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在美国,陆地海拔、坡度和覆盖情况作为猪繁殖与呼吸综合征(PRRS)疫情爆发的风险因素。

Land altitude, slope, and coverage as risk factors for Porcine Reproductive and Respiratory Syndrome (PRRS) outbreaks in the United States.

作者信息

Arruda Andréia Gonçalves, Vilalta Carles, Perez Andres, Morrison Robert

机构信息

Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, United States.

出版信息

PLoS One. 2017 Apr 17;12(4):e0172638. doi: 10.1371/journal.pone.0172638. eCollection 2017.

Abstract

Porcine reproductive and respiratory syndrome (PRRS) is, arguably, the most impactful disease on the North American swine industry. The Swine Health Monitoring Project (SHMP) is a national volunteer initiative aimed at monitoring incidence and, ultimately, supporting swine disease control, including PRRS. Data collected through the SHMP currently represents approximately 42% of the sow population of the United States. The objective of the study here was to investigate the association between geographical factors (including land elevation, and land coverage) and PRRS incidence as recorded in the SHMP. Weekly PRRS status data from sites participating in the SHMP from 2009 to 2016 (n = 706) was assessed. Number of PRRS outbreaks, years of participation in the SHMP, and site location were collected from the SHMP database. Environmental features hypothesized to influence PRRS risk included land coverage (cultivated areas, shrubs and trees), land altitude (in meters above sea level) and land slope (in degrees compared to surrounding areas). Other risk factors considered included region, production system to which the site belonged, herd size, and swine density in the area in which the site was located. Land-related variables and pig density were captured in raster format from a number of sources and extracted to points (farm locations). A mixed-effects Poisson regression model was built; and dependence among sites that belonged to a given production system was accounted for using a random effect at the system level. The annual mean and median number of outbreaks per farm was 1.38 (SD: 1.6), and 1 (IQR: 2.0), respectively. The maximum annual number of outbreaks per farm was 9, and approximately 40% of the farms did not report any outbreak. Results from the final multivariable model suggested that increments of swine density and herd size increased the risk for PRRS outbreaks (P < 0.01). Even though altitude (meters above sea level) was not significant in the final model, farms located in terrains with a slope of 9% or higher had lower rates of PRRS outbreaks compared to farms located in terrains with slopes lower than 2% (P < 0.01). Finally, being located in an area of shrubs/ herbaceous cover and trees lowered the incidence rate of PRRS outbreaks compared to being located in cultivated/ managed areas (P < 0.05). In conclusion, highly inclined terrains were associated with fewer PRRS outbreaks in US sow farms, as was the presence of shrubs and trees when compared to cultivated/ managed areas. Influence of terrain characteristics on spread of airborne diseases, such as PRRS, may help to predicting disease risk, and effective planning of measures intended to mitigate and prevent risk of infection.

摘要

猪繁殖与呼吸综合征(PRRS)可以说是对北美养猪业影响最大的疾病。猪健康监测项目(SHMP)是一项全国性的志愿者倡议,旨在监测发病率,并最终支持包括PRRS在内的猪病防控。通过SHMP收集的数据目前约占美国母猪存栏量的42%。本研究的目的是调查地理因素(包括土地海拔和土地覆盖情况)与SHMP记录的PRRS发病率之间的关联。对2009年至2016年参与SHMP的站点(n = 706)的每周PRRS状态数据进行了评估。从SHMP数据库中收集了PRRS疫情爆发次数、参与SHMP的年份以及站点位置。假设会影响PRRS风险的环境特征包括土地覆盖情况(耕地、灌木和树木)、土地海拔(海平面以上米数)和土地坡度(与周边地区相比的度数)。考虑的其他风险因素包括地区、站点所属的生产系统、畜群规模以及站点所在地区的猪密度。与土地相关的变量和猪密度以栅格格式从多个来源获取,并提取到各个点(农场位置)。构建了一个混合效应泊松回归模型;并使用系统层面的随机效应来考虑属于给定生产系统的站点之间的相关性。每个农场每年疫情爆发的平均次数和中位数分别为1.38(标准差:1.6)和1(四分位距:2.0)。每个农场每年疫情爆发的最大次数为9次;约40%的农场未报告任何疫情爆发情况。最终多变量模型的结果表明,猪密度和畜群规模的增加会增加PRRS疫情爆发的风险(P < 0.01)。尽管海拔(海平面以上米数)在最终模型中不显著,但与位于坡度低于2%的地形上的农场相比,位于坡度为9%或更高的地形上的农场PRRS疫情爆发率较低(P < 0.01)。最后,与位于耕地/管理区域相比,位于有灌木/草本覆盖和树木的区域会降低PRRS疫情爆发的发生率(P < 0.05)。总之,在美国母猪养殖场中,与耕地/管理区域相比,高度倾斜的地形与较少的PRRS疫情爆发相关,有灌木和树木的情况也是如此。地形特征对诸如PRRS等空气传播疾病传播的影响可能有助于预测疾病风险,并有效规划旨在减轻和预防感染风险的措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4c00/5393554/64dc8bda22dd/pone.0172638.g001.jpg

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