Online Program

Food inspection forecasting to optimize inspections with analytics

Tuesday, November 3, 2015 : 3:30 p.m. - 3:50 p.m.

Tom Schenk, MS, Department of Innovation and Technology, City of CHicago, Chicago, IL
Gene Leynes, MS, Department of Innovation and Technology, City of Chicago, Chicago, IL
Raed Mansour, MS, Chicago Department of Public Health, City of Chicago, Chicago, IL
Jay Bhatt, DO, MPH, MPA, FACP, Chicago Department of Public Health, Chicago, IL
According to the CDC, 1 in 6 Americans develops a food borne illness annually, which results in over 3,000 deaths per year. The estimated economic impact of these illnesses are up to $77 billion per year and as few as 2.9% of those who become sick seek medical care and most do not report their illness.

There are over 15,000 food establishments across the City of Chicago that are subject to sanitation inspections by the Chicago Department of Public Health (CDPH) with 36 sanitarians responsible for inspecting these establishments. The CDPH has systematically collected the results of nearly 100,000 sanitation inspections posted on the City’s Open Data Portal; meanwhile, other City departments have collected data on 311 complaints business characteristics, and other information also posted on the portal.

With this information, the City of Chicago’s Department of Innovation and Technology (DoIT), in collaboration with an insurance company, and the CDPH, together developed advanced analytics to forecast food establishments that are most likely to have critical violations, which are most likely to contribute to food borne illness, so that they may be inspected first. The code is written in the free and open source statistical modeling software.

The result is that food establishments with critical violations are more likely to be discovered earlier by the CDPH sanitarians. Food establishments with critical violations were discovered over one week earlier during a two-month evaluation. The formulation of these new strategies fit within the constructs of existing rules and regulations that add order for the allocation of inspectors to prioritized food establishments.

Learning Areas:

Communication and informatics
Environmental health sciences
Other professions or practice related to public health
Protection of the public in relation to communicable diseases including prevention or control
Public health or related research

Learning Objectives:
Demonstrate prioritization of food establishment inspections using predictive analytics. Demonstrate allocation of food inspectors using predictive analytics. Identify traditional and non-traditional open data sources for predictive modeling. Discuss the use of collaboration to build capacity and sustainability in innovative methods for food protection programs. Articulate the formulation of strategies involving innovations in technologies within the constructs of existing policies of food protection programs.

Keyword(s): Food Safety, Data Collection and Surveillance

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I was the primary lead on the project and co-author of the written materials.
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.