Researchers to explore spatial patterns of obesity and risk factors

South Dakota State University researchers are using the tools of spatial analysis to explore nationwide data for insights on what influences obesity.

"We can identify and map some of these regions or 'hotspots' of high and low obesity," said associate professor Michael Wimberly of SDSU's Geographic Information Science Center of Excellence. "Ultimately what we want to do is explain what some of the drivers are."

SDSU postdoctoral researcher Akihiko Michimi, who is working on the project with Wimberly, said one glaring regional difference is that the rate of obesity is high in much of the rural South United States, but low in the rural West and in New England states.

Michimi and Wimberly's first journal article about the study appeared June 29 in the American Journal of Preventive Medicine.

The SDSU study set out to map spatial patterns of obesity and risk factors nationwide by using Behavioral Risk Factor Surveillance System data from telephone surveys compiled annually by the Centers for Disease Control and Prevention. The BRFSS data includes self-reported height and weight, as well as respondents' answers to questions about their levels of physical activity, and about fruit and vegetable consumption.

"The advantage of using BRFSS compared to a variety of other data sources is that we can get wall-to-wall national coverage. They actually do sampling in every county across the United States," Wimberly said. "So we can map things, first of all, and we can also use various spatial statistics to test hypotheses about what the environmental correlates of obesity, physical activity, fruit and vegetable consumption are at a national level as opposed to other studies that have been more localized."

For example, the SDSU analysis shows that the rural South and parts of the Great Plains had low proportions of people who are physically active in their leisure time, while the rural West, New England, and the upper Midwest had high proportions.

When analyzing data for another factor — the proportion of adults consuming fruits and vegetables five times or more per day — researchers found the West Coast, New England and parts of the South had the highest proportions. But the Lower Mississippi Valley, the Great Plains and the Mid-Appalachian Mountain region had low proportions.

Michimi and Wimberly said a current idea in research is that factors in society can set up "obesogenic environments" that give rise to obesity — if factors discourage physical activity or encourage eating the wrong sorts of food, for example.

One of the angles they're currently exploring in a follow-up study is the possibility that distance from supermarkets — a possible indicator of access to nutritious foods rather than highly processed, less healthful foods — could play a role.

SDSU's preliminary analysis of data from the 48 contiguous United States showed that the probability of obesity increased with distance from supermarkets, while consumption of five or more servings of fruits and vegetables per day decreased. The research also showed clear differences between large metropolitan areas and sparsely populated rural areas.

"Sometimes people have to drive 25 or 30 miles to get to a supermarket or grocery store," Michimi said. "But big cities on the East Coast or West Coast have a high population density. If they have a large number of people, they have a large number of stores. So the distance to the supermarkets in general is much, much shorter compared to the distances to the supermarkets on the Great Plains."

Wimberly said there are no easy answers about what's responsible for obesity. But analyzing it with the tools of geography could make some less obvious factors visible.

"The geographic perspective opens up a unique window. Looking at maps, people relate very intuitively to the patterns and it really catalyzes a lot of new thought, ideas, hypotheses. That's the power of what we refer to as 'exploratory spatial data analysis,' working with the data using statistical techniques that allow us to tease out real spatial trends from the underlying noise and using that as a method for hypothesis generation. We can also pull multiple sources of data together to actually test hypotheses about the underlying relationships."