Pub Date : 2025-08-05DOI: 10.1016/j.mran.2025.100352
Mehedi Hasan , Megan Peterson , Emily K. Waldron , Nathan L. Mottern , Nicole T. Pargas , Lynn B. Gerald , Ashley A. Lowe , Amanda M. Wilson
Due to the impact of COVID-19, publicly available risk-based tools are becoming increasingly popular. However, subject experts develop most of these tools without consulting end users. Thus, this study aimed to explore users' perceptions, vision, and guidance for microbial risk assessment tool development through focus groups. This tool was intended to assist school health staff in decision-making regarding school respiratory viral outbreaks. Partnering with a school district in the Tucson metropolitan area, we conducted three focus groups with school health staff to gather feedback on a risk tool prototype. We discussed the staff’s vision for the tool, their feedback on tool capabilities and design, and how they could leverage tool output for informing decisions, advocating with administration, or educating parents, students, or staff. Focus groups were conducted at the district health office, and the transcripts were analyzed by two researchers using inductively informed themes. Thematic analysis revealed that a comprehensive microbial risk assessment tool must have the potential to manage large amounts of data, scope for incorporation of existing data management systems, have real-time data processing, and produce context-specific recommendations for advocacy. Risk tools can expand personalized risk assessment and management strategies. Directly engaging users will advance microbial risk assessment impact and implementation. In the context of schools, a collaborative, comprehensive, digital and real time microbial risk assessment tool is a timely demand by the school health staff to manage microbial risks.
{"title":"Improving a microbial risk assessment tool with direct feedback from school health staff","authors":"Mehedi Hasan , Megan Peterson , Emily K. Waldron , Nathan L. Mottern , Nicole T. Pargas , Lynn B. Gerald , Ashley A. Lowe , Amanda M. Wilson","doi":"10.1016/j.mran.2025.100352","DOIUrl":"10.1016/j.mran.2025.100352","url":null,"abstract":"<div><div>Due to the impact of COVID-19, publicly available risk-based tools are becoming increasingly popular. However, subject experts develop most of these tools without consulting end users. Thus, this study aimed to explore users' perceptions, vision, and guidance for microbial risk assessment tool development through focus groups. This tool was intended to assist school health staff in decision-making regarding school respiratory viral outbreaks. Partnering with a school district in the Tucson metropolitan area, we conducted three focus groups with school health staff to gather feedback on a risk tool prototype. We discussed the staff’s vision for the tool, their feedback on tool capabilities and design, and how they could leverage tool output for informing decisions, advocating with administration, or educating parents, students, or staff. Focus groups were conducted at the district health office, and the transcripts were analyzed by two researchers using inductively informed themes. Thematic analysis revealed that a comprehensive microbial risk assessment tool must have the potential to manage large amounts of data, scope for incorporation of existing data management systems, have real-time data processing, and produce context-specific recommendations for advocacy. Risk tools can expand personalized risk assessment and management strategies. Directly engaging users will advance microbial risk assessment impact and implementation. In the context of schools, a collaborative, comprehensive, digital and real time microbial risk assessment tool is a timely demand by the school health staff to manage microbial risks.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100352"},"PeriodicalIF":4.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-26DOI: 10.1016/j.mran.2025.100350
Jennifer E.M. McCarthy , Paul D Hynds , Declan J Bolton , Jesús M Frías Celayeta
Animal slurries and wastewater treatment sludges (WWTS) represent valuable biofertilisers in high-income, temperate regions and support transformative agri-food systems as sustainable, agricultural waste management practice. However, the presence of enteric pathogens in land-spread biowastes pose a public health risk, with food and water being critical transmission pathways. A dearth of spatiotemporally representative pathogen prevalence and concentration data from high-income, temperate regions exists to estimate the risk, achievable through quantitative microbial risk assessment (QMRA). A spatiotemporally explicit scoping review was undertaken of four waste-pathogen combinations (W-PCs) (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-Cryptosporidium parvum, broiler litter-Campylobacter jejuni, and WWTS-norovirus genogroups GI/GII) from land-spreading in high-income, temperate regions. W-PC prevalence and concentration data from 46 farm-level studies were extracted, harmonised, and pooled, to obtain representative data for meta-analyses, distribution fitting, and QMRA from land-spreading across these regions in addition to providing individual study prevalence and concentrations. Pooled mean prevalence and the total number of biowaste samples across extracted studies for each W-PC ranged from 17 % for STEC O157/O26 (N = 14,204) to 48 % for norovirus GI/GII (N = 1027). These general estimates included specific and non-specific data (i.e., serogroups, species and subspecies, or genogroups), and thus, should be interpreted with a level of caution. Pooled mean and SD concentrations ranged from norovirus GI/GII 1.3, 0.5 log10 gc ml-1 to C. jejuni 5.1, 0.7 log10 CFU g-1. Spatiotemporal heterogeneity, unstandardised reporting, and study design biases were found across studies. Therefore, increased standardised data and reporting in primary studies are required for more accurate QMRA estimates. Furthermore, pooling heterogeneous secondary data as though they were homogeneous introduces general error, and hence, highlights the requirement for future meta-analyses and distribution fitting of these data to characterise the inter- and intra- study variability in addition to uncertainty and variability from environmental sources.
{"title":"The prevalence and concentrations of four waste-pathogen combinations from land-spreading across high-income, temperate regions – A scoping review and pooled analysis","authors":"Jennifer E.M. McCarthy , Paul D Hynds , Declan J Bolton , Jesús M Frías Celayeta","doi":"10.1016/j.mran.2025.100350","DOIUrl":"10.1016/j.mran.2025.100350","url":null,"abstract":"<div><div>Animal slurries and wastewater treatment sludges (WWTS) represent valuable biofertilisers in high-income, temperate regions and support transformative agri-food systems as sustainable, agricultural waste management practice. However, the presence of enteric pathogens in land-spread biowastes pose a public health risk, with food and water being critical transmission pathways. A dearth of spatiotemporally representative pathogen prevalence and concentration data from high-income, temperate regions exists to estimate the risk, achievable through quantitative microbial risk assessment (QMRA). A spatiotemporally explicit scoping review was undertaken of four waste-pathogen combinations (W-PCs) (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-<em>Cryptosporidium parvum</em>, broiler litter-<em>Campylobacter jejuni</em>, and WWTS-norovirus genogroups GI/GII) from land-spreading in high-income, temperate regions. W-PC prevalence and concentration data from 46 farm-level studies were extracted, harmonised, and pooled, to obtain representative data for meta-analyses, distribution fitting, and QMRA from land-spreading across these regions in addition to providing individual study prevalence and concentrations. Pooled mean prevalence and the total number of biowaste samples across extracted studies for each W-PC ranged from 17 % for STEC O157/O26 (<em>N</em> = 14,204) to 48 % for norovirus GI/GII (<em>N</em> = 1027). These general estimates included specific and non-specific data (i.e., serogroups, species and subspecies, or genogroups), and thus, should be interpreted with a level of caution. Pooled mean and SD concentrations ranged from norovirus GI/GII 1.3, 0.5 log<sub>10</sub> gc ml<sup>-1</sup> to <em>C. jejuni</em> 5.1, 0.7 log<sub>10</sub> CFU g<sup>-1</sup>. Spatiotemporal heterogeneity, unstandardised reporting, and study design biases were found across studies. Therefore, increased standardised data and reporting in primary studies are required for more accurate QMRA estimates. Furthermore, pooling heterogeneous secondary data as though they were homogeneous introduces general error, and hence, highlights the requirement for future meta-analyses and distribution fitting of these data to characterise the inter- and intra- study variability in addition to uncertainty and variability from environmental sources.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100350"},"PeriodicalIF":4.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-22DOI: 10.1016/j.mran.2025.100348
Jennifer E M McCarthy , Paul D Hynds , Declan J Bolton , Jesús M Frías Celayeta
Land-spread organic wastes provide sustainable waste management across high-income, temperate regions. However, enteric pathogens in these animal manures and wastewater treatment sludges (WWTS) may pose food- and waterborne public health risks. Furthermore, these risks might increase due to climate change, with the likelihood of increasing temperature and precipitation across temperate latitudes. Quantitative microbial risk assessment (QMRA) is an established approach to estimate the potential risks, with a sparsity of spatiotemporally distributed waste-pathogen combination (W-PC) prevalence and concentrations from land-spreading existing in the literature for QMRA. Additionally, a knowledge gap exists regarding the availability of meta-models to predict pathogen prevalence based on spatially specific climatic or agricultural parameters. Accordingly, spatiotemporally representative data across high-income, temperate regions were extracted from 46 published studies based on a scoping review of four W-PC (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-Cryptosporidium parvum, broiler litter-Campylobacter jejuni, and WWTS-norovirus genogroups GI/GII) prevalence and concentrations from land-spreading. Meta-analyses and distribution fitting of these data characterised variability and uncertainty, with generalised linear mixed effects models employed to develop prevalence meta-models in addition to generalised additive models for location, shape, and scale fitted to concentrations. Mean pathogen prevalence ranged from STEC O157/O26 7 % OR 1.07 p = 0.05 to C. jejuni 39 % OR 1.48 p < 0.0001, with bioclimatic indicators, namely temperature and precipitation seasonality, significant across all meta-models. The best fit was a 2-parameter reverse Gumbel for norovirus GI/GII log10 gc ml-1 concentration (µ = 0.33, p = 0.55; σ = 0.66, p = 0.004; GAIC = 69.21). While meta-analyses and distribution fitting accounted for uncertainty and variability associated with modelled data, more standardised secondary data are required from primary research to provide more accurate QMRA estimates for ensuring microbiological safety in primary agricultural production.
陆地上散布的有机废物为高收入温带地区提供了可持续的废物管理。然而，这些动物粪便和废水处理污泥（WWTS）中的肠道病原体可能构成食物和水传播的公共卫生风险。此外，由于气候变化，这些风险可能会增加，温带地区的温度和降水可能会增加。定量微生物风险评估（QMRA）是一种评估潜在风险的成熟方法，但文献中存在时空分布的废物-病原体组合（W-PC）流行率和浓度的稀疏性。此外，在基于空间特定气候或农业参数预测病原体流行的元模型的可用性方面存在知识缺口。因此，基于对四种W-PC（即牛浆液-产大肠杆菌血清群O157/O26、牛浆液-小隐孢子虫、肉鸡窝-空肠弯曲杆菌和wwts -诺如病毒基因群GI/GII）在陆地传播的流行和浓度的范围审查，从高收入、温带地区的46项已发表研究中提取了具有时空代表性的数据。这些数据的荟萃分析和分布拟合具有可变性和不确定性的特征，除了用于拟合浓度的位置、形状和规模的广义相加模型外，还使用广义线性混合效应模型来开发患病率元模型。平均致病菌患病率从STEC O157/O26 7% OR 1.07 p = 0.05到空肠C. jejuni 39% OR 1.48 p <；0.0001，生物气候指标，即温度和降水季节性，在所有元模型中都显著。诺如病毒GI/GII log10 gc ml-1浓度的最佳拟合为2参数反向Gumbel(µ= 0.33,p = 0.55；σ = 0.66, p = 0.004；gac = 69.21)。虽然荟萃分析和分布拟合解释了与建模数据相关的不确定性和可变性，但需要从初级研究中获得更多标准化的次级数据，以提供更准确的QMRA估计，以确保初级农业生产中的微生物安全。
{"title":"Prevalence and concentrations of four waste-pathogen combinations from land-spreading across high-income, temperate regions: Meta-modelling and distribution fitting for quantitative microbial risk assessment (QMRA)","authors":"Jennifer E M McCarthy , Paul D Hynds , Declan J Bolton , Jesús M Frías Celayeta","doi":"10.1016/j.mran.2025.100348","DOIUrl":"10.1016/j.mran.2025.100348","url":null,"abstract":"<div><div>Land-spread organic wastes provide sustainable waste management across high-income, temperate regions. However, enteric pathogens in these animal manures and wastewater treatment sludges (WWTS) may pose food- and waterborne public health risks. Furthermore, these risks might increase due to climate change, with the likelihood of increasing temperature and precipitation across temperate latitudes. Quantitative microbial risk assessment (QMRA) is an established approach to estimate the potential risks, with a sparsity of spatiotemporally distributed waste-pathogen combination (W-PC) prevalence and concentrations from land-spreading existing in the literature for QMRA. Additionally, a knowledge gap exists regarding the availability of meta-models to predict pathogen prevalence based on spatially specific climatic or agricultural parameters. Accordingly, spatiotemporally representative data across high-income, temperate regions were extracted from 46 published studies based on a scoping review of four W-PC (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-<em>Cryptosporidium parvum</em>, broiler litter-<em>Campylobacter jejuni</em>, and WWTS-norovirus genogroups GI/GII) prevalence and concentrations from land-spreading. Meta-analyses and distribution fitting of these data characterised variability and uncertainty, with generalised linear mixed effects models employed to develop prevalence meta-models in addition to generalised additive models for location, shape, and scale fitted to concentrations. Mean pathogen prevalence ranged from STEC O157/O26 7 % OR 1.07 <em>p</em> = 0.05 to <em>C. jejuni</em> 39 % OR 1.48 <em>p</em> < 0.0001, with bioclimatic indicators, namely temperature and precipitation seasonality, significant across all meta-models. The best fit was a 2-parameter reverse Gumbel for norovirus GI/GII log<sub>10</sub> gc ml<sup>-1</sup> concentration (µ = 0.33, <em>p</em> = 0.55; σ = 0.66, <em>p</em> = 0.004; GAIC = 69.21). While meta-analyses and distribution fitting accounted for uncertainty and variability associated with modelled data, more standardised secondary data are required from primary research to provide more accurate QMRA estimates for ensuring microbiological safety in primary agricultural production.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100348"},"PeriodicalIF":3.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-09DOI: 10.1016/j.mran.2025.100345
Nodali Ndraha , Hsin-I Hsiao
Vibrio parahaemolyticus, a major seafood pathogen, threatens public health as oyster consumption rises. We evaluated 14 machine learning models to predict its concentrations in oysters, achieving high accuracy (Concordance Correlation Coefficient, CCC > 0.85 training, > 0.9 testing, except bag-MARS) across diverse algorithms. Processing times varied from 23 min (KNN) to 162 min (bag-RPart), highlighting computational trade-offs. Five top models—Elastic Net (EN), Random Forest (RF), XGBoost, Light Gradient-Boosting Machine (L-GBM), and Cubist (39–92 min)—were selected for their performance and efficiency, forming a robust toolkit for shellfish safety monitoring. Variable importance and partial dependence plots identified sea surface temperature (SST) and wind as primary drivers, with SST thresholds of 16–26 °C driving proliferation and wind showing mixed effects (negative >4 m/s, positive >6 m/s). Precipitation, salinity (>19 ppm), and pH (7.5–7.7) played supplementary roles. Lagged variables (e.g., SST_imX_25) underscored temporal dynamics, supporting real-time monitoring and risk assessment strategies.
{"title":"A comparison of machine learning models for predicting Vibrio parahaemolyticus in oysters","authors":"Nodali Ndraha , Hsin-I Hsiao","doi":"10.1016/j.mran.2025.100345","DOIUrl":"10.1016/j.mran.2025.100345","url":null,"abstract":"<div><div><em>Vibrio parahaemolyticus</em>, a major seafood pathogen, threatens public health as oyster consumption rises. We evaluated 14 machine learning models to predict its concentrations in oysters, achieving high accuracy (Concordance Correlation Coefficient, CCC > 0.85 training, > 0.9 testing, except bag-MARS) across diverse algorithms. Processing times varied from 23 min (KNN) to 162 min (bag-RPart), highlighting computational trade-offs. Five top models—Elastic Net (EN), Random Forest (RF), XGBoost, Light Gradient-Boosting Machine (L-GBM), and Cubist (39–92 min)—were selected for their performance and efficiency, forming a robust toolkit for shellfish safety monitoring. Variable importance and partial dependence plots identified sea surface temperature (SST) and wind as primary drivers, with SST thresholds of 16–26 °C driving proliferation and wind showing mixed effects (negative >4 m/s, positive >6 m/s). Precipitation, salinity (>19 ppm), and pH (7.5–7.7) played supplementary roles. Lagged variables (e.g., SST_imX_25) underscored temporal dynamics, supporting real-time monitoring and risk assessment strategies.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100345"},"PeriodicalIF":3.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-25DOI: 10.1016/j.mran.2025.100344
Nan Zhang, Palmira Elisa Nhantumbo, Haochen Zhang
Understanding how respiratory infectious diseases spreads is critical for effective pandemic prevention and control. This study investigated the transmission of aerosol-transmissible respiratory pathogens within an office building for postgraduate students and teachers in Beijing, using SARS-CoV-2 as representative model, focusing on real-time occupancy and close-contact behaviors. Surveillance videos and RGB-D cameras were used to collect data, and a multi–route virus transmission model was established to assess the infection risk and evaluate the effectiveness of non–pharmaceutical interventions. Student offices experienced the longest room usage time (13.2 ± 0.4 h) but a lower room occupancy intensity rate (27.1 ± 7 %) during weekdays. Close contact rate in students and teacher offices ranged from 10 to 11 %, while the conference room displayed the highest rates of 93–96 %. Teacher offices had the lowest average interpersonal distance during close contact (0.73 m), followed by teachers' conference (0.85 m). If a single infected individual were set in the building, people in the student office would face the highest hourly infection risk at 0.12 %. The use of surgical masks and increasing indoor ventilation from 0.5 to 6 air changes per hour reduces the total infection risk by 66.4–76.0 % and 45.0–65.0 %, respectively. Maintaining a distance of 1.5 m when in contact can further lower the total infection risk to 52.8–51.9 %. The findings of this study provide valuable insights for understanding the transmission dynamics of a respiratory infectious disease within the building, essential knowledge for effective prevention and control strategies.
{"title":"Human close contact behavior based respiratory diseases transmission in a university office building","authors":"Nan Zhang, Palmira Elisa Nhantumbo, Haochen Zhang","doi":"10.1016/j.mran.2025.100344","DOIUrl":"10.1016/j.mran.2025.100344","url":null,"abstract":"<div><div>Understanding how respiratory infectious diseases spreads is critical for effective pandemic prevention and control. This study investigated the transmission of aerosol-transmissible respiratory pathogens within an office building for postgraduate students and teachers in Beijing, using SARS-CoV-2 as representative model, focusing on real-time occupancy and close-contact behaviors. Surveillance videos and RGB-D cameras were used to collect data, and a multi–route virus transmission model was established to assess the infection risk and evaluate the effectiveness of non–pharmaceutical interventions. Student offices experienced the longest room usage time (13.2 ± 0.4 h) but a lower room occupancy intensity rate (27.1 ± 7 %) during weekdays. Close contact rate in students and teacher offices ranged from 10 to 11 %, while the conference room displayed the highest rates of 93–96 %. Teacher offices had the lowest average interpersonal distance during close contact (0.73 m), followed by teachers' conference (0.85 m). If a single infected individual were set in the building, people in the student office would face the highest hourly infection risk at 0.12 %. The use of surgical masks and increasing indoor ventilation from 0.5 to 6 air changes per hour reduces the total infection risk by 66.4–76.0 % and 45.0–65.0 %, respectively. Maintaining a distance of 1.5 m when in contact can further lower the total infection risk to 52.8–51.9 %. The findings of this study provide valuable insights for understanding the transmission dynamics of a respiratory infectious disease within the building, essential knowledge for effective prevention and control strategies.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100344"},"PeriodicalIF":3.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1016/j.mran.2025.100343
Hernán G. Redondo , Laurent Guillier , Virginie Desvignes , Matthias Filter , Sara M. Pires , Maarten Nauta
The risk of acquiring foodborne infections such as listeriosis is influenced by individuals’ food consumption and food storage practices. So far, quantitative microbiological risk assessment (QMRA) studies have mostly treated the related variables as independent, creating potentially unrealistic high-risk conditions, such as combinations of big portion size, high frequency of consumption, high storage temperature and long storage time. Therefore, in this study, we integrated individual food consumption and food storage data collected by the French national dietary survey INCA3 with food contamination data to estimate the risk of listeriosis in France, without assuming independence of variables. The aim was to assess and compare risks for different population groups and for different food groups, to identify risk factors and characterize high-risk groups. We adapted a QMRA model previously developed for the assessment of the number of listeriosis cases associated to ready-to-eat (RTE) foods in the EU. We modified the exposure assessment to use an individual-based approach in which consumer-specific consumption data and food safety practices are used to calculate exposure. Results showed that high-risk individuals stored their food in their refrigerator for longer and at higher temperatures prior to consumption than low-risk individuals. Smoked fish and pâte were estimated to be responsible for 66 % of the likely 393 annual cases for France. Improved characterization of high-risk individuals and their determinants for risk may contribute to better targeted food safety guidance. We demonstrated that considering individual-based data in QMRA opens the way for the establishment of risk-based measures that are specific for distinct individuals within the population.The advantage of this “individual-based” QMRA approach is that the observed variation between individual consumers in the four variables “frequency of consumption”, “portion size”, “storage time” and “storage temperature” is taken into account, and that it includes their interdependency for each individual consumer.
{"title":"Quantitative microbiological risk assessment using individual data on food storage and consumption (Part 1): A case study on listeriosis associated to ready-to-eat foods in France","authors":"Hernán G. Redondo , Laurent Guillier , Virginie Desvignes , Matthias Filter , Sara M. Pires , Maarten Nauta","doi":"10.1016/j.mran.2025.100343","DOIUrl":"10.1016/j.mran.2025.100343","url":null,"abstract":"<div><div>The risk of acquiring foodborne infections such as listeriosis is influenced by individuals’ food consumption and food storage practices. So far, quantitative microbiological risk assessment (QMRA) studies have mostly treated the related variables as independent, creating potentially unrealistic high-risk conditions, such as combinations of big portion size, high frequency of consumption, high storage temperature and long storage time. Therefore, in this study, we integrated individual food consumption and food storage data collected by the French national dietary survey INCA3 with food contamination data to estimate the risk of listeriosis in France, without assuming independence of variables. The aim was to assess and compare risks for different population groups and for different food groups, to identify risk factors and characterize high-risk groups. We adapted a QMRA model previously developed for the assessment of the number of listeriosis cases associated to ready-to-eat (RTE) foods in the EU. We modified the exposure assessment to use an individual-based approach in which consumer-specific consumption data and food safety practices are used to calculate exposure. Results showed that high-risk individuals stored their food in their refrigerator for longer and at higher temperatures prior to consumption than low-risk individuals. Smoked fish and pâte were estimated to be responsible for 66 % of the likely 393 annual cases for France. Improved characterization of high-risk individuals and their determinants for risk may contribute to better targeted food safety guidance. We demonstrated that considering individual-based data in QMRA opens the way for the establishment of risk-based measures that are specific for distinct individuals within the population.The advantage of this “individual-based” QMRA approach is that the observed variation between individual consumers in the four variables “frequency of consumption”, “portion size”, “storage time” and “storage temperature” is taken into account, and that it includes their interdependency for each individual consumer.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100343"},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.mran.2025.100342
Ursula Gonzales-Barron , Ana Sofia Faria , Anne Thebault , Laurent Guillier , Lucas Ribeiro Mendes , Lucas Ribeiro Silva , Winy Messens , Pauline Kooh , Vasco Cadavez
The collection of occurrence data of foodborne pathogens in foods faces the hindrances of dispersion of information, lack of standardisation and harmonisation, and ultimately, high expenditure in time and resources. The Pathogens-in-Foods (PIF) database was conceived as a solution to centralise published data on prevalence and concentration of pathogenic bacteria, viruses and parasites occurring in foods, obtained through systematic review (SR), and categorised in harmonised data structures under controlled terminologies. The present article outlines how PIF was constructed to adhere to the FAIR (findability, accessibility, interoperability and reusability) principles for scientific data management; and proceeds with a description of the PIF concept, which entails two phases: the SR process and the population of PIF. The protocolled SR process is supported by a well-defined search strategy, inclusion criteria, and rules for internal validation assessment; whereas the population of PIF with new data relies in data extraction, validation and release. The article then introduces a novel data quality approach, named as the CCC approach (data consistency, conformity and completeness), which ensures proper interpretation of data, richness of data, and flawless transcription of data. After a brief explanation of the three PIF components – database, back-end and front-end – the article proceeds with the exposition of the data model, as well as the capabilities of the front-end, including data search, insertion and curation. The future of PIF lies in expanding its capabilities, addressing emerging challenges, and leveraging technological advancements to maintain its relevance and utility in the evolving landscape of food safety.
{"title":"Pathogens-in-Foods (PIF): An open-access European database of occurrence data of biological hazards in foods","authors":"Ursula Gonzales-Barron , Ana Sofia Faria , Anne Thebault , Laurent Guillier , Lucas Ribeiro Mendes , Lucas Ribeiro Silva , Winy Messens , Pauline Kooh , Vasco Cadavez","doi":"10.1016/j.mran.2025.100342","DOIUrl":"10.1016/j.mran.2025.100342","url":null,"abstract":"<div><div>The collection of occurrence data of foodborne pathogens in foods faces the hindrances of dispersion of information, lack of standardisation and harmonisation, and ultimately, high expenditure in time and resources. The Pathogens-in-Foods (PIF) database was conceived as a solution to centralise published data on prevalence and concentration of pathogenic bacteria, viruses and parasites occurring in foods, obtained through systematic review (SR), and categorised in harmonised data structures under controlled terminologies. The present article outlines how PIF was constructed to adhere to the FAIR (findability, accessibility, interoperability and reusability) principles for scientific data management; and proceeds with a description of the PIF concept, which entails two phases: the SR process and the population of PIF. The protocolled SR process is supported by a well-defined search strategy, inclusion criteria, and rules for internal validation assessment; whereas the population of PIF with new data relies in data extraction, validation and release. The article then introduces a novel data quality approach, named as the CCC approach (data consistency, conformity and completeness), which ensures proper interpretation of data, richness of data, and flawless transcription of data. After a brief explanation of the three PIF components – database, back-end and front-end – the article proceeds with the exposition of the data model, as well as the capabilities of the front-end, including data search, insertion and curation. The future of PIF lies in expanding its capabilities, addressing emerging challenges, and leveraging technological advancements to maintain its relevance and utility in the evolving landscape of food safety.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100342"},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.mran.2024.100340
Alexandra Fetsch , Nunzio Sarnino , Konstantinos Koutsoumanis , Maarten Nauta , Martin Wiedmann , Katharina D.C. Stärk , Monika Ehling-Schulz , Roger Stephan , Sophia Johler
Foodborne microbial hazards lead to substantial morbidity and mortality. To assure consumer protection, a need to move from hazard-based to risk-based food safety approaches is increasingly recognized. Food-business-operators play a crucial role by implementing risk management practices in their facilities. Still, there is very limited data on current approaches to ensure microbial food safety and the profiles and perceptions of professionals assessing, managing, and communicating risks in food industry. This study addresses food safety approaches and challenges in food industry aiming to provide data on microbial risk analysis according to Codex Alimentarius. A survey elicited responses from 108 food professionals involved in microbial risk assessment, risk management, or risk communication in the food industry. The findings highlight drivers and trends relevant to food safety and the food industries’ internal decision-making processes. Most participants had risk-based food-safety management systems established. A microbial risk assessment according to Codex Alimentarius principles was conducted by 85 %. Professionals pinpointed areas that led to significant microbial incidents such as contaminated raw materials, poor hygiene, or emerging pathogens. Interestingly, one third of the participants believed that zero risk is possible, which contrasts with the scientific consensus that microbial food safety is not absolute as zero risk is not feasible. The results of this work provide insights into the implementation and understanding of microbial risk analysis from a food industrial perspective and could be leveraged to develop innovative microbial risk analysis frameworks that meet the challenges of future food systems.
{"title":"Microbial risk analysis from a food industry perspective – insights from an international survey","authors":"Alexandra Fetsch , Nunzio Sarnino , Konstantinos Koutsoumanis , Maarten Nauta , Martin Wiedmann , Katharina D.C. Stärk , Monika Ehling-Schulz , Roger Stephan , Sophia Johler","doi":"10.1016/j.mran.2024.100340","DOIUrl":"10.1016/j.mran.2024.100340","url":null,"abstract":"<div><div>Foodborne microbial hazards lead to substantial morbidity and mortality. To assure consumer protection, a need to move from hazard-based to risk-based food safety approaches is increasingly recognized. Food-business-operators play a crucial role by implementing risk management practices in their facilities. Still, there is very limited data on current approaches to ensure microbial food safety and the profiles and perceptions of professionals assessing, managing, and communicating risks in food industry. This study addresses food safety approaches and challenges in food industry aiming to provide data on microbial risk analysis according to Codex Alimentarius. A survey elicited responses from 108 food professionals involved in microbial risk assessment, risk management, or risk communication in the food industry. The findings highlight drivers and trends relevant to food safety and the food industries’ internal decision-making processes. Most participants had risk-based food-safety management systems established. A microbial risk assessment according to Codex Alimentarius principles was conducted by 85 %. Professionals pinpointed areas that led to significant microbial incidents such as contaminated raw materials, poor hygiene, or emerging pathogens. Interestingly, one third of the participants believed that zero risk is possible, which contrasts with the scientific consensus that microbial food safety is not absolute as zero risk is not feasible. The results of this work provide insights into the implementation and understanding of microbial risk analysis from a food industrial perspective and could be leveraged to develop innovative microbial risk analysis frameworks that meet the challenges of future food systems.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100340"},"PeriodicalIF":3.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-25DOI: 10.1016/j.mran.2024.100339
Hernán G. Redondo , Laurent Guillier , Virginie Desvignes , Matthias Filter , Sara M. Pires , Maarten Nauta
In a previous study, we integrated data from individual consumers collected in a dietary survey in France in a multi-food quantitative microbiological risk assessment (QMRA) for listeriosis. Here, we compared the “individual-based” modelling approach applied in that study with several other approaches where the data are treated as in more “traditional” QMRA methods, for example by assuming independent randomly sampled variables from distributions fitted through the data, instead of the observed individual data themselves. We found that assigning randomly sampled storage times instead of the reported individual storage times resulted in a higher risk estimate than the baseline, expressed as expected annual number of cases in the population. Assigning randomly sampled storage temperature and point estimates for portion size and frequency of consumption, slightly increased the estimated risk. Statistical analysis did not show dependency between portion size, frequency of consumption, storage temperature and storage time in the data set, which can be explained by the fact that only a few individuals had a large impact on the final population risk. Analysis of expected numbers of cases per age class, sex and food group showed small differences between approaches. Our analysis was challenged by the difference between a model structure where the risk is calculated per individual (when based on a dietary survey with individual data) and one where it is calculated per serving, as in “traditional” QMRA. We showed that an “individual-based” QMRA is more resource-demanding but can give fundamentally different risk estimates, which are potentially more accurate. The application of tools for efficient knowledge exchange and integration is needed to facilitate the usage of this type of QMRA.
{"title":"Quantitative microbiological risk assessment using individual data on food storage and consumption (Part 2): A comparison with traditional QMRA approaches","authors":"Hernán G. Redondo , Laurent Guillier , Virginie Desvignes , Matthias Filter , Sara M. Pires , Maarten Nauta","doi":"10.1016/j.mran.2024.100339","DOIUrl":"10.1016/j.mran.2024.100339","url":null,"abstract":"<div><div>In a previous study, we integrated data from individual consumers collected in a dietary survey in France in a multi-food quantitative microbiological risk assessment (QMRA) for listeriosis. Here, we compared the “individual-based” modelling approach applied in that study with several other approaches where the data are treated as in more “traditional” QMRA methods, for example by assuming independent randomly sampled variables from distributions fitted through the data, instead of the observed individual data themselves. We found that assigning randomly sampled storage times instead of the reported individual storage times resulted in a higher risk estimate than the baseline, expressed as expected annual number of cases in the population. Assigning randomly sampled storage temperature and point estimates for portion size and frequency of consumption, slightly increased the estimated risk. Statistical analysis did not show dependency between portion size, frequency of consumption, storage temperature and storage time in the data set, which can be explained by the fact that only a few individuals had a large impact on the final population risk. Analysis of expected numbers of cases per age class, sex and food group showed small differences between approaches. Our analysis was challenged by the difference between a model structure where the risk is calculated per individual (when based on a dietary survey with individual data) and one where it is calculated per serving, as in “traditional” QMRA. We showed that an “individual-based” QMRA is more resource-demanding but can give fundamentally different risk estimates, which are potentially more accurate. The application of tools for efficient knowledge exchange and integration is needed to facilitate the usage of this type of QMRA.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100339"},"PeriodicalIF":3.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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