Addressing Deadly Hepatitis B: UCSD Research Using @RISK and PrecisionTree Informs Public Health Policy

UCSD Research uses @RISK and PrecisionTree to Inform Public Health PolicyThe Hepatitis B virus is 100 times more infectious than HIV, and kills more than 780,000 people each year. It can cause a potentially life-threatening liver infection often leading to cirrhosis and liver cancer.  It’s also preventable with a series of vaccinations. Yet throughout the past 30 years, the U.S. Preventative Services Task Force (USPSTF) had calculated the societal cost-benefits of universal Hepatitis B screening, and found that screening the entire US population was not worth the cost.

Dr. John Fontanesi, Director of the Center for Management Science in Health at University of California San Diego School of Medicine, and his team of researchers led by Dr. Robert Gish, M.D., have helped to change this policy. After dedicated field work, @RISK modeling, and PrecisionTree analysis, the researchers found that, rather than screening the entire US population, targeting key at-risk groups would indeed result in worthwhile health outcomes.

The research team knew that in some Asian immigrant communities, the rate of Hepatitis B infection can be as high as 16-18%. They studied two very different groups of Asian immigrant communities: one made up of university students and faculty with relatively high socioeconomic status; the other made up of Laotian and Hmong and Vietnamese immigrants with lower socioeconomic status. Using Palisade software, the researchers determined which type of screening test to administer, and what location was most effective (e.g. health clinic versus community event), when considering widely different segments of the Asian immigrant population.

Dr. Fontanesi’s findings have spurred the USPSTF to re-write their recommendation to include targeted screening of Hepatitis B in certain Asian communities and populations.

He notes how Palisade’s software was pivotal in getting the message across. “When you’re trying to communicate statistics to the medical community, people can get lost, but if you show them @RISK, they get it instantly; that visual representation is so much more powerful than written text or a table of numbers.”

Read the full case study here.

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