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 policy intervention not necessarily placed in sector. Most HIAs are done prospectively and aim forecast expected impacts under assumed implementation. may quantitatively and/or qualitatively assess with this study focusing on former. A variety quantitative modelling methods exist for forecasting however, they differ application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths comprehensibility. We reviewed relevant models, so as provide public researchers considerations HIA model choice. Methods: Based an expert consultation, combined narrative literature review, we identified most models can be forecasting. narratively comparatively according their fields application, configuration purposes, counterfactual scenarios, underlying limitations strengths. Results: Seven were identified, consisting I) comparative (CRA), II) time series analysis (TSA), III) compartmental (CM), IV) structural (SM), V) agent-based (ABM), VI) microsimulations (MS), VII) artificial intelligence (AI)/ machine learning (ML). These represent approaches vary complexity set criteria Researchers must consider input assumptions match available parameter structures, resources, outputs research question, meet expectations comprehensible end-users. Conclusion: The have specific characteristics, related computational implementation, interpretation comprehensibility, which researcher should critically before