The COVID-19 pandemic has impacted our society in innumerable ways to the extent that no aspect of normal societal life has been spared the rigour of upheaval. This wide-spread change has impacted many individuals, especially those possessing considerable life challenges during pre-pandemic times.
For example prior to the COVID-19 pandemic, loneliness and social isolation were so prevalent across Europe, the USA, and China that it was described as a “behavioral epidemic” (Jeste et al., 2020). The situation has only worsened with the restrictions imposed to contain viral spread. More recently a study in BMC Geriatrics on older adults found a positive association between loneliness and emergency department use. The study evaluated the relationship between loneliness and Emergency room use over a 12-month period using follow-up survey data from the Canadian Longitudinal Study on Aging (CLSA). As Emergency departments are commonly the first point of contact into a hospital setting for older adults, this longitudinal study provided valuable information specific to types of care needed to optimize health service support for older age patients. By knowing more about how loneliness is associated with health conditions and morbidity, further work can be done to address questions of concern for both patients and the practitioners who serve them.
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 isolation levels influence their transition and trajectories of health decline or healthy aging, and how men and women differ in this association. In this way, longitudinal analysis and multilevel modeling techniques allow researchers to look at current and past conditions to 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 as part of a micro credential program in Population Health Data Analysis. The program has been designed to provide you with flexible, accessible, and practice-based learning that will take your career to the next level. Courses are offered over a twelve-week period and structured so that you can upgrade your skills and work at the same time.