Risk of Illness with Salmonella due to Consumption of Raw Unwashed Vegetables Irrigated with Water from the Bogotá River

The Bogotá River receives untreated wastewater from the city of Bogotá and many other towns. Downstream from Bogotá, water from the river is used for irrigation of crops. Concentrations of indicator organisms in the river are high, which is consistent with fecal contamination. To investigate the probability of illness due to exposure to enteric pathogens from the river, specifically Salmonella, we took water samples from the Bogotá River at six sampling locations in an area where untreated water from the river is used for irrigation of lettuce, broccoli, and cabbage. Salmonella concentrations were quantified by direct isolation and qPCR. Concentrations differed, depending on the quantification technique used, ranging between 10^7.7 and 10^9.9 number of copies of gene invA per L and 10^5.3 and 10^8.4 CFU/L, for qPCR and direct isolation, respectively. A quantitative microbial risk assessment model that estimates the daily risk of illness with Salmonella resulting from consuming raw unwashed vegetables irrigated with water from the Bogotá River was constructed using the Salmonella concentration data. The daily probability of illness from eating raw unwashed vegetables ranged between 0.62 and 0.85, 0.64 and 0.86, and 0.64 and 0.85 based on concentrations estimated by qPCR (0.47–0.85, 0.47–0.86, and 0.41–0.85 based on concentrations estimated by direct isolation) for lettuce, cabbage, and broccoli, respectively, which are all above the commonly propounded benchmark of 10^?4 per year. Results obtained in this study highlight the necessity for appropriate wastewater treatment in the region, and emphasize the importance of postharvest practices, such as washing, disinfecting, and cooking.     

Roles of Vegetable Surface Properties and Sanitizer Type on Annual Disease Burden of Rotavirus Illness by Consumption of Rotavirus-Contaminated Fresh Vegetables: A Quantitative Microbial Risk Assessment

Enteric viruses are often detected in water used for crop irrigation. One concern is foodborne viral disease via the consumption of fresh produce irrigated with virus-contaminated water. Although the food industry routinely uses chemical sanitizers to disinfect post-harvest fresh produce, it remains unknown how sanitizer and fresh produce properties affect the risk of viral illness through fresh produce consumption. A quantitative microbial risk assessment model was conducted to estimate (i) the health risks associated with consumption of rotavirus (RV)-contaminated fresh produce with different surface properties (endive and kale) and (ii) how risks changed when using peracetic acid (PAA) or a surfactant-based sanitizer. The modeling results showed that the annual disease burden depended on the combination of sanitizer and vegetable type when vegetables were irrigated with RV-contaminated water. Global sensitivity analyses revealed that the most influential factors in the disease burden were RV concentration in irrigation water and postharvest disinfection efficacy. A postharvest disinfection efficacy of higher than 99% (2-log₁₀) was needed to decrease the disease burden below the World Health Organization (WHO) threshold, even in scenarios with low RV concentrations in irrigation water (i.e., river water). All scenarios tested here with at least 99.9% (3-log₁₀) disinfection efficacy had a disease burden lower than the WHO threshold, except for the endive treated with PAA. The disinfection efficacy for the endive treated with PAA was only about 80%, leading to a disease burden 100 times higher than the WHO threshold. These findings should be considered and incorporated into future models for estimating foodborne viral illness risks.     

Comparison of Risk Predicted by Multiple Norovirus Dose–Response Models and Implications for Quantitative Microbial Risk Assessment

The application of quantitative microbial risk assessments (QMRAs) to understand and mitigate risks associated with norovirus is increasingly common as there is a high frequency of outbreaks worldwide. A key component of QMRA is the dose–response analysis, which is the mathematical characterization of the association between dose and outcome. For Norovirus, multiple dose–response models are available that assume either a disaggregated or an aggregated intake dose. This work reviewed the dose–response models currently used in QMRA, and compared predicted risks from waterborne exposures (recreational and drinking) using all available dose–response models. The results found that the majority of published QMRAs of norovirus use the ₁F₁ hypergeometric dose–response model with α = 0.04, β = 0.055. This dose–response model predicted relatively high risk estimates compared to other dose–response models for doses in the range of 1–1,000 genomic equivalent copies. The difference in predicted risk among dose–response models was largest for small doses, which has implications for drinking water QMRAs where the concentration of norovirus is low. Based on the review, a set of best practices was proposed to encourage the careful consideration and reporting of important assumptions in the selection and use of dose–response models in QMRA of norovirus. Finally, in the absence of one best norovirus dose–response model, multiple models should be used to provide a range of predicted outcomes for probability of infection.     

Risk of Norovirus Gastroenteritis from Consumption of Vegetables Irrigated with Highly Treated Municipal Wastewater—Evaluation of Methods to Estimate Sewage Quality

Quantitative microbial risk assessment was used to assess the risk of norovirus gastroenteritis associated with consumption of raw vegetables irrigated with highly treated municipal wastewater, using Melbourne, Australia as an example. In the absence of local norovirus concentrations, three methods were developed: (1) published concentrations of norovirus in raw sewage, (2) an epidemiological method using Melbourne prevalence of norovirus, and (3) an adjustment of method 1 to account for prevalence of norovirus. The methods produced highly variable results with estimates of norovirus concentrations in raw sewage ranging from 10⁴ per milliliter to 10⁷ per milliliter and treated effluent from 1 × 10^?3 per milliliter to 3 per milliliter (95th percentiles). Annual disease burden was very low using method 1, from 4 to 5 log₁₀ disability adjusted life years (DALYs) below the 10^?6 threshold (0.005–0.1 illnesses per year). Results of method 2 were higher, with some scenarios exceeding the threshold by up to 2 log₁₀ DALYs (up to 95,000 illnesses per year). Method 3, thought to be most representative of Melbourne conditions, predicted annual disease burdens >2 log₁₀ DALYs lower than the threshold (～4 additional cases per year). Sensitivity analyses demonstrated that input parameters used to estimate norovirus concentration accounted for much of the model output variability. This model, while constrained by a lack of knowledge of sewage concentrations, used the best available information and sound logic. Results suggest that current wastewater reuse behaviors in Melbourne are unlikely to cause norovirus risks in excess of the annual DALY health target.     

A Mathematical Model for Pathogen Cross‐Contamination Dynamics during the Postharvest Processing of Leafy Greens

We developed a probabilistic mathematical model for the postharvest processing of leafy greens focusing on Escherichia coli O157:H7 contamination of fresh‐cut romaine lettuce as the case study. Our model can (i) support the investigation of cross‐contamination scenarios, and (ii) evaluate and compare different risk mitigation options. We used an agent‐based modeling framework to predict the pathogen prevalence and levels in bags of fresh‐cut lettuce and quantify spread of E. coli O157:H7 from contaminated lettuce to surface areas of processing equipment. Using an unbalanced factorial design, we were able to propagate combinations of random values assigned to model inputs through different processing steps and ranked statistically significant inputs with respect to their impacts on selected model outputs. Results indicated that whether contamination originated on incoming lettuce heads or on the surface areas of processing equipment, pathogen prevalence among bags of fresh‐cut lettuce and batches was most significantly impacted by the level of free chlorine in the flume tank and frequency of replacing the wash water inside the tank. Pathogen levels in bags of fresh‐cut lettuce were most significantly influenced by the initial levels of contamination on incoming lettuce heads or surface areas of processing equipment. The influence of surface contamination on pathogen prevalence or levels in fresh‐cut bags depended on the location of that surface relative to the flume tank. This study demonstrates that developing a flexible yet mathematically rigorous modeling tool, a “virtual laboratory,” can provide valuable insights into the effectiveness of individual and combined risk mitigation options.     

Risk of Legionellosis in residential areas around farms irrigating with municipal wastewater

The conservation of freshwater is of both global and national importance, and in the United States, agriculture is one of the largest consumers of this resource. Reduction of the strain farming puts on local surface or groundwater is vital for ensuring resilience in the face of climate change, and one possible option is to irrigate with a combination of freshwater and reclaimed water from municipal wastewater treatment facilities. However, this wastewater can contain pathogens that are harmful to human health, such as Legionella pneumophila, which is a bacterium that can survive aerosolization and airborne transportation and cause severe pneumonia when inhaled. To assess an individual adult's risk of infection with L. pneumophila from a single exposure to agricultural spray irrigation, a quantitative microbial risk assessment was conducted for a scenario of spray irrigation in central Illinois, for the growing seasons in 2017, 2018, and 2019. The assessment found that the mean risk of infection for a single exposure exceeded the safety threshold of 10^–6 infections/exposure up to 1 km from a low-pressure irrigator and up to 2 km from a high-pressure irrigator, although no median risk exceeded the threshold for any distance or irrigator pressure. These findings suggest that spray irrigation with treated municipal wastewater could be a viable option for reducing freshwater consumption in Midwest farming, as long as irrigation on windy days is avoided and close proximity to the active irrigator is limited.     

Construction of a Dose–Illness Relationship via Modeling Morbidity and Application to Risk Assessment of Wastewater Reuse

A disease burden (DB) evaluation for environmental pathogens is generally performed using disability‐adjusted life years with the aim of providing a quantitative assessment of the health hazard caused by pathogens. A critical step in the preparation for this evaluation is the estimation of morbidity between exposure and disease occurrence. In this study, the method of a traditional dose–response analysis was first reviewed, and then a combination of the theoretical basis of a “single‐hit” and an “infection‐illness” model was performed by incorporating two critical factors: the “infective coefficient” and “infection duration.” This allowed a dose–morbidity model to be built for direct use in DB calculations. In addition, human experimental data for typical intestinal pathogens were obtained for model validation, and the results indicated that the model was well fitted and could be further used for morbidity estimation. On this basis, a real case of a water reuse project was selected for model application, and the morbidity as well as the DB caused by intestinal pathogens during water reuse was evaluated. The results show that the DB attributed to Enteroviruses was significant, while that for enteric bacteria was negligible. Therefore, water treatment technology should be further improved to reduce the exposure risk of Enteroviruses . Since road flushing was identified as the major exposure route, human contact with reclaimed water through this pathway should be limited. The methodology proposed for model construction not only makes up for missing data of morbidity during risk evaluation, but is also necessary to quantify the maximum possible DB.     

A Bayesian Belief Network Model Assessing the Risk to Wastewater Workers of Contracting Ebola Virus Disease During an Outbreak

During an outbreak of Ebola virus disease (EVD), hospitals’ connections to municipal wastewater systems may provide a path for patient waste bearing infectious viral particles to pass from the hospital into the wastewater treatment system, potentially posing risks to sewer and wastewater workers. To quantify these risks, we developed a Bayesian belief network model incorporating data on virus behavior and survival along with structural characteristics of hospitals and wastewater treatment systems. We applied the model to assess risks under several different scenarios of workers’ exposure to wastewater for a wastewater system typical of a mid‐sized U.S. city. The model calculates a median daily risk of developing EVD of approximately 6.1×10⁻¹² (90% confidence interval: 1.0×10⁻¹² to 5.4×10⁻⁹; mean 1.8×10⁻⁶) when no prior exposure conditions are specified. Under a worst‐case scenario in which a worker stationed in the sewer adjacent to the hospital accidentally ingests several drops (0.35 mL) of wastewater, median risk is 5.8×10⁻⁴ (90% CI: 8.8×10⁻⁷ to 9.5×10⁻²; mean 3.2×10⁻²) . Disinfection of patient waste with peracetic acid for 15 minutes prior to flushing decreases the estimated median risk to 3.8×10⁻⁷ (90% CI: 4.1×10⁻⁹ to 8.6×10⁻⁵; mean 2.9×10⁻⁵). The results suggest that requiring hospitals to disinfect EVD patient waste prior to flushing may be advisable. The modeling framework can provide insight into managing patient waste during future outbreaks of highly virulent infectious pathogens.     

Quantitative Risk Estimation for a Legionella pneumophila Infection Due to Whirlpool Use

Quantitative microbiological risk assessment was used to quantify the risk associated with the exposure to Legionella pneumophila in a whirlpool. Conceptually, air bubbles ascend to the surface, intercepting Legionella from the traversed water. At the surface the bubble bursts into dominantly noninhalable jet drops and inhalable film drops. Assuming that film drops carry half of the intercepted Legionella, a total of four (95% interval: 1–9) and 4.5×10⁴ (4.4×10⁴ – 4.7×10⁴) cfu/min were estimated to be aerosolized for concentrations of 1 and 1,000 legionellas per liter, respectively. Using a dose‐response model for guinea pigs to represent humans, infection risks for active whirlpool use with 100 cfu/L water for 15 minutes were 0.29 (～0.11–0.48) for susceptible males and 0.22 (～0.06–0.42) for susceptible females. A L. pneumophila concentration of ≥1,000 cfu/L water was estimated to nearly always cause an infection (mean: 0.95; 95% interval: 0.9–～1). Estimated infection risks were time‐dependent, ranging from 0.02 (0–0.11) for 1‐minute exposures to 0.93 (0.86–0.97) for 2‐hour exposures when the L. pneumophila concentration was 100 cfu/L water. Pool water in Dutch bathing establishments should contain <100 cfu Legionella/L water. This study suggests that stricter provisions might be required to assure adequate public health protection.     

Effect of Climate Change on the Concentration and Associated Risks of Vibrio Spp. in Dutch Recreational Waters

Currently, the number of reported cases of recreational‐ water‐related Vibrio illness in the Netherlands is low. However, a notable higher incidence of Vibrio infections has been observed in warm summers. In the future, such warm summers are expected to occur more often, resulting in enhanced water temperatures favoring Vibrio growth. Quantitative information on the increase in concentration of Vibrio spp. in recreational water under climate change scenarios is lacking. In this study, data on occurrence of Vibrio spp. at six different bathing sites in the Netherlands (2009–2012) were used to derive an empirical formula to predict the Vibrio concentration as a function of temperature, salinity, and pH. This formula was used to predict the effects of increased temperatures in climate change scenarios on Vibrio concentrations. For Vibrio parahaemolyticus, changes in illness risks associated with the changed concentrations were calculated as well. For an average temperature increase of 3.7 °C, these illness risks were calculated to be two to three times higher than in the current situation. Current illness risks were, varying per location, on average between 10^?4 and 10^?2 per person for an entire summer. In situations where water temperatures reached maximum values, illness risks are estimated to be up to 10^?2 and 10^?1. If such extreme situations occur more often during future summers, increased numbers of ill bathers or bathing‐water‐related illness outbreaks may be expected.     

Risk Ranking for Foodborne Microbial Hazards in New Zealand: Burden of Disease Estimates

Priority setting for food safety management at a national level requires risks to be ranked according to defined criteria. In this study, two approaches (disability‐adjusted life years (DALYs) and cost of illness (COI)) were used to generate estimates of the burden of disease for certain potentially foodborne diseases (campylobacteriosis, salmonellosis, listeriosis (invasive, perinatal, and nonperinatal), infection with Shiga toxin‐producing Escherichia coli (STEC), yersiniosis, and norovirus infection) and their sequelae in New Zealand. A modified Delphi approach was used to estimate the food‐attributable proportion for these diseases. The two approaches gave a similar ranking for the selected diseases, with campylobacteriosis and its sequelae accounting for the greatest proportion of the overall burden of disease by far.     

A Decision Support Tool to Compare Waterborne and Foodborne Infection and/or Illness Risks Associated with Climate Change

Climate change may impact waterborne and foodborne infectious disease, but to what extent is uncertain. Estimating climate‐change‐associated relative infection risks from exposure to viruses, bacteria, or parasites in water or food is critical for guiding adaptation measures. We present a computational tool for strategic decision making that describes the behavior of pathogens using location‐specific input data under current and projected climate conditions. Pathogen‐pathway combinations are available for exposure to norovirus, Campylobacter, Cryptosporidium, and noncholera Vibrio species via drinking water, bathing water, oysters, or chicken fillets. Infection risk outcomes generated by the tool under current climate conditions correspond with those published in the literature. The tool demonstrates that increasing temperatures lead to increasing risks for infection with Campylobacter from consuming raw/undercooked chicken fillet and for Vibrio from water exposure. Increasing frequencies of drought generally lead to an elevated infection risk of exposure to persistent pathogens such as norovirus and Cryptosporidium, but decreasing risk of exposure to rapidly inactivating pathogens, like Campylobacter. The opposite is the case with increasing annual precipitation; an upsurge of heavy rainfall events leads to more peaks in infection risks in all cases. The interdisciplinary tool presented here can be used to guide climate change adaptation strategies focused on infectious diseases.     

Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

Multi‐Exposure Pathway Model to Compare Escherichia coli O157 Risks and Interventions

The relative contributions of exposure pathways associated with cattle‐manure‐borne Escherichia coli O157:H7 on public health have yet to be fully characterized. A stochastic, quantitative microbial risk assessment (QMRA) model was developed to describe a hypothetical cattle farm in order to compare the relative importance of five routes of exposure, including aquatic recreation downstream of the farm, consumption of contaminated ground beef processed with limited interventions, consumption of leafy greens, direct animal contact, and the recreational use of a cattle pasture. To accommodate diverse environmental and hydrological pathways, existing QMRAs were integrated with novel and simplistic climate and field‐level submodels. The model indicated that direct animal contact presents the greatest risk of illness per exposure event during the high pathogen shedding period. However, when accounting for the frequency of exposure, using a high‐risk exposure‐receptor profile, consumption of ground beef was associated with the greatest risk of illness. Additionally, the model was used to evaluate the efficacy of hypothetical interventions affecting one or more exposure routes; concurrent evaluation of multiple routes allowed for the assessment of the combined effect of preharvest interventions across exposure pathways—which may have been previously underestimated—as well as the assessment of the effect of additional downstream interventions. This analysis represents a step towards a full evaluation of the risks associated with multiple exposure pathways; future incorporation of variability associated with environmental parameters and human behaviors would allow for a comprehensive assessment of the relative contribution of exposure pathways at the population level.     

Evaluating the Potential for a Helicobacter pylori Drinking Water Guideline

Helicobacter pylori is a microaerophilic, gram‐negative bacterium that is linked to adverse health effects including ulcers and gastrointestinal cancers. The goal of this analysis is to develop the necessary inputs for a quantitative microbial risk assessment (QMRA) needed to develop a potential guideline for drinking water at the point of ingestion (e.g., a maximum contaminant level, or MCL) that would be protective of human health to an acceptable level of risk while considering sources of uncertainty. Using infection and gastric cancer as two discrete endpoints, and calculating dose‐response relationships from experimental data on humans and monkeys, we perform both a forward and reverse risk assessment to determine the risk from current reported surface water concentrations of H. pylori and an acceptable concentration of H. pylori at the point of ingestion. This approach represents a synthesis of available information on human exposure to H. pylori via drinking water. A lifetime risk of cancer model suggests that a MCL be set at <1 organism/L given a 5‐log removal treatment because we cannot exclude the possibility that current levels of H. pylori in environmental source waters pose a potential public health risk. Research gaps include pathogen occurrence in source and finished water, treatment removal rates, and determination of H. pylori risks from other water sources such as groundwater and recreational water.     

Considering the Risk of Infection by Cryptosporidium via Consumption of Municipally Treated Drinking Water from a Surface Water Source in a Southwestern Ontario Community

Through the use of case‐control analyses and quantitative microbial risk assessment (QMRA), relative risks of transmission of cryptosporidiosis have been evaluated (recreational water exposure vs. drinking water consumption) for a Canadian community with higher than national rates of cryptosporidiosis. A QMRA was developed to assess the risk of Cryptosporidium infection through the consumption of municipally treated drinking water. Simulations were based on site‐specific surface water contamination levels and drinking water treatment log₁₀ reduction capacity for Cryptosporidium. Results suggested that the risk of Cryptosporidium infection via drinking water in the study community, assuming routine operation of the water treatment plant, was negligible (6 infections per 10¹³ persons per day—5th percentile: 2 infections per 10¹⁵ persons per day; 95th percentile: 3 infections per 10¹² persons per day). The risk is essentially nonexistent during optimized, routine treatment operations. The study community achieves between 7 and 9 log₁₀Cryptosporidium oocyst reduction through routine water treatment processes. Although these results do not preclude the need for constant vigilance by both water treatment and public health professionals in this community, they suggest that the cause of higher rates of cryptosporidiosis are more likely due to recreational water contact, or perhaps direct animal contact. QMRA can be successfully applied at the community level to identify data gaps, rank relative public health risks, and forecast future risk scenarios. It is most useful when performed in a collaborative way with local stakeholders, from beginning to end of the risk analysis paradigm.     

Risk Management Solutions for Climate Change–Induced Disasters

In honor of the 40th anniversary of Risk Analysis, this article suggests ways of linking risk assessment and risk perception in developing risk management strategies that have a good chance of being implemented, focusing on the problem of reducing losses from natural hazards in the face of climate change. Following a checklist for developing an implementable risk management strategy, Section 2 highlights the impact that exponential growth of CO₂ emissions is likely to have on future disaster losses as assessed by climate and social scientists. Section 3 then discusses how people perceive the risks of low-probability adverse events and the cognitive biases that lead them to underprepare for future losses. Based on this empirical evidence, Section 4 proposes a risk management strategy for reducing future losses using the principles of choice architecture to communicate the likelihood and consequences of disasters, coupled with economic incentives and well-enforced regulations.     

Risk and Benefit Perceptions of Mobile Phone and Base Station Technology in Bangladesh

Research in developed countries showed that many citizens perceive that radio signals transmitted by mobile phones and base stations represent potential health risks. Less research has been conducted in developing countries focused on citizen perceptions of risks and benefits, despite the recent and rapid introduction of mobile communication technologies. This study aims to identify factors that are influential in determining the tradeoffs that Bangladeshi citizens make between risks and benefits in terms of mobile phone technology acceptance and health concerns associated with the technology. Bangladesh was selected as representative of many developing countries inasmuch as terrestrial telephone infrastructure is insubstantial, and mobile phone use has expanded rapidly over the last decade, even among the poor. Issues of importance were identified in a small‐scale qualitative study among Bangladeshi citizens (n = 13), followed by a survey within a sample of Bangladeshi citizens (n = 500). The results demonstrate that, in general, the perceived benefits of mobile phone technology outweigh the risks. The perceived benefits are primarily related to the social and personal advantages of mobile phone use, including the ability to receive emergency news about floods, cyclones, and other natural disasters. Base stations were seen as a symbol of societal advance. The results furthermore suggest that overall risk perceptions are relatively low, in particular health risks, and are primarily driven by perceptions that related to crime and social inconvenience. Perceived health risks are relatively small. These findings show that risk communication and management may be particularly effective when contextual factors of the society where the system is implemented are taken into consideration.     

Quantitative risk assessment-epidemic curve prediction model for leafy green outbreak investigation

The objective of this study was to leverage quantitative risk assessment to investigate possible root cause(s) of foodborne illness outbreaks related to Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) infections in leafy greens in the United States. To this end, we developed the FDA leafy green quantitative risk assessment epidemic curve prediction model (FDA-LG QRA-EC) that simulated the lettuce supply chain. The model was used to predict the number of reported illnesses and the epidemic curve associated with lettuce contaminated with STEC O157 for a wide range of scenarios representing various contamination conditions and facility processing/sanitation practices. Model predictions were generated for fresh-cut and whole lettuce, quantifying the differing impacts of facility processing and home preparation on predicted illnesses. Our model revealed that the timespan (i.e., number of days with at least one reported illness) and the peak (i.e., day with the most predicted number of reported illnesses) of the epidemic curve of a STEC O157-lettuce outbreak were not strongly influenced by facility processing/sanitation practices and were indications of contamination pattern among incoming lettuce batches received by the facility or distribution center. Through comparisons with observed number of illnesses from recent STEC O157-lettuce outbreaks, the model identified contamination conditions on incoming lettuce heads that could result in an outbreak of similar size, which can be used to narrow down potential root cause hypotheses.     

Quantitative Microbial Risk Assessment of Cryptosporidiosis and Giardiasis from Very Small Private Water Supplies

This article reports a quantitative microbial risk assessment of the risk of Giardia and Cryptosporidium in very small private water supplies. Both pathogens have been implicated in causing outbreaks of waterborne disease associated with such supplies, though the risk of endemic disease is not known. For exposure assessments, we used existing data to derive regression equations describing the relationships between the concentration of these pathogens and Escherichia coli in private water supplies. Pathogen concentrations were then estimated using national surveillance data of E. coli in private water supplies in England and France. The estimated risk of infection was very high with the median annual risk being of the order of 25–28% for Cryptosporidium and 0.4% to 0.7% for Giardia, though, in the poorer quality supplies the risk could be much higher. These risks are substantially greater than for public water supplies and well above the risk considered tolerable. The observation that observed infection rates are generally much lower may indicate increased immunity in people regularly consuming water from private supplies. However, this increased immunity is presumed to derive from increased disease risk in young children, the group most at risk from severe disease.