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.

With that challenge in mind, ICF was inspired to take an unconventional approach. ICF conducted research that matched three massive data sets — including one that measures disaster preparedness — to better understand how neighborhood context can help to identify communities with high levels of obesity and physical activity burden and “SVI” vulnerabilities.

  • 500 Cities, a collaboration between the Centers for Disease Control and Prevention, Robert Wood Johnson Foundation and CDC Foundation, provides city- and census tract-level small area estimates for chronic disease risk factors, health outcomes and clinical preventive service use for the 500 largest cities in the U.S. These small area estimates will allow cities and local health departments to better understand the burden and geographic distribution of health-related variables in their jurisdictions, and assist them in planning public health interventions.
  • County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure vital health factors, including high school graduation rates, obesity, smoking, unemployment, access to healthy foods, the quality of air and water, income inequality and teen births in nearly every county in America. The annual rankings provide a revealing snapshot of how health is influenced by where we live, learn, work and play.
  • The Social Vulnerability Index, or SVI, a tool developed by the Agency for Toxic Substances and Disease Registry for emergency preparedness planning, offers index scores that collapse indicators that describe community demographics and socio-economics. Many of the indicators, like race/ethnicity and income, have been linked in research to community-level physical inactivity and obesity prevalence.

Here’s what the data said. On average, 30 percent and 25 percent of neighborhood adult residents of the 500 cities were obese or inactive. Initially, we identified cities such as New York City, Dayton/Toledo, Ohio, and Mobile, Alabama, with the highest obesity or physical activity burden. But cities like El Paso, Texas, New Orleans and San Bernardino, California, had both the highest SVI scores and high rates of obesity/physical activity burden. This means that obesity/physical activity estimates alone do not explain the variation in neighborhood health outcomes. Considering contexts can help us to identify communities for intervention before obesity/physical activity estimates peak.

The largest increase in obesity was linked to socioeconomic status. Obesity increased by 13.5 percent as the socio-economic status index increased, showing that more vulnerable, lower socioeconomic status communities were more likely to experience poor health outcomes. Household composition and disability — households with youth under 18, older adults aged 65 and older, disabled residents or a single parent — also saw obesity rise 6.3 percent as the index increased. A community’s overall SVI score was also significantly related to adult obesity along with the Gini Index of Income Inequality, indicating the impact of social determinants on increasing obesity at the neighborhood level. Physical inactivity was related to each of these SVI themes along with the minority/limited-English theme rank. This aligns with current research showing low-income communities continue to have limited access to recreation and healthy retail environments.

This is all to say that, in general, communities vulnerable to factors like the ones outlined above are also more likely to be obese and less likely to be physically active. The silver lining here is the confluence of these data sets brings us one step closer to a better local understanding of the obesity crisis. Just as important, it sets the stage for more informed approaches to data analysis and program planning for other chronic diseases, too.

To learn more about the way we’re using health survey research to improve outcomes, and let us know what you think on Facebook, Twitter or LinkedIn.

You can find ICF at APHA’s Public Health Expo in booth 1714 during the upcoming APHA Annual Meeting and Expo in Atlanta, Nov. 4-8, or learn more at icf.com/APHA2017.