Disease Surveillance: Monitoring livestock pregnancy loss

Systematically tracking and analysing reproductive loss in livestock helps with efforts to safeguard the health and productivity of food animals by identifying causes and high-risk areas.
  1. Clara Akpan  Is a corresponding author
  1. Department of Veterinary Medicine, Michael Okpara University of Agriculture, Nigeria

Raising healthy and disease-free livestock is globally important for ensuring food and economic security as well as public health. Information about the pathogens causing livestock diseases across Africa is lacking, which makes it difficult to design strategies to prevent and control such diseases across the continent. This difficulty – combined with heat stress due to extreme temperatures and humidity – reduces livestock productivity, such as growth or milk production (Janssens et al., 2020), and increases the likelihood of livestock diseases being transmitted to humans.

Livestock abortion – where a pregnancy ends early and the foetus is expelled – is distressing for both animals and farmers, and contributes to reduced productivity and profitability of livestock projects (Keshavarzi et al., 2020). Although there are multiple potential causes of abortion, several pathogens have been associated with it globally. Therefore, collecting and analyzing data on abortion rates and their timing and associated factors could help authorities detect deviations from baseline levels that signal infections or environmental stressors that warrant further investigation (Norzin et al., 2023). This would serve as a resource for prioritizing disease control strategies (Gachohi et al., 2024), allowing policymakers to allocate resources strategically, minimizing the economic burden on farmers and the broader agricultural industry.

Due to poor disease monitoring and lack of infrastructure in Africa, little is known about the causes and impacts of livestock abortions (Dórea and Vial, 2016). Data on livestock diseases in the region rarely include information on abortion cases (Thomas et al., 2022), making it difficult to launch interventions where they are most needed. Now, in eLife, Sarah Cleaveland (University of Glasgow) and colleagues from various institutes in Tanzania, the United Kingdom and New Zealand – including Felix Lankester (Washington State University) as first author – report results from a surveillance study in northern Tanzania that aimed to identify abortion-causing pathogens and their impact on animals raised for food (Lankester et al., 2024).

The research was conducted through collaboration with the Ministry of Livestock and Fisheries, and local government authorities across Tanzania. Farmers that engage in various agricultural practices – including raising livestock alone or combined with crop cultivation or sustainable farming methods (Bodenham et al., 2021) – were encouraged to report abortion cases to livestock field officers, who then reported to the researchers. If an abortion was reported within 72 hours of it occurring, appropriate samples were collected from the females (blood, milk and vaginal swab) and the aborted foetuses. Additionally, a questionnaire was used to gather history of the livestock management, and laboratory analysis was used to test for a range of microorganisms.

A total of 215 abortion cases in cattle, sheep and goats were investigated, revealing that abortions occurred more during the dry season and in exotic and cross-bred animals rather than local livestock breeds. In 19.5% of cases, abortion was attributed to identifiable pathogens, including some that cause mild to severe illness in humans (such as Brucella spp, Coxiella burnetii, Toxoplasma gondii and Rift Valley fever virus), as well as pathogens not transmissible to humans (Neospora spp and Pertivirus). The study also identified valuable information for designing future studies. Vaginal swabs from aborting animals proved more sensitive for detecting causative agents than swabs from foetuses and the placenta. Furthermore, the longer the delay between abortion and analysis of samples, the less likely the causative agent was to be identified.

The findings suggest that surveillance of livestock abortion can be used to track important disease-causing agents responsible for reproductive loss that are not easily identified through other forms of livestock disease surveillance. This valuable information also allows monitoring of diseases that can be transmitted to humans. Additionally, the observation that more abortions occurred in non-indigenous livestock than local breeds could be used to guide herd improvement programs, for example by introducing more local livestock.

One limitation of the work of Lankester et al. is that only ten different microorganisms were tested for. In the future, expanding this number may identify more causative agents. Furthermore, increasing the number of people involved in investigation and providing suitable transport for field officers could ensure abortion cases are reported and investigated more promptly (Nansikombi et al., 2023). With the knowledge provided by Lankester et al., establishing an effective reporting and investigation system could help to design disease control measures that would be implementable even in remote rural areas.

References

    1. Norzin T
    2. Ghiasbeglou H
    3. Patricio M
    4. Romanova S
    5. Zaghlool A
    6. Tanguay F
    7. Zhao L
    (2023)
    Event-based surveillance: Providing early warning for communicable disease threats
    Canada Communicable Disease Report 49:29–34.

Article and author information

Author details

  1. Clara Akpan

    Clara Akpan is in the Department of Veterinary Medicine, Michael Okpara University of Agriculture, Umudike, Nigeria

    For correspondence
    akpan.clara@mouau.edu.ng
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2415-6662

Publication history

  1. Version of Record published: May 15, 2024 (version 1)

Copyright

© 2024, Akpan

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 22
    views
  • 6
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Clara Akpan
(2024)
Disease Surveillance: Monitoring livestock pregnancy loss
eLife 13:e98828.
https://doi.org/10.7554/eLife.98828

Further reading

    1. Epidemiology and Global Health
    Xiaoxin Yu, Roger S Zoh ... David B Allison
    Review Article

    We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

    1. Ecology
    2. Epidemiology and Global Health
    Emilia Johnson, Reuben Sunil Kumar Sharma ... Kimberly Fornace
    Research Article

    Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.