Health Equity Data Commitment and Principles

Commitment to Equity in Data

The Public Health Institute at Denver Health aspires to present data humbly, recognizing numbers never tell the whole story. We strive to work with individuals and communities to learn and share their stories to improve collective understanding. Knowing that people across life circumstances have inequitable opportunities to achieve optimal health, we commit to pair numbers and stories to inform policy and systems change to improve health for all.

Data Equity Principles

  1. We recognize that systemic, social and economic factors – racism being the largest – impact health.
  2. We understand that health inequities are worse for communities who experience injustice.
  3. When we talk about people, we use a strengths based approach; when we talk about inequities we use a systems level approach. We do this to avoid judgment, blame, or marginalization of individuals or communities.
  4. We strive to include the lived experience and traditional knowledge of community members into population health analysis, because the community health experience is complex.
  5. We proactively engage communities to identify inequities, interpret findings, shape our work, and take action toward improving health.

Actions we can take to operationalize Data Equity Principles

  • Identify ways in which systemic racism affects health outcomes
  • Recognize and challenge how personal biases affect how we analyze and report on data
  • Disaggregate data by race, ethnicity, gender, nativity, ancestry, income, location and other factors to the extent possible
  • Avoid analyses that privilege a population as being “normal” or “desirable” compared to others
  • Ensure data visualizations and graphics do not stigmatize
  • Support the collection and use of new data to fill gaps in knowledge about populations underrepresented by current methods
  • Identify and address important issues for which we lack data
  • Learn more about populations and health issues with which we are not currently familiar
  • Use first person language in written descriptions to acknowledge a person’s identity beyond their condition, ability, situation, or experiences
  • Share choices we make when analyzing data and be transparent about the limitations of our analyses
  • Prioritize establishing and maintaining trust and safety of people in our community when engaging with data
  • Work with communities to collect feedback on data collected and guide decision making about future data collection efforts
  • Commit to share data with communities affected by challenges in order to share analysis, reporting and ownership of findings
  • Recognize that data can have a powerful effect on people and commit to create the time and space to process and receive feedback
  • Work to tell a compelling story through data so that we, in partnership with community, can take action with the data