Dose–response data on human exposure to oocysts of Cryptosporidium were analyzed to determine the dose corresponding to a stated risk level. A recent study of infection and illness in human volunteers subjected to controlled exposure to oocysts of Cryptosporidium parvum provided a suitable data base for dose–response analysis. The authors analyzed the study data, and using exponential and beta‐poisson dose–response models, arrived at an estimate of the waterborne concentrations associated with various risk levels.

No abstract

Every year there are estimated 300000 cases of Shigella in the United States (Bennett et al., 1987, Am. J. Prev. Med. 3, 102-114). A beta-Poisson model was fit to human dose-response information on pathogenic Shigella using the Maximum Likelihood Estimation technique (Haas, 1983, Am. J. Epidemiol. 118, 573-582). Pooled and separate data sets for the Shigella species were fit to the beta-Poisson model and 95% confidence limits and regions were calculated. Shigella dysentariae and Shigella jlexneri confidence regions and limits overlapped with each other and with the pooled data set, suggesting that this model can describe Shigella in general. The pooled Shigella model as well as the upper and lower confidence limits of the three data sets showed average exposures based on the estimated U.S. caseload of pathogenic Shigella of 0.01 to 0.014 organisms (confidence limits O.OOl-0.05) for a 7-day per annum period of exposure and ranges from 0.07 to 0.1 organisms (confidence limits 0.006-0.4). for a l-day per annum period of exposure. The plausibility of the pooled dose-response model was then evaluated by comparison with two known cruise ship outbreaks. The pooled model estimated that the two outbreaks studied could have been due to ingestion of 344 (confidence limits 72-915) Shigella cells per meal and 10.5-12 (confidence limits l-44) Shigella cells per glass of water by passengers.

Traditionally, regulators, dischargers, and even water suppliers believed that wastewater discharge meeting the levels of 200 cfu/ 100 mL of fecal coliforms in wastewater effluent was sufficient to protect against downstream microbial effects. However, these beliefs are now being challenged by emerging pathogens that are resistant to standard water and wastewater treatment processes, exhibit extended survival periods in the environment, can adversely affect sensitive subpopulations, and require extremely low doses for human infection. Based on this new information, it is estimated that discharges of emerging pathogens from conventional wastewater treatment plants as far as 160 km upstream and cumulative amounts of wastewater discharge ranging from 2 to 20 ML/d have the potential to reach a water supply intake in a viable state at significant concentrations that could exceed regulatory limits for drinking water supplies, increase endemic risk from drinking water, and/or require additional drinking water treatment. Wastewater dischargers may be able mitigate this potential effect and achieve upwards of 6 log combined removal and inactivation of emerging pathogens to mitigate drinking water effects by using alternative treatment processes, such as filtration or UV light disinfection, or optimizing these processes based on site-specific conditions. Water Environ. Res., 79, 221 (2007).

Eliminating bias while balancing sensitivity and cost are important issues for ICR monitoring of Giardia and Cryptosporidium. The proposed Information Collection Rule may require utilities to monitor source and finished waters for Giardia and Cryptosporidium and to use the data for compliance with treatment requirements imposed by the Enhanced Surface Water Treatment Rule. Variations in sample collection and analytical methods, water quality, weather conditions, and proposed averaging methods could result in widely varying detection limits; in certain cases detection limits could easily be inaccurately pushed beyond 100 organisms/100 L. The optimal balance between sample cost and laboratory analysis is achieved by examination of 1–3 membrane filters, depending on source water turbidity. Improved monitoring results and detection limits may prevent decisions to institute unnecessary and possibly expensive treatment.