We’re getting older, how can we read the signs for better health?

Date posted: 
Wednesday, April 3, 2019

 


According to The Canadian Longitudinal Study on Aging (CLSA), “for the first time in our history the number of Canadians over 65 has exceeded the number of those aged 14 and under. In fact, by 2031, one in four Canadians will be 65 or older”. This reflects positively on our countries good standard of living and high quality health care. In fact, we are living longer than ever; life expectancy in Canada today is 80 years for men and 84 years for women. These demographic milestones give us cause to both celebrate and ponder the future of our society. How can we gather evidence from our past successes to understand what to expect for the future of Canada’s population? How can we better understand why some individuals experience healthy aging while others do not? How can we detangle the complex interplay between various determinants of health over time?

Longitudinal analysis and multilevel modeling are invaluable tools for today’s health and social science researchers who want to address these kinds of questions. These methods allow researchers to study how outcomes change over time and what predicts these changes. We can use these techniques to explore dynamic relationships both within and between individuals. For example, we may examine how a person’s varying stress levels influence their transition and trajectories of healthy aging, and how men and women differ in this association. In this way, longitudinal analysis and multilevel modeling techniques allow researchers to look to the past to understand today’s outcomes and help predict outcomes in the future.

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.

Learn more about this topic or enrol in our upcoming course.

 

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.”  

 

 


Page last revised: April 3, 2019