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Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making

The dominant approach to decision-making in public health policy for infectious diseases relies heavily on expert opinion, which often applies empirical evidence to policy questions in a manner that is neither systematic nor transparent. Although systematic reviews are frequently commissioned to inf...

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Autores principales: Knight, Gwenan M., Dharan, Nila J., Fox, Gregory J., Stennis, Natalie, Zwerling, Alice, Khurana, Renuka, Dowdy, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996966/
https://www.ncbi.nlm.nih.gov/pubmed/26546234
http://dx.doi.org/10.1016/j.ijid.2015.10.024
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author Knight, Gwenan M.
Dharan, Nila J.
Fox, Gregory J.
Stennis, Natalie
Zwerling, Alice
Khurana, Renuka
Dowdy, David W.
author_facet Knight, Gwenan M.
Dharan, Nila J.
Fox, Gregory J.
Stennis, Natalie
Zwerling, Alice
Khurana, Renuka
Dowdy, David W.
author_sort Knight, Gwenan M.
collection PubMed
description The dominant approach to decision-making in public health policy for infectious diseases relies heavily on expert opinion, which often applies empirical evidence to policy questions in a manner that is neither systematic nor transparent. Although systematic reviews are frequently commissioned to inform specific components of policy (such as efficacy), the same process is rarely applied to the full decision-making process. Mathematical models provide a mechanism through which empirical evidence can be methodically and transparently integrated to address such questions. However, such models are often considered difficult to interpret. In addition, models provide estimates that need to be iteratively reevaluated as new data or considerations arise. Using the case study of a novel diagnostic for tuberculosis, a framework for improved collaboration between public health decision-makers and mathematical modellers that could lead to more transparent and evidence-driven policy decisions for infectious diseases in the future is proposed. The framework proposes that policymakers should establish long-term collaborations with modellers to address key questions, and that modellers should strive to provide clear explanations of the uncertainty of model structure and outputs. Doing so will improve the applicability of models and clarify their limitations when used to inform real-world public health policy decisions.
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spelling pubmed-49969662017-01-01 Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making Knight, Gwenan M. Dharan, Nila J. Fox, Gregory J. Stennis, Natalie Zwerling, Alice Khurana, Renuka Dowdy, David W. Int J Infect Dis Article The dominant approach to decision-making in public health policy for infectious diseases relies heavily on expert opinion, which often applies empirical evidence to policy questions in a manner that is neither systematic nor transparent. Although systematic reviews are frequently commissioned to inform specific components of policy (such as efficacy), the same process is rarely applied to the full decision-making process. Mathematical models provide a mechanism through which empirical evidence can be methodically and transparently integrated to address such questions. However, such models are often considered difficult to interpret. In addition, models provide estimates that need to be iteratively reevaluated as new data or considerations arise. Using the case study of a novel diagnostic for tuberculosis, a framework for improved collaboration between public health decision-makers and mathematical modellers that could lead to more transparent and evidence-driven policy decisions for infectious diseases in the future is proposed. The framework proposes that policymakers should establish long-term collaborations with modellers to address key questions, and that modellers should strive to provide clear explanations of the uncertainty of model structure and outputs. Doing so will improve the applicability of models and clarify their limitations when used to inform real-world public health policy decisions. 2015-11-03 2016-01 /pmc/articles/PMC4996966/ /pubmed/26546234 http://dx.doi.org/10.1016/j.ijid.2015.10.024 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Knight, Gwenan M.
Dharan, Nila J.
Fox, Gregory J.
Stennis, Natalie
Zwerling, Alice
Khurana, Renuka
Dowdy, David W.
Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
title Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
title_full Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
title_fullStr Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
title_full_unstemmed Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
title_short Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
title_sort bridging the gap between evidence and policy for infectious diseases: how models can aid public health decision-making
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996966/
https://www.ncbi.nlm.nih.gov/pubmed/26546234
http://dx.doi.org/10.1016/j.ijid.2015.10.024
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