Abstract
Data validation of safe doses of pathogens in drinking water consistent with public health goals is not possible due to the number of subjects that would be needed at each dose. Because of this problem, together with the difficulty in extrapolating pathogenic response between species, and the ability of microbes to adapt rapidly, confidence-level-dependent assessments of Cryptosporidium parvum dose-response have been developed. However, these results, even on a relative basis, are dependent on confidence level, and the lack of scientific basis for this choice hampers efforts to set water quality standards. Therefore, a predictive Bayesian dose-response assessment method was proposed previously. In this article, a hierarchical predictive population dose-response Bayesian assessment for C. parvum is presented for the infection endpoint. Available data on the infectivity of three isolates of C. parvum, genotype C, were adjusted for sensitive and antibody-positive subpopulations not proportionately represented in the data, by bootstrap analysis. The diverse mean infectivities of the isolates were used to obtain a predictive distribution for population infectivity, used in turn to obtain the predictive population dose-response function. The predictive result is a distribution of unconditional probability of infection, based on available dose-response information. Information includes theoretical and empirical evidence for the conditional beta-Poisson parametric dose-response function. Results indicate that a dose of 6 x 10(-6) oocysts per exposure corresponds to 10(-4) infections per capita year. An allowable dose corresponding to goals of the SWTR should be increased over this value to reflect the illness endpoint, while potentially being reduced to account for secondary transmission among hosts if important for gastroenteritis in developed countries.
