Spatiotemporal dynamics and environmental drivers of listeriosis hospitalizations in Spain (2016-2024): A One Health perspective
- 1Hospital Universitario Severo Ochoa (Madrid, Spain)
Abstract
BackgroundListeria monocytogenes causes invasive infections with high mortality. However, ecological studies linking human incidence to livestock and weather remain scarce. This retrospective study analyzed the geographical distribution of human listeriosis in Spain using nationwide hospitalization data, livestock censuses, and meteorological records.
MethodsNegative binomial regression models, spatial autocorrelation (Morans Index), and bivariate local indicators of spatial association (LISA) analysis were applied.
ResultsBetween 2016 and 2024, 4,391 hospital admissions were recorded. The national mean incidence was 1.03 per 100,000 inhabitants, peaking in 2019. Bovine density was the primary environmental driver, with an 11% increased risk per 10 heads/km2. Weather-livestock synergies were observed: bovine-associated risk increased exponentially at higher temperatures ([~]21{degrees}C) and lower humidity (53%), whereas caprine-associated incidence peaked at cooler temperatures ([~]13{degrees}C). Spatial mapping revealed northern clusters for bovine reservoirs and southern segregation for caprine reservoirs, with no associations for ovine or porcine livestock.
ConclusionsZoonotic risk is environmentally conditioned, with weather acting as a biological trigger. This study highlights the critical role of ruminant farming practices and the need for a "One Health" surveillance approach that integrates meteorological and farming data to predict seasonal outbreaks and optimize food chain safety.
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BibTeX
@misc{garciacarretero2026spatiotemporal,
title = {{Spatiotemporal dynamics and environmental drivers of listeriosis hospitalizations in Spain (2016-2024): A One Health perspective}},
author = {R. Garcia-Carretero and B. Valle-Borrego and C. Peiro-Villalba and C. de-Juan-Alvarez and G. Cenzual-Alvarez and S.-M. Quevedo-Soriano},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.15.26363098},
url = {https://www.medrxiv.org/content/10.64898/2026.09.15.26363098v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Spatiotemporal dynamics and environmental drivers of listeriosis hospitalizations in Spain (2016-2024): A One Health perspective
AU - R. Garcia-Carretero
AU - B. Valle-Borrego
AU - C. Peiro-Villalba
AU - C. de-Juan-Alvarez
AU - G. Cenzual-Alvarez
AU - S.-M. Quevedo-Soriano
PY - 2026
DA - 2026/09/15
PB - medRxiv
DO - 10.64898/2026.09.15.26363098
UR - https://www.medrxiv.org/content/10.64898/2026.09.15.26363098v1
AB - BackgroundListeria monocytogenes causes invasive infections with high mortality. However, ecological studies linking human incidence to livestock and weather remain scarce. This retrospective study analyzed the geographical distribution of human listeriosis in Spain using nationwide hospitalization data, livestock censuses, and meteorological records. MethodsNegative binomial regression models, spatial autocorrelation (Morans Index), and bivariate local indicators of spatial association (LISA) analysis were applied. ResultsBetween 2016 and 2024, 4,391 hospital admissions were recorded. The national mean incidence was 1.03 per 100,000 inhabitants, peaking in 2019. Bovine density was the primary environmental driver, with an 11% increased risk per 10 heads/km2. Weather-livestock synergies were observed: bovine-associated risk increased exponentially at higher temperatures ([~]21{degrees}C) and lower humidity (53%), whereas caprine-associated incidence peaked at cooler temperatures ([~]13{degrees}C). Spatial mapping revealed northern clusters for bovine reservoirs and southern segregation for caprine reservoirs, with no associations for ovine or porcine livestock. ConclusionsZoonotic risk is environmentally conditioned, with weather acting as a biological trigger. This study highlights the critical role of ruminant farming practices and the need for a "One Health" surveillance approach that integrates meteorological and farming data to predict seasonal outbreaks and optimize food chain safety.
ER -