Validation of Quantitative Microbial Risk Assessment Using Epidemiological Data from Outbreaks of Waterborne Gastrointestinal Disease

The assumptions underlying quantitative microbial risk assessment (QMRA) are simple and biologically plausible, but QMRA predictions have never been validated for many pathogens. The objective of this study was to validate QMRA predictions against epidemiological measurements from outbreaks of waterborne gastrointestinal disease. I screened 2,000 papers and identified 12 outbreaks with the necessary data: disease rates measured using epidemiological methods and pathogen concentrations measured in the source water. Eight of the 12 outbreaks were caused by Cryptosporidium, three by Giardia, and one by norovirus. Disease rates varied from 5.5 × 10^?6 to 1.1 × 10^?2 cases/person‐day, and reported pathogen concentrations varied from 1.2 × 10^?4 to 8.6 × 10² per liter. I used these concentrations with single‐hit dose–response models for all three pathogens to conduct QMRA, producing both point and interval predictions of disease rates for each outbreak. Comparison of QMRA predictions to epidemiological measurements showed good agreement; interval predictions contained measured disease rates for 9 of 12 outbreaks, with point predictions off by factors of 1.0–120 (median = 4.8). Furthermore, 11 outbreaks occurred at mean doses of less than 1 pathogen per exposure. Measured disease rates for these outbreaks were clearly consistent with a single‐hit model, and not with a “two‐hit” threshold model. These results demonstrate the validity of QMRA for predicting disease rates due to Cryptosporidium and Giardia.     

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.     

Cryptosporidium Infection Risk: Results of New Dose‐Response Modeling

Cryptosporidium human dose‐response data from seven species/isolates are used to investigate six models of varying complexity that estimate infection probability as a function of dose. Previous models attempt to explicitly account for virulence differences among C. parvum isolates, using three or six species/isolates. Four (two new) models assume species/isolate differences are insignificant and three of these (all but exponential) allow for variable human susceptibility. These three human‐focused models (fractional Poisson, exponential with immunity and beta‐Poisson) are relatively simple yet fit the data significantly better than the more complex isolate‐focused models. Among these three, the one‐parameter fractional Poisson model is the simplest but assumes that all Cryptosporidium oocysts used in the studies were capable of initiating infection. The exponential with immunity model does not require such an assumption and includes the fractional Poisson as a special case. The fractional Poisson model is an upper bound of the exponential with immunity model and applies when all oocysts are capable of initiating infection. The beta Poisson model does not allow an immune human subpopulation; thus infection probability approaches 100% as dose becomes huge. All three of these models predict significantly (>10x) greater risk at the low doses that consumers might receive if exposed through drinking water or other environmental exposure (e.g., 72% vs. 4% infection probability for a one oocyst dose) than previously predicted. This new insight into Cryptosporidium risk suggests additional inactivation and removal via treatment may be needed to meet any specified risk target, such as a suggested 10^?4 annual risk of Cryptosporidium infection.     

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.     

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.     

Development and application of a dose–response model for Elizabethkingia spp

Elizabethkingia spp. are common environmental pathogens responsible for infections in more vulnerable populations. Although the exposure routes of concern are not well understood, some hospital-associated outbreaks have indicated possible waterborne transmission. In order to facilitate quantitative microbial risk assessment (QMRA) for Elizabethkingia spp., this study fit dose–response models to frog and mice datasets that evaluated intramuscular and intraperitoneal exposure to Elizabethkingia spp. The frog datasets could be pooled, and the exact beta-Poisson model was the best fitting model with optimized parameters α  = 0.52 and β = 86,351. Using the exact beta-Poisson model, the dose of Elizabethkingia miricola resulting in a 50% morbidity response (LD₅₀) was estimated to be approximately 237,000 CFU. The model developed herein was used to estimate the probability of infection for a hospital patient under a modeled exposure scenario involving a contaminated medical device and reported Elizabethkingia spp. concentrations isolated from hospital sinks after an outbreak. The median exposure dose was approximately 3 CFU/insertion event, and the corresponding median risk of infection was 3.4E-05. The median risk estimated in this case study was lower than the 3% attack rate observed in a previous outbreak, however, there are noted gaps pertaining to the possible concentrations of Elizabethkingia spp. in tap water and the most likely exposure routes. This is the first dose–response model developed for Elizabethkingia spp. thus enabling future risk assessments to help determine levels of risk and potential effective risk management strategies.     

A Risk Assessment of Campylobacteriosis and Salmonellosis Linked to Chicken Meals Prepared in Households in Dakar,Senegal

We used a quantitative microbiological risk assessment model to describe the risk of Campylobacter and Salmonella infection linked to chicken meals prepared in households in Dakar, Senegal. The model uses data collected specifically for this study, such as the prevalence and level of bacteria on the neck skin of chickens bought in Dakar markets, time‐temperature profiles recorded from purchase to consumption, an observational survey of meal preparation in private kitchens, and detection and enumeration of pathogens on kitchenware and cooks’ hands. Thorough heating kills all bacteria present on chicken during cooking, but cross‐contamination of cooked chicken or ready‐to‐eat food prepared for the meal via kitchenware and cooks’ hands leads to a high expected frequency of pathogen ingestion. Additionally, significant growth of Salmonella is predicted during food storage at ambient temperature before and after meal preparation. These high exposures lead to a high estimated risk of campylobacteriosis and/or salmonellosis in Dakar households. The public health consequences could be amplified by the high level of antimicrobial resistance of Salmonella and Campylobacter observed in this setting. A significant decrease in the number of ingested bacteria and in the risk could be achieved through a reduction of the prevalence of chicken contamination at slaughter, and by the use of simple hygienic measures in the kitchen. There is an urgent need to reinforce the hygiene education of food handlers in Senegal.     

Outbreak-Based Giardia Dose–Response Model Using Bayesian Hierarchical Markov Chain Monte Carlo Analysis

Giardia is a zoonotic gastrointestinal parasite responsible for a substantial global public health burden, and quantitative microbial risk assessment (QMRA) is often used to forecast and manage this burden. QMRA requires dose–response models to extrapolate available dose–response data, but the existing model for Giardia ignores valuable dose–response information, particularly data from several well-documented waterborne outbreaks of giardiasis. The current study updates Giardia dose–response modeling by synthesizing all available data from outbreaks and experimental studies using a Bayesian random effects dose–response model. For outbreaks, mean doses (D) and the degree of spatial and temporal aggregation among cysts were estimated using exposure assessment implemented via two-dimensional Monte Carlo simulation, while potential overreporting of outbreak cases was handled using published overreporting factors and censored binomial regression. Parameter estimation was by Markov chain Monte Carlo simulation and indicated that a typical exponential dose–response parameter for Giardia is r = 1.6 × 10⁻² [3.7 × 10⁻³, 6.2 × 10⁻²] (posterior median [95% credible interval]), while a typical morbidity ratio is m = 3.8 × 10⁻¹ [2.3 × 10⁻¹, 5.5 × 10⁻¹]. Corresponding (logistic-scale) variance components were σ_r = 5.2 × 10⁻¹ [1.1 × 10⁻¹, 9.6 × 10⁻¹] and σ_m = 9.3 × 10⁻¹ [7.0 × 10⁻², 2.8 × 10⁰], indicating substantial variation in the Giardia dose–response relationship. Compared to the existing Giardia dose–response model, the current study provides more representative estimation of uncertainty in r and novel quantification of its natural variability. Several options for incorporating variability in r (and m) into QMRA predictions are discussed, including incorporation via Monte Carlo simulation as well as evaluation of the current study's model using the approximate beta-Poisson.     

Long‐Term Contracting: The Role of Private Information in Dynamic Supply Risk Management

We examine the critical role of evolving private information in managing supply risk. The problem features a dyadic channel where a dominant buyer operates a multiperiod inventory system with lost sales and fixed cost. He replenishes from a supplier, whose private state of production is vulnerable to random shocks and evolves dynamically over time. We characterize the optimal inventory policy with a simple semi‐stationary structure; it distorts order quantity for limiting information rent only in the initial period; the optimal payment compensates for production cost in every period but concedes real information rent only in the initial period. These properties allow us to derive an easy‐to‐implement revenue‐sharing contract that facilitates ex ante strategic planning and ex post dynamic execution. This work advances our understanding on when and how to use private information in dynamic risk management.     

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.     

Behavioral pathways to private well risk mitigation: A structural equation modeling approach

Complex, multihazard risks such as private groundwater contamination necessitate multiannual risk reduction actions including seasonal, weather-based hazard evaluations. In the Republic of Ireland (ROI), high rural reliance on unregulated private wells renders behavior promotion a vital instrument toward safeguarding household health from waterborne infection. However, to date, pathways between behavioral predictors remain unknown while latent constructs such as extreme weather event (EWE) risk perception and self-efficacy (perceived behavioral competency) have yet to be sufficiently explored. Accordingly, a nationwide survey of 560 Irish private well owners was conducted, with structural equation modeling (SEM) employed to identify underlying relationships determining key supply management behaviors. The pathway analysis (SEM) approach was used to model three binary outcomes: information seeking, post-EWE action, and well testing behavior. Upon development of optimal models, perceived self-efficacy emerged as a significant direct and/or indirect driver of all three behavior types—demonstrating the greatest indirect effect (β = −0.057) on adoption of post-EWE actions and greatest direct (β = 0.222) and total effect (β = 0.245) on supply testing. Perceived self-efficacy inversely influenced EWE risk perception in all three models but positively influenced supply awareness (where present). Notably, the presence of a vulnerable (infant and/or elderly) household member negatively influenced adoption of post-EWE actions (β = −0.131, p = 0.016). Results suggest that residential and age-related factors constitute key demographic variables influencing risk mitigation and are strongly mediated by cognitive variables—particularly self-efficacy. Study findings may help contextualize predictors of private water supply management, providing a basis for future risk-based water interventions.     

Impact of Acquired Immunity and Dose‐Dependent Probability of Illness on Quantitative Microbial Risk Assessment

Dose‐response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure‐infection step; the infection‐illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within‐host dynamics of illness. We find that at low (average) doses, the infection‐illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose‐response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.     

Comparison of Six Dose-Response Models for Use with Food-Borne Pathogens

Food-related illness in the United States is estimated to affect over six million people per year and cost the economy several billion dollars. These illnesses and costs could be reduced if minimum infectious doses were established and used as the basis of regulations and monitoring. However, standard methodologies for dose-response assessment are not yet formulated for microbial risk assessment. The objective of this study was to compare dose-response models for food-borne pathogens and determine which models were most appropriate for a range of pathogens. The statistical models proposed in the literature and chosen for comparison purposes were log-normal, log-logistic, exponential, -Poisson and Weibull-Gamma. These were fit to four data sets also taken from published literature, Shigella flexneri, Shigella dysenteriae,Campylobacter jejuni, and Salmonella typhosa, using the method of maximum likelihood. The Weibull-gamma, the only model with three parameters, was also the only model capable of fitting all the data sets examined using the maximum likelihood estimation for comparisons. Infectious doses were also calculated using each model. Within any given data set, the infectious dose estimated to affect one percent of the population ranged from one order of magnitude to as much as nine orders of magnitude, illustrating the differences in extrapolation of the dose response models. More data are needed to compare models and examine extrapolation from high to low doses for food-borne pathogens.     

A Quantitative Microbiological Risk Assessment for Campylobacter in Petting Zoos

The significance of petting zoos for transmission of Campylobacter to humans and the effect of interventions were estimated. A stochastic QMRA model simulating a child or adult visiting a Dutch petting zoo was built. The model describes the transmission of Campylobacter in animal feces from the various animal species, fences, and the playground to ingestion by visitors through touching these so‐called carriers and subsequently touching their lips. Extensive field and laboratory research was done to fulfill data needs. Fecal contamination on all carriers was measured by swabbing in 10 petting zoos, using Escherichia coli as an indicator. Carrier‐hand and hand‐lip touching frequencies were estimated by, in total, 13 days of observations of visitors by two observers at two petting zoos. The transmission from carrier to hand and from hand to lip by touching was measured using preapplied cow feces to which E. coli WG5 was added as an indicator. Via a Beta‐Poisson dose‐response function, the number of Campylobacter cases for the whole of the Netherlands (16 million population) in a year was estimated at 187 and 52 for children and adults, respectively, so 239 in total. This is significantly lower than previous QMRA results on chicken fillet and drinking water consumption. Scenarios of 90% reduction of the contamination (meant to mimic cleaning) of all fences and just goat fences reduces the number of cases by 82% and 75%, respectively. The model can easily be adapted for other fecally transmitted pathogens.     

Use of Risk Assessment and Likelihood Estimation to Analyze Spatial Distribution Pattern of Respiratory Infection Cases

Obvious spatial infection patterns are often observed in cases associated with airborne transmissible diseases. Existing quantitative infection risk assessment models analyze the observed cases by assuming a homogeneous infectious particle concentration and ignore the spatial infection pattern, which may cause errors. This study aims at developing an approach to analyze spatial infection patterns associated with infectious respiratory diseases or other airborne transmissible diseases using infection risk assessment and likelihood estimation. Mathematical likelihood, based on binomial probability, was used to formulate the retrospective component with some additional mathematical treatments. Together with an infection risk assessment model that can address spatial heterogeneity, the method can be used to analyze the spatial infection pattern and retrospectively estimate the influencing parameters causing the cases, such as the infectious source strength of the pathogen. A Varicella outbreak was selected to demonstrate the use of the new approach. The infectious source strength estimated by the Wells‐Riley concept using the likelihood estimation was compared with the estimation using the existing method. It was found that the maximum likelihood estimation matches the epidemiological observation of the outbreak case much better than the estimation under the assumption of homogeneous infectious particle concentration. Influencing parameters retrospectively estimated using the new approach can be used as input parameters in quantitative infection risk assessment of the disease under other scenarios. The approach developed in this study can also serve as an epidemiological tool in outbreak investigation. Limitations and further developments are also discussed.     

Microbiota and Dose Response: Evolving Paradigm of Health Triangle

SRA Dose‐Response and Microbial Risk Analysis Specialty Groups jointly sponsored symposia that addressed the intersections between the “microbiome revolution” and dose response. Invited speakers presented on innovations and advances in gut and nasal microbiota (normal microbial communities) in the first decade after the Human Microbiome Project began. The microbiota and their metabolites are now known to influence health and disease directly and indirectly, through modulation of innate and adaptive immune systems and barrier function. Disruption of healthy microbiota is often associated with changes in abundance and diversity of core microbial species (dysbiosis), caused by stressors including antibiotics, chemotherapy, and disease. Nucleic‐acid‐based metagenomic methods demonstrated that the dysbiotic host microbiota no longer provide normal colonization resistance to pathogens, a critical component of innate immunity of the superorganism. Diverse pathogens, probiotics, and prebiotics were considered in human and animal models (in vivo and in vitro ). Discussion included approaches for design of future microbial dose–response studies to account for the presence of the indigenous microbiota that provide normal colonization resistance , and the absence of the protective microbiota in dysbiosis. As NextGen risk analysis methodology advances with the “microbiome revolution,” a proposed new framework, the Health Triangle, may replace the old paradigm based on the Disease Triangle (focused on host, pathogen, and environment) and germophobia. Collaborative experimental designs are needed for testing hypotheses about causality in dose–response relationships for pathogens present in our environments that clearly compete in complex ecosystems with thousands of bacterial species dominating the healthy superorganism.     

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.     

Exposure Factors for Wastewater‐Irrigated Asian Vegetables and a Probabilistic Rotavirus Disease Burden Model for Their Consumption

Many farmers in water‐scarce regions of developing countries use wastewater to irrigate vegetables and other agricultural crops, a practice that may expand with climate change. There are a number of health risks associated with wastewater irrigation for human food crops, particularly with surface irrigation techniques common in the developing world. The World Health Organization (WHO) recommends using quantitative microbial risk assessment (QMRA) to determine if the irrigation scheme meets health standards. However, only a few vegetables have been studied for wastewater risk and little information is known about the disease burden of wastewater‐irrigated vegetable consumption in China. To bridge this knowledge gap, an experiment was conducted to determine volume of water left on Asian vegetables and lettuce after irrigation. One hundred samples each of Chinese chard (Brassica rapa var. chinensis), Chinese broccoli (Brassica oleracea var. alboglabra), Chinese flowering cabbage (Brassica rapa var. parachinensis), and lettuce (Lactuca sativa) were harvested after overhead sprinkler irrigation. Chinese broccoli and flowering cabbage were found to capture the most water and lettuce the least. QMRAs were then constructed to estimate rotavirus disease burden from consumption of wastewater‐irrigated Asian vegetables in Beijing. Results indicate that estimated risks from these reuse scenarios exceed WHO guideline thresholds for acceptable disease burden for wastewater use, signifying that reduction of pathogen concentration or stricter risk management is necessary for safe reuse. Considering the widespread practice of wastewater irrigation for food production, particularly in developing countries, incorporation of water retention factors in QMRAs can reduce uncertainty regarding health risks for consumers worldwide.     

Development of Dose‐Response Models to Predict the Relationship for Human Toxoplasma gondii Infection Associated with Meat Consumption

Toxoplasma gondii is a protozoan parasite that is responsible for approximately 24% of deaths attributed to foodborne pathogens in the United States. It is thought that a substantial portion of human T. gondii infections is acquired through the consumption of meats. The dose‐response relationship for human exposures to T. gondii‐infected meat is unknown because no human data are available. The goal of this study was to develop and validate dose‐response models based on animal studies, and to compute scaling factors so that animal‐derived models can predict T. gondii infection in humans. Relevant studies in literature were collected and appropriate studies were selected based on animal species, stage, genotype of T. gondii, and route of infection. Data were pooled and fitted to four sigmoidal‐shaped mathematical models, and model parameters were estimated using maximum likelihood estimation. Data from a mouse study were selected to develop the dose‐response relationship. Exponential and beta‐Poisson models, which predicted similar responses, were selected as reasonable dose‐response models based on their simplicity, biological plausibility, and goodness fit. A confidence interval of the parameter was determined by constructing 10,000 bootstrap samples. Scaling factors were computed by matching the predicted infection cases with the epidemiological data. Mouse‐derived models were validated against data for the dose‐infection relationship in rats. A human dose‐response model was developed as P (d) = 1–exp (–0.0015 × 0.005 × d) or P (d) = 1–(1 + d × 0.003 / 582.414)^?1.479. Both models predict the human response after consuming T. gondii‐infected meats, and provide an enhanced risk characterization in a quantitative microbial risk assessment model for this pathogen.     

Exposure to Chlorination By-Products from Hot Water Uses

Exposures to chlorination by-products (CBP) within public water supplies are multiroute in water. Cold water is primarily used for ingestion while a mixture of cold water and hot water is used for showering, bathing others, dish washing, etc. These latter two activities result in inhalation and dermal exposure. Heating water was observed to change the concentration of various CBP. An increase in the trihalomethanes (THM) concentrations and a decrease in the haloacetonitriles and halopropanones concentration, though an initial rise in the concentration of dichloropropanone, were observed. The extent of the increase in the THM is dependent on the chlorine residual present. Therefore, estimates of total exposure to CBP from public water supplies need to consider any changes in their concentration with different water uses. The overall THM exposures calculated using the THM concentration in heated water were 50% higher than those calculated using the THM concentration present in cold water. The estimated lifetime cancer risk associated with exposure to THM in water during the shower is therefore underestimated by 50% if the concentration of THM in cold water is used in the risk assessment.