Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses.
Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses.
复制标题
DOI：
10.1371/journal.pone.0140666
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Raghavan RK
中科院分区：
文献类型：
--
作者：
Boysen C;Davis EG;Beard LA;Lubbers BV;Raghavan RK
2012年秋天，堪萨斯州在马中爆发了前所未有的假结核棒状杆菌感染，这种疾病通常被称为鸽子热。建立了贝叶斯地质统计模型，以确定与马假结核杆菌感染相关的关键环境和气候风险因素。马（病例）感染的阳性状态由以下因素确定：特征性脓肿形成的阳性检测结果，从穿刺脓肿中获得的化脓性物质上的细菌培养阳性（n = 82），或暴露于生物体的血清学阳性证据（n = 11）。这些试验呈阴性的马（n = 172）（对照）被认为没有感染。通过审查医疗记录和/或通过电话与马主联系，获得有关马的人口统计数据和马厩位置的信息。环境和气候决定因素的协变量信息来自美国农业部（土壤属性）、美国地质调查局（土地利用/土地覆盖）、美国宇航局MODIS和美国宇航局全球可再生资源预测（气候）。采用单变量回归模型筛选候选协变量，然后采用带协变量和不带协变量的贝叶斯地统计学模型进行筛选。结果表明，较高的土壤含水量（OR = 0.53, 95% CrI = 0.25, 0.71）对马的假结核杆菌感染状况有保护作用，较高的地表温度（≥35°C） （OR = 2.81, 95% CrI = 2.21, 3.85）和栖息地破碎化（OR = 1.31, 95% CrI = 1.27, 2.22）对马的假结核杆菌感染状况有不利影响，而年龄、性别和品种对马的假结核杆菌感染状况没有影响。讨论了这些发现的预防和生态气候意义。
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.