New research shows how a novel use of data can help us better understand factors affecting obesity rates and possible solutions. This guest post comes from Donoria Evans, senior associate at ICF specializing in mixed methods program evaluation and data analysis; Adeya Powell, a mathematics professor at Georgia State University focused on social and behavioral research and applied statistics; Jane Obi, data manager with Pro-Sphere Tek Inc; Shelly-Ann Bowen, technical specialist with ICF specializing in public health research in chronic and infectious diseases; Cindy Hockaday, an associate with ICF skilled in data visualization and chronic disease prevention technical assistance; and Alicia Swann, an associate with ICF specializing in qualitative methods and chronic disease program performance monitoring. ICF is a generous sponsor of the 2017 APHA Annual Meeting and Expo and this blog.
In June, the New England Journal of Medicine reported that 10 percent of the world’s population is now obese — and the U.S. is leading the pack. The news is alarming, but not surprising. And though public health officials have long understood the dire consequences of rising obesity rates, curbing that trend is a different story altogether.
So why do high obesity rates persist in some U.S. communities but not others? Social factors — from income to housing to education — play an integral role at local, state and national levels. From a research perspective, though, it can be difficult to account for all the factors at play and even more difficult to understand why obesity manifests so differently in one place versus another.