Data are not neutral.
They are shaped by the systems that produce them.
Social Determinants of Data (SDOD) are the systemic, institutional, political, cultural, and social factors that shape how data is collected, produced, categorized, recorded, interpreted, and made missing—affecting whose realities are represented, and whose are rendered invisible.
Defining the Social Determinants of Data
Hi! I’m Luzita Francis
I’m a researcher, author, and systems thinker working at the intersection of public health, data, and institutional practice. I created the Social Determinants of Data (SDOD)—a framework for understanding how the systems that produce data shape whose realities get counted, and whose get erased.
Framework introduced by Luzita Francis (2025)
Social Determinants of Data (SDOD) are the systemic, institutional, political, cultural, and social factors that shape how data is collected, produced, categorized, recorded, interpreted and made missing—affecting whose realities are represented, and whose are rendered invisible.
The Social Determinants of Data (SDOD) is a framework that examines the systemic, institutional, and social forces that shape the quality, completeness, and representation of data. This framework draws a direct analogy to the Social Determinants of Health (SDOH): just as living conditions shape health outcomes, the structural “circuitry” of our data systems determines how we are counted—or erased—in public records.
The Core Premise: Data is Not Neutral
In a purely technical model, data collection would be objective and consistent. SDOD challenges this assumption by showing that “data disparities” reflect underlying social, institutional, and structural inequalities. When communities are rendered invisible in data, they become invisible in policy—leading to misdirected resources and preventable harm.
The SDOD Pillars of Data Equity
Addressing inequities in data requires intervention across six core domains:
- Governance: Institutional accountability, oversight, and feedback loops.
- Policy: Mandates that define whether demographic data is required or optional.
- Funding: Investment in staffing, training, and data infrastructure.
- Technology (Infrastructure and Data Standards): Design and logic of systems such as Electronic Death Registration Systems (EDRS)
- Community: Trust, language, and the social conditions shaping data disclosure.
- Practices: Training, workflows, and cultural responsiveness of the front-line workforce.
Power operates across all SDOD domains, shaping how categories are defined, whose identities are recognized, and whose lives are rendered visible or invisible in data systems.
Together, these domains shape not only data quality, but whose lives are made visible—and whose are systematically overlooked.
SDOD is a framework for researchers, public health professionals, and advocates working to build more equitable data systems.
Data quality is not just a technical issue. It is shaped by social and institutional conditions long before analysis occurs.
