The information layer: shaping data into meaning with interactive tools

Abstract Valorization and interpretation of health data are contingent upon a robust analytical framework. This framework encompasses essential processes such as data cleaning, validation, and the critical evaluation of bias and missing data. A nuanced understanding of these factors is crucial for contextualizing data through stratification, developing comprehensive analysis plans, and addressing interactions, covariates, and confounders. Such meticulous methodologies guard against ‘data fishing expeditions’ that could otherwise lead to erroneous conclusions. Healthybelgium.be exemplifies our approach for deciding on appropriate levels of data aggregation and stratification. This study serves as a foundation for deriving conclusions and formulating hypotheses regarding the health status of the national and various sub-populations. Our discussion will cover the limitations of the questions that can be answered with the available data, the resulting opportunities for data enhancement, and the subsequent valorization of findings for knowledge advancement. Furthermore, we will present the interactive tools (through the use of R Shiny) developed by Sciensano to disseminate information broadly, making it accessible and comprehensible to diverse audiences. These tools embody a balance of strength and limitation, facilitating an informed dialogue among stakeholders. The ongoing refinement of these tools, influenced by continuous learning and insights, exemplifies our commitment to evolving public health intelligence.