In Data We Trust: Challenges and Opportunities Facing Data-Driven Policymaking in Public Health

National management of public health crises suffers from our inability to collect and utilize data effectively in developing response policies. The reality became painfully apparent with the ongoing coronavirus pandemic but is mirrored in past crises like the U.S. opioid epidemic. Where do the gaps between data and data-driven policymaking in healthcare arise? In this thesis, using these two crises as case studies, I characterize the relationships between data, public health, and policymaking and identify pitfalls that have stymied their effective integration.
I synthesize academic literature, policy memos, and news media to describe how the federal government struggles to design legislation for crisis mitigation due to its reliance on hierarchical information pathways, which are misaligned with crisis geographies and challenged by temporal strain. I instead promote a hybrid data management strategy combining current infrastructure with proxy approaches from literature. At the local level, I place secondary sources in conversation with expert testimony to tease out data’s “public life,” and I argue that creating lasting local change will require governments to cultivate an increased public understanding of data. Across government strata, I characterize the confusion created in policymaking from the inclusion of racial data and suggest we rethink our concept of race in healthcare.
I propose an original framework for better data-driven policymaking in public health through opening bidirectional lines of communication, equipping healthcare providers and citizens to understand data at the local level, and reevaluating our understanding of traditional statistics (mortality rates and racial categorization) at all levels of government. This framework shifts the conversation surrounding data in public health policymaking from a tone of reactiveness to one of proactive readiness and, ultimately, helps save lives.