Health PEI

BackgroundGuided self-help family-based treatment (GSH-FBT) is emerging as a promising, more efficient alternative to traditional family-based treatment (FBT). The present study is designed to examine the real-world implementation of GSH-FBT at pediatric treatment sites across nine provinces in Canada.MethodsImplementation teams at each site consisting of a GSH-FBT coach, a medical provider, and a program administrator will be formed. Clinician coaches will be trained in this new modality and supported with weekly GSH-FBT consultation. Each site will recruit ten families with an adolescent with anorexia nervosa and the parents will undergo ten virtual GSH-FBT sessions. The implementation approach will be evaluated using qualitative and quantitative methods. Outcomes of interest include (1) treatment fidelity, (2) treatment wait times, (3) change in adolescent symptoms and parent/caregiver self-efficacy, (4) change in provider readiness, attitudes, and confidence towards the intervention, and (5) the overall experience of the implementation of the intervention from the perspective of the provider teams, and participant families.DiscussionThe findings of this study will help to identify factors important to the acceptability and implementation of GSH-FBT in real-world clinical settings.Trial registrationThis study was first registered with clinicaltrials.gov (registration # NCT06851273) on February 12, 2025 (url: https://clinicaltrials.gov/study/NCT06851273?id=NCT06851273&rank=1).

Infectious animal diseases such as Foot-and-Mouth Disease, Bluetongue, Avian Influenza, and emerging zoonoses pose a major threat to animal and public health, food security, and trade. Effective disease surveillance is critical to early detect disease outbreaks, minimize health risks, and economic impact. Surveillance combines systematic data collection, analysis, and reporting, with passive surveillance playing an important role in the Netherlands. Farmers and veterinarians are legally required to report notifiable diseases, while voluntary reporting through the veterinary helpdesk and postmortem examinations at Royal GD provides additional signals. This dissertation provides the first scientific evaluation of the Dutch passive surveillance system. Four key objectives were addressed: assessing representativeness of collected data, evaluating its contribution to disease detection, determining its ability to detect changes in animal health status, and identifying motivations and barriers for participation. Findings showed that data coverage generally matched farm, however certain regions showed over- or underrepresentation. Passive surveillance proved vital in detecting Highly Pathogenic Avian Influenza in poultry, while also showing potential in identifying emerging or poorly defined diseases through patterns of excess mortality in cattle. Farmers and veterinarians demonstrated willingness to engage, with trust and farmer-veterinarian relationship as important factors. The study concludes that Dutch surveillance is effective but can be strengthened. Improvements include clearer definitions of excess mortality, better understanding of sector differences, and maintaining trust through transparent communication. Future developments, including syndromic surveillance and Precision Livestock Farming, can further enhance early detection and safeguard both animal and public health.

AAC Malcolm is a hulless, six-row spring barley ( Hordeum vulgare L.) cultivar adapted to the growing conditions of eastern Canada. It was evaluated in the Ontario Barley Orthogonal Test (2018–2020) before being registered in Canada in 2023. AAC Malcolm is high-yielding and good lodging resistance. AAC Malcolm is recommended for commercial production in eastern Canada.

Le Réseau canadien de surveillance des anomalies congénitales a été créé en 2002 pour combler les lacunes dans la surveillance nationale des anomalies congénitales et pour soutenir la viabilité de systèmes de surveillance des anomalies congénitales de haute qualité et fondés sur la population dans les provinces et les territoires. Cet article présente les méthodologies de chaque système local de surveillance des anomalies congénitales, en faisant ressortir les similitudes et les divergences, afin de contribuer à l’amélioration des efforts de surveillance des anomalies congénitales à l’échelle nationale.

Canada has one of the highest incidences of colorectal cancer (CRC) worldwide. CRC screening improves CRC outcomes and is cost-effective. This study compares Canadian CRC screening programs using essential elements of an organized screening program outlined by the International Agency for Research on Cancer (IARC). We collaborated with the Cancer Screening in 5 continents (CanScreen5) program, an initiative of IARC. Standardized data collection forms were sent to representatives of provincial and territorial CRC screening programs. Twenty-five questions were selected to reflect IARC's essential elements of an organized screening program. We performed a qualitative analysis of Canada's CRC screening programs and compared programs within Canada and internationally. CRC screening programs exist in 10 provinces and 2 territories. None of the programs in Canada met all the essential criteria of an organized screening program outlined by IARC. Three programs do not send invitations to participate in screening. Among those that do, 4 programs do not include a stool test kit in the invitations. While all provinces met the essential elements for leadership, governance, finance, and access to essential services, there was more heterogeneity in the domains of service delivery as well as information systems and quality assurance. There is considerable heterogeneity in the design of CRC screening programs in Canada and worldwide. Programs should strive to meet all the essential IARC criteria for organized screening if local resources allow, such as issuing invitations and implementing systems to track and compare outcomes to maximize screening program quality, effectiveness, and impact.

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.