ArticleBMC infectious diseases2024
Using priorities between human and livestock bacterial antimicrobial resistance (AMR) to identify data gaps in livestock AMR surveillance.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Antimicrobial resistance in the Northern Territory, Australia: insights from a systems thinking approach to human health.One health outlook · 2026Article
- Understanding antibiotic decision-making for bovine respiratory disease: a survey of UK farm veterinarians.Frontiers in veterinary science · 2026Article
- Geography, Antimicrobial Resistance, and Genomics ofMicroorganisms · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
backgroundBacterial antimicrobial resistance (AMR) is a global threat to both humans and livestock. Despite this, there is limited global consensus on data-informed, priority areas for intervention in both sectors. We compare current livestock AMR data collection efforts with other variables pertinent to human and livestock AMR to identify critical data gaps and mutual priorities.
methodsWe globally synthesized livestock AMR data from open-source surveillance reports and point prevalence surveys stratified for six pathogens (Escherichia coli, Staphylococcus aureus, non-typhoidal Salmonella, Campylobacter spp., Enterococcus faecalis, Enterococcus faecium) and eleven antimicrobial classes important in human and veterinary use, published between 2000 and 2020. We also included all livestock species represented in the data: cattle, chickens, pigs, sheep, turkeys, ducks, horses, buffaloes, and goats. We compared this data with intended priorities calculated from: disability-adjusted life years (DALYs), livestock antimicrobial usage (AMU), livestock biomass, and a global correlation exercise between livestock and human proportion of resistant isolates.
resultsResistance to fluoroquinolones and macrolides in Staphylococcus aureus were identified as priorities in many countries but, less than 10% of these reported livestock AMR data. Resistance data for Escherichia coli specific to cattle, chickens, and pigs, which we prioritized, were also well collected. AMR data collection on non-typhoidal Salmonella and other livestock species were often not prioritized. Of 232 categories prioritized by at least one country, data were only collected for 48% (n = 112).
conclusionsThe lack of livestock AMR data globally for broad resistance in Staphylococcus aureus could underplay their zoonotic threat. Countries can bolster livestock AMR data collection, reporting, and intervention setting for Staphylococcus aureus as done for Escherichia coli. This framework can provide guidance on areas to strengthen AMR surveillance and decision-making for humans and livestock, and if done routinely, can adapt to resistance trends and priorities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.