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Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses.

Boysen C, Davis EG, Beard LA, Lubbers BV, Raghavan RK - PLoS ONE (2015)

Bottom Line: Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate).Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates.Preventative and ecoclimatic significance of these findings are discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, United States of America.

ABSTRACT
Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (≥ 1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (≥ 35°C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed.

No MeSH data available.


Related in: MedlinePlus

Number of positive pigeon fever cases diagnosed between 2005–2013 (a), and during 2012/13 (b) at the Veterinary Health Center and Kansas State Veterinary Diagnostic Laboratory, Kansas State University.
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pone.0140666.g002: Number of positive pigeon fever cases diagnosed between 2005–2013 (a), and during 2012/13 (b) at the Veterinary Health Center and Kansas State Veterinary Diagnostic Laboratory, Kansas State University.

Mentions: The observed spatial distribution of C. pseudotuberculosis infection in Kansas based on VHC and KSVDL medical records is present in Fig 1. Most cases recorded during the study period were reported during the fall of 2012 (Sep–Nov) (Fig 2A and 2B), indicating that the infection had occurred during the preceding summer months or late spring in the region. From the univariate, deterministic, non-spatial logistic models, five variables retained their statistical significance and others were discarded. Higher soil moisture levels had a protective effect (OR = 1.22, 95% CI =1.08, 1.30) on C. pseudotuberculosis infection status in horses; and, mixed forests (OR = 1.18, 95% CI = 1.04, 1.12), grassland/herbaceous cover (OR = 1.22, 95% CI = 1.08, 1.30), total edge contrast index (OR = 1.33, 95% CI = 1.17, 2.21), and day time land surface temperature (≥35°C) (OR = 2.28 95% CI = 2.14, 3.31) had significantly increased the odds of infection status in horses. Of these variables, mixed forest and grassland/herbaceous cover did not retain significance in the Bayesian geostatistical model with covariates, and, individual host factor covariates (age, sex and breed) were not significantly associated with case status when they were inserted one at a time. Box plots showing differences in statistical distribution of significant univariate variables and those retained in the Bayesian geostatistical model surrounding case and control locations are present in Figs 3 and 4, respectively.


Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses.

Boysen C, Davis EG, Beard LA, Lubbers BV, Raghavan RK - PLoS ONE (2015)

Number of positive pigeon fever cases diagnosed between 2005–2013 (a), and during 2012/13 (b) at the Veterinary Health Center and Kansas State Veterinary Diagnostic Laboratory, Kansas State University.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608828&req=5

pone.0140666.g002: Number of positive pigeon fever cases diagnosed between 2005–2013 (a), and during 2012/13 (b) at the Veterinary Health Center and Kansas State Veterinary Diagnostic Laboratory, Kansas State University.
Mentions: The observed spatial distribution of C. pseudotuberculosis infection in Kansas based on VHC and KSVDL medical records is present in Fig 1. Most cases recorded during the study period were reported during the fall of 2012 (Sep–Nov) (Fig 2A and 2B), indicating that the infection had occurred during the preceding summer months or late spring in the region. From the univariate, deterministic, non-spatial logistic models, five variables retained their statistical significance and others were discarded. Higher soil moisture levels had a protective effect (OR = 1.22, 95% CI =1.08, 1.30) on C. pseudotuberculosis infection status in horses; and, mixed forests (OR = 1.18, 95% CI = 1.04, 1.12), grassland/herbaceous cover (OR = 1.22, 95% CI = 1.08, 1.30), total edge contrast index (OR = 1.33, 95% CI = 1.17, 2.21), and day time land surface temperature (≥35°C) (OR = 2.28 95% CI = 2.14, 3.31) had significantly increased the odds of infection status in horses. Of these variables, mixed forest and grassland/herbaceous cover did not retain significance in the Bayesian geostatistical model with covariates, and, individual host factor covariates (age, sex and breed) were not significantly associated with case status when they were inserted one at a time. Box plots showing differences in statistical distribution of significant univariate variables and those retained in the Bayesian geostatistical model surrounding case and control locations are present in Figs 3 and 4, respectively.

Bottom Line: Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate).Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates.Preventative and ecoclimatic significance of these findings are discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, United States of America.

ABSTRACT
Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (≥ 1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (≥ 35°C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed.

No MeSH data available.


Related in: MedlinePlus