Development of a prognostic prediction model to estimate the risk of multiple chronic diseases

12:30 to 1:30pm EST (9:30am to 10:30am PST) | All sessions will be delivered live and online via the Gotowebinar system.

This webinar is part of the Power of Population Data Science Series

Methodological complexity may be a barrier to developing tools for multimorbidity risk prediction. Prognostic prediction models estimate patients’ risk of disease incidence, typically for a single disease independent of other chronic diseases incidence. When estimating the risk of multiple chronic diseases, these models fail to describe the dependence between the incidence of multiple diseases leading to inaccurate perceptions of risk.

This webinar presents a copula-based model to estimate the risk of the incidence of multiple chronic diseases, while accounting for the dependence that exists between the incidence of these diseases. The research team used the CPCSSN database: a pan-Canadian collection of de-identified primary care electronic medical records from nearly 2 million patients that have been made available for research and surveillance. The webinar will present this method and discuss its advantages and limitations.

View original IJPDS article at:

View recorded presentation below.

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Jason Black Jason Black is an epidemiologist/biostatistician who has a special research interest in real-world health records analysis. He has worked extensively with the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database with a focus on predicting chronic disease incidence among primary care patients. He has worked as a data analyst in both government and academic settings, where he acted as the statistical lead on various projects using health administrative and electronic medical record data.

Jaky KueperJaky Kueper is a combined PhD candidate in Epidemiology and Computer Science at Western University. Her research integrates epidemiology and computer science, especially machine learning, to develop, apply, and evaluate data-analytic methods for supporting care decisions in primary health care settings. She is particularly interested in using this work to promote health equity for vulnerable and complex populations, such as those with multimorbidity. Her doctoral work is supported by a CIHR CGS-D and she is a recent graduate of the pan-Canadian Transdisciplinary Understanding and Training on Research in Primary Health Care (TUTOR-PHC) program.


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