Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making
| Autor: | Gwenan M. Knight, Renuka Khurana, Alice Zwerling, David W. Dowdy, Gregory J. Fox, Nila J. Dharan, Natalie Stennis |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
0301 basic medicine
Microbiology (medical) medicine.medical_specialty Bridging (networking) Process (engineering) Decision Making Communicable Diseases models theoretical Article lcsh:Infectious and parasitic diseases 03 medical and health sciences 0302 clinical medicine Models Humans Medicine Tuberculosis lcsh:RC109-216 030212 general & internal medicine Empirical evidence theoretical Structure (mathematical logic) business.industry Management science Mechanism (biology) Public health Public health practice General Medicine 030104 developmental biology Infectious Diseases Systematic review Key (cryptography) Public Health business |
| Zdroj: | International Journal of Infectious Diseases, Vol 42, Iss C, Pp 17-23 (2016) International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases |
| ISSN: | 1201-9712 |
| Popis: | SUMMARY 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. |
| Databáze: | OpenAIRE |
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