Unintentional injury deaths associated with sport and recreation in Québec, Canada, 2006-2019.

OBJECTIVES: This study examined trends in the frequencies and rates of deaths associated with unintentional injuries in sport and recreation in Québec, Canada, for the period January 2006-December 2019. METHODS: In this descriptive retrospective study, data were extracted from the database of the Bureau du coroner du Québec. Incidence rates were calculated using participation data from the Étude des blessures subies au cours de la pratique d'activités récréatives et sportives au Québec (ÉBARS) and Canadian census population data. Poisson regression was used to investigate changes in death rates over the 14-year period by estimating incidence rate ratios. RESULTS: There were 1937 unintentional injury deaths and the population-based death rate was 1.72 per 100 000 person-years. The participation-based rate was 1.40 per 100 000 participant-years, considering the 24 matching activities in both ÉBARS' editions. Using both population-based and participation-based denominators, separate analyses consistently showed declining death rates in non-motorised navigation and cycling. Deaths related to all-terrain vehicles, snowmobiles, swimming, cycling, motorised navigation and non-motorised navigation activities accounted for 80.2% of all deaths. Drowning was documented as a cause of death in 39.3% of all fatalities. Males represented 86.8% of all deaths, with males aged 18-24 years and 65 and over having the highest rates. CONCLUSION: The death rates of unintentional injury deaths associated with non-motorised navigation and cycling declined, from January 2006 to December 2019. The characteristics and mechanisms of drowning deaths and fatalities that occurred in activities associated with higher death frequencies and rates need to be further investigated.

Clowning as a therapeutic intervention with the elderly: a literature review / Le clown comme intervention thérapeutique auprès de personnes âgées : revue de la littérature

Clown interventions with elderly have been a field of study for twenty years. They concern interventions in hospitals or nursing homes, and particularly on people with cognitive disorders and/or behavioral disorders. The literature also studies the clown interventions in palliative care services. Some studies concern the clowns themselves. Most of writings about clowns intervening in a therapeutic context analyze these interventions through the effect of humor. Other studies, particularly those which are interested in the interventions on people presenting cognitive disorders, focus on the relational qualities of the clown. This synthesis of the literature proposes to make a state of the art on the interventions of clowns with an elderly public, taking into consideration all the characteristics and possible effects of this practice. Thus, we will begin with a description of the interventions of clowns and the specificity of those intervening for the elderly. We will then divide our discussion according to the different targets of the interventions: people with cognitive disorders and palliative care. Next, we will focus on the studies on clowns themselves. If most of studies show positive effects of the clown, almost all mention methodological difficulties. Moreover, the studies take into consideration one of the characteristics of the clown and never the clown as a whole. Furthermore, it will be interesting to observe this practice in non-institutional context.

Les interventions de clowns auprès d'un public âgé sont devenues un champ d'études depuis une vingtaine d'années. Cette revue de la littérature propose un état des connaissances sur les modalités et les effets de ce type d'intervention. Chez des personnes présentant des troubles cognitifs et/ou des troubles du comportement, les interventions clownées auraient un effet sur l'agitation et l'agressivité, comme sur la qualité de vie et le repli sur soi. Dans les services de soins palliatifs, l'aspect poétique et décalé du clown faciliterait l'expression de soi et l'humour permettrait de reconnaître la personne en fin de vie dans son humanité. Les écrits sur les clowns intervenant dans un cadre thérapeutique analysent soit l'effet de l'humour, soit les qualités relationnelles du clown. Cette synthèse de la littérature prend en considération toutes les caractéristiques et effets possibles de cette pratique et propose des pistes de recherches complémentaires.

Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19.

Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by modelling daily mortality counts as distinct phases in the COVID-19 outbreak. We first consider the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We then explore subsequent phases with more recent data. Various countries that have been adversely affected by the epidemic are considered, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony.

Identification of Pre-Clinical Alzheimer's Disease in a Population of Elderly Cognitively Normal Participants.

Alzheimer's disease (AD) has a long pathological process, with an approximate lead-time of 20 years. During the early stages of the disease process, little evidence of the building pathology is identifiable without cerebrospinal fluid and/or imaging analyses. Clinical manifestations of AD do not present until irreversible pathological changes have occurred. Given an opportunity to provide treatment prior to irreversible pathological change, this study aims to identify a subgroup of cognitively normal (CN) participants from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), where subtle changes in cognition are indicative of early AD-related pathology. Using a Bayesian method for unsupervised clustering via mixture models, we define an aggregate measure of posterior probabilities (AMPP score) establishing the likelihood of pre-clinical AD. From Baseline through to 54 months, visuo-spatial function had the greatest contribution to the AMPP score, followed by attention and processing speed and visual memory. Participants with the highest AMPP scores had both increasing neo-cortical amyloid burden and decreasing hippocampus volume over 54 months, compared to those in the lowest category with stable amyloid burden and hippocampus volume. The identification of a possible pre-clinical stage in CN participants via this method, without the aid of disease specific biomarkers, represents an important step in utilizing the strength of cognitive composite scores for the early detection of AD pathology.

Overfitting Bayesian Mixture Models with an Unknown Number of Components.

This paper proposes solutions to three issues pertaining to the estimation of finite mixture models with an unknown number of components: the non-identifiability induced by overfitting the number of components, the mixing limitations of standard Markov Chain Monte Carlo (MCMC) sampling techniques, and the related label switching problem. An overfitting approach is used to estimate the number of components in a finite mixture model via a Zmix algorithm. Zmix provides a bridge between multidimensional samplers and test based estimation methods, whereby priors are chosen to encourage extra groups to have weights approaching zero. MCMC sampling is made possible by the implementation of prior parallel tempering, an extension of parallel tempering. Zmix can accurately estimate the number of components, posterior parameter estimates and allocation probabilities given a sufficiently large sample size. The results will reflect uncertainty in the final model and will report the range of possible candidate models and their respective estimated probabilities from a single run. Label switching is resolved with a computationally light-weight method, Zswitch, developed for overfitted mixtures by exploiting the intuitiveness of allocation-based relabelling algorithms and the precision of label-invariant loss functions. Four simulation studies are included to illustrate Zmix and Zswitch, as well as three case studies from the literature. All methods are available as part of the R package Zmix, which can currently be applied to univariate Gaussian mixture models.

Quantitative risk assessment from farm to fork and beyond: a global Bayesian approach concerning food-borne diseases.

A novel approach to the quantitative assessment of food-borne risks is proposed. The basic idea is to use Bayesian techniques in two distinct steps: first by constructing a stochastic core model via a Bayesian network based on expert knowledge, and second, using the data available to improve this knowledge. Unlike the Monte Carlo simulation approach as commonly used in quantitative assessment of food-borne risks where data sets are used independently in each module, our consistent procedure incorporates information conveyed by data throughout the chain. It allows "back-calculation" in the food chain model, together with the use of data obtained "downstream" in the food chain. Moreover, the expert knowledge is introduced more simply and consistently than with classical statistical methods. Other advantages of this approach include the clear framework of an iterative learning process, considerable flexibility enabling the use of heterogeneous data, and a justified method to explore the effects of variability and uncertainty. As an illustration, we present an estimation of the probability of contracting a campylobacteriosis as a result of broiler contamination, from the standpoint of quantitative risk assessment. Although the model thus constructed is oversimplified, it clarifies the principles and properties of the method proposed, which demonstrates its ability to deal with quite complex situations and provides a useful basis for further discussions with different experts in the food chain.