Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review
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 assu...
| Autores: | , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/57428 |
| Acceso en línea: | http://hdl.handle.net/10230/57428 http://dx.doi.org/10.34172/ijhpm.2023.7103 |
| Access Level: | acceso abierto |
| Palabra clave: | Health Impact Assessment Ex-Ante Impact Evaluation Forecast Modelling Policy |
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Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature reviewMueller, Natalie, 1988-Anderle, RodrigoBrachowicz, NicolaiGraziadei, HeltonLloyd, Simon J.de Sampaio Morais, GabrielPietro Sironi, AlbertoGibert, KarinaTonne, CathrynNieuwenhuijsen, Mark J.Rasella, DavideHealth Impact AssessmentEx-Ante Impact EvaluationForecastModellingPolicyBackground: 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 (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agentbased models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users. 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.Kerman University of Medical Sciences202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57428http://dx.doi.org/10.34172/ijhpm.2023.7103reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésInternational Journal of Health Policy and Management. 2023; 12(1): 1-13© 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 (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.http://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:recercat.cat:10230/574282026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| title |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| spellingShingle |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review Mueller, Natalie, 1988- Health Impact Assessment Ex-Ante Impact Evaluation Forecast Modelling Policy |
| title_short |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| title_full |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| title_fullStr |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| title_full_unstemmed |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| title_sort |
Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review |
| dc.creator.none.fl_str_mv |
Mueller, Natalie, 1988- Anderle, Rodrigo Brachowicz, Nicolai Graziadei, Helton Lloyd, Simon J. de Sampaio Morais, Gabriel Pietro Sironi, Alberto Gibert, Karina Tonne, Cathryn Nieuwenhuijsen, Mark J. Rasella, Davide |
| author |
Mueller, Natalie, 1988- |
| author_facet |
Mueller, Natalie, 1988- Anderle, Rodrigo Brachowicz, Nicolai Graziadei, Helton Lloyd, Simon J. de Sampaio Morais, Gabriel Pietro Sironi, Alberto Gibert, Karina Tonne, Cathryn Nieuwenhuijsen, Mark J. Rasella, Davide |
| author_role |
author |
| author2 |
Anderle, Rodrigo Brachowicz, Nicolai Graziadei, Helton Lloyd, Simon J. de Sampaio Morais, Gabriel Pietro Sironi, Alberto Gibert, Karina Tonne, Cathryn Nieuwenhuijsen, Mark J. Rasella, Davide |
| author2_role |
author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Health Impact Assessment Ex-Ante Impact Evaluation Forecast Modelling Policy |
| topic |
Health Impact Assessment Ex-Ante Impact Evaluation Forecast Modelling Policy |
| description |
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 (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agentbased models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users. 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. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023 2023 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10230/57428 http://dx.doi.org/10.34172/ijhpm.2023.7103 |
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http://hdl.handle.net/10230/57428 http://dx.doi.org/10.34172/ijhpm.2023.7103 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
International Journal of Health Policy and Management. 2023; 12(1): 1-13 |
| dc.rights.none.fl_str_mv |
http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/4.0 |
| eu_rights_str_mv |
openAccess |
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application/pdf application/pdf |
| dc.publisher.none.fl_str_mv |
Kerman University of Medical Sciences |
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Kerman University of Medical Sciences |
| dc.source.none.fl_str_mv |
reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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