Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.

The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pi...

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Autores principales:	James D Tamerius, Andrew C Comrie
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/222e74ca661e46f9bdb3581f9f432f94
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spelling	oai:doaj.org-article:222e74ca661e46f9bdb3581f9f432f942021-11-18T06:51:44ZCoccidioidomycosis incidence in Arizona predicted by seasonal precipitation.1932-620310.1371/journal.pone.0021009https://doaj.org/article/222e74ca661e46f9bdb3581f9f432f942011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21701590/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.James D TameriusAndrew C ComriePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 6, p e21009 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q James D Tamerius Andrew C Comrie Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.
description	The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.
format	article
author	James D Tamerius Andrew C Comrie
author_facet	James D Tamerius Andrew C Comrie
author_sort	James D Tamerius
title	Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.
title_short	Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.
title_full	Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.
title_fullStr	Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.
title_full_unstemmed	Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.
title_sort	coccidioidomycosis incidence in arizona predicted by seasonal precipitation.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/222e74ca661e46f9bdb3581f9f432f94
work_keys_str_mv	AT jamesdtamerius coccidioidomycosisincidenceinarizonapredictedbyseasonalprecipitation AT andrewccomrie coccidioidomycosisincidenceinarizonapredictedbyseasonalprecipitation
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Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation.

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