This project is an investigation of the gene by environment (GxE) interactions’ effect on substance use outcomes among refugee communities. Substance use disorders (SUDs) are a major public health concern, affecting individuals and communities worldwide. The etiology of SUDs is complex, involving a combination of genetic, environmental, and social factors. In recent years, there has been growing interest in the role of gene by environment interactions in the development of SUDs, particularly in vulnerable populations such as refugees. Refugee populations are exposed to a range of environmental stressors that may interact with genetic factors to increase their risk of SUDs. However, a number of studies describe a “refugee paradox,” where despite having been exposed to risk factors that can lead to SUDs, they are less likely to develop SUDs. Understanding these gene by environment interactions in refugee communities is crucial for not only understanding this phenomenon, but developing effective prevention and treatment strategies for this population. This thesis aims to investigate the gene by environment interactions underlying substance use in refugee communities and to analyze different methods for gene by environment analyses, ultimately determining which method is best suited for this population.

Santé is an event planning company that aims to address the growing need for mental health support among university students. The company's focus is on creating events that are specifically designed to help students cope with stress. Santé's events offer a variety of activities and resources that cater to students' mental and emotional needs. From outdoor walks to movie night sessions, Santé's events aim to create a safe and welcoming space for students to de-stress and connect with others. With a team of experienced event planners, Santé is dedicated to providing high-quality events that promote mental wellness and help students navigate the challenges of university life.

Background: Creation and reuse of reliable clinical code sets could accelerate the use of EHR data for research. To support that vision, there is an imperative need for methodologically. driven, transparent and automatic approaches to create error-free clinical code sets. Objectives: Propose and evaluate an automatic, generalizable, and knowledge-based approach that uses as starting point a correct and complete knowledge base of ingredients (e.g., the US Drug Enforcement Administration Controlled Substance repository list includes fentanyl as an opioid) to create medication code sets (e.g., Abstral is an opioid medication with fentanyl as ingredient). Methods: Algorithms were written to convert lists of ingredients into medication code sets, where all the medications are codified in the RxNorm terminology, are active medications and have at least one ingredient from the ingredient list. Generalizability and accuracy of the methods was demonstrated by applying them to the discovery of opioid and anti-depressant medications. Results: Errors (39 (1.73%) and 13 (6.28%)), obsolete drugs (172 (7.61%) and 0 (0%)) and missing medications (1,587 (41.26%) and 1,456 (87.55%)) were found in publicly available opioid and antidepressant medication code sets, respectively. Conclusion: The proposed knowledge-based algorithms to discover correct, complete, and up to date ingredient-based medication code sets proved to be accurate and reusable. The resulting algorithms and code sets have been made publicly available for others to use.