Child Abuse and Sudden Infant Death

In times past, parents were usually blamed when an infant died suddenly and unexpectedly. A more caring attitude evolved with the adoption of the term ‘cot death’ or ‘sudden infant death syndrome’ (SIDS). Recent research looking at the factors responsible for SIDS has focused on aspects of child care and parental behaviour. Epidemiological studies confirm that several risk factors associated with child maltreatment are also to be found in many families who experience a sudden infant death. Social deprivation and other well-known associated features linked to maltreatment are correlated with sudden death in infancy. It is suggested that both physical abuse (suffocation is the most usual mechanism proposed) and neglect (failure to thrive, failure to recognize and treat illness) are important mechanisms. Infanticide (filicide) has existed widely over past centuries and its continuation in modern times is reflected in the highest murder rates of all age groups in infancy. However, much fatal abuse and neglect remains hidden, as demonstrated from the results of detailed fatality reviews. The evidence linking child abuse and neglect with sudden unexpected death in infancy is reviewed.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Action collective,action de mouvement social: la contribution du syndicalisme à la liberté au travail dans un exemple néo-zélandais

Les syndicats peuvent contribuer à la liberté au travail par une action collective axée sur l'intérêt des membres, mais aussi en nouant des partenariats avec d'autres acteurs de la société civile, dans le cadre d'un mouvement social. Pour traiter ce dernier aspect, encore peu documenté, les auteurs s'appuient sur les définitions de la liberté de Berlin (1988) et MacCallum (1967) puis sur la théorie de la démocratie radicale de Laclau et Mouffe (2019). Ils illustrent leur propos par une étude de cas portant sur les stratégies déployées par des syndicats néozélandais en vue d'accompagner l'action officielle pour une transition juste face au défi climatique.     

Quantitative risk assessment-epidemic curve prediction model for leafy green outbreak investigation

The objective of this study was to leverage quantitative risk assessment to investigate possible root cause(s) of foodborne illness outbreaks related to Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) infections in leafy greens in the United States. To this end, we developed the FDA leafy green quantitative risk assessment epidemic curve prediction model (FDA-LG QRA-EC) that simulated the lettuce supply chain. The model was used to predict the number of reported illnesses and the epidemic curve associated with lettuce contaminated with STEC O157 for a wide range of scenarios representing various contamination conditions and facility processing/sanitation practices. Model predictions were generated for fresh-cut and whole lettuce, quantifying the differing impacts of facility processing and home preparation on predicted illnesses. Our model revealed that the timespan (i.e., number of days with at least one reported illness) and the peak (i.e., day with the most predicted number of reported illnesses) of the epidemic curve of a STEC O157-lettuce outbreak were not strongly influenced by facility processing/sanitation practices and were indications of contamination pattern among incoming lettuce batches received by the facility or distribution center. Through comparisons with observed number of illnesses from recent STEC O157-lettuce outbreaks, the model identified contamination conditions on incoming lettuce heads that could result in an outbreak of similar size, which can be used to narrow down potential root cause hypotheses.