Literature DB >> 21902922

Use of geographic information systems technology to track critical health code violations in retail facilities available to populations of different socioeconomic status and demographics.

Valerie L Darcey1, Jennifer J Quinlan.   

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.

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Year:  2011        PMID: 21902922     DOI: 10.4315/0362-028X.JFP-11-101

Source DB:  PubMed          Journal:  J Food Prot        ISSN: 0362-028X            Impact factor:   2.077


  8 in total

1.  Inspection Frequency, Sociodemographic Factors, and Food Safety Violations in Chain and Nonchain Restaurants, Philadelphia, Pennsylvania, 2013-2014.

Authors:  Sarah E Leinwand; Karen Glanz; Brendan T Keenan; Charles C Branas
Journal:  Public Health Rep       Date:  2017-01-06       Impact factor: 2.792

Review 2.  The impact of socioeconomic status on foodborne illness in high-income countries: a systematic review.

Authors:  K L Newman; J S Leon; P A Rebolledo; E Scallan
Journal:  Epidemiol Infect       Date:  2015-01-20       Impact factor: 2.451

Review 3.  Foodborne illness incidence rates and food safety risks for populations of low socioeconomic status and minority race/ethnicity: a review of the literature.

Authors:  Jennifer J Quinlan
Journal:  Int J Environ Res Public Health       Date:  2013-08-15       Impact factor: 3.390

4.  Geospatial analysis of salmonellosis and its association with socioeconomic status in Texas.

Authors:  Anand Gourishankar
Journal:  Fam Med Community Health       Date:  2021-10

5.  An Evaluation of a Virtual Food Safety Program for Low-Income Families: Applying the Theory of Planned Behavior.

Authors:  Juan C Archila-Godínez; Han Chen; Leah Klinestiver; Lia Rosa; Tressie Barrett; Shauna C Henley; Yaohua Feng
Journal:  Foods       Date:  2022-01-26

6.  Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach.

Authors:  Rachel A Oldroyd; Michelle A Morris; Mark Birkin
Journal:  Int J Environ Res Public Health       Date:  2021-11-30       Impact factor: 3.390

7.  Evaluating area-level spatial clustering of Salmonella Enteritidis infections and their socioeconomic determinants in the greater Toronto area, Ontario, Canada (2007 - 2009): a retrospective population-based ecological study.

Authors:  Csaba Varga; David L Pearl; Scott A McEwen; Jan M Sargeant; Frank Pollari; Michele T Guerin
Journal:  BMC Public Health       Date:  2013-11-15       Impact factor: 3.295

8.  Association between community socioeconomic factors, animal feeding operations, and campylobacteriosis incidence rates: Foodborne Diseases Active Surveillance Network (FoodNet), 2004-2010.

Authors:  Rachel E Rosenberg Goldstein; Raul Cruz-Cano; Chengsheng Jiang; Amanda Palmer; David Blythe; Patricia Ryan; Brenna Hogan; Benjamin White; John R Dunn; Tanya Libby; Melissa Tobin-D'Angelo; Jennifer Y Huang; Suzanne McGuire; Karen Scherzinger; Mei-Ling Ting Lee; Amy R Sapkota
Journal:  BMC Infect Dis       Date:  2016-07-22       Impact factor: 3.090

  8 in total

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