{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:16:06Z","timestamp":1772856966766,"version":"3.50.1"},"reference-count":55,"publisher":"BMJ","issue":"3","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"unspecified","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Glob Health"],"accepted":{"date-parts":[[2026,1,28]]},"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Q fever is a zoonotic infection caused by the bacterium\n                      <jats:italic>Coxiella burnetii<\/jats:italic>\n                      . 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>A total of 513\u2009cases 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 \u2018Mont R\u00e9mire\u2019 in the municipality of \u2018R\u00e9mire-Montjoly\u2019. 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>This study identifies priority areas where public health actions and research efforts should be focused, including slaughterhouses, farms and the surrounding wildlife.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjgh-2025-020069","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T15:20:31Z","timestamp":1772810431000},"page":"e020069","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Querying Q fever: spatiotemporal patterns and environmental 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