Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2021 Sep 10;12(1):5384.
doi: 10.1038/s41467-021-25655-8.

Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia

Affiliations
Meta-Analysis

Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia

Daniel Schar et al. Nat Commun. .

Abstract

Antimicrobial resistance (AMR) is a growing threat to human and animal health. However, in aquatic animals-the fastest growing food animal sector globally-AMR trends are seldom documented, particularly in Asia, which contributes two-thirds of global food fish production. Here, we present a systematic review and meta-analysis of 749 point prevalence surveys reporting antibiotic-resistant bacteria from aquatic food animals in Asia, extracted from 343 articles published in 2000-2019. We find concerning levels of resistance to medically important antimicrobials in foodborne pathogens. In aquaculture, the percentage of antimicrobial compounds per survey with resistance exceeding 50% (P50) plateaued at 33% [95% confidence interval (CI) 28 to 37%] between 2000 and 2018. In fisheries, P50 decreased from 52% [95% CI 39 to 65%] to 22% [95% CI 14 to 30%]. We map AMR at 10-kilometer resolution, finding resistance hotspots along Asia's major river systems and coastal waters of China and India. Regions benefitting most from future surveillance efforts are eastern China and India. Scaling up surveillance to strengthen epidemiological evidence on AMR and inform aquaculture and fisheries interventions is needed to mitigate the impact of AMR globally.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Annual trends in the proportion of drugs with resistance greater than 50% (P50) in each survey.
(a) P50 for cultured aquatic animals (n = 558); and (b) wild-caught aquatic animals (n = 81). The horizontal box lines represent the first quartile, the median, and the third quartile. Whiskers denote the range of points within the first quartile −1.5× the interquartile range and the third quartile +1.5× the interquartile range. Each survey is represented by a dot with horizontal jitter for visibility. Regression lines are fitted using generalized linear models, with a solid line indicating statistical significance (p = 0.003); 95% confidence intervals are shown in shaded areas.
Fig. 2
Fig. 2. Antimicrobial resistance in foodborne pathogens isolated from aquatic animals in Asia.
Gray bars represent 95% proportion confidence intervals. Resistance is shown for pathogen-drug combinations recommended for susceptibility testing (Supplementary Table S1) and with 10 or more isolates tested. (For drug acronyms, see Supplementary Note 2).
Fig. 3
Fig. 3. Geographic distribution of antimicrobial resistance in freshwater environments in Asia.
The proportion of antimicrobial compounds in each survey with resistance higher than 50% (P50) at continental scale (a); eastern Turkey (b); southern India (c); Yangtze River drainage basin in China (d); and the Mekong River delta (e).
Fig. 4
Fig. 4. Future survey locations prioritized to reduce uncertainty in antimicrobial resistance in freshwater environments in Asia.
The background color gradient (blue) represents weighted uncertainty in multi-drug resistance (see “Methods” section). An initial set of 50 future surveys optimized to reduce uncertainty in multi-drug resistance is displayed (red).
Fig. 5
Fig. 5. Antimicrobial resistance in marine environments in Asia.
Transparency reflects low survey density; areas of higher relative survey density are represented by increased opacity.

References

    1. The State of World Fisheries and Aquaculture 2020. 10.4060/ca9229en (FAO, 2020).
    1. Fisheries and aquaculture software. FishStatJ-Software for Fishery and Aquaculture Statistical Time Series. (2016).
    1. Van Boeckel TP, et al. Global trends in antimicrobial use in food animals. Proc. Natl Acad. Sci. USA. 2015;112:5649–5654. doi: 10.1073/pnas.1503141112. - DOI - PMC - PubMed
    1. Van Boeckel TP, et al. Reducing antimicrobial use in food animals. Science. 2017;357:1350–1352. doi: 10.1126/science.aao1495. - DOI - PMC - PubMed
    1. Schar D, Klein EY, Laxminarayan R, Gilbert M, Van Boeckel TP. Global trends in antimicrobial use in aquaculture. Sci. Rep. 2020;10:21878. doi: 10.1038/s41598-020-78849-3. - DOI - PMC - PubMed

Publication types

MeSH terms

Substances