Data fitting approach more critical than exposure scenarios and treatment of censored data for quantitative microbial risk assessment

Recreational waters are a source of many diseases caused by human viral pathogens, including norovirus genogroup II (NoV GII) and enterovirus (EV). Water samples from the Arenales river in Salta, Argentina, were concentrated by ultrafiltration and analyzed for the concentrations of NoV GII and EV by quantitative PCR. Out of 65 samples, 61 and 59 were non-detects (below the Sample Limit of Detection limit, SLOD) for EV and NoV GII, respectively. We hypothesized that a finite number of environmental samples would lead to different conclusions regarding human health risks based on how data were treated and fitted to existing distribution functions. A quantitative microbial risk assessment (QMRA) was performed and the risk of infection was calculated using: (a) two methodological approaches to find the distributions that best fit the data sets (methods H and R), (b) four different exposure scenarios (primary contact for children and adults and secondary contact by spray inhalation/ingestion and hand-to-mouth contact), and (c) five alternatives for treating censored data. The risk of infection for NoV GII was much higher (and exceeded in most cases the acceptable value established by the USEPA) than for EV (in almost all the scenarios within the recommended limit), mainly due to the low infectious dose of NoV. The type of methodology used to fit the monitoring data was critical for these datasets with numerous non-detects, leading to very different estimates of risk. Method R resulted in higher projected risks than Method H. Regarding the alternatives for treating censored data, replacing non-detects by a unique value like the average or median SLOD to simplify the calculations led to the loss of information about the particular characteristics of each sample. In addition, the average SLOD was highly impacted by extreme values (due to events such as precipitations or point source contamination). Instead, using the SLOD or half- SLOD captured the uniqueness of each sample since they account for the history of the sample including the concentration procedure and the detection method used. Finally, substitution of non-detects by Zero is not realistic since a negative result would be associated with a SLOD that can change by developing more efficient and sensitive methodology; hence this approach would lead to an underestimation of the health risk. Our findings suggest that in most cases the use of the half-SLOD approach is appropriate for QMRA modeling.