Studies of farm animals' infections can shed light on diarrhea in hospitals

10-Feb-2010 - USA

Why would the National Institutes of Health (NIH) fund a veterinary epidemiologist to study the bug that is the leading cause of infectious diarrhea in human hospitals? Because Yrjo Grohn, professor of epidemiology and chair of the Department of Population medicine and Diagnostic Sciences in Cornell's College of Veterinary Medicine, has extensive experience using mathematical modeling to understand the spread of such common food-borne pathogens as Salmonella, Listeria and E. coli in farm animal populations. Now, Grohn and his colleagues have found an important new application for their empirical animal models - the study of infectious disease in human populations.

While the use of animal models to advance our understanding of human diseases is well established, this type of research has traditionally been conducted at the level of the individual organism. But similar work can also be done at the population level, writes Grohn in a review published in Nature Reviews Microbiology.

Grohn and his team, postdoctoral associates Cristina Lanzas and Renata Ivanek and DVM/Ph.D. dual-degree student Patrick Ayscue, make the case that humans and farm animals share many pathogens and transmission mechanisms.

"We propose that farm animal populations, coupled with mathematical models, are well-suited model systems to study infectious disease population dynamics that are relevant to control of human infectious diseases," they write. According to the researchers, the same factors that contribute to outbreaks in livestock, such as crowding, close contact, poor hygiene and contaminated objects, are also prevalent in human settings such as hospitals, the military and schools.

For the NIH-supported study, Grohn is using the modeling expertise that he has gained working with livestock to quantify how the infection spread by Clostridium difficile is introduced and passed around the hospital environment. This work will also help determine risk factors for susceptibility and design control measures.

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