Pandemic preparedness: Potential of routine general practice data for infectious disease early signal detection

Le résumé fourni par la source

In this proof-of-concept study, we explored the potential of structured general practice data for early signal detection, to enhance pandemic preparedness. We used electronic health record data from the Family Medicine Network (FaMe-Net) in the Netherlands between 2014 and 2024. Descriptive evaluation with surveillance attributes was performed, and the Farrington flexible method was used to construct an alert threshold for respiratory, gastroenteritis, rash, infectious conjunctivitis, and fever syndromes. Alert signals were generated, indicating when the observed weekly count exceeded the threshold. This data registry performed adequately for most surveillance attributes. We analysed 634708 diagnostic codes and 9 infectious disease syndromes in the data registry. In 154 weeks, the number of alert signals ranged from 3 to 20. We found similarities and variations in the number of alert signals between infectious syndrome definitions within the different data types. Structured general practice data showed potential for surveillance and early signal detection of infectious diseases because it performed well on most surveillance attributes and weekly counts of infectious disease syndromes led to the generation of alert signals. Further validation of these alert signals is needed, to underpin their early warning potential for timely public health action.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.