One Health Surveillance of Antimicrobial Resistance

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

One Health AMR surveillance links antimicrobial resistance data from humans, animals, and the environment to identify resistance hotspots and transmission pathways across sectors.
Design a program by defining clear objectives, selecting sample types and standardized testing methods, then integrating data through shared metadata and interpretation frameworks.
A key limitation is that surveillance systems vary in sampling design and laboratory methods, so direct comparisons between sectors require harmonized protocols and quality controls.

At a Glance

One Health antimicrobial resistance surveillance is a coordinated approach to monitoring resistance in bacterial populations across human clinical settings, animal production systems, and environmental reservoirs. The table below outlines the core components of a surveillance program.

Component	Human Sector	Animal Sector	Environmental Sector
Primary sample types	Clinical isolates, fecal specimens, urine, blood cultures	Fecal samples, nasal swabs, milk, carcass swabs	Water, soil, wastewater, manure, surface swabs
Testing approach	Antimicrobial susceptibility testing using standardized methods	Antimicrobial susceptibility testing with veterinary-specific breakpoints	Culture-based isolation followed by susceptibility testing or molecular detection
Data integration	Patient metadata, clinical outcomes, treatment history	Species, production type, antimicrobial use records	Geographic location, season, land use, waste management
Main limitation	Clinical sampling bias toward symptomatic cases	Production sector variation in sampling frequency	Environmental detection does not confirm transmission or source

Defining One Health Surveillance for Antimicrobial Resistance

Antimicrobial resistance is the ability of microorganisms to survive exposure to drugs that would normally inhibit their growth or kill them. The World Health Organization describes antimicrobial resistance as a global public health concern that requires coordinated action across human health, animal health, and environmental sectors. One Health surveillance operationalizes this coordination by collecting, analyzing, and interpreting resistance data from multiple domains to inform interventions.

The core premise of One Health surveillance is that resistant bacteria and resistance genes do not respect sector boundaries. Humans, animals, and the environment share ecosystems, and resistant organisms can move between them through direct contact, food consumption, water, and waste streams. A surveillance program that examines only one sector will miss the broader picture of how resistance emerges, spreads, and persists.

A functional One Health surveillance program answers specific questions. These include which resistance mechanisms are present in a region, whether the same bacterial clones or resistance genes appear in humans and animals, and whether environmental reservoirs contribute to clinical infections. The program must also track changes over time to detect emerging threats and evaluate the impact of stewardship interventions.

The design of a One Health surveillance program begins with a clear statement of purpose. The purpose determines the sample types, the testing methods, the data to be collected, and the interpretation framework. A program designed to detect emerging resistance in foodborne pathogens will look different from a program designed to monitor resistance in commensal bacteria as an ecological indicator. Both are valid, but they require different sampling strategies and analytical approaches.

The Global Context and Rationale for Integrated Surveillance

The global burden of antimicrobial resistance is substantial, and the WHO identifies it as a major threat to modern medicine. Resistance affects the treatment of bacterial infections in humans and animals, and it complicates procedures such as surgery, chemotherapy, and organ transplantation that depend on effective antibiotics. The WHO emphasizes that antimicrobial resistance is driven by the use and misuse of antimicrobials in humans, animals, and agriculture, and that the problem requires coordinated action across sectors.

The rationale for One Health surveillance is that resistance does not respect administrative or species boundaries. A resistant bacterium that emerges in a livestock operation can enter the food chain, contaminate water, or spread to farm workers and their families. Similarly, resistant bacteria from human clinical settings can enter wastewater and spread into the environment. Surveillance that captures only one sector will miss these connections and will provide an incomplete picture of the resistance problem.

The WHO supports the development of national action plans for antimicrobial resistance that include surveillance as a core component. These plans call for integrated surveillance systems that can detect resistance in humans, animals, and the environment, and that can share data across sectors. The WHO also emphasizes the importance of antimicrobial stewardship, which is the coordinated effort to optimize the use of antimicrobials to preserve their effectiveness.

The rationale for One Health surveillance is also economic. Resistance increases the cost of treating infections, extends hospital stays, and reduces the productivity of livestock operations. Surveillance data can guide the use of antimicrobials, reduce unnecessary use, and help target interventions to the areas where they will have the greatest impact.

Core Principles of One Health Surveillance Design

Define the Surveillance Objective

The first step in designing a One Health surveillance program is to define the objective in measurable terms. The objective should state what the program will detect, in which populations, and for what purpose. Examples include detecting the emergence of carbapenem resistance in environmental waters, comparing the prevalence of extended-spectrum beta-lactamase producing bacteria in poultry and humans, or tracking the spread of a specific resistant clone across a region.

The objective determines the scope of the program. A program focused on food safety will prioritize samples from food animals and food products. A program focused on environmental contamination will prioritize water, soil, and wastewater samples. A program focused on clinical outcomes will prioritize human clinical isolates. The objective also determines the frequency of sampling, the number of sites, and the duration of the program.

Select the Surveillance Population

The surveillance population is the group of humans, animals, or environmental sites from which samples will be collected. The population must be defined in a way that allows for meaningful comparisons over time and across sectors. For humans, the population might be patients in a specific hospital, residents of a specific community, or a national sample of clinical isolates. For animals, the population might be a specific species, a production type, or a geographic region. For the environment, the population might be a set of water sampling sites, soil types, or wastewater treatment plants.

The population definition must include the inclusion and exclusion criteria. For example, a program that monitors clinical isolates from humans might exclude isolates from patients who have been hospitalized for more than 48 hours, because these isolates are more likely to be healthcare-associated. A program that monitors livestock might exclude animals that have received antimicrobial treatment in the previous 14 days, because treatment can select for resistant bacteria.

Choose the Sampling Strategy

The sampling strategy determines how samples are collected, how many samples are collected, and how often. The strategy must be statistically sound to produce reliable estimates of resistance prevalence. A convenience sample, such as the isolates that happen to be submitted to a laboratory, may be biased and may not represent the target population. A probability sample, in which every member of the population has a known chance of being selected, is more likely to produce unbiased estimates.

The sampling strategy also determines the sample size. The sample size must be large enough to detect the expected level of resistance with a reasonable degree of precision. A program that expects a low prevalence of resistance will need a larger sample size than a program that expects a high prevalence. The sample size calculation should be based on the expected prevalence, the desired precision, and the acceptable error rate.

Standardize Testing Methods

The testing methods must be standardized across all sectors to produce comparable results. The Clinical and Laboratory Standards Institute (CLSI) publishes standards for antimicrobial susceptibility testing that are used in clinical laboratories. These standards define the methods for performing susceptibility tests, the quality control organisms to use, and the interpretation criteria for classifying isolates as susceptible, intermediate, or resistant.

Standardization is essential for One Health surveillance because results from different laboratories must be comparable. If one laboratory uses a different method or different interpretation criteria than another, the results cannot be combined. The CLSI standards provide a common framework for testing, but the specific breakpoints may differ between human and veterinary applications. The surveillance program must specify which breakpoints will be used and how the results will be interpreted.

Integrate Data Across Sectors

The data from each sector must be integrated into a common framework to allow for comparison. The integration requires a common data model that includes the same fields for each sector. The fields include the bacterial species, the antimicrobial tested, the susceptibility result, the sample type, the geographic location, and the date of collection.

The data model must also include metadata that describes the context of the sample. For a human sample, the metadata might include the patient age, sex, and clinical diagnosis. For an animal sample, the metadata might include the species, the production type, and the housing system. For an environmental sample, the metadata might include the water source, the season, and the land use in the surrounding area.

The data integration process must be designed to handle the differences between sectors. The human clinical isolates are collected from patients with infections, while the animal isolates may be collected from healthy animals or from animals with clinical disease. The environmental isolates may be collected from water or soil and may not be associated with any clinical disease. The program must account for these differences when interpreting the results.

Designing a One Health Surveillance Program

Step 1: Establish the Governance Structure

The governance structure defines who is responsible for the program, how decisions are made, and how the data will be used. The structure should include representatives from the human health, animal health, and environmental health sectors. The governance structure should also include a data management team, a laboratory team, and an interpretation team.

The governance structure must define the roles and responsibilities of each participant. The human health sector might be responsible for collecting clinical isolates and patient data. The animal health sector might be responsible for collecting livestock samples and production data. The environmental sector might be responsible for collecting water and soil samples. The data management team is responsible for integrating the data and ensuring its quality.

Step 2: Define the Data Collection Protocol

The data collection protocol describes the procedures for collecting, labeling, and transporting samples. The protocol must be written and followed consistently by all participants. The protocol should include the type of sample to be collected, the volume or quantity, the container, the transport conditions, and the maximum time between collection and processing.

The protocol must also include the labeling requirements. Each sample must be labeled with a unique identifier that links it to the metadata. The identifier should include the date, the location, and the sample type. The metadata should be recorded on a data collection form that is completed at the time of collection.

Step 3: Select the Laboratory Methods

The laboratory methods must be selected based on the objective of the program and the resources available. The methods include culture-based methods, which isolate the bacteria from the sample, and molecular methods, which detect the resistance genes directly. The culture-based methods are the standard for antimicrobial susceptibility testing, but they require viable bacteria and take time to produce results. The molecular methods are faster and can detect resistance genes even when the bacteria are not viable, but they do not provide information about the phenotype.

The laboratory methods must be validated before they are used in the program. The validation should include the testing of known reference strains to confirm that the methods produce the expected results. The reference strains should be obtained from a culture collection, such as the American Type Culture Collection (ATCC), which provides authenticated biological materials for quality control.

Step 4: Develop the Data Management System

The data management system must be designed to store, integrate, and analyze the data from all sectors. The system should include a database that stores the sample data, the metadata, and the test results. The system should also include a data entry interface that allows the laboratory staff to enter the results and the field staff to enter the metadata.

The data management system must include quality checks to identify errors in the data. The checks should include the validation of the data fields, the verification of the sample identifiers, and the detection of duplicate records. The system should also include a mechanism for correcting errors and documenting the corrections.

Step 5: Analyze and Interpret the Data

The data analysis must be designed to answer the questions that were defined in the objective. The analysis should include the calculation of the resistance prevalence for each sector, the comparison of the prevalence across sectors, and the detection of trends over time. The analysis should also include the identification of the resistance patterns, such as the presence of multidrug-resistant organisms.

The interpretation of the data must consider the limitations of the surveillance system. The data from a clinical surveillance system may not represent the general population, because the samples are collected from patients who are already sick. The data from an environmental surveillance system may not represent the risk to humans, because the presence of resistant bacteria in the environment does not always lead to human infection.

Step 6: Report and Disseminate the Results

The results of the surveillance program must be reported to the stakeholders in a format that is useful for decision-making. The report should include the summary of the findings, the interpretation of the data, and the recommendations for action. The report should be distributed to the participants in the program, the public health authorities, and the veterinary authorities.

The report should also be used to evaluate the program itself. The evaluation should assess whether the program is meeting its objectives, whether the data is of sufficient quality, and whether the program is cost-effective. The evaluation should lead to changes in the program design, the sampling strategy, or the testing methods.

Sample Types and Collection Methods

Human Clinical Samples

Human clinical samples are collected from patients with suspected infections. The most common sample types include blood, urine, sputum, wound swabs, and stool. The samples are submitted to a clinical microbiology laboratory for culture and antimicrobial susceptibility testing.

The collection of human clinical samples is subject to the clinical context. The samples are collected when a patient has signs of infection, and the results are used to guide the treatment of the patient. The surveillance program must use the clinical isolates that are already being collected for patient care, and it must not require additional samples from patients.

The metadata for human clinical samples should include the patient age, sex, the clinical diagnosis, the specimen type, and the location of the patient. The metadata should also include the date of collection and the date of the test.

Animal Samples

Animal samples are collected from livestock, poultry, and other animals. The sample types include fecal samples, nasal swabs, milk samples, and tissue samples. The samples are collected from healthy animals, from animals with clinical disease, or from the environment of the animal.

The sampling strategy for animal samples depends on the production system. In a large commercial operation, the samples may be collected from a representative sample of the animals. In a small farm, the samples may be collected from all of the animals. The sampling strategy must be designed to produce a representative estimate of the resistance prevalence in the population.

The metadata for animal samples should include the species, the breed, the age, the production type, the housing system, and the geographic location. The metadata should also include the history of antimicrobial use in the animal or the herd.

Environmental Samples

Environmental samples are collected from water, soil, wastewater, and other environmental sources. The sample types include surface water, groundwater, wastewater, soil, and sediment. The samples are collected from sites that are likely to be affected by human or animal activity.

The sampling strategy for environmental samples must consider the spatial and temporal variation in the environment. The samples should be collected at multiple sites and at multiple times to capture the variation. The samples should also be collected from sites that are upstream and downstream of potential sources of contamination.

The environmental metadata should include the type of water or soil, the geographic location, the season, and the land use in the surrounding area. The metadata should also include the presence of nearby human or animal populations.

Antimicrobial Susceptibility Testing Methods

Disk Diffusion Method

The disk diffusion method is a phenotypic method that tests the susceptibility of a bacterium to a panel of antimicrobials. The method involves placing a standardized inoculum of the bacterium on the surface of an agar plate and then placing the disks containing the antimicrobials on the plate. The plate is incubated, and the zone of inhibition around each disk is measured.

The zone of inhibition is compared to the breakpoints that are published by the CLSI. The breakpoints are the zone diameters that define the susceptible, intermediate, and resistant categories. The method is simple and inexpensive, and it is used for the routine testing of clinical isolates.

Broth Dilution Method

The broth dilution method is a quantitative method that determines the minimum inhibitory concentration (MIC) of an antimicrobial. The method involves the preparation of a series of dilutions of the antimicrobial in a broth medium, and the inoculation of each dilution with a standardized inoculum of the bacterium. The plate is incubated, and the lowest concentration of the antimicrobial that inhibits the growth of the bacterium is the MIC.

The MIC is compared to the breakpoints that are published by the CLSI. The breakpoints are the MIC values that define the susceptible, intermediate, and resistant categories. The method is more quantitative than the disk diffusion method, but it is more labor-intensive and more expensive.

Molecular Methods

Molecular methods detect the resistance genes directly in the sample or in the isolated bacterium. The methods include polymerase chain reaction (PCR), which amplifies a specific DNA sequence, and whole-genome sequencing, which determines the complete DNA sequence of the bacterium.

The molecular methods can detect the resistance genes that are present in the bacterium, even when the bacterium is not viable. The methods can also detect the resistance genes that are not expressed, which may not be detected by the phenotypic methods. The molecular methods are faster than the phenotypic methods, but they require specialized equipment and expertise.

The molecular methods are used in the surveillance program to complement the phenotypic methods. The molecular methods can be used to identify the resistance mechanisms, to detect the spread of a specific clone, and to track the movement of resistance genes across sectors.

Data Integration and Interpretation

The Need for a Common Data Model

The data from the human, animal, and environmental sectors must be integrated into a common data model to allow for comparison. The data model must include the same fields for each sector, and the fields must be defined in the same way. The data model must also include the metadata that describes the context of each sample.

The data model must be designed to handle the differences in the data. The human clinical isolates are collected from patients with infections, while the animal isolates may be collected from healthy animals. The environmental isolates may be collected from water or soil without any clinical context. The data model must include the fields that allow the analyst to distinguish between the different types of samples.

The Analysis of Resistance Prevalence

The prevalence of resistance is the proportion of isolates that are resistant to a specific antimicrobial. The prevalence is calculated for each sector, for each antimicrobial, and for each bacterial species. The prevalence is also calculated for the different sample types within each sector.

The prevalence of resistance is compared across the sectors to identify the differences and the similarities. The comparison must be adjusted for the differences in the sample types and the testing methods. The comparison must also be adjusted for the differences in the population, such as the age and the health status of the animals.

The Identification of Transmission Pathways

The data integration can be used to identify the transmission pathways of resistant bacteria. The transmission pathways are the routes by which the resistant bacteria move from one sector to another. The pathways include the food chain, the water, the direct contact, and the environment.

The identification of the transmission pathways requires the comparison of the bacterial isolates from different sectors. The comparison can be done using the phenotypic methods, which compare the resistance patterns, or the molecular methods, which compare the DNA sequences. The molecular methods are more discriminating and can identify the transmission of a specific clone.

The identification of the transmission pathways is used to design the interventions. The interventions can be targeted at the point of transmission, such as the food processing plant, the water treatment plant, or the farm.

Biosafety and Laboratory Containment

The laboratory work in a One Health surveillance program must be conducted in a safe manner. The biosafety principles are described in the Biosafety in Microbiological and Biomedical Laboratories (BMBL) manual, which is published by the CDC and NIH. The BMBL provides the risk assessment, the containment, and the decontamination principles for the laboratory.

The risk assessment must be conducted for each laboratory procedure. The risk assessment should consider the pathogenicity of the bacterium, the route of transmission, the concentration of the bacterium, and the procedures that are used. The risk assessment should be used to determine the biosafety level of the laboratory and the practices that are required.

The containment of the laboratory is the combination of the laboratory practices, the safety equipment, and the facility design. The containment is used to protect the laboratory workers and the environment from the exposure to the bacterium. The containment level is determined by the risk assessment and the biosafety level of the laboratory.

The decontamination of the laboratory is the process of removing or killing the bacteria from the surfaces, the equipment, and the waste. The decontamination is used to prevent the spread of the bacteria from the laboratory. The decontamination methods include the autoclaving, the chemical disinfection, and the incineration.

The laboratory staff must be trained in the biosafety principles and the laboratory practices. The training should include the proper use of the safety equipment, the proper handling of the samples, and the proper disposal of the waste. The training should also include the emergency procedures for the spills and the exposures.

Common Failure Patterns in One Health Surveillance

Inconsistent Sampling Methods

A common failure in One Health surveillance is the use of inconsistent sampling methods across the sectors. The human samples may be collected from the clinical isolates, while the animal samples may be collected from the healthy animals. The environmental samples may be collected from the water, while the human samples may be collected from the urine. The inconsistency makes it difficult to compare the results across the sectors.

The solution is to standardize the sampling methods across the sectors. The program should define the sample types, the sampling frequency, and the sample size for each sector. The program should also define the inclusion and exclusion criteria for the samples.

Lack of Standardized Testing

Another common pattern is the lack of standardized testing methods. The different laboratories may use different methods for the antimicrobial susceptibility testing, and the results may not be comparable. The different laboratories may also use different breakpoints for the interpretation of the results.

The solution is to use the standardized testing methods that are published by the CLSI. The program should also use the same breakpoints for the interpretation of the results. The program should also use the same quality control organisms for the testing.

Incomplete Metadata

The incomplete metadata is a common failure in the One Health surveillance. The metadata is the information that describes the context of the sample, such as the date, the location, and the species. The incomplete metadata makes it difficult to interpret the results and to identify the patterns.

The solution is to require the complete metadata for each sample. The program should define the required fields for the metadata and should verify the completeness of the metadata at the time of the collection.

Lack of Data Integration

The lack of data integration is a common failure in the One Health surveillance. The data from the different sectors may be stored in different databases, and the data may not be combined for the analysis. The lack of integration makes it difficult to identify the transmission pathways and the hotspots.

The solution is to use a common data model and a common database for the data from all sectors. The program should also use the common data management system for the data entry, the storage, and the analysis.

Limitations of One Health Surveillance

The Clinical Bias

The clinical isolates are collected from the patients who are sick, and the isolates may not represent the general population. The clinical isolates are more likely to be resistant than the isolates from the healthy individuals, because the patients are more likely to have been treated with the antimicrobials. The clinical bias can lead to an overestimation of the resistance prevalence in the population.

The Environmental Detection

The environmental detection of the resistant bacteria does not always mean that the bacteria are a risk to the human health. The bacteria may be present in the environment, but they may not be able to cause the infection. The environmental detection also does not provide the information about the transmission of the bacteria to the humans.

The Temporal and Spatial Variation

The resistance prevalence can vary over the time and the space. The prevalence may be higher in the certain seasons, in the certain geographic regions, or in the certain production types. The surveillance program must be designed to capture the variation, and the analysis must be adjusted for the variation.

The Cost and the Resources

The One Health surveillance program is expensive and requires the resources. The program requires the laboratory equipment, the laboratory supplies, the trained staff, and the data management system. The cost of the program must be weighed against the benefits of the program.

Professional Escalation Criteria

The surveillance program should have the criteria for the escalation of the findings. The escalation is the process of the reporting of the findings to the higher authorities, such as the public health authorities or the regulatory authorities. The escalation is triggered by the specific findings, such as the detection of a new resistance mechanism, the detection of a resistant clone in the food chain, or the detection of a high prevalence of resistance in a specific population.

The escalation criteria should be defined in the program protocol. The criteria should include the type of the finding, the threshold for the escalation, and the authority to be notified. The escalation should be documented, and the response should be tracked.

The escalation criteria should also include the criteria for the investigation. The investigation is the process of the collection of the additional data to confirm the finding and to identify the source. The investigation should be conducted by the epidemiology team, and the results should be reported to the authorities.

Records and Measurements

The surveillance program must maintain the records of the data collection, the testing, and the analysis. The records should include the sample collection forms, the laboratory test results, the quality control data, and the analysis reports. The records should be stored in a secure location and should be retained for the required period.

The program should also maintain the records of the quality control. The quality control records should include the results of the testing of the reference strains, the results of the proficiency testing, and the corrective actions that were taken. The quality control records should be reviewed on a regular basis to ensure the quality of the testing.

The program should also maintain the records of the training. The training records should include the training of the laboratory staff, the field staff, and the data management staff. The training records should be reviewed to ensure that the staff are qualified to perform the tasks.

Common Failure Patterns in the Interpretation

Overinterpretation of the Data

A common failure is the overinterpretation of the data. The data may show a difference in the resistance prevalence between the sectors, but the difference may be due to the sampling or the testing. The overinterpretation can lead to the wrong conclusions and the wrong interventions.

The solution is to use the statistical methods to assess the significance of the differences. The analysis should also consider the limitations of the data, such as the sample size and the sampling bias.

Underinterpretation of the Data

A common failure is the underinterpretation of the data. The data may show a pattern that is important, but the pattern may be missed because the analysis is not thorough. The underinterpretation can lead to the missed opportunities for the intervention.

The solution is to use the multiple methods for the analysis, and to review the data from the different perspectives. The analysis should also be reviewed by the experts in the different sectors.

The Failure to Act on the Data

A common failure is the failure to act on the data. The surveillance program may produce the data, but the data may not be used to inform the decisions. The failure to act can lead to the continued spread of the resistance.

The solution is to have a clear process for the translation of the data into the action. The process should include the reporting of the data to the decision-makers, the development of the recommendations, and the implementation of the interventions.

The Role of the Laboratory in One Health Surveillance

The laboratory is the core of the One Health surveillance program. The laboratory is responsible for the isolation of the bacteria, the identification of the bacteria, and the testing of the susceptibility. The laboratory is also responsible for the quality control of the testing and the reporting of the results.

The laboratory must be accredited and must follow the standards that are published by the CLSI. The laboratory must also participate in the proficiency testing programs to ensure the quality of the testing. The proficiency testing programs provide the laboratory with the samples that are tested by the reference laboratory, and the results are compared.

The laboratory must also have the capacity to handle the different types of samples. The laboratory must be able to handle the clinical samples, the animal samples, and the environmental samples. The laboratory must also be able to handle the different types of bacteria, including the bacteria that are resistant to the multiple antimicrobials.

The laboratory must also have the capacity to perform the molecular testing. The molecular testing is used to identify the resistance mechanisms and to track the transmission of the resistance. The molecular testing requires the specialized equipment and the expertise.

The Role of the Epidemiologist in One Health Surveillance

The epidemiologist is responsible for the design of the surveillance program, the analysis of the data, and the interpretation of the results. The epidemiologist is also responsible for the reporting of the results and the recommendations for the interventions.

The epidemiologist must have the training in the epidemiology of the infectious diseases and the biostatistics. The epidemiologist must also have the knowledge of the human health, the animal health, and the environmental health. The epidemiologist must be able to integrate the data from the different sectors and to identify the patterns.

The epidemiologist must also be able to communicate the findings to the different audiences. The epidemiologist must be able to communicate the findings to the public health authorities, the veterinary authorities, and the environmental authorities. The epidemiologist must also be able to communicate the findings to the farmers and the public.

The Role of the Farmer in One Health Surveillance

The farmer is a key participant in the One Health surveillance program. The farmer is responsible for the collection of the animal samples and the provision of the metadata. The farmer is also responsible for the implementation of the interventions that are recommended by the surveillance program.

The farmer must be trained in the collection of the samples. The farmer must also be trained in the recording of the metadata, such as the antimicrobial use and the production data. The farmer must also be trained in the implementation of the interventions, such as the biosecurity measures and the antimicrobial stewardship.

The farmer must also be aware of the importance of the surveillance program. The farmer must understand that the surveillance program is designed to protect the health of the animals and the public. The farmer must also understand that the surveillance program can help to improve the productivity of the farm.

The Use of the Surveillance Data for the Stewardship

The surveillance data can be used to inform the antimicrobial stewardship. The stewardship is the use of the antimicrobials to optimize the treatment of the infections and to minimize the development of the resistance. The surveillance data can be used to identify the resistance patterns and to guide the selection of the antimicrobials.

The surveillance data can also be used to evaluate the effectiveness of the stewardship interventions. The surveillance data can be used to track the changes in the resistance prevalence over the time. The surveillance data can also be used to identify the areas where the stewardship is needed.

The surveillance data can also be used to inform the policy. The surveillance data can be used to identify the need for the regulation of the antimicrobial use. The surveillance data can also be used to identify the need for the investment in the new antimicrobials.

The Use of the Surveillance Data in the Research

The surveillance data can be used to support the research on the antimicrobial resistance. The surveillance data can be used to identify the new resistance mechanisms, the new resistance genes, and the new transmission pathways. The surveillance data can also be used to evaluate the effectiveness of the interventions.

The surveillance data can also be used to support the development of the new diagnostic tests. The surveillance data can be used to identify the need for the new tests and to evaluate the performance of the new tests.

The surveillance data can also be used to support the development of the new antimicrobials. The surveillance data can be used to identify the need for the new antimicrobials and to evaluate the effectiveness of the new antimicrobials.

The Use of the Surveillance Data in the Policy

The surveillance data can be used to inform the policy on the antimicrobial resistance. The surveillance data can be used to identify the need for the regulation of the antimicrobial use, the need for the investment in the surveillance, and the need for the investment in the research.

The surveillance data can also be used to evaluate the effectiveness of the policy. The surveillance data can be used to track the changes in the resistance prevalence over the policy. The surveillance data can also be used to identify the gaps in the policy.

The surveillance data can also be used to inform the international policy. The surveillance data can be used to compare the resistance prevalence across the countries and to identify the need for the international cooperation.

The Future of One Health Surveillance

The future of the One Health surveillance is the integration of the data from the different sectors into a common platform. The platform will allow the real-time monitoring of the resistance and the identification of the hotspots. The platform will also allow the sharing of the data across the countries and the regions.

The future of the One Health surveillance is also the use of the new technologies. The new technologies include the whole-genome sequencing, the metagenomics, and the artificial intelligence. The new technologies will allow the more detailed analysis of the resistance and the more accurate prediction of the threats.

The future of the One Health surveillance is also the engagement of the different stakeholders. The stakeholders include the farmers, the veterinarians, the physicians, the public health authorities, and the environmental authorities. The engagement of the stakeholders will ensure that the surveillance is relevant and that the data is used.

Frequently Asked Questions

What is the difference between antimicrobial resistance and antimicrobial susceptibility testing?

Antimicrobial resistance is the ability of a bacterium to resist the effects of an antimicrobial drug. Antimicrobial susceptibility testing is the laboratory method used to determine whether a specific bacterium is susceptible, intermediate, or resistant to a specific antimicrobial. The testing is performed using standardized methods, such as those published by the Clinical and Laboratory Standards Institute, and the results are used to guide the treatment of infections and to monitor the prevalence of resistance.

How does One Health surveillance differ from traditional surveillance?

Traditional surveillance typically focuses on a single sector, such as human clinical isolates or animal isolates. One Health surveillance integrates data from human, animal, and environmental sources to identify the transmission pathways and the hotspots of resistance. The integration requires a common data model, standardized testing methods, and a coordinated governance structure.

What are the minimum inhibitory concentration and why is it important?

The minimum inhibitory concentration (MIC) is the lowest concentration of an antimicrobial that inhibits the visible growth of a bacterium. The MIC is determined using the broth dilution method and is compared to the breakpoints that are published by the CLSI. The MIC is important because it provides a quantitative measure of the susceptibility of the bacterium and can be used to detect the changes in the susceptibility over the time.

What are the common mechanisms of antimicrobial resistance?

The common mechanisms of antimicrobial resistance include the production of enzymes that inactivate the antimicrobial, the alteration of the target site of the antimicrobial, the efflux of the antimicrobial from the cell, and the reduced permeability of the cell to the antimicrobial. The mechanisms can be detected using the molecular methods, such as the polymerase chain reaction and the whole-genome sequencing.

How are the quality control organisms used in the antimicrobial susceptibility testing?

The quality control organisms are the reference strains that are used to verify the performance of the antimicrobial susceptibility testing. The quality control organisms are obtained from the culture collections, such as the American Type Culture Collection, and are tested in parallel with the clinical isolates. The results of the quality control organisms are compared to the expected results, and the testing is considered valid if the results are within the acceptable range.

What are the limitations of the environmental surveillance for antimicrobial resistance?

The environmental surveillance for antimicrobial resistance has several limitations. The presence of the resistant bacteria in the environment does not always indicate a risk to human health. The environmental samples may not be representative of the broader environment, and the sampling may be biased by the location and the time. The environmental surveillance also does not provide the information about the transmission of the resistant bacteria to the humans.

How can the surveillance data be used to improve the antimicrobial stewardship?

The surveillance data can be used to identify the patterns of resistance in the human, animal, and environmental sectors. The data can be used to guide the selection of the antimicrobials for the treatment of the infections, to identify the areas where the antimicrobial use should be reduced, and to evaluate the effectiveness of the stewardship interventions. The data can also be used to inform the policy on the antimicrobial use.

What are the biosafety considerations for the laboratory testing of the resistant bacteria?

The laboratory testing of the resistant bacteria must be conducted in a manner that protects the laboratory workers and the environment. The biosafety principles are described in the Biosafety in Microbiological and Biomedical Laboratories, which is published by the CDC and NIH. The principles include the risk assessment, the containment, the decontamination, and the training of the laboratory staff.

Using the Evidence

Source	Best use in this topic	Important limitation
Biosafety in Microbiological and Biomedical Laboratories	official guidance	Check the linked page for current local requirements
Microbiology Resources	official guidance	Check the linked page for current local requirements
Antimicrobial Resistance	official guidance	Check the linked page for current local requirements

Related Bacteriology Guides

References and Further Reading

Biosafety in Microbiological and Biomedical Laboratories. CDC and NIH.
Microbiology Resources. American Society for Microbiology.
Antimicrobial Resistance. World Health Organization.
Bacteriological Analytical Manual. U.S. Food and Drug Administration.
Antimicrobial Susceptibility Testing. Clinical and Laboratory Standards Institute.
Culture Collections. American Type Culture Collection.
NCBI Pathogen Detection. National Center for Biotechnology Information.
Antimicrobial Resistance: a One Health Perspective.. Microbiology spectrum, 2018.
Genomic surveillance for antimicrobial resistance - a One Health perspective.. Nature reviews. Genetics, 2024.
Exploiting genomics for antimicrobial resistance surveillance at One Health interfaces.. The Lancet. Microbe, 2023.
One Health antimicrobial resistance surveillance roundtable.. Journal of medical microbiology, 2025.

This article is educational and does not replace validated laboratory procedures, clinical diagnosis, institutional biosafety review, or public-health guidance.