Achieving Health Equity Through Equitable Data Systems
In recent years, especially throughout the COVID-19 pandemic and its devastating impact on communities, the persistence of health inequities has been highlighted. Data are the building blocks to help us better understand these health inequities.
Too often, data falls short in representing the big picture because equity is not emphasized in the data collection process. Partnering with communities in defining data questions, collection, analysis and use are needed steps to transform our public health data system.
Researchers and public health practitioners are recognizing their power to shift approaches to health inequities by centering these conversations on community-level participation and voices. Acknowledging this power, the CDC Foundation, with insights from CDC, has developed Principles for Using Public Health Data to Drive Equity to create a more equitable data life cycle.
Public health practitioners and policy decision-makers rely on population data to identify health inequities, allocate financial and human resources, enact laws and regulations and establish public services. It is essential that this data gathering process, from collection and reporting to analysis, dissemination and stewardship, is grounded in equity principles. Achieving health equity requires every data practitioner, including funders, program managers, community partners and state leaders, to effectively harness the full power of data in a way that embraces innovation, inclusion and community voice.
The CDC Foundation’s data equity principles acknowledge the power data has in shaping community landscapes and encourage a shift in mindset to embed a focus on equity throughout the entire data life cycle.
The data equity principles represent a synthesis and reflection of the existing information in the public health field on equitable data systems. For example, the principles draw on the recommendations and lessons from the National Commission to Transform Public Health Data Systems, convened by the Robert Wood Johnson Foundation.
The principles aim to help end-users build data systems that more robustly focus on equity, prioritize deeper community engagement and connect the social and economic factors that impact health. The five data equity principles are:
- Recognize and define systemic factors: Recognize and define systemic, social and economic factors that affect individual health outcomes and communities’ ability to thrive.
- Use equity-mindedness as the guide for language and action: Use equity-mindedness as the guide for language and action in a continual process of learning, disaggregating data and questioning assumptions about relevance and effectiveness.
- Allow for cultural modification: Proactively include participants from the communities of interest in research and program design to allow for cultural modifications to standard data collection tools, analysis and sharing.
- Create shared data agreements: Collaborate with agencies and the community to generate a shared data development agenda ensuring a plan for data completeness, access and prioritized use to answer high-interest questions.
- Facilitate data sovereignty: Facilitate data sovereignty by paving the way for communities to govern the collection, ownership, dissemination and application of their data.
Historically, public health agencies and researchers have collected data to better understand inequities and barriers to health but without the consistent inclusion of the communities on whom the work is focused. The accountability of data systems to the communities whose health and wellbeing they seek to support is central to each of the data equity principles.
The goal is for communities who have been historically excluded from shaping their population narratives to exercise their agency and to have access to the data to use it to improve their daily lives. When each data user shifts their mindset to be more equity-focused and inclusive as they collect, analyze and manage community data, it allows communities to contribute to each step, including the interpretation of the data and the prioritization of its use.
Equitable data practices create the ability for government, local and private organizations to work with community partners to collaboratively design plans, identify barriers and community assets and dismantle health disparities.
Moving toward more equitable data practices happens in various spheres, such as research and program management, across sectors such as social services, philanthropy and public health, and across organizational levels, such as local, state and federal government. By providing ideas for how different actors can apply the data equity principles in their data work, the CDC Foundation hopes to showcase the many ways data can be effectively used to foster health equity.
