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A knowledge gap exists in the role of restaurant type on the prediction of attaining the highest grade possible from the local health inspection agency. This study identified disparities using logistic regression between the issuance of a Grade A and restaurant type and location. This study tested the eight most inspected types of restaurants within the City of New York and calculated the odds ratios of their receiving the highest inspection grade by the New York City Department of Health and Mental Hygiene. A fitted equation has been proposed for the prediction of receiving the highest inspection grade based upon the citywide results of these eight restaurant types from calendar years 2011 and 2012. The results suggest that certain styles of restaurants have lower odds of receiving the highest grade in comparison to American-style restaurants.
Because U.S. restaurants are inspected at least annually against criteria in the U.S. Food and Drug Administration Model Food Code, large amounts of data are generated and should be systematically reviewed. The purpose of this study was to determine the relationships among the data obtained through health department inspections, the contributing factors to foodborne illness identified by the Centers for Disease Control and Prevention, and the risks of outbreaks of norovirus, Salmonella, and Clostridium perfringens infection associated with a specific restaurant. These agents were chosen for the analysis because they cause the majority of foodborne illnesses. A theoretical predictive assessment tool was built that extracts data from routine health department inspection reports for specific restaurants to establish a risk profile for each restaurant and identify the likelihood of a norovirus, Salmonella, or C. perfringens outbreak at that restaurant. The tool was used to examine inspection reports from restaurants known to have had confirmed norovirus, Salmonella, and C. perfringens outbreaks. Although evaluation of an extensive data set revealed lack of an overall association between outbreak inspection scores and routine inspection scores obtained at outbreak restaurant locations, certain specific violations were significantly more likely to be recorded. Significant differences in types of violations recorded during outbreak and routine inspections were determined. When risks based on violation type can be identified, targeted actions may be able to be prioritized and implemented to help decrease illnesses.
Controversy surrounds the use of posted restaurant inspection scores and grades. There is much debate about how well a score or grade conveys risks to potential diners, and questions remain about how the public interprets posted scores and grades, regardless of how they are derived. To determine how such scores and grades are perceived, the authors surveyed a sample of Maryland college students and food safety professionals about what a posted inspection score of 86 means and what a letter grade of C means. There was no clear consensus about the meaning of the scores and grades described in the surveys. The majority of respondents felt that a restaurant should be either open or closed, and that the public should not have to decipher the meaning of a posted sign. The response of the sample is especially significant given that many respondents claimed that they would not eat at a restaurant with either a posted letter grade of C or a posted score of 86. Although these results do not come from a random sample, they nevertheless suggest that the public has a limited understanding of such signs and, at the same time, bases dining decisions on them. Thus, environmental health professionals must carefully consider how the public can be better educated about signs, how the signs can be less ambiguous, and whether posted restaurant inspection results are even advisable in their current form.
Foodborne illness surveillance based on consumer complaints detects outbreaks by finding common exposures among callers, but this process is often difficult. Laboratory testing of ill callers could also help identify potential outbreaks. However, collection of stool samples from all callers is not feasible. Methods to help screen calls for etiology are needed to increase the efficiency of complaint surveillance systems and increase the likelihood of detecting foodborne outbreaks caused by Salmonella. Data from the Minnesota Department of Health foodborne illness surveillance database (2000 to 2008) were analyzed. Complaints with identified etiologies were examined to create a predictive model for Salmonella. Bootstrap methods were used to internally validate the model. Seventy-one percent of complaints in the foodborne illness database with known etiologies were due to norovirus. The predictive model had a good discriminatory ability to identify Salmonella calls. Three cutoffs for the predictive model were tested: one that maximized sensitivity, one that maximized specificity, and one that maximized predictive ability, providing sensitivities and specificities of 32 and 96%, 100 and 54%, and 89 and 72%, respectively. Development of a predictive model for Salmonella could help screen calls for etiology. The cutoff that provided the best predictive ability for Salmonella corresponded to a caller reporting diarrhea and fever with no vomiting, and five or fewer people ill. Screening calls for etiology would help identify complaints for further follow-up and result in identifying Salmonella cases that would otherwise go unconfirmed; in turn, this could lead to the identification of more outbreaks.