title: Use of geographic information systems technology to track critical health code violations in retail facilities available to populations of different socioeconomic status and demographics.
abstract: Research shows that community socioeconomic status (SES) predicts, based on food service types available, whether a population has access to healthy food. It is not known, however, if a relationship exists between SES and risk for foodborne illness (FBI) at the community level. Geographic information systems (GIS) give researchers the ability to pinpoint health indicators to specific geographic locations and detect resulting environmental gradients. It has been used extensively to characterize the food environment, with respect to access to healthy foods. This research investigated the utility of GIS in determining whether community SES and/or demographics relate to access to safe food, as measured by food service critical health code violations (CHV) as a proxy for risk for FBI. Health inspection records documenting CHV for 10,859 food service facilities collected between 2005 and 2008 in Philadelphia, PA, were accessed. Using an overlay analysis through GIS, CHV were plotted over census tracts of the corresponding area. Census tracts (n = 368) were categorized into quintiles, based on poverty level. Overall, food service facilities in higher poverty areas had a greater number of facilities (with at least one CHV) and had more frequent inspections than facilities in lower poverty areas. The facilities in lower poverty areas, however, had a higher average number of CHV per inspection. Analysis of CHV rates in census tracts with high concentrations of minority populations found Hispanic facilities had more CHV than other demographics, and Hispanic and African American facilities had fewer days between inspections. This research demonstrates the potential for utilization of GIS mapping for tracking risks for FBI. Conversely, it sheds light on the subjective nature of health inspections, and indicates that underlying factors might be affecting inspection frequency and identification of CHV, such that CHV might not be a true proxy for risk for FBI.
authors: Department of Nutrition Sciences, College of Nursing and Health Professions, Drexel University, Philadelphia, Pennsylvania 19104, USA.
authors: Valerie L
authors: VL
authors: Darcey
authors: Jennifer J
authors: JJ
authors: Quinlan
mesh_terms: D003132:Commerce / Q000592:standards* / Q000706:statistics & numerical data; D003710:Demography; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D005523:Food Supply / Q000592:standards* / Q000706:statistics & numerical data; D005517:Foodborne Diseases / Q000453:epidemiology*; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D006801:Humans; D008913:Minority Groups; D011159:Population Surveillance; D011203:Poverty; D018570:Risk Assessment; D012959:Socioeconomic Factors