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What's Missing from Data Modernization? A Focus on Structural Racism.

Jamila M Porter1, Brian C Castrucci1, Jacquelynn Y Orr2

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Health Equity
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Summary
This summary is machine-generated.

Public health data modernization must dismantle structural racism. Integrating six principles ensures equitable data ecosystems, community governance, and policy change for population health.

Keywords:
community powerdata justicedata modernizationhealth equitypublic healthstructural racism

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Area of Science:

  • Public Health
  • Data Science
  • Health Equity

Background:

  • Public health data modernization initiatives often neglect the impact of structural racism.
  • Existing data life cycles may perpetuate racial inequities.
  • A comprehensive approach is needed to address systemic issues in public health data.

Purpose of the Study:

  • To outline principles for modernizing public health data systems.
  • To emphasize the integration of anti-racism and justice in data practices.
  • To guide government agencies in creating equitable data ecosystems.

Main Methods:

  • Conceptual framework development.
  • Literature review on structural racism and data governance.
  • Synthesis of principles for equitable data modernization.

Main Results:

  • Six core principles for data modernization: dismantling structural racism, centering justice, community data governance, driving population-level change, engaging nonprofits, and securing government commitments.
  • Highlighting the critical role of government agencies in leading and financing these changes.

Conclusions:

  • Public health data modernization must explicitly address and dismantle structural racism.
  • Implementing these principles is essential for creating just and equitable data ecosystems.
  • Government commitment is crucial for policy and practice changes in public health data.