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This study proposes a novel methodology for risk assessment of products with extremely low risk. The method is based on the analysis of those iterations that result in illness occurrence. It is demonstrated using a hypothetical scenario on listeriosis from pasteurized milk heated at 72°C–75 °C for 15–20 s and analysed which combinations of factors resulted in illness. Sixty-one cases of listeriosis were predicted from 10 billion simulations, representing a realistically large number of servings for this product. According to the model simulations, the illness cases were caused by extremely high doses resulting from three rare situations occurring concurrently: high initial level, less effective pasteurization (due to high thermotolerance of L. monocytogenes contaminants) and extremely high microbial growth during domestic storage (due to poor storage conditions). However, listeriosis was not always observed even if the three situations occurred. Infectivity (dose–response parameter) had the strongest relevance to illness occurrence. Notably, the results of the sensitivity analysis varied depending on the output variable (microbial concentration at exposure or illness occurrence). Furthermore, the correlation-based sensitivity analysis for the illness occurrence provided unreliable results, discouraging this approach for food products with an extremely low illness probability. Considering that number of illnesses and not exposure is the output variable most relevant for risk management, we propose an innovative method based on graphical representations for sensitivity analysis in low-risk products.

Connectedness among older people is essential for healthy communities, especially among rural populations where limited social interaction and associated health effects may be cause for concern. In this qualitative descriptive study, we explored older rural people's perception of connectedness through a communication application. The study assessed 10 participants (mean age = 76.2 years) living in rural Japan who regularly participated in a senior citizens' club. From July 2019 to January 2020, the participants used a social media application developed by our research team to meet the needs of older people. Semi-structured interviews were conducted. Six themes representing older rural people's perception of connectedness were identified: (1) thoughtful consideration for members strengthened even without them meeting face-to-face, (2) encouragement received from familiar members, (3) joy in sharing daily routine with neighbors, (4) courage to advance through face-to-face interaction, (5) willingness to continue club membership, and (6) fear of disrupting club's harmony. Participants who used the application felt compensated for the lack of social interaction opportunities in rural settings and strengthened their existing relationships.

Aims The biological events occurring during human digestion help to understand the mechanisms underlying the dose-response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine. Methods and Results The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased. Conclusions Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived. Significance and Impact of the Study Competition experiments in a small-intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose-response relationships.

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 10(5) and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10(4). The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 10(1), 10(2), and 10(3) and growth with average of initial cell numbers of 10(-1), 10(0), and 10(1). The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10(1) were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

We developed 'Kikoeru', a pedometer and communication application (app) for older people, with functions that include voice conversations and messages, photograph sharing, and pedometer sharing among users. This pilot study aimed to clarify the effects of Kikoeru on social connectedness related to interactions through the app, subjective health, loneliness, and setting a target number of steps for seven men aged 65 and older who lived at home. Survey items consisted of the Ando-Osada-Kodama loneliness scale, target number of steps, and subjective health ratings. We utilized interviews, seeking to determine whether users felt connected when using the app. The app usage days were collected from the server. The social and physical effects of the app were assessed using a mix of quantitative and qualitative methods. Participants used the app for 54.9 days on average during the 60-day intervention period. Interviews revealed four social connectedness benefits: 'constructing reliance', 'remembering to consider friends', 'awareness of friends' daily lives and encouragement for me', and 'taking an interest in friends'. Loneliness decreased for four out of seven people, remained the same for three, and did not worsen for anyone. The target number of steps increased for three people, remained the same for four, and did not decrease for anyone. Average subjective health improved from 63.6 to 72.9 points out of 100 after the intervention. Results showed participants experienced social connectedness, reduced loneliness, and improved their target steps and health by using Kikoeru. Future large-scale studies with expanded sample sizes should be conducted.

The objective of this study was to separately describe the fitting uncertainty and the variability of individual cell in bacterial survival kinetics during isothermal and non-isothermal thermal processing. The model describing bacterial survival behavior and its uncertainties and variabilities during non-isothermal inactivation was developed from survival kinetic data for Bacillus simplex spores under fifteen isothermal conditions. The fitting uncertainties in the parameters used in the primary Weibull model was described by using the bootstrap method. The variability of individual cells in thermotolerance and the true randomness in the number of dead cells were described by using the Markov chain Monte Carlo (MCMC) method. A second-order Monte Carlo (2DMC) model was developed by combining both the uncertainties and variabilities. The 2DMC model was compared with reduction behavior under three non-isothermal profiles for model validation. The bacterial death estimations were validated using experimentally observed surviving bacterial count data. The fitting uncertainties in the primary Weibull model parameters, the individual thermotolerance heterogeneity, and the true randomness of inactivated spore counts were successfully described under all the iso-thermal conditions. Furthermore, the 2DMC model successfully described the variances in the surviving bacterial counts during thermal inactivation for all three non-isothermal profiles. As a template for risk-based process designs, the proposed 2DMC simulation approach, which considers both uncertainty and variability, can facilitate the selection of appropriate thermal processing conditions ensuring both food safety and quality.