five

Variables included logistic regression analysis.

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://figshare.com/articles/dataset/Variables_included_logistic_regression_analysis_/26823877
下载链接
链接失效反馈
官方服务:
资源简介:
Background Coxiella burnetii, the causative agent of Q fever, and Rift Valley fever virus are two under-researched zoonotic pathogens in Ethiopia. Potential outbreaks of these diseases, in light of the high dependency of nomadic pastoralists on their livestock, poses a risk to both human and animal health in addition to risking the pastoralists livelihoods. Our study aimed to determine the seroprevalence and associated risk factors for Q fever and Rift Valley fever in pastoral communities in the Afar region of north-eastern Ethiopia. Methodology/Principal findings This cross-sectional study screened pastoralists (n = 323) and their livestock (n = 1377) for IgG antibodies to Coxiella burnetii and Rift Valley fever virus. A seroprevalence for Q fever of 25.0% (95%CI 18.6–32.6) was found in pastoralists and 34.3% (95%CI 27.9–41.3) in livestock overall; with 51.9% in goats (95%CI 44.9–58.8), 39.9% in sheep (95%CI 24.6–51.2), 16.3% in camels (95%CI 10.4–24.6) and 8.8% in cattle (95%CI 5.0–15.0). For Rift Valley fever the seroprevalence in pastoralists was 6.1% (95%CI 3.3–11.0) and 3.9% (95%CI 2.6–5.7) in livestock overall; cattle had the highest seroprevalence (8.3%, 95%CI 3.3–19.2), followed by goats (2.7%; 95%CI 1.4–5.1), sheep (2.5%; 95%CI 1.0–5.9) and camels (1.8%; 95%CI 0.4–6.9). Human Q fever seropositivity was found to be associated with goat abortions (OR = 2.11, 95%CI 1.18–3.78, p = 0.011), while Rift Valley fever seropositivity in livestock was found to be associated with cattle abortions (OR = 2.52, 95%CI 1.05–6.08, p = 0.039). Conclusions/Significance This study provides evidence for a notable exposure to both Q fever and Rift Valley fever in pastoralists and livestock in Afar. The outbreak potential of these pathogens warrants ongoing integrated human and animal surveillance requiring close collaboration of the human and animal health sectors with community representatives following a One Health approach.
创建时间:
2024-08-23
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作