Spatial Distribution and Characteristics of Restaurant Inspection Results in Toronto, Ontario, 2017–2018

Deborah Li Ting Ng, M. Anne Harris, Tor H. Oiamo, Ian Young Biblographic citation: Food Protection Trends, vol. 40, no. 4, pp. 232-240, Jul 2020 Volume 40, Issue 4: Pages 232–240

Food safety inspection data have been analyzed for relationships between violations and sociodemographic neighborhood factors, but spatial trends of inspection results have rarely been investigated. This study included a descriptive analysis, mapping, and identification of geospatial clustering patterns and hot spots of 2017/2018 restaurant inspection results (pass, conditional pass/closed) obtained from Toronto’s public disclosure system. Negative binomial regression modeling was conducted to identify associations between census demographic information and the rate of conditional pass/closed outcomes. Of 5,950 first annual restaurant inspections performed in 2017, 5,510 (92.6%) restaurants passed, 438 (7.4%) attained a conditional pass, and 2 (0.03%) were closed. Of 6,457 first annual restaurant inspections in 2018, 5,907 (91.5%) restaurants passed, 540 (8.4%) attained a conditional pass, and 10 (0.15%) were closed. The Global Moran’s I statistic showed positive and significant spatial autocorrelation (P < 0.01) of conditional pass/closed counts and percentages in both years. Additional hot spot analyses identified four and three census tract clusters in 2017 and 2018, respectively. Census-tract information on low-income households, immigration status, and non-official languages spoken at home were associated with rates of conditional pass/closed inspection outcomes. The findings provide insights into spatial characteristics of results of food premise inspections, which can inform food safety policy and practice.

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