Recording of the Webinar Series: The Power of Population Data Science
Courtesy of Population Data BC
We are delighted to present the recordings from the brand new Population Data BC webinar series entitled ‘The Power of Population Data Science’. This series aims to highlight the value of data linkage and related data-intensive analytics by profiling some of the most recent articles that have been published right here in IJPDS, by national and international Population Data Scientists.
This webinar series will be of particular interest to researchers, analysts, health professionals and members of the public who are interested in learning more about Population Data Science and how work in this emerging field is making substantive contributions to informing population health-related policies for the betterment of our communities.
The world of rapidly expanding data has provided many new and challenging opportunities to address a myriad of issues facing human populations. Population Data Scientists see the potential social and individual benefits that can be realized through data-intensive analytics and collaborative work involving data linkage methods. Data linkage allows information on an individual from one data source to be linked to information on the same individual from another data source. Using the linked data makes it possible to gain a more comprehensive understanding than could be obtained from either data source individually.
Linked data used for secondary analysis often involves population-based, longitudinal data that was originally collected for another purpose. Linkage may take place across data sets in a single domain (e.g. health) or across domains (e.g. health, education, environment, and early childhood). This work can provide an unbiased picture of the entire population, is cost-effective, relative to other data collection mechanisms, and enables studies to be done that could not otherwise be performed.
The use of linked data to support better health outcomes exists across many research areas, for example:
- Analysing patient characteristics, treatment costs and outcomes of care to identify the most cost effective healthcare, thereby influencing provider behavior
- Applying advanced analytics to patient profiles (e.g., segmentation and predictive modeling) to identify individuals who would benefit from preventative care or lifestyle changes
- Disease profiling to identify predictive events and support prevention measures
The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance
January 9 2019
Lisa Lix, University of Manitoba, Canada
Multi-jurisdictional epidemiological research in Canada: Challenges and opportunities
December 5 2018
Amanda Butler, Simon Fraser University, Canada
Wayne Jones, Centre for Applied Research in Mental Health and Addiction, Canada
Visualising Logistic Regression: Application of coloring book technique
November 6 2018
Dr. Andriy Koval, University of Central Florida, USA
Probabilistic linkage of national immunisation and state-based health records for a cohort of 1.9 million births to evaluate Australia’s childhood immunisation program
October 25 2018
Dr. Heather F Gidding, University of New South Wales, Australia
Dr. Hannah Moore, Telethon Kids Institute, Australia
Future Directions in Probabilistic Linkage
October 11 2018
Dr. James Doidge, University College London, UK
Dr. Harvey Goldstein, University of Bristol and University College London, UK
Are you a Population Data Scientist?
September 27 2018
Dr. Kim McGrail, University of British Columbia, Canada
Dr. Kerina Jones, Swansea University, UK