TI Best Evidence Webinar
TITLE: Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies
ABOUT THE TOPIC: Can a dramatic reduction in the incidence of childhood asthma be explained by falling antibiotic use in infancy? We present data from population, prospective cohort and microbiota studies that support that hypothesis. We’ll discuss methods, their interpretation and our view of next steps in research and translation.
PRESENTER: Professor David Patrick is an Infectious Disease Physician and Epidemiologist, Director of Research at the British Columbia Centre for Disease Control and Professor of Population and Public Health at UBC. His interest is in fostering interdisciplinary approaches to the control of emerging infectious diseases in populations. His current focus is on tracking and controlling antimicrobial resistance and on the relationship between antibiotic use and atopic disease at population level. During the pandemic, he has been chairing BC’s COVID-19 Strategic Research Advisory Committee and working on several fronts to advance the research response in BC.
DATE: Wednesday, January 13th, 2021
TIME: 19:00 – 20:15 Pacific Standard Time PST [UTC -7 convert to your local time]
CME CREDITS: MainPro+/MOC Section 1 credits: 1.0. You must register, attend the webinar and complete the evaluation in order to receive your certificate.
By the end of this session, participants should be able to:
- Review what is known about the relationship between the developing immune system, the gut microbiota and childhood’s most prevalent chronic disease
- Understand the application of regression methods to exploring associations at population level
- Understand the application of analyses within a prospective cohort design to studying similar relationships at individual level
- Learn how study of the microbiota strengthens our understanding of the biological plausibility of an association between antibiotic use and atopic disease
- Discuss possible confounders and how they may be addressed methodologically
- Discuss approaches to further explore generalizability and causal implications of observed associations