Study of micro-signals: proposed analysis methodology based on data from the Lille Poison Control and Toxicovigilance Center

This project aimed to enhance the Lille Poison Control and Toxicovigilance Center health surveillance and proactive response to intoxication-related issues by integrating a micro-signal analysis methodology. We developed a methodology employing Poisson distribution and historical limits to identify unusual signals marked by significant variations, often obscured by case age, continuous information flow (due to the 24/7 operational nature of the service), or team rotations. A severity score was created to prioritize micro-signals based on case seriousness to facilitate rapid and appropriate interventions. Additionally, a dashboard was developed to visualize detected micro-signals as a heat map, improving accessibility and decision-making for poison center professionals. Testing the methodology with historical Lille Poison Control and Toxicovigilance Center data demonstrated its viability and the medical relevance of the severity score calculations. It highlighted the effect of national measures on the use of an anxiolytic that had not been detected by the teams at the time. In addition, when the values returned by the severity score are interpreted by class of substance, rather than individually, similar orders of magnitude are observed (e.g., mushrooms, anxiolytics in our case study). Our approach shows potential for improving patient care and responsiveness in toxicovigilance, particularly within the French Lille Poison Control and Toxicovigilance Center network. One limitation of the current dashboard is that it only carries out analysis by product. Future enhancements include the integration of a thesaurus which will also allow analysis by class. This study integrated a micro-signal analysis method into a health surveillance system to detect hidden trends in poison center data, improving emergency response and substance management.