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                <term xml:lang="en">Big data</term>
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                <term xml:lang="en">Environmental health</term>
                <term xml:lang="en">SNDS</term>
                <term xml:lang="en">Pollution</term>
                <term xml:lang="en">Congenital hypothyroidism</term>
                <term xml:lang="fr">Intelligence artificielle</term>
                <term xml:lang="fr">Santé environnementale</term>
                <term xml:lang="fr">Données massives</term>
                <term xml:lang="fr">SNDS</term>
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              <p>Objectif : Étudier comment l’intégration des données massives (Big Data) de santé (Système National des Données de Santé [SNDS] et données des Centres Régionaux de Dépistage Néonatal [CRDN]) et environnementales (Système d’Information en Santé-Environnement sur les Eaux, SISE-Eaux et Cartothèque de la Qualité de l’Air) permet d’explorer les associations entre pollution environnementale et hypothyroïdie congénitale, et de détecter précocement des signaux épidémiques à grande échelle. Méthodes : Ce travail de synthèse s’appuie sur trois études publiées par notre équipe. Le premier analyse les tendances spatiales et temporelles de l’incidence de l’hypothyroïdie congénitale et acquise en France entre 2014 et 2019 grâce au SNDS, base médico-administrative couvrant l’ensemble de la population française. Le second évalue, à l’échelle régionale (Picardie), l’association entre les concentrations de TSH néonatale et l’exposition prénatale à divers polluants de l’air et de l’eau, via un croisement entre données de dépistage néonatal et données environnementales locales. Le troisième examine, à l’échelle nationale, le lien entre exposition prénatale à certains polluants (perchlorate, nitrates, PM) et la survenue de l’hypothyroïdie congénitale à partir d’une cohorte issue du SNDS. Résultats : Les analyses mettent en évidence une association entre plusieurs polluants environnementaux et des anomalies de la fonction thyroïdienne néonatale. L’exploitation conjointe du SNDS et de données environnementales massives permet une détection fine de signaux épidémiques émergents sur l’ensemble du territoire. Conclusions : L’intégration des données massives de santé et d’environnement ouvre de nouvelles perspectives pour la surveillance épidémiologique automatisée et la compréhension des liens entre pollution et maladies thyroïdiennes.</p>
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