The study findings are published in the September 22 edition of the journal PLoS ONE. 

MDR-TB is a form of tuberculosis that is resistant to at least two of the primary antibiotics used to treat the disease. The World Health Organization estimates that MDR-TB affects between 0.5 and 2 million people each year worldwide, but there were only 111 cases reported in the U.S. in 2006.

For the analysis, the researchers developed a computer model to simulate the potential for MDR-TB epidemics. Eighty-one scenarios covering a 500-year period were created with varying levels of treatment quality, diagnosis accuracy, microbial fitness and the degree of immunogenicity of drug-susceptible TB.

According to the study, when 75 percent of active TB cases are detected, improving therapeutic compliance from 50 percent to 75 percent can reduce the probability of an epidemic from 45 percent to 15 percent. Paradoxically, improving the case-detection rate from 50 percent to 75 percent when compliance with directly observed treatment is constant at 75 percent increases the probability of MDR-TB epidemics from 3 percent to 45 percent.

"The ability of MDR-TB to spread depends on the prevalence of drug-susceptible TB," said David Bishai, MD, PhD, MPH, senior author of the study and associate professor in the departments of Population, Family and Reproductive Health and International Health at the Johns Hopkins Bloomberg School of Public Health. "The most successful approach to reduce this risk for MDR-TB epidemics in the U.S. would be to ensure that populations around the world combine high rates of case findings that are tightly coupled to high compliance with directly observed drug therapy."

The authors of "Heightened Vulnerability to MDR-TB Epidemics after Controlling Drug-Susceptible TB" include Jason D. Bishai, an undergraduate student at Stanford University and William R. Bishai, MD, PHD, professor with the Johns Hopkins School of Medicine and co-director of the Johns Hopkins Center for Tuberculosis Research.

The research was funded in part by an award to Jason Bishai from the Robert Wood Johnson Foundation Young Epidemiology Scholars Contest and by NIH grant 5R01AI079590-03.

An interactive TB computer simulation used by the research team is available at mdr.tbtools.org.

Note: If no author is given, the source is cited instead.

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