A causal inference framework for analyzing large administrative healthcare databases with a focus on multiple sclerosis

Provincial health authorities routinely collect patient information on a massive scale, but health researchers face the challenge of exploring cause-and-effect relationships using these non-randomized population-based data sources. Machine learning methods are increasingly used to analyze these large datasets, although they do not inherently take causal structures (i.e., how the variables affect each other) into consideration and may lead to less-than-optimal or even erroneous conclusions.

Health researchers urgently need new big-data analytic methods that are geared towards extracting causal explanations rather than merely increasing prediction accuracy. This project will develop innovative biostatistical methodologies that will better equip health researchers to infer causation from big-data sources.

As a motivating problem, with a bias reduction goal in mind, Dr. Karim will investigate potential benefits of disease-modifying drugs in multiple sclerosis patients 50 years of age or older. Ultimately, this methodological development will enable health researchers to convert information into actionable knowledge for other common, chronic conditions, leading to cost-effective medical decision making and improving the health of Canadians.

Improving outcomes in the treatment of eating disorders: Self-compassion in patients, families and clinicians

Self-compassion refers to an individual's capacity to be mindful, recognize our common humanity in times of hardship, and to practice self-kindness in times of suffering. It has been shown to be beneficial in working with individuals with chronic health conditions, such as HIV/AIDS, diabetes, and eating disorders. However, many individuals have difficulty with this skill and experience barriers to being self-compassionate. 

This research will help us understand how self-compassion can benefit individuals with chronic health conditions. We will interview patients in treatment, recovered patients, and clinicians about their experiences with self-compassion. Their responses will be used to design an intervention that aims to increase capacity of individuals with chronic health conditions to benefit from self-compassion. We will also explore self-compassion in family members and clinicians to increase understanding of what gets in the way of a collaborative stance, shown to be most helpful to individuals with chronic health conditions. We will share results of this research with clinicians, patients and families locally at education days and provincial video conferences, and nationally and internationally through workshops, conference presentations and publications.