ArticleBMC veterinary research2026
Spatial distribution and determinants of antimicrobial resistance in livestock across pathogen types, animal species and antimicrobial classes.
Article in BMC veterinary research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) in livestock is a growing global health concern with important implications for food security, human and animal health. However, the spatial distribution and determinants of AMR in livestock systems remain insufficiently characterised. This study examined spatial patterns and contributions of selected AMR drivers in livestock AMR, using data from the ResistanceBank database, which encompasses livestock species, bacterial pathogens, and antimicrobial categories. This study included all data compiled in the Resistancebank database, including prevalence studies published between 2000 and 2021 and 33,186 resistance data points compiled from 93 countries. Spatial dependence was evaluated using Global and Local Moran's I statistics, while the influence of livestock species, pathogens, and antimicrobial classes was analysed using beta regression and Extreme Gradient Boosting (XGBoost) machine learning models. The spatial analysis includes only countries listed in the Resistancebank database and displays them. Global Moran's I revealed significant positive spatial autocorrelation in AMR proportions (I = 0.1911, p = 0.0246; Z-score = 1.9676, expected I = - 0.0127), indicating geographic clustering of resistance. High-high clusters were identified across South and East Asia, the Middle East, parts of Sub-Saharan Africa, and South America, whereas low-low clusters occurred in Southern Africa, Southeast Asia, and several European regions. Beta regression showed that cattle (β = -0.5953, p = 0.0263) and sheep (β = -0.7873, p = 0.0034) contributed less to AMR variation than buffalo, whereas highly important antimicrobials were associated with increased proportions of resistance (β = 0.3365, p < 0.0001). The XGBoost model demonstrated slightly better predictive performance (Root Mean Squared Error (RMSE) = 0.3428) than beta regression (RMSE = 0.3471). These findings reveal pronounced spatial clustering of AMR in livestock and underscore the need for strengthened global surveillance, improved antimicrobial stewardship and integrated One Health strategies to mitigate the spread of AMR.
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.