Ten years ago, the Canadian Partnership for Tomorrow’s Health began recruiting Canadian citizens to participate in what is now one of Canada’s largest population health studies. Today more than 330,000 citizens between the ages of 30 and 74 years of age are voluntarily sharing their detailed health and lifestyle information over several decades. The contributions made by these participants enable researchers to study the environmental and biological causes of disease, thereby improving the health of Canadians.
But how do researchers disentangle such enormous amounts of data to make important discoveries?
Longitudinal analysis and multi-level modeling are valuable tools that researchers can use to explore and learn from this kind of population data. Data collected on the same individuals over time provides unique benefits and opportunities for researchers who want to learn more about the inner workings of societal health. With the use of longitudinal analysis and multi-level modelling such studies allow researchers to investigate how outcomes change over time, and how outcomes occurring at one time point relate to information collected in the past. This is a powerful way we can make connections across different domains of life such as health, education, environment, and significant societal events.
We can also use these techniques to explore dynamic relationships both within and between individuals. For example, we may examine how a person’s involvement in certain kinds of work or environmental settings influences their transition and trajectories of healthy aging, and how individuals from different socio-economic backgrounds differ in this association.
With answers provided through the application of longitudinal analysis and multi-level modeling, we can make informed decisions and address the evolving needs of new health policies to support improved health for today’s population and those of the future.
The ability to address these questions by analyzing longitudinal health data using multilevel models are key components of what the PHDA 05 Longitudinal Analysis and Multi-level Modeling of Population Health Data course offers.
Laura Holder is a Senior Analyst with the Analytics and Special Projects team at the Western Office of the Canadian Institute for Health Information (CIHI) and is the instructor for the Longitudinal Analysis and Multi-level Modeling of Population Health Data (PHDA 05) course being offered this May.
“I am excited to share my knowledge of an analytic skillset that I know is an important and practical tool for researchers. Using multilevel models for longitudinal analysis opens the door to studying all kinds of important research questions—the course emphasizes hands-on training so you’ll be confident applying the methods in your own work afterwards.”