Trend Analysis

CODA -CERVA Centrum voor Onderzoek in Diergeneeskunde en Agrochemie Centre de Recherches et d Etudes Vétérinaires et Agrochimiques Antimicrobial Resistance in commensal Escherichia coli from livestock in Belgium: Trend Analysis 2011-2016 Unit Epidemiology and Risk Assessment (ERA-SURV) Context This report summarises the results of the trend analysis of the data related to antimicrobial resistance in Escherichia coli (E. coli) during six consecutive years (2011-2016) regarding commensal intestinal flora of several livestock categories in Belgium: - Veal calves - Young beef cattle - Slaughter pigs - Broiler chickens Commensal E. coli is regarded as a general indicator for resistance amongst Gram-negative bacteria. It can be frequently isolated from all animal species and is therefore suitable for comparisons and surveillance programmes. During sampling, faecal material was taken at the slaughterhouse or directly at the farms depending on the animal category. E. coli were isolated and thereafter tested for their susceptibility to a panel of several antimicrobial substances. The objectives of this study were two-fold: - To provide a trend analysis of the prevalence of resistant strains over the six consecutive years. The results were compared for the six years and then analysed to check whether the observed trends (increase or decrease) were statistically significant. - To evaluate the level of multi-resistance and its trend over the same period: using the same data, we calculated for each animal category the proportion of multiresistant strains (i.e. resistance to more than two antimicrobials (= at least three) by the same strain) and checked whether there was a significant trend. 1

I. Material and Methods A. Sampling Samples of fresh faeces were collected each year by agents of the Federal Agency for the Safety of the Food Chain (FASFC) according to standardized technical sampling instructions (PRI codes) as part of a nationwide surveillance programme. Samples were taken from the following categories of food-producing animals: - Veal calves: young cattle kept in specialized units for fattening and slaughtered at an average age of 8 months. In 2011, faecal samples were taken on the floor at the farm level (PRI-516: 10 animals/farm of 7 months or younger), while in 2012, 2013, 2014, 2015, 2016 the samples were taken directly from the rectum of the animals at the slaughterhouse (PRI-036: 1 animal sampled/farm) - Beef Cattle (meat production): young animals (7 months or younger) from farms raising beef cattle for meat production. Faecal samples were taken from the floor at the farm (PRI-515:1 sample consisted of a pool of faeces collected from different spots on the floor representing 10 animals). - Broiler chickens: samples were taken at the slaughter house (PRI-019: pools of pairs of caeca from 10 chickens /batch) - Fattening pigs: faecal samples of fattening pigs older than 3 months were taken from the rectum at the slaughterhouse (PRI-035: 1 animal /origin farm). Following EFSA recommendations and in order to allow resistance trends to be detected with an acceptable confidence and precision (http://www.efsa.europa.eu/en/scdocs/doc/141r.pdf), the target sample size for each animal category was fixed to 170 isolates. In order to improve representativeness, the sampling was stratified by province proportionally to the number of registered herds or slaughterhouses. B. Isolation of the strains and antimicrobial susceptibility testing Isolates of E. coli strains were obtained from the faecal samples at the laboratories of the two animal health associations ARSIA and DGZ. The isolation methods are described in the annual reports of CODA-CERVA on antimicrobial resistance in E. Coli (http://www.codacerva.be/images/pdf/report%20commensal%20e.%20coli%202014.pdf) and were performed according to the standard operating procedures (SOP). The isolates were sent to the National Reference Laboratory (CODA-CERVA) for confirmation of identification and antimicrobial susceptibility testing. Susceptibility was tested by a micro-dilution technique (Trek Diagnostics) as it is described in the annual reports. The antimicrobials which were followed up in 2011-2015 and 2016 and those common to the six years (2011-2016) are all presented in Table A. For each strain and each antimicrobial substance, the Minimal Inhibitory Concentration (MIC) was recorded: MIC is defined as the lowest concentration by which no visible growth could be detected. MICs were semi-automatically recorded and stored in a database (Annex 1). 2

Table A. Panel of antimicrobials tested during 2011-2016 for E. coli (in blue: antimicrobials only tested in 2011-2013; in green: only tested in 2014-2016; in black: tested during the 6 consecutive years) Symbol AMP AZI CHL CIP COL FFN FOT GEN KAN MERO NAL SMX STR TAZ TGC TET TMP Antimicrobial Ampicillin Azithromycin Chloramphenicol Ciprofloxacin Colistin Florphenicol Cefotaxime Gentamicin Kanamycin Meropenem Nalidixic acid Sulphamethoxazole Streptomycin Ceftazidime Tigecycline Tetracycline Trimethoprim C. Data The datasets for 2011-2016 were formatted in Excel files by the Department of Bacteriology of CODA-CERVA and validated by the FASFC. They included identification of the samples corresponding to each isolate recorded in the LIMS merged with the corresponding MIC value for each tested antibiotic. After several steps of cross-checking and cleaning of the data, six yearly distinct data sets were produced, imported and analysed in SAS 9.2 software and R freeware. Emphasis was put on verifying that the animal category of the sample was correct. If it could not be confirmed, the sample was excluded. The final annual datasets contained the following fields: i. isolate identification number, ii. animal category, iii. sampling date and iiii. MIC values for each of the tested antimicrobials. D. Statistical Methods All subsequent statistics were carried out using SAS 9.2 software and R freeware. 1. Prevalence Quantitative MIC values were converted into binary qualitative values (Resistant/Susceptible) based on the susceptibility breakpoints defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). The ECOFFs (Epidemiological cut-offs values) were used in order to define strains as Resistant (R) or Susceptible (S) (Annex 1). 3

For each animal category and year, the proportion of resistant isolates (p) was calculated per tested antimicrobial (resistance prevalence), as well as the associated 95% confidence interval (CI). In order to avoid interval boundaries outside 0-1, which does not make sense for probabilities, CI were constructed for logit(p). 2. Trend Analysis The trends analysis aims at finding models to describe the variation of antimicrobial resistance over the years and to check if this variation is significant or not. Several statistical methods were used to model the probability of an isolate to be resistant: logistic regression models (in the univariate model each antimicrobial was considered separately), a linear Generalized Estimating Equations model (GEE) and a non-linear mixed model (both multivariate models; taking into account the possible correlation between antimicrobial substances in a single model; assuming an unstructured correlation matrix in the GEE). The models with the smallest Akaike information criterion (AIC) were selected and presented in the results. The results are described in the form of Odds Ratio (OR), where an OR > 1 means that the probability to be resistant increases with time. Plots representing the log odds for each year were also produced for each antimicrobial and animal category. The odds represent here the probability to be resistant on the probability to be susceptible. In this study, the effects of the different antimicrobials were assessed on an individual level. Hence, the 5% significance levels were specified for each antimicrobial separately. If the interest is in making a statement on the entire pool of antimicrobials jointly, a family wise significance level should be specified. In order to adjust the p-values and reduce the chances of obtaining false-positive results (type I errors; i.e. detection of a trend when in reality there is no trend) when several dependent or independent statistical tests are being performed simultaneously on a single data set, both the Bonferroni s correction method and the linear step-up method of Benjamini and Hochberg (1995) were applied to the GEE (linear multivariate model) and the resulting corrected p-values were produced and presented in annex for documentation. 3. Multi-resistance Multi-resistance was considered in this report as resistance by an isolate to at least three antimicrobials belonging to any three antimicrobial families as recommended by EFSA (2008, 2014). These antimicrobials were: ampicillin, cefotaxime and/or ceftazidime, chloramphenicol, ciprofloxacin and/or nalidixic acid, colistine, gentamycin, sulphonamides, tetracycline and trimethoprim. Thus, nine antimicrobials belonging to different classes were considered in this part of the analysis. Based on this, for each animal category, the estimate for the prevalence of multi-resistant isolates was calculated together with the 95% CI, calculated using normal distribution. 4

In addition, logistic regression models were used to check whether there was a significant trend over the years regarding the prevalence of multi-resistant strains, for each animal category. In addition, a diversity index was calculated for multi-resistance: Diversity index: Weighted entropy This indice is calculated to describe the degree of diversity of multi-resistance for a specific year and a specific animal category. The weighted entropy index takes into account order and will take higher values when multi-resistance is more frequent for large number of antimicrobials. Therefore, a higher weighted entropy index reflects a shift to multi-resistance to a greater number of antibiotics. This latter index was calculated using R software based on the formula of Guiasu (1971). II. Results A. Prevalence The following table summarizes the data obtained in 2011, 2012, 2013, 2014, 2015 and 2016 regarding prevalence of resistant isolates for each animal category and each tested antimicrobial substance: - N = number of tested samples - Prevalence of resistant isolates and confidence intervals 5

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Resistance (%) CODA-CERVA B. Trend Analysis N.B: Detailed outputs of the multiple comparisons corrections are presented in Annex 2. N.B.2: In this report the adjective high was used in case of a prevalence of resistant strains higher than 50%. However, the significance of a given level of resistance will depend on the particular antimicrobial and its importance in human and veterinary medicine. a) Veal Calves: (N= 34 (2011); 181 (2012); 202 (2013); 188 (2014); 196 (2015);174 (216) As shown in figure 1, high levels of resistance (>50%) were observed for the six consecutive years for TET, SMX, AMP. Resistance was > 40% for the six consecutive years for TMP. 100 Resistance strains prevalence Veal calves - E. coli 90 80 70 60 50 40 30 20 10 0 2011 2012 2013 2014 2015 2016 AMP CHL CIP COL FOT GEN NAL SMX TAZ TET TMP figure 1 Based on the results of the linear multivariate model (GEE), statistically significant decrease of resistance over time was observed for all tested substances except for FOT (figure 2). 7

Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper CODA-CERVA figure 2 The detailed odds ratios obtained from the non-linear multivariate model are shown in table 1 and the log odds of the logistic regression are plotted in figure 3. We notice that a constant significant decrease that began in 2012 but slowed down and stopped for all substances in 2016. table 1 (OR1= year 2012 vs 2011: OR2= year 2013 vs 2012;OR3= year 2014 vs 2013; OR4= year 2015 vs 2014; OR5= year 2015 vs 2016) Substance OR1 OR2 OR3 OR4 OR5 AMP 0,66 0,49 0,84 0,72 0,57 0,87 0,80 0,72 0,89 0,90 0,79 1,00 1,00 0,77 1,23 CHL 0,65 0,48 0,81 0,70 0,56 0,84 0,79 0,71 0,87 0,89 0,77 1,01 1,00 0,75 1,25 CIP 0,64 0,47 0,80 0,69 0,55 0,82 0,76 0,68 0,84 0,84 0,72 0,96 0,93 0,69 1,18 COL 0,57 0,30 0,83 0,60 0,38 0,81 0,65 0,50 0,80 0,71 0,43 0,98 0,77 0,26 1,28 FOT 0,58 0,33 0,82 0,64 0,39 0,89 0,78 0,62 0,94 0,95 0,65 1,26 1,16 0,49 1,83 GEN 0,69 0,38 1,00 0,74 0,50 0,98 0,81 0,67 0,95 0,89 0,66 1,11 0,97 0,52 1,42 NAL 0,66 0,48 0,84 0,70 0,56 0,84 0,77 0,69 0,85 0,84 0,72 0,96 0,92 0,67 1,16 SMX 0,60 0,08 0,75 0,66 0,07 0,80 0,76 0,04 0,85 0,89 0,06 1,00 1,03 0,13 1,28 TAZ 0,59 0,14 0,87 0,64 0,12 0,88 0,72 0,08 0,88 0,82 0,14 1,10 0,93 0,29 1,49 TET 0,72 0,16 1,04 0,76 0,09 0,93 0,83 0,05 0,93 0,91 0,06 1,03 1,00 0,13 1,25 TMP 0,63 0,09 0,80 0,67 0,07 0,80 0,72 0,04 0,79 0,77 0,05 0,87 0,83 0,10 1,03 8

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figure 3 (year0: 0=2011; 1=2012; 2=2013; 3=2014; 4=2015 5=2016) b) Beef cattle: N= 154 (2011) ; 175 (2012) ; 204 (2013); 164 (2014); 180 (2015); 176 (2016)) Globally, significantly lower prevalences of resistance were observed in E. coli from beef cattle compared to veal calves: resistance prevalences are smaller than 45 %. However, the highest resistance prevalences were observed against the same substances than for veal calves: AMP, SMX, TET and TMP (figure 4). 10

Resistance (%) CODA-CERVA 50 Resistance strains prevalence Beef cattle - E. coli 40 30 20 10 0 2011 2012 2013 2014 2015 2016 AMP CHL CIP COL FOT GEN NAL SMX TAZ TET TMP figure 4 Based on the results of the linear multivariate model (GEE), statistically significant decrease of resistance over time was observed for all tested substances except for COL and GEN (figure 5). 11

figure 5 As we can see from the results of the NL mixed model (table 2), a decreasing trend has started in 2012 for AMP. However, in 2016 all upper confidence s are close to 1. Results of the logistic regression are plotted in Figure 6. 12

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Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper CODA-CERVA figure 6 (where year0: 0=2011; 1=2012; 2=2013; 3=2014; 4=2015; 5=2016) table 2 (OR1= year 2012 vs 2011: OR2= year 2013 vs 2012, OR3= year 2014 vs 2013 and OR4= year 2015 vs 2014; OR5= 2016 vs 2015 ) Substance OR1 OR2 OR3 OR4 OR5 AMP 0,87 0,65 1,08 0,85 0,72 0,97 0,82 0,75 0,90 0,80 0,67 0,93 0,78 0,57 0,99 CHL 1,14 0,76 1,51 1,00 0,83 1,32 0,89 0,80 0,99 0,80 0,65 0,95 0,71 0,49 0,94 CIP 0,90 0,58 1,21 0,83 0,67 1,00 0,77 0,67 0,88 0,72 0,54 0,90 0,66 0,39 0,94 COL 1,17-0,24 2,59 0,85 0,39 1,31 0,63 0,29 0,97 0,47 0,00 0,94 0,35 0 0,90 FOT 0,94 0,40 1,47 0,85 0,59 1,11 0,77 0,59 0,94 0,70 0,41 0,98 0,63 0,21 1,05 GEN 1,79 0,58 2,99 1,35 0,88 1,81 1,07 0,86 1,28 0,85 0,60 1,11 0,68 0,33 1,03 NAL 0,83 0,54 1,11 0,79 0,63 0,94 0,75 0,64 0,85 0,71 0,52 0,89 0,67 0,38 0,96 SMX 0,92 0,71 1,12 0,90 0,78 1,02 0,89 0,82 0,96 0,88 0,76 1,00 0,87 0,67 1,07 TAZ 0,97 0,36 1,57 0,83 0,56 1,09 0,71 0,52 0,89 0,60 0,31 0,90 0,52 0,12 0,91 TET 0,89 0,55 1,23 0,92 0,79 1,05 0,89 0,81 0,97 0,86 0,73 0,99 0,83 0,61 1,04 TMP 0,97 0,70 1,25 0,89 0,75 1,02 0,81 0,73 0,89 0,74 0,61 0,87 0,68 0,47 0,88 14

Resistance (%) CODA-CERVA c) Broiler Chickens ( N= 420 (2011) ; 320 (2012) ; 234 (2013); 158 (2014); 152 (2015); 167 (2016)) A high prevalence of resistance (>50%) was observed for broiler chickens for the six consecutive years for several substances: AMP (the highest level all substances and species included), SMX, CIP, and TMP (figure 7). Prevalence for NAL was >60% for five consecutive years and was >40% in 2016. 100 90 80 70 60 50 40 30 20 10 Resistance strains prevalence Chickens - E.coli 0 2011 2012 2013 2014 2015 2016 AMP CHL CIP COL FOT GEN NAL SMX TAZ TET TMP figure 7 Based on the results of the linear multivariate model (GEE), statistically significant decrease of resistance over time was observed for all tested substances except for CIP, AMP (substance with high levels of resistance) and GEN (figure 8). 15

Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper CODA-CERVA figure 8 For CIP the NL mixed and the logistic regression models indicate a significant increasing trend followed by a significant decreasing trend starting in 2014. The odds ratio based on the NL mixed model are presented in table 3 and those based on the logistic regression are plotted in figure 9. Table 3 (OR1= year 2012 vs 2011; OR2= year 2013 vs 2012; OR3= year 2014 vs 2013; OR4= year 2015 vs 2014; OR5= year 2016 vs 2015) Substance OR1 OR2 OR3 OR4 OR5 AMP 0,78 0,63 0,92 0,85 0,74 0,95 0,94 0,87 1,02 1,04 0,89 1,20 1,16 0,87 1,44 CHL 1,22 0,98 1,47 1,03 0,93 1,14 0,88 0,82 0,95 0,76 0,65 0,86 0,65 0,50 0,80 CIP 1,51 1,19 1,83 1,15 1,04 1,26 0,91 0,85 0,97 0,72 0,64 0,81 0,57 0,46 0,69 COL 5,56 <0.001 188,07 0,34 <0.001 0,71 0,01 <0.001 0,04 0,00 0,00 0,00 0,00 0,00 0,00 FOT 0,80 0,62 0,97 0,77 0,68 0,86 0,74 0,66 0,82 0,72 0,57 0,86 0,69 0,47 0,91 GEN 1,35 0,78 1,93 1,15 0,91 1,40 1,00 0,85 1,15 0,87 0,63 1,11 0,76 0,42 1,09 NAL 1,36 1,08 1,64 1,06 0,96 1,17 0,86 0,80 0,91 0,69 0,61 0,77 0,56 0,45 0,67 SMX 0,80 0,67 0,94 0,84 0,76 0,93 0,89 0,83 0,96 0,95 0,83 1,07 1,00 0,80 1,21 TAZ 0,84 0,64 1,03 0,79 0,69 0,89 0,75 0,66 0,83 0,70 0,55 0,85 0,66 0,44 0,89 TET 0,79 0,60 0,97 0,83 0,75 0,91 0,85 0,79 0,90 0,86 0,76 0,96 0,87 0,70 1,04 TMP 0,88 0,74 1,02 0,89 0,81 0,97 0,90 0,84 0,96 0,91 0,81 1,02 0,92 0,74 1,10 16

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figure 9 (where year0: 0=2011; 1=2012; 2=2013; 3=2014; 4=2015; 5=2016) 18

Resistance (%) CODA-CERVA d) Pigs: (N= 157 (2011) ; 217 (2012) ; 206 (2013); 184 (2014); 186 (2015); 173 (2016)) The prevalence of resistance for SMX and TET was above 40% during the six consecutive years (figure 10). 70 Resistance strain prevalence Pigs - E. coli 60 50 40 30 20 10 0 2011 2012 2013 2014 2015 2016 AMP CHL CIP COL FOT GEN NAL SMX TAZ TET TMP figure 10 Based on the results of the linear multivariate model (GEE) (figure 11), a significant decrease of resistance over time was observed for SMX, TMP,TET, CIP,NAL. For COL and GEN, prevalences of these two substances are very low (<4%). 19

Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper Estimate Lower Upper CODA-CERVA figure 11 Based on the NL mixed model (table 4), only NAL has continued to significantly decreasing since 2012. Results of the logistic regression are plotted in Figure 12. Table 4 (OR1= year 2012 vs 2011; OR2= year 2013 vs 2012; OR3= year 2014 vs 2013; OR4= year 2015 vs 2014; OR5= year 2016 vs 2015) Substance OR1 OR2 OR3 OR4 OR5 AMP 0,82 0,67 0,98 0,88 0,77 0,98 0,94 0,87 1,01 1,01 0,89 1,13 1,08 0,86 1,30 CHL 0,99 0,75 1,23 0,96 0,83 1,10 0,94 0,86 1,01 0,91 0,78 1,03 0,88 0,67 1,09 CIP 0,54 0,42 0,65 0,59 0,47 0,71 0,73 0,62 0,83 0,89 0,65 1,14 1,10 0,60 1,60 COL 1,07-0,01 2,25 0,97 0,40 1,53 0,88 0,54 1,21 0,79 0,26 1,34 0,72-0,10 1,54 FOT 0,51 0,40 0,61 0,56 0,34 0,77 0,83 0,64 1,02 1,24 0,69 1,79 1,86 0,44 3,28 GEN 0,82 0,24 1,40 0,82 0,48 1,17 0,82 0,59 1,06 0,83 0,39 1,26 0,83 0,11 1,54 NAL 0,59 0,38 0,80 0,58 0,45 0,71 0,57 0,43 0,70 0,55 0,30 0,80 0,54 0,16 0,91 SMX 0,86 0,69 1,03 0,88 0,77 0,99 0,90 0,84 0,96 0,92 0,81 1,03 0,94 0,75 1,14 TAZ 0,53 0,38 0,69 0,60 0,39 0,82 0,81 0,63 0,99 1,09 0,62 1,57 1,47 0,39 2,55 TET 0,92 0,66 1,17 0,83 0,73 0,93 0,87 0,81 0,94 0,92 0,81 1,03 0,97 0,77 1,16 TMP 0,93 0,74 1,13 0,93 0,81 1,04 0,92 0,85 0,98 0,91 0,80 1,02 0,90 0,72 1,09 20

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Figure 12 (where year0: 0=2011; 1=2012; 2=2013; 3=2014; 4=2015; 5=2016) 22

2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016 Prevalence CODA-CERVA E. Multi-resistance Prevalence of multi-resistance The proportion of multi-resistant strains (= strains resistant to at least three antimicrobials) was very high for broiler chickens (>62%) and high for veal calves (>50%) during the six consecutive years (Table 5 and Figure 13). Figure 14 displays the distribution of multi-resistance patterns per animal category (i.e, number of isolates resistant to 0, 1.9 of the antimicrobial tested). Between 12%-26%, 48%- 71%, 5%-12%, 25%-41%, of, respectively, calves, cattle, chicken and pig isolates, were fully susceptible (=no resistance) to all tested antimicrobials. Table 5: proportion of multi-resistant strains (+95% CI) Veal calves Beef cattle Chickens Pigs 2011 70.59 (54.45-86.73) 24.68 (17.79-31.56) 77.86 (73.87-81.84) 53.50 (45.62-61.39) 2012 72.93 (66.39-79.46) 32.57 (25.56-39.58) 81.88 (77.63-86.12) 53.92 (47.23-60.6) 2013 66.83 (60.28-73.38) 23.04 (17.21-28.87) 76.92 (71.48-82.36) 48.54 (41.66-55.43) 2014 56.38 (49.23-63.54) 20.73 (14.46-27) 62.03 (54.37-69.68) 47.83 (40.54-55.11) 2015 51.02 (43.96-58.08) 16.67 (11.17-22.16) 70.39 (63.05-77.73) 36.56 (29.57-43.54) 2016 58.05 (50.64-65.45 15.91 (10.45-21.37) 68.86 (61.77-75.96) 45.09 (37.60-52.57) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% veal calves beef cattle chickens pigs figure 13 23

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Veal calves 2011 2012 2013 2014 2015 2016 9 8 7 6 5 4 3 2 1 0 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Beef cattle 2011 2012 2013 2014 2015 2016 8 7 6 5 4 3 2 1 0 24

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Chickens 2011 2012 2013 2014 2015 2016 9 8 7 6 5 4 3 2 1 0 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Pigs 2011 2012 2013 2014 2015 2016 8 7 6 5 4 3 2 1 0 figure 14 25

Table 6 and 7 present the OR (the ratio of the odds for a one unit increase in the time) for the probability to be multi-resistance obtained from the logistic models. For veal calves, the decrease becomes significant from 2012 onwards and one year later for pigs and broiler chickens (Table 7). In all cases, there was still a decreasing trend in 2014 which stopped in 2015 for veal calves and pigs. In 2016, the probability to be multi resistant is only significantly decreasing in beef cattle in which category a decreasing trend has been observed since 2014 but confident interval is close to 1. Table 6: OR and CI95% regarding to probability to be multi-resistant (logistic regression, 2011-2016) Species OR 95%CI veal calves 0.827 0.756-0.904 beef cattle 0.851 0.779-0.930 chickens 0.872 0.815-0.934 pigs 0.893 0.832-0.959 Table 7: OR and CI95% regarding to probability to be multi-resistant (logistic regression, year by year) Years compared veal calves beef cattle chicken pig 2012 vs 2011 0.62 (0.44-0.87) 0.94 (0.73-1.21) 0.86 (0.71-1.04) 0.85 (0.69-1.05) 2013 vs 2012 0.70 (0.57-0.86) 0.89 (0.77-1.03) 0.87 (0.78-0.97) 0.87 (0.77-0.98) 2014 vs 2013 0.79 (0.71-0.88) 0.85 (0.78-0.93) 0.87 (0.81-0.93) 0.89 (0.83-0.86) 2015 vs 2014 0.89 (0.79-1.00) 0.81 (0.69-0.94) 0.88 (0.78-0.99) 0.91 (0.81-1.03) 2016 vs 2015 1.00 (0.79-1.26) 0.76 (0.59-0.99) 0.89 (0.72-1.10) 0.94 (0.76-1.15) Indice of diversity: Weighted Entropy : As shown in table 8, the value of the indices decreased over time for all species. The index is globally lower for pigs compared with other species, meaning that for this species, multiresistance to a high number of antibiotics is less frequent than for the others. Table 8 year Veal calves Beef cattle Chickens Pigs 2011 0.68 0.52 0.64 0.48 2012 0.7 0.63 0.79 0.48 2013 0.63 0.55 0.62 0.4 2014 0.54 0.59 0.59 0.32 2015 0.54 0.48 0.57 0.33 2016 0.50 0.41 0.58 0.36 26

III. Discussion and conclusions To summarize the findings, data from six consecutive years (2011 to 2016) indicate an overall decreasing trend of E. coli antimicrobial resistance in Belgian production animals, especially since 2013 but in 2016 an increase of resistance prevalence is noticed, principally in pigs and chickens. Nevertheless, as described in this report, the increase of resistance prevalence is not significant. TET prevalence in calves increases the most in 2016 (+9% resistant E. coli strains). GEN is the only substance which prevalence decreases in 2016 in all animal categories. There is globally a high level of resistance to AMP, SMX, TET and TMP in all animal species, but to a lesser extent in beef cattle. The common patterns of resistance to AMP, SMX, TMP and TET and combinations thereof often feature as a component of multi-resistance patterns, and are probably related to the presence of class 1 or class 2 integrons, which generally carry genes conferring resistance to these antimicrobials (Marchant et al., 2013; EFSA and ECDC, 2015). Although other risk factors have been described, antimicrobial use is recognized as the main selector for antimicrobial resistance and a correlation with resistance was pointed out in Belgium (Cargnel et al., 2017). In Belgium, antimicrobial sales data for use in animals are being collected on an annual basis since 2009 (BelVet-SAC, 2016). In 2016, a decrease of 20,0% in the sales of antimicrobials has been observed since 2011. The AIC criteria of the non-linear multivariate model and from the logistic regression are similar (a little bit lower for the last one) and they can be considered for the final results. The levels of antimicrobial resistance are very high in veal calves for AMP, SMX and TET (more than 50% of isolates are resistant during the six consecutive years). Although the linear multivariate analysis highlight a significant decrease in resistance, except for FOT (non-significant but prevalence are extremely low), it cannot be affirmed by the non-linear analysis that the significant decreases observed for from 2012 to 2015, depending on the substance, continued afterward. In beef cattle, resistance prevalence is globally lower than in other species and a significant decrease of resistance for more than 80% substances since 2014 by both univariate and the NL mixed models is observed. Based on the logistic regression, resistance decreases in all substances since 2012, even not always at a significant level, it can be assessed that in 2016, the decrease is only significant for CHL and TMP. Chicken present substance with a high level a resistance (e.i. AMP, SMX, CIP, TMP are >50% resistance during the 6 years). An increasing trend which had been previously detected for in this species for CIP but no other increase in any substance was noticed after 2013. An increase of resistance prevalence was detected for 7/11 substances in 2016 compared to 2015, but was not statistically significant. Despite these decreasing trends of resistance in the previous year, in 2016, still more than 90% of E. coli chicken strains are resistant to at least one of the antimicrobials in the panel. The high resistance to quinolones 27

in chicken is especially worrisome because of a higher resistance percentage for ciprofloxacin compared to nalidixic acid, suggesting the presence of plasmid mediated quinolone resistance (Strahilevitz et al., 2009). The collection of antimicrobial use data in the entire poultry chain might expose why selection pressure on the intestinal flora in broiler chickens remains high. In pigs, except for SMX, for the second year antimicrobial resistance is lower than 50% for all tested substance but the prevalence increased in all substance except for GEN. Resistance in chickens and pigs should be monitored because the non-linear models reveals non-significant OR >1. The proportions of multi-resistant strains remained high during the six consecutive years, especially in chickens and veal calves (>65% and >55%, respectively). Moreover, in chickens, an increase was seen again in 2015, resulting in a borderline significant decreasing trend for the 5 consecutive years. E. coli strains from broiler chickens were also resistant to the highest number of antimicrobials (highest entropy), whereas E. coli from pigs had the lowest entropy. In a previous report a significant decreasing trend of the multiresistant strains of E. coli was found from 2012 onwards by linear and non-linear analysis. A decreasing trend is observed with the linear analysis but was borderline in 2015 compared to 2014 and is non-significant in 2016 compared to 2015 for calves, chicken and pig. Again, multi-resistance and entropy reveal the broiler chicken production as the animal production sector where additional measures should be taken in terms of the use of antimicrobials. Multi-resistance rapidly decreased since 2012 in veal calves, but increased in 2016 and is still resistant to a high number of different antimicrobial classes (entropy. E. coli from beef cattle remain the less multi-resistant for 6 consecutive years. 28

IV. References BelVet-SAC. Belgian Veterinary Surveillance of Antibacterial Consumption, 2016. National consumption report 2015. Available online: http://www.belvetsac.ugent.be/pages/home/ CARGNEL M., SARRAZIN S., CALLENS B., WATTIAU P., WELBY S., Assessing the possible association between veterinary antimicrobial consumption and resistance in indicator E. coli isolated from farm animals in Belgium. Poster presented at the SVEPM Conference & Annual General Meeting of the Society 2017, Inverness, Scotland. Available at: http://hdl.handle.net/2268/208958 EFSA and ECDC. European Food Safety Authority and European Centre for Disease Prevention and Control, 2015. EU Summary Report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals and food in 2013. EFSA Journal 2015;13(2):4036, 178 pp. Available at: www.efsa.europa.eu/efsajournal Marchant, M., Vinue, L., Torres, C., Moreno, M.A., 2013. Change of integrons over time in Escherichia coli isolates recovered from healthy pigs and chickens. Vet Microbiol 163, 124-132. Strahilevitz, J., Jacoby, G.A., Hooper, D.C., Robicsek, A., 2009. Plasmid-mediated quinolone resistance: a multifaceted threat. Clin Microbiol Rev 22, 664-689. 29

ANNEX 1: List of antimicrobials tested over 2011-2016 and Epidemiological cut-off values (ECOFF) Resistant strain if MIC value of the isolate > Cut-off Symbol Antimicrobial Cut-off value (mg/ml) AMP Ampicillin 8 CHL Chloramphenicol 16 CIP Ciprofloxacin 0,064 COL Colistin 2 FOT Cefotaxime 0,25 GEN Gentamicin 2 NAL Nalidixic acid 16 SMX Sulphonamide 64 TAZ Ceftazidime 0,5 TET Tetracycline 8 TMP Trimethoprim 2 Outputs of the univariate logistic regression model (2011-2016) The LOGISTIC Procedure SPECIES= calve year0 at substance=amp 0.837 0.766 0.915 year0 at substance=chl 0.815 0.742 0.895 year0 at substance=cip 0.778 0.706 0.858 year0 at substance=col 0.654 0.519 0.824 year0 at substance=fot 0.800 0.642 0.997 year0 at substance=gen 0.824 0.695 0.978 year0 at substance=nal 0.784 0.710 0.866 year0 at substance=smx 0.814 0.743 0.891 year0 at substance=taz 0.735 0.590 0.915 30

year0 at substance=tet 0.863 0.785 0.950 year0 at substance=tmp 0.737 0.674 0.806 The LOGISTIC Procedure SPECIES= cattle year0 at substance=amp 0.823 0.753 0.901 year0 at substance=chl 0.900 0.811 0.998 year0 at substance=cip 0.785 0.690 0.893 year0 at substance=col 0.748 0.519 1.076 year0 at substance=fot 0.793 0.647 0.970 year0 at substance=gen 1.043 0.880 1.236 year0 at substance=nal 0.760 0.665 0.869 year0 at substance=smx 0.893 0.825 0.967 year0 at substance=taz 0.745 0.601 0.925 year0 at substance=tet 0.889 0.814 0.971 year0 at substance=tmp 0.819 0.743 0.902 The LOGISTIC Procedure SPECIES= chicken 1.006 0.981 year0 at substance=amp 0.930 0.861 1.003 year0 at substance=chl 0.917 0.857 0.981 year0 at substance=cip 0.943 0.884 1.006 year0 at substance=col 0.740 0.547 1.001 31

1.006 0.981 year0 at substance=fot 0.757 0.689 0.832 year0 at substance=gen 1.027 0.898 1.174 year0 at substance=nal 0.885 0.831 0.942 year0 at substance=smx 0.888 0.831 0.949 year0 at substance=taz 0.766 0.695 0.844 year0 at substance=tet 0.843 0.793 0.897 year0 at substance=tmp 0.900 0.846 0.957 The LOGISTIC Procedure SPECIES= pig year0 at substance=amp 0.941 0.876 1.010 year0 at substance=chl 0.934 0.861 1.014 year0 at substance=cip 0.692 0.594 0.806 year0 at substance=col 0.930 0.657 1.315 year0 at substance=fot 0.791 0.608 1.030 year0 at substance=gen 0.820 0.625 1.077 year0 at substance=nal 0.571 0.470 0.693 year0 at substance=smx 0.900 0.838 0.967 year0 at substance=taz 0.770 0.599 0.990 year0 at substance=tet 0.876 0.815 0.941 year0 at substance=tmp 0.921 0.858 0.989 32

Outputs of the univariate logistic regression model, year by year The LOGISTIC Procedure SPECIES= calve AMP year0 at year0=0 0.649 0.723 0.907 year0 at year0=1 0.723 0.587 0.890 year0 at year0=2 0.804 0.724 0.893 year0 at year0=3 0.895 0.792 1.010 year0 at year0=4 0.996 0.788 1.257 year0 at year0=5 1.108 0.772 1.589 CHL year0 at year0=0 0.623 0.453 0.857 year0 at year0=1 0.701 0.577 0.852 year0 at year0=2 0.790 0.715 0.872 year0 at year0=3 0.889 0.778 1.016 year0 at year0=4 1.001 0.781 1.283 year0 at year0=5 1.127 0.774 1.641 CIP year0 at year0=0 0.614 0.852 0.852 year0 at year0=1 0.683 0.560 0.833 year0 at year0=2 0.759 0.685 0.841 year0 at year0=3 0.844 0.732 0.973 year0 at year0=4 0.939 0.722 1.220 year0 at year0=5 1.044 0.704 1.550 33

COL year0 at year0=0 0.547 0.291 1.027 year0 at year0=1 0.600 0.417 0.864 year0 at year0=2 0.659 0.528 0.821 year0 at year0=3 0.723 0.495 1.056 year0 at year0=4 0.794 0.415 1.516 year0 at year0=5 0.871 0.342 2.219 FOT year0 at year0=0 0.491 0.260 0.928 year0 at year0=1 0.617 0.423 0.901 year0 at year0=2 0.776 0.634 0.949 year0 at year0=3 0.975 0.713 1.334 year0 at year0=4 1.226 0.699 2.150 year0 at year0=5 1.540 0.670 3.542 GEN year0 at year0=0 0.669 0.392 1.141 year0 at year0=1 0.737 0.535 1.014 year0 at year0=2 0.811 0.685 0.960 year0 at year0=3 0.893 0.693 1.151 year0 at year0=4 0.983 0.622 1.553 year0 at year0=5 1.083 0.548 2.139 34

NAL year0 at year0=0 0.639 0.459 0.890 year0 at year0=1 0.701 0.573 0.856 year0 at year0=2 0.768 0.692 0.852 year0 at year0=3 0.842 0.728 0.974 year0 at year0=4 0.923 0.706 1.206 year0 at year0=5 1.012 0.677 1.513 SMX year0 at year0=0 0.572 0.402 0.812 year0 at year0=1 0.662 0.531 0.825 year0 at year0=2 0.767 0.686 0.857 year0 at year0=3 0.888 0.785 1.004 year0 at year0=4 1.028 0.810 1.305 year0 at year0=5 1.191 0.822 1.726 TAZ year0 at year0=0 0.559 0.298 1.048 year0 at year0=1 0.639 0.441 0.924 year0 at year0=2 0.730 0.594 0.897 year0 at year0=3 0.834 0.596 1.167 year0 at year0=4 0.954 0.529 1.719 year0 at year0=5 1.090 0.460 2.586 35

TET year0 at year0=0 0.699 0.488 1.002 year0 at year0=1 0.764 0.610 0.956 year0 at year0=2 0.834 0.744 0.934 year0 at year0=3 0.911 0.800 1.036 year0 at year0=4 0.995 0.775 1.276 year0 at year0=5 1.086 0.739 1.597 TMP year0 at year0=0 0.621 0.447 0.861 year0 at year0=1 0.668 0.545 0.818 year0 at year0=2 0.718 0.648 0.796 year0 at year0=3 0.773 0.684 0.873 year0 at year0=4 0.831 0.658 1.050 year0 at year0=5 0.895 0.625 1.281 The LOGISTIC Procedure species=cattle AMP year0 at year0=0 0.857 0.668 1.099 year0 at year0=1 0.839 0.727 0.968 year0 at year0=2 0.822 0.751 0.901 year0 at year0=3 0.806 0.687 0.945 year0 at year0=4 0.789 0.604 1.033 year0 at year0=5 0.774 0.526 1.137 36

CHL year0 at year0=0 1.116 0.826 1.507 year0 at year0=1 1.000 0.840 1.190 year0 at year0=2 0.895 0.803 0.998 year0 at year0=3 0.802 0.665 0.966 year0 at year0=4 0.718 0.524 0.984 year0 at year0=5 0.643 0.409 1.012 CIP year0 at year0=0 0.889 0.631 1.253 year0 at year0=1 0.831 0.685 1.008 year0 at year0=2 0.776 0.677 0.890 year0 at year0=3 0.725 0.565 0.929 year0 at year0=4 0.677 0.450 1.018 year0 at year0=5 0.632 0.355 1.125 COL year0 at year0=0 1.095 0.423 2.835 year0 at year0=1 0.897 0.520 1.485 year0 at year0=2 0.705 0.451 1.101 year0 at year0=3 0.566 0.249 1.287 year0 at year0=4 0.454 0.123 1.675 year0 at year0=5 0.364 0.059 2.233 37

FOT year0 at year0=0 0.922 0.540 1.573 year0 at year0=1 0.849 0.628 1.146 year0 at year0=2 0.781 0.628 0.972 year0 at year0=3 0.719 0.483 1.071 year0 at year0=4 0.662 0.346 1.267 year0 at year0=5 0.610 0.244 1.522 GEN year0 at year0=0 1.567 0.901 2.727 year0 at year0=1 1.289 0.926 1.796 year0 at year0=2 1.061 0.878 1.282 year0 at year0=3 0.873 0.653 1.166 year0 at year0=4 0.718 0.434 1.189 year0 at year0=5 0.591 0.282 1.238 NAL year0 at year0=0 0.839 0.593 0.189 year0 at year0=1 0.795 0.654 0.966 year0 at year0=2 0.752 0.652 0.868 year0 at year0=3 0.712 0.549 0.924 year0 at year0=4 0.674 0.441 1.031 year0 at year0=5 0.638 0.351 1.162 SMX 38

year0 at year0=0 0.920 0.733 1.155 year0 at year0=1 0.906 0.794 1.035 year0 at year0=2 0.893 0.825 0.967 year0 at year0=3 0.880 0.769 1.006 year0 at year0=4 0.867 0.689 1.090 year0 at year0=5 0.854 0.613 1.190 TAZ year0 at year0=0 0.962 0.551 1.681 year0 at year0=1 0.830 0.610 1.131 year0 at year0=2 0.717 0.557 0.922 year0 at year0=3 0.619 0.389 0.984 year0 at year0=4 0.534 0.254 1.121 year0 at year0=5 0.461 0.164 1.297 TET year0 at year0=0 0.948 0.737 0.219 year0 at year0=1 0.918 0.793 1.062 year0 at year0=2 0.889 0.813 0.971 year0 at year0=3 0.860 0.738 1.002 year0 at year0=4 0.833 0.642 1.079 year0 at year0=5 0.806 0.554 1.172 TMP 39

TMP year0 at year0=0 0.961 0.734 1.257 year0 at year0=1 0.883 0.757 1.029 year0 at year0=2 0.811 0.733 0.898 year0 at year0=3 0.746 0.623 0.892 year0 at year0=4 0.685 0.508 0.924 year0 at year0=5 0.630 0.411 0.964 The LOGISTIC Procedure species=chicken AMP year0 at year0=0 0.773 0.622 0.959 year0 at year0=1 0.853 0.755 0.964 year0 at year0=2 0.942 0.869 1.020 year0 at year0=3 1.039 0.898 1.203 year0 at year0=4 1.147 0.899 1.464 year0 at year0=5 1.266 0.894 1.794 CHL year0 at year0=0 1.201 1.002 1.439 year0 at year0=1 1.031 0.933 1.139 year0 at year0=2 0.885 0.822 0.953 year0 at year0=3 0.760 0.662 0.873 year0 at year0=4 0.653 0.521 0.181 40

CHL year0 at year0=5 0.560 0.408 0.770 CIP year0 at year0=0 1.446 1.209 1.730 year0 at year0=1 1.148 1.040 1.269 year0 at year0=2 0.912 0.855 0.972 year0 at year0=3 0.724 0.641 0.817 year0 at year0=4 0.575 0.469 0.705 year0 at year0=5 0.456 0.341 0.611 COL year0 at year0=0 8.425 2.253 31.503 year0 at year0=1 0.387 0.143 1.049 year0 at year0=2 0.018 0.001 0.222 year0 at year0=3 <0.001 <0.001 0.056 year0 at year0=4 <0.001 <0.001 0.015 year0 at year0=5 <0.001 <0.001 0.004 FOT year0 at year0=0 0.807 0.646 1.010 year0 at year0=1 0.776 0.687 0.876 year0 at year0=2 0.746 0.669 0.832 year0 at year0=3 0.717 0.584 0.880 41

FOT year0 at year0=4 0.689 0.499 0.952 year0 at year0=5 0.662 0.424 1.035 GEN year0 at year0=0 1.304 0.896 1.898 year0 at year0=1 1.146 0.927 1.415 year0 at year0=2 1.006 0.870 1.165 year0 at year0=3 0.884 0.677 1.154 year0 at year0=4 0.777 0.500 1.206 year0 at year0=5 0.682 0.365 1.273 NAL year0 at year0=0 1.318 1.106 1.571 year0 at year0=1 1.064 0.964 1.173 year0 at year0=2 0.858 0.805 0.914 year0 at year0=3 0.692 0.614 0.780 year0 at year0=4 0.559 0.457 0.683 year0 at year0=5 0.451 0.338 0.600 SMX year0 at year0=0 0.797 0.661 0.960 year0 at year0=1 0.844 0.760 0.938 year0 at year0=2 0.894 0.836 0.957 42

SMX year0 at year0=3 0.948 0.837 1.073 year0 at year0=4 1.004 0.816 1.237 year0 at year0=5 1.064 0.790 1.434 TAZ year0 at year0=0 0.837 0.664 1.056 year0 at year0=1 0.792 0.699 0.899 year0 at year0=2 0.750 0.669 0.841 year0 at year0=3 0.710 0.573 0.879 year0 at year0=4 0.672 0.479 0.941 year0 at year0=5 0.636 0.399 1.013 43

TET year0 at year0=0 0.821 0.757 0.974 year0 at year0=1 0.833 0.794 0.917 year0 at year0=2 0.845 0.794 0.900 year0 at year0=3 0.858 0.764 0.963 year0 at year0=4 0.870 0.717 1.057 year0 at year0=5 0.883 0.669 1.165 TMP year0 at year0=0 0.880 0.742 1.043 year0 at year0=1 0.891 0.810 0.980 year0 at year0=2 0.901 0.846 0.960 year0 at year0=3 0.912 0.812 1.025 year0 at year0=4 0.923 0.760 1.121 year0 at year0=5 0.934 0.708 1.233 The LOGISTIC Procedure species=pig AMP year0 at year0=0 0.814 0.659 1.004 year0 at year0=1 0.874 0.773 0.988 year0 at year0=2 0.939 0.873 1.008 year0 at year0=3 1.009 0.896 1.136 year0 at year0=4 1.084 0.883 1.331 year0 at year0=5 1.165 0.864 1.569 44

CHL year0 at year0=0 0.976 0.770 1.237 year0 at year0=1 0.955 0.832 1.096 year0 at year0=2 0.934 0.861 1.014 year0 at year0=3 0.914 0.795 1.050 year0 at year0=4 0.894 0.705 1.035 year0 at year0=5 0.875 0.619 1.235 CIP year0 at year0=0 0.491 0.340 0.708 year0 at year0=1 0.599 0.489 0.734 year0 at year0=2 0.732 0.633 0.845 year0 at year0=3 0.893 0.681 1.170 year0 at year0=4 1.090 0.698 1.701 year0 at year0=5 1.330 0.709 2.496 COL year0 at year0=0 1.156 0.423 3.159 year0 at year0=1 1.035 0.578 1.852 year0 at year0=2 0.927 0.645 1.331 year0 at year0=3 0.830 0.445 1.548 year0 at year0=4 0.743 0.259 2.133 year0 at year0=5 0.666 0.146 3.033 FOT 45

year0 at year0=0 0.380 0.194 0.746 year0 at year0=1 0.567 0.391 0.822 year0 at year0=2 0.845 0.679 1.051 year0 at year0=3 1.260 0.822 1.931 year0 at year0=4 1.879 0.900 3.923 year0 at year0=5 2.803 0.970 8.098 GEN year0 at year0=0 0.761 0.375 1.543 year0 at year0=1 0.795 0.533 1.184 year0 at year0=2 0.830 0.635 1.086 year0 at year0=3 0.867 0.532 1.416 year0 at year0=4 0.906 0.402 2.044 year0 at year0=5 0.947 0.298 3.011 NAL year0 at year0=0 0.565 0.371 0.860 year0 at year0=1 0.571 0.455 0.717 year0 at year0=2 0.578 0.459 0.729 year0 at year0=3 0.585 0.381 0.898 year0 at year0=4 0.592 0.305 1.150 year0 at year0=5 0.599 0.242 1.486 SMX 46

SMX year0 at year0=0 0.856 0.693 1.057 year0 at year0=1 0.877 0.775 0.993 year0 at year0=2 0.899 0.837 0.966 year0 at year0=3 0.922 0.820 1.037 year0 at year0=4 0.945 0.771 1.159 year0 at year0=5 0.969 0.720 1.305 TAZ year0 at year0=0 0.448 0.240 0.383 year0 at year0=1 0.606 0.428 0.857 year0 at year0=2 0.819 0.658 1.019 year0 at year0=3 1.107 0.729 1.679 year0 at year0=4 1.496 0.738 3.031 year0 at year0=5 2.021 0.735 5.558 TET year0 at year0=0 0.794 0.643 0.981 year0 at year0=1 0.833 0.736 0.943 year0 at year0=2 0.874 0.814 0.939 year0 at year0=3 0.918 0.815 1.033 year0 at year0=4 0.963 0.785 1.181 year0 at year0=5 1.010 0.750 1.361 TMP 47

year0 at year0=0 0.942 0.764 1.162 year0 at year0=1 0.932 0.825 1.053 year0 at year0=2 0.922 0.858 0.990 year0 at year0=3 0.911 0.810 1.026 year0 at year0=4 0.901 0.735 1.106 year0 at year0=5 0.891 0.662 1.200 ANNEX 2: GEE linear model with multiple comparisons corrections (p-values) CALVES Test probz Bonferroni Linear Stepup AMP 0.0003 0.0035 0.0005 CHL <0.0001 0.0008 0.0002 CIP <0.0001 <0.0001 <0.0001 COL 0.0002 0.0021 0.0004 FOT 0.0877 0.9643 0.0877 GEN 0.0283 0.3118 0.0312 NAL <0.0001 <0.0001 <0.0001 SMX <0.0001 0.0008 0.0002 TAZ 0.0129 0.1414 0.0157 TET 0.0064 0.0709 0.0089 TMP <0.0001 <0.0001 <0.0001 48

CATTLE Test probz Bonferroni Linear Stepup AMP <0.0001 0.0002 <0.0001 CHL 0.0364 0.4007 0.0445 CIP <0.0001 0.001 0.0003 COL 0.0515 0.5667 0.0567 FOT 0.0164 0.1808 0.0226 GEN 0.5232 1.0000 0.5232 NAL <0.0001 0.0002 <0.0001 SMX 0.0054 0.0597 0.0099 TAZ 0.0025 0.0270 0.0054 TET 0.0084 0.0924 0.0132 TMP <0.0001 0.003 <0.0001 CHICKEN Test probz Bonferroni Linear Stepup AMP 0.0636 0.7 0.0778 CHL 0.0101 0.1116 0.0140 CIP 0.1035 1.0000 0.1139 COL <0.0001 <0.0001 <0.0001 FOT <0.0001 <0.0001 <0.0001 GEN 0.5927 1.0000 0.5927 NAL 0.0004 0.0039 0.0008 SMX 0.0006 0.0069 0.0012 49

Test probz Bonferroni Linear Stepup TAZ <0.0001 <0.0001 <0.0001 TET <0.0001 <0.0001 <0.0001 TMP 0.0012 0.0137 0.002 PIG Test probz Bonferroni Linear Stepup AMP 0.0533 0.5865 0.0977 CHL 0.0752 0.8272 0.1182 CIP <0.0001 0.0004 0.0002 COL 0.7936 1.0000 0.7936 FOT 0.1870 1.000 0.2285 GEN 0.3135 1.0000 0.3449 NAL <0.0001 <0.0001 <0.0001 SMX 0.0022 0.0240 0.0060 TAZ 0.1086 1.000 0.1494 TET 0.0002 0.0019 0.0006 TMP 0.0144 0.1588 0.0318 50

Results of the univariate (logistic regression) and multivariate (GEE) analysis are summarized hereafter in a table using simple symbols in order to get an overall picture of the situation over the six consecutive years and to easily make comparisons between animal categories. All indicated trends (, ) were statistically significant (p = 0.05) both in univariate (logistic regression) and multivariate (GEE) analysis, even after using correction methods for multiple testing (Bonferroni and Linear step-up method), unless otherwise mentioned. Veal Calves Beef Cattle Chickens Pigs AMP ++ ++ CHL 1 CIP ++ COL 3 FOT 2 1 GEN 1 NAL SMX ++ 1 ++ TAZ 1 2 TET 1++ 1 TMP 1 ++ = High prevalence (> 50%) for the 6 consecutive years = decreasing trend of resistance detected* 1=Trend not significant after p value adjustment with Bonferroni method 2= Trend not significant in multivariate analysis (GEE) 3=Trend not significant in univariate analysis (logistic regression) but significant in multivariate analysis (GEE). *statistically significant trend (5% significance level) detected at least once during the 6 years 51