Survival and predictive modeling of Listeria monocytogenes under simulated human gastric conditions in the presence of bovine milk products.

Listeria monocytogenes is an opportunistic foodborne pathogen which has been implicated in many outbreaks of foodborne diseases. This study evaluated the effects of gastric acidity and gastric digestion time of adults, L. monocytogenes strain and food type on the survival of L. monocytogenes under simulated stomach conditions of adults in in vitro gastric models with dynamic pH changes occurring throughout the exposure. Individual strains as well as a cocktail of L. monocytogenes, inoculated at 8 log CFU/mL in filtered bovine milk products, 0 % milk, 2 % milk, 2 % chocolate milk and 3.25 % milk, were introduced to the gastric models for 2 h. The survival of L. monocytogenes depended on a combination of factors, including gastric acidity and gastric digestion time of adults, L. monocytogenes strain, food type and recovery method (P < 0.05). The survival rates of L. monocytogenes inoculated in 2 % milk after a 2-h exposure to simulated gastric fluids with pH values of 1.5, 2.0 and 3.0 were 0.003 to 0.040 %, 22.7 to 43.4 % and 16.6 to 27.2 %, respectively. Fluid milk containing a higher milk fat content (3.25 % vs 0 % milk) protected L. monocytogenes from being inactivated when they were exposed to the human stomach model with a gastric acidity of pH 2.0. Compared to 0 % and 3.25 % milk, L. monocytogenes survived the best in 2 % chocolate milk, which appears to be due to the presence of milk fat (2 %) and the additional nutrients that are present in chocolate milk. A predictive mathematical model was developed that captured the population of the strains of L. monocytogenes under the in vitro conditions. This study advances our understanding of the behaviour of L. monocytogenes under various human gastric conditions and provides key parameters that can affect the survival of L. monocytogenes in the stomachs of adults. The mathematical models developed in this study can be used as a supplementary tool to help predict the survival of L. monocytogenes under similar scenarios and for relevant risk-assessment studies.

A scoping review of factors potentially linked with antimicrobial-resistant bacteria from turkeys (iAM.AMR Project).

Antimicrobial resistance (AMR) is a complex problem that is a threat to global public health. Consumption of turkey meat may be an important source of foodborne exposure to resistant bacteria; recent outbreaks of multi-drug-resistant Salmonella Reading in Canada and the USA have implicated raw turkey products. To better understand the epidemiology of AMR in farmed turkey production, a scoping review was conducted. The objectives were to identify (1) modifiable factors potentially associated with antimicrobial-resistant Campylobacter, Enterococcus, Escherichia coli and Salmonella enterica along the farm-to-fork pathway in turkeys, and (2) data gaps with respect to factors potentially associated with AMR and Canadian commercial turkey production. A comprehensive search of the peer-reviewed literature was conducted in 2019 and updated in 2021. Thirteen references were included, reporting 36 factors. Antimicrobial use factors and their potential associations with AMR were most frequently reported (n = 15 factors; 42%), followed by biosecurity (n = 11; 31%) and management practices (n = 10; 28%). This review revealed important data gaps; no factors pertaining to S. enterica or to stages other than the farm (e.g. abattoir, retail) were identified, and only one Canadian reference was identified. These findings will inform priorities for future research and surveillance regarding turkeys and AMR.

The performance of phenomenological models in providing near-term Canadian case projections in the midst of the COVID-19 pandemic: March - April, 2020.

BACKGROUND: The COVID-19 pandemic has had an unprecedented impact on citizens and health care systems globally. Valid near-term projections of cases are required to inform the escalation, maintenance and de-escalation of public health measures, and for short-term health care resource planning. METHODS: Near-term case and epidemic growth rate projections for Canada were estimated using three phenomenological models: the logistic model, Generalized Richard's model (GRM) and a modified Incidence Decay and Exponential Adjustment (m-IDEA) model. Throughout the COVID-19 epidemic in Canada, these models have been validated against official national epidemiological data on an ongoing basis. RESULTS: The best-fit models estimated that the number of COVID-19 cases predicted to be reported in Canada as of April 1, 2020 and May 1, 2020 would be 11,156 (90 % prediction interval: 9,156-13,905) and 54,745 (90 % prediction interval: 54,252-55,239). The three models varied in their projections and their performance over the first seven weeks of their implementation. Both the logistic model and GRM under-predicted cases reported a week following the projection date in nearly all instances. The logistic model performed best at the early stages, the m-IDEA model performed best at the later stages, and the GRM performed most consistently during the full period assessed. CONCLUSIONS: All three models have yielded qualitatively comparable near-term forecasts of cases and epidemic growth for Canada. Under or over-estimation of projected cases and epidemic growth by these models could be associated with changes in testing policies and/or public health measures. Simple forecasting models can be invaluable in projecting the changes in trajectory of subsequent waves of cases to provide timely information to support the pandemic response.

Exploring the percentage of COVID-19 cases reported in the community in Canada and associated case fatality ratios.

While surveillance can identify changes in COVID-19 transmission patterns over time and space, sections of the population at risk, and the efficacy of public health measures, reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases. Our primary objective was to estimate the percentage of cases reported in the general community, considered as those that occurred outside of long-term care facilities (LTCFs), in specific provinces and Canada as a whole. We applied a methodology using the delay-adjusted case fatality ratio (CFR) to all cases and deaths, as well as those representing the general community. Our second objective was to assess whether the assumed CFR (mean = 1.38%) was appropriate for calculating underestimation of cases in Canada. Estimates were developed for the period from March 11th, 2020 to September 16th, 2020. Estimates of the percentage of cases reported (PrCR) and CFR varied spatially and temporally across Canada. For the majority of provinces, and for Canada as a whole, the PrCR increased through the early stages of the pandemic. The estimated PrCR in general community settings for all of Canada increased from 18.1% to 69.0% throughout the entire study period. Estimates were greater when considering only those data from outside of LTCFs. The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07% on March 11th, 2020 to 2.00% on September 16th, 2020. Therefore, the true CFR in the general community in Canada was likely less than 2% on September 16th. According to our analysis, some provinces, such as Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatchewan reported a greater percentage of cases as of September 16th, compared to British Columbia, Ontario, and Québec. This could be due to differences in testing rates and criteria, demographics, socioeconomic factors, race, and access to healthcare among the provinces. Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study. The estimates provide context to the summative state of the pandemic in Canada, and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

Assessing the progression of the COVID-19 pandemic in Canada using testing data and time-dependent reproduction numbers.

OBJECTIVES: To compare a mathematical tool and time-dependent reproduction number (Rt) estimates to assess the COVID-19 pandemic progression in a Canadian context. METHODS: Total number of reported cases were plotted against total number of tests for COVID-19 performed over time, with and without smoothing, for Canada and some Canadian provinces individually. Changes in curvature profile were identified as either convex or concave as indicators of pandemic acceleration or deceleration, respectively. Rt estimates were calculated on an exponential growth rate. RESULTS: For Canada as a whole, the testing graphs had a slightly concave profile and a coincident decrease in Rt estimates. Saskatchewan more recently had a convex profile with a gradual shift to a concave profile and also demonstrated a gradual decline in Rt estimates. Curves and Rt estimates for Alberta, British Columbia, Manitoba, Nova Scotia, Ontario and Quebec displayed a gradual shift towards concavity over time and an overall decrease in Rt estimates, which is suggestive of a positive impact of public health interventions implemented federally and provincially. CONCLUSION: The present analyses compared a mathematical tool to Rt estimates to ascertain the status of the pandemic in Canada. Caution should be taken when interpreting results due to factors such as varying testing protocols, available testing data unique to each province and limitations inherent to each method, which may generate different results using the two approaches. Analysis of testing data may complement metrics obtained from surveillance data to allow for a weight-of-evidence approach to assess the status of the COVID-19 pandemic.

RéSUMé: OBJECTIFS: Comparer un outil mathématique aux estimations du taux de reproduction en fonction du temps (Rt) pour évaluer la progression de la pandémie de la COVID-19 dans le contexte canadien. MéTHODES: Le nombre total de cas signalés a été comparé au nombre total de tests à la COVID-19 effectués au fil du temps, avec et sans lissage, pour le Canada et certaines provinces canadiennes individuellement. Les modifications du profil de courbure identifiées comme étant convexes ou concaves seraient des indicateurs respectivement d'une accélération ou d'une décélération de la pandémie. Le calcul des estimations du Rt a été réalisé en fonction du taux de croissance exponentiel. RéSULTATS: Pour l'ensemble du Canada, la légère concavité des graphiques relatifs aux tests coïncidait avec la diminution des estimations du Rt. Plus récemment, la Saskatchewan avait un profil convexe avec un passage progressif à un profil concave et a également démontré une baisse progressive des estimations du Rt. Les courbes et les estimations du Rt pour l'Alberta, la Colombie-Britannique, le Manitoba, la Nouvelle-Écosse, l'Ontario et le Québec ont montré un glissement progressif vers la concavité au fil du temps et une diminution globale des estimations du Rt, ce qui suggère un impact positif des interventions de santé publique mises en œuvre au niveau fédéral et provincial. CONCLUSION: Les présentes analyses ont comparé un outil mathématique aux estimations de Rt pour déterminer l'état de la pandémie au Canada. Les résultats doivent être interprétés avec prudence en raison de certains facteurs tels que les différences entre provinces en ce qui concerne les protocoles de réalisation des tests et la disponibilité des données relatives aux tests. De plus, une limite inhérente à la méthodologie de cette étude est la possibilité d'obtenir des résultats différents en fonction de l'approche utilisée. L'analyse des données des tests pourrait être complémentaire à celle des données de surveillance pour permettre une approche fondée sur le poids de la preuve dans le cadre de l'évaluation de l'état de la pandémie de la COVID-19.

An agent-based simulator for the gastrointestinal pathway of Listeria monocytogenes.

We developed an agent-based gastric simulator for a human host to illustrate the within host survival mechanisms of Listeria monocytogenes. The simulator incorporates the gastric physiology and digestion processes that are critical for pathogen survival in the stomach. Mathematical formulations for the pH dynamics, stomach emptying time, and survival probability in the presence of gastric acid are integrated in the simulator to evaluate the portion of ingested bacteria that survives in the stomach and reaches the small intestine. The parameters are estimated using in vitro data relevant to the human stomach and L. monocytogenes. The simulator predicts that 5%-29% of ingested bacteria can survive a human stomach and reach the small intestine. In the absence of extensive scientific experiments, which are not feasible on the grounds of ethical and safety concerns, this simulator may provide a supplementary tool to evaluate pathogen survival and subsequent infection, especially with regards to the ingestion of small doses.

A farm-to-fork quantitative risk assessment model for Salmonella Heidelberg resistant to third-generation cephalosporins in broiler chickens in Canada.

Salmonella Heidelberg resistant to ceftiofur (a third-generation cephalosporin antimicrobial agent) in broiler chicken products pose a risk to public health in Canada. The objective of this study was to assess the extent of that risk and to evaluate the effect of intervention measures along the agri-food chain. A stochastic farm-to-fork quantitative microbial risk assessment model was developed following the Codex Alimentarius Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance. Different scenarios were analyzed to assess the individual relative effects of 18 possible interventions in comparison to a baseline scenario. The baseline scenario represented the first year of on-farm antimicrobial use surveillance in the Canadian broiler industry and the year before an industry-imposed ban on the preventive use of antimicrobials of very high importance to human health (2013), where 31.3% of broiler flocks consisted of birds to which ceftiofur was administered. The baseline scenario predicted an average probability of illness of 1.1 per 100,000 servings (SE: 0.064 per 100,000), corresponding to an average of 22,000 human infections (SE: 1900) with ceftiofur-resistant S. Heidelberg per year, which is likely an overestimation. This risk was reduced by 90% or 20% when two separate scenarios designed to capture the effect of withdrawing preventive ceftiofur use from poultry production were simulated using different approaches; data used for the former scenario were confounded by other potential concomitant control measures (e.g. Salmonella vaccination programme), so the true effect likely lies somewhere between the two estimates. A theoretical 'worst case' scenario where all flocks had birds exposed to ceftiofur increased the risk by 107%. A 50% reduction in the probability of human prior exposure to antimicrobials, which has a selective and competitive effect for Salmonella spp. following ingestion of contaminated products, reduced the risk by 65%. Other promising measures that could be considered for further risk management included improved cleaning and disinfection between broiler flocks on farm (risk reduction by 26%), exclusive use of air chilling (risk reduction by 34%), and the improvement of meat storage and preparation conditions, e.g., no temperature abuse at retail (risk reduction by 88%). These findings showed the importance of a structured approach to assessing and potentially implementing effective interventions to reduce the risk associated with ceftiofur-resistant S. Heidelberg at different steps along the agri-food chain. Major data gaps included information on concentrations of resistant bacteria, cross contamination at processing and how ceftiofur-resistant S. Heidelberg behave in comparison with susceptible ones, e.g., in terms of growth and survival ability, as well as pathogenicity and virulence.

A novel IDEA: The impact of serial interval on a modified-Incidence Decay and Exponential Adjustment (m-IDEA) model for projections of daily COVID-19 cases.

The SARS-CoV-2 virus causes the disease COVID-19, and has caused high morbidity and mortality worldwide. Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term. A modified Incidence Decay and Exponential Adjustment (m-IDEA) model was developed to predict the progression of infectious disease outbreaks. The modification allows for the production of precise daily estimates, which are critical during a pandemic of this scale for planning purposes. The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19. Both deterministic and stochastic approaches were applied. Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts, and performance was retrospectively assessed. The performance of the m-IDEA for projection cases in the near-term was improved using shorter serial intervals (1-4 days) at early stages of the pandemic, and longer serial intervals at mid- to late-stages (5-9 days) thus far. This, coupled with epidemiological reports, suggests that the serial interval of COVID-19 might increase as the pandemic progresses, which is rather intuitive: Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures, public caution and social distancing, thus increasing the time between transmission events. In most cases, the stochastic approach captured the majority of future reported incidence data, because it accounts for the uncertainty around the serial interval of COVID-19. As such, it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.

A within-flock model of Salmonella Heidelberg transmission in broiler chickens.

As part of the development of a quantitative microbial risk assessment (QMRA) model of third-generation cephalosporins (3GC)-resistant Salmonella Heidelberg, a compartmental (SEIR) model for S. Heidelberg transmission within a typical Canadian commercial broiler chicken flock was developed. The model was constructed to estimate the within-flock prevalence and the bacterial concentration in the barn environment at pre-harvest, and to assess the effect of selected control measures. The baseline scenario predicted an average within-flock prevalence of 23.5 % (95 % tolerance interval: 15.7-31.4) and an average bacterial concentration of 3.579 (0-4.294) log CFU/g of feces in the barn environment at pre-harvest (on the day the flock is sent to slaughter). Because vertical introduction of S. Heidelberg into the barn was already uncommon in the baseline scenario, vaccination of broiler parent flocks appeared to have a negligible effect, while vaccination of broiler chicken flocks substantially reduced the bacterial concentration at pre-harvest. Cleaning and disinfection between batches markedly reduced the within-flock prevalence at pre-harvest, but the effect on bacterial concentration was limited outside of the beginning of the production period. Extending downtime between batches by 7 days had little effect on within-flock prevalence or bacterial concentration of S. Heidelberg when compared to the baseline scenario. This study provides a basis to describe S. Heidelberg dynamics within a broiler chicken flock and to predict the within-flock prevalence and bacterial concentration at pre-harvest, and includes a description of the limitations and data gaps. The results of these analyses and associated uncertainties are critical information for populating QMRA models of the downstream impacts on public health from on-farm and other food-chain practices. Specifically, the study findings will be integrated into a broader farm-to-fork QMRA model to support the risk-based control of S. Heidelberg resistant to 3GC in broiler chicken in Canada.

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges.

Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of 'omics' techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.

Factors potentially linked with the occurrence of antimicrobial resistance in selected bacteria from cattle, chickens and pigs: A scoping review of publications for use in modelling of antimicrobial resistance (IAM.AMR Project).

Antimicrobial resistance is a complex issue with a large volume of published literature, and there is a need for synthesis of primary studies for an integrated understanding of this topic. Our research team aimed to have a more complete understanding of antimicrobial resistance in Canada (IAM.AMR Project) using multiple methods including the literature reviews and quantitative modelling. To accomplish this goal, qualitative features of publications (e.g., geographical location, study population) describing potential relationships between the occurrence of antimicrobial resistance and factors (e.g., antimicrobial use; management system) were of particular interest. The objectives of this review were to (a) describe the available peer-reviewed literature reporting potential relationships between factors and antimicrobial resistance; and (b) to highlight data gaps. A comprehensive literature search and screening were performed to identify studies investigating factors potentially linked with antimicrobial resistance in Campylobacter species, Escherichia coli and Salmonella enterica along the farm-to-fork pathway (farm, abattoir (slaughter houses) and retail meats) for the major Canadian livestock species (beef cattle, broiler chicken and pigs). The literature search returned 14,966 potentially relevant titles and abstracts. Following screening of titles, abstracts and full-text articles, the qualitative features of retained studies (n = 28) were extracted. The most common factors identified were antimicrobial use (n = 13 studies) and type of farm management system (e.g., antibiotic-free, organic; n = 8). Most studies were conducted outside of Canada and involved investigations at the farm level. Identified data gaps included the effect of vaccination, industry-specific factors (e.g., livestock density) and factors at sites other than farm along the agri-food chain. Further investigation of these factors and other relevant industry activities are needed for the development of quantitative models that aim to identify effective interventions to mitigate the occurrence of antimicrobial resistance along the agri-food chain.

Exposure to antimicrobial-resistant Escherichia coli through the consumption of ground beef in Western Canada.

Emergence and dissemination of antimicrobial resistance (AMR) in food-borne bacteria is a public health issue. Retail meat is considered an important carrier for antimicrobial-resistant Escherichia coli. Currently, resistance of E. coli strains to third generation cephalosporins are of particular concern, with significant potential animal and public health consequences. Resistance to tetracyclines is also a concern, due to high prevalence of this resistance and important co-resistance patterns. However, the actual likelihood of exposure to antimicrobial-resistant bacteria via the consumption of retail meats, beyond the simple frequencies of resistance found in samples of meat at the grocery store, in Canada remains to be investigated. This study was conducted to estimate the potential human exposure to ceftriaxone- and tetracycline-resistant E.coli (CREC and TREC; the hazards of interest) through the consumption of ground beef in Western Canada. Our exposure assessment simulation model included five modules: 1) estimation of prevalence and concentration of the hazards of interest in retail ground beef samples collected by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS), 2011-2014; 2) potential growth of the hazards during transportation from retail to home; 3) potential growth or reduction of the hazards during home storage; 4) thermal inactivation of the hazards due to cooking; and 5) consumption. The outputs of the model were presented as the probability of consumers' exposure to various threshold levels of the hazards [10 to 106 colony forming units (CFU)] in one serving of cooked ground beef. Overall, the probabilities of exposure to high threshold levels of the hazards (>4 log10 CFU/serving) were estimated to be lower than 0.12% in the study population (2011-2014). The mean probabilities of exposure to at least 1 log10 CFU CREC and TREC in a single meal containing cooked ground beef were 0.067% (SD: 0.001%) and 1.20% (SD: 0.02%), respectively. This probability substantially decreased as the threshold level of exposure increased to ≥6 log10 CFU. The probability of exposure to TREC was consistently greater than that for CREC. Cooking led to a prominent drop in the mean concentration of the hazards (4.7 log10 CFU/g). The findings from this research could inform the policy-making process and provide suggestions for adjustments in future retail surveillance plans. In addition, important knowledge gaps in this area have been highlighted.

Risk assessment modelling of fecal shedding caused by extended-spectrum cephalosporin-resistant Escherichia coli transmitted through waste milk fed to dairy pre-weaned calves.

Waste milk feeding is a common practice in dairy operations. Regardless of the benefits of this practice to the dairy farmers, concerns from the potential dissemination of antimicrobial-resistant bacteria through the gut and subsequent shedding by calves into the environment are increasing. In this study, we employed Monte Carlo simulation to assess the risk of shedding extended-spectrum cephalosporin-resistant Escherichia coli (ESC-R E. coli) caused by waste milk feeding in pre-weaned calves using an exponential dose-response model fit to data for E. coli O157:H7 in cattle. Data from pertinent studies were included in our model to predict the risk of shedding. The median (5th and 95th percentiles) for the daily risk of shedding ESC-R E. coli by calves fed only contaminated waste milk was predicted to be 2.9 × 10-3 (2.1 × 10-3, 3.7 × 10-3), representing a median daily risk of 29 out of 10,000 calves shedding ESC-R E. coli due to exclusive feeding of waste milk containing ESC-R E. coli. This median value was reduced by 94% when accounting for the proportion of waste milk that does not contain ESC-R E. coli. The overall risk of shedding ESC-R E. coli through the pre-weaning period for farms that feed waste milk to calves was 5.7 × 10-3 (2.4 × 10-3, 1.1 × 10-2), representing 57 out of 10,000 calves. When accounting for the proportion of farms that do not feed waste milk, the pre-weaning period risk was reduced by 23%. By varying the prevalence of ESC-R E. coli in waste milk using values of 3, 1.5, and 1%, the daily risk of shedding decreased by factors of 50, 65, and 82%, respectively, which supports the reduction of contamination or discontinuation of feeding waste milk containing ESC-R E. coli as major mitigation measures to reduce the risk of shedding caused by ingestion of resistant bacteria. It is anticipated that the effects of antimicrobial residues in waste milk, which was not considered herein due to lack of data, would further increase risks. Although waste milk feeding to calves may be economically beneficial to the dairy farmers, there exists the risk of dissemination of ESC-resistant bacteria into the environment.

A systematic review and meta-analysis of the effects of extreme weather events and other weather-related variables on Cryptosporidium and Giardia in fresh surface waters.

Global climate change is expected to impact drinking water quality through multiple weather-related phenomena. We conducted a systematic review and meta-analysis of the relationship between various weather-related variables and the occurrence and concentration of Cryptosporidium and Giardia in fresh surface waters. We implemented a comprehensive search in four databases, screened 1,228 unique citations for relevance, extracted data from 107 relevant articles, and conducted random-effects meta-analysis on 16 key relationships. The average odds of identifying Cryptosporidium oocysts and Giardia cysts in fresh surface waters was 2.61 (95% CI = 1.63-4.21; I² = 16%) and 2.87 (95% CI = 1.76-4.67; I² = 0%) times higher, respectively, during and after extreme weather events compared to baseline conditions. Similarly, the average concentration of Cryptosporidium and Giardia identified under these conditions was also higher, by approximately 4.38 oocysts/100 L (95% CI = 2.01-9.54; I(2) = 0%) and 2.68 cysts/100 L (95% CI = 1.08-6.55; I² = 48%). Correlation relationships between other weather-related parameters and the density of these pathogens were frequently heterogeneous and indicated low to moderate effects. Meta-regression analyses identified different study-level factors that influenced the variability in these relationships. The results can be used as direct inputs for quantitative microbial risk assessment. Future research is warranted to investigate these effects and potential mitigation strategies in different settings and contexts.

Bioavailability of copper and zinc in mining soils.

The soil-contact exposure pathway can be the main driver of ecological risk assessments. There is currently no standard method to measure bioavailability of metals in soil to ecological receptors, yet the influence of metal bioavailability on toxicity has been known for decades and is a major factor influencing risk to ecological receptors. Bioavailability is to a large degree governed by varying soil characteristics within and among sites, yet ecological screening benchmarks are often derived on a total-concentration basis. We compared a calcium chloride (CaCl2) extraction, cyclodextrin extraction, simulated earthworm gut (SEG) test, earthworm kinetic bioaccumulation test, and metal residues in plant tissues with a battery of invertebrate and toxicity tests using mining soils consisting of high organic-matter content cocontaminated with copper (Cu) and zinc (Zn). Earthworm (Eisenia andrei) tissue concentrations of Cu and Zn were regulated and were not predictive of invertebrate toxicity. All chemical measures of bioavailability correlated with several biological responses; however, CaCl2-extractable Cu and SEG-extractable Cu and Zn best predicted effects to E. andrei. Total Cu concentrations in soil best correlated with effects to plants. Overall, a chemical measure was the best predictor of toxicity to each organism compared with biological measures, although the exact measure was dependent on organism and end point. Chemical-extraction techniques provide relatively quick, inexpensive indicators of essential metal bioavailability compared with biological measures; however, no single measure was indicative of all effects to all organisms.

Species extrapolation for the 21st century.

Safety factors are used in ecological risk assessments to extrapolate from the toxic responses of laboratory test species to all species representing that group in the environment. More accurate extrapolation of species responses is important. Advances in understanding the mechanistic basis for toxicological responses and identifying molecular response pathways can provide a basis for extrapolation across species and, in part, an explanation for the variability in whole organism responses to toxicants. We highlight potential short- and medium-term development goals to meet our long-term aspiration of truly predictive in silico extrapolation across wildlife species' response to toxicants. A conceptual approach for considering cross-species extrapolation is presented. Critical information is required to establish evidence-based species extrapolation, including identification of critical molecular pathways and regulatory networks that are linked to the biological mode of action and species' homologies. A case study is presented that examines steroidogenesis inhibition in fish after exposure to fadrozole or prochloraz. Similar effects for each compound among fathead minnow, medaka, and zebrafish were attributed to similar inhibitor pharmacokinetic/pharmacodynamic distributions and sequences of cytochrome P45019A1/2 (CYP19A1/2). Rapid advances in homology modeling allow the prediction of interactions of chemicals with enzymes, for example, CYP19 aromatase, which would eventually allow a prediction of potential aromatase toxicity of new compounds across a range of species. Eventually, predictive models will be developed to extrapolate across species, although substantial research is still required. Knowledge gaps requiring research include defining differences in life histories (e.g., reproductive strategies), understanding tissue-specific gene expression, and defining the role of metabolism on toxic responses and how these collectively affect the power of interspecies extrapolation methods.

Comparison of biological and chemical measures of metal bioavailability in field soils: test of a novel simulated earthworm gut extraction.

Bioavailability of metals in soil is a major factor influencing estimates of risk associated with exposure of ecological receptors. Metal concentrations in soil are often compared to ecological screening benchmarks, which are based on total concentrations in soil. Often, the total concentration is not correlated with toxicity. No standardised method exists for determining the bioavailability of metals in soil to ecological receptors. Several surrogate measures of bioavailability were compared to the results of a battery of toxicity tests using copper (Cu), lead (Pb), and zinc (Zn)-contaminated soils collected from a former industrial area. A calcium chloride (CaCl(2)) extraction, cyclodextrin (HPCD) extraction, simulated earthworm gut (SEG) test, and earthworm bioaccumulation test were performed using the soils. Extractable metals using the CaCl(2) solution were not correlated with any biological responses of earthworms (Eisenia andrei), collembola (Folsomia candida), northern wheatgrass (Elymus lanceolatus), or alfalfa (Medicago sativa L.). Concentrations of metals in the HPCD extracts were highly variable and were not adequate for revealing adverse effects. E. andrei tissue concentrations were variable but were predictive of adverse effects to invertebrates. The results of the SEG test correlated with most of the biological endpoints. Bioavailable Cu was correlated with adverse effects to invertebrates and plants using the SEG test. Overall, coefficients of determination associated with the relationships between the biological responses and each measure of bioavailability indicated that those for the SEG test were greater than those for the other surrogate measures of bioavailability. Further validation is required before this test is routinely used to estimate metal bioavailability and toxicity.

Uptake and elimination of cadmium and zinc by Eisenia andrei during exposure to low concentrations in artificial soil.

Bioaccumulation factors (BAFs) are often used in the risk assessment process to estimate trophic transfer of contaminants such as metals from soil. BAFs can be derived from laboratory studies through the determination of steady-state concentrations or kinetic estimation methods. In this study, bioaccumulation tests were performed with artificial soil spiked at low levels with cadmium or zinc to determine uptake and elimination kinetics of both metals by the compost worm Eisenia andrei. The metal-amended soils were acclimated for 21 days prior to the test, after which worms were individually incubated in the soils. The uptake phase comprised 0-21 days, after which the test organisms were transferred into clean soil and the elimination phase continued for an additional 21 days. Subsamples of soil and earthworms (whole body) were collected from independent replicates throughout the uptake phase and elimination phase and analyzed for total metal concentrations. Uptake of Cd in E. andrei increased linearly with time and did not reach steady state within the testing period. Cd uptake and excretion were described by a one-compartment first-order kinetics model. Zn concentrations rapidly increased in E. andrei after 1 day of exposure but subsequently decreased to background levels throughout the remainder of the uptake phase; internal Zn concentrations did not change from background levels during the elimination phase. Kinetic BAFs were calculated for Cd and Zn. Cd is a nonessential metal that is bioaccumulated at a relatively rapid rate, while Zn is an essential metal, and as such, it is regulated by E. andrei. Metal essentiality and concentration significantly impact bioaccumulation of metals by terrestrial invertebrates.

Development of a simulated earthworm gut for determining bioaccessible arsenic, copper, and zinc from soil.

Soil physicochemical characteristics and contamination levels alter the bioavailability of metals to terrestrial invertebrates. Current laboratory-derived benchmark concentrations used to estimate risk do not take into account site-specific conditions, such as contaminant sequestration, and site-specific risk assessment requires a battery of time-consuming and costly toxicity tests. The development of an in vitro simulator for earthworm bioaccessibility would significantly shorten analytical time and enable site managers to focus on areas of greatest concern. The simulated earthworm gut (SEG) was developed to measure the bioaccessibility of metals in soil to earthworms by mimicking the gastrointestinal fluid composition of earthworms. Three formulations of the SEG (enzymes, microbial culture, enzymes and microbial culture) were developed and used to digest field soils from a former industrial site with varying physicochemical characteristics and contamination levels. Formulations containing enzymes released between two to 10 times more arsenic, copper, and zinc from contaminated soils compared with control and 0.01 M CaCl2 extractions. Metal concentrations in extracts from SEG formulation with microbial culture alone were not different from values for chemical extractions. The mechanism for greater bioaccessible metal concentrations from enzyme-treated soils is uncertain, but it is postulated that enzymatic digestion of soil organic matter might release sequestered metal. The relevance of these SEG results will need validation through further comparison and correlation with bioaccumulation tests, alternative chemical extraction tests, and a battery of chronic toxicity tests with invertebrates and plants.