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Spatial and seasonal determinants of Lyme borreliosis incidence in France, 2016 to 2021.
Fu, Wen; Bonnet, Camille; Septfons, Alexandra; Figoni, Julie; Durand, Jonas; Frey-Klett, Pascale; Rustand, Denis; Jaulhac, Benoît; Métras, Raphaëlle.
Afiliação
  • Fu W; Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Paris, France.
  • Bonnet C; Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Paris, France.
  • Septfons A; Santé publique France, Saint-Maurice, France.
  • Figoni J; Santé publique France, Saint-Maurice, France.
  • Durand J; Laboratoire Tous Chercheurs, Université de Lorraine, INRAE, UMR 1136 Interactions Arbres-Microorganismes, Nancy, France.
  • Frey-Klett P; Laboratoire Tous Chercheurs, Université de Lorraine, INRAE, UMR 1136 Interactions Arbres-Microorganismes, Nancy, France.
  • Rustand D; Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
  • Jaulhac B; French National Reference Center for Borrelia, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
  • Métras R; Institut de Bactériologie, Fédération de Médecine Translationnelle de Strasbourg, University of Strasbourg,ITI InnoVec, Strasbourg, France.
Euro Surveill ; 28(14)2023 04.
Article em En | MEDLINE | ID: mdl-37022210
BackgroundLyme borreliosis (LB) is the most widespread hard tick-borne zoonosis in the northern hemisphere. Existing studies in Europe have focused mainly on acarological risk assessment, with few investigations exploring human LB occurrence.AimWe explored the determinants of spatial and seasonal LB variations in France from 2016 to 2021 by integrating environmental, animal, meteorological and anthropogenic factors, and then mapped seasonal LB risk predictions.MethodsWe fitted 2016-19 LB national surveillance data to a two-part spatio-temporal statistical model. Spatial and temporal random effects were specified using a Besag-York-Mollie model and a seasonal model, respectively. Coefficients were estimated in a Bayesian framework using integrated nested Laplace approximation. Data from 2020-21 were used for model validation.ResultsA high vegetation index (≥ 0.6) was positively associated with seasonal LB presence, while the index of deer presence (> 60%), mild soil temperature (15-22 °C), moderate air saturation deficit (1.5-5 mmHg) and higher tick bite frequency were associated with increased incidence. Prediction maps show a higher risk of LB in spring and summer (April-September), with higher incidence in parts of eastern, midwestern and south-western France.ConclusionWe present a national level spatial assessment of seasonal LB occurrence in Europe, disentangling factors associated with the presence and increased incidence of LB. Our findings yield quantitative evidence for national public health agencies to plan targeted prevention campaigns to reduce LB burden, enhance surveillance and identify further data needs. This approach can be tested in other LB endemic areas.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cervos / Doença de Lyme Tipo de estudo: Incidence_studies / Prognostic_studies / Risk_factors_studies Limite: Animals / Humans País/Região como assunto: Europa Idioma: En Revista: Euro Surveill Assunto da revista: DOENCAS TRANSMISSIVEIS Ano de publicação: 2023 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cervos / Doença de Lyme Tipo de estudo: Incidence_studies / Prognostic_studies / Risk_factors_studies Limite: Animals / Humans País/Região como assunto: Europa Idioma: En Revista: Euro Surveill Assunto da revista: DOENCAS TRANSMISSIVEIS Ano de publicação: 2023 Tipo de documento: Article País de afiliação: França