An analysis of antibiotic prescribing practices for enteric bacterial infections within FoodNet Canada sentinel sites.

OBJECTIVES: Understanding the current state of antibiotic treatment guidelines and prescribing practices for bacterial enteric infections is critical to inform antibiotic stewardship initiatives. This study aims to add to the current understanding through three objectives: (i) to identify and summarize published treatment guidelines for bacterial enteric infections; (ii) to describe observed antibiotic prescribing practices for bacterial enteric infections across three sentinel sites in Canada; and (iii) to assess concordance between observed antibiotic prescribing and treatment guidelines. METHODS: An environmental scan of treatment guidelines for bacterial enteric infections was conducted and recommendations were collated. A descriptive analysis of cases of bacterial enteric illnesses captured in FoodNet Canada's sentinel site surveillance system between 2010 and 2018 was performed. Antibiotic-use data were self-reported by cases via an enhanced questionnaire. RESULTS: Ten treatment guidelines were identified in the environmental scan. There was substantial variation between guidelines for both when to prescribe antibiotics and which antibiotics were recommended. Of the 5877 cases of laboratory-confirmed bacterial enteric illness in the three sites, 49% of cases reported having received an antibiotic prescription. Of particular significance was the finding that 21% of verotoxigenic Escherichia coli cases received a prescription. Of the 17 antibiotics recommended in the guidelines, 14 were used in practice. In addition to these, 18 other antibiotics not included in any of the guidelines reviewed were also prescribed. CONCLUSIONS: Our study suggests that a substantial proportion of enteric bacterial infections in Canada are prescribed antibiotics. These findings highlight the need to standardize treatment guidelines for enteric illnesses and could be used to inform future stewardship programme development.

Outbreak of Salmonella Chailey Infections Linked To Precut Coconut Pieces - United States and Canada, 2017.

Foodborne salmonellosis causes an estimated 1 million illnesses and 400 deaths annually in the United States (1). In recent years, salmonellosis outbreaks have been caused by foods not typically associated with Salmonella. On May 2, 2017, PulseNet, CDC's national molecular subtyping network for foodborne disease surveillance, identified a cluster of 14 Salmonella Chailey isolates with a rare pulsed-field gel electrophoresis (PFGE) pattern. On May 29, Canadian health officials informed CDC that they were also investigating a cluster of five Salmonella Chailey infections in British Columbia with the same PFGE pattern. Nineteen cases were identified and investigated by CDC, U.S. state health departments, the Public Health Agency of Canada, and the British Columbia Centre for Disease Control. Isolates from all cases were highly related by whole genome sequencing (WGS). Illness onset dates ranged from March 10 to May 7, 2017. Initial interviews revealed that infected persons consumed various fresh foods and shopped at grocery chain A; focused questionnaires identified precut coconut pieces from grocery chain A as a common vehicle. The Canadian Food Inspection Agency (CFIA) and the U.S. Food and Drug Administration (FDA) conducted a traceback investigation that implicated a single lot of frozen, precut coconut as the outbreak source. Grocery chain A voluntarily removed precut coconut pieces from their stores. This action likely limited the size and scope of this outbreak.

Using environmental health officers' opinions to inform the source attribution of enteric disease: further analysis of the "most likely source of infection".

BACKGROUND: Policies and programs are needed to mitigate the burden of enteric disease in Canada. Source attribution, a goal of FoodNet Canada, can inform such strategies and can be accomplished with the information provided by expert opinion. This includes environmental health officers' (EHOs) opinions on the "most likely source of infection" (MLSI) of confirmed cases of enteric disease that are investigated by the Fraser Health Authority in British Columbia, FoodNet Canada's second sentinel site. METHODS: Exposure data from the MLSI were categorized into ten groups and summarized for five enteric disease groups using endemic cases in the first analysis, and a combination of endemic and international travel cases for the second analysis. An exploratory analysis was also conducted on risk setting information in the MLSI. The final analysis involved using a logistic regression model (Wald test) to describe the inherent biases in the data. RESULTS: Exposure proportions, by disease group, were similar to those of an analysis of MLSI data from FoodNet Canada's Ontario sentinel site. Food exposure represented the greatest proportion of overall enteric disease (32.0%), as well as for salmonellosis (45.0%), verotoxigenic E. coli (VTEC) infection (38.1%), and campylobacteriosis (30.0%) cases. The majority of parasitic diseases (41.2%) were attributed to water exposure. Food safety practices and consuming unpasteurized products were more frequently reported for campylobacteriosis (19.7% and 5.4%, respectively) compared to other enteric diseases. More VTEC infection was attributed to domestic travel (4.8%) than the other enteric diseases. Among endemic and international travel-related cases combined, VTEC infection was attributed more to endemic food exposure (35.5%) than international travel (16.1%), but similar proportions of campylobacteriosis were attributed to endemic food exposure (25.1%) and international travel (25.1%). Variations existed in the exposure and risk setting information that EHOs included in the MLSI, and in their propensity to enter food sources over other types of exposures. CONCLUSIONS: Results from the MLSI analysis for exposure, risk setting, and EHO bias, are valid contributions for informing source attribution. Important considerations from this work, including strategies to standardize and improve the quality of MLSI data, will enhance source attribution hypotheses.