Why This Work
Before my work in data and research, I spent years working as a medical Spanish interpreter.
In that role, I was often present at the point where information entered the system—during patient intake, clinical encounters, and documentation. I saw firsthand how language barriers, time constraints, assumptions, and institutional practices shaped what was recorded—and what was not.
Those experiences stayed with me.
When I later encountered large-scale data gaps in my research, I recognized similar patterns at a different scale. What I had seen at the individual level—small moments of omission, assumption, or constraint—was reflected in the data itself.
The issue was not just missing data.
It was how the system produced missingness.
Luzita
Francis
I am a researcher and systems thinker working at the intersection of public health, data, and institutional practice.
I hold dual master’s degrees from the University of North Carolina at Chapel Hill: a Master of Science in Information Science (MSIS) and a Master of Healthcare Administration (MHA). My work focuses on how data systems operate in practice—particularly how institutional processes shape data quality, completeness, and representation.
My master’s research examined COVID-19 mortality data in North Carolina, with a focus on missing ethnicity data in death records. What initially appeared to be a data quality issue revealed something more complex: patterns of missingness that were not random, but structured.
This work led to the development of the Social Determinants of Data (SDOD)—a framework for understanding how systemic, institutional, and social forces shape the production of data.
The Social Determinants of Data
The Social Determinants of Data (SDOD) framework emerged from this intersection of lived experience and research.
It builds on the idea that data is not neutral. Data is produced through systems—systems shaped by policy, technology, training, governance, and social context.
Understanding data, therefore, requires understanding those systems.
This framework is intended to provide a way to:
- support more equitable and accurate data practices
- identify structural sources of data gaps
- connect data quality to institutional conditions
Current Work
I continue to explore the applications of SDOD across public health, policy, and data systems.
I am currently preparing a peer-reviewed article introducing the SDOD framework for publication, and developing this website as an open resource for researchers, public health professionals, and practitioners working on data equity.
I am also interested in opportunities to apply SDOD in practice — including consulting, collaboration, and speaking on data quality, health equity, and the structural conditions that shape public health data systems.
Get in touch.
I’d love to hear your thoughts.