Model Choice for Quantitative Health Impact Assessment and Modelling: An Expert Consultation and Narrative Literature Review.
Ex-Ante Impact Evaluation
Forecast
Health Impact Assessmen
Modelling
Policy
Journal
International journal of health policy and management
ISSN: 2322-5939
Titre abrégé: Int J Health Policy Manag
Pays: Iran
ID NLM: 101619905
Informations de publication
Date de publication:
2023
2023
Historique:
received:
19
01
2022
accepted:
28
01
2023
medline:
16
8
2023
pubmed:
14
8
2023
entrez:
14
8
2023
Statut:
ppublish
Résumé
Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/ or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice. Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths. Seven relevant models for health impacts forecasting were identified, consisting of ( The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.
Sections du résumé
BACKGROUND
Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/ or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice.
METHODS
Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths.
RESULTS
Seven relevant models for health impacts forecasting were identified, consisting of (
CONCLUSION
The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.
Identifiants
pubmed: 37579425
doi: 10.34172/ijhpm.2023.7103
pii: 7103
pmc: PMC10461835
doi:
pii:
Types de publication
Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7103Informations de copyright
© 2023 The Author(s); Published by Kerman University of Medical Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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