ReviewMicrobiology spectrum2018
Source Attribution and Risk Assessment of Antimicrobial Resistance.
Review in Microbiology spectrum, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Vancomycin-resistant Enterococcus prevalence and its association along the food chain: a systematic review and meta-analysis.The Journal of antimicrobial chemotherapy · 2025Pooled it
- Animal sources of antimicrobial-resistant bacterial infections in humans: a systematic review.Epidemiology and infection · 2023Pooled it
- A statistical modelling approach for source attribution meta-analysis of sporadic infection with foodborne pathogens.Zoonoses and public health · 2022Pooled it
- Global characteristics and trends of research on construction dust: based on bibliometric and visualized analysis.Environmental science and pollution research international · 2020Pooled it
- Transmission, Health Risks and Attenuation Strategies of Antibiotic Resistance Genes in Soil-Plant Systems.Environment & health (Washington, D.C.) · 2026Review
- Companions in antimicrobial resistance: examining transmission of common antimicrobial-resistant organisms between people and their dogs, cats, and horses.Clinical microbiology reviews · 2025Review
- Quantifying national burdens of foodborne disease-Four imperatives for global impact.PLOS global public health · 2025Review
- Antibiotic Use Characteristics and Patterns in Humans and Poultry in Wakiso District, Uganda.The American journal of tropical medicine and hygiene · 2024Article
- Using priorities between human and livestock bacterial antimicrobial resistance (AMR) to identify data gaps in livestock AMR surveillance.BMC infectious diseases · 2024Article
- Review
- A Scoping Review of Antimicrobial Usage and Antimicrobial Resistance in Beef Cow-Calf Herds in the United States and Canada.Antibiotics (Basel, Switzerland) · 2023Article
- Antimicrobial Resistance in the Global Health Network: Known Unknowns and Challenges for Efficient Responses in the 21st Century.Microorganisms · 2023Review
- Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research.PloS one · 2023Article
- Comparison of approaches for source attribution of ESBL-producing Escherichia coli in Germany.PloS one · 2022Article
- Overview of Evidence of Antimicrobial Use and Antimicrobial Resistance in the Food Chain.Antibiotics (Basel, Switzerland) · 2020Review
- Metagenomics-Based Approach to Source-Attribution of Antimicrobial Resistance Determinants - Identification of Reservoir Resistome Signatures.Frontiers in microbiology · 2020Article
- Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance.Frontiers in public health · 2020Article
- Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms.EFSA journal. European Food Safety Authority · 2019Article
- Mathematical modelling for antibiotic resistance control policy: do we know enough?BMC infectious diseases · 2019Article
- Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges.Frontiers in microbiology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Source attribution and microbial risk assessment methods have been widely applied for the control of several foodborne pathogens worldwide by identifying (i) the most important pathogen sources and (ii) the risk represented by specific foods and the critical points in these foods' production chains for microbial control. Such evidence has proved crucial for risk managers to identify and prioritize effective food safety and public health strategies. In the context of antimicrobial resistance (AMR) from livestock and pets, the utility of these methods is recognized, but a number of challenges have largely prevented their application and routine use. One key challenge has been to define the hazard in question: Is it the antimicrobial drug use in animals, the antimicrobial-resistant bacteria in animals and foods, or the antimicrobial resistance genes that can be transferred between commensal and pathogenic bacteria in the animal or human gut or in the environment? Other important limitations include the lack of occurrence and transmission data and the lack of evidence to inform dose-response relationships. We present the main principles, available methods, strengths, and weaknesses of source attribution and risk assessment methods, discuss their utility to identify sources and estimate risks of AMR from livestock and pets, and provide an overview of conducted studies. In addition, we discuss remaining challenges and current and future opportunities to improve methods and knowledge of the sources and transmission routes of AMR from animals through food, direct contact, or the environment, including improvements in surveillance and developments in genotypic typing methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.