Natalie Mueller - ISGlobal, Barcelona, Spain
Rodrigo Anderle - Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Salvador, Brazil
Nicolai Brachowicz - ISGlobal, Barcelona, Spain
Helton Graziadei - School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro, Brazil
Simon J. Lloyd - ISGlobal, Barcelona, Spain
Gabriel de Sampaio Morais - Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Salvador, Brazil
Alberto Pietro Sironi - Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Salvador, Brazil
Karina Gibert - Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya (IDEAI-UPC), Barcelona, Spain
Cathryn Tonne - ISGlobal, Barcelona, Spain
Mark Nieuwenhuijsen - ISGlobal, Barcelona, Spain
Davide Rasella - ISGlobal, Barcelona, Spain

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.

Health Impact Assessment, Ex-Ante Impact Evaluation, Forecast, Modelling, Policy