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

Public health data modernization efforts frequently overlook the far-reaching effects of structural racism across the data life cycle. Modernizing data requires creating data ecosystems grounded in six principles: dismantling structural racism and building community power explicitly; centering justi...

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Bibliographic Details
Published inHealth equity Vol. 7; no. 1; pp. 699 - 702
Main Authors Porter, Jamila M., Castrucci, Brian C., Orr, Jacquelynn Y.
Format Journal Article
LanguageEnglish
Published 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA Mary Ann Liebert, Inc., publishers 01.10.2023
Mary Ann Liebert
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Summary:Public health data modernization efforts frequently overlook the far-reaching effects of structural racism across the data life cycle. Modernizing data requires creating data ecosystems grounded in six principles: dismantling structural racism and building community power explicitly; centering justice in all stages of data collection and analysis; ensuring communities can govern their data; driving positive population-level change; engaging nonprofit organizations; and obtaining commitments from governments to make changes in policy and practice. As government agencies spearhead and finance data modernization initiatives, it is imperative that they address structural racism head-on and integrate these principles into all aspects of their work.
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ISSN:2473-1242
2473-1242
DOI:10.1089/heq.2023.0086