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

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.