Katri Jalava

An Outbreak of Gastrointestinal Illness and Erythema Nodosum from Grated Carrots Contaminated with Yersinia pseudotuberculosis

BACKGROUND Outbreaks of Yersinia pseudotuberculosis infection have been epidemiologically linked to fresh produce, but the bacterium has not been recovered from the food items implicated. In May 2003, a cluster of gastrointestinal illness and erythema nodosum was detected among schoolchildren who had eaten lunches prepared by the same institutional kitchen. METHODS We conducted a case-control study and trace-back, environmental, and laboratory investigations. Case patients had culture-confirmed Y. pseudotuberculosis O:1 infection, erythema nodosum, or reactive arthritis. Bacterial isolates from clinical and environmental samples were compared using pulsed-field gel electrophoresis (PFGE). RESULTS Of 7392 persons at risk, 111 (1.5%) met the case definition; 76 case patients and 172 healthy control subjects were enrolled in the case-control study. Only raw grated carrots were significantly associated with illness in a logistic-regression model (multivariable odds ratio, 5.7 [95% confidence interval, 1.7-19.5]); a dose response was found for increasing amount of consumption. Y. pseudotuberculosis O:1 isolates from 39 stool specimens and from 5 (42%) of 12 soil samples that contained carrot residue and were obtained from peeling and washing equipment at the production farm were indistinguishable by PFGE. CONCLUSIONS Carrots contaminated early in the production process caused a large point-source outbreak. Our findings enable the development of evidence-based strategies to prevent outbreaks of this emerging foodborne pathogen.

Novel Microbiological and Spatial Statistical Methods to Improve Strength of Epidemiological Evidence in a Community-Wide Waterborne Outbreak

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9–16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak.

Agricultural, socioeconomic and environmental variables as risks for human verotoxigenic Escherichia coli (VTEC) infection in Finland.

BackgroundVerotoxigenic E. coli (VTEC) is the cause of severe gastrointestinal infection especially among infants. Between 10 and 20 cases are reported annually to the National Infectious Disease Register (NIDR) in Finland. The aim of this study was to identify explanatory variables for VTEC infections reported to the NIDR in Finland between 1997 and 2006. We applied a hurdle model, applicable for a dataset with an excess of zeros.MethodsWe enrolled 131 domestically acquired primary cases of VTEC between 1997 and 2006 from routine surveillance data. The isolated strains were characterized by virulence type, serogroup, phage type and pulsed-field gel electrophoresis. By applying a two-part Bayesian hurdle model to infectious disease surveillance data, we were able to create a model in which the covariates were associated with the probability for occurrence of the cases in the logistic regression part and the magnitude of covariate changes in the Poisson regression part if cases do occur. The model also included spatial correlations between neighbouring municipalities.ResultsThe average annual incidence rate was 4.8 cases per million inhabitants based on the cases as reported to the NIDR. Of the 131 cases, 74 VTEC O157 and 58 non-O157 strains were isolated (one person had dual infections). The number of bulls per human population and the proportion of the population with a higher education were associated with an increased occurrence and incidence of human VTEC infections in 70 (17%) of 416 of Finnish municipalities. In addition, the proportion of fresh water per area, the proportion of cultivated land per area and the proportion of low income households with children were associated with increased incidence of VTEC infections.ConclusionsWith hurdle models we were able to distinguish between risk factors for the occurrence of the disease and the incidence of the disease for data characterised by an excess of zeros. The density of bulls and the proportion of the population with higher education were significant both for occurrence and incidence, while the proportion of fresh water, cultivated land, and the proportion of low income households with children were significant for the incidence of the disease.

Binary Regression Models with Log-Link in the Cohort Studies

Regression models have been used to control confounding in food borne cohort studies, logistic regression has been commonly used due to easy converge. However, logistic regression provide estimates for OR only when RR estimate is lower than 10%, an unlikely situation in food borne outbreaks. Recent developments have resolved the binary model convergence problems applying log link. Food items significant in the univariable analysis were included for the multivariable analysis of two recent Finnish norovirus outbreaks. We used both log and logistic regression models in R and Bayesian model in Winbugs by SPSS and R. The log-link model could be used to identify the vehicle in the two norovirus outbreak datasets. Convergence problems were solved using Bayesian modelling. Binary model applying log link provided accurate and useful estimates of RR estimating the true risk, a suitable method of choice for multivariable analysis of outbreak cohort studies.