scholarly journals Health economic model to measure the impact of a stemi initiative in Romania

2015 ◽  
Vol 18 (3) ◽  
pp. A50
Author(s):  
E. Kim ◽  
A. Yoculan
2016 ◽  
Vol 32 (6) ◽  
pp. 400-406 ◽  
Author(s):  
Kevin Marsh ◽  
Michael Ganz ◽  
Emil Nørtoft ◽  
Niels Lund ◽  
Joshua Graff-Zivin

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.


2020 ◽  
Vol 41 (8) ◽  
pp. 1033-1041
Author(s):  
Rishi Mandavia ◽  
Yvette M. Horstink ◽  
Janneke P.C. Grutters ◽  
Evie Landry ◽  
Carl May ◽  
...  

2019 ◽  
Vol 39 (4) ◽  
pp. 347-359 ◽  
Author(s):  
Anna Heath ◽  
Ioanna Manolopoulou ◽  
Gianluca Baio

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.


2011 ◽  
Vol 14 (7) ◽  
pp. A486-A487 ◽  
Author(s):  
B. Nagy ◽  
L. Nagyjanosi ◽  
S. Nagyistok ◽  
J. Józwiak-Hagymásy ◽  
Z. Dessewffy ◽  
...  

2018 ◽  
Author(s):  
Lydia Turkson ◽  
Hannah Mamuszka ◽  
Kyla Grimshaw ◽  
Erica Marie Marshall

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