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Spatial and seasonal determinants of Lyme borreliosis incidence in France, 2016 to 2021
- Wen Fu1 , Camille Bonnet1 , Alexandra Septfons2 , Julie Figoni2 , Jonas Durand3 , Pascale Frey-Klett3 , Denis Rustand4 , Benoît Jaulhac5,6 , Raphaëlle Métras1,7,8
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View Affiliations Hide AffiliationsAffiliations: 1 Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique, Paris, France 2 Santé publique France, Saint-Maurice, France 3 Laboratoire Tous Chercheurs, Université de Lorraine, INRAE, UMR 1136 Interactions Arbres-Microorganismes, Nancy, France 4 Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 5 French National Reference Center for Borrelia, Hôpitaux Universitaires de Strasbourg, Strasbourg, France 6 Institut de Bactériologie, Fédération de Médecine Translationnelle de Strasbourg, University of Strasbourg,ITI InnoVec, Strasbourg, France 7 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom 8 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United KingdomWen Fuwen.fu iplesp.upmc.fr
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Citation style for this article: Fu Wen, Bonnet Camille, Septfons Alexandra, Figoni Julie, Durand Jonas, Frey-Klett Pascale, Rustand Denis, Jaulhac Benoît, Métras Raphaëlle. Spatial and seasonal determinants of Lyme borreliosis incidence in France, 2016 to 2021. Euro Surveill. 2023;28(14):pii=2200581. https://doi.org/10.2807/1560-7917.ES.2023.28.14.2200581 Received: 15 Jul 2022; Accepted: 12 Jan 2023
Abstract
Lyme 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.
We 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.
We 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.
A 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.
We 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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