Dataset Information

A Systematic Review of Beef Meat Quantitative Microbial Risk Assessment Models.

ABSTRACT: Each year in Europe, meat is associated with 2.3 million foodborne illnesses, with a high contribution from beef meat. Many of these illnesses are attributed to pathogenic bacterial contamination and inadequate operations leading to growth and/or insufficient inactivation occurring along the whole farm-to-fork chain. To ensure consumer health, decision-making processes in food safety rely on Quantitative Microbiological Risk Assessment (QMRA) with many applications in recent decades. The present study aims to conduct a critical analysis of beef QMRAs and to identify future challenges. A systematic approach, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was used to collate beef QMRA models, identify steps of the farm-to-fork chain considered, and analyze inputs and outputs included as well as modelling methods. A total of 2343 articles were collected and 67 were selected. These studies focused mainly on western countries and considered Escherichia coli (EHEC) and Salmonella spp. pathogens. Future challenges were identified and included the need of whole-chain assessments, centralization of data collection processes, and improvement of model interoperability through harmonization. The present analysis can serve as a source of data and information to inform QMRA framework for beef meat and will help the scientific community and food safety authorities to identify specific monitoring and research needs.

SUBMITTER: Tesson V

PROVIDER: S-EPMC7037662 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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A Systematic Review of Beef Meat Quantitative Microbial Risk Assessment Models.

Tesson Vincent V Federighi Michel M Cummins Enda E de Oliveira Mota Juliana J Guillou Sandrine S Boué Géraldine G

International journal of environmental research and public health 20200121 3

Each year in Europe, meat is associated with 2.3 million foodborne illnesses, with a high contribution from beef meat. Many of these illnesses are attributed to pathogenic bacterial contamination and inadequate operations leading to growth and/or insufficient inactivation occurring along the whole farm-to-fork chain. To ensure consumer health, decision-making processes in food safety rely on Quantitative Microbiological Risk Assessment (QMRA) with many applications in recent decades. The present ...[more]

PMID: 31973083

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Project description:The aim of this study was to perform a quantitative microbial risk assessment (QMRA) of Shiga toxin-producing Escherichia coli hemolytic uremic syndrome (STEC-HUS) linked to the consumption of Kosher beef produced in Argentina and consumed in Israel in children under 14 years. A probabilistic risk assessment model was developed to characterize STEC prevalence and contamination levels in the beef supply chain (cattle primary production, cattle transport, processing and storage in the abattoir, for export and at retail, and home preparation and consumption). The model was implemented in Microsoft Excel 2016 with the @Risk add-on package. Results of 302 surveys with data collected in Israel were as follows: 92.3% of people consumed beef, mostly at home, and 98.2% preferred levels of cooking that ensured STEC removal from the surface of beef cuts. The preferred degree of ground beef doneness was "well-done" (48.2%). Cooking preference ranged from red to "medium-well done" (51.8%). Median HUS probability from Argentinean beef cut and ground beef consumption in children under 14 years old was <10-15 and 8.57x10-10, respectively. The expected average annual number of HUS cases and deaths due to beef cut and ground beef consumption was zero. Risk of infection and HUS probability correlated with salting effect on E. coli count, processing raw beef before vegetables, ways of storage and refrigeration temperature at home, joint consumption of salad and beef cuts, degree of beef doneness and cutting board washing with detergent after each use with beef and vegetables. The STEC-HUS risk in Israel from consumption of bovine beef produced in Argentina was negligible. The current QMRA results were similar to those of previous beef cut consumption QMRA in Argentina and lower than any of the QMRA performed worldwide in other STEC-HUS linked to ground beef consumption. This study confirms the importance of QMRA to estimate and manage the risk of STEC-HUS from beef consumption. The impact variables identified in the sensitivity analysis allowed us to optimize resources and time management, to focus on accurate actions and to avoid taking measures that would not have an impact on the risk of STEC-HUS.

Project description:Data below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low [10%], medium [35%], high [65%], and severe [90%]) and one real-life censoring example (97%) and were informed by enterovirus data assuming a lognormal distribution with a limit of detection (LOD) of 2.3 genome copies/liter. For each data set, five methods for handling left-censored data were applied: (i) substitution with LOD/[Formula: see text], (ii) lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, (iii) Kaplan-Meier estimation (KM), (iv) imputation method using MLE to estimate distribution parameters (MI method 1), and (v) imputation from a uniform distribution (MI method 2). Each data set mean was used to estimate enterovirus dose and infection risk. Root mean square error (RMSE) and bias were used to compare estimated and known doses and infection risks. MI method 1 resulted in the lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 × 10-2 from known values under 97% censoring. MI method 2 was the next overall best method. For medium to severe censoring, MI method 1 may result in the least error. If unsure of the distribution, MI method 2 may be a preferred method to avoid distribution misspecification.IMPORTANCE This study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists.

Project description:IntroductionUnderstanding the balance between the benefits and risks of vaccination is essential to ensure informed and adequate public health decision making. Quantitative benefit-risk models (qBRm) represent useful tools to help decision makers with supporting benefit-risk assessment throughout the lifecycle of a medical product. However, few initiatives have been launched to harmonise qBRm approaches, specifically for vaccines.ObjectivesThe aim of this paper was to identify publications about qBRm applied to vaccines through a systematic literature review, and to describe their characteristics.MethodsMedline, Scopus and Institute for Scientific Information Web of Knowledge databases were searched to identify articles in English, published from database inceptions up to December 2019. The search strategy included the combination of three key concepts: 'benefit-risk', 'modelling' and 'vaccines'. Data extracted included the modelling context and the methodological approaches used.ResultsOf 3172 publications screened, 48 original publications were included. Most of the selected studies were published over the past decade and focused on rotavirus (15), dengue (10) and influenza (6) vaccines. The majority (30) of studies reported analyses related to high-income countries. The methodology of the studies differed, particularly in modelling techniques, benefit-risk measures, and sensitivity analyses. The present work also pointed out a high level of variability in the quality of reporting across studies, with particular regard to input parameters and methodological approaches.ConclusionsThis review provides an extensive list of qBRm applied to vaccines. Discrepancies across studies were identified during our review. While the number of published qBRm studies is increasing, no reporting guidance for qBRm applied to vaccines is currently available. This may affect decision makers' confidence in the results and their benefit-risk assessment(s); therefore, the development of such reporting guidance is highly needed.

Project description:IntroductionHospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital.MethodsA systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings.ResultsAmong 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7-0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%.ConclusionAvailable data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM.Prospero registration numberSteve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778.

Dataset Information

A Systematic Review of Beef Meat Quantitative Microbial Risk Assessment Models.

Publications

A Systematic Review of Beef Meat Quantitative Microbial Risk Assessment Models.

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