NIH Seeks to Break New Ground in Reducing Health Disparities

Doctors have long known that different populations have different risks for chronic illness. Certain ethnic groups, for instance, are more likely to develop type 2 diabetes than others. But why? The National Institutes of Health aims to find out. It’s Network on Inequality, Complexity, and Health will take a broad look at factors that influence disease and aim to make positive changes.

Instead of focusing on a single area of study – social factors, let’s say, or genetic markers – the new institute will try to understand all of the variables that make groups susceptible to disease or other health problems. Scientists from various disciplines will work together on the project, which is a collaboration between the NIH’s Office of Behavioral and Social Sciences Research and the University of Michigan’s School of Public Health, Ann Arbor.

There’s a name for this new, broad-based approach: systems science. Experts in biology, economics, education, computer science, sociology, mathematics, epidemiology and ecology will collaborate on research.

According to the NIH, “Factors such as access to health care, neighborhood environment, educational opportunities, physiology and genetics all may interact over the course of a person’s life to influence risk for diseases like diabetes and cardiovascular disease.”

Computer models will be used to sketch both problems and possible solutions. The diverse group of scientists then plans to produce reports, articles and books that delve into the complex interrelations that previous work may have missed.

Their ultimate aim? Finding interventions that reduce differences in health risks between groups.

“Much of the health disparities research conducted to date took place within single disciplines, and therefore could not comprehensively approach the multitude of factors that are involved,” said principal investigator George A. Kaplan of the University of Michigan School of Public Health. “NICH will fundamentally change this approach by embracing perspectives from the biological to the societal, while employing cutting-edge simulation methods from computer science.”

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