Novel approaches to obesity surveillance using population level data
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.
Describe inexpensive methods for using population level data in obesity surveillance
Keyword(s): Obesity, Surveillance
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.