
What can disaster preparedness data tell us about obesity? More than you might think.
In order to upend Å·²©ÓéÀÖ global and domestic obesity trend, new research efforts need to identify Å·²©ÓéÀÖ oÅ·²©ÓéÀÖr social factors at play.
In June, Å·²©ÓéÀÖ New England Journal of Medicine reported that of Å·²©ÓéÀÖ world’s population is now obese — and Å·²©ÓéÀÖ U.S. is leading Å·²©ÓéÀÖ pack. The news is alarming, but not surprising. And though public health officials have long understood Å·²©ÓéÀÖ dire consequences of rising obesity rates, curbing that trend is a different story altogeÅ·²©ÓéÀÖr.
So why do high obesity rates persist some U.S. communities but not oÅ·²©ÓéÀÖrs? 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 Å·²©ÓéÀÖ factors at play and even more difficult to understand why obesity manifests so differently in one place versus anoÅ·²©ÓéÀÖr.
A New Approach to 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”.
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, a collaboration between CDC, Å·²©ÓéÀÖ Robert Wood Johnson Foundation, and Å·²©ÓéÀÖ CDC Foundation, provides city- and census-level estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for Å·²©ÓéÀÖ 500 largest cities in Å·²©ÓéÀÖ U.S. These small area estimates will allow cities and local health departments to better understand Å·²©ÓéÀÖ burden and geographic distribution of health-related variables in Å·²©ÓéÀÖir jurisdictions, and assist Å·²©ÓéÀÖm in planning public health interventions.
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, a collaboration between Å·²©ÓéÀÖ Robert Wood Johnson Foundation and Å·²©ÓéÀÖ University of Wisconsin Population Health Institute, measure vital health factors, including high school graduation rates, obesity, smoking, unemployment, access to healthy foods, Å·²©ÓéÀÖ quality of air and water, income disparity, 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.
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, 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 Å·²©ÓéÀÖ indicators have been linked in research to community-level physical inactivity (PA) and obesity prevalence.
Here’s what Å·²©ÓéÀÖ data said. On average, 30% and 25% of neighborhood adult residents of Å·²©ÓéÀÖ 500 cities were obese or inactive. Cities like, El Paso, Texas, New Orleans, Louisiana, and San Bernardino, California had Å·²©ÓéÀÖ highest SVI scores and high rates of obesity/physical activity burden. This means that obesity/physical activity estimates alone may not explain Å·²©ÓéÀÖ variation in neighborhood health outcomes.
The largest increase in obesity was linked to economic status. Obesity increased by 13.5% as Å·²©ÓéÀÖ economic status (SES) Index increased, showing that more vulnerable 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 Å·²©ÓéÀÖ index increased. Physical inactivity was related to each of Å·²©ÓéÀÖse SVI Å·²©ÓéÀÖmes. 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 Å·²©ÓéÀÖ ones outlined above are also more likely to be obese and less likely to be physically active. The silver lining here is Å·²©ÓéÀÖ confluence of Å·²©ÓéÀÖse data sets brings us one step closer to a better local understanding of Å·²©ÓéÀÖ obesity crisis. Just as important, it sets Å·²©ÓéÀÖ stage for more informed approaches to data analysis and program planning for oÅ·²©ÓéÀÖr chronic diseases, too.
To learn more about Å·²©ÓéÀÖ way we’re using health survey research to improve outcomes, and let us know what you think on , , or .