Background Quantifying diagnostic transitions across development is needed to estimate the long-term burden of mental illness. This study estimated patterns of diagnostic transitions from childhood to adolescence and from adolescence to early adulthood. Methods Patterns of diagnostic transitions were estimated using data from three prospective, longitudinal studies involving close to 20,000 observations of 3,722 participants followed across multiple developmental periods covering ages 9 to 30. Common DSM psychiatric disorders were assessed in childhood (ages 9 to 12; two samples), adolescence (ages 13 to 18; three samples), and early adulthood (ages 19 to age 32; three samples) with structured psychiatric interviews and questionnaires. Results Having a disorder at an early period was associated with at least a 3-fold increase in odds for having a disorder at a later period. Homotypic and heterotypic transitions were observed for every disorder category. The strongest evidence of continuity was seen for behavioral disorders (particularly ADHD) with less evidence for emotional disorders such as depression and anxiety. Limited evidence was found in adjusted models for behavioral disorders predicting later emotional disorders. Adult substance disorders were preceded by behavioral disorders, but not anxiety or depression. Conclusions Having a disorder in childhood or adolescence is a potent risk factor for a range of psychiatric problems later in development. These findings provide further support for prevention and early life intervention efforts and suggest that treatment at younger ages, while justified in its own right, may also have potential to reduce the risk for disorders later in development.

Canada's health care system must be prepared for the possibility of a significant influx of ICU patients during the second wave of swine-origin H1N1. Efficient vaccination and other disease prevention measures can reduce the attack rate to manageable levels.

Reducing the attack rate among children, whether through vaccination or additional measures, such as social distancing, will be critical to ensure sufficient pediatric intensive care unit capacity for continued pediatric care.

Objective: Prevalence estimates for mood and anxiety disorders in Canada are available, but various methodological approaches have produced inconsistent results. Simulation studies involve careful examination of available data by an expert modelling team working together with subject matter experts. Simulation can integrate datasets and literature-based estimates from various sources into a coherent mathematical representation of the underlying total population epidemiology. Methods:Supported by the Mental Health Commission of Canada, a simulation modelling project for mental disorders in Canada was recently undertaken. The modelling was carried out by RiskAnalytica using their Life at Risk platform. Specification and calibration of the model occurred in consultation with national and international experts.Results: To reconcile estimates of incidence and prevalence, recall bias needed to be represented in the model. This suggests that the population prevalence of mood and anxiety disorders has been underestimated by population surveys and may explain a discrepancy observed in the age-specific prevalence in population surveys as compared with studies using administrative data. The number of Canadians with mood and anxiety disorders is projected to increase in upcoming decades as a result of population growth, but, based on conservative assumptions, an increased prevalence proportion is not anticipated.Conclusions: Simulation models can act as a platform for economic analyses and epidemiologic projections and can support the rapid exploration of what-if scenarios, thereby informing policy decisions. This first national-level simulation provides a high level overview of mood and anxiety disorder epidemiology in Canada. W W WObjectif : Des estimations de la prévalence des troubles anxieux et de l'humeur au Canada sont disponibles, mais diverses approches méthodologiques ont produit des résultats incohérents. Les études en simulation font appel à un examen minutieux des données disponibles par une équipe experte en modélisation qui collabore avec des experts en la matière. La simulation peut intégrer des ensembles de données et des estimations fondées sur la littérature issus de sources diverses dans une représentation mathématique cohérente de l'épidémiologie sous-jacente dans la population totale.Méthodes : Avec l'appui de la Commission de la santé mentale du Canada, un projet de modélisation par simulation pour les troubles mentaux au Canada a été récemment entrepris. La modélisation a été exécutée par RiskAnalytica, au moyen de leur plateforme Life at Risk. La spécification et le calibrage du modèle ont été effectués en consultation avec des experts nationaux et internationaux. Résultats :Afin de concilier les estimations de l'incidence et de la prévalence, il a fallu représenter des biais de rappel dans le modèle. Cela laisse entendre que la prévalence des troubles anxieux et de l'humeur dans la population a été sous-estimée par les enquêtes auprès de la population et peut expliquer l'écart observé dans la préva...

We developed early warning algorithms for influenza using data from the Alberta Real-Time Syndromic Surveillance Net (ARTSSN). In addition to looking for signatures of potential pandemics, the model was operationalized by using the algorithms to provide regular weekly forecasts on the influenza trends in Alberta during 2012-2014. We describe the development of the early warning model and the predicted influenza peak time and attack rate results. We report on the usefulness of this model using real-time ARTSSN data, discuss how it was used by decision makers and suggest future enhancements for this promising tool in influenza planning and preparedness.