Document Type : Review Article
ISGlobal, Barcelona, Spain
Universitat Pompeu Fabra (UPF), Barcelona, Spain
CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
Institute of Collective Health (ISC), Federal University of Bahia (UFBA), Salvador, Brazil
School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro, Brazil
Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya (IDEAI-UPC), Barcelona, Spain
Hospital Clínic—Universitat de Barcelona, Barcelona, Spain
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 (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.
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.