This webinar is part of the Power of Population Data Science Series
Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageous because it does not require individual-level data to be shared across jurisdictional boundaries.
The Public Health Agency of Canada (PHAC) established the Canadian Chronic Disease Surveillance System (CCDSS) in 2009 to facilitate standardized, national estimates of chronic disease prevalence, incidence, and outcomes. It draws upon expertise from PHAC epidemiologists and analysts, provincial/territorial experts in administrative data, and academic researchers. The distributed model used by the CCDSS enables efficient sharing of aggregate data for provinces and territories to produce reports and open data resources.
Topics that will be covered in this session include the process, structure, benefits, and challenges of PHAC’s distributed model for chronic disease surveillance using linked administrative data.
View original IJPDS article at: https://ijpds.org/article/view/433
Watch recorded presentation below.
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Speaker
Lisa Lix, PhD is a Professor in the Department of Community Health Sciences and Tier I Canada Research Chair in Methods for Electronic Health Data Quality at the University of Manitoba. She is also Director of the Data Science Platform within the George and Fay Yee Centre for Healthcare Innovation.
Dr. Lix’s areas of research expertise include statistical methods to evaluate the quality of administrative health databases, the analysis of repeated measures and longitudinal data, and statistical methods for patient-reported outcomes. She collaborates widely on projects about population health and the association between chronic disease, prescription drug use, and quality of life.
She is former co-chair of the CCDSS Science Committee and currently co-chairs the CCDSS Data Quality Working Group
