Approaches to risk-benefit assessment of seafood consumption: lessons learned from an evidence scan.

Qualitative and quantitative risk-benefit assessments (RBA) can be used to support public health decisions in food safety. We conducted an evidence scan to understand the state of the science regarding RBA in seafood to help inform seafood dietary advice in the United States. We collected published RBA studies assessing seafood consumption, designed inclusion and exclusion criteria to screen these studies, and conducted systematic data extraction for the relevant studies published since 2019. Our findings indicate the selection of health risks and benefits does not generally follow a systematic approach. Uncertainty and variability in RBAs is often not addressed, and quantitative RBAs making use of a single health metric generally have not been leveraged to directly support published regulatory decisions or dietary guidance. To elevate the role of RBA in supporting regulatory decision-making, risk assessors and risk managers must work together to set expectations and goals. We identified the need for a prioritization phase (e.g., multicriteria decision analysis model) to determine the risks and benefits of greatest public health impact to inform the RBA design. This prioritization would consider not only the degree of public health impact of each risk and benefit, but also the potential for risks and benefits to converge on common health outcomes and their importance to subpopulations. Including a prioritization could improve the utility of the RBAs to better inform risk management decisions and advance public health. Our work serves to guide the United States Food and Drug Administration's approaches to RBA in foods.

Implementing a risk-risk analysis framework to evaluate the impact of food intake shifts on risk of illness: a case study with infant cereal.

In food safety, process pathway risk assessments usually estimate the risk of illness from a single hazard and a single food and can inform food safety decisions and consumer advice. To evaluate the health impact of a potential change in diet, we need to understand not only the risk posed by the considered hazard and food but also the risk posed by the substitution food and other potential hazards. We developed a framework to provide decision-makers with a multi-faceted evaluation of the impact of dietary shifts on risk of illness. Our case study explored exposure to inorganic arsenic (iAs) and aflatoxins through consumption of infant cereals and the risk of developing lung, bladder and liver cancer over a lifetime. The estimated additional Disability-Adjusted Life Year (DALY) in the U.S. from exposure to iAs and aflatoxin based on available contamination and consumption patterns of infant rice and oat cereal is 4,921 (CI 90% 414; 9,071). If all infant cereal consumers shift intake (maintaining equivalent serving size and frequency) to only consuming infant rice cereal, the predicted DALY increases to 6,942 (CI 90% 326; 12,931). If all infant cereal consumers shift intake to only consuming infant oat cereal, the predicted DALY decreases to 1,513 (CI 90% 312; 3,356). Changes in contaminant concentrations or percent consumers, that could occur in the future, also significantly impact the predicted risk. Uncertainty in these risk predictions is primarily driven by the dose-response models. A risk-risk analysis framework provides decision-makers with a nuanced understanding of the public health impact of dietary changes and can be applied to other food safety and nutrition questions.

Survey of Salmonella in raw tree nuts at retail in the United States.

The objective of this survey was to estimate the prevalence, contamination level, and genetic diversity of Salmonella in selected raw, shelled tree nuts (Brazil nuts, cashews, hazelnuts, macadamia nuts, pecans, pine nuts, pistachios, and walnuts) at retail markets in the United States. A total of 3,374 samples of eight tree nuts were collected from different types of retail stores and markets nationwide between September 2015 and March 2017. These samples (375 g) were analyzed using a modified FDA's BAM Salmonella culture method. Of the 3,374 samples, 15 (0.44%) (95% confidence interval [CI] [0.25, 0.73]) were culturally confirmed as containing Salmonella; 17 isolates were obtained. Among these isolates, there were 11 serotypes. Salmonella was not detected in Brazil nuts (296), hazelnuts (487), pecans (510), pine nuts (500), and walnuts (498). Salmonella prevalence estimates in cashews (510), macadamia (278), and pistachios (295) were 0.20% (95% CI [<0.01, 1.09]), 2.52% (95% CI [1.02, 5.12]), and 2.37% (95% CI [0.96, 4.83]), respectively. The rates of Salmonella isolation from major/big-chain supermarkets (1381), small-chain supermarkets (328), discount/variety/drug stores (1329), and online (336) were 0.29% (95% CI [0.08, 0.74]), 0.30% (95% CI [0.01, 1.69]), 0.45% (95% CI [0.17, 0.98]), and 1.19% (95% CI [0.33, 3.02]), respectively. Salmonella prevalence in organic (530) and conventional (2,844) nuts was not different statistically (P = 0.0601). Of the enumerated samples (15), 80% had Salmonella levels ≤0.0092 most probable number (MPN)/g. The highest contamination level observed was 0.75 MPN/g. The prevalence and contamination levels of Salmonella in the tree nuts analyzed were generally comparable to previous reports. Pulsed-field gel electrophoresis, serotype, and sequencing data all demonstrated that Salmonella population in nuts is very diverse genetically. PRACTICAL APPLICATION: The prevalence, contamination level, and genetic diversity of Salmonella in eight types of tree nuts (3,374 samples collected nationwide) revealed in this survey could help the development of mitigation strategies to reduce public health risks associated with consumption of these nuts.

A Quantitative Risk Assessment of Human Salmonellosis from Consumption of Walnuts in the United States.

We assessed the risk of human salmonellosis from consumption of shelled walnuts in the United States and the impact of 0- to 5-log reduction treatments for Salmonella during processing. We established a baseline model with Salmonella contamination data from 2010 to 2013 surveys of walnuts from California operations to estimate baseline prevalence and levels of Salmonella during preshelling storage and typical walnut processing stages, considered U.S. consumption data, and applied an adapted dose-response model from the Food and Agriculture Organization and the World Health Organization to evaluate risk of illness per serving and per year. Our baseline model predicted 1 case of salmonellosis per 100 million servings (95% confidence interval [CI], 1 case per 3 million to 1 case per 2 billion servings) of walnuts untreated during processing and uncooked by consumers, resulting in an estimated 6 cases of salmonellosis per year (95% CI, <1 to 278 cases) in the United States. A minimum 3-log reduction treatment for Salmonella during processing of walnuts eaten alone or as an uncooked ingredient resulted in a mean risk of <1 case per year. We modeled the impact on risk per serving of three atypical situations in which the Salmonella levels were increased by 0.5 to 1.5 log CFU per unit pretreatment during processing at the float tank or during preshelling storage or posttreatment during partitioning into consumer packages. No change in risk was associated with the small increase in levels of Salmonella at the float tank, whereas an increase in risk was estimated for each of the other two atypical events. In a fourth scenario, we estimated the risk per serving associated with consumption of walnuts with Salmonella prevalence and levels from a 2014 to 2015 U.S. retail survey. Risk per serving estimates were two orders of magnitude larger than those of the baseline model without treatment. Further research is needed to determine whether this finding reflects variability in Salmonella contamination across the supply or a rare event affecting a portion of the supply.

Risk Assessment of Salmonellosis from Consumption of Alfalfa Sprouts and Evaluation of the Public Health Impact of Sprout Seed Treatment and Spent Irrigation Water Testing.

We developed a risk assessment of human salmonellosis associated with consumption of alfalfa sprouts in the United States to evaluate the public health impact of applying treatments to seeds (0-5-log10 reduction in Salmonella) and testing spent irrigation water (SIW) during production. The risk model considered variability and uncertainty in Salmonella contamination in seeds, Salmonella growth and spread during sprout production, sprout consumption, and Salmonella dose response. Based on an estimated prevalence of 2.35% for 6.8 kg seed batches and without interventions, the model predicted 76,600 (95% confidence interval (CI) 15,400-248,000) cases/year. Risk reduction (by 5- to 7-fold) predicted from a 1-log10 seed treatment alone was comparable to SIW testing alone, and each additional 1-log10 seed treatment was predicted to provide a greater risk reduction than SIW testing. A 3-log10 or a 5-log10 seed treatment reduced the predicted cases/year to 139 (95% CI 33-448) or 1.4 (95% CI <1-4.5), respectively. Combined with SIW testing, a 3-log10 or 5-log10 seed treatment reduced the cases/year to 45 (95% CI 10-146) or <1 (95% CI <1-1.5), respectively. If the SIW coverage was less complete (i.e., less representative), a smaller risk reduction was predicted, e.g., a combined 3-log10 seed treatment and SIW testing with 20% coverage resulted in an estimated 92 (95% CI 22-298) cases/year. Analysis of alternative scenarios using different assumptions for key model inputs showed that the predicted relative risk reductions are robust. This risk assessment provides a comprehensive approach for evaluating the public health impact of various interventions in a sprout production system.

Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.

Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21°C to 24°C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of δ per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of ρ per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (δ) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be implemented in a probabilistic exposure assessment or a quantitative microbial risk assessment.

Predicting survival of Salmonella in low-water activity foods: an analysis of literature data.

Factors such as temperature, water activity (aw), substrate, culture media, serotype, and strain influence the survival of Salmonella in low-aw foods. Predictive models for Salmonella survival in low-aw foods at temperatures ranging from 21 to 80(u) C and water activities below 0.6 were previously developed. Literature data on survival of Salmonella in low-aw foods were analyzed in the present study to validate these predictive models and to determine global influencing factors. The results showed the Weibull model provided suitable fits to the data in 75% of the curves as compared with the log-linear model. The secondary models predicting the time required for log-decimal reduction (log δ) and shape factor (log ß) values were useful in predicting the survival of Salmonella in low-aw foods. Statistical analysis indicated overall fail-safe secondary models, with 88% of the residuals in the acceptable and safe zones (<0.5 log CFU) and a 59% correlation coefficient (R(2) = 0.35). A high variability in log δ-values and log ß-values was observed, emphasizing the importance of experimental design. Factors of significant influence on the times required for first log-decimal reduction included temperature, aw, product, and serotype. Log ß-values were significantly influenced by serotype, the type of inoculum (wet or dry), and whether the recovery media was selective or not. The results of this analysis provide a general overview of survival kinetics of Salmonella in low-aw foods and its influencing factors.

Data analysis of the inactivation of foodborne microorganisms under high hydrostatic pressure to establish global kinetic parameters and influencing factors.

The inactivation rate of foodborne microorganisms under high hydrostatic pressure (HHP) is influenced by factors such as substrate, species, strain, temperature, pH, and stage of growth of the cell. In this study, 445 D(P)-values from previously published data were analyzed, including those from bacterial spores, vegetative cells, and yeasts. Three secondary linear inactivation models with pressure and/or temperature as process parameters were tested to estimate global log D(P)-, z(P)-, and z(T)-values, and the influence of these parameters and additional factors was assessed. The results show that significant differences in microbial resistance are mainly the result of temperature, highlighting the need for its inclusion as a process parameter. Perhaps due to the large number of data and very distinct factors, the remaining factors showed no significant differences in microbial resistance, except in the case of Clostridium spp. in soy milk, which showed decreased resistance in this substrate compared with its behavior in other products. These results serve to establish priorities among factors influencing HHP inactivation and to estimate global kinetic parameters as a basis for setting target levels of inactivation. Moreover, they can be used as a benchmark for comparison of microbial HHP inactivation data gathered in future studies.