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Lyme disease cases reported in seven Canadian provinces from 2009 to 2019 through the Lyme Disease Enhanced Surveillance System are described herein by demographic, geography, time and season. The proportion of males was greater than females. Bimodal peaks in incidence were observed in children and older adults (≥60 years of age) for all clinical signs except cardiac manifestations, which were more evenly distributed across age groups. Proportions of disease stages varied between provinces: Atlantic provinces reported mainly early Lyme disease, while Ontario reported equal proportions of early and late-stage Lyme disease. Early Lyme disease cases were mainly reported between May through November, whereas late Lyme disease were reported in December through April. Increased awareness over time may have contributed to a decrease in the proportion of cases reporting late disseminated Lyme disease. These analyses help better describe clinical features of reported Lyme disease cases in Canada. [ABSTRACT FROM AUTHOR]

Background: With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like. Objective: To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them. Design: 12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques. Setting: Virtual sessions. Participants: Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders Results: The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants. Limitations: Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective. Conclusions: These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped. [ABSTRACT FROM AUTHOR]

Introduction: Inadequate pregnancy monitoring for pregnant migrant women without medical insurance (PMWMI) exposes them to severe complications during childbirth and consequences for the health of their child (e.g. preterm delivery, low birth weight, etc.). This scoping review aims to identify existing interventions globally to improve access to perinatal care for PMWMI. It will also highlight the strengths, weaknesses as well as the costs of these interventions. Materials and methods: The methodological framework developed by Arksey & O'Malley (2005) will be used. An electronic search of studies from 2000 to 2021, published in French or English, will be conducted in 12 databases. Publication in Websites of non-governmental organizations working on migrant women without medical insurance issues will be also searched. All articles related to perinatal follow-up and care of PMWMIs, regardless of design, will be included. Editorial comments will be excluded. Outcomes of interest will focus on the impacts, strengths, weaknesses, and cost of interventions. Selection of articles and data extraction will be done by two independent researchers following the Tricco et al. (2018) reporting guide. Finally, a deliberative workshop with experts will allow to identify the most promising and appropriate interventions that can facilitate access to perinatal services by PMWMIs in the Quebec province of Canada. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada's provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs. [ABSTRACT FROM AUTHOR]