Online Program

Novel approaches to obesity surveillance using population level data

Monday, November 2, 2015 : 11:10 a.m. - 11:30 a.m.

Benjamin Cooper, MPH, Institute for Public Health, Washington University in St Louis, St Louis, MO
Matt Steiner, MPH, MSW, Center for Health Information, Planning and Research, City of Saint Louis Department of Health, St. Louis, MO
Carl Filler, MSW, Department of Health, City of St. Louis, St. Louis, MO
background In 2014 the St. Louis City Department of Health (STLDOH) launched an obesity surveillance program. However, they had little population level data. A partnership with the Public Health Data & Training Center within the Institute for Public Health at Washington University in St. Louis, Missouri was formed.

methods The STLDOH obtained publicly available height, weight, age, gender and zip code data from the Missouri Department of Motor Vehicles. Records where cleaned, geocoded and BMI computed. BMI values were adjusted for self-reporting bias according to published recommendations in the literature. Simultaneously, the Data Center requested similar data abstracted from patient records at Barnes hospital, a large urban facility in St. Louis. Comparative analyses will be conducted to determine data agreement.

results A final dataset of 171,995 DMV records from St. Louis City zip codes was obtained with ages ranging from 16-90 years. Zip code level gender distribution ranged from 69% to 40% for males and 60% to 31% for females.  Approximately 63% of males and 59% of females were in the overweight or obese categories with zip code level rates as high as 72% and 79% respectively.

discussion Public health departments are under increasing pressure to do more with less. This approach to obesity surveillance is low cost and applicable to many other cities around the country. Geocoded data can be merged with Census data to provide additional information for planning and intervention work.

Learning Areas:


Learning Objectives:
Describe inexpensive methods for using population level data in obesity surveillance

Keyword(s): Obesity, Surveillance

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have extensive experience working with large datasets, including address level data and geocoding for GIS analyses.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

Back to: 3146.1: Epidemiology of Obesity