Difference between revisions of "Chiolero 2023 Eur J Epidemiol"
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{{Publication | {{Publication | ||
|title=Chiolero A, Tancredi S, Ioannidis JPA (2023) Slow data public health. Eur J Epidemiol https://doi.org/10.1007/s10654-023-01049-6 | |title=Chiolero A, Tancredi S, Ioannidis JPA (2023) Slow data public health. Eur J Epidemiol 38:1219-25. https://doi.org/10.1007/s10654-023-01049-6 | ||
|info=[https:// | |info=[https://link.springer.com/article/10.1007/s10654-023-01049-6 Open Access] | ||
|authors=Chiolero A, Tancredi S, Ioannidis JPA | |authors=Chiolero A, Tancredi S, Ioannidis JPA | ||
|year=2023 | |year=2023 |
Latest revision as of 18:45, 29 February 2024
Chiolero A, Tancredi S, Ioannidis JPA (2023) Slow data public health. Eur J Epidemiol 38:1219-25. https://doi.org/10.1007/s10654-023-01049-6 |
Β» Open Access
Chiolero A, Tancredi S, Ioannidis JPA (2023) Eur J Epidemiol
Abstract: 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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