Modelling Bodyweight to Avoid Anthelmintic Underdosing of Goats in Resources Limited Settings
Research Square (Research Square)(2022)
摘要
Abstract There is a need to improve the management of endemic diseases of livestock in resource limited settings as a means towards improved human nutrition and livelihoods. Gastrointestinal nematode infections (GIN) are some of the most significant endemic diseases, and improved management would significantly improve animal health and production. Anthelmintics play a role in GIN management; however, very few anthelmintics are available in many low-middle income countries. Utilising a limited range of anthelmintic classes may increase selection for anthelmintic resistance, as well as reducing mitigation strategies once resistance establishes. Strategies to reduce other selective pressures for anthelmintic resistance are therefore of heightened importance. Avoiding anthelmintic underdosing is one such strategy, and whilst it may appear simple, it can be challenging in settings without access to accurate bodyweight measurement. Many previous studies have used thoracic girth as a practical proxy for bodyweight in goats; however, they have rarely considered the potential impact of natural variation on therapeutic doses. Here, the relationship between bodyweight and thoracic girth was modelled using data from 820 goats from three Malawian biomes in two seasons, with the specific aim of avoiding underestimation of bodyweight. This resulted in the development of a simple linear regression (readily convertible into a weigh tape) that was internally cross-validated and then successfully tested against data from an additional 352 Malawian goats (from the same 3 biomes, across two seasons). Further testing using a historical dataset of 150 goats from Assam, India suggests that a more globally generalisable approach would be feasible.
更多查看译文
关键词
anthelmintic underdosing,goats,bodyweight
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要