharmacy Annual Meeting; April 23-26, 2018; Boston, MA.

Cost of achieving HbA1c and weight loss treatment targets with IDegLira vs insulin glargine U100 plus insulin aspart in the USA

Background

Compared with basal-bolus insulin therapy (insulin glargine U100 plus insulin aspart), IDegLira has been shown to be associated with similar improvements in HbA1c, with superior weight loss and reduced hypoglycemia in patients with type 2 diabetes. The present analysis evaluated the cost per patient with type 2 diabetes achieving HbA1c-focused and composite treatment targets with IDegLira and insulin glargine U100 plus insulin aspart (≤4 times daily).

Methods

The proportions of patients achieving treatment targets were obtained from the treat-to-target, non-inferiority DUAL VII study (NCT02420262). The annual cost per patient achieving target (cost of control) was analyzed from a US healthcare payer perspective. The annual cost of control was assessed for eight prespecified endpoints and four post-hoc endpoints.

Results

The number needed to treat to bring one patient to targets of HbA1c <7.0% and HbA1c ≤6.5% was similar with IDegLira and insulin glargine U100 plus insulin aspart. However, when weight gain and/or hypoglycemia were included, the number needed to treat was lower with IDegLira. IDegLira and insulin glargine U100 plus insulin aspart had similar costs of control for HbA1c <7.0%. However, cost of control values were substantially lower with IDegLira when the more stringent target of HbA1c ≤6.5% was used, and when patient-centered outcomes of hypoglycemia risk and impact on weight were included.

Conclusion

IDegLira was shown to be a cost-effective treatment vs insulin glargine U100 plus insulin aspart for patients with type 2 diabetes not achieving glycemic targets on basal insulin in the USA.

External validation of type 2 diabetes computer simulation models: definitions, approaches, implications and room for improvement-a protocol for a systematic review

BACKGROUND

Type 2 diabetes mellitus (T2DM), a highly prevalent chronic disease, puts a large burden on individual health and health care systems. Computer simulation models, used to evaluate the clinical and economic effectiveness of various interventions to handle T2DM, have become a well-established tool in diabetes research. Despite the broad consensus about the general importance of validation, especially external validation, as a crucial instrument of assessing and controlling for the quality of these models, there are no systematic reviews comparing such validation of diabetes models. As a result, the main objectives of this systematic review are to identify and appraise the different approaches used for the external validation of existing models covering the development and progression of T2DM.

METHODS

We will perform adapted searches by applying respective search strategies to identify suitable studies from 14 electronic databases. Retrieved study records will be included or excluded based on predefined eligibility criteria as defined in this protocol. Among others, a publication filter will exclude studies published before 1995. We will run abstract and full text screenings and then extract data from all selected studies by filling in a predefined data extraction spreadsheet. We will undertake a descriptive, narrative synthesis of findings to address the study objectives. We will pay special attention to aspects of quality of these models in regard to the external validation based upon ISPOR and ADA recommendations as well as Mount Hood Challenge reports. All critical stages within the screening, data extraction and synthesis processes will be conducted by at least two authors. This protocol adheres to PRISMA and PRISMA-P standards.

DISCUSSION

The proposed systematic review will provide a broad overview of the current practice in the external validation of models with respect to T2DM incidence and progression in humans built on simulation techniques.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017069983 .

Cost of diabetic eye, renal and foot complications: a methodological review

INTRODUCTION

Diabetic retinopathy (DR), diabetic kidney disease (DKD) and diabetic foot ulcer (DFU) represent a public health and economic concern that may be assessed with cost-of-illness (COI) studies.

OBJECTIVES

(1) To review COI studies published between 2000 and 2015, about DR, DKD and DFU; (2) to analyse methods used.

METHODS

Disease definition, epidemiological approach, perspective, type of costs, activity data sources, cost valuation, sensitivity analysis, cost discounting and presentation of costs may be described in COI studies. Each reviewed study was assessed with a methodological grid including these nine items.

RESULTS

The five following items have been detailed in the reviewed studies: epidemiological approach (59 % of studies described it), perspective (75 %), type of costs (98 %), activity data sources (91 %) and cost valuation (59 %). The disease definition and the presentation of results were detailed in fewer studies (respectively 50 and 46 %). In contrast, sensitivity analysis was only performed in 14 % of studies and cost discounting in 7 %. Considering the studies showing an average cost per patient and per year with a societal perspective, DR cost estimates were US $2297 (range 5-67,486), DKD cost ranged from US $1095 to US $16,384, and DFU cost was US $10,604 (range 1444-85,718).

DISCUSSION

This review reinforces the need to adequately describe the method to facilitate literature comparisons and projections. It also recalls that COI studies represent complementary tools to cost-effectiveness studies to help decision makers in the allocation of economic resources for the management of DR, DKD and DFU.

Evaluation of the Short-Term Cost-Effectiveness of IDegLira Versus Continued Up-Titration of Insulin Glargine U100 in Patients with Type 2 Diabetes in the USA

Introduction

Effective glycemic control can reduce the risk of complications and their related costs in type 2 diabetes mellitus (T2DM). However, many patients fail to reach glycemic targets, often because of adverse effects of treatment (including hypoglycemia or weight gain). The present analysis evaluated the short-term cost-effectiveness of IDegLira versus continued up-titration of insulin glargine U100 in patients with T2DM failing to achieve glycemic control on basal insulin in the US setting.

Methods

The cost per patient achieving treatment target (cost of control) was assessed for various single and composite endpoints for the entire trial population and in patients with baseline glycated hemoglobin (HbA1c) >8.0% and HbA1c >9.0%. The proportions of patients achieving treatment targets were analyzed using data obtained in the DUAL V study. Costs were accounted based on published wholesale acquisition costs.

Results

When assessing the full trial population, IDegLira was associated with lower annual cost of control than continued up-titration of insulin glargine U100 for patients achieving HbA1c ≤6.5% without confirmed hypoglycemia (by $10,608), HbA1c ≤6.5% without weight gain (by $29,215), and HbA1c ≤6.5% without confirmed hypoglycemia and weight gain (by $57,351). A similar pattern was observed when multifactorial treatment targets were based on achieving a glycemic target of 7.0%. When only HbA1c was considered, IDegLira was associated with a lower cost per patient achieving HbA1c ≤6.5% (by $3306) but cost of control was equivalent for a target of HbA1c <7.0%. In patients with baseline HbA1c >8.0% and HbA1c >9.0%, IDegLira was associated with a lower cost of control for all treatment targets.

Conclusion

The significantly greater clinical efficacy in terms of bringing patients to treatment targets identified in the DUAL V study results in lower cost of control values for IDegLira versus continued up-titration of insulin glargine U100 in the USA. This suggests IDegLira is a cost-effective treatment option in the USA.

Funding

Novo Nordisk A/S and Novo Nordisk Inc.

Economic impact of hypoglycemia among insulin-treated patients with diabetes

OBJECTIVE

The objective of this study was to assess the cost of hypoglycemic events among insulin-treated patients with diabetes and the potential cost savings to a hypothetical US health plan and employer of reducing hypoglycemic events with a device intervention.

METHODS

A cost-calculator model was developed to estimate the direct costs of hypoglycemic events, accounting for diabetes type, age, and event severity. Model inputs were derived from published incidence rates of hypoglycemic events and direct medical costs. Assumed intervention efficacy was based on published studies of an emerging technology which yielded 72.2% (LGS Trial; ACTRN12610000024044) and 31.8% (ASPIRE Trial; NCT01497938) reductions in severe and non-severe hypoglycemic events, respectively. Model outcomes-including the number of severe (requiring medical assistance) and non-severe events, and direct/indirect medical costs (excluding intervention costs)-were evaluated over a 1-year period for a hypothetical health plan and employer perspectives.

RESULTS

In a health plan with 10 million enrollees, patients without the intervention would have experienced 0.09 and 14.60 severe and non-severe hypoglycemic events per patient per year (PPPY), respectively (vs 0.02 severe and 9.96 non-severe events with the intervention). This translated into total direct medical cost savings of $45 million ($177 PPPY) for the health plan. For an employer with 100,000 employees, the intervention would have yielded additional savings of $492 PPPY in indirect costs.

CONCLUSION

Insulin-treated patients experience hypoglycemic events, which are associated with substantial direct and indirect medical costs. The cost savings of reducing hypoglycemic events need to be weighed against the costs of using diabetes device interventions.

Cost-effectiveness of a risk-based secondary screening programme of type 2 diabetes

OBJECTIVE

The objective of this study was to develop a long-term economic model for type 2 diabetes to describe the entire spectrum of the disease over a wide range of healthcare programmes. The model evaluates a public health, risk-based screening programme in a country specific setting.

METHODS

The lifespan of persons and important phases of the disease and related interventions are recorded in a Markov model, which first simulates the effect of screening, then replicates important complications of diabetes, follows the progression of individuals through physiological variables and finally calculates outcomes in monetary and naturalistic units.

RESULTS

The introduction of the screening programme nearly doubled the proportion of diagnosed patients at the age of 50 and prolonged life expectancy. Three-yearly screening gained 0.0229 quality adjusted life years for an additional €83 per person compared with no screening and resulted an incremental cost-effectiveness ratio of €3630/quality adjusted life years.

CONCLUSION

From the economic perspective introduction of the 3-yearly screening programme is justifiable and it provides a good value for money. Copyright © 2016 John Wiley & Sons, Ltd.

Comparative cost-effectiveness of metformin-based dual therapies associated with risk of cardiovascular diseases among Chinese patients with type 2 diabetes: Evidence from a population-based national cohort in Taiwan

OBJECTIVE

To assess the cost-effectiveness of metformin-based dual therapies associated with cardiovascular disease (CVD) risk in a Chinese population with type 2 diabetes.

METHODS

We utilized Taiwan's National Health Insurance Research Database (NHIRD) 1997-2011, which is derived from the claims of National Health Insurance, a mandatory-enrollment single-payer system that covers over 99% of Taiwan's population. Four metformin-based dual therapy cohorts were used, namely a reference group of metformin plus sulfonylureas (Metformin-SU) and metformin plus acarbose, metformin plus thiazolidinediones (Metformin-TZD), and metformin plus glinides (Metformin-glinides). Using propensity scores, each subject in a comparison cohort was 1:1 matched to a referent. The effectiveness outcome was CVD risk. Only direct medical costs were included. The Markov chain model was applied to project lifetime outcomes, discounted at 3% per annum. The bootstrapping technique was performed to assess uncertainty in analysis.

RESULTS

Metformin-glinides was most cost-effective in the base-case analysis; Metformin-glinides saved $194 USD for one percentage point of reduction in CVD risk, as compared to Metformin-SU. However, for the elderly or those with severe diabetic complications, Metformin-TZD, especially pioglitazone, was more suitable; as compared to Metformin-SU, Metformin-TZD saved $840.1 USD per percentage point of reduction in CVD risk. Among TZDs, Metformin-pioglitazone saved $1831.5 USD per percentage point of associated CVD risk reduction, as compared to Metformin-rosiglitazone.

CONCLUSIONS

When CVD is considered an important clinical outcome, Metformin-pioglitazone is cost-effective, in particular for the elderly and those with severe diabetic complications.

A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus

BACKGROUND

Critiques of cost-effectiveness modelling in type 1 diabetes mellitus (T1DM) are scarce and are often undertaken in combination with type 2 diabetes mellitus (T2DM) models. However, T1DM is a separate disease, and it is therefore important to appraise modelling methods in T1DM.

OBJECTIVES

This review identified published economic models in T1DM and provided an overview of the characteristics and capabilities of available models, thus enabling a discussion of best-practice modelling approaches in T1DM.

METHODS

A systematic review of Embase(®), MEDLINE(®), MEDLINE(®) In-Process, and NHS EED was conducted to identify available models in T1DM. Key conferences and health technology assessment (HTA) websites were also reviewed. The characteristics of each model (e.g. model structure, simulation method, handling of uncertainty, incorporation of treatment effect, data for risk equations, and validation procedures, based on information in the primary publication) were extracted, with a focus on model capabilities.

RESULTS

We identified 13 unique models. Overall, the included studies varied greatly in scope as well as in the quality and quantity of information reported, but six of the models (Archimedes, CDM [Core Diabetes Model], CRC DES [Cardiff Research Consortium Discrete Event Simulation], DCCT [Diabetes Control and Complications Trial], Sheffield, and EAGLE [Economic Assessment of Glycaemic control and Long-term Effects of diabetes]) were the most rigorous and thoroughly reported. Most models were Markov based, and cohort and microsimulation methods were equally common. All of the more comprehensive models employed microsimulation methods. Model structure varied widely, with the more holistic models providing a comprehensive approach to microvascular and macrovascular events, as well as including adverse events. The majority of studies reported a lifetime horizon, used a payer perspective, and had the capability for sensitivity analysis.

CONCLUSIONS

Several models have been developed that provide useful insight into T1DM modelling. Based on a review of the models identified in this study, we identified a set of 'best in class' methods for the different technical aspects of T1DM modelling.

Methods applied in cost-effectiveness models for treatment strategies in type 2 diabetes mellitus and their use in Health Technology Assessments: a systematic review of the literature from 2008 to 2013

OBJECTIVE

To identify and compare health-economic models that were developed to evaluate the cost-effectiveness of treatments for type 2 diabetes mellitus (T2DM), and their use within Health Technology Assessments (HTAs).

METHODS

In total, six commonly used databases were searched for articles published between October 2008 and January 2013, using a protocolized search strategy and inclusion criteria. The websites of HTA organizations in nine countries, and proceedings from five relevant conferences, were also reviewed. The identified new health-economic models were qualitatively assessed using six criteria that were developed based on technical components, and characteristics related to the disease or the treatments being assessed. Finally, the number of times the models were applied within HTA reports, published literature, and/or major conferences was determined.

RESULTS

Thirteen new models were identified and reviewed in depth. Most of these were based on identical key data sources, and applied a similar model structure, either using Markov modeling or microsimulation techniques. The UKPDS equations and panel regressions were frequently used to estimate the occurrence of diabetes-related complications and the probability of developing risk factors in the long term. The qualitative assessment demonstrated that the CARDIFF, Sheffield T2DM and ECHO T2DM models seem technically equipped to appropriately assess the long-term health-economic consequences of chronic treatments for patients with T2DM. It was observed that the CORE model is the most widely described in literature and conferences, and the most often applied model within HTA submissions, followed by the CARDIFF and UKPDS models.

CONCLUSION

This research provides an overview of T2DM models that were developed between 2008 and January 2013. The outcomes of the qualitative assessments, combined with frequent use in local reimbursement decisions, prove the applicability of the CORE, CARDIFF and UKPDS models to address decision problems related to the long-term clinical and economic consequences of new and existing T2DM treatments.

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.

Policy evaluation in diabetes prevention and treatment using a population-based macro simulation model: the MICADO model

AIMS

To test a simulation model, the MICADO model, for estimating the long-term effects of interventions in people with and without diabetes.

METHODS

The MICADO model includes micro- and macrovascular diseases in relation to their risk factors. The strengths of this model are its population scope and the possibility to assess parameter uncertainty using probabilistic sensitivity analyses. Outcomes include incidence and prevalence of complications, quality of life, costs and cost-effectiveness. We externally validated MICADO's estimates of micro- and macrovascular complications in a Dutch cohort with diabetes (n = 498,400) by comparing these estimates with national and international empirical data.

RESULTS

For the annual number of people undergoing amputations, MICADO's estimate was 592 (95% interquantile range 291-842), which compared well with the registered number of people with diabetes-related amputations in the Netherlands (728). The incidence of end-stage renal disease estimated using the MICADO model was 247 people (95% interquartile range 120-363), which was also similar to the registered incidence in the Netherlands (277 people). MICADO performed well in the validation of macrovascular outcomes of population-based cohorts, while it had more difficulty in reflecting a highly selected trial population.

CONCLUSIONS

Validation by comparison with independent empirical data showed that the MICADO model simulates the natural course of diabetes and its micro- and macrovascular complications well. As a population-based model, MICADO can be applied for projections as well as scenario analyses to evaluate the long-term (cost-)effectiveness of population-level interventions targeting diabetes and its complications in the Netherlands or similar countries.

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.

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes.

Evaluating the Cost of Bringing People with Type 2 Diabetes Mellitus to Multiple Targets of Treatment in Canada

PURPOSE

Evidence suggests that clinical outcomes for people with type 2 diabetes mellitus can be improved through multifactorial treatment. The key challenges in the successful treatment of type 2 diabetes include maintaining tight glycemic control, minimizing the risk of hypoglycemia, controlling cardiovascular risk factors, and reducing or controlling weight. The aim of the present analysis was to evaluate the cost per patient achieving a composite clinical end point (glycosylated hemoglobin <7%, with no weight gain and no hypoglycemic events) in patients with type 2 diabetes in Quebec, Quebec, Canada, receiving liraglutide 1.2 mg, liraglutide 1.8 mg, thiazolidinedione, sulfonylurea, insulin glargine, sitagliptin, or exenatide.

METHODS

The proportion of patients achieving control was taken from a meta-analysis that was based on the Phase III trial program of liraglutide. Treatment costs, estimated from a health care payer perspective, were calculated on the basis of the trials included in the meta-analysis and captured the study drug, needles, self-monitoring of blood glucose (SMBG) test strips, SMBG lancets, and other antidiabetes medications received. Cost-effectiveness in terms of cost per patient achieving the composite end point (cost of control) was evaluated with an economic model developed in Microsoft Excel. No discounting was applied to cost or clinical outcomes because these were not projected beyond a 1-year time horizon. Sensitivity analyses were performed.

FINDINGS

Liraglutide 1.8 mg was associated with the lowest number needed to treat, with 3 patients needing to be treated to bring 1 patient to the composite end point. Pioglitazone was associated with the highest number needed to treat, with 17 patients requiring treatment to bring 1 patient to the composite end point. Evaluation of only annual pharmacy costs indicated that liraglutide 1.8 mg was the most costly treatment at Can$2780 per patient per year. Pioglitazone and glimepiride were associated with the lowest direct annual costs. Combining the clinical efficacy data with the annual cost of medications produced cost of control values of Can$6070 (liraglutide 1.2 mg), Can$6949 (liraglutide 1.8 mg), Can$7237 (glimepiride), Can$7704 (exenatide), Can$8297 (insulin glargine), Can$8741 (pioglitazone), and Can$9270 (sitagliptin) per patient achieving the composite end point.

IMPLICATIONS

Liraglutide 1.2 mg and 1.8 mg were associated with the lowest cost of control values, driven by the high proportion of patients achieving the composite end point, which offset the higher medication costs. A relatively low cost of control value was achieved for glimepiride, driven by low acquisition costs, despite relatively few patients achieving the composite end point.

Validation of the IHE Cohort Model of Type 2 Diabetes and the impact of choice of macrovascular risk equations

BACKGROUND

Health-economic models of diabetes are complex since the disease is chronic, progressive and there are many diabetic complications. External validation of these models helps building trust and satisfies demands from decision makers. We evaluated the external validity of the IHE Cohort Model of Type 2 Diabetes; the impact of using alternative macrovascular risk equations; and compared the results to those from microsimulation models.

METHODS

The external validity of the model was analysed from 12 clinical trials and observational studies by comparing 167 predicted microvascular, macrovascular and mortality outcomes to the observed study outcomes. Concordance was examined using visual inspection of scatterplots and regression-based analysis, where an intercept of 0 and a slope of 1 indicate perfect concordance. Additional subgroup analyses were conducted on 'dependent' vs. 'independent' endpoints and microvascular vs. macrovascular vs. mortality endpoints.

RESULTS

Visual inspection indicates that the model predicts outcomes well. The UKPDS-OM1 equations showed almost perfect concordance with observed values (slope 0.996), whereas Swedish NDR (0.952) and UKPDS-OM2 (0.899) had a slight tendency to underestimate. The R2 values were uniformly high (>0.96). There were no major differences between 'dependent' and 'independent' outcomes, nor for microvascular and mortality outcomes. Macrovascular outcomes tended to be underestimated, most so for UKPDS-OM2 and least so for NDR risk equations.

CONCLUSIONS

External validation indicates that the IHE Cohort Model of Type 2 Diabetes has predictive accuracy in line with microsimulation models, indicating that the trade-off in accuracy using cohort simulation might not be that large. While the choice of risk equations was seen to matter, each were associated with generally reasonable results, indicating that the choice must reflect the specifics of the application. The largest variation was observed for macrovascular outcomes. There, NDR performed best for relatively recent and well-treated patients, while UKPDS-OM1 performed best for the older UKPDS cohort.

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.

Review of models used in economic analyses of new oral treatments for type 2 diabetes mellitus

BACKGROUND

Economic models are considered to be important, as they help evaluate the long-term impact of diabetes treatment. To date, it appears that no article has reviewed and critically appraised the cost-effectiveness models developed to evaluate new oral treatments [glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors] for type 2 diabetes mellitus (T2DM).

OBJECTIVES

This study aimed to provide insight into the utilization of cost-effectiveness modelling methods. The focus of our study was aimed at the applicability of these models, particularly around the major assumptions related to the clinical parameters (glycated haemoglobin [A1c], systolic blood pressure [SBP], lipids and weight) used in the models, and subsequent clinical outcomes.

METHODS

MEDLINE and EMBASE were searched from 1 January 2004 to 14 February 2013 in order to identify published cost-effectiveness evaluations for the treatment of T2DM by new oral treatments (GLP-1 receptor agonists and DPP-4 inhibitors). Once identified, the articles were reviewed and grouped together according to the type of model. The following data were captured for each study: comparators; country; evaluation and key cost drivers; time horizon; perspective; discounting rates; currency/year; cost-effectiveness threshold, sensitivity analysis; and cost-effectiveness analysis curves.

RESULTS

A total of 15 studies were identified in our review. Nearly all of the models utilized a health care payer perspective and provided a lifetime horizon. The CORE Diabetes Model, UK Prospective Diabetes Study (UKPDS) Outcomes Model, Cardiff Diabetes Model, Centers for Disease Control and Prevention (CDC) Diabetes Cost-Effectiveness Group Model and Diabetes Mellitus Model were cited. With the exception of two studies, all of the studies made significant assumptions surrounding the impact of GLP-1 receptor agonists or DPP-4 inhibitors on clinical parameters and subsequent short- and long-term outcomes. Moreover, often the differences in the clinical parameters were relatively small (e.g. 1 or 2 mmHg in blood pressure) and would not be considered by many as clinically important. Yet, the impact of these small clinical changes often resulted in large lifetime changes in health outcomes in the models. In particular, many studies assumed that changes in weight associated with the therapies would equate to improved outcomes, despite limited evidence for this assumption. Although the new oral treatments were regarded as cost effective in most studies based upon the studies reviewed, the validity of these projections, particularly for the longer time frames, is questionable. Indeed, although most of these studies have been conducted in the last 5 years, recent trial evidence has already questioned the validity of most of these studies.

CONCLUSION

It is clear that a number of changes are required in the evaluation of diabetes therapies. First and foremost, the basic models need to be updated to include contemporary important clinical trial data assessing hard clinical outcomes in patients with diabetes. Second, there should be less emphasis on 40-year or lifetime costs and consequences of the therapies and a greater focus on short-term (5-year) and intermediate-term (10-year) outcomes. Practice is continually evolving, and the probability that these models would provide any valid predictions beyond 10 years is remote. Third, all modellers should immediately remove the basic assumption that small clinically inconsequential changes in A1c, SBP, lipids and weight result in major clinical improvements in patients. 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. We need to explore why cost-saving interventions could benefit further from adding patient characteristics, which may be able to better predict the use of lower-cost alternatives. Moreover, the vast array of different clinical, cost and utility data used in the different models reviewed makes it apparent that a uniform methodology should be developed for diabetes economic models. In this manner, future models could be run using the same data, which would allow for more acceptable comparability between studies.

Stratified patient-centered care in type 2 diabetes: a cluster-randomized, controlled clinical trial of effectiveness and cost-effectiveness

OBJECTIVE

Diabetes treatment should be effective and cost-effective. HbA1c-associated complications are costly. Would patient-centered care be more (cost-) effective if it was targeted to patients within specific HbA1c ranges?

RESEARCH DESIGN AND METHODS

This prospective, cluster-randomized, controlled trial involved 13 hospitals (clusters) in the Netherlands and 506 patients with type 2 diabetes randomized to patient-centered (n=237) or usual care (controls) (n=269). Primary outcomes were change in HbA1c and quality-adjusted life years (QALYs); costs and incremental costs (USD) after 1 year were secondary outcomes. We applied nonparametric bootstrapping and probabilistic modeling over a lifetime using a validated Dutch model. The baseline HbA1c strata were <7.0% (53 mmol/mol), 7.0-8.5%, and >8.5% (69 mmol/mol).

RESULTS

Patient-centered care was most effective and cost-effective in those with baseline HbA1c>8.5% (69 mmol/mol). After 1 year, the HbA1c reduction was 0.83% (95% CI 0.81-0.84%) (6.7 mmol/mol [6.5-6.8]), and the incremental cost-effectiveness ratio (ICER) was 261 USD (235-288) per QALY. Over a lifetime, 0.54 QALYs (0.30-0.78) were gained at a cost of 3,482 USD (2,706-4,258); ICER 6,443 USD/QALY (3,199-9,686). For baseline HbA1c 7.0-8.5% (53-69 mmol/mol), 0.24 QALY (0.07-0.41) was gained at a cost of 4,731 USD (4,259-5,205); ICER 20,086 USD (5,979-34,193). Care was not cost-effective for patients at a baseline HbA1c<7.0% (53 mmol/mol).

CONCLUSIONS

Patient-centered care is more valuable when targeted to patients with HbA1c>8.5% (69 mmol/mol), confirming clinical intuition. The findings support treatment in those with baseline HbA1c 7-8.5% (53-69 mmol/mol) and demonstrate little to no benefit among those with HbA1c<7% (53 mmol/mol). Further studies should assess different HbA1c strata and additional risk profiles to account for heterogeneity among patients.

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.

Model performance evaluation (validation and calibration) in model-based studies of therapeutic interventions for cardiovascular diseases : a review and suggested reporting framework

Decision analytic models play an increasingly important role in the economic evaluation of health technologies. Given uncertainties around the assumptions used to develop such models, several guidelines have been published to identify and assess 'best practice' in the model development process, including general modelling approach (e.g., time horizon), model structure, input data and model performance evaluation. This paper focuses on model performance evaluation. In the absence of a sufficient level of detail around model performance evaluation, concerns regarding the accuracy of model outputs, and hence the credibility of such models, are frequently raised. Following presentation of its components, a review of the application and reporting of model performance evaluation is presented. Taking cardiovascular disease as an illustrative example, the review investigates the use of face validity, internal validity, external validity, and cross model validity. As a part of the performance evaluation process, model calibration is also discussed and its use in applied studies investigated. The review found that the application and reporting of model performance evaluation across 81 studies of treatment for cardiovascular disease was variable. Cross-model validation was reported in 55 % of the reviewed studies, though the level of detail provided varied considerably. We found that very few studies documented other types of validity, and only 6 % of the reviewed articles reported a calibration process. Considering the above findings, we propose a comprehensive model performance evaluation framework (checklist), informed by a review of best-practice guidelines. This framework provides a basis for more accurate and consistent documentation of model performance evaluation. This will improve the peer review process and the comparability of modelling studies. Recognising the fundamental role of decision analytic models in informing public funding decisions, the proposed framework should usefully inform guidelines for preparing submissions to reimbursement bodies.

Economic evaluations of adult weight management interventions: a systematic literature review focusing on methods used for determining health impacts

BACKGROUND

One of the challenges when undertaking economic evaluations of weight management interventions is to adequately assess future health impacts. Clinical trials commonly measure impacts using surrogate outcomes, such as reductions in body mass index, and investigators need to decide how these can best be used to predict future health effects. Since obesity is associated with an increased risk of numerous chronic diseases occurring at different future time points, modelling is needed for predictions.

OBJECTIVE

To assess the methods used in economic evaluations to determine health impacts of weight management interventions and to investigate whether differences in methods affect the cost-effectiveness estimates.

METHODS

Eight databases were systematically searched. Included studies were categorized according to a decision analytic approach and effect measures incorporated.

RESULTS

A total of 44 articles were included; 21 evaluated behavioural interventions, 12 evaluated surgical procedures and 11 evaluated pharmacological compounds. Of the 27 papers that estimated future impacts, eleven used Markov modelling, seven used a decision tree, five used a mathematical application, two used patient-level simulation and the modelling method was unclear in two papers. The most common types of effects included were co-morbidity treatment costs, heath-related quality of life due to weight loss and gain in survival. Only 12 of the studies included heath-related quality of life gains due to reduced co-morbidities and only one study included productivity gains. Despite consensus that trial-based analysis on its own is inadequate in guiding resource allocation decisions, it was used in 39% of the studies. Several of the modelling papers used model structures not suitable for chronic diseases with changing health risks. Three studies concluded that the intervention dominated standard care; meaning that it generated more quality-adjusted life-years (QALYs) for less cost. The incremental costs per QALY gained varied from $US235 to $US56,836 in the remaining studies using this outcome measure. An implicit hypothesis of the review was that studies including long-term health effects would illustrate greater cost effectiveness compared with trial-based studies. This hypothesis is partly confirmed with three studies arriving at dominating results, as these reach their conclusion from modelling future co-morbidity treatment cost savings. However, for the remaining studies there is little indication that decision-analytic modelling disparities explain the differences.

CONCLUSIONS

This is the first literature review comparing methods used in economic evaluations of weight management interventions, and it is the first time that observed differences in study results are addressed with a view to methodological explanations. We conclude that many studies have methodological deficiencies and we urge analysts to follow recommended practices and use models capable of depicting long-term health consequences.

Impact of screening and early detection of impaired fasting glucose tolerance and type 2 diabetes in Canada: a Markov model simulation

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a major global health problem. An estimated 20%-50% of diabetic subjects in Canada are currently undiagnosed, and around 20%-30% have already developed complications. Screening for high blood glucose levels can identify people with prediabetic conditions and permit introduction of timely and effective prevention. This study examines the benefit of screening for impaired fasting glucose (IFG) and T2DM. If intervention is introduced at this prediabetic stage, it can be most effective in delaying the onset and complications of T2DM.

METHODS

Using a Markov model simulation, we compare the cost-effectiveness of screening for prediabetes (IFG) and T2DM with the strategy of no screening. An initial cohort of normoglycemic, prediabetic, or undiagnosed diabetic adults with one or more T2DM risk factors was used to model the strategies mentioned over a 10-year period. Subjects without known prediabetes or diabetes are screened every 3 years and persons with prediabetes were tested for diabetes on an annual basis. The model weighs the increase in quality-adjusted life-years (QALYs) associated with early detection of prediabetes and earlier diagnosis of T2DM due to lifestyle intervention and early treatment in asymptomatic subjects.

RESULTS

Costs for each QALY gained were $2281 for conventional screening compared with $2890 for no screening. Thus, in this base-case analysis, conventional screening with a frequency of once every 3 years was favored over no screening. Furthermore, conventional screening was more favorable compared with no screening over a wide range of willingness-to-pay thresholds. Changing the frequency of screening did not affect the overall results. Screening persons without diabetes or prediabetes on an annual basis had small effects on the cost-effectiveness ratios. Screening with a frequency of once every 5 years resulted in the lowest cost per QALY ($2117). Lack of screening costs the health care system $4812 more than the cost of screening once every 5 years.

CONCLUSION

The increased cost per QALY of not screening is due to the costs of complications caused downstream of T2DM. By ensuring that IFG screening occurs every 3 years for those without prediabetes and every year for those with prediabetes, the health and financial benefits related to T2DM are improved in Canada.

The quality of three decision-analytic diabetes models: a systematic health economic assessment

AIMS

Diabetes mellitus has important economic impacts worldwide. Interventions to prevent diabetes-related complications are often analyzed using model-based cost-effectiveness analyses. As model results are usually influenced by structural assumptions and by the data used, decision-makers should be able to assess the quality of diabetes models. The aim of this study was to assess the quality of selected diabetes models and to determine if modeling recommendations by the American Diabetes Association are considered.

METHODS

The quality of three selected diabetes models (Archimedes Model, CDC Model and Center for Outcomes Research [CORE] Diabetes Model) was assessed using systematic methods.

RESULTS

This systematic approach to assess model quality proved to be feasible and highlighted two areas for improvement: the rationale for model structure and methods to identify parameter values, which should be presented more transparently.

CONCLUSIONS

Overall, the need for a quality assessment of diabetes models is emphasized.

A health economic analysis of clinical islet transplantation

Islet cell transplantation is in clinical development for type 1 diabetes. There are no data on the cost in relationship to its benefits. We performed a cost-effectiveness analysis and made a comparison with standard insulin therapy, using Markov modeling and Monte Carlo simulations. The patient population was adults aged 20 yr suffering from hypoglycemia unawareness. Data were estimates from literature and clinical trials: costs were based on the situation in the United States. For insulin therapy, cumulative cost per patient during a 20-yr follow-up was $663,000, and cumulative effectiveness was 9.3 quality-adjusted life years (QALY), the average cost-effectiveness ratio being $71,000 per QALY. Islet transplantation had a cumulative cost of $519,000, a cumulative effectiveness of 10.9 QALY, and an average cost-effectiveness ratio of $47,800. During the first 10 yr, costs for transplantation were higher, but cumulative effectiveness was higher from the start onwards. In sensitivity analyses, the need for one instead of two transplants during the first year did not affect the conclusions, and islet transplantation remained cost-saving up to an initial cost of the procedure of $240,000. This exploratory evaluation shows that islet cell transplantation is more effective than standard insulin treatment, and becomes cost-saving at about 9-10 yr after transplantation.

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.