Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach

Abstract

Background. Effectively communicating complex statistical model outputs is a major challenge in public health. This study introduces the FARSI approach (Fast, Accessible, Reliable, Secure, Informative) as a framework to enhance the translation of intricate statistical findings into actionable insights for policymakers and stakeholders. We apply this framework in a real-world case study on chronic disease monitoring in Italy. Methods. The FARSI framework outlines key principles for developing user-friendly tools that improve the translation of statistical results. We applied these principles to create an open-access web application using R Shiny, designed to communicate chronic disease prevalence estimates from a Bayesian spatio-temporal logistic model. The case study highlights the importance of an intuitive design for fast accessibility, validated data and expert feedback for reliability, aggregated data for security, and insights into prevalence population subgroups, which were previously unobservable, for informativeness. Results. The web application enables stakeholders to explore disease prevalence across populations and geographical area through dynamic visualizations. It facilitates public health monitoring by, for instance, identifying disparities at the local level and assessing risk factors such as smoking. Its user-friendly interface enhances accessibility, making statistical findings more actionable. Conclusions. The FARSI framework provides a structured approach to improving the communication of complex research findings. By making statistical models more accessible and interpretable, it supports evidence-based decision-making in public health and increases the societal impact of research.