Slow data public health [essay].

Chiolero, Arnaud; Tancredi, Stefano; Ioannidis, John P A (2023). Slow data public health [essay]. European journal of epidemiology, 38(12), pp. 1219-1225. Springer 10.1007/s10654-023-01049-6

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Surveillance and research data, despite their massive production, often fail to inform evidence-based and rigorous data-driven health decision-making. In the age of infodemic, as revealed by the COVID-19 pandemic, providing useful information for decision-making requires more than getting more data. Data of dubious quality and reliability waste resources and create data-genic public health damages. We call therefore for a slow data public health, which means focusing, first, on the identification of specific information needs and, second, on the dissemination of information in a way that informs decision-making, rather than devoting massive resources to data collection and analysis. A slow data public health prioritizes better data, ideally population-based, over more data and aims to be timely rather than deceptively fast. Applied by independent institutions with expertise in epidemiology and surveillance methods, it allows a thoughtful and timely public health response, based on high-quality data fostering trustworthiness.

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