Simulation of decontamination and transmission of Escherichia coli O157:H7, Salmonella Enteritidis, and Listeria monocytogenes during handling of raw vegetables in domestic kitchens

Epidemiological data indicates that a large number of foodborne illnesses are attributed to cross-contamination during food preparation in the domestic kitchen. The objectives of this study were to evaluate the efficiency of household washing practices in removing Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella Enteritidis on artificially contaminated lettuce and to determine the transfer rate of these three foodborne pathogens from contaminated lettuce to wash water, tomato, cabbage, and cutting boards during washing and cutting processes. Washing under the running tap water with scrubbing for 60 s was the most effective method in reducing pathogen populations by 1.86–2.60 log10 CFU/g. Also, final rinsing and scrubbing practices were found to enhance the efficiency of washing treatment. In this study, the transfer rates of S. Enteritidis, E. coli O157:H7, and L. monocytogenes from cutting board to cabbage and tomato via cutting process (17.5–31.7%) were higher (P < 0.05) than from wash water to cabbage and tomato (0.8–23.0%) during washing treatment. Overall, our findings suggest that wash water and cutting board can be potential vehicles in the dissemination of foodborne pathogens. Therefore, there is a need to promote consumer awareness for proper handling practices in the kitchen to minimise the risk of foodborne infection

Comparing Bayesian and Maximum Likelihood Predictors in Structural Equation Modeling of Children’s Lifestyle Index

Several factors may influence children’s lifestyle. The main purpose of this study is to introduce a children’s lifestyle index framework and model it based on structural equation modeling (SEM) with Maximum likelihood (ML) and Bayesian predictors. This framework includes parental socioeconomic status, household food security, parental lifestyle, and children’s lifestyle. The sample for this study involves 452 volunteer Chinese families with children 7–12 years old. The experimental results are compared in terms of root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error metrics. An analysis of the proposed causal model suggests there are multiple significant interconnections among the variables of interest. According to both Bayesian and ML techniques, the proposed framework illustrates that parental socioeconomic status and parental lifestyle strongly impact children’s lifestyle. The impact of household food security on children’s lifestyle is rejected. However, there is a strong relationship between household food security and both parental socioeconomic status and parental lifestyle. Moreover, the outputs illustrate that the Bayesian prediction model has a good fit with the data, unlike the ML approach. The reasons for this discrepancy between ML and Bayesian prediction are debated and potential advantages and caveats with the application of the Bayesian approach in future studies are discussed

mHealth Apps Assessment among Postpartum Women with Obesity and Depression

Background: Pregnancy has become the main constituent for women to become overweight or obese during the postpartum phase. This could lead women to suffer from postpartum depression as well. Information technology (IT) has become more prevalent in the healthcare industry. It offers patients the opportunity to manage their health conditions via the use of several applications, one being the mHealth applications. Objective: The main purpose of this study is to experiment and understand the effects the mHealth applications (i.e., fitness and nutrition applications) have on the body mass index (BMI) and depression levels amongst postpartum women. Methods: Online questionnaires were sent to postpartum women within one year after their pregnancy, of which 819 completed questionnaires were returned. The frequency of the mHealth applications usage was categorized into daily, weekly, rarely and never streams. Therefore, the frequency of use of the mHealth applications for BMI and depression levels was analyzed based on the available statistical data. Descriptive statistics, ANOVA, and Dunnet tests were applied to analyze the experimental data. Results: Out of 819 respondents, 37.9% and 42.1% of them were overweight and obese, respectively. Almost 32.9% of the respondents were likely depressed, and 45.6% were at an increased risk. This study reports that only 23.4% and 28.6% of respondents never used the fitness and nutrition applications. The impact of the frequency of using the fitness applications on BMI and depression levels was obvious. This means that with the increased use of the fitness applications, there was also a significant effect in maintaining and decreasing the BMI and depression levels amongst Malaysians postpartum women. However, from the data of weekly and daily use of fitness applications, we found that the contribution toward the BMI and depression levels was high (p = 0.000). However, nutrition applications amongst the users were not significant within the main variables (p &gt; 0.05). From the Dunnet test, the significance of using the fitness applications within the depression levels started from daily usage, whereas for BMI, it started from weekly usage. Conclusion: The efficiency of the fitness applications toward the BMI and depression levels has been proven in this research work. While nutrition applications did not affect the BMI and depression levels, some of the respondents were still categorized as weekly and daily users. Thus, the improvements in BMI and depression levels are associated with the types of mHealth app that had been used

Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling

The main purpose of the current article is to introduce a framework of the complexity of childhood obesity based on the family environment. A conceptual model that quantifies the relationships and interactions among parental socioeconomic status, family food security level, child’s food intake and certain aspects of parental feeding behaviour is presented using the structural equation modeling (SEM) concept. Structural models are analysed in terms of the direct and indirect connections among latent and measurement variables that lead to the child weight indicator. To illustrate the accuracy, fit, reliability and validity of the introduced framework, real data collected from 630 families from Urumqi (Xinjiang, China) were considered. The framework includes two categories of data comprising the normal body mass index (BMI) range and obesity data. The comparison analysis between two models provides some evidence that in obesity modeling, obesity data must be extracted from the dataset and analysis must be done separately from the normal BMI range. This study may be helpful for researchers interested in childhood obesity modeling based on family environment

Firm Sustainability Performance Index Modeling

The main objective of this paper is to bring a model for firm sustainability performance index by applying both classical and Bayesian structural equation modeling (parametric and semi-parametric modeling). Both techniques are considered to the research data collected based on a survey directed to the China, Taiwan, and Malaysia food manufacturing industry. For estimating firm sustainability performance index we consider three main indicators include knowledge management, organizational learning, and business strategy. Based on the both Bayesian and classical methodology, we confirmed that knowledge management and business strategy have significant impact on firm sustainability performance index

An Application of Moderation Analysis: the Situation of School Size in the Relationship among Principal's Leadership Style, Decision Making Style, and Teacher Job Satisfaction

The main purpose of this research is to illustrate that school size a moderator can effect on the relationship among principal’s leadership style (PLS), principal's decision making style (PDMS), and teacher job satisfaction (TJS). Moreover, in this article, some hypotheses have been proposed to verify the existing relations among TJS, PLS, and PDMS. Based on samples randomly chosen from primary, secondary and high schools in Chinese educational systems in China, the required data are gathered through a mail survey, and the proposed hypotheses are tested via moderation analysis and structural equation modeling

Effect of Social Media on Child Obesity: Application of Structural Equation Modeling with the Taguchi Method

Through public health studies, specifically on child obesity modeling, research scholars have been attempting to identify the factors affecting obesity using suitable statistical techniques. In recent years, regression, structural equation modeling (SEM) and partial least squares (PLS) regression have been the most widely employed statistical modeling techniques in public health studies. The main objective of this study to apply the Taguchi method to introduce a new pattern rather than a model for analyzing the body mass index (BMI) of children as a representative of childhood obesity levels mainly related to social media use. The data analysis includes two main parts. The first part entails selecting significant indicators for the proposed framework by applying SEM for primary and high school students separately. The second part introduces the Taguchi method as a realistic and reliable approach to exploring which combination of significant variables leads to high obesity levels in children. AMOS software (IBM, Armonk, NY, USA) was applied in the first part of data analysis and MINITAB software (Minitab Inc., State College, PA, USA) was utilized for the Taguchi experimental analysis (second data analysis part). This study will help research scholars view the data and a pattern rather than a model, as a combination of different factor levels for target factor optimization

Mahalanobis distance.

<p>Mahalanobis distance.</p

Normality test of endogenous variables.

<p>Normality test of endogenous variables.</p

Validity and reliability based on AVE and Cronbach’s Alpha values.

<p>Validity and reliability based on AVE and Cronbach’s Alpha values.</p