Comparison of pathogen-derived ‘total risk’ with indicator-based correlations for recreational (swimming) exposure

Abstract

Typical recreational water risk to swimmers is assessed using epidemiologically derived correlations by means of fecal indicator bacteria (FIB). It has been documented that concentrations of FIB do not necessarily correlate well with protozoa and viral pathogens, which pose an actual threat of illness and thus sometimes may not adequately assess the overall microbial risks from water resources. Many of the known pathogens have dose-response relationships; however, measuring water quality for all possible pathogens is impossible. In consideration of a typical freshwater receiving secondarily treated effluent, we investigated the level of consistency between the indicator-derived correlations and the sum of risks from six reference pathogens using a quantitative microbial risk assessment (QMRA) approach. Enterococci and E. coli were selected as the benchmark FIBs, and norovirus, human adenovirus (HAdV), Campylobacter jejuni, Salmonella enterica, Cryptosporidium spp., and Giardia spp. were selected as the reference pathogens. Microbial decay rates in freshwater and uncertainties in exposure relationships were considered in developing our analysis. Based on our exploratory assessment, the total risk was found within the range of risk estimated by the indicator organisms, with viral pathogens as dominant risk agents, followed by protozoan and bacterial pathogens. The risk evaluated in this study captured the likelihood of gastrointestinal illnesses only, and did not address the overall health risk potential of recreational waters with respect to other disease endpoints. Since other highly infectious pathogens like hepatitis A and Legionella spp. were not included in our analysis, these estimates should be interpreted with caution.

Microbial Indicators of Fecal Pollution: Recent Progress and Challenges in Assessing Water Quality

“…Indicator-based risk assessment requires defining the relationships between the indicator concentration and the index pathogens selected for consideration, typically a subset of pathogens expected to account for the majority of the risk and for which dose-response relationships have been characterized [55,175]. A substantial body of research characterizing processes affecting indicator-pathogen relationships has culminated in the recent publication of several comprehensive reviews and metaanalyses of the occurrence, transport, and persistence of indicators and common index pathogens in fecal waste streams and surface water [17,27,[177][178][179].…”

Occurrence of coliphage in raw wastewater and in ambient water: A meta-analysis

“…Evidence from microbial risk assessments (Soller et al 2010;Soller et al 2015;Sunger et al 2018) and epidemiology studies (Cabelli et al 1982, Lee et al 1997, Colford et al 2005, Wiedenmann et al 2006, Colford et al 2007, Wade et al 2010, Griffith et al 2016 illustrates that viruses cause the majority of gastrointestinal illnesses associated with primary contact recreation in surface waters impacted by human sources. Further, U.S. outbreak surveillance data point to viruses as the leading pathogen group responsible for ambient recreational water outbreaks (Jiang et al 2007, Sinclair et al 2009, Hlavsa 2015.…”

A Somatic Coliphage Threshold Approach To Improve the Management of Activated Sludge Wastewater Treatment Plant Effluents in Resource-Limited Regions

“…Additionally, data from other contexts were used when contextualized data were lacking. Similar to other studies, it is likely that these assumptions influenced the model output (51,53,70). Although wastewater contains a wide-variety of disease-causing viruses, it is not possible to estimate the disease burden of all pathogens given the lack of disease-related and dose-response data.…”

“…Similarly, it was difficult to identify the dilution factor of the WWTP effluent entering the river due to constant fluctuations in river flow rates and volume. Consequently, this parameter was defined as a distribution between a conservative (99:1) and maximum (50,000:1) assumption (53). If the dilution factor was greater, then the cumulative annual disease burden estimates would be much lower.…”

“…Nevertheless, it is important to recognize that the dose-response curves and parameter distributions may not reflect realistic human populations, which can highly influence model outputs (53,70). This is particularly true for EV, which is based upon a non-human model (51,58), and NoVGI because there is no agreement on which parameters are most appropriate (62).…”