Acknowledgements

We would like to thank the following for their support during this project:

• Drs. Carl Ribble (University of Calgary), John Prescott (University of Guelph), Karen Liljebjelke (University of Calgary), Sylvia Checkley (University of Calgary), Tim McAllister (Agriculture and Agri-Food Canada), and Vic Gannon (Public Health Agency of Canada) for their feedback and guidance regarding antimicrobial resistance.
• Jay Son (Sunnybrook Research Institute), Julia Kupis (W21C, University of Calgary), and Zoe Thomson (W21C, University of Calgary) along with Drs. Caroline Ritter (University of Calgary), David Hall (University of Calgary), Jan Tomka (NAFC-RIAP Nitra), Karin Lienhard (W21C, University of Calgary), and Lis Alban (Danish Agriculture and Food Council) for their assistance with language translation of studies not available in English.
• Jill de Grood and the W21C Research and Innovation Centre for project coordination and oversight.
• Canadian Institutes of Health Research and Alberta Innovates – Health Solutions for their support through Dr. Karen Tang’s fellowship awards.

Abbreviations

AGISAR

Advisory Group on Integrated Surveillance of Antimicrobial Resistance

AGP

antimicrobial growth promoter

AMR

antimicrobial resistance

AMU

antimicrobial use

CI

confidence interval

CIA

Critically Important Antimicrobials

DALY

disability adjusted life years

DANMAP

Danish Integrated Antimicrobial Resistance Monitoring and Research Programme

FAO

Food and Agriculture Organization

GRADE

grading of recommendations assessment, development and evaluation

MARAN

Monitoring of Antimicrobial Resistance and Antibiotic Usage in Animals in the Netherlands

MeSH

medical subject heading

MIC

minimum inhibitory concentration

MRSA

methicillin resistant Staphylococcus aureus

N/A

not application

NR

not reported

OIE

World Organization of Animal Health

OR

odds ratio

PICOD

populations, intervention, control/comparator, outcome, design

PRISMA

preferred reporting items for systematic reviews and meta-analyses

RAB

reduced antibiotic farms

RAB-CD

reduced antibiotic with cleaning and disinfection protocol farms

RCT

randomized controlled trials

RD

risk difference

UK

United Kingdom

USA

United States

WHO

World Health Organization

WHO

FERG World Health Organization Foodborne Diseases Epidemiology Reference Group

Introduction

In this report, we present a systematic review on the effect of interventions that restrict the use of one or more antibiotics in food animals on the prevalence of antibiotic resistant bacteria in food animals and humans. This review has been commissioned to inform the development of a proposed WHO Guideline on the use of antibiotics in food animals, which may then serve to inform policy makers and regulatory officials on this important issue (1).

Antimicrobial agents are commonly used in livestock agriculture and aquaculture for therapeutic and non-therapeutic indications (2–4). These include treatment of active infections, prevention of infections in the absence of active disease, and for growth promotion. Though antimicrobial agents include drugs to treat viral, fungal, bacterial, and parasitic diseases, we have been asked to focus on antibiotics, or antibacterial drugs, for the purposes of this review.

As we gain more insight into the complexity of antibiotic resistance, there is recognition that anthropogenic activities and the wide use of antibiotics in human medicine and animal agriculture have contributed to the growing genetic pool of resistance genes found in bacteria isolated from humans, animals, and the environment (5–8). There is a growing concern that the routine use of antibiotics in food animals provides strong evolutionary pressure for potentially zoonotic bacteria to develop resistance to antibiotics commonly used to treat humans (2, 9, 10). Pathogenic (e.g., Salmonella spp., Campylobacter spp.) and commensal (e.g., Escherichia coli, Enterococcus spp.) bacteria, including those carrying resistance genes, can be transmitted from animals to humans through food, as well as by direct contact with animals, or through environmental sources such as contaminated water. As a result, national and international antimicrobial resistance (AMR) monitoring programs have been implemented to assess and monitor the extent of this problem (11–14). Many countries have also enforced a ban on the use of specified antimicrobial agents in the feed of food animals (15).

Recognizing that the use of antibiotics in food animals may lead to broader public health consequences, the WHO created the list of Critically Important Antimicrobials for human medicine (CIA) (10, 16). This list classifies antimicrobials by level of importance in human medicine; it serves to prioritize the preservation of the effectiveness of antibiotics that are most important in the treatment of human infections. There is a need for recommendations on the use and restrictions of antibiotics in food animals, which specifically considers the antibiotics in the WHO CIA list. This forms the basis of the proposed WHO Guideline, the development of which may be informed by this systematic review.

There is consensus that the use of antibiotics is prevalent in food animals and that this leads to the development of antibiotic resistance in these animals. Furthermore, food animals are a source of antibiotic resistant bacteria for humans, which may then cause significant disease, morbidity, and mortality. There is, however, not consensus regarding the level of impact that antibiotic use in food animals has on antibiotic resistance in the general human population. The issue of antibiotic resistance is highly complex with a number of different biological drivers involved in the selection and persistence of antibiotic resistance genes both in the natural environment and in the presence of antibiotic use (5, 17). In this systematic review and meta-analysis, we have been asked to address the following two research questions, as articulated by the WHO terms of reference (1):

Methods

f. GRADE tables

Judgments around the global quality of evidence required assessments of the validity of the results of individual studies for the outcomes of interest. Explicit criteria were used in making these judgments. Specifically, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group has developed a standardized and transparent methodology for assessing the global quality of evidence (28). This approach has been adopted by a number of agencies and decision-making groups, including the WHO.

The quality of the outcome measures was assessed by using a standard GRADE approach as described by Guyatt et al. (29). The GRADE evidence tables for the primary outcome (absolute risk difference in the prevalence of antibiotic resistance in bacteria isolated from intervention compared to control groups) were prepared for animal and human studies separately. For animal studies, although a number of stratifications was available for presentation, for brevity we limited our strata to those identified as highest priority/critically important antimicrobials as outlined by the WHO (30). As described in GRADE methodology, evidence derived from RCTs starts as high quality evidence and observational studies as low quality evidence supporting estimate of intervention effects (Table 1).

Table 1

Quality assessment criteria adapted from Guyatt (2011) ⁽²⁹⁾.

Five factors were assessed to potentially downgrade the evidence (risk of bias, inconsistency, indirectness, imprecision, and publication bias), while three factors were assessed to potentially upgrade the level of evidence (large effect, dose response, all plausible confounders or biases would result in an underestimate of the effect size). Overall, the quality of evidence for the primary outcome could fall into one of four categories including very low, low, moderate, and high. Using a consensus approach, all investigators were involved in the judgement of upgrading or downgrading the level of evidence and providing detailed reasons for doing so in the GRADE tables and accompanying notes.

Results

a. Identification of studies

The initial search strategy identified a total of 9,008 citations. An additional 56 were identified by contacting experts in the field of antibiotic use and resistance, and another 82 were identified through searching the reference lists of studies that were included in this systematic review. From these, 3,201 duplicates were removed, and 5,945 records were screened for eligibility through title and abstract review. After removal of the 5,559 records that were not relevant to either of the two research questions, 386 full-text articles were reviewed. Of these, a total of 181 studies addressed either PICOD 1 or PICOD 2 and were therefore included in the systematic review (15, 31–210). The most common reasons for exclusion of articles at the full-text review stage were the absence of interventions that aimed to reduce antibiotic use in animals and the absence of a comparator group (see Figure 1 for full PRISMA flow diagram).

Figure 1

Flow diagram of the study selection process.

Of the 181 studies, 160 reported on the outcome of antibiotic resistance in animals only, two reported on the outcome of antibiotic resistance in humans only, and 19 included both outcomes. That is, 179 studies addressed the first research question (PICOD 1) (15, 31–108, 110–161, 163–206–210), while 21 studies addressed the second research question (PICOD 2) (51, 62, 66, 72–74, 78, 90, 98, 100, 107, 109, 111, 112, 151, 162, 179, 181, 184, 196, 197). The results from the animal studies and human studies are reported separately. There were 19 studies that addressed both PICOD 1 and PICOD 2 and were included in both animal and human analyses.

b. Animal studies

i. Study characteristics

Of the 179 animal studies, 148 were articles in peer-reviewed journals, 20 were abstracts only without accompanying full-text articles, nine were dissertations, and two were government or organization reports. Poultry was the most commonly studied animal population, followed by swine and dairy cattle (see Table 2 for a summary of study characteristics and Table 3 for detailed characteristics of individual studies).

Table 2

Summary of study characteristics for animal studies.

Table 3

Study characteristics of individual animal studies.

Interventions were classified into four categories: 1) Externally imposed bans or restrictions of antibiotic use (n=36 studies); 2) Organic interventions, as defined by the study and the country-specific regulations for organic certification (n=87 studies); 3) Self-reported and self-labeling of antibiotic-free and related interventions, such as free-range or pasture (n=38 studies); and 4) Voluntary reduction or cessation of antibiotic use (n=29 studies). These intervention categories were not mutually exclusive. For example, studies that reported antibiotic resistance in antibiotic-free versus organic versus conventional meats were considered to include both “Organic” and “Antibiotic-Free” interventions.

A variety of samples were tested for antibiotic resistance across studies. Most commonly, studies used faecal or caecal samples (n=106 studies), followed by meat or carcass samples (n=53 studies). In addition, a wide variety of bacteria were studied including Campylobacter spp., Enterococcus spp., Enterobacteriaceae family of bacteria, and Staphylococcus spp. See Appendix 3 for flow charts illustrating the sample points, sample types, and bacteria studied for each of the four classes of interventions.

Studies were predominantly from the United States (n=81). A large number of studies were European in origin (n=78), with many of these comparing antibiotic resistance across different European countries. The countries in Europe with greatest representation were Denmark (n=21) and Spain (n=11). Few studies originated from Asia (n=6) and only one study was from Africa. Five studies did not specify the country of origin (Figure 2).

Figure 2

Geographic representation of countries* from which animal studies (human studies in parentheses) originate, with enlarged European insert. (The global map is currently being reproduced and to be uploaded in due course)

ii. Study quality

Study quality was assessed for all 179 studies that examine antibiotic resistance in animals (Table 4).

Table 4

Assessment of study quality for individual animal studies.

Table 5

Proportion of studies meeting study quality criteria.

An area of weaker study quality was the general lack of description of the study populations and management practices on farms and slaughterhouses. Without a clear description of sample characteristics, it was often difficult to assess whether intervention and control groups were comparable and whether they were representative of the source population. There also tended to be a lack of description of the interventions that aimed to reduce antibiotic use in animals, specifically in studies that sampled at the retail level (e.g., studies that compared antibiotic resistance in retail meats labeled as organic or antibiotic-free or other related labels versus conventional meats). For labels without rigorous requirements or certification such as for pasture-raised, free-range, or raised without antibiotics, the label itself, without a clear description of the underlying farm management practices, provides no detail regarding co-interventions and degree of antibiotic use, such as whether antibiotics are allowed therapeutically. For studies with organic interventions, many studies again tended to use the term “organic” to describe the intervention without any other detail, and despite rigorous processes for certification, this label alone is an inadequate description of the intervention, given that organic requirements are generally country or region-specific. Furthermore, few studies adjusted for potential confounders, despite the many variables that are likely to confound the complex association between antibiotic resistance and antibiotic use in animals. To address these study quality limitations, we conducted stratified analysis based on study quality criteria to determine whether there were any differences in pooled effect estimates for higher versus lower quality studies (see section “Stratified analysis: study quality criteria”).

iii. Synthesis of results

Of the 179 studies reporting antibiotic resistance in animals, 81 were included in meta-analyses. Because of the heterogeneity of the studies, with different bacteria, sample types (faeces versus carcass versus milk samples), and sample points (farm versus slaughter versus retail), we chose to conduct a series of meta-analyses rather than pooling all studies simultaneously in a single meta-analysis. We pooled the absolute risk differences in antibiotic resistance for individual antibiotic classes for: 1) Enterobacteriaceae in faecal samples; 2) Enterobacteriaceae in meat samples; 3) Enterococcus spp. in faecal samples; 4) Campylobacter spp. in faecal samples; 5) Campylobacter spp. in meat samples; and 6) Staphylococcus spp. in milk samples.

1. Enterobacteriaceae in faecal samples

The Enterobacteriaceae are a large family of bacteria, though all studies that were meta-analyzed reported antibiotic resistance for only three groups: 1) E. coli, 2) Salmonella spp., and 3) non-specified faecal coliforms. The most commonly studied bacterial species was E. coli. Depending on the antibiotic class, absolute risk differences in antibiotic resistance in Enterobacteriaceae were pooled for 16 to 21 studies (Table 6).

Table 6

Pooled absolute risk differences of antibiotic resistance for Enterobacteriaceae isolates in faecal samples.

All pooled estimates across antibiotic classes were less than zero, indicating that the pooled risk of antibiotic resistance in the intervention group was lower than that in the control group. These were statistically significant, with the upper limit of the 95% confidence interval not crossing 0, for all antibiotic classes except for cephalosporins and quinolones. The pooled absolute risk difference of antibiotic resistance was highest for tetracyclines at −0.16 (95% CI −0.27, −0.05). This corresponds to a 16% reduction in the percentage of isolates that are antibiotic resistant in the intervention group compared to the control group. The pooled absolute risk difference was lowest for cephalosporins and quinolones at −0.01 (95% CI −0.04, 0.01) and −0.01 (95% CI −0.02, −0.00) respectively, corresponding to a 1% reduction in the percentage of isolates that are antibiotic resistant in the intervention group compared to the control group for both antibiotic classes. See Appendix 4 Figures 1–7 for forest plots of absolute risk differences of antibiotic resistance for each of the seven classes of antibiotics. There was significant heterogeneity across studies, with an I² between 92.8% and 99.3% and Cochran Q test p-values <0.05 for all antibiotic classes for which meta-analysis was undertaken. Meta-analysis was not undertaken for the following antibiotic classes due to insufficient numbers of studies: aminocyclitols (2 studies), carbapenems (1 study), nitrofurantoins (2 studies), and polymyxins (4 studies).

Figure 1

Forest plot of absolute risk differences of antibiotic resistance to aminoglycosides for Enterobacteriaceae isolates in faecal samples.

Figure 7

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Enterobacteriaceae isolates in faecal samples.

A meta-analysis of 19 studies reporting multi-drug resistance to antibiotics was also conducted. The pooled absolute risk difference of multi-drug resistance was −0.24 (95% CI −0.32, −0.17). That is, the pooled proportion of isolates that were resistant to multiple antibiotics was 24% lower in intervention groups compared to control groups (Figure 3).

Figure 3

Forest plot of absolute risk differences of multi-drug resistance for Enterobacteriaceae isolates in faecal samples.

2. Enterobacteriaceae in meat samples

Similar to faecal samples, all studies of Enterobacteriaceae in meat samples studied E. coli, Salmonella spp., or unspecified coliforms. Depending on the antibiotic class, the absolute risk differences in antibiotic resistance in Enterobacteriaceae were pooled for 11 to 13 studies (Table 7).

Table 7

Pooled absolute risk differences of antibiotic resistance for Enterobacteriaceae isolates in meat samples.

All pooled estimates across antibiotic classes were less than zero, indicating that the pooled risk of antibiotic resistance in the intervention group was lower than that in the control group. These were statistically significant for all antibiotic classes except for cephalosporins (risk difference −0.07 [95% CI −0.14, 0.01]). The pooled absolute risk difference of antibiotic resistance was highest for sulfonamides at −0.23 (95% CI −0.32, −0.13). See Appendix 4 Figures 8–14 for forest plots of absolute risk differences of antibiotic resistance for each of the seven classes of antibiotics. There was significant heterogeneity across studies, with an I² between 82.3% and 97.9% and Cochran Q test p-value <0.05 for all studies for which meta-analysis was undertaken. Meta-analysis was not undertaken for the following antibiotic classes due to insufficient numbers of studies: carbapenems (1 study), cyclic esters (3 studies), macrolides (1 study), and nitrofurantoins (5 studies).

Figure 8

Forest plot of absolute risk differences of antibiotic resistance to aminoglycosides for Enterobacteriaceae isolates in meat samples.

Figure 14

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Enterobacteriaceae isolates in meat samples.

In addition, a meta-analysis of 14 studies reporting multi-drug resistance to antibiotics was conducted. The pooled absolute risk difference of multi-drug resistance was −0.32 (95% CI −0.43, −0.22). That is, the proportion of isolates that were resistant to multiple antibiotics was 32% lower in intervention groups compared to control groups (Figure 4).

Figure 4

Forest plot of absolute risk differences of multi-drug resistance for Enterobacteriaceae isolates in meat samples.

3. Enterococcus spp. in faecal samples

For Enterococcus spp. in faecal samples, depending on the antibiotic class, the absolute risk differences in antibiotic resistance were pooled for 7 to 12 studies (Table 8).

Table 8

Pooled absolute risk differences of antibiotic resistance for Enterococcus spp. isolates in faecal samples.

Similar to the faecal and meat samples for Enterobacteriaceae, all pooled estimates across antibiotic classes were less than zero, indicating that the pooled risk of antibiotic resistance in the intervention group was lower than that in the control group. These were statistically significant for all antibiotic classes. The pooled absolute risk difference of antibiotic resistance was highest for macrolides at −0.39 (95% CI −0.56, −0.23). It was the lowest for penicillins, though the absolute risk difference was still −10% for this antibiotic class (−0.10 [95% CI −0.18, −0.02]). See Appendix 4 Figures 15–20 for forest plots of absolute risk differences of antibiotic resistance for each of the six classes of antibiotics. There was significant heterogeneity across studies, with an I² between 96.4% and 98.8% and Cochran Q test p-value <0.05 for all studies for which meta-analysis was undertaken. No meta-analysis of multi-drug resistance to antibiotics was conducted due to insufficient numbers of studies reporting this outcome. Meta-analysis was also not undertaken for the following antibiotic classes due to insufficient numbers of studies: amphenicols (5 studies), carbapenems (1 study), cyclic esters (1 study), cyclic polypeptides (2 studies), glycylcyclines (1 study), lincosamides (3 studies), lipopeptides (1 study), nitrofurantoins (3 studies), oxazolidinones (2 studies), quinolones (3 studies), and rifamycins (1 study).

Figure 15

Forest plot of absolute risk differences in antibiotic resistance to aminoglycosides for Enterococcus spp. isolates in faecal samples.

Figure 20

Forest plot of absolute risk differences in antibiotic resistance to tetracyclines for Enterococcus spp. isolates in faecal samples.

4. Campylobacter spp. in faecal samples

Depending on the antibiotic class, the absolute risk differences in antibiotic resistance were pooled for 7 to 11 studies (Table 9).

Table 9

Pooled absolute risk differences of antibiotic resistance for Campylobacter spp. isolates in faecal samples.

Similar to the previous meta-analyses, nearly all pooled estimates across antibiotic classes were less than zero, indicating that the pooled risk of antibiotic resistance in the intervention group was lower than that in the control group. However, this was statistically significant only for macrolides (pooled absolute risk difference of −0.15 [95% CI −0.26, −0.04]) and tetracyclines (pooled absolute risk difference of −0.12 [95% CI −0.20, −0.03]). See Appendix 4 Figures 21–26 for forest plots of absolute risk differences of antibiotic resistance for each of the six classes of antibiotics. There was significant heterogeneity across studies, with an I² between 77.5% and 99.4% and Cochran Q test p-value <0.05 for all studies for which meta-analysis was undertaken. Meta-analysis was not undertaken for multi-drug antibiotic resistance or for resistance to the following antibiotic classes due to insufficient numbers of studies: carbapenems (1 study), cephalosporins (1 study), cyclic esters (1 study), lincosamides (3 studies), nitrofurantoins (1 study), oxazolidinones (1 study), and sulfonamides (3 studies).

Figure 21

Forest plot of absolute risk differences of antibiotic resistance to aminoglycosides for Campylobacter spp. isolates in faecal samples.

Figure 26

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Campylobacter spp. isolates in faecal samples.

5. Campylobacter spp. in meat samples

Meta-analyses were conducted only for three antibiotic classes, due to insufficient numbers of studies reporting resistance to the other antibiotic classes (aminoglycosides [5 studies], amphenicols [5 studies], penicillins [3 studies], and sulfonamides [1 study]). Seven studies reporting on antibiotic resistance to macrolides were pooled, nine were pooled for quinolones, and seven were pooled for tetracyclines. The pooled absolute risk difference for both macrolides and quinolones were less than 0, although neither was statistically significant (−0.04 [95% CI −0.17, 0.09], Appendix 4 Figure 27 for macrolides; −0.08 (95% CI −0.17, 0.01], Appendix 4 Figure 28 for quinolones). The pooled absolute risk difference for tetracyclines was approximately 0 (0.01 [95% CI −0.19, 0.21; Appendix 4 Figure 29) indicating that there was no difference in the pooled risk of antibiotic resistance in the intervention group compared to the control group for this antibiotic class. There was significant heterogeneity, with an I² between 94.1% and 97.2%. Cochran Q test p-values were all <0.05. No meta-analysis of multi-drug resistance to antibiotics was conducted due to insufficient numbers of studies reporting this outcome.

Figure 27

Forest plot of absolute risk differences of antibiotic resistance to macrolides for Campylobacter spp. isolates in meat samples.

Figure 28

Forest plot of absolute risk differences of antibiotic resistance to quinolones for Campylobacter spp. isolates in meat samples.

Figure 29

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Campylobacter spp. isolates in meat samples.

6. Staphylococcus spp. in milk samples

Depending on the antibiotic class, the absolute risk differences in antibiotic resistance were pooled for 6 to 10 studies (Table 10).

Table 10

Pooled absolute risk differences in antibiotic resistance for Staphylococcus spp. isolates in milk samples.

Similar to the meta-analyses for Enterobacteriaceae and Enterococcus spp., all pooled estimates across antibiotic classes were less than zero, indicating that the pooled risk of antibiotic resistance in the intervention group was lower than in the control group. This was statistically significant for all antibiotic groups except aminoglycosides. The pooled absolute risk difference of antibiotic resistance was highest for lincosamides at −0.09 (95% CI −0.16, −0.02). It was the lowest, and also not statistically significant, for aminoglycosides at −0.04 (95% CI −0.13, 0.05). See Appendix 4 Figures 30–35 for forest plots of absolute risk differences of antibiotic resistance for each of the six classes of antibiotics. For most antibiotic classes, there was significant heterogeneity across studies, with an I² between 78.6% and 91.3%. However, there was very little visual or statistical heterogeneity for the antibiotic class of sulfonamides only, with an I² of 0.0% and p-value for Cochran Q Test of 0.68. Meta-analysis was not undertaken for multi-drug antibiotic resistance or for resistance to the following antibiotic classes due to insufficient numbers of studies: amphenicols (4 studies), cephalosporins (4 studies), cyclic polypeptides (1 study), glycopeptides (3 studies), nitrofurantoins (1 study), oxazolidinones (1 study), pseudomonic acids (1 study), quinolones (5 studies), rifamycins (2 studies), steroid antibacterials (1 study), and streptogramins (2 studies).

Figure 30

Forest plot of absolute risk differences in antibiotic resistance to aminoglycosides for Staphylococcus spp. isolates in milk samples.

Figure 35

Forest plot of absolute risk differences in antibiotic resistance to tetracyclines for Staphylococcus spp. isolates in milk samples.

iv. Stratified analysis by intervention type

Due to the heterogeneity of interventions and results across studies, stratified analysis was performed by intervention type. This was done for Enterobacteriaceae in faecal samples (Appendix 4 Figures 36–42), Enterococcus spp. in faecal samples (Appendix 4 Figures 43–49), and Campylobacter spp. in faecal samples (Appendix 4 Figures 50–55). Overall, there were no clear patterns that emerged from these stratified meta-analyses. That is, absolute risk differences of antibiotic resistance were similar across all intervention types, with no one intervention being consistently associated with a higher or lower absolute risk difference when compared to the others. Of note, there were no external bans or regulation type interventions for studies reporting outcomes in Enterobacteriaceae in faecal samples. There were no voluntary restriction interventions for studies reporting outcomes in Enterococcus spp. or Campylobacter spp. in faecal samples.

Figure 36

Forest plot of absolute risk differences of antibiotic resistance to aminoglycosides for Enterobacteriaceae isolates in faecal samples, stratified by intervention.

Figure 42

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Enterobacteriaceae isolates in faecal samples, stratified by intervention type.

Figure 43

Forest plot of absolute risk differences of antibiotic resistance to aminoglycosides for Enterococcus spp. isolates in faecal samples, stratified by intervention.

Figure 49

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Enterococcus spp. isolates in faecal samples, stratified by intervention.

Figure 50

Forest plot of absolute risk differences of antibiotic resistance to aminoglycosides for Campylobacter spp. isolates in faecal samples, stratified by intervention.

Figure 55

Forest plot of absolute risk differences of antibiotic resistance to tetracyclines for Campylobacter spp. isolates in faecal samples, stratified by intervention.

Despite stratification by intervention type, visual and statistical heterogeneity remained high, indicating that intervention type alone did not explain the heterogeneity of results. Stratified analysis was not performed for Enterobacteriaceae or Campylobacter spp. in meat samples as these studies consisted primarily of a single intervention type (organic intervention). Similarly, all studies included in meta-analysis for Staphylococcus spp. in milk samples represented organic interventions, so stratified analysis could not be performed in this group either.

v. Stratified analysis by quality criteria

Of our component-based assessment of study quality (see “Study quality” below), the three criteria deemed most important were the following:

• Were animals in the intervention and comparison groups recruited from the same source population?
• Were animals included in the intervention and comparison groups recruited over the same period of time?
• Was there adequate adjustment of important potential confounders?

Due to the low numbers of studies meeting any of these three quality criteria, stratified meta-analysis within the six meta-analytic groups based on bacteria and sample type was not possible. We therefore performed a global meta-analysis for all animal studies that contained the necessary data for meta-analysis, using a pooled single effect estimate for each study (pooling within studies across all antibiotic classes, sample types, and bacterial groups). Stratification and meta-regression based on each of these three quality criteria, in addition to whether studies were published as full-text articles in peer-reviewed journals, was conducted to determine whether there was a difference in the pooled effect estimates between higher quality studies (i.e. studies meeting each of these quality criteria) and lower quality studies (Table 11).

Table 11

Stratified analysis and meta-regression of pooled absolute risk differences in overall antibiotic resistance.

Overall, meta-analyses of both higher quality and lower quality studies demonstrated statistically significant risk reductions in antibiotic resistance in intervention compared to control groups, although effect estimates appeared to be lower for higher quality compared to lower quality studies for the three quality criteria. On meta-regression, however, none of these differences were statistically significant. There did not appear to be any differences in the effect estimates when analysis was stratified for studies that were published as full-text articles in peer-reviewed journals compared to non peer-reviewed articles such as meeting abstracts and reports found in the grey literature.

vi. Publication bias

A funnel plot was produced, including all animal studies that were meta-analyzed. Visual inspection of the funnel plot did reveal visual asymmetry (Figure 5), although Begg’s test for funnel plot asymmetry was not statistically significant (p=0.16).

Figure 5

Funnel plot for animal studies included in meta-analysis.

The visual asymmetry of the funnel plot suggested the potential for publication bias, despite the non-significant results on statistical testing, and therefore, sensitivity analysis using the trim and fill method was performed. There was no change in the risk reduction of antibiotic resistance in animals with interventions that reduced antibiotic use with and without imputation (both −0.13 [95% CI −0.15, −0.11]), suggesting that publication bias, if present, likely had minimal effect on our findings. Of note, the pooled risk difference seen on the funnel plot is −0.03 rather than at −0.13 as the creation of the funnel plot required the use of a fixed effects model (which gives a pooled effect estimate of −0.03). We prefer the use of a random effects model (which gives a pooled effect estimate of −0.13), recognizing the heterogeneity across studies in terms of interventions, regions, bacterial species, and antibiotics tested.

There are other factors that can contribute to funnel plot asymmetry other than publication bias. These include heterogeneity, such as when the magnitude of effect differs based on sample sizes and when there are differences in baseline antibiotic risk across studies. Given the known clinical heterogeneity of studies, with variable sample sizes, animal and bacterial groups studied, samples studied, and countries studied (all leading to probable differences in baseline risk), the funnel plot asymmetry is therefore not surprising and may not be attributable to publication bias alone.

vii. Qualitative description of study results

We conducted a qualitative descriptive analysis of the 63 studies that examined phenotypic antibiotic resistance in animals, which could not be included into the series of meta-analyses. Table 12 summarizes the study results, where red represents higher, green indicates lower, and yellow indicates no difference in the prevalence of antibiotic resistance in the intervention group compared to the comparator group respectively.

Table 12

Trends in the prevalence of antibiotic resistance by bacterial class for studies not included in meta-analyses.

The results of this analysis very closely reflected the findings from the series of meta-analyses described previously. The summary of the trends found in these 63 studies was:

1. The majority of studies of antibiotic resistance in animals focused on Enterobacteriaceae.
2. For Enterobacteriaceae, Enterococcus spp., and Staphylococcus spp., the majority of studies reported a reduction in the absolute risk difference of antibiotic resistance with any intervention that aimed to reduce antibiotic use in animals, across all antibiotic classes and sample types. There were also a large number of studies that reported no statistically significant difference between intervention and control groups, and proportionately few studies that reported an increase of antibiotic resistance with interventions that aimed to reduce antibiotic use in animals.
3. Antibiotic resistance in Campylobacter spp. appeared to follow a different pattern compared to the other bacterial groups. Specifically, in most studies, no statistically significant difference in antibiotic resistance was detected in animals with interventions that reduced antibiotic use. There were only a small numbers of studies showing decreased antibiotic resistance with interventions that aimed to reduce antibiotic use in animals.

viii. Synthesis of results from studies reporting genetic elements

Fifty-four studies reported genotypic resistance results. See Table 13 for a description of the study characteristics, genes that were screened, and author conclusions on the genetic data for each of these studies.

Table 13

Study characteristics and conclusions from studies that report on genotypic resistance.

Of these 54 studies, 46 reported the prevalence of genetic resistance elements in intervention groups compared to control groups or provided data where this could be calculated (Table 14).

Table 14

Summary of results for the 46 studies reporting prevalence of resistance genes in intervention versus control groups.

These 46 studies reported changes in antibiotic resistance genes with interventions that reduce antibiotics in food animals, shedding light on the mechanisms of antibiotic resistance. The 17 animal studies that performed genetic screening for both phenotypically susceptible and resistant isolates reported a total of 264 associations; 58 of these demonstrated lower prevalence of resistance genes in intervention compared to control groups, while 201 demonstrated no statistically significant difference in prevalence of resistance genes between the groups. These results corroborate the findings from the meta-analyses and the qualitative description of phenotypic results; a considerable number of studies showed that interventions that reduce antibiotic use in food animals also reduced resistance genes, especially those genes that are associated with the restricted drug. For some genes such as bla_shv, tetB, tetQ, this association was supported by two or more studies. Almost no studies showed an increase in resistance genes with interventions that reduced antibiotic use in animals. For many genes, there was no statistical difference in the prevalence of resistance genes in the intervention group compared to the comparator group. The lack of a statistically significant difference does not necessarily indicate the lack of association between prevalence of resistance genes and interventions, as this may be an issue of insufficient power. Individual studies had small sample sizes overall, with some studies conducting genetic screening only on a subset of isolates. Studies tended to be underpowered to detect differences in the prevalence of resistant genes between groups. Non-statistically significant trends are not represented in the table above.

The majority of studies that screened for resistant genes only in phenotypically resistant isolates showed no difference in the prevalence of resistance genes between intervention and control groups, which was expected. These results are helpful in exploring the presence of resistance genes that drive phenotypic antibiotic resistance, but do not provide information regarding overall prevalence of resistance genes in intervention and control groups. That is, phenotypically resistant isolates would be expected to have genetic resistance elements regardless of whether an intervention that reduced antibiotic use was implemented. We have presented the data from these studies not to show differences in prevalence of resistance between intervention and control groups, but to provide a comprehensive description of genetic evidence in this research area. Studies rarely evaluated genes that were not related to the restricted antibiotic, so the complete effect of any single intervention across a wide range of resistance genes remains unclear.

ix. GRADE tables

An overall assessment of the strength of evidence for the effect of interventions that reduce antibiotic use in food animals on antibiotic resistance in this population was completed using the GRADE framework (Table 15).

Table 15

PICOD 1 GRADE assessment.

Because of the observational nature of studies, the quality rating started at “Low” before any other quality criteria were considered, based on the GRADE framework. There were limitations in risk of bias, particularly with the cross-sectional study designs and minimal adjustment for potential confounders. However, these limitations did not downgrade the quality rating due to the considerable volume of evidence and the consistency of findings across many different layers of stratification (across all bacterial groups, animal species, antibiotic classes, and sample types).

c. Human studies

i. Study characteristics

Twenty-one studies examined antimicrobial resistance in humans (see Table 16 for a summary of study characteristics and Table 17 for detailed characteristics for individual studies).

Table 16

Summary of study characteristics for human studies.

Table 17

Summary of the 21 studies examining antibiotic resistance in humans.

The majority of studies (n=12) were in farmers and employees of farms, along with their family members. Six studies examined antibiotic resistance from patient samples provided by laboratories, either in hospital or in outpatient facilities, while five studies were in healthy adults without disease. The most common intervention studied was externally imposed bans or restrictions of antibiotic use in animals (n=9); two studies included organic interventions, five included an antibiotic-free or a similar intervention, and five consisted of voluntary limitations. The most commonly studied bacteria were Staphylococcus spp. and Enterococcus spp. Most studies tested resistance to a single class of antibiotics, specifically to penicillins for Staphylococcus spp. and glycopeptides for Enterococcus spp.

ii. Study quality

Study quality was assessed for all 21 human studies (Tables 18 and 19).

Table 18

Assessment of study quality for individual human studies.

Table 19

Numbers of human studies meeting each study quality criterion.

The strengths and weaknesses of these studies were similar to the studies that examined antibiotic resistance in animals described previously. They tended to have well-described research objectives and outcome variables. The human studies did tend to have a better description of characteristics of the study sample, compared with animal studies, although only nine of 21 studies met this quality criterion. One of the most significant quality concerns was that study populations tended not to be representative of the general population with over half of studies being in farm workers. Study findings and conclusions that support a reduction of antibiotic resistance in farmers with interventions that reduce antibiotic use in animals may therefore not be generalizable to the general populations. Furthermore, the link between antibiotic use in animals and antibiotic resistance in humans is complex and the causal mechanisms are unclear and likely multi-faceted. Despite this complexity, only four studies were considered to have adequately adjusted for potential confounding variables. Other issues include the lack of information reported in the studies to demonstrate that intervention and control groups were comparable and low sample sizes, with one study including only a total of nine participants (151).

iii. Synthesis of results

There were two main groups of studies within the 21 included human studies: 1) studies examining antibiotic resistance in farmers or those with direct contact with livestock (n=12); and 2) studies examining antibiotic resistance patterns in those without direct contact with livestock animals (n=9). In the second group, the link between antibiotic use in animals and antibiotic resistance in humans was often indirect and implied. That is, authors tended to report antibiotic resistance trends in humans, using surveillance data, before and after a reduction in use or withdrawal of antibiotics in animals. Any changes to these trends were then attributed to changes in antibiotic use in animals, though many other factors may have contributed to these temporal changes and were often not considered. In both groups of studies, most, but not all, studies reported lower antibiotic resistance in the intervention versus the comparator group. Two Danish studies reported an increased prevalence of antibiotic resistance over time in the general human population, despite a restriction of antibiotic use in animals in this country (62, 179).

Few studies examined genetic elements of bacterial antibiotic resistance. In studies of farm workers, genetic data from two studies (51, 184) suggested that antibiotic resistance in bacteria isolated from these workers originated from animals. One study (162) suggested that methicillin-resistant S. aureus (MRSA) was livestock-associated on conventional farms, but that MRSA that was isolated on antibiotic-free farms did not have features of livestock association. This suggests that resistant S. aureus can be found on farms where there is no selective pressure from animal uses of antibiotics because of other non-animal sources of resistance. Only one study in the nine that examined antibiotic resistance trends in non-farm workers commented on the genetic origins of resistance. This study (100) indicated that the genetic and virulence factors of antibiotic resistant E. coli in humans more closely resembled E. coli isolates in animals compared to antibiotic susceptible isolates in humans.

Of the 21 studies that reported antibiotic resistance in humans and its association with interventions that reduced antibiotic use in animals, 13 were able to be meta-analyzed. All 13 studies showed either no difference or a lower risk of antibiotic resistance in the intervention group compared to the control group. Results from nine of the 13 studies were statistically significant, with absolute risk differences ranging from −9% to −85%. The pooled estimate of the absolute risk difference of antibiotic resistance, across all classes of antibiotics, was −0.24 (95% CI −0.42, −0.06). That is, the pooled prevalence of antibiotic resistant bacteria in humans was 24% lower in intervention groups where there was reduced used of antibiotics in animals, compared to control groups (Figure 6).

Figure 6

Pooled absolute risk differences of antibiotic resistance in humans.

The study by Harper et al. (90) was the only abstract included in the meta-analysis, whereas the other 12 were full-text articles published in peer-reviewed journals. It did also have significant limitations in quality, but whether these limitations were due to restrictions in reporting (as it was a meeting abstract) versus actual limitations in study validity are unclear. We performed a sensitivity analysis, removing the study by Harper et al. from the meta-analysis. This resulted in only a very slight decrease to the pooled absolute risk difference (to −0.21 [95% CI −0.40, −0.03]).

Similar to the animal studies, a high degree of heterogeneity was evident, with an I² value of 97.4% and a p-value for the Cochran Q test <0.05. Stratified analysis by intervention group, bacterial group, or sample type was not completed due to insufficient numbers of studies.

The conclusions of the remaining eight studies that could not be meta-analyzed are summarized in Table 20.

Table 20

Study synopsis and author conclusions for the 8 human studies for which no meta-analysis could be undertaken.

iv. Stratified analysis by human population

We conducted stratified analysis, based on the human population. That is, meta-analysis was stratified into 1) studies examining antibiotic resistance in farm workers or those with direct contact with livestock animals; and 2) studies examining antibiotic resistance in humans without known direct contact with livestock animals (Figure 7)

Figure 7

Stratified meta-analysis by human population.

Although the pooled effect estimates were statistically significant in both populations, the pooled effect was stronger in farm workers (−0.29 [95% CI −0.54, −0.04]) compared to humans without direct contact with livestock animals (−0.09 [95% CI −0.13, −0.05]). That is, the pooled prevalence of antibiotic resistant bacteria in humans with direct contact to livestock animals was 29% lower when interventions that reduce antibiotic use in the livestock animals were implemented. For humans without direct contact to livestock animals, the pooled prevalence of antibiotic resistant bacteria was 9% lower when interventions that reduce antibiotic use in animals were implemented.

v. Publication bias

A funnel plot was produced, which included the 13 human studies that were meta-analyzed. Visual inspection of the funnel plot did reveal visual asymmetry (Figure 8), and Begg’s test for funnel plot asymmetry was statistically significant (p=0.05).

Figure 8

Funnel plot for human studies included in meta-analysis.

Sensitivity analysis using the trim and fill method was performed. There was no change in the risk reduction of antibiotic resistance in humans with interventions that reduced antibiotic use in animals with (−0.27 [95% CI −0.43, −0.10]) and without (−0.24 [95% −0.42, −0.06]) imputation, suggesting that publication bias, if present, likely had minimal effect on our findings. Similar to the animal studies, known clinical heterogeneity, including heterogeneity in human populations studied, bacteria studied, and countries of origin, may have also contributed to the funnel plot asymmetry. The asymmetry therefore may not be solely attributable to publication bias.

vi. GRADE tables

An overall assessment of the strength of evidence for the effect of interventions that reduce antibiotic use in food animals on antibiotic resistance in humans was completed using the GRADE framework (Table 21).

Table 21

PICOD 2 GRADE assessment.

Because of the observational nature of studies, the quality rating started at “Low” before any other quality criteria were considered. There was direct evidence that reduction in antibiotic use in food animals was associated with a 29% reduction in antibiotic resistance in humans who have direct contact with these animals. Although the mechanisms are unclear and the evidence is more indirect, a 9% reduction in antibiotic resistance for human populations without direct contact with food animals was still demonstrated, suggesting that the benefit of interventions that reduce antibiotic use in animals may extend broadly. There were limitations in risk of bias, particularly with the observational study designs and minimal adjustment for potential confounders. These limitations did not further downgrade the quality rating due to the consistency of findings across different bacterial groups, animal species, antibiotic classes, and sample types, and because of the similarity of findings when considering animal data (PICOD 1).

Discussion

In this systematic review of 181 studies, an association was found between interventions that restrict antibiotic use and reduction in prevalence of antibiotic resistant bacteria in animals and animal products and also in the human population. When considering only the studies that examined antibiotic resistance in food animals, this association was consistent across all the studied bacterial groups, animal populations, and sample types, although the association appeared to be weaker for Campylobacter species. In addition, all interventions that reduced the use of antibiotics, whether the reduction was voluntary or government-imposed, and whether it included complete withdrawal of all antibiotics or limitation of use of only certain antibiotics for certain indications, seemed to be effective in reducing antibiotic resistance in animals. The magnitude of effect depended upon the antibiotic class studied, baseline antibiotic resistance, sample types, and bacterial species. Overall, reducing antibiotic use decreased prevalence of antibiotic resistant bacteria by about 15% and decreased prevalence of multi-drug resistant bacteria by between 24–32%. Furthermore, with some evidence in the literature suggesting that a ban of antibiotic growth-promoters may result in increased use of therapeutic antibiotics in animals (211), it is reassuring that our meta-analysis did not reveal any evidence of increased resistance to antibiotics with such interventions.

The evidence for reduction in prevalence of antibiotic resistant bacteria in humans as a result of reduced antibiotic use in food animals was more limited and less robust than for the reduction of antibiotic resistance in the animals themselves. Meta-analysis of 13 of the 21 human resistance studies showed similar results to the meta-analyses of the animal studies. Interventions to reduce antibiotic use in animals were associated with a 24% absolute reduction in the prevalence of antibiotic resistant bacteria in humans. With nine of the 13 meta-analyzed studies being in farm workers and their direct contacts, generalizability of the findings to the general population may be limited. However, our stratified meta-analysis did suggest that reduction in antibiotic resistance in humans may extend beyond just farm workers, although the effect appears weaker for those without direct contact with animals.

Three recent systematic reviews have explored antibiotic resistance in bacteria isolated from organically—versus conventionally—farmed food animals (212–214). Their conclusions were similar to the ones we have reached, although all focused only on the single type of intervention. In addition, the review by Young et al. (214) included studies that examined resistance in Campylobacter species only, while the review by Wilhelm et al. included studies that tested dairy products only (213). Our systematic review is, to our knowledge, the first to include studies that examine all types of interventions that aim to reduce antibiotic use in animals, with no limitation on the type of sample collected, the animal species included, or the bacterial species tested. Therefore, this is by far the most comprehensive review on this topic. We also believe that this is the first systematic review of studies examining the association between interventions to reduce antibiotic use in food animals and changes to antibiotic resistance in bacteria in humans. Given that a key consideration for restricting antibiotic use in food animals is its potential to decrease the level of antibiotic resistance in zoonotic bacteria, this summary of evidence was commissioned by WHO to inform the development of policy recommendations for public health.

The question posed by the WHO regarding the effect on antibiotic resistance in humans, when interventions to reduce antibiotic use in food animals are undertaken, is complex. Because of the many considerations in cross-species transmission of resistant bacteria and their genetic elements, this research question cannot be easily studied and any interpretations of research findings will require a host of assumptions and inferences. There is biologic plausibility that interventions to reduce antibiotic use in food animals may reduce antibiotic resistance in humans. The results of the meta-analysis undertaken on the human studies included in our systematic review consistently suggest that antibiotic resistant bacteria can be exchanged between livestock and farm workers. It is plausible that antibiotic resistant bacteria can also be exchanged between animals and the general population; however, the evidence for this is weaker and less consistent and more indirect in the studies included in our systematic review. Transmission from food animals to the human population can occur through contaminated animal retail products (215), although the risk of this may be low if animal products are adequately prepared and cooked. Resistance can also be transmitted through the environment through animal faecal matter, wastewater, and contaminated produce (216).

Further adding to the complexity of the issue of antibiotic resistance is that a number of different biological drivers are involved in the selection and persistence of antibiotic resistance genes both in the natural environment and in the presence of antibiotic use (17). That is, development and persistence of resistance does not depend solely on the use of antibiotics, as it can evolve naturally in bacterial populations and may provide survival benefits for bacteria in the absence of antibiotic selection pressure. With these caveats in mind, our systematic review has shown that reducing the level of antibiotic resistance in livestock populations is likely a beneficial strategy for animals and humans. Though we do not fully understand the selection and mechanisms of cross-species transmission of resistant bacteria and their genetic elements, it seems clear that the health of humans, animals, and the ecosystem are intricately linked. In this regard it is evident that a One Health approach will be required to address the problem of antimicrobial resistance (217). Many jurisdictions have recognized this and have therefore implemented comprehensive surveillance of antimicrobial resistance in both the human and animal population and better co-ordination between public health and veterinary antimicrobial resistance reporting systems (218–220). These tracking systems have the potential to provide high-quality information, such as detailed genomic data, necessary to track resistant bacteria in diverse settings, to better understand the links between antibiotic resistance in animals and in humans (221).

There are some caveats and limitations to our systematic review and meta-analyses. Despite considerable heterogeneity across both food animal and human sets of studies, we chose to pool results through meta-analyses. This is partially because we anticipated a priori that there would be heterogeneity across studies, given the wide variety of settings studied and interventions described and tested. Anticipating this, we used random effect models in all of our analyses and conducted stratified analyses to explore contributors to heterogeneity. In the animal studies (PICOD 1), meta-analyses were conducted separately for groups that could not be combined in a biologically sensible way. For example, we did not pool antibiotic resistance results for all bacterial groups as their origins, resistance patterns, and isolation techniques differ widely. The bacterial species were therefore analyzed within four distinct and biologically similar groups. Even with stratification in our meta-analytic approach, there remained significant heterogeneity as demonstrated by Cochran Q test results and high I² values. Despite this heterogeneity, the conclusions from the meta-analyses were remarkably consistent regardless of the bacteria studied, the antibiotic class to which resistance was tested, or the sample type. Further stratification by intervention type had no effect on these associations, highlighting the robustness of these conclusions. We conducted meta-analysis on human studies (PICOD 2) without any stratification by bacterial or sample type given the low number of studies in each category. We recognized the heterogeneity of these studies, but given the consistency of findings in the series of meta-analyses that were undertaken for the animal studies, there did not appear to be significant effect modification by intervention type, antibiotic tested, or sample tested that would be a contraindication to meta-analysis.

As with any systematic review, our study is limited by the varied quality and nature of the underlying studies. While studies had clear strengths in reporting their objectives, hypotheses, and outcomes clearly, areas of deficiency included lack of description of study groups, lack of description of interventions, lack of a control group for longitudinal studies, and inadequate adjustment for potential confounders. The large majority of studies were observational, and therefore could not prove causality between reduction in antibiotic use and reduction in the prevalence of antibiotic resistant bacteria. The issue of causality was especially problematic for human studies as noted above, where linkages between bacterial resistance in food animals and bacterial resistance in humans were indirect and implied. Lastly, the majority of studies were from North America and Europe, while only one study originated from India, China, or Brazil—three of the top five global users of antibiotics in livestock and in humans (222). This likely reflects the geographic areas where there has been greatest and least focus, respectively, on the reduction of antibiotic use in animals and may limit the generalizability of our findings. Furthermore, the majority of the included studies had a cross-sectional design, limiting causal inferences of associations between interventions and the reduction in antibiotic use.

The body of literature that we have identified in the systematic review has definite limitations. As noted above, these include limitations in study designs and quality and the issues of making causal inferences in this very complex area. However, we also note a substantial body of evidence that strongly suggests reduction of the prevalence of antibiotic resistant bacteria in food animals when antibiotic use is reduced in this population, and the smaller, albeit not insignificant body of evidence suggesting that such interventions may also reduce the prevalence of antibiotic resistant bacteria in humans (particularly those with direct contact with food animals). This evidence is not only substantial in its volume, but also in its consistency. The findings held regardless of bacteria studied, food animals in question, interventions implemented, samples studied, and regardless of the quality of the studies. They held when considering phenotypic resistance and genotypic resistance. The mechanisms may be indirect when considering transmission from humans to animals, but are biologically plausible. Therefore, despite the limitations posed by the quality of studies and the methodological issues and assumptions that are made in them, it would be imprudent to entirely discount this body of evidence given its coherence and consistency.

As human and veterinary medicine public health researchers, our mandate in this WHO-commissioned work has been the summarization and presentation of the evidence on the relationship between various antibiotic reduction interventions and antibiotic resistance patterns. Our findings reveal that there is a large body of evidence suggesting that interventions that restrict antibiotic use in food animals is associated with reduction in antibiotic resistance in these animals, and a smaller body of evidence showing a similar effect in humans. Decision-makers will need to determine whether these findings are sufficient to recommend widespread antibiotic reduction interventions.

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APPENDIX 1. MEDLINE search strategy

Notes on search terms:

• Antibiotic-related keywords were derived from the WHO List of Critically Important Antimicrobials for Human Medicine (http://apps.who.int/iris/bitstream/10665/77376/1/9789241504485_eng.pdf) and the OIE List of Antimicrobial Agents of Veterinary Importance (http://www.oie.int/fileadmin/Home/eng/Our_scientific_expertise/docs/pdf/Eng_OIE_List_antimicrobials_May2015.pdf).
• Keywords for species used in aquaculture were derived from the FAO Fisheries list of animal species used in aquaculture at http://www.fao.org/docrep/W2333E/W2333E00.htm.

APPENDIX 2. Grey literature search strategy

The following websites/agencies/documents were included in our grey literature search:

APPENDIX 3. Study characteristics flow charts

Figure 1Flowchart depicting the species, sample point, sample type and bacteria investigated within studies where organic production systems, which reduced or eliminated antibiotic use in animals, were implemented

*

Faeces includes caecum samples, cloacal swabs, rectal swabs, intestinal tract, colon content

Figure 2Flowchart depicting species, sample point, sample type and bacteria investigated within studies where externally imposed bans or restrictions were placed on antibiotic use in food animals

*

Faeces includes caecum samples and cloacal swabs

Figure 3Flowchart depicting the species, sample point, sample type and bacteria investigated within studies with interventions that were self-identified to be antibiotic free, raised without antibiotics, or other similar labels

*

Faeces includes rectal swabs and caecum content

Figure 4Flowchart depicting the species, sample point, sample type and bacteria investigated in studies where there was a voluntary limitation on the use of antibiotics within production systems

*

Faeces includes cloacal swabs, rectal swabs, and caecum samples

APPENDIX 4. Forest plots

Figure 2

Forest plot of absolute risk differences of antibiotic resistance to amphenicols for Enterobacteriaceae isolates in faecal samples.

Figure 3

Forest plot of absolute risk differences of antibiotic resistance to cephalosporins for Enterobacteriaceae isolates in faecal samples.

Figure 4

Forest plot of absolute risk differences of antibiotic resistance to penicillins for Enterobacteriaceae isolates in faecal samples.

Figure 5

Forest plot of absolute risk differences of antibiotic resistance to quinolones for Enterobacteriaceae isolates in faecal samples.

Figure 6

Forest plot of absolute risk differences of antibiotic resistance to sulfonamides for Enterobacteriaceae isolates in faecal samples.

Figure 9

Forest plot of absolute risk differences of antibiotic resistance to amphenicols for Enterobacteriaceae isolates in meat samples.

Figure 10

Forest plot of absolute risk differences of antibiotic resistance to cephalosporins for Enterobacteriaceae isolates in meat samples.

Figure 11

Forest plot of absolute risk differences of antibiotic resistance to penicillins for Enterobacteriaceae isolates in meat samples.

Figure 12

Forest plot of absolute risk differences of antibiotic resistance to quinolones for Enterobacteriaceae isolates in meat samples.

Figure 13

Forest plot of absolute risk differences of antibiotic resistance to sulfonamides for Enterobacteriaceae isolates in meat samples.

Figure 16

Forest plot of absolute risk differences in antibiotic resistance to glycopeptides for Enterococcus spp. isolates in faecal samples.

Figure 17

Forest plot of absolute risk differences in antibiotic resistance to macrolides for Enterococcus spp. isolates in faecal samples.

Figure 18

Forest plot of absolute risk differences in antibiotic resistance to penicillins for Enterococcus spp. isolates in faecal samples.

Figure 19

Forest plot of absolute risk differences in antibiotic resistance to streptogramins for Enterococcus spp. isolates in faecal samples.

Figure 22

Forest plot of absolute risk differences of antibiotic resistance to amphenicols for Campylobacter spp. isolates in faecal samples.

Figure 23

Forest plot of absolute risk differences of antibiotic resistance to macrolides for Campylobacter spp. isolates in faecal samples.

Figure 24

Forest plot of absolute risk differences of antibiotic resistance to penicillins for Campylobacter spp. isolates in faecal samples.

Figure 25

Forest plot of absolute risk differences of antibiotic resistance to quinolones for Campylobacter spp. isolates in faecal samples.

Figure 31

Forest plot of absolute risk differences in antibiotic resistance to lincosamides for Staphylococcus spp. isolates in milk samples.

Figure 32

Forest plot of absolute risk differences in antibiotic resistance to macrolides for Staphylococcus spp. isolates in milk samples.

Figure 33

Forest plot of absolute risk differences in antibiotic resistance to penicillins for Staphylococcus spp. isolates in milk samples.

Figure 34

Forest plot of absolute risk differences in antibiotic resistance to sulfonamides for Staphylococcus spp. isolates in milk samples.

Figure 37

Forest plot of absolute risk differences of antibiotic resistance to amphenicols for Enterobacteriaceae isolates in faecal samples, stratified by intervention type.

Figure 38

Forest plot of absolute risk differences of antibiotic resistance to cephalosporins for Enterobacteriaceae isolates in faecal samples, stratified by intervention.

Figure 39

Forest plot of absolute risk differences of antibiotic resistance to penicillins for Enterobacteriaceae isolates in faecal samples, stratified by intervention.

Figure 40

Forest plot of absolute risk differences of antibiotic resistance to quinolones for Enterobacteriaceae isolates in faecal samples, stratified by intervention.

Figure 41

Forest plot of absolute risk differences of antibiotic resistance to sulfonamides for Enterobacteriaceae isolates in faecal samples, stratified by intervention type.

Figure 44

Forest plot of absolute risk differences of antibiotic resistance to amphenicols for Enterococcus spp. isolates in faecal samples, stratified by intervention.

Figure 45

Forest plot of absolute risk differences of antibiotic resistance to glycopeptides for Enterococcus spp. isolates in faecal samples, stratified by intervention.

Figure 46

Forest plot of absolute risk differences of antibiotic resistance to macrolides for Enterococcus spp. isolates in faecal samples, stratified by intervention.

Figure 47

Forest plot of absolute risk differences of antibiotic resistance to penicillins for Enterococcus spp isolates in faecal samples, stratified by intervention.

Figure 48

Forest plot of absolute risk differences of antibiotic resistance to streptogramins for Enterococcus spp. isolates in faecal samples, stratified by intervention.

Figure 51

Forest plot of absolute risk differences of antibiotic resistance to amphenicols for Campylobacter spp. isolates in faecal samples, stratified by intervention.

Figure 52

Forest plot of absolute risk differences of antibiotic resistance to macrolides for Campylobacter spp. isolates in faecal samples, stratified by intervention.

Figure 53

Forest plot of absolute risk differences of antibiotic resistance to penicillins for Campylobacter spp. isolates in faecal samples, stratified by intervention.

Figure 54

Forest plot of absolute risk differences of antibiotic resistance to quinolones for Campylobacter spp. isolates in faecal samples, stratified by intervention.