Using Data to Advance Racial Equity: Lessons from the DREAM Learning Community

This post has been updated. It was originally published on Sept. 3, 2024.

November 07, 2024 | Sowmya Kuruganti

In 2022, ASTHO, with support from CDC’s Division of Reproductive Health, launched the Data Road Map for Racial Equity Advancement in Maternal and Child Health (DREAM) Learning Community. There were 11 state health department teams—made up of leadership, programmatic, and equity staff—that participated in this project, with the goal of building internal capacity to address issues of racial equity through a data-to-action cycle. The project’s key lessons offer strategies for state and territorial health agencies (S/THAs) to support ASTHO’s commitment to achieving optimal health for all by addressing the impacts of structural racism and driving systemic (or groundwater-level) change.

Highlighting Underrepresented Communities in Data

Disaggregating data by race and ethnicity is often listed as a key step in identifying and addressing the root causes of health inequities. However, states may have limited or no quantitative data about certain populations of interest, due to missing or inaccurate race and ethnicity data or biases present during data collection. Although quantitative analysis methods can help to examine this data, their assumptions or practices may be counterproductive from an equity standpoint. For instance, using proxy measures that rely on similarities between populations may be insufficient when examining health disparities based on their differences. Other techniques recommended for understanding small numbers and populations (e.g., rolling averages, synthetic estimates, and small area estimates) come with their own considerations, such as being aware of how policy and structural changes over time can influence data and ensuring estimates are accurate and reflect the experiences of the population.

Alternatively, qualitative methods such as key informant interviews can address equity concerns by preventing data erasure of groups that are too small to be represented within quantitative analysis. By providing the detailed perspectives of a few people in their own voices, qualitative data provides contextual depth when it is not possible to increase breadth of data collection. Furthermore, mixed methods use both quantitative and qualitative methods to draw inferences based on their combined strengths.

Regardless of the data practices at hand, DREAM Learning Community participants agree that agencies must consult communities when identifying and addressing the inequities they face. Truly meaningful data equity goes a step further and centers processes led by communities and people with lived experience.

Rebuilding Relationships with Communities

DREAM Learning Community participants all agreed that increasing community engagement is a key step in their goals to advance racial equity. While historic inequities and challenges in fostering strong interactions between S/THAs and communities can make this difficult, S/THAs can implement a few practices to help rebuild these relationships:

• Acknowledge when they are not the right communicator or messenger, identify a partner who is, and consult and codesign with them.
• Engage communities and people with lived experience early and continuously throughout the data-to-action cycle (e.g., ask what they want to learn from data collection, share data, provide time to process information, and ask if data aligns with or represents what they know and experience).
• Implement solutions based on community input and feedback, rather than repeatedly revisiting questions about what issues are burdening communities.

Each suggestion acknowledges that those who are most impacted by a problem are best equipped to design and implement effective solutions based on their lived experiences. When structural racism is a fundamental cause of health inequities, the ultimate solution lies in S/THAs shifting power back to these communities.

Embracing Structural Change for Community-Led Efforts

Advancing racial equity by empowering communities and people with lived experience requires structural change in all lanes of work. This includes:

• Prioritizing long-term funding to uphold and build upon current equity infrastructure. For instance, to shift power and build relationships with communities, S/THAs must build sustained investment into their infrastructure instead of relying on special grants.
• Equipping communities to leverage and grow existing assets, skill sets, and resident knowledge. The TIER Community Evaluator model at Tufts University exemplifies how to engage communities at all stages of a data-to-action cycle while investing in them through training, mentorship, and adequate compensation.
• Encouraging de-siloing among government entities, organizations, and communities. The BARHII framework illustrates that addressing the root causes of inequities lies in strategic partnerships and systemic structural changes, which should ensure that policies and practices can support the capacity of communities to advocate for needed solutions.
• Leveraging supports, such as ASTHO’s STAR Center, to strengthen administrative and workforce capacity as well as cultural shifts and change management to promote racial equity work. Building long-term relationships with communities requires investment, commitment, and trust building, despite administrative, workforce, and financial barriers.