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Front. Microbiol. | doi: 10.3389/fmicb.2018.02304

Learning something from nothing: The critical importance of rethinking microbial non-detects

 Alex Ho Shing Chik1, 2, 3, Philip J. Schmidt1, 4 and  Monica B. Emelko1*
  • 1Department of Civil and Environmental Engineering, University of Waterloo, Canada
  • 2Institute of Hydraulic Engineering and Water Resources Management, Technische Universität Wien, Austria
  • 3Department of Earth Sciences, Faculty of Geosciences, Utrecht University, Netherlands
  • 4Philip J. Schmidt Technical Consulting Inc., Canada

Accurate estimation of microbial concentrations is necessary to inform many important environmental science and public health decisions and regulations. Critically, widespread misconceptions about laboratory-reported microbial non-detects (NDs) have led to their erroneous description and handling as “censored” values. This ultimately compromises their interpretation and undermines efforts to describe and model microbial concentrations accurately. Herein, these misconceptions are dispelled by 1) discussing the critical differences between discrete microbial observations and continuous data acquired using analytical chemistry methodologies and 2) demonstrating the bias introduced by statistical approaches tailored for chemistry data and misapplied to discrete microbial data. Notably, these approaches especially preclude the accurate representation of low concentrations and those estimated using microbial methods with low or variable analytical recovery, which can be expected to result in non-detects. Techniques that account for the probabilistic relationship between observed data and underlying microbial concentrations have been widely demonstrated, and their necessity for handling non-detects (in a way which is consistent with the handling of positive observations) is underscored herein. Habitual reporting of raw microbial observations and sample sizes is proposed to facilitate accurate estimation and analysis of microbial concentrations.

Keywords: qmra, microbial risk assessment, zeros, Detection limit, Censored data, Presence-absence, pathogens

Received: 10 Mar 2018; Accepted: 10 Sep 2018.

Edited by:

Aldo Corsetti, Università degli Studi di Teramo, Italy

Reviewed by:

Antonio Valero, Universidad de Córdoba, Spain
Maria Aponte, Università degli Studi di Napoli Federico II, Italy  

Copyright: © 2018 Chik, Schmidt and Emelko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Monica B. Emelko, University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, Ontario, Canada, mbemelko@uwaterloo.ca