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Canadian Journal of Public Health = Revue Canadienne de Santé Publique logoLink to Canadian Journal of Public Health = Revue Canadienne de Santé Publique
. 2024 Dec 10;116(4):582–597. doi: 10.17269/s41997-024-00969-4

Quantitative microbial risk assessment of acute gastrointestinal illness attributable to freshwater recreation in Ontario

Henry Ngo 1, E Jane Parmley 2, Nicole Ricker 3, Charlotte Winder 2, Heather M Murphy 1,✉
PMCID: PMC12629550  PMID: 39658778

Abstract

Objectives

The burden of disease associated with acute gastrointestinal illness (AGI) in Canada is estimated to be ~ 20 million cases/year. One known risk factor for developing AGI is recreation in freshwater bodies such as lakes. The proportion of cases attributable to freshwater recreation in Canada, however, is currently unknown. The study objective was to estimate the risk of developing AGI from exposure to Giardia, Cryptosporidium, Campylobacter, Escherichia coli O157:H7, norovirus, and Salmonella during freshwater recreation in Ontario, Canada.

Methods

A quantitative microbial risk assessment (QMRA) was conducted to estimate the number of AGI cases per 1000 recreational events associated with freshwater recreation. QMRA utilizes four steps: hazard identification, exposure assessment, dose–response modelling, and risk characterization. A probabilistic model was developed using the following inputs accounting for uncertainty and variability: published data on pathogen prevalence and concentration in freshwaters in Ontario (hazard identification), recreator water ingestion volumes (exposure), pathogen-specific dose–response models, and ratios between numbers of infections and symptomatic disease cases to estimate illness risks (risk characterization).

Results

The mean estimated AGI risk associated with recreation ranged from 0.8 to 36.7 cases per 1000 swimmers (5th–95th probability interval: 0–226.3 cases/1000) which is in line with previous studies conducted in Lake Ontario, as well as prior QMRAs of freshwater recreation. Upper range predicted values exceeded the Health Canada guideline of less than 20 cases per 1000 recreators.

Conclusion

This study shows that QMRA can be used to estimate disease risk in the absence of large-scale epidemiological studies. The results demonstrate a range of risk that is in line with exposure to pristine (low risk estimates) and more contaminated waters (high risk estimates) and capture the potential risk to vulnerable populations.

Supplementary Information

The online version contains supplementary material available at 10.17269/s41997-024-00969-4.

Keywords: Quantitative microbial risk assessment, Freshwater recreation, Canada, Pathogens, Waterborne disease, Disease burden, Attribution, Enteric disease

Introduction

The Public Health Agency of Canada (PHAC) estimates that approximately 19.5 to 20.5 million AGI cases occur annually across Canada (Thomas et al., 2013, 2017). Of these cases, 4 million are estimated to be attributable to foodborne transmission, while 103,230 and 334,966 cases are estimated to be attributable to water use from small water systems or private wells and from large municipal tap water systems, respectively (Thomas & Murray, 2014; Murphy et al., 2016a, 2016b). Attribution of illness cases to different sources is important for understanding the drivers of enteric disease in Canada. With adequate data, attribution estimates may be the basis for effective resource allocation and targeted interventions to reduce enteric disease. Currently, however, no equivalent attribution estimates exist in Canada for AGI transmitted through freshwater recreation (Janicki et al., 2018).

Monitoring programs for enteric diseases in Canada such as through the National Enteric Disease Surveillance Program (NESP) and Ontario’s integrated Public Health Information System (iPHIS) does not capture the full extent of cases associated with freshwater recreation (Public Health Agency of Canada, 2020; Public Health Ontario, 2022). In addition, these systems record infections by causative pathogen instead of transmission route, and may be subject to underreporting due to under-ascertainment caused by under-diagnosis and under-reporting (Majowicz et al., 2005; Thomas et al., 2006).

Freshwater recreation may contribute to a large proportion of AGI cases in the Canadian population because of the abundance of freshwater bodies and popularity of water recreation. In 2010, approximately 1.4% of Canadians over the age of 15, and 24% below the age of 15 engaged in swimming activities at least once per week (Statistics Canada, 2013). More recent data from 2016 have also estimated that 22% of Canadians over age 15 engaged in secondary water-contact activities such as canoeing, kayaking, and motorboating at least once annually (Statistics Canada, 2017). Additionally, the diversity of possible secondary water-contact activities has increased in recent years to include activities such as kiteboarding, stand-up paddleboarding, and dragon boating. Globally, surface water recreation has been repeatedly identified as a risk factor for the development of AGI. One prior review found AGI to be the predominant adverse health outcome among water recreators, with 77.1% of studies reporting symptomatic cases (Adhikary et al., 2022). Other reviews have also shown that the relative risk of AGI for swimmers can range between two and three times greater than for non-swimmers (King et al., 2014; Prüss, 1998; Russo et al., 2020).

Modelling methods such as quantitative microbial risk assessment (QMRA) represent a practical means of estimating AGI risk on a national scale. The QMRA method presents several advantages including the ability to model illness risk for specific pathogens and exposure pathways, as well as the ability to quickly generate results in a cost-effective manner relative to large-scale epidemiological studies. Consequently, QMRA is a recommended approach by the World Health Organization (WHO) for evaluating microbial hazards associated with waterborne exposures (World Health Organization, 2016). In Canada, QMRAs are also recommended by Health Canada as a tool for evaluating the safety of drinking water systems (Health Canada, 2018). Previous analyses of QMRA estimates have demonstrated their results to be in line with epidemiological studies of similar scale and scope (Burch, 2019; Soller et al., 2016). In addition, this risk assessment approach has been successfully applied to estimate illness risk for recreators in both freshwater (McGinnis et al., 2022; Soller et al., 2017) and marine water in the United States (Soller et al., 2010), as well as in other regions such as Europe and Asia (Federigi et al., 2019).

The objective of this study was to conduct a preliminary QMRA to estimate the risk of AGI for freshwater recreators in Ontario, Canada. The results generated will identify data gaps and provide preliminary estimates for moving towards a Canada-wide burden estimate for AGI associated with freshwater recreation.

Methods

An overview of the QMRA model is presented in Fig. 1 and was conducted using four standard QMRA steps: hazard identification, exposure assessment, dose–response modelling, and risk characterization (Haas et al., 2014).

Fig. 1.

Fig. 1

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Overview of QMRA model

Hazard identification

Six enteric pathogens of concern for Canadian freshwater recreators were included in the QMRA: Giardia, Cryptosporidium, Campylobacter, Escherichia coli O157:H7, norovirus, and Salmonella. These pathogens were identified by cross-referencing Health Canada’s recreational water guidelines with PHAC’s 2018 FoodNet Canada report on enteric disease surveillance data (Health Canada, 2012; PHAC, 2019). Apart from Escherichia coli O157:H7, which was chosen to account for risk factors from recreation in rural waterways such as agricultural activities, pathogens were selected if they were simultaneously reported as a leading cause of AGI incidence in Canada and included on the list of national notifiable diseases (PHAC, 2009b). Pathogen concentrations in surface waters for the QMRA were taken from data compiled in a prior review used to inform a drinking water burden of disease QMRA by Murphy et al. (2016b). This review was supplemented by an additional literature search of studies published after the Murphy et al. (2016b) review was completed (date range 2014–2022). The review focused on retrieving papers from Ontario and the Great Lakes region that monitored for pathogens and presented both prevalence and concentration data. A summary of all the data examined can be found in the Supplementary Material (Table S1). The final model inputs, including distributions, are presented in Table 1.

Table 1.

Inputs for modelling the probability of pathogen detection in water and range of pathogen concentrations

Exposure assessment: Pathogen inputs
Parameter Distribution Units Data sources used to generate distributions
Probability that sample has Giardia Binomial(1, (Pert(0, 25.07, 77.78)/100)) Unitless (Butler et al., 2021; Dorner et al., 2007; Lenaker et al., 2017; Murphy et al., 2016b; Payment et al., 2000; Schets et al., 2008; Wilkes et al., 2009, 2011)
Standardized possible concentration range of Giardia Weibull(0.735, 3.52) Cysts/L (Payment et al., 2000)
Probability that sample has Cryptosporidium Binomial(1, (Pert(0, 71.33, 77.78)/100)) Unitless (Corsi et al., 2016; Lenaker et al., 2017; Sales-Ortells et al., 2015; Schets et al., 2008)(Butler et al., 2021; Corsi et al., 2016; Lenaker et al., 2017; Murphy et al., 2016b; Payment et al., 2000; Pintar et al., 2012; Ruecker et al., 2007, 2012; Sales-Ortells et al., 2015; Schets et al., 2008; Wilkes et al., 2009, 2011, 2013)
Standardized possible concentration range of Cryptosporidium Lognorm(0.25, 2.453) Oocysts/L (Wilkes et al., 2011)
Probability that sample has E. coli O157:H7 Binomial(1, (Pert(0, 1.35, 8.5)/100)) Unitless (Corsi et al., 2016; Dorner et al., 2007; Johnson et al., 2003; Jokinen et al., 2011; Lenaker et al., 2017; Petit et al., 2017; Wilkes et al., 2009, 2011)
Standardized concentration range of E. coli O157:H7 Uniform (0.001, 0.01) CFU1/L (Murphy et al., 2016b; Won et al., 2013)
Probability that sample has Campylobacter Binomial(1, (Pert(0, 34.65, 90)/100)) Unitless (Butler et al., 2021; Corsi et al., 2016; Dorner et al., 2007; Jokinen et al., 2011; Khan et al., 2013; Lenaker et al., 2017; Sales-Ortells et al., 2015; St-Pierre et al., 2009; Van Dyke et al., 2010; Wilkes et al., 2009, 2011)
Standardized concentration range of Campylobacter Lognorm(58, 325) CFU1/L (Corsi et al., 2016)
Probability that sample has norovirus Binomial(1, (Pert(0, 6.25, 10)/100)) Unitless (Corsi et al., 2014, 2016; Lenaker et al., 2017; Lodder & de Roda Husman, 2005; Sedji et al., 2018; Westrell et al., 2006; Wyn-Jones et al., 2011)
Standardized concentration range of norovirus Lognorm(13, 100) gc2/L (Corsi et al., 2016)
Probability that sample has Salmonella Binomial(1, (Pert(0.3, 24.27, 79.17)/100)) Unitless (Corsi et al., 2016; Haley et al., 2009; Lenaker et al., 2017; Weidhaas et al., 2018)
Standardized concentration range of Salmonella Lognorm(0.2, 258.25) CFU1/L (Corsi et al., 2016)
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1 CFU/L colony-forming units per litre

2gc/L genomic copies per litre

A probabilistic model was chosen to account for variability and uncertainty in model inputs. Binomial distributions were used to model the probability of pathogen detection/presence using pathogen prevalence data reported in the literature (i.e., number of positive samples over total samples). Pathogen concentrations in water were modelled by fitting PERT, lognormal, and Weibull distributions to encompass the range, means, and medians reported in literature. In instances where a pathogen was not detected, it was assumed to have a concentration of 0. A detailed description of the process used to generate each distribution is presented in Text S1 and Tables S2 and S3. Pathogen concentrations reported using different units than those required by dose–response models were converted using published ratios from the literature (Table 2).

Table 2.

Conversion ratios for pathogen concentrations

Pathogen Conversion Source
Giardia 1 gc/L = 4.5 cysts/L (Stokdyk et al., 2020)
Cryptosporidium 1 gc/L = 13.5 oocysts/L (Stokdyk et al., 2019)
Campylobacter 0.073 gc/L = 1 CFU/L (Corsi et al., 2016)
E. coli 5.6 gc/L = 1 CFU/L (Corsi et al., 2016)
Norovirus3 N/A N/A
Salmonella 5.6 gc/L = 1 CFU/L (Corsi et al., 2016)
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All values were standardized to litre volumes. The standard units used for Cryptosporidium and Giardia were cysts and oocysts, respectively. The standard unit for bacterial pathogens was CFU (colony-forming units)

Exposure assessment and dose–response modelling

Exposure and dose–response inputs for this QMRA were based on models previously used for recreational water in the literature (McGinnis et al., 2022). For this study, the risk associated with primary water-contact activities (swimming and wading) was estimated as these activities represent the scenarios with the greatest water exposure, and also a secondary contact activity (fishing), with lower direct water exposure. Primary and secondary contact events were estimated to be at 30 and 60 min in duration respectively. Event durations chosen for each activity were derived from studies observing behaviours of water recreators (Dufour et al., 2017; McGinnis et al., 2022). The corresponding ingestion volumes for each activity were calculated using mL/60-min ratios (Table 3). Thirty-minute recreation events were assumed to result in half the ingestion volume of 60-min events.

Table 3.

Exposure distributions for swimming, wading, and fishing

Exposure assessment inputs
Parameter Distribution Units Source
Volume ingested per 60-min swimming event Log10-normal (1.154, 0.557) mL/60 min (Boehm et al., 2018)
Volume ingested per 60-min wading event Ln-normal (1.31, 1.74) mL/60 min (Dufour et al., 2017)
Volume ingested per 60-min fishing event Ln-normal (1.28, 1.72) mL/60 min (Dorevitch et al., 2011)
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Dose–response models for each pathogen were selected using a similar rationale to McGinnis et al. (2022) and are summarized in Table 4. Models were selected based on study characteristics such as sample size, ability to model extraneous factors such as virus aggregation (norovirus), and representation in the literature of being used in prior recreational water QMRAs. Campylobacter and norovirus were modelled using two different dose–response models predicting greater (Teunis et al., 2005, 2008b) or lower (Schmidt et al., 2013; Schmidt, 2015) risk after exposure regardless of dose, respectively.

Table 4.

Dose–response models utilized

Dose–response inputs
Parameter Distribution Source
Giardia dose–response Exponential (r = 0.0199) (Rose et al., 1991)
Cryptosporidium dose–response Exponential (r = 0.028) (Messner et al., 2001)
E. coli dose–response Exact beta-Poisson (a = 0.248, b = 48.8) (Teunis et al., 2008a)
Campylobacter dose–response 11 Hypergeometric beta-Poisson (a = 0.1453, b = 8.007) (Schmidt et al., 2013)
Campylobacter dose–response 21 Hypergeometric beta-Poisson (a = 0.024, b = 0.011) (Teunis et al., 2005)
Norovirus dose–response 11 Hypergeometric beta-Poisson (a = 0.04, b = 0.055) (Teunis et al., 2008b)
Norovirus dose–response 21 Hypergeometric beta-Poisson (a = 2.91, beta = 2734) (Schmidt, 2015)
Salmonella dose–response Exponential (r = 0.00752) (Rose et al., 1995)
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1Two models were used for Campylobacter and norovirus to account for the range of infection risk associated with different data and assumptions used to create the models. Campylobacter model 2 and norovirus model 1 predict higher infection risk, while Campylobacter model 1 and norovirus model 2 predict lower infection risk

Risk characterization

Separate models for each recreational activity were developed to account for differences in exposure rates. Individual risk after exposure to any pathogen was defined as the probability of symptomatic illness and calculated as a fraction of the probability of infection after exposure using published conversion ratios from the EPA (US EPA, 2010) for Giardia, Cryptosporidium, and Salmonella, and the literature for Campylobacter (Soller et al., 2010), E. coli O157:H7 (Teunis et al., 2008a), and norovirus (Teunis et al., 2008b). The models used for these calculations are presented in Table 5. Sensitivity analyses using Spearman correlations were generated to determine the primary inputs influencing the probability of infection in the model. All analyses were conducted in @Risk Version 8.4.0 (Palisade, 2022).

Table 5.

Distributions used to convert predicted infections to symptomatic illnesses

Risk characterization: Infection to illness models
Parameter Distribution Units Source
Illnesses per Giardia infection Uniform (0.2, 0.7) N/A (US EPA, 2010)
Illnesses per Cryptosporidium infection Uniform (0.2, 0.7) N/A (US EPA, 2010)
Illnesses per Campylobacter infection Pert (0.1,0.28,0.6) N/A (Soller et al., 2010)
Illnesses per E. coli O157:H7 infection Point estimate (1) N/A Teunis et al., 2008a
Illnesses per norovirus infection Uniform (0.3,0.8) N/A Teunis et al., 2008b
Illnesses per Salmonella infection Point estimate (0.2) N/A (US EPA, 2010)
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This calculation was not carried out for E. coli O157:H7 exposure as the dose–response output was illnesses as opposed to infections

Results

Four multi-pathogen risk estimates (A, B, C, and D) were generated to account for using two different dose–response models for Campylobacter and norovirus. The combinations for the dose–response models used for Campylobacter and norovirus in each multi-pathogen model are summarized in Table 6. For ease of discussion, the Campylobacter and norovirus dose–response models that predict higher risk (norovirus model 1; Campylobacter model 2) will be referred to as “high risk” and the ones that produce lower risk estimates will be called “low risk” (norovirus model 2; Campylobacter model 1). Models A and B both utilized the high risk norovirus dose–response model (Teunis et al., 2008b) with model A including the low risk Campylobacter dose–response model (Schmidt et al., 2013) and model B including the higher risk Campylobacter dose–response model (Teunis et al., 2005). Models C and D shared the lower risk norovirus dose–response model (Schmidt, 2015) with model C including the lower risk Campylobacter dose–response model (Schmidt et al., 2013) and model D including the high risk Campylobacter model.

Table 6.

Campylobacter and norovirus dose–response model combinations used for models A, B, C, and D

Campylobacter low risk (Schmidt et al., 2013) Campylobacter high risk (Teunis et al., 2005)
Norovirus high risk (Teunis et al., 2008b) A B
Norovirus low risk (Schmidt, 2015) C D
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The estimated AGI risk per 1000 recreation events from exposure to Giardia, Cryptosporidium, Campylobacter, E. coli O157:H7, norovirus, and Salmonella is summarized in Table 7. In all instances, models B and D predicted a greater risk than models A and C. For models B and D, the risks associated with wading and fishing were approximately half that associated with swimming. The mean estimated numbers of illnesses for swimming were 35.7 and 36.7 cases per 1000 events for models B and D, respectively. The probability interval (PI) 5th and 95th percentiles were 0 to 224.0 cases and 0 to 226.3 cases per 1000 events for models B and D, respectively. The mean and 95th percentiles values exceeded the US EPA and Health Canada primary recreation guidelines of 32 and 20 cases per 1000 recreation events, respectively (Health Canada, 2012; US EPA, 2012). For models A and C, the mean estimated risks for swimmers were 1.8 and 0.8 cases per 1000 events, and the probability interval 5th and 95th percentiles were 0 to 3.7 cases and 0 to 5.9 cases, respectively.

Table 7.

Estimated AGI risk per swimming, wading, and fishing exposure associated with any pathogen

Swimming: Cases of illness per 1000 swimming events
Model A Model B Model C Model D
Mean 1.8 36.7 0.8 35.7
5th percentile 0.0 0.0 0.0 0.0
50th percentile 0.1 0.3 0.1 0.2
95th percentile 5.9 226.3 3.7 224.0
Wading: Cases of illness per 1000 wading events
Model A Model B Model C Model D
Mean 1.1 18.8 0.4 18.1
5th percentile 0.0 0.0 0.0 0.0
50th percentile 0.0 0.1 0.0 0.1
95th percentile 2.4 92.0 1.5 86.0
Fishing: Cases of illness per 1000 fishing events
Model A Model B Model C Model D
Mean 1.0 19.0 0.5 18.5
5th percentile 0.0 0.0 0.0 0.0
50th percentile 0.0 0.1 0.0 0.1
95th percentile 2.5 97.7 1.5 93.9
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The estimated individual AGI risk per recreation event based on exposure to each pathogen is summarized in Figs. 2, 3, and 4. Across all models and recreational activities, a negligible risk was associated with Cryptosporidium and E. coli O157:H7 exposure. For models B and D, the largest AGI risk across all recreation types was primarily associated with Campylobacter and norovirus with a lower level of risk associated with Salmonella and Giardia. For models A and C, the primary pathogens associated with risk were reversed with Salmonella and Giardia being the highest, followed by Campylobacter and norovirus. The differences in pathogen-specific risk estimates produced using the different Campylobacter and norovirus dose–response models were 1log10 and 2log10, respectively.

Fig. 2.

Fig. 2

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Estimated AGI risk per swimming event. Crosses within boxes represent the mean number of illnesses. Boxes represent the 25th to 75th percentile range of estimated risk with tails representing 5th and 95th values. Numbered Campylobacter and norovirus results refer to the different dose–response models applied

Fig. 3.

Fig. 3

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Estimated AGI risk per wading exposure. Crosses within boxes represent the mean number of illnesses. Boxes represent the 25th to 75th percentile range of estimated risk with tails representing 5th and 95th values. Numbered Campylobacter and norovirus results refer to the different dose–response models applied

Fig. 4.

Fig. 4

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Estimated AGI risk per fishing event. Crosses within boxes represent the mean number of illnesses. Boxes represent the 25th to 75th percentile range of estimated risk with tails representing 5th and 95th values. Numbered Campylobacter and norovirus results refer to the different dose–response models applied

Sensitivity analysis

The results of the sensitivity analyses are presented in Tables S4, S5, and S6 for the swimming, wading, and fishing models, respectively. The primary input driving risk in all models was the probability of Campylobacter being present in waterways, followed by the volume of water ingested during recreation events. The third and fourth most contributing inputs for all models and recreational activities were the binomial probability of Giardia being present followed by the probability of norovirus being present for model A, and Salmonella for models B through D.

Discussion

Conditions at freshwater recreation sites in Ontario should be expected to be anywhere from pristine to contaminated. Recreation sites are subject to differing weather conditions and varying amounts of human and animal interaction. Possible sources of microbial contaminants that may impact these sites include stormwater or agricultural runoff, wildlife, treated wastewater effluent, combined sewer overflows (CSO), and leaking septic systems (Dixon et al., 2011; Jokinen et al., 2011; Mattison et al., 2007; Michel et al., 1999; Moorehead et al., 1990; Statistics Canada, 2019). The predicted risk estimates capture the range in potential contamination sources impacting recreational waterways across the province of Ontario.

Using quantitative microbial risk assessment, a preliminary estimate for recreational waterborne disease risk associated with exposure to Giardia, Cryptosporidium, Campylobacter, E. coli O157:H7, norovirus, and Salmonella from swimming for the province of Ontario, Canada, was developed. Depending on the dose–response models used for norovirus and Campylobacter, the mean estimated number of illnesses ranged from 0.8 to 36.7 cases per 1000 swimming events with 5th and 95th probability intervals ranging from 0 to 226.3 cases per 1000 swimming events.

The higher risk estimates produced by models B and D may reflect the risk faced by children under age 5 in Ontario as a result of the Campylobacter dose–response model being based on outbreak data among children (Teunis et al., 2005). Additionally, the results for models B and D were similar to those of prior studies of younger children which observed an AGI risk of up to 1.67 times greater than that of adults (Arnold et al., 2016; Wade et al., 2008) as a result of behaviours such as longer swim durations, greater frequencies of swim events, greater frequencies of head immersions, and increased water ingestion (Schets et al., 2011). In addition, these higher risk estimates may also be potentially representative of other vulnerable subpopulations such as immunocompromised individuals, individuals over age 65, and pregnant women (Gerba et al., 1996).

The mean and 95th percentiles values of the higher risk models exceeded the US EPA and Health Canada primary recreation guidelines of 32 and 20 cases per 1000 recreation events, respectively (Health Canada, 2012; US EPA, 2012). These results are in line with prior QMRAs and epidemiological studies on recreational waterborne disease.

Comparisons to prior QMRAs

Urban QMRAs

Results from the Ontario QMRA model presented herein were in line with several prior recreational water QMRAs conducted under various water quality settings (Table 8). In Puerto Rico, a QMRA conducted by Soller et al. (2010) estimated that recreating in waters influenced by treated wastewater could result in an AGI risk of 2 cases per 1000 swimmers. These results were approximate to the mean risk predicted by models A and C of 1.8 and 0.8 cases, respectively. The Ontario model was expected to underpredict relative to Soller et al. (2010) because the current model did not account for illnesses associated with enterovirus or adenovirus. In contrast, a similar QMRA of coastal waters in the USA by Schoen and Ashbolt (2010) for waters impacted by treated sewage effluent predicted a greater risk of 3.1 cases per 1000 swimmers using only norovirus, Giardia, Cryptosporidium, and Salmonella as reference pathogens.

Table 8.

Results of the present study relative to prior reported risk values in QMRAs conducted in various water quality conditions

Study Study location Waterway type Pathogens Mean (cases/1000) Median (cases/1000) Range (cases/1000)
Current Study (this article) Ontario FW1; urban and rural, various conditions Giardia, Cryptosporidium, Campylobacter, E. coli O157:H7, norovirus, and Salmonella 0.8 to 36.7 0.1 to 0.3 3.7 to 226.3
(McGinnis et al., 2022) Philadelphia FW; urban, CSO impacted Bacteroides HF183, Giardia, Cryptosporidium, E. coli O157:H7, and norovirus

4 to 31

39 to 250
(Soller et al., 2017) San Diego SW2; urban, stormwater impacted Norovirus, adenovirus, enterovirus, Campylobacter jejuni, and Salmonella enterica 0.6 25.2
36 226.2
(McBride et al., 2013) California FW and SW; stormwater impacted Salmonella, Cryptosporidium, Giardia, enterovirus, adenovirus, norovirus, and rotavirus 47.5 to 81.2
(Wilkes et al., 2013) Ontario FW; rural, cattle impacted Cryptosporidium, Giardia, and E. coli O157:H7  ~ 1  ~ 1
(Pintar et al., 2010) Ontario FW; lakes, fecal impacted Cryptosporidium 4.3 to 9.9 3.6 to 8.2 11.1 to 25.6
FW; rivers, fecal impacted 1.53E − 02 to 3.54E − 02 7.2E − 03 to 1.7E − 02 5.5E − 02 to 0.1
(Schoen & Ashbolt, 2010) USA Unspecified; impacted by treated WW3 Norovirus, Giardia, Cryptosporidium, and Salmonella 3.1
Unspecified; fecal impacted Campylobacter jejuni and Salmonella enterica − 0.036
(Soller et al., 2010) Puerto Rico SW; urban; impacted by treated WW Norovirus, adenovirus, Cryptosporidium, Giardia, and Salmonella  ~ 2
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1FW freshwater

2SW saltwater

3WW wastewater

Recreation in untreated sewage-impacted freshwater as reported by McGinnis et al. (2022) in the city of Philadelphia showed higher risk estimates. The mean reported risk in this US study was 4‒31 cases per 1000 recreators and 39‒250 cases per 1000 recreators depending on the norovirus dose–response model used. These values were approximate to the 95th percentile estimates produced from models A and C, and models B and D respectively, and suggest that the Ontario model’s higher-end risk estimates are representative of urban waterways impacted by human sewage and stormwater run-off. Differences between the Ontario model and the Philadelphia estimates may also be attributed to differences in methodology. The Philadelphia model used concentration measurements of human Bacteroides (HF183), a human sewage marker, to estimate pathogen concentrations in waterways and then recreator risk in turn (McGinnis et al., 2022). Pathogens were consequently assumed to be almost always present in the Philadelphia estimate as human Bacteroides density in sewage-impacted waters was consistently elevated. Conversely, the current Ontario estimate used literature data directly reporting pathogen prevalence and concentration across a range of waterways, thereby assuming that they were only conditionally present.

In another higher-risk scenario involving stormwater-impacted waters, the current Ontario model predicted a greater risk at the 95th percentile for models B and D compared to a study conducted by McBride et al. (2013) in Southern California. In the McBride model, the mean individual illness risk after exposure to norovirus was predicted to be 47.5–81.2 cases per 1000 swimmers. The McBride estimate differs from the Ontario estimate in that it measured pathogen density in stormwater discharges diluted to mimic impacted waterways. This meant that pathogens were also assumed to be almost always present. Campylobacter was also not included as a reference pathogen; however, this was noted to have been because of a lack of detections as opposed to a difference in methodology (McBride et al., 2013). In this scenario, the Ontario model was expected to predict higher risk relative to the McBride estimate due to the inclusion of additional pathogens. As such, these results support the possibility of the upper range of the Ontario model being representative of stormwater exposures in recreators as well. A similar QMRA by Soller et al. (2017) for stormwaters in San Diego estimated AGI risk at the 95th percentile to be 25.2 cases per 1000 recreators using a lower-risk norovirus dose–response model, and 226.2 cases using a higher-risk model. One key difference between the San Diego models and models B and D from the present study is that the former did not include Campylobacter, the primary driver of risk in the Ontario model, as a reference pathogen. Disparities in the lower-end estimates may also be attributed to the Ontario estimate including data derived from both rural and urban freshwater environments as opposed to using solely urban data. Campylobacteriosis has been previously observed to have a greater prevalence in rural populations in Manitoba and Québec as a result of exposure to water impacted by livestock and wildlife; therefore, the estimates are also likely capturing health risk due to exposure to rural waterways (Green et al., 2006; Lévesque et al., 2013). Results from the high-risk scenarios from the San Diego and Ontario studies, however, were almost identical. Both results were generated using the same dose–response model with the same assumptions (Teunis et al., 2008b), and support the validity of the Ontario model in stormwater scenarios.

Rural QMRAs

A QMRA in Ontario conducted by Pintar et al. (2010) predicted that swimmers at recreation sites impacted by fecal contamination from human and wildlife sources had a mean AGI rate of 4.3 to 9.9 cases per 1000 swimmers due to Cryptosporidium exposure. These results differed from the current model where the risk associated with Cryptosporidium was predicted to be negligible. This disparity can be attributed to differences in the datasets used, with the present study drawing prevalence and concentration data from a much larger pool of 13 studies as opposed to four. As a result of having a greater pool of data to draw from, the data in our Ontario model were fit using a non-uniform distribution that underestimated risk relative to the Pintar study by estimating a lower average concentration of Cryptosporidium in waterways. In another Ontario-based QMRA for waters impacted by cattle feces, Wilkes et al. (2013) estimated a mean AGI risk approaching 1 case per 1000 swimmers for Cryptosporidium, Giardia, and E. coli O157:H7. These results agreed with the present study, where Cryptosporidium, Giardia, and E. coli also had a mean AGI risk of less than 1 case per 1000 events for swimmers and waders.

Comparisons to epidemiological studies

The model results overlap with two prospective cohort studies observing enteric disease rates in Lake Ontario recreators (Seyfried et al., 1985; Young et al., 2023). Seyfried et al. (1985) reported attributable enteric disease rates, defined as any instance of stomachaches, nausea, diarrhea, or vomiting, of 13.3 cases per 1000 waders and 10.6 cases per 1000 swimmers. Young et al. (2023) reported an attributable risk of 39.9 cases per 1000 beachgoers exposed to water using a stricter case definition of either diarrhea (≥ 3 loose stools in 24 h), vomiting, nausea with stomachaches, or missed daily activities. Although the mean estimates are lower, these epidemiological studies would have captured all potential causes of AGI while the current Ontario model estimated AGI associated with only the six reference pathogens selected. Therefore, the model results were expected to underestimate the total number of illnesses as compared to the epidemiological investigations. The model would not have captured cases caused by other pathogens of concern such as Shigella, adenovirus, enterovirus, and other potential unknown disease agents (Schuster et al., 2005). Finally, the prospective cohort studies focused only on Lake Ontario and were therefore not representative of all waterways found in Ontario such as pristine lakes in rural regions less impacted by human or animal fecal waste.

Study strengths and limitations

The present QMRA provides the first multi-pathogen estimate of AGI risk for Ontario freshwater recreators. Strengths of this study include the use of a comprehensive set of reference pathogens encompassing the most reported causes of waterborne AGI in Ontario (Schuster et al., 2005; Public Health Agency of Canada, 2009a, b; Health Canada, 2012), and the inclusion of both pathogen prevalence and concentration in the model. The model also accounted for uncertainty in the dose–response models by applying two different models for Campylobacter and norovirus (Van Abel et al., 2017).

However, several limitations also exist for the models described in this research. The first is that the scope of this QMRA was specifically limited to AGI cases caused by the selected reference pathogens in natural surface waters. As such, the model may underestimate risk if another pathogen driving AGI risk is present in waterways. Based on past outbreak data, these pathogens may include rotavirus, hepatitis A, Shigella, and pathogenic Streptococci (Schuster et al., 2005). Additionally, the model does not include exposure to Cyanobacteria toxins which can also manifest gastrointestinal symptoms. Infections with different manifestations such as otitis externa (swimmer’s ear), which has an estimated disease burden of 6 million annual cases in the USA (Collier et al., 2021), was also not modelled. The model further underestimated AGI risk by excluding cases attributable to recreation in managed waterways such as swimming pools and water parks. Finally, land exposure to beach sand and water sediments was not within the scope of the current work, but may also contribute to recreational waterborne disease burden (Heaney et al., 2012; Yamahara et al., 2012; Whitman et al., 2014; Robalo et al., 2023).

Furthermore, the model relied on literature sources for pathogen inputs. The number of available pathogen datasets (representative of the Ontario context) that contained quantitative data was limited, particularly for pathogens such as Salmonella. Only four Salmonella datasets stemming from the northeastern USA were used in the present study. As a result, the estimated risk associated with Salmonella may be more representative of US portions of the Great Lakes region as opposed to Ontario. In addition, gray literature was also not included which may have subjected the estimate to publication biases. It is also possible that the review may have missed other published pathogen literature as an extensive systematic review was not conducted given time and resource constraints. Another data limitation encountered was the lack of data regarding the number of recreators in Ontario. The closest equivalent found was a 2015 survey recording 6.3 million visits to Ontario beaches in 2015 (Ontario Ministry of Tourism, Culture, and Sport, 2017). Using the data provided in this 2015 survey, one can estimate the number of potential individual beach-day exposures to be ~ 16.52 million based on the fact that 69% of visits took place in the summer months (Jul–Sept), with an average of 3.8 visitors per visit, and assuming that each visit equated to 1 day spent at the beach (average visit duration was 3.8 nights). If one assumes 44% of the ~ 16.52 million beachgoers went swimming (using data from Young et al., 2023), one could crudely estimate a mean of 5815 to 266,742 cases of AGI per year could be associated with swimming at Ontario beaches. Additional data about the number of beach visitors who participated in water recreation, and what type of activities they participated in, would allow for the development of a more robust estimate of cases per annum in the Ontario population. Similarly, site-specific data would also allow for the estimate to provide actionable information for recreation sites informing public health decisions such as beach closures.

Conclusion

Despite the methodological and data limitations, the present work produced preliminary estimates that captured a range of reasonable recreational waterborne disease risks in line with other published studies (QMRA and epidemiological) representative of the range of waterway conditions found across Ontario, from pristine to impacted by sewage overflows, stormwater, and agricultural activity. The results of this work, however, highlighted several data gaps that need to be filled, including:

  1. Pathogen-specific data by waterway characteristics such as type (lakes or rivers) and location (urban or rural), particularly for Salmonella.

  2. Population-level data for recreators by waterway describing the number of recreation events by activity (such as swimming and kayaking) both spatially and temporally.

  3. Risk estimates for secondary contact recreation such as boating, kayaking, canoeing, and newer recreational activities such as stand-up paddleboarding, kitesurfing, and dragon-boating.

Filling these data gaps will help to produce more robust risk estimates. Pathogen-specific data by waterway type will allow estimates to better reflect differences in prevalence, concentration, and subsequent risk associated with each water environment. Population-level and exposure data will allow for direct estimates for subgroups in the population such as children. In addition, these data will support recreational waterborne disease burden estimates similar to existing Canada-wide estimates for total AGI cases (Thomas et al., 2013), and estimates of other transmission routes such as food and drinking water (Murphy et al., 2016a, 2016b; Thomas & Murray, 2014).

Contributions to knowledge

What does this study add to existing knowledge?

  • The study provides a preliminary estimate of recreational waterborne disease risk for the province of Ontario, Canada.

  • The results suggest recreational disease risk can exceed that of Health Canada guidelines.

  • The results demonstrate a range of risk that is in line with exposure to pristine (low-risk estimates) and more contaminated waters (high-risk estimates) and captures the potential risk to vulnerable populations such as young children, the elderly, and immunocompromised populations (high-risk estimates).

What are the key implications for public health interventions, practice, or policy?

  • We demonstrate that QMRA is a useful approach for estimating recreational waterborne disease risk and could be used to inform decision making in the absence of epidemiological data.

  • The work highlights key knowledge and data gaps for the generation of recreational waterborne disease burden estimates.

  • Refined burden and disease attribution estimates will help inform public health interventions to reduce recreational waterborne disease in Canada.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 312 KB) (311.9KB, docx)

Acknowledgements

We would like to thank Mark Borchardt and Tucker Burch from the USDA/USGS of the Laboratory for Infectious Diseases in Marshfield, Wisconsin, for providing a raw dataset of pathogen data for Campylobacter and Salmonella from the published article by Corsi et al. (2016).

The research in the current CJPH article was included in Henry Ngo’s Masters of Pathobiology thesis at the University of Guelph: Ngo, H. (2024). Estimating the Burden of Enteric Disease Associated with Natural Water Recreation in Ontario, Canada. [Thesis] University of Guelph, Ontario, Canada.

Author contributions

HN contributed to the analysis, data curation, and investigation of this work. He was also involved in writing of the original manuscript and editing of the final manuscript. CW provided critical review and editing and revising of the manuscript. EJP contributed to the analysis and editing of the manuscript. NR contributed to the conceptualization and editing of the manuscript. HMM was involved in the conceptualization of this work, she oversaw and supervised the project administration, secured the resources and funding, contributed to the analysis, methodology, visualization and writing and editing of the original and final drafts of the manuscript. All authors approved the final version of the manuscript.

Funding

This research was supported in part by Dr. Heather Murphy’s Tier II Canada Research Chair in One Health through the Canada Research Chairs program (Grant #950–232787). Ngo was partially funded through an Ontario Veterinary College graduate student scholarship.

Data availability

Data are available in Supplementary Materials.

Code availability

N/A.

Declarations

Ethics approval

N/A.

Consent to participate

N/A.

Consent for publication

N/A.

Conflict of interest

Murphy is a member of the Health Professionals Advisory Board for the International Joint Commission that advises the Canadian and US governments on transboundary water issues related to health. She has also received funding from Drexel University’s Academy of Natural Science through the William Penn Foundation to conduct a risk assessment of recreational waterborne disease in the City of Philadelphia (Grant # ANS Award # DWRF-19–01). Her Canada Research Chair program also includes an area on studying recreational waterborne disease in Canada (Grant #950–232787). Parmley is engaged in research funded by the Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council, the Public Health Agency of Canada, and the Canadian Safety and Security Program. She is currently President of the Board of Directors of the Centre for Coastal Health, past president of the Canadian Association of Veterinary Epidemiology and Preventive Medicine, treasurer and member of the Board of Directors of the McEachran Institute, and a member of the Advisory Council for Research Directions: One Health. Prior to February 2019, she was employed by the Public Health Agency of Canada.

Footnotes

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