Evidence map›Paper›PMID 40586036›Full record

ArticleFrontiers in veterinary science2025

Investigating the role of environmental factors in the French highly pathogenic avian influenza epizootic in 2022-2023.

Maryem Ben Salem, Mathieu Andraud, Stéphanie Bougeard, Virginie Allain, Morgane Salines, Rodolphe Thomas, Audrey Schmitz, Legrand Saint-Cyr, Karine Fiore, Sophie Le Bouquin and 1 more

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Maryem Ben SalemEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Mathieu AndraudEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Stéphanie BougeardEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Virginie AllainEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Morgane SalinesEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Rodolphe ThomasEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Audrey SchmitzAvian and Rabbit Virology, Immunology and Parasitology Unit (VIPAC) and French National Reference Laboratory for Avian Influenza, Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Legrand Saint-CyrSocial Sciences, Economics and Society Department (DISSES), ANSES, Maisons-Alfort, France.
Karine FioreSocial Sciences, Economics and Society Department (DISSES), ANSES, Maisons-Alfort, France.
Sophie Le BouquinEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.
Axelle ScoizecEpidemiology, Animal Health and Welfare Unit (EpiSaBe), Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The recurring epizootics of highly pathogenic avian influenza (HPAI) in France have been associated with changes in the epidemiological landscape, such as higher frequency of detections in wild birds and introductions into backyard farms. This highlights the need for a deeper understanding of the factors that drive the spread of HPAI, particularly environmental ones, which, unlike other factors, are still understudied. Methods: In this study, we examined various farm and environmental variables around the 2022-2023 outbreak sites in France to unravel potential common traits among detected outbreaks. From August 2022 to March 2023, 397 poultry farms were infected, including different species and production types. For each outbreak, the farm characteristics and variables related with their direct environment within a 2 km radius were collected. Based on the Gower distance, accounting for qualitative and quantitative variable, clusters were identified using k-medoid partitioning algorithm. A random forest analysis was further used to hierarchize the relative role of each variable in the clustering process, to assess the importance of the farm structural and environmental conditions on the outbreak occurrence. To disentangle the impact of environmental factors from intrinsic herd characteristics, the method was applied twice: first, using the whole dataset including the farm characteristics and environmental variables (first scenario); second, accounting exclusively for the environmental variables (second scenario). Results: Overall, farm variables such as farm type were crucial in the clustering process, overpassing most of the environmental factors, although the distance from "particular risk zones" and the coastline were also important. However, the clusters obtained with the second scenario that counts only for the environmental variables, remained consistent with the first scenario. Discussion: This shows a non-negligible impact of environmental conditions on the probability of viral introduction in poultry herds. This study used an innovative approach to explore how HPAI dynamics can be influenced by external factors, which could help in the design of risk zones at the national level.

Indexed as

classificationclusteringepidemiologymachine learningpoultryrandom forest

Identifiers

PMID40586036
PMCPMC12202217

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.