Data for Equity: Creating an Antiracist, Intersectional Approach to Data in a Local Health Department.

Objective: To develop recommendations to embed equity into data work at a local health department and a framework for antiracist data praxis.
Design: A working group comprised staff from across the agency whose positions involved data collection, analysis, interpretation, or communication met during April-July 2018 to identify and discuss successes and challenges experienced by staff and to generate recommendations for achieving equitable data practices.
Setting: Local health department in New York City.
Results: The recommendations encompassed 6 themes: strengthening analytic skills, communication and interpretation, data collection and aggregation, community engagement, infrastructure and capacity building, and leadership and innovation. Specific projects are underway or have been completed.
Conclusions: Improving equity in data requires changes to data processes and commitment to racial and intersectional justice and process change at all levels of the organization and across job functions. We developed a collaborative model for how a local health department can reform data work to embed an equity lens. This framework serves as a model for jurisdictions to build upon in their own efforts to promote equitable health outcomes and become antiracist organizations.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc.)