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PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona

Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of...

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Autores principales: Kollath, Daniel R., Mihaljevic, Joseph R., Barker, Bridget M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9045372/
https://www.ncbi.nlm.nih.gov/pubmed/35319247
http://dx.doi.org/10.1128/spectrum.01483-21
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author Kollath, Daniel R.
Mihaljevic, Joseph R.
Barker, Bridget M.
author_facet Kollath, Daniel R.
Mihaljevic, Joseph R.
Barker, Bridget M.
author_sort Kollath, Daniel R.
collection PubMed
description Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen. IMPORTANCE The inhalation of environmental infectious propagules from the fungal pathogens Coccidioides immitis and Coccidioides posadasii by susceptible mammals can result in coccidioidomycosis (Valley fever). Arizona is known to be a region where the pathogen is hyperendemic, and reported cases are increasing throughout the western United States. Coccidioides spp. are naturally occurring fungi in arid soils. Little is known about ecological factors that influence the growth of these fungi, and a higher environmental burden may result in increases in human exposure and therefore case rates. By examining case and climate data from Arizona and using generalized additive statistical models, we were able to examine the relationship between disease outbreaks and climatic variables and predict seasonal time points of increased infection risk.
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spelling pubmed-90453722022-04-28 PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona Kollath, Daniel R. Mihaljevic, Joseph R. Barker, Bridget M. Microbiol Spectr Research Article Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen. IMPORTANCE The inhalation of environmental infectious propagules from the fungal pathogens Coccidioides immitis and Coccidioides posadasii by susceptible mammals can result in coccidioidomycosis (Valley fever). Arizona is known to be a region where the pathogen is hyperendemic, and reported cases are increasing throughout the western United States. Coccidioides spp. are naturally occurring fungi in arid soils. Little is known about ecological factors that influence the growth of these fungi, and a higher environmental burden may result in increases in human exposure and therefore case rates. By examining case and climate data from Arizona and using generalized additive statistical models, we were able to examine the relationship between disease outbreaks and climatic variables and predict seasonal time points of increased infection risk. American Society for Microbiology 2022-03-23 /pmc/articles/PMC9045372/ /pubmed/35319247 http://dx.doi.org/10.1128/spectrum.01483-21 Text en Copyright © 2022 Kollath et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Kollath, Daniel R.
Mihaljevic, Joseph R.
Barker, Bridget M.
PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona
title PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona
title_full PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona
title_fullStr PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona
title_full_unstemmed PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona
title_short PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona
title_sort pm10 and other climatic variables are important predictors of seasonal variability of coccidioidomycosis in arizona
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9045372/
https://www.ncbi.nlm.nih.gov/pubmed/35319247
http://dx.doi.org/10.1128/spectrum.01483-21
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