Antimicrobial Resistance in Aquaculture Environments: Unravelling the Complexity and Connectivity of the Underlying Societal Drivers

Abstract

Food production environments in low- and middle-income countries (LMICs) are recognized as posing significant and increasing risks to antimicrobial resistance (AMR), one of the greatest threats to global public health and food security systems. In order to maximize and expedite action in mitigating AMR, the World Bank and AMR Global Leaders Group have recommended that AMR is integrated into wider sustainable development strategies. Thus, there is an urgent need for tools to support decision makers in unravelling the complex social and environmental factors driving AMR in LMIC food-producing environments and in demonstrating meaningful connectivity with other sustainable development issues. Here, we applied the Driver-Pressure-State-Impact-Response (DPSIR) conceptual framework to an aquaculture case study site in rural Bangladesh, through the analysis of distinct social, microbiological, and metagenomic data sets. We show how the DPSIR framework supports the integration of these diverse data sets, first to systematically characterize the complex network of societal drivers of AMR in these environments and second to delineate the connectivity between AMR and wider sustainable development issues. Our study illustrates the complexity and challenges of addressing AMR in rural aquaculture environments and supports efforts to implement global policy aimed at mitigating AMR in aquaculture and other rural LMIC food-producing environments.

Keywords: Antimicrobial resistance; DPSIR; LMIC; antibiotic; aquaculture; environment; food production; framework.

Figure 1

Driver-Pressure-State-Impact-Response (DPSIR) conceptual framework. We have adapted this framework to be able to systematically structure the issue of AMR in a rural LMIC aquaculture environment. Image is adapted from the European Environment Agency website.

Figure 2

Microbiological data set showing resistance of bacteria isolated from diseased fish. Data collected by Quality Feed Ltd. Bacteria were isolated from diseased fish brought to the company’s laboratory for diagnosis between October 2019 and September 2020. Isolates were tested for resistance to these antibiotics, using the disc diffusion method, and classified as sensitive (green), intermediate susceptibility (yellow), or resistant (red), according to the criteria outlined in Materials and Methods. Bars show total number of isolates tested for each month. Testing for resistance to levofloxacin and neomycin did not begin until May 2020. All antibiotics tested were classified as either critically (∗∗) or highly (∗) important for human health, according to the World Health Organization’s List of Critically Important Antimicrobials for Human Medicine.

Figure 3

Metagenomic data set showing AMR genes detected in fish pond water samples. Between April 2017 and February 2018, pond water samples were collected from ponds at six farms, at four seasonal time points: (A) monsoon (Jul/Aug), (B) postmonsoon (Oct/Nov), (C) winter (Jan/Feb), and (D) premonsoon (Apr/May). All DNA was extracted from the samples and subjected to shotgun metagenomic sequencing. The resulting data sets were analyzed for the presence of AMR genes. Individual genes detected (shown on y-axis) are color coded according to drug class. To allow comparison with the microbiological data set in Figure 2, please note the following antibiotics and their classes: amoxicillin (class: penams), erythromycin (macrolides), trimethoprim (diaminopyrimidines), chlorotetracycline, doxycycline, oxytetracycline (tetracyclines), enrofloxacin, ciprofloxacin (fluoroquinolones), colistin (polymyxins (not detected), neomycin (aminoglycosides), sulfadiazine, and sulfamethoxazole (sulfonamides).

Figure 4

Presence of humans and animal pathogens. In our social data set, farmers were asked whether people (A) and animals (B) on their farm suffer from infectious diseases, as an indication of pathogen presence, and whether these diseases are treated with antibiotics.

Figure 5

DPSIR network mapping. The DPSIR results (Table 1) were used to generate a network map using Gephi software. Nodes are scaled according to number of connections in a directional flow from drivers to impact (Figure 1); i.e., larger nodes indicate greater contributions toward AMR. The impact (AMR) is colored in red, states in green, and pressures/drivers in blue, with darker blue indicating greater sizes of nodes. BMP: best management practices. WWT: wastewater treatment.