A new computational model developed by scientists from the University of Chicago could help improve the allocation of U.S. biomedical research resources. The tool, called the Research Opportunity Index (ROI), measures disparities between resources dedicated to a disease and its relative burden on society. ROI identifies diseases that receive a disproportionate share of biomedical resources, which represent opportunities for high-impact investment or for the realignment of existing resources. It is designed to provide an unbiased, data-driven framework to help scientific and political communities assess resource investment and identify unmet medical needs. ROI is described in the August issue of Nature Biotechnology.
"The misalignment of resources in biomedical research could be likened to poor budgeting of household finances," said senior study author Andrey Rzhetsky, PhD, professor of genetic medicine and senior fellow at the Institute for Genomics and Systems Biology at the University of Chicago. "It would be bad to spend all your money on food, for example, and have nothing for rent. Resources are finite and attention to each problem ideally should be proportional to the need."
The biomedical research community is increasingly faced with difficult choices when it comes to allocating finite resources, both human and financial. Meanwhile, there are few unbiased methods to determine how to focus resources for the best return on investment.
Rzhetsky and his colleagues addressed this problem by creating a mathematical framework called a Research Opportunity Index (ROI). It estimates the societal burden of 1,400 medical conditions in the U.S. over a 12-year timespan, based on frequency of diagnosis and health care insurance costs, as well as research publications, awarded grants and clinical trials for each condition. The index then calculates misalignments, allowing the team to create an "investment portfolio" of the resources dedicated to each disease, relative to its burden on the U.S. health care system.
The team found that breast cancer, for example, stands out as a disease with higher dedicated resources than its relative societal burden. On the opposite end of the spectrum, Hashimoto's thyroiditis falls among the conditions with the most investment potential. The autoimmune disorder is the most common cause of hypothyroidism and occurs when the immune system attacks the thyroid gland, disrupting the body's balance of hormones. While it has nearly the same incidence among women as breast cancer, there were only 16 open clinical trials for Hashimoto's disease as of August 2015, according to a list on clinicaltrials.gov. Breast cancer had 2,205.
Rzhetsky and his colleagues acknowledge that the question of what makes a condition more deserving of funding than any other ailment is complex and multifaceted. But the team hopes this new tool can help the community decide on how best to allocate funds and other resources. By providing a framework based on unbiased quantitative analytics and big data, they hope to identify diseases that are high-impact and rewarding targets for additional investment.
"Some diseases stick in the public's attention," Rzhetsky said. "We have a distorted map of the frequency and importance of events from media and articles, and without special efforts to reconcile the reality, we are inherently biased."
The team are now working to incorporate other models of funding distribution into their index to account for additional variables. For example, the "trendy model," which supports investment for diseases with large emotional impact, suggests that even though this reduces funding for other diseases, there may still be benefits as basic science discoveries are added to the scientific and medical community.
"With the availability of more and more data analytics in health care, it's the right time to use data to direct the investments of drug discovery and biomedical research for the common good," said the study's first author, Lixia Yao, PhD, assistant professor at the University of North Carolina at Charlotte.
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