In order to upend the global and domestic obesity trend, new research efforts need to identify the other social factors at play.
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 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.
A New Approach to Social Determinant Data
With that challenge in mind, we were inspired to take an unconventional approach. We 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 (PA) burden and social vulnerability index (SVI) “vulnerabilities”.
500 Cities, a collaboration between CDC, the Robert Wood Johnson Foundation, and the CDC Foundation, provides city- and census-level 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 (CHR), 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 (SVI), a tool developed by Agency for Toxic Substances and Disease Registry (ATSDR) 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 (PA) and obesity prevalence.
Here’s what the data said. On average, 30% and 25% of neighborhood adult residents of the 500 cities were obese or inactive. Cities like, El Paso, Texas, New Orleans, Louisiana, and San Bernardino, California had the highest SVI scores and high rates of obesity/physical activity burden. This means that obesity/physical activity estimates alone may not explain the variation in neighborhood health outcomes.
The largest increase in obesity was linked to socioeconomic status. Obesity increased by 13.5% as the socioeconomic status (SES) 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% 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.
Looking Ahead to Better Data
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.