Patterns of antimicrobial resistance in Salmonella isolates from fattening pigs in Spain

Abstract

Background: Swine are considered a major source of foodborne salmonellosis, a public health issue further complicated by the circulation of multidrug-resistant Salmonella strains that threaten the safety of the food chain. The current study aimed to identify patterns that can help to understand the epidemiology of antimicrobial resistance (AMR) in Salmonella in pigs in Spain through the application of several multivariate statistical methods to data from the AMR national surveillance programs from 2001 to 2017.

Results: A total of 1,318 pig Salmonella isolates belonging to 63 different serotypes were isolated and their AMR profiles were determined. Tetracycline resistance across provinces in Spain was the highest among all antimicrobials and ranged from 66.7% to 95.8%, followed by sulfamethoxazole resistance (range: 42.5% - 77.8%), streptomycin resistance (range: 45.7% - 76.7%), ampicillin resistance (range: 24.3% - 66.7%, with a lower percentage of resistance in the South-East of Spain), and chloramphenicol resistance (range: 8.5% - 41.1%). A significant increase in the percentage of resistant isolates to chloramphenicol, sulfamethoxazole, ampicillin and trimethoprim from 2013 to 2017 was observed. Bayesian network analysis showed the existence of dependencies between resistance to antimicrobials of the same but also different families, with chloramphenicol and sulfamethoxazole in the centre of the networks. In the networks, the conditional probability for an isolate susceptible to ciprofloxacin that was also susceptible to nalidixic acid was 0.999 but for an isolate resistant to ciprofloxacin that was also resistant to nalidixic acid was only 0.779. An isolate susceptible to florfenicol would be expected to be susceptible to chloramphenicol, whereas an isolate resistant to chloramphenicol had a conditional probability of being resistant to florfenicol at only 0.221. Hierarchical clustering further demonstrated the linkage between certain resistances (and serotypes). For example, a higher likelihood of multidrug-resistance in isolates belonging to 1,4,[5],12:i:- serotype was found, and in the cluster where all isolates were resistant to tetracycline, chloramphenicol and florfenicol, 86.9% (n = 53) of the isolates were Typhimurium.

Conclusion: Our study demonstrated the power of multivariate statistical methods in discovering trends and patterns of AMR and found the existence of serotype-specific AMR patterns for serotypes of public health concern in Salmonella isolates in pigs in Spain.

Keywords: 1,4,[5],12:i:-; Bayesian network analysis; Hierarchical clustering; Multidrug resistance; Multivariate analysis; Typhimurium.

Fig. 1

Venn diagrams illustrating the number of resistotypes in Salmonella isolates of serotypes Typhimurium, Rissen, Derby, and 1,4,[5],12:i:- from pigs recovered through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme. (a) contains 1,154 isolates with seven antimicrobial susceptibility results between 2001 and 2013 (D_am7); (b) contains 680 isolates with ten antimicrobial susceptibility results between 2008 and 2017 (D_am10)

Fig. 2

Spatial trends adjusted by empirical Bayesian smoothing in the proportion of Salmonella isolates from pigs resistant to twelve antimicrobials from 2001 to 2017, collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme. Provinces in grey indicate where there was no isolate TET tetracycline, CHL chloramphenicol, CIP ciprofloxacin, NAL nalidixic acid, GEN gentamicin, FFC florfenicol, CTX cefotaxime, SMX sulfamethoxazole, AMP ampicillin, TMP trimethoprim, CAZ ceftazidime, STR streptomycin

Fig. 3

Temporal trends in the percentage of resistant Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme towards seven antimicrobials from 2001 to 2013. Red dots are the observed percentage of resistant isolates; black lines are the fitted values of generalized estimating equation models using the binary results (i.e., resistant and susceptible) TET tetracycline, CHL chloramphenicol, CIP ciprofloxacin, NAL nalidixic acid, GEN gentamicin, FFC florfenicol, CTX cefotaxime

Fig.4

Temporal trends in the percentage of resistant Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme towards ten antimicrobials from 2008 to 2017. Red dots are the observed percentage of resistant isolates; black lines are the fitted values of generalized estimating equation models using the binary results (i.e., resistant and susceptible) TET tetracycline, CHL chloramphenicol, CIP ciprofloxacin, NAL nalidixic acid, GEN gentamicin, CTX cefotaxime, SMX sulfamethoxazole, AMP ampicillin, TMP trimethoprim, CAZ ceftazidime

Fig. 5

Estimated pairwise correlations obtained from the generalized estimating equations for the binary results (i.e., resistant and susceptible) of antimicrobial susceptibility testing for ten antimicrobials TET tetracycline, CHL chloramphenicol, CIP ciprofloxacin, NAL nalidixic acid, GEN gentamicin, CTX cefotaxime, SMX sulfamethoxazole, AMP ampicillin, TMP trimethoprim, CAZ ceftazidime

Fig. 6

Bayesian networks for (A) seven and (B) ten binary antimicrobial susceptibility testing results of Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2001 and 2013 for (A) and between 2008 and 2017 for (B). TET: tetracycline; CHL: chloramphenicol; CIP: ciprofloxacin; NAL: nalidixic acid; GEN: gentamicin; FFC: florfenicol; CTX: cefotaxime; SMX: sulfamethoxazole; AMP: ampicillin; TMP: trimethoprim; CAZ: ceftazidime; r: resistant; s: susceptible

Fig. 7

Hierarchical clusters using the binary antimicrobial susceptibility testing results of seven antimicrobials among 1,154 Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2001 and 2013 (left: the proportion of isolates resistant to seven antimicrobials; right: the composition of serotypes in each of the clusters). Cluster 0 shows the serotype distribution of all isolates in the dataset (D_am7). Only serotypes accounting for ≥5% of the isolates in each particular cluster are shown in the graph

Fig. 8

Spatial distribution of six clusters elicited from hierarchical clustering using binary antimicrobial susceptibility testing results of 836 Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2001 and 2013. Provinces in grey indicate where there was no isolate

Fig. 9

Temporal distribution of six clusters elicited from hierarchical clustering using binary antimicrobial susceptibility testing results of 836 Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2001 and 2013

Fig. 10

Hierarchical clusters using the binary antimicrobial susceptibility testing results of ten antimicrobials among 680 Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2011 and 2017 (left: the proportion of isolates resistant to ten antimicrobials; right: the composition of serotypes in each of the clusters). Cluster 0 shows the serotype distribution of all isolates in the dataset (D_am10). Only serotypes accounting for ≥5% of the isolates in each particular cluster are shown in the graph

Fig. 11

Spatial distribution of six clusters elicited from hierarchical clustering using binary antimicrobial susceptibility testing results of 399 Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2011 and 2017. Provinces in grey indicate where there was no isolate

Fig. 12

Temporal distribution of six clusters elicited from hierarchical clustering using binary antimicrobial susceptibility testing results of 399 Salmonella isolates from pigs collected through the Spanish Veterinary Antimicrobial Resistance Surveillance Network programme between 2011 and 2017