Antibiotic use in animal agriculture may be responsible for many resistant human infections, but quantifying this burden is challenging due to lack of data. Key data needed includes detailed antibiotic use patterns in animals, molecular characterization of bacteria from food animals and surrounding environments, and bacteria from human infections. While techniques exist, current monitoring programs are limited and do not systematically collect this information. Expanding data collection through strengthened federal programs could help assign an evidence-based burden from agricultural antibiotic misuse.

1. Exploring the Societal Burden of
Animal Agricultural Antibiotic Use
Keeve Nachman, PhD, MHS
Johns Hopkins Center for a Livable Future
Department of Environmental Health Sciences
Johns Hopkins Bloomberg School of Public Health

2. Background – why are we concerned?
• Antibiotics are routinely used in animal agriculture
• Therapeutic uses (treatment, control)
• Non-therapeutic uses (prevention, growth promotion)
• Same drugs or drug classes as those used in humans
• Bacteria in the animal production setting can become resistant
• Those bacteria leave the farm and can cause infections in people
• Resistant infections are difficult to treat (and expensive!)
• Resistance is worsening, and we are running out of treatment options
• Our existing approaches to solving the problem aren’t likely to work, and
we have no plans to evaluate them in a meaningful way

3. Antibiotics
administered at
low doses
Susceptible
bacteria
eliminated
Resistant
bacteria survive
and multiply

4. Thesis statement
• What we know about antibiotic use in animal agriculture suggests it
may be responsible for a significant fraction of resistant infections in
humans
Corollary statement:
• We lack the data to quantify the externalized societal costs stemming
from misuse of antibiotics in animal agriculture

5. AMR societal burden estimates
• No credible clinical or ag-specific estimates exist!
• US
• Morbidity and mortality burden estimated by CDC
• 2M infections and 23,000 deaths (20% are assumed to be food-borne)
• Underestimate
• Economic burden estimates
• Lack of universal/standard accepted methodology for estimation
• $20B USD for direct healthcare costs, plus societal/productivity costs of $35B USD
• Data are inadequate to differentiate clinical and agricultural contributions
• Global burden
• Review on AMR (2016) – 10M deaths and $100 trillion USD
• Very inclusive estimate (includes malaria, TB, HIV, and other bacterial)

6. Key differences in human vs. animal ABX use
Human Veterinary
Dose ranges Therapeutic only Primarily subtherapeutic
Duration Specified, brief Majority of animal lifespan
Oversight Physician-prescribed OTC and veterinarian-prescribed*
Drug selection Often targeted Preventive uses not targeted
Animal use contributes more “selection pressure” – increasing
likelihood of killing weaker bacteria and promoting existing (and
new) resistant bugs!

7. What we’d need to know to assign burden
• Primary goal – identify fraction of human infections originating from
agricultural misuse
• To do this, we would ideally have:
• Antibiotic use pattern information for animals
• Molecular characterization of bacteria from food animals and environmental
media around production sites
• Molecular characterization of bacteria isolated from humans with infections
• With this information, we would be able to confidently link infections to
farms (when a link exists), and match antibiotic uses to patterns of
resistance observed in human infections.

8. But what do we have/what are we already doing?
• Antibiotic use pattern information for animals
• Antibiotic sales data from drug manufacturers (ADUFA)
• Spotty, low resolution, non-geographic antibiotic use data (NAHMS, ARMS)
• Molecular characterization of bacteria from food animals and
environmental media around production sites
• Food animals only, to a limited extent, not molecular characterization (NARMS)
• Molecular characterization of bacteria isolated from humans with
infections
• To a very limited extent, not molecular characterization (NARMS)
• Goals of clinical practice do not typically include source determination

9. Gaps and opportunities for filling them
• Moving towards better use data
• Agencies have publicly recognized needs for usage data
• Especially in the context of their plan to address misuse
• Funding and compliance will be ongoing issues
• Molecular characterization is becoming easier and less expensive
• Still not a routine part of clinical practice
• Federal programs would need to be expanded to take better advantage of
these techniques
• More bugs (not just GI pathogens, but others, too)
• Stronger sampling program
• Compliance?

10. Conclusions
• We are not able to pin a specific quantitative burden of human AMR
infections on agricultural or human misuse.
• We aren’t collecting the data we’d need to do it, and there are many
barriers to doing so – most involving political will
• Do we need to pin down a number definitively before taking more
meaningful action than we are?