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. 2017 Jul 27;15(7):e04872.
doi: 10.2903/j.efsa.2017.4872. eCollection 2017 Jul.

ECDC/EFSA/EMA second joint report on the integrated analysis of the consumption of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from humans and food-producing animals: Joint Interagency Antimicrobial Consumption and Resistance Analysis (JIACRA) Report

European Centre for Disease Prevention and Control (ECDC); European Food Safety Authority (EFSA); European Medicines Agency (EMA)

ECDC/EFSA/EMA second joint report on the integrated analysis of the consumption of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from humans and food-producing animals: Joint Interagency Antimicrobial Consumption and Resistance Analysis (JIACRA) Report

European Centre for Disease Prevention and Control (ECDC) et al. EFSA J. .

Abstract

The second ECDC/EFSA/EMA joint report on the integrated analysis of antimicrobial consumption (AMC) and antimicrobial resistance (AMR) in bacteria from humans and food-producing animals addressed data obtained by the Agencies' EU-wide surveillance networks for 2013-2015. AMC in both sectors, expressed in mg/kg of estimated biomass, were compared at country and European level. Substantial variations between countries were observed in both sectors. Estimated data on AMC for pigs and poultry were used for the first time. Univariate and multivariate analyses were applied to study associations between AMC and AMR. In 2014, the average AMC was higher in animals (152 mg/kg) than in humans (124 mg/kg), but the opposite applied to the median AMC (67 and 118 mg/kg, respectively). In 18 of 28 countries, AMC was lower in animals than in humans. Univariate analysis showed statistically-significant (p < 0.05) associations between AMC and AMR for fluoroquinolones and Escherichia coli in both sectors, for 3rd- and 4th-generation cephalosporins and E. coli in humans, and tetracyclines and polymyxins and E. coli in animals. In humans, there was a statistically-significant association between AMC and AMR for carbapenems and polymyxins in Klebsiella pneumoniae. Consumption of macrolides in animals was significantly associated with macrolide resistance in Campylobacter coli in animals and humans. Multivariate analyses provided a unique approach to assess the contributions of AMC in humans and animals and AMR in bacteria from animals to AMR in bacteria from humans. Multivariate analyses demonstrated that 3rd- and 4th-generation cephalosporin and fluoroquinolone resistance in E. coli from humans was associated with corresponding AMC in humans, whereas resistance to fluoroquinolones in Salmonella spp. and Campylobacter spp. from humans was related to consumption of fluoroquinolones in animals. These results suggest that from a 'One-health' perspective, there is potential in both sectors to further develop prudent use of antimicrobials and thereby reduce AMR.

Keywords: antimicrobial consumption; antimicrobial resistance; ecological analysis; food‐producing animals; logistic regression; partial least square path modeling; public health.

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Figures

Figure 1
Figure 1
Available sets of data related to AMC and AMR in humans and food‐producing animals in the reporting countries and the possible relationships investigated in this report

  1. Note: The relationship between AMC in humans and AMR in food‐producing animals was not addressed in this report.

Figure 2
Figure 2
Comparison of clinical breakpoints for resistance (intermediate and resistant categories combined) and epidemiological cut‐off values used to interpret MIC data reported for Salmonella spp. from humans and food‐producing animals
Figure 3
Figure 3
Comparison of clinical breakpoints for resistance (intermediate and resistant categories combined) and epidemiological cut‐off values used to interpret MIC data reported for Campylobacter spp. from humans and food‐producing animals
Figure 4
Figure 4
Comparison of clinical breakpoints for resistance (intermediate and resistant categories combined) used for invasive E. coli from humans and epidemiological cut‐off values used to interpret MIC data reported for indicator E. coli from food‐producing animals
Figure 5
Figure 5
Diagram showing the initial model considered to assess the potential relationships between antimicrobial resistance in bacteria from humans (AMR human) and antimicrobial consumption in humans (AMC human), antimicrobial consumption in animals (AMC animal) (whether as direct or indirect influential factor), and antimicrobial resistance in bacteria in animals (AMR animal)
Figure 6
Figure 6
Comparison of biomass‐corrected consumption of antimicrobials (mg/kg of estimated biomass) in humans and food‐producing animals by country, EU/EEA MSs, 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector AMC of the 2014 total national AMC for EU/EEA MSs that provided data for both sectors is 10%.

  2. Note: 1) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials in humans and animals, please see Section 14.

  3. 2) The average figure represents the population‐weighted mean of data from included countries.

Figure 7
Figure 7
Comparison of consumption of selected antimicrobial classes in humans and food‐producing animals, EU/EEA MSs, 2014

  1. Notes: 1) The y‐axis scale differs between the graphs A, B and C.

  2. 2) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials in humans and animals, please see Section 14.

  3. 3) Classes not included for human medicine were monobactams (ATC group J01DF), other cephalosporins and penems (J01DI), streptogramins (J01 FG), glycopeptides, imidiazoles, nitrofurans, steroid antimicrobials and other antimicrobials (J01XX). Substances not included for food‐producing animals were bacitracin (ATCvet group QA07AA93 and QJ01XX10), paromomycin (QJ01GB92) and spectinomycin (QJ01XX04).

Figure 8
Figure 8
Biomass‐corrected consumption of 3rd‐ and 4th‐generation cephalosporins in humans and food‐producing animals by country, EU/EEA MSs, 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector from the 2014 total national consumption of antimicrobials for EU/EEA MSs that provided data for both sectors was 51.1%.

  2. 1) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials in humans and animals, please see Section 14.

  3. 2) The average figure represents the population‐weighted mean of data from included countries.

Figure 9
Figure 9
Logistic regression analysis curves of the total (community and hospital) consumption of 3rd‐ and 4th‐generation cephalosporins in humans, expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to 3rd‐generation cephalosporins in invasive E. coli from humans, EU/EEA, (1) 2013, (2) 2014 and (3) 2015 (see also Table 7)

  1. Dots represent countries included in the analysis.

Figure 10
Figure 10
Logistic regression analysis curves of the total (community and hospital) consumption of 3rd‐ and 4th‐generation cephalosporins in humans, expressed in DDD per 1,000 inhabitants, and per day and the probability of resistance to 3rd‐generation cephalosporins in S. Infantis isolates from humans, EU/EEA, (1) 2013 and (2) 2014 (see also Table 8)

  1. Dots represent countries included in the analysis.

Figure 11
Figure 11
Logistic regression analysis curves of the total (community and hospital) consumption of 3rd‐ and 4th‐generation cephalosporins in humans, expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to 3rd‐generation cephalosporins in S. Enteritidis from humans, EU/EEA, 2015 (see also Table 8)

  1. Dots represent countries included in the analysis.

Figure 12
Figure 12
Logistic regression analysis curves of the consumption of 3rd‐ and 4th‐generation cephalosporins in food‐producing food‐producing animals and the probability of resistance to cefotaxime in (1) indicator E. coli and (2) Salmonella spp. from food‐producing animals, 2014–2015 (see also Table 9)

  1. Dots represent the countries involved in the analysis. The category ‘food‐producing animals’ includes broilers, pigs and cattle for 2013, and broilers, turkeys, pigs and calves for 2014–2015. The scale used in these graphs is adapted according to the range of probabilities of resistance observed, in order to best show the distribution of data points.

Figure 13
Figure 13
Logistic regression analysis curves of the probability of resistance to 3rd‐generation cephalosporins in invasive E. coli from humans and the probability of resistance in indicator E. coli (SIMR) from food‐producing animals (combined data for 2014–2015) (see also Table 11)

  1. Dots represent countries included in the analysis. Note: The figure displays a non‐significant correlation. When two countries (outliers) were excluded from the analysis, a significant correlation was found.

Figure 14
Figure 14
PLS‐PM model of resistance to 3rd‐generation cephalosporins in human invasive E. coli (2014–2015) considering resistance to 3rd‐generation cephalosporins in indicator E. coli from animals (pigs in 2015 and poultry in 2014), consumption of 3rd‐ and 4th‐generation cephalosporins in humans (average in 2014–2015, expressed in DDD per 1,000 inhabitants and per day) and in animals (in pigs in 2015, expressed in DDDvet/kg of estimated biomass)

  1. 26 countries: AT*, BE, BG, CY, CZ*†, DE*, DK, EE, ES*†, FI, FR, HR, HU, IE, IT, LT †, LV, NL, NO, PL, PT, RO, SE, SI, SK †, UK (goodness‐of‐fit = 0.686).

    †For these countries, the AMC in pigs in 2014 was used as a surrogate of that for 2015 (missing data).

    *For these countries, the AMC at the hospital was estimated.

Figure 15
Figure 15
Population‐corrected consumption of fluoroquinolones and other quinolones in humans and food‐producing animals by country, EU/EEA MSs, 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector from the 2014 total national consumption of antimicrobials for EU/EEA MSs that provided data for both sectors was 14.0%.

  2. 1) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials in humans and animals, please see Section 14.

  3. 2) The average figure represents the population‐weighted mean of data from included countries.

Figure 16
Figure 16
Logistic regression analysis curves of the total (community and hospital) consumption of fluoroquinolones in humans, expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to fluoroquinolones in human invasive E. coli, EU/EEA, (1) 2013, (2) 2014 and (3) 2015 (see also Table 14)

  1. Dots represent countries included in the analysis.

Figure 17
Figure 17
Logistic regression analysis curves of the total (community and hospital) consumption of fluoroquinolones in humans, expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to fluoroquinolones in monophasic S. Typhimurium from humans, EU/EEA, 2014 (see also Table 15)

  1. Dots represent countries included in the analysis.

Figure 18
Figure 18
Logistic regression analysis curves of the consumption of fluoroquinolones and other quinolones in food‐producing animals and the probability of resistance to ciprofloxacin in (1) indicator E. coli and (2) Salmonella spp. from food‐producing animals, as well as in (3) C. jejuni from broilers and cattle and (4) C. coli from broilers and pigs, in 2013 (see also Table 17)

  1. Dots represent the countries involved in the analysis. The category ‘food‐producing animals’ includes cattle, broilers and pigs in 2013.

Figure 19
Figure 19
Logistic regression analysis curves of consumption of fluoroquinolones and other quinolones in food‐producing animals and the probability of resistance to ciprofloxacin in (1) indicator E. coli and (2) Salmonella spp. from food‐producing animals for 2014–2015 (see also Table 17)

  1. Dots represent the countries involved in the analysis. The category ‘food‐producing animals’ includes broilers, turkeys, pigs and calves for 2014–2015.

Figure 20
Figure 20
Logistic regression analysis curves of the estimated consumption of all quinolones in pigs and the probability of resistance to ciprofloxacin in indicator E. coli from slaughter pigs in 2013 (1) and 2015 (2), and of the estimated consumption of all quinolones in poultry and the probability of resistance to ciprofloxacin in indicator E. coli from poultry in 2014 (3), in Salmonella spp. from poultry in 2013 (4) and 2014 (5) and in Campylobacter jejuni from poultry in 2014 (6) (see also Table 18)

  1. Dots represent the countries involved in the analysis. The category ‘poultry’ includes broilers for 2013 and broilers and turkeys for 2014. The scale used in graphs (5) and (6) is adapted according to the range of probabilities of resistance observed, in order to best show the distribution of data points. In graph (6), the dashed curve means that the corresponding association is not significant, although it becomes significant while disregarding the three outlying dots in the upper left hand corner of the graph.

Figure 21
Figure 21
Logistic regression analysis curves of the probability of resistance to fluoroquinolones in E. coli from food‐producing animals and humans, 2013–2015 (see also Table 19)

  1. Dots represent countries included in the analysis.

Figure 22
Figure 22
Logistic regression analysis curves of the probability of resistance to fluoroquinolones in invasive E. coli from humans and the probability of resistance in indicator E. coli (SIMR) from food‐producing animals (combined data for 2014–2015) (see also Table 19)

  1. Dots represent countries included in the analysis.

Figure 23
Figure 23
Logistic regression analysis curves of the probability of resistance to fluoroquinolones in S. Infantis from food‐producing animals and humans, 2013 (see also Table 20)

  1. Dots represent countries included in the analysis.

Figure 24
Figure 24
Logistic regression analysis curves of the probability of resistance to fluoroquinolones in Campylobacter jejuni from food‐producing animals and humans, (1) 2013 and (2) 2014 (see also Table 21)

  1. Dots represent countries included in the analysis.

Figure 25
Figure 25
Logistic regression analysis curves of the probability of resistance to fluoroquinolones in Campylobacter coli from broilers and humans, 2013 (see also Table 21)

  1. Dots represent countries included in the analysis.

Figure 26
Figure 26
Diagram of the PLS‐PM of resistance to fluoroquinolones in human invasive E. coli (2014 and 2015) considering resistance to fluoroquinolones in indicator E. coli from animals (pigs 2015 and poultry 2014), consumption of fluoroquinolones and other quinolones in humans (2014–2015 average, expressed in DDD per 1,000 inhabitants and per day), in animals (pigs in 2015 and poultry in 2014, expressed in DDDvet/kg of estimated biomass)

  1. 26 countries: AT*, BE, BG, CY, CZ*†, DE*, DK, EE, ES*†, FI, FR, HR, HU, IE, IT, LT †, LV, NL, NO, PL, PT, RO, SE, SI, SK †, UK (Goodness‐of‐fit = 0.668).

    †For these countries, the estimated consumption in pigs in 2014 was used as a proxy for 2015 missing data.

    *For these countries, consumption in hospital was estimated.

Figure 27
Figure 27
Diagram of the PLS‐PM model of resistance to fluoroquinolones in Salmonella spp. from humans (in 2014 and 2015) considering resistance to fluoroquinolones in Salmonella spp. from animals (in pigs in 2015 and in poultry in 2014), consumption of fluoroquinolones and other quinolones in humans (average 2014–2015, expressed in DDD per 1,000 inhabitants and per day), in animals (in pigs in 2015 and in poultry in 2014, expressed in DDDvet/kg of estimated biomass)

  1. 10 countries involved: BE, DE*, DK, ES*†, FR, HU, PT, RO, SK †, UK (Goodness‐of‐fit = 0.627).

    †For these countries, the estimated consumption in pigs in 2014 was used as a proxy for 2015 missing data.

    *For these countries, consumption in hospital was estimated.

Figure 28
Figure 28
Diagram of the PLS‐PM model of resistance to fluoroquinolones in Campylobacter jejuni in humans (in 2014) considering resistance to fluoroquinolones in Campylobacter jejuni from animals (poultry in 2014), consumption of fluoroquinolones and other quinolones in humans (expressed in DDD per 1,000 inhabitants and per day in 2014 and 2015), in animals (poultry and pigs, 2014, expressed in DDDvet/kg of estimated biomass)

  1. 15 countries: AT*, CY, DK, ES*, FI, FR, IS, IT, LT, NL, PT, RO, SI, SK, UK (Goodness‐of‐fit = 0.617).

    *For these countries, consumption in hospital was estimated.

Figure 29
Figure 29
Population‐corrected consumption of polymyxins in humans and food‐producing animals by country in 28 EU/EEA MSs (A) and in humans only in 30 countries (B) in 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector from the 2014 total national consumption of antimicrobials for EU/EEA MSs that provided data for both sectors is 51.4%.

    1) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials by humans and animals, please see Section 14.

    2) The average figure represents the population‐weighted mean of data from included countries.

Figure 30
Figure 30
Logistic regression analysis curves of the total (community and hospital) consumption of polymyxins in humans expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to polymyxins in invasive K. pneumoniae from humans, EU/EEA, (1) 2014 and (2) 2015 (see also Table 23)

  1. Dots represent countries included in the analysis.

Figure 31
Figure 31
Logistic regression analysis curves of hospital consumption of polymyxins in humans expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to polymyxins in invasive K. pneumoniae from humans, EU/EEA, 2015 (see also Table 23)
Figure 32
Figure 32
Logistic regression analysis curves of the consumption of polymyxins in food‐producing animals and the probability of resistance to colistin in indicator E. coli from food‐producing animals for 2014–2015 (see also Table 24)

  1. Dots represent the countries involved in the analysis. The category ‘food‐producing animals’ include broilers, turkeys, pigs and calves for 2014–2015.

    The scale used in the graph is adapted according to the range of probabilities of resistance observed, in order to best show the distribution of data points.

Figure 33
Figure 33
Logistic regression analysis curves of the estimated consumption of polymyxins in pigs/poultry and the probability of resistance to colistin in indicator E. coli isolates from (1) poultry in 2014 and (2) from slaughter pigs in 2015 (see Table 25)

  1. Dots represent the countries involved in the analysis. The poultry category includes broilers and turkeys. The scale used in graph (2) was adapted according to the range of probabilities of resistance observed, in order to best show the distribution of data points.

Figure 34
Figure 34
Population‐corrected consumption of macrolides for humans and food‐producing animals by country, EU/EEA MSs, 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector from the 2014 total national consumption of antimicrobials for EU/EEA MSs that provided data for both sectors is 4.2%.

    1) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials in humans and animals, please see Section 14.

    2) The average figure represents the population‐weighted mean of data from included countries.

Figure 35
Figure 35
Logistic regression analysis curves of the consumption of macrolides in food‐producing animals and the probability of resistance to erythromycin in (1) C. coli from broilers and pigs and (2) C. jejuni from broilers and cattle in 2013 (see also Table 27)

  1. Dots represent the countries involved in the analysis.

    Note: 1) The scale used in graph (2) was adapted according to the range of probabilities of resistance observed, in order to best show the distribution of data points.

    2) In graph (2), the dashed curve means that the association is not significant.

Figure 36
Figure 36
Logistic regression analysis curves of the estimated consumption of macrolides in poultry and the probability of resistance to erythromycin in C. jejuni from broilers and turkeys in 2014 (see also Table 28)

  1. Dots represent the countries involved in the analysis.

Figure 37
Figure 37
Logistic regression analysis curves of the probability of resistance to macrolides in C. coli from food‐producing animals (broilers) and humans, 2013 (see also Table 29)

  1. Dots represent countries included in the analysis.

Figure 38
Figure 38
Logistic regression analysis curves of the consumption of macrolides in food‐producing animals and the probability of resistance to macrolides in C. coli and C. jejuni from humans, EU/EEA MSs, 2013–2015 (see also Table 30)

  1. Dots represent the countries involved in the analysis.

    The scale used in graphs (3), (4) and (5) is adapted according to the range of probabilities of resistance observed, in order to best show the distribution of data points.

Figure 39
Figure 39
Population‐corrected consumption of tetracyclines for humans and food‐producing animals by country, EU/EEA MSs, 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector from the 2014 total national consumption of antimicrobials for EU/EEA MSs that provided data for both sectors is 2.9%.

  2. Notes: 1) The estimates presented are crude and must be interpreted with caution. For limitations that hamper the comparison of consumption of antimicrobials in humans and animals, please see Section 14.

  3. 2) The average figure represents the population‐weighted mean of data from included countries.

Figure 40
Figure 40
Logistic regression analysis curves of the total (community and hospital) consumption of tetracyclines in humans expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to tetracyclines in S. Enteritidis isolates from humans, EU/EEA, 2013 and 2015 (see also Table 31)

  1. Dots represent countries included in the analysis.

Figure 41
Figure 41
Logistic regression analysis curves of the consumption of tetracyclines in food‐producing animals and the probability of resistance to tetracyclines in indicator E. coli from food‐producing animals in (1) 2013 and (2) 2014–2015, in Salmonella spp. from food‐producing animals in (3) 2013 and (4) 2014–2015, and in (5) C. jejuni from broilers and cattle in 2013 (see also Table 33)

  1. Dots represent the countries included in the analysis.

    Category ‘food‐producing animals’ includes broilers, cattle and pigs for 2013 and broilers, turkeys, pigs and calves for 2014–2015.

Figure 42
Figure 42
Logistic regression analysis curves of the estimated consumption of tetracyclines in pigs and the probability of resistance to tetracyclines in indicator E. coli from slaughter pigs in (1) 2013 and (2) 2015 (see also Table 34)

  1. Dots represent the countries included in the analysis.

Figure 43
Figure 43
Logistic regression analysis curves of the estimated consumption of tetracyclines in poultry and the probability of resistance to tetracyclines in indicator E. coli from poultry in 2014 (see also Table 34)

  1. Dots represent the countries included in the analysis.

Figure 44
Figure 44
Logistic regression analysis curves of the probability of resistance to tetracyclines in S. Enteritidis from broilers and humans, 2014 (see also Table 35)

  1. Dots represent countries included in the analysis.

Figure 45
Figure 45
Logistic regression analysis curves of the probability of resistance to tetracyclines in S. Infantis from broilers and humans, 2013–2014 (see also Table 35)

  1. Dots represent countries included in the analysis.

Figure 46
Figure 46
Logistic regression analysis curves of the probability of resistance to tetracyclines in Salmonella spp. from humans and the probability of resistance to tetracyclines in Salmonella spp. (SIMR) from food‐producing animals (combined data for 2014–2015) (see also Table 35)

  1. Dots represent countries included in the analysis.

Figure 47
Figure 47
Logistic regression analysis curves of the probability of resistance to tetracyclines in Campylobacter jejuni from broilers, turkeys and humans, 2013–2014 (see also Table 36)

  1. Dots represent countries included in the analysis.

Figure 48
Figure 48
Diagram of the PLS‐PM model of resistance to tetracyclines in Salmonella spp. in humans (in 2014 and 2015) considering resistance to tetracyclines in Salmonella spp. from animals (in pigs in 2015 and in poultry in 2014), consumption of tetracyclines in humans (average 2014–2015, expressed in DDD per 1,000 inhabitants and per day), in animals (in pigs in 2015 and in poultry in 2014, expressed in DDDvet/kg of estimated biomass)

  1. 11 countries involved: BE, CY, DE*, DK, ES*†, FR, HU, PT, RO, SK †, UK (goodness‐of‐fit =  0.736).

    †For these countries, estimated consumption in pigs in 2014 was used as a proxy for 2015 missing data.

    *For these countries, consumption in hospital was estimated.

Figure 49
Figure 49
Diagram of the PLS‐PM of resistance to tetracyclines in Campylobacter jejuni from humans (2014 and 2015) considering resistance to tetracyclines in C. jejuni from animals (poultry 2014), consumption of tetracyclines in humans (2014–2015 average, expressed in DDD per 1,000 inhabitants and per day), in animals (in pigs in 2014 and poultry in 2014 ‐ expressed in DDDvet/kg of estimated biomass)

  1. 14 countries: AT*, CY, DK, ES*, FI, FR, IT, LT, NL, PT, RO, SI, SK, UK.

    *For these countries consumption in hospital was estimated (goodness‐of‐fit = 0.689).

Figure 50
Figure 50
Consumption of carbapenems in humans (in the community and at the hospital) expressed in DDD per 1,000 inhabitants and per day, by country, EU/EEA, 2014

  1. Asterisk (*) denotes that only community consumption was provided for human medicine. The population‐weighted mean proportion (%) of the hospital sector from the 2014 total national consumption of antimicrobials for EU/EEA MSs that provided data for both sectors is 94.1%.

Figure 51
Figure 51
Logistic regression analysis curve of the total (community and hospital) consumption of carbapenems in humans, expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to carbapenems in invasive E. coli from humans, EU/EEA, 2014 (see also Table 38)
Figure 52
Figure 52
Logistic regression analysis curves of the total (community and hospital) consumption of carbapenems, expressed in DDD per 1,000 inhabitants and per day, and the probability of resistance to carbapenems in invasive K. pneumoniae from humans, EU/EEA, 2013–2015 (see also Table 39)

  1. Dots represent countries involved in the analysis.

Figure 53
Figure 53
Logistic regression analysis curves of the consumption and ‘corrected’ consumption of 3rd‐ and 4th‐generation cephalosporins in food‐producing animals and the corresponding probability of resistance to 3rd‐ and 4th‐generation cephalosporins in indicator E. coli from food‐producing animals for (1) 2013 and (2) 2014–2015 (see also Table 40)

  1. Dots represent the countries included in the analysis. Note: In the absence of 2013 resistance data, proxy data for years prior to 2013 may have been used. Regarding resistance data, food‐producing animals include broilers, pigs and cattle for 2013 and broilers, turkeys, pigs and veal calves for 2014–2015. In graphs (1.a). and (2.b.), the dashed curve means that the association is not significant.

Figure 54
Figure 54
Logistic regression analysis curves of the consumption and ‘corrected’ consumption of fluoroquinolones in food‐producing animals and the corresponding probability of resistance to fluoroquinolones in indicator E. coli from food‐producing animals for (1) 2013 and (2) 2014–2015 (see also Table 41)

  1. Dots represent the countries included in the analysis.

    Note: In the absence of 2013 resistance data, proxy data for years prior to 2013 may have been used. Regarding resistance data, food&–hyphen;producing animals include broilers, pigs and cattle for 2013 and broilers, turkeys, pigs and veal calves for 2014–2015.

Figure 55
Figure 55
Logistic regression analysis curves of the consumption and ‘corrected’ consumption of tetracyclines in food‐producing animals and the corresponding probability of resistance to tetracyclines in indicator E. coli isolates from food‐producing animals for (1) 2013 and (2) 2014–2015 (see also Table 42)

  1. Dots represent the countries included in the analysis.

    Note: In the absence of 2013 resistance data, proxy data for years prior to 2013 may have been used. Regarding resistance data, food‐producing animals include broilers, pigs and cattle for 2013 and broilers, turkeys, pigs and veal calves for 2014–2015.

Figure 56
Figure 56
Logistic regression analysis curves of the total national consumption of antimicrobials in food‐producing animals and (1) the probability of complete susceptibility to the harmonised set of substances tested in indicator E. coli isolates from food‐producing animals for 2013 and (2) the probability of complete susceptibility to the harmonised set of substances tested in indicator E. coli isolates from food‐producing animals for 2014–2015 (see also Table 43)

  1. Dots represent the countries included in the analysis.

    Regarding resistance data, the category ‘food‐producing animals’ includes broilers, pigs and cattle for 2013, and broilers, turkeys, pigs and veal calves for 2014–2015.

Figure B.1
Figure B.1
Example of calculation of the animal biomass ratio of each antimicrobial VMP presentation authorised for pigs
Figure B.2
Figure B.2
Calculation of weighted sales for an antimicrobial VMP presentation (X) authorised both for pigs and poultry
Figure B.3
Figure B.3
Indicator expressing exposure to an antimicrobial substance
Figure B.4
Figure B.4
Example on calculation of exposure of pigs to antimicrobial X for administration orally and by injection, respectively
Figure B.5
Figure B.5
Diagram of the PLS‐PM of resistance to 3rd‐ and 4th‐generation cephalosporins in human invasive E. coli (2014 and 2015) considering resistance to 3rd‐ and 4th‐generation cephalosporins in indicator E. coli from food‐producing animals (pigs and calves < 1 year in 2015 and poultry in 2014), consumption of 3rd‐ and 4th‐generation cephalosporins in humans (2014–2015 average, expressed in mg/kg of biomass), in animals (2014–2015 average, food‐producing animals, expressed in mg/kg PCU)

  1. 26 countries: AT, BE, BG, CY, CZ, DE, DK, EE, ES, FI, FR, HR, HU, IE, IT, LT, LV, NL, NO, PL, PT, RO, SE, SI, SK, UK.

Figure B.6
Figure B.6
Diagram of the PLS‐PM of resistance to fluoroquinolones in human invasive E. coli (2014 and 2015) considering resistance to fluoroquinolones in indicator E. coli from food‐producing animals (pigs and veal in 2015 and poultry in 2014), consumption of fluoroquinolones and other quinolones in humans (2014–2015 average, expressed in mg/kg of biomass), in animals (2014–2015 average, food‐producing animals, expressed in mg/kg PCU)

  1. 26 countries: AT, BE, BG, CY, CZ, DE, DK, EE, ES, FI, FR, HR, HU, IE, IT, LT, LV, NL, NO, PL, PT, RO, SE, SI, SK, UK.

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