title: Analytical recovery of protozoan enumeration methods: have drinking water QMRA models corrected or created bias?
abstract: Quantitative microbial risk assessment (QMRA) is a tool to evaluate the potential implications of pathogens in a water supply or other media and is of increasing interest to regulators. In the case of potentially pathogenic protozoa (e.g. Cryptosporidium oocysts and Giardia cysts), it is well known that the methods used to enumerate (oo)cysts in samples of water and other media can have low and highly variable analytical recovery. In these applications, QMRA has evolved from ignoring analytical recovery to addressing it in point-estimates of risk, and then to addressing variation of analytical recovery in Monte Carlo risk assessments. Often, variation of analytical recovery is addressed in exposure assessment by dividing concentration values that were obtained without consideration of analytical recovery by random beta-distributed recovery values. A simple mathematical proof is provided to demonstrate that this conventional approach to address non-constant analytical recovery in drinking water QMRA will lead to overestimation of mean pathogen concentrations. The bias, which can exceed an order of magnitude, is greatest when low analytical recovery values are common. A simulated dataset is analyzed using a diverse set of approaches to obtain distributions representing temporal variation in the oocyst concentration, and mean annual risk is then computed from each concentration distribution using a simple risk model. This illustrative example demonstrates that the bias associated with mishandling non-constant analytical recovery and non-detect samples can cause drinking water systems to be erroneously classified as surpassing risk thresholds.
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1. pj2schmi@alumni.uwaterloo.ca
authors.forename: P J
authors.initials: PJ
authors.lastname: Schmidt
authors.forename: M B
authors.initials: MB
authors.lastname: Emelko
authors.forename: M E
authors.initials: ME
authors.lastname: Thompson
mesh_terms: D000818:Animals; D015982:Bias; D003198:Computer Simulation; D003458:Cryptosporidium / Q000302:isolation & purification*; D060766:Drinking Water / Q000469:parasitology*; D005872:Giardia / Q000302:isolation & purification*; D008962:Models, Theoretical; D033661:Oocysts / Q000166:cytology; D011336:Probability; D018570:Risk Assessment / Q000379:methods*; D018508:Water Purification
chemical_list: D060766:Drinking Water