scholarly journals INCORPORATING ENVIRONMENTAL OUTCOMES INTO A HEALTH ECONOMIC MODEL

2016 ◽  
Vol 32 (6) ◽  
pp. 400-406 ◽  
Author(s):  
Kevin Marsh ◽  
Michael Ganz ◽  
Emil Nørtoft ◽  
Niels Lund ◽  
Joshua Graff-Zivin
Keyword(s):  
Health Economic ◽  
Environmental Impact ◽  
Economic Model ◽  
Economic Evaluations ◽  
Combination Regimen ◽  
Simple Method ◽  
Life Years ◽  
Health Economic Model ◽  
Economic Analyses ◽  
The Impact

Objectives: Traditional economic evaluations for most health technology assessments (HTAs) have previously not included environmental outcomes. With the growing interest in reducing the environmental impact of human activities, the need to consider how to include environmental outcomes into HTAs has increased. We present a simple method of doing so.Methods: We adapted an existing clinical-economic model to include environmental outcomes (carbon dioxide [CO2] emissions) to predict the consequences of adding insulin to an oral antidiabetic (OAD) regimen for patients with type 2 diabetes mellitus (T2DM) over 30 years, from the United Kingdom payer perspective. Epidemiological, efficacy, healthcare costs, utility, and carbon emissions data were derived from published literature. A scenario analysis was performed to explore the impact of parameter uncertainty.Results: The addition of insulin to an OAD regimen increases costs by 2,668 British pounds per patient and is associated with 0.36 additional quality-adjusted life-years per patient. The insulin-OAD combination regimen generates more treatment and disease management-related CO2 emissions per patient (1,686 kg) than the OAD-only regimen (310 kg), but generates fewer emissions associated with treating complications (3,019 kg versus 3,337 kg). Overall, adding insulin to OAD therapy generates an extra 1,057 kg of CO2 emissions per patient over 30 years.Conclusions: The model offers a simple approach for incorporating environmental outcomes into health economic analyses, to support a decision-maker's objective of reducing the environmental impact of health care. Further work is required to improve the accuracy of the approach; in particular, the generation of resource-specific environmental impacts.

scholarly journals Microsimulation model for the health economic evaluation of osteoporosis interventions: study protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. e028365
Author(s):  
Lei Si ◽  
John A Eisman ◽  
Tania Winzenberg ◽  
Kerrie M Sanders ◽  
Jacqueline R Center ◽  
...  
Keyword(s):  
Health Economic ◽  
Economic Evaluation ◽  
User Interface ◽  
Economic Model ◽  
Health Economic Evaluation ◽  
Economic Models ◽  
Economic Evaluations ◽  
Microsimulation Model ◽  
Study Results ◽  
Health Economic Model

IntroductionOsteoporosis is a systemic skeletal disease that is characterised by reduced bone strength and increased fracture risk. Osteoporosis-related fractures impose enormous disease and economic burden to the society. Although many treatments and health interventions are proven effective to prevent fractures, health economic evaluation adds evidence to their economic merits. Computer simulation modelling is a useful approach to extrapolate clinical and economic outcomes from clinical trials and it is increasingly used in health economic evaluation. Many osteoporosis health economic models have been developed in the past decades; however, they are limited to academic use and there are no publicly accessible health economic models of osteoporosis.Methods and analysisWe will develop the Australian osteoporosis health economic model based on our previously published microsimulation model of osteoporosis in the Chinese population. The development of the model will follow the recommendations for the conduct of economic evaluations in osteoporosis by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases and the US branch of the International Osteoporosis Foundation. The model will be a state-transition semi-Markov model with memory. Clinical parameters in the model will be mainly obtained from the Dubbo Osteoporosis Epidemiology Study and the health economic parameters will be collected from the Australian arm of the International Costs and Utilities Related to Osteoporotic Fractures Study. Model transparency and validates will be tested using the recommendations from Good Research Practices in Modelling Task Forces. The model will be used in economic evaluations of osteoporosis interventions including pharmaceutical treatments and primary care interventions. A user-friendly graphical user interface will be developed, which will connect the user to the calculation engine and the results will be generated. The user interface will facilitate the use of our model by people in different sectors.Ethics and disseminationNo ethical approval is needed for this study. Results of the model validation and future economic evaluation studies will be submitted to journals. The user interface of the health economic model will be publicly available online accompanied with a user manual.

scholarly journals The Potential Added Value of Novel Hearing Therapeutics: An Early Health Economic Model for Hearing Loss

2020 ◽  
Vol 41 (8) ◽  
pp. 1033-1041
Author(s):  
Rishi Mandavia ◽  
Yvette M. Horstink ◽  
Janneke P.C. Grutters ◽  
Evie Landry ◽  
Carl May ◽  
...  
Keyword(s):  
Health Economic ◽  
Hearing Loss ◽  
Economic Model ◽  
Added Value ◽  
Health Economic Model ◽  
Early Health

scholarly journals Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression

2019 ◽  
Vol 39 (4) ◽  
pp. 347-359 ◽  
Author(s):  
Anna Heath ◽  
Ioanna Manolopoulou ◽  
Gianluca Baio
Keyword(s):  
Health Economic ◽  
Nonlinear Regression ◽  
Economic Model ◽  
Trial Design ◽  
Expected Value ◽  
Moment Matching ◽  
Sample Sizes ◽  
Sample Information ◽  
Health Economic Model ◽  
The Cost

Background. The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with the greatest net benefit. However, despite recent developments, EVSI analysis can be slow, especially when optimizing over a large number of different designs. Methods. This article develops a method to reduce the computation time required to calculate the EVSI across different sample sizes. Our method extends the moment-matching approach to EVSI estimation to optimize over different sample sizes for the underlying trial while retaining a similar computational cost to a single EVSI estimate. This extension calculates the posterior variance of the net monetary benefit across alternative sample sizes and then uses Bayesian nonlinear regression to estimate the EVSI across these sample sizes. Results. A health economic model developed to assess the cost-effectiveness of interventions for chronic pain demonstrates that this EVSI calculation method is fast and accurate for realistic models. This example also highlights how different trial designs can be compared using the EVSI. Conclusion. The proposed estimation method is fast and accurate when calculating the EVSI across different sample sizes. This will allow researchers to realize the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models. Limitations. Our method involves rerunning the health economic model, which can be more computationally expensive than some recent alternatives, especially in complex models.

scholarly journals PDB82 Development of a Health Economic Model to Compare the Prevention, Treatment and Management Strategies of Type 2 Diabetes

2011 ◽  
Vol 14 (7) ◽  
pp. A486-A487 ◽  
Author(s):  
B. Nagy ◽  
L. Nagyjanosi ◽  
S. Nagyistok ◽  
J. Józwiak-Hagymásy ◽  
Z. Dessewffy ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Economic ◽  
Economic Model ◽  
Management Strategies ◽  
Health Economic Model
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