The variability introduced by partial sample analysis to numbers of Cryptosporidium oocysts and Giardia cysts reported under the information collection rule

Subsampling of the concentrated sample pellet resulting from the informatio n collection rule (ICR) method for detection of Cryptosporidium oocysts and Giardia cysts is often required to facilitate the terminal microscopy step . Variability in counts of (oo)cysts in subsamples suggests that sampling e rror may occur as a consequence of subsampling and subsequent partial analy sis of this pellet. Variability is modeled hers as a Poisson distribution a nd is incorporated into a model that accounts for other uncertainties in th e detection procedure. The result is to greatly expand confidence intervals (CI) around reported concentrations. For example, the 90% CI around the co ncentration reported based on detection of one (oo)cyst in a pellet subsamp le is increased nearly threefold when sampling error arising from partial s ample analysis is added to other potential sources of variability. Consider ation of a distribution in which (oo)cysts are clumped, such as a negative binomial distribution, further increases uncertainty, producing confidence intervals ranging over several orders of magnitude. The relevance of the un certainty arising from partial sample analysis to risk assessment is demons trated. For example, to produce water with an acceptable 1 in 10,000 risk o f infection, the level of water treatment prescribed may increase from 3- t o 4.5- for greater log removal of(oo)cysts. Implications of this additional uncertainty in the ICR method on reported (oo)cyst concentrations argue fo r expanded reporting procedures to include an indication of the uncertainti es associated with reported concentrations and/or improvements in the detec tion method. (C)! 1999 Elsevier Science Ltd. All rights reserved.