Querying Q fever

Spatiotemporal patterns and environmental drivers in French Guiana

Bibliographic Data

ID21879529
AuthorsAnissa Desmoulin (0000-0001-7204-481X, Université de Perpignan), Amanda Esparon (Université de Perpignan), Fabrice Quet (0000-0003-4777-5527, University of French Guiana), Claire Teillet (0009-0006-0655-5690, Université de Perpignan), Pauline Thill (Université de Lille), Mathieu Nacher (0000-0001-9397-3204, Clinical Investigation Centre of French Guiana, CIC Inserm 1424, Inserm UA17 Santé des Populations en Amazonie, CHU de Guyane, Cayenne, French Guiana), Emmanuel Roux (0000-0003-2266-8207, Université de Perpignan), Thibault Catry (0000-0001-9514-1751, Université de Perpignan), Loïc Epelboin (0000-0002-3481-5991, Clinical Investigation Centre of French Guiana, CIC Inserm 1424, Inserm UA17 Santé des Populations en Amazonie, CHU de Guyane, Cayenne, French Guiana)
Year2026
Volume11
Issue3
Pagese020069
Publication date2026-03-01
Open AccessYes
TypeARTICLE
VenueBMJ Global Health (JOURNAL)
Journal identifiersISSN: 2059-7908 • E-ISSN: 2059-7908
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/bmjgh-2025-020069
PMID41771663
OpenAlexW7133296693
LanguageEN
References cited39

BACKGROUND: . French Guiana, largely covered by the Amazon rainforest, is considered a hyper-endemic region. While ruminants are the primary reservoirs worldwide, the reservoir in French Guiana remains debated, likely relying on wild fauna. This study aimed to identify spatiotemporal clusters of human Q fever in the Cayenne area and investigate their relationships with environmental factors using remote sensing data. METHODS: A retrospective study was conducted on acute Q fever human cases from January 2007 to December 2017. Cases were aggregated into regular grids, and explanatory variables derived from remote sensing data or local sources. Clusters were identified using spatial autocorrelation and spatiotemporal scanning. A generalized Poisson additive model was applied for explanatory modelling. FINDINGS: A total of 513 cases of acute Q fever were aggregated within 1205 analysis units. Spatial and spatiotemporal analyses identified six clusters, all classified as hotspots. An epicentre was detected at the base of 'Mont Rémire' in the municipality of 'Rémire-Montjoly'. Several risk factors were associated with the occurrence of acute Q fever cases: proximity to forests (edf: 4.05), wild live mammals watching (edf: 1.00), slaughterhouse (edf: 6.11), density of potentially unfit housing (edf: 6.53) and spatial distribution (edf: 2.00). INTERPRETATION: This study identifies priority areas where public health actions and research efforts should be focused, including slaughterhouses, farms and the surrounding wildlife

Data collection · Public health · Mosquito-borne diseases and control · Vector-borne infectious diseases · Vibrio bacteria research studies

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Open DOIOpen Access