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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118810/ https://www.ncbi.nlm.nih.gov/pubmed/21701590 http://dx.doi.org/10.1371/journal.pone.0021009 |
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author | Tamerius, James D. Comrie, Andrew C. |
author_facet | Tamerius, James D. Comrie, Andrew C. |
author_sort | Tamerius, James D. |
collection | PubMed |
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 | Online Article Text |
id | pubmed-3118810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-31188102011-06-23 Coccidioidomycosis Incidence in Arizona Predicted by Seasonal Precipitation Tamerius, James D. Comrie, Andrew C. PLoS One Research Article 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. Public Library of Science 2011-06-20 /pmc/articles/PMC3118810/ /pubmed/21701590 http://dx.doi.org/10.1371/journal.pone.0021009 Text en Tamerius, Comrie. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Tamerius, James D. Comrie, Andrew C. Coccidioidomycosis Incidence in Arizona Predicted by Seasonal Precipitation |
title | 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_short | Coccidioidomycosis Incidence in Arizona Predicted by Seasonal Precipitation |
title_sort | coccidioidomycosis incidence in arizona predicted by seasonal precipitation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118810/ https://www.ncbi.nlm.nih.gov/pubmed/21701590 http://dx.doi.org/10.1371/journal.pone.0021009 |
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