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International Journal of Environmental... Jan 2022Food safety inspections are a key health protection measure applied by governments to prevent foodborne illness, yet they remain the subject of sustained... (Review)
Review
Food safety inspections are a key health protection measure applied by governments to prevent foodborne illness, yet they remain the subject of sustained criticism. These criticisms include inconsistency and inadequacy of methods applied to inspection, and ineffectiveness in preventing foodborne illness. Investigating the validity of these criticisms represent important areas for further research. However, a defined construct around the meanings society attributes to food safety inspection must first be established. Through critical examination of available literature, this review identified meanings attributed to food safety inspection and explicates some of the key elements that compose food safety inspection as a social construct. A total of 18 meanings were found to be attributed to food safety inspection. Variation in meanings were found between consumers, food business associates and food safety inspectors. For some, inspection meant a source of assurance, for others a threat to fairness, while most view inspection as a product of resources and inspector training. The meanings were then examined in light of common criticisms directed at food safety inspection, to expound their influence in how food safety inspection is realized, shaped, and rationalized. This review highlights the influence of sociological factors in defining food safety inspection. .
Topics: Food Inspection; Food Safety; Foodborne Diseases; Humans
PubMed: 35055611
DOI: 10.3390/ijerph19020789 -
Philosophical Transactions of the Royal... Jul 2023The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and... (Review)
Review
The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps-causal modelling, observation, estimation, detection and attribution-for robust attribution. This workflow provides evidence of biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.
Topics: Ecosystem; Biodiversity
PubMed: 37246383
DOI: 10.1098/rstb.2022.0182 -
Zoonoses and Public Health Aug 2022Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for... (Meta-Analysis)
Meta-Analysis
Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for modelling frameworks that synthetize the quantitative evidence at our disposal and reduce reliance on expert elicitations. Here, we develop a statistical model within a Bayesian estimation framework to integrate attribution estimates from expert elicitations with estimates from microbial subtyping and case-control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (Campylobacter, Salmonella, Shiga toxin-producing E. coli [STEC] O157 and Listeria). For each pathogen, we pooled the published fractions of human cases attributable to each animal reservoir from the microbial subtyping studies, accounting for the uncertainty arising from the different typing methods, attribution models, and year(s) of data collection. We then combined the population attributable fractions (PAFs) from the case-control studies according to five transmission pathways (domestic food, environment, direct animal contact, human-human transmission and travel) and 11 groups within the foodborne pathway (beef/lamb, pork, poultry meat, eggs, dairy, fish/shellfish, fruit/vegetables, beverages, grains, composite foods and food handlers/vermin). The attribution estimates were biologically plausible, allowing the human cases to be attributed in several ways according to reservoirs, transmission pathways and food groups. All pathogens were predominantly foodborne, with Campylobacter being mostly attributable to the chicken reservoir, Salmonella to pigs (albeit closely followed by layers), and Listeria and STEC O157 to cattle. Food-wise, the attributions reflected those at the reservoir level in terms of ranking. We provided a modelling solution to reach consensus attribution estimates reflecting the empirical evidence in the literature that is particularly useful for policy-making and is extensible to other pathogens and domains.
Topics: Animals; Bayes Theorem; Campylobacter; Cattle; Cattle Diseases; Escherichia coli; Food Microbiology; Foodborne Diseases; Listeria; Models, Statistical; Ovum; Salmonella; Sheep; Sheep Diseases; Swine; Swine Diseases
PubMed: 35267243
DOI: 10.1111/zph.12937 -
Scientific Reports Jul 2023People tend to expect mental capabilities in a robot based on anthropomorphism and often attribute the cause and responsibility for a failure in human-robot interactions...
People tend to expect mental capabilities in a robot based on anthropomorphism and often attribute the cause and responsibility for a failure in human-robot interactions to the robot. This study investigated the relationship between mind perception, a psychological scale of anthropomorphism, and attribution of the cause and responsibility in human-robot interactions. Participants played a repeated noncooperative game with a human, robot, or computer agent, where their monetary rewards depended on the outcome. They completed questionnaires on mind perception regarding the agent and whether the participant's own or the agent's decisions resulted in the unexpectedly small reward. We extracted two factors of Experience (capacity to sense and feel) and Agency (capacity to plan and act) from the mind perception scores. Then, correlation and structural equation modeling (SEM) approaches were used to analyze the data. The findings showed that mind perception influenced attribution processes differently for each agent type. In the human condition, decreased Agency score during the game led to greater causal attribution to the human agent, consequently also increasing the degree of responsibility attribution to the human agent. In the robot condition, the post-game Agency score decreased the degree of causal attribution to the robot, and the post-game Experience score increased the degree of responsibility to the robot. These relationships were not observed in the computer condition. The study highlights the importance of considering mind perception in designing appropriate causal and responsibility attribution in human-robot interactions and developing socially acceptable robots.
Topics: Humans; Robotics; Social Behavior; Emotions; Social Perception; Reward
PubMed: 37507519
DOI: 10.1038/s41598-023-39435-5 -
Risk Analysis : An Official Publication... Dec 2023Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of...
Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.
Topics: Animals; Humans; Swine; Campylobacter Infections; Bayes Theorem; Chickens; Australia; Multilocus Sequence Typing; Campylobacter; Campylobacter jejuni; Ruminants; Gastroenteritis
PubMed: 37032319
DOI: 10.1111/risa.14138 -
Journal of Food Protection Jun 2020The economic burden of foodborne illness has been estimated to be as high as US$90 billion annually. For policy purposes, it is often important to understand not only...
ABSTRACT
The economic burden of foodborne illness has been estimated to be as high as US$90 billion annually. For policy purposes, it is often important to understand not only the overall cost of illness but also the costs associated with individual products or groups of products. In this study, I estimate the cost of foodborne illnesses from 29 pathogens associated with nongame meat and poultry products that are regulated by the U.S. Department of Agriculture. To complete this, I merge results from a food attribution model with results from an illness model and an economic burden of illness model. The food attribution model uses outbreak and expert elicitation data to attribute foods to pathogens. The illness model is a replication of the 2011 study published by the Centers for Disease Control and Prevention. The economic cost model is an updated version of previously published studies that include costs for medical care, lost productivity, loss of life, and pain and suffering. The primary attribution model, based largely on Interagency Food Safety Analytics Collaboration assumptions, estimates that meat and poultry products are vectors for 30.9% of all foodborne illnesses. This translates into 2.9 million annual illnesses, yielding economic costs of up to $20.3 billion. The costliest food-pathogen pairs include Campylobacter spp. in poultry ($6.9 billion), Salmonella spp. in chicken and pork ($2.8 and $1.9 billion, respectively), and Toxoplasma gondii in pork ($1.9 billion). Results based on alternative attribution and economic model assumptions are also presented, generating meat and poultry attribution estimates ranging from 27.1 to 36.7% and economic costs of $8.1 to $22.5 billion.
Topics: Animals; Cost of Illness; Disease Outbreaks; Food Microbiology; Foodborne Diseases; Meat; Poultry; United States
PubMed: 32032420
DOI: 10.4315/JFP-19-548 -
General Psychiatry 2022The biomedical model, which limits itself to finding the attributions of organic disease, is challenged by gastrointestinal (GI) symptoms. Simultaneously, physicians'... (Review)
Review
The biomedical model, which limits itself to finding the attributions of organic disease, is challenged by gastrointestinal (GI) symptoms. Simultaneously, physicians' attribution of GI symptoms to underlying psychological issues is not readily accepted by patients and can negatively affect the clinical rapport between doctor and patient. In reality, psychosocial aspects are involved in many functional disorders and organic diseases, not just in mental disorders. Time is overdue for gastroenterologists to recognise the inadequacy and limitations of conventional gastroenterology and consider the role of psychological, social and biological variables throughout the entire clinical course of the illness, as is shown in George Engel's model. This review discusses the following: (1) the current challenges of using the conventional clinical model for both functional and organic GI illness, (2) the inadequacy and limitations of explaining GI symptoms simply as psychological disorders, (3) the exploration of the symptom-centred, stepped reattribution clinical model, (4) the clarification of psychosomatic medical concepts for use in gastroenterology, and (5) the significance of a systematic and interdisciplinary framework for a comprehensive psychosomatic model in gastroenterology.
PubMed: 36447756
DOI: 10.1136/gpsych-2022-100856 -
The Journal of Head Trauma...In participants with traumatic brain injury (TBI) and peer controls, examine (1) differences in negative attributions (interpret ambiguous behaviors negatively); (2)...
OBJECTIVE
In participants with traumatic brain injury (TBI) and peer controls, examine (1) differences in negative attributions (interpret ambiguous behaviors negatively); (2) cognitive and emotional factors associated with negative attributions; and (3) negative attribution associations with anger responses, life satisfaction, and participation.
SETTING
Two TBI outpatient rehabilitation centers.
PARTICIPANTS
Participants with complicated mild to severe TBI (n = 105) and peer controls (n = 105).
DESIGN
Cross-sectional survey study.
MAIN MEASURES
Hypothetical scenarios describing ambiguous behaviors were used to assess situational anger and attributions of intent, hostility, and blame. Executive functioning, perspective taking, emotion perception and social inference, alexithymia, aggression, anxiety, depression, participation, and life satisfaction were also assessed.
RESULTS
Compared with peer controls, participants with TBI rated behaviors significantly more intentional, hostile, and blameworthy. Regression models explained a significant amount of attribution variance (25%-43%). Aggression was a significant predictor in all models; social inference was also a significant predictor of intent and hostility attributions. Negative attributions were associated with anger responses and lower life satisfaction.
CONCLUSION
People with TBI who have higher trait aggression and poor social inferencing skills may be prone to negative interpretations of people's ambiguous actions. Negative attributions and social inferencing skills should be considered when treating anger problems after TBI.
Topics: Aggression; Brain Injuries; Cross-Sectional Studies; Hostility; Humans; Risk Factors; Social Perception
PubMed: 32769831
DOI: 10.1097/HTR.0000000000000600 -
Neuropsychopharmacology : Official... Nov 2020Context, the information surrounding an experience, can significantly alter the meaning and the affective responses to events. Yet the biological mechanisms through... (Randomized Controlled Trial)
Randomized Controlled Trial
Context, the information surrounding an experience, can significantly alter the meaning and the affective responses to events. Yet the biological mechanisms through which context modulate experiences are not entirely understood. Here, we hypothesized that the µ-opioid system-extensively implicated in placebo effects, a clinical phenomenon thought to rely on contextual processing-modulates the effects of contextual information on emotional attributions in patients with depression. To test this hypothesis, 20 unmedicated patients with depression completed a randomized, double-blind, placebo-controlled, crossover study of one dose of 50 mg of naltrexone, or placebo immediately before completing two sessions of the Contextual Framing fMRI task. This task captures effects of valenced contextual cues (pleasant vs. unpleasant) on emotional attribution (the rating of subtle emotional faces: fearful, neutral, or happy). Behaviorally, we found that emotional attribution was significantly moderated by the interaction between contextual cues and subtle emotional faces, such that participants' ratings of valenced faces (fearful and happy), compared to neutral, were more negative during unpleasant, compared to pleasant context cues. At a neural level, context-induced blood-oxygen-level-dependent responses in the ventromedial prefrontal cortex, the dorsal anterior cingulate, the dorsolateral prefrontal cortex, and the lateral orbitofrontal cortex, significantly moderated the effects of context on emotional attribution, and were blunted by naltrexone. Furthermore, the effects of naltrexone on emotional attribution were partially abolished in more severely depressed patients. Our results provide insights into the molecular alterations underlying context representation in patients with depression, providing pivotal early data for future treatment studies.
Topics: Cross-Over Studies; Depression; Double-Blind Method; Emotions; Facial Expression; Humans; Magnetic Resonance Imaging; Naltrexone
PubMed: 32843703
DOI: 10.1038/s41386-020-00809-2 -
Frontiers in Endocrinology 2023Visceral adipose tissue plays a central role in obesity and metabolic syndrome and is an independent risk factor for both cardiovascular and metabolic disorders.... (Review)
Review
Visceral adipose tissue plays a central role in obesity and metabolic syndrome and is an independent risk factor for both cardiovascular and metabolic disorders. Increased visceral adipose tissue promotes adipokine dysregulation and insulin resistance, leading to several health issues, including systemic inflammation, oxidative stress, and activation of the renin-angiotensin-aldosterone system. Moreover, an increase in adipose tissue directly and indirectly affects the kidneys by increasing renal sodium reabsorption, causing glomerular hyperfiltration and hypertrophy, which leads to increased proteinuria and kidney fibrosis/dysfunction. Although the interest in the adverse effects of obesity on renal diseases has grown exponentially in recent years, the relationship between obesity and renal prognosis remains controversial. This may be attributed to the long clinical course of obesity, numerous obesity-related metabolic complications, and patients' attributes. Multiple individual attributes influencing the pathophysiology of fat accumulation make it difficult to understand obesity. In such cases, it may be effective to elucidate the pathophysiology by conducting research tailored to individual attributes from the perspective of attribute-based medicine/personalized medicine. We consider the appropriate use of clinical indicators necessary, according to attributes such as chronic kidney disease stage, level of visceral adipose tissue accumulation, age, and sex. Selecting treatments and clinical indicators based on individual attributes will allow for advancements in the clinical management of patients with obesity and chronic kidney disease. In the clinical setting of obesity-related nephropathy, it is first necessary to accumulate attribute-based studies resulting from the accurate evaluation of visceral fat accumulation to establish evidence for promoting personalized medicine.
Topics: Humans; Intra-Abdominal Fat; Obesity; Metabolic Syndrome; Kidney; Renal Insufficiency, Chronic
PubMed: 36843595
DOI: 10.3389/fendo.2023.1097596