Giardiasis in NSW: Identification of Giardia duodenalis assemblages contributing to human and cattle cases, and an epidemiological assessment of sporadic human giardiasis.

Two genetic assemblages (A and B) of the protozoan parasite species, Giardia duodenalis, infect humans, domestic animals and wildlife. In New South Wales, Australia, over 2000 sporadic human giardiasis cases are reported annually, but parasite sources and links between sporadic cases are unknown. This study describes G. duodenalis assemblages contributing to human and cattle cases in NSW, and examines demographic, spatial, and temporal distributions of NSW human infections and G. duodenalis assemblages. Genotyping by PCR-restriction fragment length polymorphism of the glutamate dehydrogenase (gdh) gene identified G. duodenalis assemblage B as the most common (86%) cause of infection among human cases (n=165). Approximately 37% of cattle DNA samples were PCR positive (18S rRNA, gdh), and G. duodenalis assemblages E (69%) or B (31%) were identified from these samples. Human assemblage A was more common among older age groups, and seasonality in the geographic dispersal of human assemblage A was observed. The results of this study indicate G. duodenalis assemblage B is highly prevalent among humans in NSW, and the potential for cross-species transmission exists between humans and cattle in this region. Spatio-temporal and demographic distributions of human assemblage A and B are highlighted, and risk factors associated with these dispersal patterns warrants further research.

Infection risk assessment of diarrhea-related pathogens in a tropical canal network.

A quantitative microbial risk assessment (QMRA) of Cryptosporidium, Giardia and diarrhegenic Escherichia coli (DEC) infection was performed using Monte Carlo simulations to estimate the human health risks associated with the use of canal water for recreational purposes, unrestricted and restricted irrigation in a tropical peri-urban area. Three canals receiving municipal, agricultural, and, predominantly, industrial wastewater were investigated. Identification of pathogenic protozoans revealed the major presence of Cryptosporidium hominis and both assemblages A and B of Giardia lamblia. The highest individual infection risk estimate was found to be for Giardia in an exposure scenario involving the accidental ingestion of water when swimming during the rainy season, particularly in the most polluted section, downstream of a large wholesale market. The estimated annual risks of diarrheal disease due to infection by the protozoan parasites were up to 120-fold greater than the reported disease incidence in the vicinity of the studied district and the entire Thailand, suggesting a significant host resistance to disease beyond our model's assumptions. In contrast, annual disease risk estimates for DEC were in agreement with actual cases of diarrhea in the study area.

QMRA (quantitative microbial risk assessment) and HACCP (hazard analysis and critical control points) for management of pathogens in wastewater and sewage sludge treatment and reuse.

Hazard Analysis and Critical Control Points (HACCP) was applied for identifying and controlling exposure to pathogenic microorganisms encountered during normal sludge and wastewater handling at a 12,500 m3/d treatment plant utilising tertiary wastewater treatment and mesophilic sludge digestion. The hazardous scenarios considered were human exposure during treatment, handling, soil application and crop consumption, and exposure via water at the wetland-area and recreational swimming. A quantitative microbial risk assessment (QMRA), including rotavirus, adenovirus, haemorrhagic E. coli, Salmonella, Giardia and Cryptosporidium, was performed in order to prioritise pathogen hazards for control purposes. Human exposures were treated as individual risks but also related to the endemic situation in the general population. The highest individual health risk from a single exposure was via aerosols for workers at the belt press for sludge dewatering (virus infection risk = 1). The largest impact on the community would arise if children ingested sludge at the unprotected storage site, although in the worst-case situation the largest number of infections would arise through vegetables fertilised with sludge and eaten raw (not allowed in Sweden). Acceptable risk for various hazardous scenarios, treatment and/or reuse strategies could be tested in the model.

Assessment of methods for detection of infectious Cryptosporidium oocysts and Giardia cysts in reclaimed effluents.

This study evaluates the occurrence of Cryptosporidium oocysts and Giardia cysts in reclaimed effluents if method 1623 with the Envirochek capsule filters (standard and high-volume [HV] filters) and a modified version of the Information Collection Rule method (ICR) with the polypropylene yarn-wound cartridge filter are used. The recovery efficiency of the analytical methods was evaluated with samples of reagent, tap, and reclaimed water by using flow cytometer-sorted spike suspensions. (Oo)cyst recovery efficiency determined filter performance and method reproducibility in the water matrix tested. Method 1623 with the Envirochek HV capsule filter generated significantly higher recovery rates than did the standard Envirochek filter and the modified ICR method. Notwithstanding, large variations in recovery rates (>80%) occurred with samples of reclaimed water, and none of the water quality parameters analyzed in the reclaimed effluents could explain such variability. The highest concentrations of indigenous oocysts were detected by method 1623 with the HV filter, which provided a sufficient number of oocysts for further confirmation of infectious potential. Confirmation of species and potential infectivity for all positive protozoan samples was made by using a nested PCR restriction fragment polymorphism assay and the focus detection method most-probable-number assay, respectively. The methodology and results described in the present investigation provide useful information for the establishment of pathogen numeric standards for reclaimed effluents used for unrestricted irrigation.

Quantifying water pathogen risk in an epidemiological framework.

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature-based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing the a priori range of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.