Economic models in type 2 diabetes

Yi, Yang; Philips, Z; Bergman, Garrett E.; Burslem, Kate

doi:10.1185/03007995.2010.494451

Cited by 38 publications

(28 citation statements)

References 68 publications

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“…A total of 11 diabetes models, described in Yi et al 21 and/or Tarride et al ,22 were reviewed. They were the IMS CDM,28 UKPDS,29 Archimedes,30 EAGLE,31 DCCT,32 DMM,33 Eastman,34 35 GDM,36 JADE,37 DiDACT38 and CDC39 models.…”

Section: Resultsmentioning

confidence: 99%

“…Details on the IMS CDM have been described and summarised in a number of published articles (eg, Palmer et al, 28 Yi et al 21 and Tarride et al 22). It uses 15 Markov submodels to simulate outcomes and costs for type 1 and type 2 diabetes patient cohorts, covering a range of macrovascular, microvascular and treatment-related complications with interaction between the submodels 28.…”

Section: Resultsmentioning

confidence: 99%

“…The diabetes models that were assessed were those evaluated in two recent reviews of diabetes models by Yi et al 21 and Tarride et al 22 Criteria for diabetes models from the American Diabetes Association (ADA)23 and criteria specific to the needs of this economic evaluation were used to assess the established models. These criteria included demonstrated internal and external validity, a long-term time horizon, interdependence between modelled complications, capture of glycated haemoglobin, analysis of quality-adjusted life-years (QALYs) and previous use in the published literature and in health technology assessment (HTA) submissions to reimbursement organisations.…”

Section: Methodsmentioning

confidence: 99%

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

2013

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ObjectivesTo 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).DesignExperts 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.SettingNational Health Service in England and Wales.ParticipantsExperts in monogenic diabetes whose collective expertise spans the length of the patient care pathway.Primary and secondary outcomesA 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.ResultsFive 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.ConclusionsDefining 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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

2013

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“…4.2.3). In the field of diabetes, for example, at least six out of ten important economic models are not capable to accurately account for diabetes duration [31]. This may not be necessary in every research question, but if highly accurate results are needed, modelling the disease duration should be considered.…”

Section: Discussionmentioning

confidence: 99%

Lexis Diagram and Illness-Death Model: Simulating Populations in Chronic Disease Epidemiology

et al. 2014

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Chronic diseases impose a tremendous global health problem of the 21st century. Epidemiological and public health models help to gain insight into the distribution and burden of chronic diseases. Moreover, the models may help to plan appropriate interventions against risk factors. To provide accurate results, models often need to take into account three different time-scales: calendar time, age, and duration since the onset of the disease. Incidence and mortality often change with age and calendar time. In many diseases such as, for example, diabetes and dementia, the mortality of the diseased persons additionally depends on the duration of the disease. The aim of this work is to describe an algorithm and a flexible software framework for the simulation of populations moving in an illness-death model that describes the epidemiology of a chronic disease in the face of the different times-scales. We set up a discrete event simulation in continuous time involving competing risks using the freely available statistical software R. Relevant events are birth, the onset (or diagnosis) of the disease and death with or without the disease. The Lexis diagram keeps track of the different time-scales. Input data are birth rates, incidence and mortality rates, which can be given as numerical values on a grid. The algorithm manages the complex interplay between the rates and the different time-scales. As a result, for each subject in the simulated population, the algorithm provides the calendar time of birth, the age of onset of the disease (if the subject contracts the disease) and the age at death. By this means, the impact of interventions may be estimated and compared.

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“…In ihr kommen entscheidungsanalytische Modelle zum Einsatz, die die Evidenz unterschiedlicher Studien miteinander verknüpfen. Für eine chronische Erkrankung wie Diabetes mellitus sind derartige Modelle von hoher Relevanz: Allein für den Typ-2-Diabetes wurden mindestens 20 verschiedene Modelle entwickelt [19]. Entsprechend wurden von der Amerikanischen Diabetes Gesellschaft (ADA) bereits Modellierungsleitlinien erstellt.…”

Section: Möglichkeiten Und Grenzenunclassified

Health-economic modelling for diabetes mellitus

2011

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Die gesundheitsökonomische Evaluation gewinnt international sowie inDeutschland zunehmend an Bedeutung. In ihr kommen entscheidungsanalytische Modelle zum Einsatz, die die Evidenz unterschiedlicher Studien miteinander verknüpfen. Für eine chronische Erkrankung wie Diabetes mellitus sind derartige Modelle von hoher Relevanz: Allein für den Typ-2-Diabetes wurden mindestens 20 verschiedene Modelle entwickelt [19]. Entsprechend wurden von der Amerikanischen Diabetes Gesellschaft (ADA) bereits Modellierungsleitlinien erstellt. Viele Diabetesforscher sind jedoch nicht mit der gesundheitsökonomischen Modellierung vertraut. Daher werden ihre Grundprinzipien hier vorgestellt. Die Rolle der entscheidungsanalytischen Modellierung in der gesundheitsöko-nomischen Evaluation Bei der entscheidungsanalytischen Modellierung in der gesundheitsökonomi-schen Evaluation handelt es sich um einen systematischen Ansatz, der die Ergebnisse unterschiedlicher Quellen in mathematischen Modellen zusammenführt [3, 4]. Ziel dieses Ansatzes ist es, die Konsequenzen unterschiedlicher Handlungsalternativen gemäß dem besten Stand der Wissenschaft abzubilden. Die Handlungsalternativen des Modells sollen alle relevanten Alternativen umfassen. Der Zeithorizont des Modells soll so gewählt werden, dass alle Konsequenzen abgebildet werden, die für eine Entscheidung von Bedeutung sind. In der Regel handelt es sich dabei um einen lebenslangen Zeithorizont. > Eine gesundheitsöko-nomische Evaluation sollte alle gesundheitlichen und wirtschaftlichen Folgen einbeziehen Gesundheitsökonomische Evaluationen können -anstelle von Modellen -auch auf klinischen Studien beruhen, in denen alle relevanten Informationen direkt erhoben werden. Allerdings können Studien kaum alle langfristigen Konsequenzen einer chronischen Erkrankung wie Diabetes mellitus direkt erheben. Da eine gesundheitsökonomische Evaluation alle gesundheitlichen und wirtschaftlichen Folgen einbeziehen sollte, ist es in der Praxis kaum möglich, auf eine Modellierung zu verzichten.

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Economic models in type 2 diabetes

Cited by 38 publications

References 68 publications

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

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

Lexis Diagram and Illness-Death Model: Simulating Populations in Chronic Disease Epidemiology

Health-economic modelling for diabetes mellitus

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