To promote the health and well-being of all, especially people from marginalized groups, public health data systems need to provide information that empowers decision makers to address the complex and interconnected social factors that affect health. These factors continue to drive and perpetuate inequitable health for many. Historically marginalized groups continue to be underrepresented or completely absent from public health data. Communities that have always existed on the margins of society still do not have a voice or any real power within the current system. Health data sets are siloed, inconsistently classified, and not interoperable between different systems and sectors.
This isn’t an accident. Policies such as redlining and racialized residential segregation, private lending practices, and zoning regulations are just some of the root causes of inequities in public health. The result is a fractured, disconnected public health data system that cannot adequately account for all interests and fails too many people, particularly in times of public health crisis. Without disruption and transformation, these same dynamics will perpetuate the inequity we see today.
The public health data system consists of multiple actors, with varied connections and relationships across sectors. In the graphic below, we explain the current issues and causes, and displayed in a Prezi presentation for a more interactive look. The root causes influence relational issues, where unequal power dynamics, particularly between communities and actors responsible for data collection and governance, contribute to weaker connections and coordination within the system. For example, failure on the part of these actors to meaningfully engage or include communities and non-Western perspectives in their activities contributes to the low trust these communities have in the wider system and further discourages dialogue with system leaders. These relational dynamics result in inefficient policies such as inconsistent guidelines for collecting race and ethnicity data and insensitive data collection practices that are not appropriate for particular cultures or contexts.
Current System Dynamics
Among many acts to improve our current system, public health actors including health facility administrators and elected officials must work to transform the underlying structures of the public health data system. Public health data modernization efforts play an important role in this work, but an intentional focus on equity is required to ensure modernized data systems don’t just lead to new methods of perpetuating harm.
The Robert Wood Johnson Foundation is leading work on these structural reforms. Their transformative investment in 2021 funded a portfolio of 16 projects that focus on data governance, data and equity, innovative data analytics, and capacity building for data literacy. These projects center equity and concern for people’s well-being in discourse on health and how we define, collect, or share public health data. The foundation’s projects focus on structural changes in practices, policies, and the flow of resources; relational changes focused on shifting relationships, connections, and power dynamics within the current data system; and transformational changes to catalyze shifts in mental models and traditional narratives and public health data.
The Work of RWJF Funded Partners
RWJF’s investments will result in stronger connections and improve coordination between the public health sector and other sectors. The goal is to increase the level of community engagement within the public health data system and give communities more power over and access to their data. The funded projects are already demonstrating early signs of progress in reforming policies, promoting more effective practices, and redirecting resources toward a more equitable data system. Examples include the following:
- The University of New Mexico project, “Employing an Intersectionality Framework in Revising Office of Management and Budget Standards for Collecting Administrative Race and Ethnicity Data,” advocates for complete and consistent data for different groups, disaggregated by race and ethnicity and for considering intersectionality in data collection and analysis.
- In related work, The Leadership Education Fund advances state and federal policies for meaningful data disaggregation, increasing access to accurate and consistent disaggregated data that accurately capture community conditions and needs. Disaggregated data can enhance care delivery and improve health outcomes for different communities; this project supports research and advocacy to identify gaps and advance work to achieve racial equity.
- The University of Pennsylvania’s Actionable Intelligence for Social Policy is implementing data governance processes that center communities and their concerns. This work will enhance cross-sector data sharing and integration to transform data about individuals into action-oriented information that can be used to build stronger, healthier communities.
The current portfolio of grants will provide proof points on how to catalyze the transformation of the public health data ecosystem and generate broader momentum for change. In practice, this work will empower and meaningfully engage communities in all aspects of the data life cycle, to include governance, collection, analysis, and use. This approach will spur investment in and support the development of new skills among the public health workforce, and help establish modern, efficient, and interoperable data systems.
Special thanks to Drew Koleros, Divya Vohra, and our partners at Bowman Performance Consulting for their vision leadership, and implementation of the data ecosystems mapping.
