The methodology of quantitative risk assessment studies involves several key steps to evaluate the health impact of external factors or policies. These steps include defining counterfactual scenarios, assessing exposure levels, quantifying risks using dose-response functions, and conducting uncertainty analyses. The main focus is on quantifying the population impact of these factors, often expressed in terms of disease cases or disability-adjusted life-years (DALYs). The study area and population are crucial, and the choice of these should align with the policy or factor being assessed. Exposure assessment tools, such as environmental models and questionnaires, are used to estimate exposure metrics that are coherent with the dose-response function. The selection of exposure metrics and the spatial resolution of models are important considerations, as they can significantly affect the accuracy of the risk assessment. Additionally, the study should consider the side effects and remote consequences of the intervention, and the impact should be estimated for both current and future scenarios. The paper also discusses methodological gaps, such as the handling of emerging factors with partial data and the need for clearer terminology in health impact assessment and human-based risk assessment studies.The methodology of quantitative risk assessment studies involves several key steps to evaluate the health impact of external factors or policies. These steps include defining counterfactual scenarios, assessing exposure levels, quantifying risks using dose-response functions, and conducting uncertainty analyses. The main focus is on quantifying the population impact of these factors, often expressed in terms of disease cases or disability-adjusted life-years (DALYs). The study area and population are crucial, and the choice of these should align with the policy or factor being assessed. Exposure assessment tools, such as environmental models and questionnaires, are used to estimate exposure metrics that are coherent with the dose-response function. The selection of exposure metrics and the spatial resolution of models are important considerations, as they can significantly affect the accuracy of the risk assessment. Additionally, the study should consider the side effects and remote consequences of the intervention, and the impact should be estimated for both current and future scenarios. The paper also discusses methodological gaps, such as the handling of emerging factors with partial data and the need for clearer terminology in health impact assessment and human-based risk assessment studies.