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EPIDEMIOLOGY The Secret Life of Hospital Bugs Non-resistant Bacteria Shown to Be Hidden Ally in Fight Against Drug-resistant Strains There is a hidden microbial drama lurking below the superficial rhythm of hospital life—the rush of incoming patients and their often rapid release. Patients enter the hospital carrying multitudes of bacteria as part of their normal microbial flora. Most come in with pathogens that, if they go on to cause infection, are treatable. But during their stay patients may become infected with strains that resist treatment, either through contamination or, ironically, as a consequence of treatment. "There's a lot of antibiotics in hospitals, which promotes the development of resistant strains. But it also changes the ecology of the normal bacterial flora in individual patients, and in some cases, makes them more susceptible to colonization by something that would otherwise not have made it in," says Marc Lipsitch, assistant professor of epidemiology at HSPH. So commonly is this scenario played out that many hospitals have launched massive campaigns to combat resistant strains—everything from reducing or cutting out the use of certain antibiotics to cycling antibiotics to strict regimens of hand washing and glove and gown use. Marc Lipsitch and colleagues have developed a model that could help to monitor and guide hospital attempts to reduce drug-resistant strains of bacteria. It turns out their efforts may be getting a boost from an unexpected source. In the Feb. 15 Proceedings of the National Academy of Sciences, Lipsitch, working with colleagues at Emory University, suggests that the influx of fresh troops of susceptible strains—carried by newly admitted patients—may help to quash antibiotic resistant strains in hospitals. By outnumbering the resistants, which are rarely carried by incoming patients and are generally much more dependent on the hospital for their transmission, the incoming bacteria outcompete the resistant bugs for space and nutrients. This "wash out" effect is greatest when the resistant strains are already being targeted by hospital-based interventions. Applying the Model Lipsitch's proposal, which grows out of a mathematical model he developed with Emory's Bruce Levin and Carl Bergstrom, explains a number of puzzling observations. For example, programs to reduce antibiotic-resistant pathogens in hospitals have been observed to work with astonishing speed—in a matter of weeks or months rather than the years required to reduce resistance in communities outside the hospital—but no one knew why. In fact, the model predicts that successful interventions will work fast. "There's a constant onslaught of sensitive bacteria, and unless resistant bacteria can reproduce fast enough to keep up with the fact that they're being washed out and not brought in so fast, they'll decline and decline quickly," says Lipsitch. Also perplexing is that hospitals have found it possible to reduce resistant strains by targeting transmission of all bugs, "which doesn't make sense evolutionarily—you usually need to select directly," Lipsitch says. Again, the model predicts that this will happen because while hospital interventions reduce the number of resistant bugs along with those that are susceptible, the susceptible bugs are being replenished by the influx of new patients and the resistant strains are not, being dependent almost entirely on hospital transmission. The admission of new patients carrying bacterial strains susceptible to treatment can help loosen the foothold of resistant strains in a hospital. To build their model, Marc Lipsitch and his colleagues tracked the behavior of three classes of patients—those carrying susceptible bacteria (red box), no bacteria (upper gray box) and resistant strains (lower gray box). They observed the rate at which people in each box were admitted and discharged and also the rate at which they moved from one box to another as a result of infection (colonization) or treatment (cleared). The high admission rate of patients with susceptible strains enables susceptible bugs to outnumber and outcompete resistant bacteria, which depend primarily on hospital infection for their transmission. Beyond explaining why efforts to control drug-resistant strains work, the model could provide standards for judging whether particular interventions are succeeding. If a hospital launches a campaign and does not see results rapidly, their efforts may be failing. "They need to see why the resistant bug is persisting in the hospital—whether it's coming in at a very rapid rate or there's some environmental reservoir that keeps infecting people, which hasn't been cleaned up," Lipsitch says. "Maybe there are health care workers who are persistently colonized with resistants or one patient who has been in there a long time. So the model tells you when to worry and where to possibly look." A Fresh Perspective Researchers have been building mathematical models of infectious diseases such as malaria and measles since the early twentieth century, but only in the last few years have their attempts gained widespread attention. In 1994, researchers presented a mathematical model and supporting experimental data showing that during the latent stage of HIV infection, the virus is being killed and replenished at an enormously fast rate. Working with one of those researchers, Martin Novack of Oxford University, Lipsitch became intrigued by the possibility of combining mathematical and experimental models to understand how microbes act inside the body of a single individual. At Emory, he developed a mouse model to test propositions about how antibiotic treatment selects for resistant bacterial strains within a host. The mouse model proved ineffective so he switched the focus of his mouse experiments to another interest, research on vaccines, and has a paper on the subject in press in Vaccine. Turning to epidemiological records for ideas, he became intrigued by the widespread belief that tuberculosis patients who developed multidrug-resistant strains often did so because they did not adhere strictly to their drug regimen. "No one had a specific mechanism for why this happens. No one could say this happens, then this, then this," says Lipsitch. Using mathematics, Lipsitch and Levin proposed that multidrug resistance developed, in large part, because the TB therapy was killing off individually resistant strains at slower rates than susceptible strains. During periods of lax therapy, these individually resistant strains would be able to expand their numbers and possibly accumulate additional mutations. It is the susceptible strains that appear to have the upper hand in Lipsitch's hospital model. That model, like the TB study, provides a mechanism for several widespread but poorly explained assumptions—namely that hospital interventions work quickly and that resistant strains are the hardest hit. A third point considered by Lipsitch and his colleagues is the counterintuitive observation that patients who are receiving a single drug will be at greater risk of developing resistance to other drugs. Lipsitch explains that the drug, by wiping out one strain of bacteria, makes more room in the patient for colonization by other strains, including the resistants that grow in hospitals. Curiously, the use of a single drug has the opposite effect at the hospital level—it actually brings down total prevalence of strains resistant to other drugs. Lipsitch explains that though a strain resistant to drug B may gain a foothold in a treated patient, it is still being targeted hospital-wide by drug A. In addition, it is being washed away by the tide of susceptible strains that arrives with incoming patients. "So you get this funny thing where you look at an individual and see a positive association between the use of one drug and resistance to another, but if you ask about the policy of the hospital as a whole, it may be the other way," Lipsitch says. Ultimately he hopes the model will be used to guide hospital policies. "The aim is to be able to say what do you expect to be more effective—a 20 percent reduction in use of this drug or an intensive hand washing program," he says. "But I don't think the model is at this level yet." —Misia Landau