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

Abstract

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.

Designing and developing a digital equity dashboard for the emergency department

“…The annual National Healthcare Quality and Disparities Report shows that significant racial discrepancies in treatment still exist, with black patients receiving worse care than white patients for almost 40% of quality and safety measures in 2021 10 . National organizations continue to draw much needed attention to examining these inequities in health care delivery, 11,12 and health systems in response have begun stratifying their quality and performance measures 13 by demographic detail to identify existing disparities 14,15 …”

“…10 National organizations continue to draw much needed attention to examining these inequities in health care delivery, 11 , 12 and health systems in response have begun stratifying their quality and performance measures 13 by demographic detail to identify existing disparities. 14 , 15 …”

“…10 National organizations continue to draw much needed attention to examining these inequities in health care delivery, 11,12 and health systems in response have begun stratifying their quality and performance measures 13 by demographic detail to identify existing disparities. 14,15 Although EDs may track operational metrics such as wait time, length of stay, door-to-clinician time, and other aspects of care, the lack of more frequent monitoring and data transparency poses barriers to recognizing and confronting patterns of inequity. Currently, data are typically provided in a limited format tailored only to those involved in clinical and financial operations.…”

Incorporating a Health Equity Lens Into Surveillance Information Systems: Opportunities and Challenges

“…The Council of State and Territorial Epidemiologists develops case definitions for STDs and other notifiable conditions, and the CDC provides detailed message mapping guides, 19 but jurisdictions have discretion in how they collect and aggregate demographic information in their public health surveillance data. 20 Walters et al, 2 Gutierrez-Mock et al, 3 and Gould et al 4 identify excellent strategies to improve the collection and use of SOGI and REAL data in surveillance systems. These include adding or modifying data fields to allow for recording gender identity such as Oregon's "X" category for nonbinary persons on death certificates, advocating for state and federal laws to require the collection of nuanced SOGI and REAL data, providing ongoing training for personnel who collect these data, enhancing data sharing agreements between local public health and death investigation systems, and implementing formal initiatives such as New York City's "Data for Equity" workgroups to integrate an equity perspective into data work.…”

“…They advocate for complete and accurate SOGI data for a comprehensive understanding of disparities and to improve public health interventions for sexual and gender minority populations. Gould et al4 provide a compelling explanation of how quantitative data are a social construct and inherent racial biases in the collection, analysis, and reporting of public health data. Beyond public health, there is growing recognition within the health care sector about the value of collecting and using high-quality SOGI and racial, ethnic, and language (REAL) data to advance health equity 5,6…”