Lopes Antunes Ana Carolina, Ersbøll Annette Kjær, Bihrmann Kristine, Toft Nils
Section for Diagnostics and Scientific Advice, National Veterinary Institute, Technical University of Denmark, Denmark.
National Institute of Public Health, University of Southern Denmark, Denmark.
Prev Vet Med. 2017 Sep 15;145:41-48. doi: 10.1016/j.prevetmed.2017.06.008. Epub 2017 Jun 20.
The aim of this study was to explore spatio-temporal mortality patterns in Danish swine herds from December 2013 to October 2015, and to discuss the use of mortality data for syndromic surveillance in Denmark. Although it has previously been assessed within the context of syndromic surveillance, the value of mortality data generated on a regular and mandatory basis from all swine herds remains unexplored in terms of swine surveillance in Denmark. A total of 5010 farms were included in the analysis, corresponding to 1896 weaner herds, 1490 sow herds and 3839 finisher herds. The spatio-temporal analysis included data description for spatial, temporal, and spatio-temporal cluster analysis for three age groups: weaners (up to 30kg), sows and finishers. Logistic regression models were used to assess the potential factors associated with finisher and weaner herds being included within multiple-herd clusters. The spatio-temporal distribution of mortality changed over time, and suggested a general increase in mortality for the months of January and July for the three age groups. A large number of single-herd clusters (i.e. clusters with only one herd), and fewer multiple-herd clusters (i.e. clusters with at least two herds) were found. The herd size affected whether weaner herds were within multiple-herd clusters, and factors such farm type, SPF status and presence of atrophic rhinitis had an impact on finisher herds being inside vs. outside multiple-herd clusters in the univariable analysis. However, due to a strong correlation between variables, only farm type remained in the multivariable analysis for the finisher herds. The higher mortality observed for the months of January and July could be linked to infrequent updates of the data used to calculate mortality. The presence of single-herd clusters might indicate welfare and disease issues, while multiple-herd clusters could suggest the presence of infectious diseases within the cluster area. The impact of farm type is linked to the fact that larger farms specialize in only one age group, with high biosecurity and more specialized personnel, and subsequently a lower mortality. Mortality data have a potential use in disease surveillance. However, detected clusters might not be due to disease, but the result of changes such as herd management practices. Further analysis to explore other spatio-temporal monitoring methods is needed before mortality data can be incorporated into a Danish disease monitoring system.
本研究旨在探究2013年12月至2015年10月丹麦猪群的时空死亡模式,并讨论死亡数据在丹麦症状监测中的应用。尽管此前已在症状监测背景下进行过评估,但就丹麦猪群监测而言,定期且强制收集的所有猪群死亡数据的价值仍未得到充分探索。分析共纳入5010个猪场,包括1896个断奶仔猪群、1490个母猪群和3839个育肥猪群。时空分析包括对三个年龄组(断奶仔猪(体重达30千克)、母猪和育肥猪)的空间、时间和时空聚类分析的数据描述。使用逻辑回归模型评估育肥猪群和断奶仔猪群被纳入多群聚类的潜在因素。死亡的时空分布随时间变化,表明三个年龄组在1月和7月的死亡率普遍上升。发现了大量单群聚类(即仅包含一个猪群的聚类),多群聚类(即至少包含两个猪群的聚类)较少。猪群规模影响断奶仔猪群是否在多群聚类中,在单变量分析中,农场类型、无特定病原体状态和萎缩性鼻炎的存在等因素对育肥猪群是否在多群聚类中有影响。然而,由于变量之间存在强相关性,在育肥猪群的多变量分析中仅保留了农场类型。1月和7月观察到的较高死亡率可能与用于计算死亡率的数据更新不频繁有关。单群聚类的存在可能表明福利和疾病问题,而多群聚类可能表明聚类区域内存在传染病。农场类型的影响与以下事实有关:较大的农场仅专注于一个年龄组,生物安全水平高且人员更专业,因此死亡率较低。死亡数据在疾病监测中具有潜在用途。然而,检测到的聚类可能并非由疾病引起,而是猪群管理实践等变化的结果。在将死亡数据纳入丹麦疾病监测系统之前,需要进一步分析以探索其他时空监测方法。