Learning Something From Nothing: The Critical Importance of Rethinking Microbial Non-detects

Abstract

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 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.

Identification of eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) infection levels in Northern bobwhite quail (Colinus virginianus) of the Rolling Plains, TX using a mobile research laboratory: Implications for regional surveillance

“…Nevertheless, the issue remains that samples that are generated outside the threshold of detection in qPCR, or non-detects, need to be considered in order to appropriately assess overall infection regionally. There is much debate in how to treat these non-detects in quantitative analyses, with the aim to reduce bias where possible [[22], [23], [24]]. At present, there is not a singularly accepted method for handling non-detects in qPCR [25].…”

Application of QMRA to MAR operations for safe agricultural water reuses in coastal areas

“…The count of Total Coliforms (TCs) and E. coli ( Chik et al., 2018 ) was determined using the Colilert®-18 Test (IDEXX Laboratories Inc., www.idexx.co.uk ), according to the manufacturer’s protocol. The number of the positive tray−wells both for TCs and E. coli , was converted into the most probable number (MPN) per 100 mL using the conversion factor provided by the IDEXX Laboratories Inc.…”

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

“…A single presence/absence analysis is commonly regarded as yielding qualitative data because it does not allow estimation of the microbial concentration. A positive result may be interpreted as censored count data because one or more discrete microorganisms is needed for detection to occur while a negative result is essentially a count of zero (Chik, Schmidt, & Emelko, ). A negative result cannot prove absence in the source because low concentration, small aliquot volume, or imperfect analytical recovery can cause nondetects when target microorganisms are present.…”