Integrating Data Science and Ethnography for Critically Applied Rapid Surveillance of Emerging Health Inequalities

Public health surveillance is a basic function of an effective modern state. Yet this surveillance is a deeply political phenomenon; the specifics of which data are collected, about whom, and the speed and format of how they are aggregated and disseminated are shaped by social and political priorities. Surveillance, then, can serve as a powerful tool for tracking social inequalities, or alternatively, a mechanism by which they remain invisible. Here, using a series of case studies, I develop and implement a model of public health surveillance that is rapid, critically-applied, mixed-methods in nature, and especially focused on the measurement of emerging or shifting health inequalities. A key aim is the advancement of new techniques to fill data gaps, especially by drawing on mixed methods research. While public health surveillance has traditionally been nearly synonymous with quantitative methods, here I seek to combine data science approaches with qualitative social science techniques, especially ethnography. A nuanced data science approach especially drawing on novel data sources, can offer robust prediction in a real-time fashion, which can further yield detailed insights about emerging health disparities. Similarly, ethnography represents a manner of accessing common-sense, on-the-ground logics around social dynamics and health disparities that can often be surprisingly difficult to characterize with traditional survey methodologies and other quantitative approaches. This model of public health surveillance is also critically-applied, meaning grounded in social theory (but not fully subservient to it) and ultimately oriented towards affecting real change. In this fashion, critically-applied surveillance work can be thought of as a first step in assessing, interrogating, and challenging the structural violence that pervades modern societies. In sum, in the 14 case studies contained within this body of work, I develop and implement a critically-applied and inequalities-oriented, heavily mixed-methods approach to public health surveillance, which can be employed widely to improve inequality monitoring across many areas of public health and clinical practice. This model is applied here especially to the study of shifting trends in the North American overdose crisis, studying clandestine, illicit, and complex dynamics that are difficult to assess with traditional approaches