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.
Highlights
Whether describing pathogens in water or the density of red blood cells, the concentration of discrete objects cannot be measured directly
Concentration is estimated by enumerating or detecting the objects in finite sample portions; such approaches are used extensively in health, food, and water applications. These estimates are required for decision making, during which they are typically evaluated against concentration-based criteria or targets (Dickey et al, 1999; Lund et al, 2000; Havelaar et al, 2001; Gerba and Rose, 2003; Gracias and McKillip, 2004; Koepke et al, 2007; Schijven and de Roda Husman, 2011; Davis, 2014; World Health Organization, 2017). This underscores the importance of accurate representation and analysis of detection- and enumeration-based data, especially where the protection of public health is at stake
This has often led to a desire to quantify enough of these objects by modifying the Rethinking Microbial Non-detects enumerated sample portion so that the count falls in a range that is deemed acceptable (Emelko et al, 2008; American Public Health Agency et al, 2017; United States Food and Drug Administration, 2017)
Summary
Whether describing pathogens in water or the density of red blood cells, the concentration of discrete objects cannot be measured directly. Concentration is estimated by enumerating or detecting the objects in finite sample portions (e.g., volumes); such approaches are used extensively in health, food, and water applications These estimates are required for decision making, during which they are typically evaluated against concentration-based criteria or targets (Dickey et al, 1999; Lund et al, 2000; Havelaar et al, 2001; Gerba and Rose, 2003; Gracias and McKillip, 2004; Koepke et al, 2007; Schijven and de Roda Husman, 2011; Davis, 2014; World Health Organization, 2017). Rethinking Microbial Non-detects enumerated sample portion so that the count falls in a range that is deemed acceptable (Emelko et al, 2008; American Public Health Agency et al, 2017; United States Food and Drug Administration, 2017) When this is not possible, resulting NDs are widely reported as being less than a detection limit (e.g.,
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