For most of the twentieth century, people didn’t worry about an illness like strep throat or an infected cut because they could go to the doctor for a quick dose of antibiotics, which was invariably followed by a quick recovery.
Slowly, however, microbes developed resistance to the miracle drugs, and in the United States, at least two million infections and 23,000 deaths are attributable to antibiotic-resistant bacteria each year, according to the Centers for Disease Control and Prevention.
Researchers in the Voiland College of Engineering and Architecture are leading a five-year, $4.9 million initiative to look at social determinants of antimicrobial resistance in human and animal populations. The initiative, called the Community Health Analytics Initiative (CHAI), will boost WSU’s ability to analyze extensive datasets known as “big data” and to promote information-based health care research. Supported by WSU, the multidisciplinary initiative, in partnership with the College of Veterinary Medicine, the College of Medicine, and the College of Arts and Sciences, tackles grand challenges in health as well as equity and opportunity. The initiative, slated to become a University research institute, includes the establishment of a new graduate degree program in health analytics.
“Data science has been of great interest to the University, and this project is a natural outgrowth of that interest,” said Behrooz Shirazi, Huie-Rogers chair professor and director in the School of Electrical Engineering and Computer Science, who is leading the initiative. “The planned research institute brings together researchers in math and computing with the School of Global Animal Health and the new College of Medicine to begin making sense of data that health care researchers have been and will be collecting.”
Medicine has traditionally been done by looking at a patient’s symptoms, giving standard tests based on those symptoms, and then prescribing a standard dose of medicine. In addition, medical studies are often controlled experiments, which are limited, expensive, and time consuming, he said. Community-based analytics, on the other hand, allow researchers to find patient-specific information that would be very difficult to spot with traditional research methods in a large community and region—and oftentimes more quickly and cost effectively.
“We need to move away from the cookie cutter solutions we now have in medicine,” he said. “We want to get a lot more precise about patient data in the context of community information and develop more proactive public health solutions.”
The initiative will initially focus on antimicrobial resistance in eastern Washington. The region provides an opportunity for a fresh way of looking at the problem since many studies have been focused in urban areas near academic medical centers.
The researchers don’t know what they are going to find.
Eastern Washington has many people who work with and around livestock who may be exposed to antibiotics and antibiotic-resistant pathogens. How much microbial resistance occurs in eastern Washington? How much might those people interact with others in their community to spread pathogens?
“Those are questions we don’t know for a population like ours, and these issues haven’t really been looked at in a rural area,” said Eric Lofgren, an assistant professor in the School of Global Animal Health who is part of the CHAI project.
What they learn will be applicable to eastern Washington as well as to other parts of the country. Collaborating across disciplines will help the computer science researchers better understand and verify their data.
“Faculty in the Elson S. Floyd College of Medicine are already involved in big data projects across different levels of analysis,” said John Roll, vice dean for research in the Elson S. Floyd College of Medicine. “This is one facet in our developing research portfolio that has tremendous potential for development over time. The partnerships that CHAI will cultivate will greatly enhance this development.”
The researchers will be able to use health analytics and computer science to model how communities interact with pathogens, building a virtual version of eastern Washington. They might model how an emerging pathogen might spread, develop preexisting theories, and come up with potential solutions before such a scenario actually occurs.
“The computer scientists let us get to the big questions in a way that traditional public health tools struggle with,” said Lofgren. “If you can get a broad outline of what might happen, you’ve got at least a little knowledge built up and can react quickly.”