We report an outbreak of Shiga toxin-producing Escherichia coli (STEC) associated paediatric haemolytic uraemic syndrome linked to the consumption of raw cow’s milk soft cheeses. From 25 March to 27 May 2019, 16 outbreak cases infected with STEC O26 (median age: 22 months) were identified. Interviews and trace-back investigations using loyalty cards identified the consumption of raw milk cheeses from a single producer. Trace-forward investigations revealed that these cheeses were internationally distributed.

Hundreds of waterborne disease outbreaks (WBDO) of acute gastroenteritis (AGI) due to contaminated tap water are reported in developed countries each year. Such outbreaks are probably under-detected. The aim of our study was to develop an integrated approach to detect and study clusters of AGI in geographical areas with homogeneous exposure to drinking water. Data for the number of AGI cases are available at the municipality level while exposure to tap water depends on drinking water networks (DWN). These two geographical units do not systematically overlap. This study proposed to develop an algorithm which would match the most relevant grouping of municipalities with a specific DWN, in order that tap water exposure can be taken into account when investigating future disease outbreaks. A space-time detection method was applied to the grouping of municipalities. Seven hundred and fourteen new geographical areas (groupings of municipalities) were obtained compared with the 1,310 municipalities and the 1,706 DWN. Eleven potential WBDO were identified in these groupings of municipalities. For ten of them, additional environmental investigations identified at least one event that could have caused microbiological contamination of DWN in the days previous to the occurrence of a reported WBDO.

Waterborne disease outbreaks (WBDO) of acute gastrointestinal illness (AGI) are a public health concern in France. Their occurrence is probably underestimated due to the lack of a specific surveillance system. The French health insurance database provides an interesting opportunity to improve the detection of these events. A specific algorithm to identify AGI cases from drug payment reimbursement data in the health insurance database has been previously developed. The purpose of our comparative study was to retrospectively assess the ability of the health insurance data to describe WBDO. Data from the health insurance database was compared with the data from cohort studies conducted in two WBDO in 2010 and 2012. The temporal distribution of cases, the day of the peak and the duration of the epidemic, as measured using the health insurance data, were similar to the data from one of the two cohort studies. However, health insurance data accounted for 54 cases compared to the estimated 252 cases accounted for in the cohort study. The accuracy of using health insurance data to describe WBDO depends on the medical consultation rate in the impacted population. As this is never the case, data analysis underestimates the total number of AGI cases. However this data source can be considered for the development of a detection system of a WBDO in France, given its ability to describe an epidemic signal.