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

Young, Pamela L.; Komisar, Simeon J.

doi:10.1016/s0043-1354(98)00495-3

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…Enumeration of only a portion of a sample (e.g., subsampling between sample-processing steps) is common in some methods. Partial sample analysis has been included elsewhere in the statistical analysis of enumeration and recovery data ,, , and can be incorporated into the probabilistic models presented herein by adding a parameter to the analytical error distributions (see Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

“…If analytical recovery is less than 100% and each seeded particle in the sample is assumed to have an equal probability (equal to analytical recovery) of yielding an observation, then the observation of seeded particles is a Bernoulli process and the number of particles observed in the sample is binomially distributed . This assumption is made in both the beta-binomial and beta-Poisson models and has been widely used in other models that address analytical recovery − ,,,, . If analytical recovery can exceed 100% (e.g., due to counting errors), then it can not be regarded as a probability and the observation of seeded particles is not a Bernoulli process.…”

Section: Methodsmentioning

confidence: 99%

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Quantification of Analytical Recovery in Particle and Microorganism Enumeration Methods

Schmidt

Emelko

Reilly

2010

Environ. Sci. Technol.

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Enumeration-based methods that are often used to quantify microorganisms and microscopic discrete particles in aqueous systems may include losses during sample processing or errors in counting. Analytical recovery (the capacity of the analyst to successfully count each microorganism or particle of interest in a sample using a specific enumeration method) is frequently assessed by enumerating samples that are seeded with known quantities of the microorganisms or particles. Probabilistic models were developed to account for the impacts of seeding and analytical error on recovery data, and probability intervals, obtained by Monte Carlo simulation, were used to evaluate recovery experiment design (i.e., seeding method, number of seeded particles, and number of samples). The method of moments, maximum likelihood estimation, and credible intervals were used to statistically analyze recovery experiment results. Low or uncertain numbers of seeded particles were found to result in variability in recovery data that was not due to analytical recovery, and should be avoided if possible. This additional variability was found to reduce the reproducibility of experimental results and necessitated the use of statistical analysis techniques, such as maximum likelihood estimation using probabilistic models that account for the impacts of sampling and analytical error in recovery data.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantification of Analytical Recovery in Particle and Microorganism Enumeration Methods

Schmidt

Emelko

Reilly

2010

Environ. Sci. Technol.

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“…The author and colleagues 2 – 4 have previously documented the problems associated with the analytical methods used for recovery and detection of Cryptosporidium oocysts and Giardia cysts in water, from sample collection to microscopic identification. Losses during sampling and processing can lead to underestimation, whereas subsample analysis and improper identification can lead to overestimation 5 – 7 . Collaborative testing of the Information Collection Rule (ICR) protozoan method has shown that although the average percent recoveries of cysts and oocysts varied among studies, overall variability (attributable to poor precision and bias) remained high, with false‐positive and false‐negative results often reported.…”

Section: Issues Surround Current Analytical Methodsmentioning

confidence: 99%

Sydney's 1998 water quality crisis

Clancy¹

2000

Journal AWWA

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Events in Sydney, Australia, illustrate the pitfalls of overreliance on pathogen monitoring data in making decisions about water treatment and public health. A drinking water quality crisis occurred in Sydney, Australia, from July through September 1998. High concentrations of Cryptosporidium oocysts and Giardia cysts were repeatedly observed in water samples collected in the distribution system, although no increase in waterborne disease was detected. The reported concentrations of both cysts and oocysts ranged from nondetects to thousands of parasites per 100 L of finished water. Based on these monitoring data, three boil‐water advisories were issued over a nine‐week period. A formal government inquiry was ordered to investigate the cause of the contamination. The author visited the facilities involved and found that the laboratory providing the monitoring data had significant quality assurance and quality control problems, rendering the protozoan data suspect. Furthermore, data from other laboratories indicated the original reports were in error. In the author's opinion, reliance on poor‐quality monitoring data created a water quality crisis when, in fact, no water quality problems or threats to public health existed.

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“…The precision of laboratory recovery rates for Cryptosporidium has long been of concern [ Gimbel and Nahrstedt , 1996; Walker , 1999, chap. 18; Bukhari et al , 1999; Young and Komisar , 1999; Connell et al , 2000]. Because the mean recovery rate for Cryptosporidium parvum is approximately 11% [ Messner , 2000], failure to include recovery in the model would result in severe underestimation of concentrations C ij and exaggeration of their variability [ Stedinger and MacKay , 1998].…”

Section: Bayesian Modeling Of Pathogen Countsmentioning

confidence: 99%

“…There are many imprecisely controlled processes in the ICR method that could be responsible for variability. Some organisms could pass through or be trapped in the filter; others could be lost during the flotation or staining procedure, or are hidden by other particles in the water [ Young and Komisar , 1999]. Even for a given probability of counting an organism (recovery rate), the number of oocysts counted by the laboratory is still random.…”

Section: Bayesian Modeling Of Pathogen Countsmentioning

confidence: 99%

Modeling the U.S. national distribution of waterborne pathogen concentrations with application to Cryptosporidium parvum

Crainiceanu

Stedinger

Ruppert

et al. 2003

Water Resources Research

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[1] This paper provides a general statistical methodology for modeling environmental pathogen concentrations in natural waters. A hierarchical model of pathogen concentrations captures site and regional random effects as well as random laboratory recovery rates. Recovery rates were modeled by a generalized linear mixed model. Two classes of pathogen concentration models are differentiated according to their ultimate purpose: water quality prediction or health risk analysis. A fully Bayesian analysis using Markov chain Monte Carlo (MCMC) simulation is used for statistical inference. The applicability of this methodology is illustrated by the analysis of a national survey of Cryptosporidium parvum concentrations, in which 93% of the observations were zero counts.

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

Cited by 6 publications

References 16 publications

Quantification of Analytical Recovery in Particle and Microorganism Enumeration Methods

Quantification of Analytical Recovery in Particle and Microorganism Enumeration Methods

Sydney's 1998 water quality crisis

Modeling the U.S. national distribution of waterborne pathogen concentrations with application to Cryptosporidium parvum

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