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Validation of the Prevention Impacts Simulation Model (PRISM)
INTRODUCTION: Demonstrating the validity of a public health simulation model helps to establish confidence in the accuracy and usefulness of a model’s results. In this study we evaluated the validity of the Prevention Impacts Simulation Model (PRISM), a system dynamics model that simulates health, m...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Centers for Disease Control and Prevention
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879963/ https://www.ncbi.nlm.nih.gov/pubmed/33544072 http://dx.doi.org/10.5888/pcd18.200225 |
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author | Yarnoff, Benjamin Honeycutt, Amanda Bradley, Christina Khavjou, Olga Bates, Laurel Bass, Sarah Kaufmann, Rachel Barker, Lawrence Briss, Peter |
author_facet | Yarnoff, Benjamin Honeycutt, Amanda Bradley, Christina Khavjou, Olga Bates, Laurel Bass, Sarah Kaufmann, Rachel Barker, Lawrence Briss, Peter |
author_sort | Yarnoff, Benjamin |
collection | PubMed |
description | INTRODUCTION: Demonstrating the validity of a public health simulation model helps to establish confidence in the accuracy and usefulness of a model’s results. In this study we evaluated the validity of the Prevention Impacts Simulation Model (PRISM), a system dynamics model that simulates health, mortality, and economic outcomes for the US population. PRISM primarily simulates outcomes related to cardiovascular disease but also includes outcomes related to other chronic diseases that share risk factors. PRISM is openly available through a web application. METHODS: We applied the model validation framework developed independently by the International Society of Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making modeling task force to validate PRISM. This framework included model review by external experts and quantitative data comparison by the study team. RESULTS: External expert review determined that PRISM is based on up-to-date science. One-way sensitivity analysis showed that no parameter affected results by more than 5%. Comparison with other published models, such as ModelHealth, showed that PRISM produces lower estimates of effects and cost savings. Comparison with surveillance data showed that projected model trends in risk factors and outcomes align closely with secular trends. Four measures did not align with surveillance data, and those were recalibrated. CONCLUSION: PRISM is a useful tool to simulate the potential effects and costs of public health interventions. Results of this validation should help assure health policy leaders that PRISM can help support community health program planning and evaluation efforts. |
format | Online Article Text |
id | pubmed-7879963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Centers for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-78799632021-03-02 Validation of the Prevention Impacts Simulation Model (PRISM) Yarnoff, Benjamin Honeycutt, Amanda Bradley, Christina Khavjou, Olga Bates, Laurel Bass, Sarah Kaufmann, Rachel Barker, Lawrence Briss, Peter Prev Chronic Dis Original Research INTRODUCTION: Demonstrating the validity of a public health simulation model helps to establish confidence in the accuracy and usefulness of a model’s results. In this study we evaluated the validity of the Prevention Impacts Simulation Model (PRISM), a system dynamics model that simulates health, mortality, and economic outcomes for the US population. PRISM primarily simulates outcomes related to cardiovascular disease but also includes outcomes related to other chronic diseases that share risk factors. PRISM is openly available through a web application. METHODS: We applied the model validation framework developed independently by the International Society of Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making modeling task force to validate PRISM. This framework included model review by external experts and quantitative data comparison by the study team. RESULTS: External expert review determined that PRISM is based on up-to-date science. One-way sensitivity analysis showed that no parameter affected results by more than 5%. Comparison with other published models, such as ModelHealth, showed that PRISM produces lower estimates of effects and cost savings. Comparison with surveillance data showed that projected model trends in risk factors and outcomes align closely with secular trends. Four measures did not align with surveillance data, and those were recalibrated. CONCLUSION: PRISM is a useful tool to simulate the potential effects and costs of public health interventions. Results of this validation should help assure health policy leaders that PRISM can help support community health program planning and evaluation efforts. Centers for Disease Control and Prevention 2021-02-04 /pmc/articles/PMC7879963/ /pubmed/33544072 http://dx.doi.org/10.5888/pcd18.200225 Text en https://creativecommons.org/licenses/by/4.0/Preventing Chronic Disease is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited. |
spellingShingle | Original Research Yarnoff, Benjamin Honeycutt, Amanda Bradley, Christina Khavjou, Olga Bates, Laurel Bass, Sarah Kaufmann, Rachel Barker, Lawrence Briss, Peter Validation of the Prevention Impacts Simulation Model (PRISM) |
title | Validation of the Prevention Impacts Simulation Model (PRISM) |
title_full | Validation of the Prevention Impacts Simulation Model (PRISM) |
title_fullStr | Validation of the Prevention Impacts Simulation Model (PRISM) |
title_full_unstemmed | Validation of the Prevention Impacts Simulation Model (PRISM) |
title_short | Validation of the Prevention Impacts Simulation Model (PRISM) |
title_sort | validation of the prevention impacts simulation model (prism) |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879963/ https://www.ncbi.nlm.nih.gov/pubmed/33544072 http://dx.doi.org/10.5888/pcd18.200225 |
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