Associations between Legionellosis and meteorological variables in Ohio, 2001-2023

Legionellosis, a respiratory disease caused by Legionella bacteria, has increased in incidence across the United States, yet the environmental factors influencing its rise remain unclear. We examined monthly Legionellosis case counts from 2001–2023 in four populous Ohio counties using Distributed Lag Nonlinear Models (DLNMs). The models assessed delayed and nonlinear associations between disease incidence and meteorological variables, including temperature extremes, precipitation, humidity, soil moisture, and large-scale climate indices. Significant lagged relationships were found for soil moisture, relative humidity, temperature, and oscillation indices, though associations and delayed effects varied based up geography. Elevated soil moisture and humidity were most consistently linked to increased risk. These results indicate that both local weather and broader climatic conditions influence Legionellosis patterns in Ohio. As climate change intensifies humidity and precipitation extremes, the environmental conditions favoring Legionella growth and transmission are likely to become more frequent, underscoring the importance of climate-informed public health surveillance.

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Publication Details

Journal
Infectious Disease Modelling
Published
2026-04-01
DOI
https://doi.org/10.1016/j.idm.2026.03.013
Primary Topic
Legionella and Acanthamoeba research
Type
article
Field-Weighted Citation Impact
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article

Associations between Legionellosis and meteorological variables in Ohio, 2001-2023

Joni Downs, Hansamali Abeysinghe Mudiyanselage, Jaryd Hinch, Nji Mukwa Njoh et al.
Infectious Disease Modelling
Legionella and Acanthamoeba research
article

Associations between Legionellosis and meteorological variables in Ohio, 2001-2023

Joni Downs, Hansamali Abeysinghe Mudiyanselage, Jaryd Hinch, Nji Mukwa Njoh, Saurav Chakraborty
article en

Abstract

Legionellosis, a respiratory disease caused by Legionella bacteria, has increased in incidence across the United States, yet the environmental factors influencing its rise remain unclear. We examined monthly Legionellosis case counts from 2001–2023 in four populous Ohio counties using Distributed Lag Nonlinear Models (DLNMs). The models assessed delayed and nonlinear associations between disease incidence and meteorological variables, including temperature extremes, precipitation, humidity, soil moisture, and large-scale climate indices. Significant lagged relationships were found for soil moisture, relative humidity, temperature, and oscillation indices, though associations and delayed effects varied based up geography. Elevated soil moisture and humidity were most consistently linked to increased risk. These results indicate that both local weather and broader climatic conditions influence Legionellosis patterns in Ohio. As climate change intensifies humidity and precipitation extremes, the environmental conditions favoring Legionella growth and transmission are likely to become more frequent, underscoring the importance of climate-informed public health surveillance.

Infectious Disease Modelling
University of South Florida (US)
Climate action
Openalex Percentile: Top 27%
Legionella and Acanthamoeba research
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