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.