Risk factors for tick attachment in companion animals in Great Britain: A spatiotemporal analysis covering 2014-2021

Arsevska Elena, Hengl Tomislav, Singleton David, Noble Peter-John M., Caminade Cyril, Eneanya Obiora A., Jones Philip H., Medlock Jolyon, Hansford Kayleigh M., Bonannella Carmelo, Radford Alan D.. 2024. Risk factors for tick attachment in companion animals in Great Britain: A spatiotemporal analysis covering 2014-2021. Parasites and Vectors, 17:29, 19 p.

Url - jeu de données - Entrepôt autre : https://doi.org/10.5281/zenodo.7625174 / Url - jeu de données - Entrepôt autre : https://opengeohub-msahin.shinyapps.io/GB_tickprobability

Résumé : Background: Ticks are an important driver of veterinary health care, causing irritation and sometimes infection to their hosts. We explored epidemiological and geo-referenced data from > 7 million electronic health records (EHRs) from cats and dogs collected by the Small Animal Veterinary Surveillance Network (SAVSNET) in Great Britain (GB) between 2014 and 2021 to assess the factors affecting tick attachment in an individual and at a spatiotemporal level. Methods: EHRs in which ticks were mentioned were identified by text mining; domain experts confirmed those with ticks on the animal. Tick presence/absence records were overlaid with a spatiotemporal series of climate, environment, anthropogenic and host distribution factors to produce a spatiotemporal regression matrix. An ensemble machine learning spatiotemporal model was used to fine-tune hyperparameters for Random Forest, Gradient-boosted Trees and Generalized Linear Model regression algorithms, which were then used to produce a final ensemble meta-learner to predict the probability of tick attachment across GB at a monthly interval and averaged long-term through 2014–2021 at a spatial resolution of 1 km. Individual host factors associated with tick attachment were also assessed by conditional logistic regression on a matched case–control dataset. Results: In total, 11,741 consultations were identified in which a tick was recorded. The frequency of tick records was low (0.16% EHRs), suggesting an underestimation of risk. That said, increased odds for tick attachment in cats and dogs were associated with younger adult ages, longer coat length, crossbreeds and unclassified breeds. In cats, males and entire animals had significantly increased odds of recorded tick attachment. The key variables controlling the spatiotemporal risk for tick attachment were climatic (precipitation and temperature) and vegetation type (Enhanced Vegetation Index). Suitable areas for tick attachment were predicted across GB, especially in forests and grassland areas, mainly during summer, particularly in June. Conclusions: Our results can inform targeted health messages to owners and veterinary practitioners, identifying those animals, seasons and areas of higher risk for tick attachment and allowing for more tailored prophylaxis to reduce tick burden, inappropriate parasiticide treatment and potentially TBDs in companion animals and humans. Sentinel networks like SAVSNET represent a novel complementary data source to improve our understanding of tick attachment risk for companion animals and as a proxy of risk to humans.

Mots-clés Agrovoc : fouille de textes, facteur de risque, chien, santé animale, épidémiologie, distribution spatiale, chat, surveillance épidémiologique, distribution géographique, vecteur de maladie, danger pour la santé, maladie transmissible par tiques, zone suburbaine, analyse du risque

Mots-clés géographiques Agrovoc : France

Mots-clés libres : Ticks, Risk factors, Ensemble machine learning, Space-time, Companion animals, Electronic health records, Great Britain

Agences de financement européennes : European Commission

Agences de financement hors UE : Biotechnology and Biological Sciences Research Council, British Small Animal Veterinary Association

Programme de financement européen : H2020

Auteurs et affiliations

• Arsevska Elena, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0002-6693-2316 - auteur correspondant
• Hengl Tomislav, Wageningen University (NLD)
• Singleton David, University of Liverpool (GBR)
• Noble Peter-John M., University of Liverpool (GBR)
• Caminade Cyril, ICTP (ITA)
• Eneanya Obiora A., The Carter Center (USA)
• Jones Philip H., University of Liverpool (GBR)
• Medlock Jolyon, NIHR (GBR)
• Hansford Kayleigh M., NIHR (GBR)
• Bonannella Carmelo, Wageningen University and Research Centre (NLD)
• Radford Alan D., University of Liverpool (GBR)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/608297/)

Métriques

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