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.

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other Backward Classes.” This method obscures the diversity of experiences, indicators of well-being, and health outcomes between castes, tribes, and other communities in the “scheduled” category. This study analyzes data on 699,686 women from 4,260 castes, tribes and communities in the 2015-2016 Demographic and Health Survey of India to: (1) examine the diversity within and overlap between general, government-defined community categories in both wealth, infant mortality, and education, and (2) analyze how infant mortality is related to community category membership and socioeconomic status (measured using highest level of education and household wealth). While there are significant differences between general, government-defined community categories (e.g., scheduled caste, backward class) in both wealth and infant mortality, the vast majority of variation between communities occurs within these categories. Moreover, when other socioeconomic factors like wealth and education are taken into account, the difference between general, government-defined categories reduces or disappears. These findings suggest that focusing on measures of education and wealth at the household level, rather than general caste categories, may more accurately target those individuals and households most at risk for poor health outcomes. Further research is needed to explain the mechanisms by which discrimination affects health in these populations, and to identify sources of resilience, which may inform more effective policies.