Discerning Strain Effects in Microbial Dose-Response Data

Coleman, Margaret E.; Marks, Harry M.; Golden, Neal J.; Latimer, Heejeong K.

doi:10.1080/15287390490428134

Cited by 15 publications

(9 citation statements)

References 52 publications

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“…This was actually the case in our study, although it was difficult to draw any firm conclusion on the posterior distributions, especially for the type-dependant parameter. The type-and source-parameters (q i and a j ) are conceived as 'black boxes', and they allow differences between types and between sources to be taken into account, which is of primary importance (Blaser and Newman, 1982;D'Aoust, 1989;Sarwari et al, 2001;Coleman et al, 2004;Bollaerts et al, 2008;Jones et al, 2008). However, their nature is not defined.…”

Section: Discussionmentioning

confidence: 99%

Attribution of the French human Salmonellosis cases to the main food-sources according to the type of surveillance data

David

Sandérs

Bemrah³

et al. 2013

Preventive Veterinary Medicine

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Salmonella are the most common bacterial cause of foodborne infections in France and ubiquitous pathogens present in many animal productions. Assessing the relative contribution of the different food-animal sources to the burden of human cases is a key step towards the conception, prioritization and assessment of efficient control policy measures. For this purpose, we considered a Bayesian microbial subtyping attribution approach based on a previous published model (Hald et al., 2004). It requires quality integrated data on human cases and on the contamination of their food sources, per serotype and microbial subtype, which were retrieved from the French integrated surveillance system for Salmonella. The quality of the data available for such an approach is an issue for many countries in which the surveillance system has not been designed for this purpose. In France, the sources are monitored simultaneously by an active, regulation-based surveillance system that produces representative prevalence data (as ideally required for the approach) and a passive system relying on voluntary laboratories that produces data not meeting the standards set by Hald et al. (2004) but covering a broader range of sources. These data allowed us to study the impact of data quality on the attribution results, globally and focusing on specific features of the data (number of sources and contamination indicator). The microbial subtyping attribution model was run using an adapted parameterization previously proposed (David et al., 2012). A total of 9076 domestic sporadic cases were included in the analyses as well as 9 sources among which 5 were common to the active and the passive datasets. The greatest impact on the attribution results was observed for the number of sources. Thus, especially in the absence of data on imported products, the attribution estimates presented here should be considered with caution. The results were comparable for both types of surveillance, leading to the conclusion that passive data constitute a potential cost-effective complement to active data collection, especially interesting because the former encompass a greater number of sources. The model appeared robust to the type of surveillance, and provided that some methodological aspects of the model can be enhanced, it could also serve as a risk-based guidance tool for active surveillance systems.

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Section: Discussionmentioning

confidence: 99%

Attribution of the French human Salmonellosis cases to the main food-sources according to the type of surveillance data

David

Sandérs

Bemrah³

et al. 2013

Preventive Veterinary Medicine

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“…If no such specific serotypes can be found, even among the less frequent ones, then a solution would be to apply either the simple model or Mullner's model. However Mullner's model seems preferable as it accounts for the differences between types and between sources, differences that have been documented in the literature . Another option to explore could be to combine Mullner's approach with use of the data‐based information on the specific type‐dependent parameters.…”

Section: Discussionmentioning

confidence: 99%

The Bayesian Microbial Subtyping Attribution Model: Robustness to Prior Information and a Proposition

Guillemot

Bemrah³

et al. 2012

Risk Analysis

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Attributing foodborne illnesses to food sources is essential to conceive, prioritize, and assess the impact of public health policy measures. The Bayesian microbial subtyping attribution model by Hald et al. is one of the most advanced approaches to attribute sporadic cases; it namely allows taking into account the level of exposure to the sources and the differences between bacterial types and between sources. This step forward requires introducing type and source-dependent parameters, and generates overparameterization, which was addressed in Hald's paper by setting some parameters to constant values. We question the impact of the choices made for the parameterization (parameters set and values used) on model robustness and propose an alternative parameterization for the Hald model. We illustrate this analysis with the 2005 French data set of non-typhi Salmonella. Mullner's modified Hald model and a simple deterministic model were used to compare the results and assess the accuracy of the estimates. Setting the parameters for bacterial types specific to a unique source instead of the most frequent one and using data-based values instead of arbitrary values enhanced the convergence and adequacy of the estimates and led to attribution estimates consistent with the other models' results. The type and source parameters estimates were also coherent with Mullner's model estimates. The model appeared to be highly sensitive to parameterization. The proposed solution based on specific types and data-based values improved the robustness of estimates and enabled the use of this highly valuable tool successfully with the French data set.

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“…as mentioned by (Ayuso‐Gabella et al., ). In addition, in many microbial risk assessments, the ‘default’ assumption used for dose–response does not account for strain or serotype variability in pathogenicity and virulence, other than perhaps, recognising the existence of avirulent strains (Coleman et al., ). Also, it is known that the dose–response relationship may differ for healthy adults vs those persons being part of a more susceptible population, which is particularly well documented for L. monocytogenes (Goulet et al., ; Pouillot et al., ).…”

Section: Required Scientific Evidence and Data Relevant For Considerimentioning

confidence: 99%

Guidance on the requirements for the development of microbiological criteria

Ricci¹,

Allende²,

Bolton³

et al. 2017

EFS2

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The European Food Safety Authority asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific opinion providing: (i) a review of the approaches used by the BIOHAZ Panel to address requests from risk managers to suggest the establishment of microbiological criteria; (ii) guidance on the required scientific evidence, data and methods/tools necessary for considering the development of microbiological criteria for pathogenic microorganisms and indicator microorganisms; (iii) recommendations on methods/tools to design microbiological criteria and (iv) guidelines for the requirements and tasks of risk assessors, compared to risk managers, in relation to microbiological criteria. This document provides guidance on approaches when: (i) a quantitative microbial risk assessment (QMRA) is available, (ii) prevalence and concentration data are available, but not a QMRA model, and (iii) neither a QMRA nor prevalence and/or concentration data are available. The role of risk assessors should be focused on assessing the impact of different microbiological criteria on public health and on product compliance. It is the task of the risk managers to: (1) formulate unambiguous questions, preferably in consultation with risk assessors, (2) decide on the establishment of a microbiological criterion, or target in primary production sectors, and to formulate the specific intended purpose for using such criteria, (3) consider the uncertainties in impact assessments on public health and on product compliance and (4) decide the point in the food chain where the microbiological criteria are intended to be applied and decide on the actions which should be taken in case of noncompliance. It is the task of the risk assessors to support risk managers to ensure that questions are formulated in a way that a precise answer can be given, if sufficient information is available, and to ensure clear and unambiguous answers, including the assessment of uncertainties, based on available scientific evidence.

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Discerning Strain Effects in Microbial Dose-Response Data

Cited by 15 publications

References 52 publications

Attribution of the French human Salmonellosis cases to the main food-sources according to the type of surveillance data

Attribution of the French human Salmonellosis cases to the main food-sources according to the type of surveillance data

The Bayesian Microbial Subtyping Attribution Model: Robustness to Prior Information and a Proposition

Guidance on the requirements for the development of microbiological criteria

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