Abstract When conducting health impact assessments (HIA), researchers often face the challenges of trying to align as closely as possible with policy scenarios. This phase concerns the translation of real-world policies into estimates usable in models. Most of the time, researchers need to address a policy-impact gap between what is needed by the policymakers and what is believed to be reliable data fitted for the model. These mismatches can stem from differences in how exposure and outcomes are studied compared to the policy plan. By looking at a case study of the planned Belgian policy to reduce tobacco selling points, challenges during the pre-modelling phase were identified. The question of what an effect size should be and its importance in the HIA process were considered fundamental for the analysis. In particular, literature on exposure to tobacco sales points differed in outcomes, as it would target proximity to someone’s home, while the policy itself focuses on the reduction of points of sale of a specific store type. Similarly, smoking behaviour can be measured in various ways, such as initiation, cessation, relapse rates, or overall smoking prevalence. The policies’ effect on these various outcomes differed significantly. In this presentation, the discussion will focus on the alignment of the existing literature with the specific details outlined in the policy. We will explore the challenges that arose when existing research criteria and assumptions did not fully match the policy, taking the new Belgian tobacco plan as an example. Lastly, we will highlight the opportunity to set up monitoring systems to evaluate tobacco interventions to extract reliable estimates for HIA and strengthen evidence-based tobacco policies in the future.