TY - JOUR ID - 4401 TI - Model Choice for Quantitative Health Impact Assessment and Modelling: An Expert Consultation and Narrative Literature Review JO - International Journal of Health Policy and Management JA - IJHPM LA - en SN - AU - Mueller, Natalie AU - Anderle, Rodrigo AU - Brachowicz, Nicolai AU - Graziadei, Helton AU - Lloyd, Simon J. AU - de Sampaio Morais, Gabriel AU - Pietro Sironi, Alberto AU - Gibert, Karina AU - Tonne, Cathryn AU - Nieuwenhuijsen, Mark AU - Rasella, Davide AD - ISGlobal, Barcelona, Spain AD - Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Salvador, Brazil AD - School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro, Brazil AD - Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya (IDEAI-UPC), Barcelona, Spain AD - ISGlobal, Barcelona, Spain Y1 - 2023 PY - 2023 VL - 12 IS - Issue 1 SP - 1 EP - 13 KW - Health Impact Assessment KW - Ex-Ante Impact Evaluation KW - Forecast KW - Modelling KW - Policy DO - 10.34172/ijhpm.2023.7103 N2 - 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. UR - https://www.ijhpm.com/article_4401.html L1 - https://www.ijhpm.com/article_4401_7c942a23ef6fba850d5216c452f94db5.pdf ER -