AMR surveillance in animals

Introduction

Welcome to AMR surveillance in animals. This module is aimed at animal health professionals, including laboratory scientists, veterinary services professionals, policy-makers and data scientists such as epidemiologists.

This module builds on the module Antimicrobial resistance in animals, where we explored in depth the challenge of antimicrobial resistance (AMR) in animals. This module begins by introducing the concepts of surveillance and monitoring, including the different types of surveillance systems and surveillance data. Next, we will consider the purposes of surveillance, and the importance of One Health integration. We will go on to think about surveillance systems in the national context, considering different data sources and the flow of information and feedback. Finally, we will look at how surveillance systems are designed, and the importance of evaluating surveillance. Throughout this module there are opportunities to reflect on how the principles from the module can be applied in your own setting.

Antimicrobials are compounds that kill or inhibit the growth of any type of microorganism. In this module, we will use the term ‘antimicrobial’ to refer to any drug that is active against bacteria, i.e. antibacterial medicines. We include poultry when using the term ‘livestock’, and ‘food-producing animals’ refers to livestock and aquaculture species. We use the term ‘stakeholders’ to refer to professionals, people or organisations involved in food producing animal systems.

After completing this module, you will be able to:

• explain the importance of AMR, AMU and residues data when tackling the AMR challenge
• describe and identify the main types of surveillance systems
• explain how local surveillance data is obtained from animals, and the importance of this data in national surveillance networks
• reflect on how your role fits within local and national AMR surveillance networks, and explain the roles of the different stakeholders within the surveillance networks
• explain the purpose of AMR-related surveillance and monitoring in animals, and the importance of conducting surveillance in different groups of animals
• list some of the key points to consider when designing a sampling strategy for AMR surveillance in animals.

1 AMR/AMU surveillance in animals: main concepts

This section introduces surveillance and monitoring, and recapitulates the concepts introduced in the module Antimicrobial resistance in animals and other introductory modules relating to the types of data used for surveillance of AMR. We will also consider different types of surveillance systems.

After completing this section you will be able to:

• explain the importance of AMR, AMU and residues data when tackling the AMR challenge
• describe and identify the main types of surveillance systems.

1.1 Monitoring and surveillance to address the AMR challenge

Antimicrobial resistance (AMR) surveillance is defined as the collection, validation, analysis and reporting of relevant microbiological and epidemiological data on antimicrobial resistance in foodborne bacteria from humans, animals and food, and on relevant antimicrobial use (AMU) in humans and animals (WHO 2017).

As Figure 1 shows, surveillance is a method of systematically collecting data that can be used by stakeholders to take action by planning and implementing more effective, evidence-based public health policies and strategies relevant to the prevention and control of disease or disease outbreaks. The prompt dissemination of information to those who need to know is as essential as ensuring the quality, validity and comparability of the data.

Figure 1 Overview of surveillance activities.

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A flow chart showing surveillance activities. Data collection (which includes various types of data) is followed by analysis and interpretation, and then reporting and dissemination – which are passed on to stakeholders and become actions.

Figure 1 Overview of surveillance activities.

Surveillance is not in itself a scientific research activity, although some research projects make use of data collected in surveillance systems. Sometimes, surveillance systems in low and middle-income countries (LMICs) are funded or operated by research organisations, which can lead to some confusion between what is surveillance and what is research. The main difference between research and surveillance is that research tries to generate new knowledge, whereas surveillance aims to monitor a known health issue and/or detect emerging health issues.

Surveillance and monitoring are critical components of the response to AMR. They allow for:

• assessing the scale of the AMR problem at a local, national or global level
• measuring patterns of AMR over time in a particular place or places
• informing public health policies on AMU
• improving health outcomes in people and animals.

1.2 Types of surveillance

Surveillance systems can be classified as either passive or active, based on how the data are obtained.

Active surveillance is ‘the investigator-initiated collection of animal health-related data using a defined protocol to perform actions that are scheduled in advance. Decisions about whether information is collected, and what information should be collected from which animals, is made by the investigator’ (Hoinville, 2013). In the context of AMR surveillance in animals, the investigator can be any stakeholder in charge of the surveillance system: this is often the competent authorities, such as national veterinary services. Active surveillance involves the use of surveys and active testing for data collection in healthy animals and animal products. Data collection of samples and information can take place at farms, abattoirs, markets or retail.

Passive surveillance is ‘the observer-initiated provision of animal health-related data (e.g. voluntary notification of suspect disease) or the use of existing data for surveillance. Decisions about whether information is provided, and what information is provided from which animals, is made by the data provider’ (Hoinville, 2013). In other words, passive surveillance relies on data that has been collected for other purposes. This is usually data from animals with symptoms of disease, including results of microbiological culture and antimicrobial susceptibility testing (AST) of isolates from diagnostic samples and other associated information provided, such as clinical information.

These data are obtained when farmers or practitioners send clinical samples to diagnostic laboratories and AMR tests are performed to choose an appropriate treatment. The main reason for generating the data is to improve animal health and profitability of the farmer; however, this system provides data to monitor resistance, and is particularly useful in LMICs where there is a lack of resources needed for active surveillance. Because passive surveillance relies on the voluntary actions of farmers and animal health providers to report the occurrence of disease, the data obtained will be dependent on their motivation and awareness and will contain some degree of bias.

Enhanced passive surveillance is an observer-initiated provision of animal health-related data with active investigator involvement; for example, by actively encouraging producers to report certain types of disease, or actively following up suspect disease reports.

1.3 AMR surveillance data

1.3.1 Types of AMR surveillance data and their uses

AMR surveillance can involve collecting various types of data. Table 1 shows the different types of data that can be obtained in animal production systems (previously introduced in the module Antimicrobial resistance in animals). The different types of data provide different information about how to address AMR and consequently have different uses.

The types of data described in Table 1 are useful in themselves, but can also be combined to provide information that is more valuable. For example, data on the prevalence of resistant bacteria present on a farm could be analysed alongside AMU records to look at the impact of AMU on AMR. Another approach would be to compare data from different sample types, such as manure from poultry farms and pond water, and sediments from fish farms, to explore possible transmission of resistant bacteria.

1.3.2 Metrics of AMU

To compare quantitative data reported on antimicrobial agents intended for use in animals between regions and over time, a standardised measurement is needed that accounts for the fact that animal populations vary in size and composition. There is a consensus in the scientific community that a relative measure is needed, where the amount of antimicrobial (the numerator) is divided by a denominator that summarises the population at risk of being treated.

These can be weight-based metrics (e.g. mg or kg of active ingredient) or dose-based metrics (e.g. DDD). Data to estimate weight-based metrics are more often available, being a more accessible option for AMU monitoring at a worldwide level, whereas dose-based metrics provide a more accurate metric (Moreno et al., 2020).

Activity 4: Types of surveillance data and sources

Timing: Allow about 15 minutes

Imagine a scenario where you would like to investigate the presence of resistant bacteria in a food production system and establish a baseline prevalence.

1. Which type of surveillance systems would you choose?

Answer

1. Active surveillance; specifically, sentinel surveillance if there are limited resources.

Look at the system in Figure 2. This is a general representation of a food production system illustrating the many different connections between drug providers (shown in red), farms (terrestrial livestock and aquaculture production systems) and following parts of the food chain (shown in yellow), the environment (shown in blue) and waste from food chain operations and human health (hospitals (shown in brown) This illustrates the central role of the environment in these types of systems.

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Figure 2 A food production system and the connections within it.

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A complex flow chart showing various connections between manufacturers of antimicrobials, the environment, terrestrial livestock and aquaculture production systems, wildlife, and wastewater, before entering the food chain.

Figure 2 A food production system and the connections within it.

2. Which type of data could be useful to investigate prevalence of resistant bacteria in animals and where it could be collected?
3. Identify the points in the system to obtain other, different types of AMR surveillance data. Can you think how these data could be useful in combination?

Answer

Figure 3 is an annotated version of Figure 2 that highlights points where different types of AMR surveillance data could be collected.

2. The first type of data you could collect is on the presence of resistant bacteria. Samples to detect resistant bacteria could be collected on farms, at abattoirs, at markets, from wildlife, in hospitals and primary care, and from the surrounding environment. These are illustrated by the blue dots in the amended figure.

3. Another type of data you could collect to complement the data on resistant bacteria is AMU data at farm level, as indicated by the red dot in Figure 3. AMU data would be useful to understand the potential role of AMU on resistance profiles collected from livestock present on the farms where samples were collected.

   Data on AMC in animals could be collected from the pharmaceutical industry, such as drug sellers and pharmacies, as illustrated by the purple dots in Figure 3.

   Data on antimicrobial residues could be obtained through sampling at abattoirs or in the environment, as illustrated by the yellow dots in the Figure 3, which could be compared to data on AMU in farms, to verify that the products reported match the substance identified.

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Figure 3 An illustration of a food production system and the connections within it, with points where different AMR surveillance data can be collected or obtained.

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The flow chart in Figure 2, with ‘hotspots’ added to it that show where resistant bacteria and ARGs data could be collected; where AMU data could be collected; where AMC can be obtained; and where antimicrobial residues data could be obtained.

Figure 3 An illustration of a food production system and the connections within it, with points where different AMR surveillance data can...

2 Purposes of AMR/AMU surveillance and monitoring in animals

In this section we will consider why surveillance is conducted. You will be introduced to the purposes of AMR/AMU surveillance in animals, and the importance of conducting and integrating surveillance in different animals and humans. These aspects will be further expanded and referred to in Sections 3 and 4.

After completing this section, you will be able to explain the purpose of AMR surveillance and monitoring in animals, and the importance of conducting surveillance in different groups of animals.

2.1 Purposes of AMR surveillance and monitoring in animals

There are many reasons why we may want to conduct surveillance. The purpose of a surveillance system describes why it is necessary and what it will accomplish. Surveillance activities should be designed for a specific purpose.

There are different ways to classify the various purposes of surveillance systems. Figure 4 is based on the terrestrial and aquatic animal health codes (OIE, 2019) which suggests six classifications of surveillance purposes – numbered in the figure – based on what surveillance will accomplish.

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Figure 4 Overview of the purposes of AMR surveillance and monitoring, based on OIE and RISKSUR classifications.

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Surveillance purposes include baseline and distribution AMR data, trends and sources of AMR in bacteria, and the emergence of new AMR mechanisms. Policy purposes include designing measures and policies to provide data for risk analyses, areas for future research and outbreak investigation, and as a basis for policy recommendations for animal and human health; policy purposes also include evaluating and assessing existing measures and policies, such as antimicrobial prescribing practices and prudent use recommendations, and the effect of mitigation measures.

Figure 4 Overview of the purposes of AMR surveillance and monitoring, based on OIE and RISKSUR classifications.

In addition, the RISKSUR consortium – a group of European experts in animal health surveillance, part of a project called Risk-based Animal Health Surveillance Systems – described the purposes as either a surveillance purpose, describing information to be obtained about occurrence of a health hazard (such as resistant bacteria), or a policy purpose, which describes how surveillance information is used by policy-makers to inform policy objectives (Hoinville, 2013).

2.2 Benefits of surveillance

To understand the benefits of surveillance, it is useful to consider:

• the source of the data (food-producing animals, food animal products, feed or environment)
• the bacterial target or focus of the investigation (pathogenic bacteria for the animal; commensal and zoonotic bacteria)
• the scope of the surveillance (farm level, national, industry or environment).

2.2.1 Benefits to animal health and welfare

Surveillance activities of resistance in pathogens in food-producing animals will generate evidence to inform the control of bacterial diseases, and have benefits for animal health and welfare. This can be at the farm level, and benefit the farmer, but it could also be as part of a national surveillance programme to control a disease, which could also benefit the industry. An example of this can be a control programme to address mastitis caused by methicillin-resistant Staphylococcus aureus (MRSA). These kinds of surveillance activities are passive, relying on the submission of clinical samples from the farmers or practitioners.

For example, the monitoring of resistance in Escherichia coli from pigs with diarrhoea in Sweden (Figure 5) showed that multidrug resistance to ampicillin and trimethoprim-sulphamethoxazole increased from 20 to 42% in the last years (Aspevall et al., 2020). This type of data is also used to inform prescribing practices and recommendations, such as to support the routine susceptibility testing in cases of diarrhoea before administering the antimicrobial. The effect on the prevalence of resistance has not yet been reported.

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Figure 5 Resistance (%) in Escherichia coli from pigs in Sweden, 1995–2019. Clinical isolates from faecal samples or from samples taken post-mortem from the gastro-intestinal tract (Aspevall et al., 2020).

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Resistance to ampicillin rose from approximately 8% in 1995 to 42% in 2019; trimethoprim-sulphamethoxazole increased from approximately 15% in 1995 to approximately 38% in 2019. Resistance to tetracycline was 30% in 1995 and just under 30% in 2019, despite falling to just over 10% in 2016.

Figure 5 Resistance (%) in Escherichia coli from pigs in Sweden, 1995–2019. Clinical isolates from faecal samples or from samples taken ...

2.2.2 Benefits to public health

AMR surveillance activities can also have a public health benefit, if the focus is foodborne pathogens and zoonotic bacteria in animals. As explained in the module Antimicrobial resistance in animals, zoonotic bacteria are transmitted between animals and humans, and cause infection in humans. These can be commensal bacteria in animals, so surveillance activities need to target healthy animals at the farm or abattoir, and animal products in the food chain.

In Sweden, there is active screening for E. coli with resistance to extended spectrum cephalosporins, collecting faecal samples in healthy food animal species in farms, in abattoirs and meat samples in retail. The bacterial isolates with reduced susceptibility to cephalosporins were further investigated by genome sequence for the presence of transferable genes coding ESC resistance, and regularly compared to clinical human E. coli isolates. Few isolates from different sources were reported to present resistance to more than one antibiotic (that is, multiresistant); however, no transferable genes coding for ESC were detected (Aspevall et al., 2020).

Also in Sweden, clinical E. coli isolates from passive surveillance in animals and humans are compared to identify potential common resistance. Challenges were described in these systems due to the different type of cut-off value used. Data for bacteria from humans are interpreted with clinical breakpoints, as the minimum inhibitory concentration (MIC) of antibiotics that is effective in more than 80% of cases, in patients with infectious diseases and presented as the proportion of isolates with clinical resistance. In contrast, data for bacteria from animals are mainly interpreted with epidemiological cut-off values (ECOFF) and presented as the proportion of isolates of non-wild type. A EUCAST report explains how to use the equivalence between the different information (EUCAST, n.d.). More details about the surveillance activities, analytic methods and interpretation of results can be found in SWEDRES/SVARM 2019. (Aspevall et al., 2020).

In Denmark, the Danish Integrated Antimicrobial Resistance Monitoring and Research Programme (DANMAP) includes surveillance of Campylobacter spp. and non-typhoidal Salmonella spp. with the aim of establishing trends and sources of resistance. The system collects information from chicken, cattle and humans. More details on the characteristics of surveillance can be found in the DANMAP report (Korsgaard et al., 2020).

Figure 6 shows resistance profiles of Campylobacter jejuni across the different species, comparing the distribution of resistance. Some noticeable features include the following:

• The highest percentage of resistant C. jejuni isolates to both ciprofloxacin and tetracycline was from humans returning from overseas.
• The percentage of resistant C. jejuni to both ciprofloxacin and tetracycline has increased in broilers and human domestic cases in the last years.
• In cattle, the main resistance found was to ciprofloxacin. It is important to note that fluoroquinolones (which include ciprofloxacin) have not been used in Denmark for animal production for ten years.

Whole genome sequencing from the isolates was applied only to human isolates that allowed the investigation of a nationwide outbreak due to a common C. jejuni clone resistant to ciprofloxacin, nalidixic acid and tetracycline. However, establishing the role of outbreaks in general resistance trends remains a challenge.

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Figure 6 Distribution (%) of AMR profiles in Campylobacter jejuni from broilers, cattle and human cases in Denmark, 2015–19. The number of isolates included each year is shown in parentheses, where broilers include isolates from Danish broiler meat. (CIP = all isolates with ciprofloxacin resistance but not tetracycline resistance; TET = all isolates with tetracycline resistance but not ciprofloxacin resistance; CIP TET = all isolates with both ciprofloxacin and tetracycline resistance; other resistance = all isolates without both ciprofloxacin and tetracycline resistance. CIP TET, CIP and TET isolates may be resistant to erythromycin, nalidixic acid or streptomycin.)

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The trend over 2015–19 is that the distribution of CIP TET AMR profiles in broilers and domestic cases have increased at the expense of fully sensitive distribution; in cattle and travel cases, the distribution has remained fairly consistent.

Figure 6 Distribution (%) of AMR profiles in Campylobacter jejuni from broilers, cattle and human cases in Denmark, 2015–19. The number ...

2.2.3 Monitoring indicator bacteria from healthy animals

Now let’s look at monitoring indicator bacteria from healthy animals, whose presence is monitored because it can give an indication of the magnitude of the AMR problem.

E. coli and Enterococcus spp. can serve as indicators of resistance in an animal population because they:

• are present as commensals in the gut microbiota of healthy animals and humans
• can acquire AMR both via mutations in chromosomal genes and horizontal transfer of ARGs
• have the potential to cause infections in both animals and humans, and to transfer AMR to pathogenic bacteria of the same or other species.

Monitoring the prevalence of acquired resistance in such commensal bacteria can indicate the magnitude of selective pressure from the use of antimicrobials in an animal population.

For example, monitoring resistance in indicator bacteria such as E.coli has been conducted in Denmark since 2010 in broiler chickens, cattle and pigs. Figure 7 shows that the occurrence of ampicillin, sulfonamide, tetracycline and trimethoprim resistance was significantly higher in E. coli from pigs compared with isolates from cattle and broilers. In contrast, resistance to ciprofloxacin in isolates from broilers (red in the figure) was higher than in isolates from cattle and pigs, and it continues a slow but increasing trend. All antimicrobials in the figure are also used in human health.

This information has been used to assess the zoonotic risk linked to transfer of resistance to critically important antimicrobials from animals to humans. You can read more about the interpretation and usefulness of data in Section 7.2.2 of the DANMAP report (Korsgaard et al., 2020). This information is routinely compared to reports from other EU member states to understand the trends.

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Figure 7 Resistance (%) among Escherichia coli isolates from broilers (top left), cattle (top right) and pigs (below) in Denmark – all the isolates were obtained from the caeca at the abattoirs. The number of isolates appear in parenthesis.

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There is a variety of resistance in broilers between 0 and 30%. Resistance among all isolates in cattle is less than around 10%. Resistance in cattle varies: some isolates are between 20 and 40%, while others are around 5% or lower.

Figure 7 Resistance (%) among Escherichia coli isolates from broilers (top left), cattle (top right) and pigs (below) in Denmark – all ...

2.2.4 Understanding the epidemiology of AMR

As explained in the Antimicrobial resistance in animals module, resistant bacteria from animal systems can be transmitted indirectly to the environment through water sources, manure and soil, and can be accumulated or transmitted to other animal populations. Similarly, resistance present in the environment can also be transmitted to farm animals.

Surveillance activities targeting the environment and wild animals are needed to be able to establish baseline data, sources of resistant bacteria or hotspots to be regularly monitored, and identify where interventions can be implemented to reduce the risk of AMR in humans, animals and the environment. For example, AMR surveillance has been conducted in wild animals in Sweden to investigate the source of strains of MRSA, which has been described in the human population in other countries in Europe (Bengtsson et al., 2017).

Establishing baseline data and monitoring AMU in animals would allow for the production of improved prescribing and prudent use guidelines that can lead to a reduction of AMU in animals, and further reduce the risk of development of resistant bacteria in animals and humans. Currently the information that is reported refers to antimicrobial consumption, as explained in Section 1.3.1. This data is also useful for the national and global level because it can:

• provide information on trends and the type of antimicrobials used
• inform policies
• be compared to data from the human sector.

Figure 8 shows the distribution of consumption of antimicrobials by animal species in Denmark compared to the live biomass, as the mass of the species. The pig and cattle sectors present the highest biomass, as the weight of living organisms of a species (43% and 39% respectively), but the pig sector was reported to use 75% of all veterinary prescribed antimicrobials in animals in Denmark. This is due to the large amount of production and consumption of antimicrobials. Cattle present a comparable biomass: the majority of cattle consists of dairy cows, which have low consumption of antimicrobials in Denmark.

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Figure 8 Distribution of live biomass and AMC in main animal species, tonnes, Denmark. The live biomass is estimated from census data (pigs, cattle, and pet animals) and production data (poultry, fur animals, and aquaculture).

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The pie chart for live biomass shows pigs, 43%; cattle >1 year, 39%; cattle 1 year, 10%; fur animals, 4%; cattle

Figure 8 Distribution of live biomass and AMC in main animal species, tonnes, Denmark. The live biomass is estimated from census data ...

In Denmark, all antimicrobials are available on prescription only. Data on all sales of veterinary prescription medicine from the pharmacies, private companies, feed mills and veterinarians are sent electronically to a central database called VetStat, which is hosted by Danish national authorities.

Before 2001, available data was antimicrobials sales derived from pharmaceutical companies. In addition, the national action plan (NAP) against AMR set several goals, including the reduction of AMU. To achieve this, a Yellow Card initiative was implemented in 2010, mainly targeting pig farmers as the bigger users, using surveillance at herd level, and establishing threshold values for AMU in individual herds. Legal action would be taken against farmers with high AMU. Decreased AMU was observed in the following year. It is important that this measure is complemented by health management at the farm to prevent diseases.

If you are interested to know more about potential legislation in the last 20 years in Denmark, visit Section 4.1 of the DANMAP report (Korsgaard et al., 2020). An FAO report includes lessons learnt from this process (FAO/Denmark Ministry of Environment, 2019).

2.3 One Health integration of AMR surveillance

3 Surveillance systems in the national context

In this section, you will learn about the flows of information in national surveillance systems.

After completing this section you will be able to:

• explain how local surveillance data is obtained from animals, and the importance of this data in national surveillance networks
• reflect on how your role fits within local and national AMR surveillance networks, and explain the roles of the different stakeholders within the surveillance networks.

3.1 Data sources

The first point in a surveillance network is the data source. As explained in Section 1, there are several different types of data that can be collected for AMR surveillance, and these can be collected from a variety of sources. For example, local AMR surveillance data from animal systems can be obtained from biological samples and from records or surveys.

Figure 9 outlines information adapted from the active surveillance systems of AMR in food-producing animals (OIE, 2019). The figure shows the relationship between the sampling source, point in the food system where the sample can be obtained, which type of sample and what information is obtained.

For example, if the data source is food-producing animals, data can be collected at the farm, or post-harvest at the abattoir. Samples that could be collected on farms include AMU records or biological samples such as faeces or bulk milk. This can provide information on AMU, prevalence of resistant bacteria and the relationship between AMU and AMR.

Some considerations:

• Figure 9 only refers to the sources of data to investigate resistant bacteria and AMU in healthy food-producing animal populations. This would be relevant to investigate commensal and zoonotic foodborne bacteria that are a threat for public health.
• Clinical samples from animals could be a source of data for passive surveillance of animal pathogens. This would be of interest for the countries to address infectious diseases.
• The same sources of data outlined could be tested for the presence of antimicrobial residues. In the case of food-producing animals (such as testing the milk), this would provide an indication of recent treatment with antimicrobials or lack of compliance with the withdrawal period.

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Figure 9 Overview of types of samples in relation to the sources and output (adapted from OIE, 2019). This would be for active surveillance of healthy animals. Colours indicate the same type of data source.

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For data from food-producing animals on farms, samples include records and faeces/bulk milk. The output is AMU and the relationship between AMU and AMR.

For data from food-producing animals in slaughterhouses or abattoirs, samples include faeces, caeca or intestines, or carcasses. The output is the hygiene of the process and contamination during slaughter.

Data from animal food products is taken from processing and packing, and points of sale in markets and other retail. Samples include food products, and the output is the hygiene of the process and contamination during processing and handling, and exposure data for animals.

Data from feed is taken from farms and feed mills. Samples include animal feed, and the output is exposure data for consumers.

Data from the environment is taken from various points. Samples include , water, soil, manure and waste, and the output is the occurrence of resistant bacteria from animals in the immediate or wider environment.

A general output for all data sources other than the environment is the prevalence of resistant bacteria.

Figure 9 Overview of types of samples in relation to the sources and output (adapted from OIE, 2019). This would be for active ...

3.2 The flow of samples, information and feedback

Surveillance data can have different uses at the individual, group, national or global level, which should correspond with the purpose of the surveillance systems.

For example, at the individual and farm level, in animals with disease, data can be utilised to improve animal health by informing treatment decisions (i.e. choice of therapy). When individual or group-level data are aggregated and analysed at local and/or national level, they can inform prescribing guidelines and identify key actions.

Figure 10 shows the general flow of information in the surveillance systems. It starts in the data sources, explained in the previous section, as part of the local surveillance. The information needs to be reported nationally and globally, to address the AMR challenge.

To summarise:

• at the individual or group level, surveillance data allow veterinary and animal health professionals to make better informed clinical decisions to ensure better animal health outcomes
• at the national level, surveillance data guide policy and ensure appropriate and timely animal health and public health interventions
• at the global level, surveillance data could provide early warnings of emerging threats and data to identify and act on long-term trends.

Figure 10 The flow of AMR surveillance data.

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AMR surveillance data starts as local, then national, then global as an information flow. The feedback based on it is initially global, then becomes national, then local.

Figure 10 The flow of AMR surveillance data.

Local surveillance is the cornerstone of AMR surveillance. Figure 11 shows the journey of the sample, from sampling to the surveillance network, and points where decisions are taken. Data collected by local surveillance sites are reported and collated to provide a national overview of AMR.

Local surveillance comprises six broad task areas, all of which are critical for good quality data:

• collecting/receiving specimens and logging specimen and relevant clinical information
• performing tests
• monitoring and documenting data
• interpreting data
• reporting and communicating test results to the person submitting the test
• reporting and communicating results/data beyond the laboratory (local/national/global).

Figure 11 The local AMR surveillance process.

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Sampling leads to specimens, which are then tested in a laboratory. The result then both feeds back into further sampling, and the wider surveillance network.

Figure 11 The local AMR surveillance process.

3.3 The role of the animal health stakeholders

As explained in the module AMR surveillance and you, the responsibility for AMR surveillance is distributed across various professional roles. The next activity aims to provide you with an opportunity to reflect on your role and responsibilities in relation to the surveillance activities.

Activity 8: The role of the animal health stakeholders

Timing: Allow 20 minutes

Think about the surveillance activity you are involved in: for example, it could be data collection, data validation, data analysis and interpretation, reporting or dissemination of surveillance results and information.

Use the information from Activity 7, where you identified the journey of the sample, flow of information and feedback.

1. Can you identify the main stakeholders and institutions conducting the surveillance activities? You can think of people and organisations involved in data collection, data analysis and interpretation, dissemination of information, provision of feedback, decision-making, etc.
2. What is your role? What are your responsibilities? Is it a local or national role?
3. Which other professionals do you interact with from the animal health sector and human health sector? What information do you share? Do you share results or take joint decisions?
4. Who plays a role in communication and dissemination of information?
5. Do you provide or receive feedback of results?
6. What do you think are the barriers to integration of surveillance across sectors?

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Discussion

Your answers will be specific to your setting. You might have thought about professionals you interact with who might depend on your role. (For question 6, potential problems in some systems could include a lack of time to discuss information with other professionals, lack of communication channels to other professionals (such as human and veterinary clinicians), language barriers, and differing priorities and ways of working.)

Similarly, you might be in charge of analysing samples, and know that the purpose is to detect or understand trends, but other animal health stakeholders can be communicating information to farmers, designing policies, or conducting risk analysis, or will only use the data to assess actions.

Figure 12 might help you to reflect on the connections in your setting. It is only a generic representation of the connections between the stakeholders involved in surveillance, such as the veterinary service professionals, laboratory team or surveillance team, and it shows the information they exchange and the direction of the information. This might be different in your setting.

Figure 12 Possible scenario of national AMR surveillance in the animal sector. This figure provides a similar outline to that presented in Figure 10, but here we focus on the key stakeholders acting in the network.

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Veterinary service professionals and a laboratory team interact to produce lab results, which are passed on to a surveillance team. The data that this team produceds is passed on to a national AMR surveillance system.

Figure 12 Possible scenario of national AMR surveillance in the animal sector. This figure provides a similar outline to that presented ...

4 Design of surveillance and monitoring programmes

In this section, you will be introduced to the principles of designing AMR surveillance and monitoring in animals.

Before we begin, what do you think are the key points to consider when designing a sampling strategy for AMR surveillance in animals?

4.1 Overview of surveillance design

We will start by looking at an overview of the key points that you need to consider when designing a surveillance strategy. These are outlined below, and in Figure 13:

• Surveillance purpose: Why do we need to do the surveillance? What is the overall aim?
• Surveillance system: What is the most appropriate type of surveillance system to use to achieve our surveillance purpose?
• Surveillance objective: What are the goals we need to achieve to meet the purpose of our surveillance?
• Surveillance components: What activities will our surveillance system include? (Elements for the design of a surveillance components are explained in Section 4.3.)
• Documentation: How will we document our surveillance system?
• Evaluation: How will we evaluate our surveillance system?

Figure 13 Overview of the main points to consider when designing a sampling strategy for AMR surveillance in animals (adapted from RISKSUR).

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Surveillance design needs to design the relevant surveillance components: that is, the different activities to achieve the surveillance objective. These could include the target population, sources of samples, hazard selection, and sampling frame and sample calculation.

Figure 13 Overview of the main points to consider when designing a sampling strategy for AMR surveillance in animals (adapted from RISKSUR).

4.2 Surveillance objectives

Surveillance objectives are the goal(s) that, when met, will result in the collection and analysis of data in order to achieve the purpose of the system. Objectives in animal health surveillance could include the following:

• Prevalence estimation (of resistance in a given bacterial species): This is the most common objective in AMR surveillance in animals. It is needed to be able to establish baseline data and monitor trends.
• Early detection: To detect the emergence of new resistance patterns and/or mechanisms – this may be through sentinel surveillance or syndromic surveillance.
• Case detection: This may be adequate to calculate the number of units needed to be tested to detect cases of antimicrobial residues in export products.
• Freedom from disease: This may be a relevant objective in countries that are trying to eliminate a particular type of resistant bacteria, such as MRSA.

4.3 Designing a surveillance system

4.3.1 Surveillance components

When designing a surveillance system, we need to consider what activities are required to pursue the surveillance objective. These activities are known as surveillance components.

A surveillance component is ‘a single surveillance activity (defined by the source of data and the methods used for its collection) used to investigate the occurrence of one or more hazards in a specified population’ (Hoinville et al., 2013). For example, this might be conducting sampling and testing at the slaughterhouse to investigate the presence of the extended-spectrum beta-lactamase (ESBL)E. coli, which is a common indicator bacterium.

A surveillance system may comprise a range of surveillance components (and the associated organisational structures) used to investigate the occurrence of a single hazard in a specified population.

A surveillance portfolio is a range of surveillance components (and the associated organisational structures) used to investigate more than one hazard in a specified population.

Surveillance components included in national AMR surveillance systems can be:

• statistically based surveys
• sampling and testing of food-producing animals on the farm, at live animal markets or at slaughter
• an organised sentinel programme, for example targeted sampling of food-producing animals, herds, flocks and vectors (such as birds or rodents)
• analysis of veterinary practice and diagnostic laboratory records
• sampling and testing of products of animal origin intended for human consumption
• sampling and testing of feed ingredients or feed.

4.3.2 Designing surveillance components

There are some essential surveillance design elements that must be considered when designing surveillance of AMR in animal systems:

4.4 Evaluation of surveillance

The capacity of surveillance systems to accurately describe patterns of diseases is of public health importance.

Therefore, regular and relevant evaluations of these systems are critical in order to improve their performance and efficiency.

Evaluation is defined as the ‘systematic and objective assessment of the relevance, adequacy, progress, efficiency, effectiveness and impact of a course of actions, in relation to objectives and taking into account the resources and facilities that have been deployed’ (Drewe et al., 2015). The importance of surveillance evaluation was highlighted by a project undertaken in the UK by the Royal Veterinary College, Animal and Plant Health Agency, and Scottish Agricultural College. This project generated a novel SuRveillance EVALuation framework (SERVAL), which was designed to be generic so that it can be used to evaluate any animal health surveillance system (Drewe et al., 2015).

The key steps within the framework are as follows:

1. Define the scope of the evaluation.
2. Characterise the surveillance system to be evaluated.
3. Design the evaluation.
4. Conduct the evaluation.
5. Reporting and communication.

If you are interested to know more about evaluation of surveillance, you may be interested to explore a case study in your own time (Drewe et al., 2017).

5 End-of-module quiz

Well done – you have reached the end of this module and can now do the quiz to test your learning.

This quiz is an opportunity for you to reflect on what you have learned rather than a test, and you can revisit it as many times as you like. 

• End-of-module quiz

Open the quiz in a new tab or window by holding down ‘Ctrl’ (or ‘Cmd’ on a Mac) when you click on the link.

6 Summary

In this module you have learned the main types of surveillance systems. You have learned the different purposes of surveillance and their importance. You have explored the process of obtaining local surveillance data and reflected on your role and responsibilities. You have been introduced to the main elements to consider when designing a sampling strategy for AMR surveillance in animals. Throughout the module you have had opportunities to reflect on AMR in an animal industry setting relevant to you.

You should now be able to:

• explain the importance of AMR, AMU and residues data when tackling the AMR challenge
• describe and identify the main types of surveillance systems
• explain how local surveillance data is obtained from animals, and the importance of this data in national surveillance networks
• reflect on how your role fits within local and national AMR surveillance networks, and explain the roles of the different stakeholders within the surveillance networks
• explain the purpose of AMR-related surveillance and monitoring in animals, and the importance of conducting surveillance in different groups of animals
• list some of the key points to consider when designing a sampling strategy for AMR surveillance in animals.

Now that you have completed this module, consider the following questions:

• What is the single most important lesson that you have taken away from this module?
• How relevant is it to your work?
• Can you suggest ways in which this new knowledge can benefit your practice?

When you have reflected on these, go to your reflective blog  and note down your thoughts.

7 Your experience of this module

Now that you have completed this module, take a few moments to reflect on your experience of working through it. Please complete a survey to tell us about your reflections. Your responses will allow us to gauge how useful you have found this module and how effectively you have engaged with the content. We will also use your feedback on this pathway to better inform the design of future online experiences for our learners.

Many thanks for your help.

Now go to the survey.

References

Acknowledgements

This free course was collaboratively written by Maria Garza and was reviewed by Lucy Brunton, Claire Gordon, Natalie Moyen, Peter Taylor and Hilary MacQueen.

Except for third party materials and otherwise stated (see terms and conditions), this content is made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Licence.

The material acknowledged below is Proprietary and used under licence (not subject to Creative Commons Licence). Grateful acknowledgement is made to the following sources for permission to reproduce material in this free course:

Module image: © Peter Hermes Furian/123RF

Figures 1, 2, 3 and 4: Garza, M.

Figure 5: Swedres-Svarm 2019. Sales of antibiotics and occurrence of resistance in Sweden. Solna/Uppsala ISSN1650-6332

Figures 6, 7 and 8: DANMAP (2019) Use of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from food animals, food and humans in Denmark. ISSN 1600-2032.

An FAO report: FAO and Denmark Ministry of Environment and Food – Danish Veterinary and Food Administration. 2019. Tackling antimicrobial use and resistance in pig production: lessons learned from Denmark. Rome. 52 pp. Licence: CC BY-NC-SA 3.0 IGO.

Figures 10, 11 and 12: © The Open University

Figure 13: Surveillance design framework, https://survtools.org/wiki/surveillance-design-framework/doku.php, This file is licensed under the Creative Commons Attribution-Noncommercial-ShareAlike Licence http://creativecommons.org/licenses/by-nc-sa/4.0/

An FAO report on monitoring and surveillance of AMR: FAO. 2019. Monitoring and surveillance of antimicrobial resistance in bacteria from healthy food animals intended for consumption. Regional Antimicrobial Resistance Monitoring and Surveillance Guidelines – Volume 1. Bangkok. This file is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO license (CC BY-NC-SA 3.0 IGO), https://creativecommons.org/licenses/ by-nc-sa/3.0/igo

A 12-point checklist for surveillance of diseases of aquatic organisms: a novel approach to assist multidisciplinary teams in developing countries: © 2021 Bondad-Reantaso, M. G. et al., Reviews in Aquaculture published by John Wiley & Sons Australia, Ltd. 1This is an open access article under the terms of the Creative Commons Attribution License, which permits use,distribution and reproduction in any medium, provided the original work is properly cited.

A case study: Drewe, J.A., Floyd, T. and Stärk, K.D.C. (2017) SERVAL: A generic framework for the evaluation of animal health surveillance, Version 1.3., 26 January 2017, available online at https://www.rvc.ac.uk/research/research-centres-and-facilities/veterinary-epidemiology-economics-and-public-health/projects/serval

Every effort has been made to contact copyright owners. If any have been inadvertently overlooked, the publishers will be pleased to make the necessary arrangements at the first opportunity.