Data Equity

Let's Think About

Data Equity

Sharon Welburn, PhD

Learning Objectives

• Define data equity
• Identify key sources of bias and inequity across the data life cycle (collection, analysis, interpretation, dissemination)
• Evaluate a real-world example of inequitable data practices and propose improvements
• Discuss strategies to promote equity in public health data systems

Interactive question

Missing Data in Health Data

• Use of administrative data has limitations.
• How are race/ethnicity data collected?
• What's the extent of missing data? How do we handle missing data?

Missing Data in Disease Surveillance

• Differences by town
• Variability over time
• How does missing data affect disease surveillance?
• Partially due to reluctance to provide race/ethnicity information. Why?
• Partially due to staffing. Why?
• Other contributors?

Fairness in CVD Risk Prediction

• Most studies examining CVD risk are in US white / European white individuals. How does this affect our risk predicting algorithms?
• What about other SES determinants?

Fairness in CVD Risk Prediction

• Most studies examining CVD risk are in US white / European white individuals. How does this affect our risk predicting algorithms?
• What about other SES determinants?

The Digital Divide

What digital data gaps can you see?

Set of principles and practices to guide anyone who works with data ... through a lens of justice, equity, and inclusivity

Data.org

Sharing findings from the data, framed in an accessible and relevant way, to appropriate audiences.

Develop a mission/purpose for data integration, understand the local context & indentify appropriate variables.

Using data to develop findings, interpretations, and conclusions, while also understanding the potential for biases in this stage.

Process of gathering information to inform research, program, or policy. Includes primary & secondary data collection.

Practices associated with who can securely obtain, view, or use data and for what purpose.

Equitable Actions Through the Data Life Cycle...There are opportunities for improving data equity throughout the entire data life cycle. Use these to consider how data equity could be achieved.

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Data Equity Game

• Each group will be given a scenario + a list of questions to consider.
• Take 15 minutes to analyze the information and answer the questions.
• Afterwards, choose a spokesperson to share out.
• In the share out, describe your scenario and your responses to the questions.

Sharing findings from the data, framed in an accessible and relevant way, to appropriate audiences.

Develop a mission/purpose for data integration, understand the local context & indentify appropriate variables.

Using data to develop findings, interpretations, and conclusions, while also understanding the potential for biases in this stage.

Process of gathering information to inform research, program, or policy. Includes primary & secondary data collection.

Practices associated with who can securely obtain, view, or use data and for what purpose.

Equitable Actions Through the Data Life Cycle...There are opportunities for improving data equity throughout the entire data life cycle. Use these to consider how data equity could be achieved.

Interactive question

Questions?