Data for Equity: Creating an Antiracist, Intersectional Approach to Data in a Local Health Department : Journal of Public Health Management and Practice

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

Gould, L. Hannah PhD, MBA; Farquhar, Stephanie E. PhD; Greer, Sophia MPH; Travers, Madeline PhD; Ramadhar, Lisa MPH; Tantay, L. MA; Gurr, Danielle MPH; Baquero, María PhD; Vasquez, Ayanna MD, MS

Author Information
Journal of Public Health Management and Practice: January/February 2023 - Volume 29 - Issue 1 - p 11-20
doi: 10.1097/PHH.0000000000001579
  • Open

Abstract

Public health agencies make thousands of decisions each year based on data. Although the most thoughtful courses of action are rooted in high-quality, well-collected data, even the best evidence-based decisions can perpetuate harm if equity has not been considered in all aspects of the data collection, analysis, interpretation, and dissemination. Which questions are asked and deemed important and to whom, and how these questions are framed determine what data are collected and analyzed, ultimately influencing the myriad ways in which those data are interpreted and communicated, and the many stakeholders that use them.1,2 When equity is not considered in these processes, the resulting data can reinforce structural and institutional racism and other systems of oppression.3

As the field of public health in the United States begins to confront racism and other systems of oppression by more directly acknowledging and addressing their roles in shaping the health of individuals and their communities,4 data are often sidelined in reform efforts, despite their centrality in public health practice. This is likely because data, particularly quantitative data, are viewed by many as objective and not susceptible to the biases of the oppressive interpersonal, institutional, and structural systems that influence us all.5 However, data are influenced by the point of view of the people creating, collecting, and interpreting them, and are therefore replete with subjectivity.

More specifically, data are a social construct, made for and by people, and the ways that data are collected and used in public health practice is an act of power with profound consequences.6 Data can be used to perpetuate myths that disparities in health outcomes might be caused by some underlying biological differences between races.7 Data can designate some groups as healthier and making the ‘right’ choices, while suggesting that other groups cannot or will not make healthy choices and are suffering health consequences as a result. For example, presenting obesity rates by racialized group does not take into consideration structurally racist factors, such as how residential segregation differentially distributes affordable fresh food8 or how inequities in the labor market create racial pay gaps.9 In addition, the absence of data collection of certain groups renders them invisible.10 For example, some races/ethnicities are not represented in data collection at all, or are ultimately grouped heterogeneously for analysis and presentation. Similarly, people who experience oppression due to the societal enforcement of a binary understanding of gender identity and gender expression have historically been invisible in data.11

To be a truly antiracist and intersectional public health agency and effectively eliminate health disparities requires recognition of the subjectivity of data and of the power of data to dictate and reinforce narratives, accompanied by intentional reform of data practices. Although the public health community has made strides in foregrounding racial equity in public health rhetoric,12 fewer resources are available that address how to actually incorporate equity principles in the collection, analysis, and reporting of data that influence public health decisions.

In 2015, the New York City Department of Health and Mental Hygiene (“Health Department') launched Race to Justice (https://www1.nyc.gov/site/doh/health/health-topics/race-to-justice.page), an internal reform effort to embed racial equity and social justice within the agency through organizational alignment and capacity building (short-term outcomes) designed to produce changes in policies, practices, and norms (intermediate outcomes), and ultimately lead to measurable reductions in health inequities (community-level impact). This work began on the premise that an organization that reflects equity in its organizational identity and culture will realize equity for the communities it serves. To achieve this reform, 4 work groups were established as part of the first phase (Finance, Workforce, Community Engagement, and Communications). The work groups were charged with examining these domains to put forward recommendations to advance equity. This initial phase of work did not include a work group around data; however, as the initiative progressed, it became evident that the agency needed a work group to examine and innovate data practices related to equity. It was impossible to grow staff's capacity to do equitable data work and to consistently track progress toward racial equity and social justice goals without an intentional, focused effort. This led to the creation of Data for Equity in 2018, the first institutionally recognized and centralized effort to develop an approach to support agency staff that work with data to apply an intersectional, antiracist equity lens to their work. This included identifying resource needs around social justice and racial and gender equity-informed data analysis and communications, identifying initial guidance for staff working with data, and developing recommendations for leadership on the types of tools and trainings that should be offered.

This article describes (1) the process and resulting Data for Equity recommendations developed in 2018, and (2) the initial progress toward implementing some of the recommended actions. We also describe our framework for antiracist data praxis. The hope is that other jurisdictions can use these recommendations to guide their own efforts to address structural racism in data.

Methods

Planning and development of Data for Equity were led by staff members from the Division of Epidemiology and the Center for Health Equity. Implementation planning was supported as part of an agency pilot to use the Government Alliance on Race and Equity (GARE) Racial Equity Toolkit in project planning.13

The first step was to create a working group comprising staff from across the agency who worked with data. We envisioned this broadly to include staff who touch data during collection and analysis and those not traditionally considered “data people” like those who communicate about (eg, external affairs) or make policy decisions based on data (Figure). Deputy commissioners (aka, division leaders/leadership) were asked to nominate 1 to 2 people from their division to serve on this working group. Before convening the first meeting, members of the planning team conducted in-depth/semistructured interviews with each division's representatives to assess their baseline needs and competencies around equity and data and to understand each division's approach to and needs around equity in data.

F1
FIGURE:
Touch Points for Working With Data Envisioned by Data for EquityThis figure is available in color online (www.JPHMP.com).

The working group comprised 24 members from 12 divisions and met 3 times during April-July 2018 to identify and discuss successes and challenges experienced by staff and to generate recommendations for the agency. The first meeting focused on discussing what objectives/goals, resources, and institutional support would be necessary for the Health Department to collect, analyze, and disseminate data using an equity framework. During the second meeting, representatives from each division presented on successes and challenges with bringing an equity perspective to data work that they had recorded in their individual divisions. Recurring themes included a lack of tools and trainings that help connect data to the Agency's Race to Justice Initiative, a lack of framework and infrastructure for community participation and engagement in data work, the absence of qualitative data expertise and work, and discomfort with not having best practices for making data collection and analysis processes more equitable (eg, more inclusive sampling frameworks, tools to analyze groups of small size, demonstrating validity and reliability of qualitative work in historically quantitative settings). During the third meeting, the group focused on developing recommendations, prioritizing each one based on perceived need, urgency, and feasibility. Final recommendations were approved for implementation by the Commissioner of Health in March 2019.

Results

Recommendations

The final Data for Equity 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 recommendations in each of the 6 areas described later are shown in Table 1.

TABLE 1 - Data for Equity Recommendations, NYC Department of Health and Mental Hygiene, 2019
Data collection and aggregation. Expand use of existing data and enhance Health Department data collection
Create agencywide protocols for using data disaggregation consistently during collection, analysis, and reporting of subgroup data, including recommendations to address sparse numbers in current Health Department data collection.
Develop agencywide, standardized list of questions for collecting data on demographics, for example, race/ethnicity/nationality/sexual orientation and gender identity (SOGI); provide internal guidance on using these standards.
Regularly conduct survey on social determinants of health.
Add additional populations to existing Health Department surveys, including persons in long-term care, people involved in the criminal justice system, and students.
Conduct focused data collection for certain populations, focusing on those who have been historically marginalized or are hard to describe using traditional surveillance methodologies (eg, older adults, justice involved, transgender, or gender nonconforming people).
Develop language to include in the scopes of work of vendors who collect data on behalf of Health Department to ensure alignment with Health Department principles of racial equity and social justice.
Strengthening analytic skills: strengthen skills of Health Department staff to incorporate equity into their analytic work
Create a guidance document for analysts including equity conventions process questions to provoke thought and reflection.
Develop and provide regular and ongoing Data for Equity/social epidemiology training for all Health Department analysts, including training in qualitative analysis and mixed methods.
Develop tasks and standards for analysts that ensure that analyses routinely incorporate social determinants of health/social epidemiology/social justice in all phases of their work. Similarly, develop tasks and standards (the agency's performance management tool) for managers that ensure that they support and empower analysts to conduct these analyses.
Data communication, interpretation, and dissemination. Improve how the Health Department communicates and disseminates data findings
Create an equity-focused data communications guide, including guidance on how to message and visualize disparities without perpetuating racism and oppression and how to incorporate historical and contemporary context into data findings.
Create a writing workshop focused on integrating health equity into data products, including guidance on plain language.
Publish special reports on the health of historically marginalized populations that clearly explain the history of marginalization and its effects on health (eg, Latino Health Report).
Community engagement: integrate New Yorkers into Health Department data collection, analysis, communication, and dissemination
Partner with Race to Justice Community Engagement Work group (CEWG) to adapt the community engagement framework for Health Department data work.
Develop and provide training for all analysts on the community engagement framework.
Provide training in community-based participatory action research.
Develop process to incorporate non-Health Department partners in research, including institutional review board process and human subjects training.
Create an external advisory panel, including persons with lived experience, to review and guide Health Department data products and publications.
Infrastructure and capacity building: build infrastructure and capacity for data and skills and knowledge sharing across the Health Department
Conduct equity skills survey and create a list of content experts across the agency.
Create a SharePort site [the agency's intranet page] for Data for Equity to provide a central repository of training materials and resources.
Create position for dedicated Equity Epidemiologist. Ideally, create a dedicated social epidemiology unit. This person/unit will manage this portfolio of work and provide technical assistance to the agency.
Provide additional resources to the Public Health Library to fund better access to peer-reviewed journals in related fields (ie, social science, geography, urban planning, economics, etc) and fund a position to help staff conduct comprehensive literature reviews incorporating different disciplines.
Work within existing infrastructure and working groups for Health Department data (eg, Data Task Force, Epi Grand Rounds, National Public Health Week) to share best practices and challenges in developing data-focused equity skills.
Leadership and innovation: provide leadership and innovation in using data with an equity framework
Establish permanent Data for Equity working group at Health Department to hold agency accountable for these recommendations.
Hold a hackathon/competition to reimagine how Health Department visualizes data with an equity lens.
Share results of Health Department's Data for Equity initiative through peer-reviewed publications, Health Department reports, and presentations at meetings.
Use the Health Data for NYC (HD4NYC) initiative to conduct innovative research on selected topics related to health equity.

1. Data collection and aggregation: expand use of existing data and enhance Health Department data collection.

This recommendation focuses on expanding the Health Department's ability to collect and use data with an equity lens. Specifically, the actions included in this recommendation will help ensure improved data collection by holding vendors accountable for including antiracist principles in their work, strengthening data collection for hard-to-reach or marginalized populations and about the social determinants of health, and by creating consistent standards for data disaggregation and demographic data collection. Collectively, these recommendations help ensure that the Health Department collects data that are more reflective of NYC populations, and that analyses using these data are collected using categories that align more meaningfully with how people identify themselves.

2. Strengthening analytic skills: strengthen skills of Health Department staff to incorporate equity into their analytic work.

Encompassed within this recommendation are projects to strengthen staff analytic skills, including social epidemiology and mixed-methods training, and development of a guidance document that formalizes equity conventions for Health Department analyses. This recommendation also includes developing performance metrics to ensure that analysts and their managers routinely include equity principles in all aspects of their work. Work on these recommended products is in progress.

3. Data communication, interpretation, and dissemination: improve how the Health Department communicates and disseminates data findings.

This recommendation focuses on improving communication around data, including creating an equity-focused communications guide, developing a writing workshop to train staff to integrate equity into data products, and publishing special reports on the health of historically marginalized populations. These recommendations will help the Health Department communicate more equitably by training staff how to better visualize and message about disparities without perpetuating racism and oppression.

4. Community engagement: integrate New Yorkers into Health Department data collection, analysis, communication, and dissemination.

Centering the experience and knowledge of NYC's many communities into the Health Department's data work is a key tenet of Data for Equity recommendations. This recommendation includes adapting the agency's community engagement framework (https://www1.nyc.gov/site/doh/health/health-topics/race-to-justice.page) to data work, developing training for analysts on this framework as well as on community-based participatory action research, developing a process to incorporate non-Health Department partners in research, including institutional review board process and human subjects training, and creating an external advisory panel, including persons with lived experience, to review and guide Health Department data products and publications. Work on these recommended products has not yet begun.

5. Infrastructure and capacity building: build infrastructure and capacity for data and skills and knowledge sharing across the Health Department.

6. Leadership and innovation: provide leadership and innovation in using data with an equity framework.

These latter 2 recommendations support actions to build capacity and institutionalize this work within the Health Department as well as to provide leadership in the field and share these products externally. Recommended actions include assessing the agency's baseline capacity to incorporate equity into data-related work, creating a platform via the agency's internal Web site and internal working groups to share information and resources, creating permanent staff positions to support the work, resourcing the public health library, making a permanent work group structure, enhancing academic collaborations, and sharing of Data for Equity products externally.

Completed projects

Since finalization of the recommendations, work on several specific projects has been advanced by individual staff and program efforts. Brief, high-level summaries of some successful projects are provided later to highlight initial efforts toward achieving the recommended actions.

Data disaggregation guidance

During the summer of 2019, we conducted 5 focus groups of approximately 5 to 11 staff and reviewed current data disaggregation practices for 28 Health Department data sets to create agencywide protocols for improving data disaggregation by consistently using more inclusive and collaborative methods during collection, analysis, and reporting of subgroup data, including recommendations to address sparse numbers in current Health Department data collection (including considerations for privacy and confidentiality). The product was a guidance document currently available internally for analysts.

Standardized questions on gender identity

A working group was established to develop guidance for standardizing the collection, analysis, and reporting of gender-related data in the agency and for improving inclusivity in these processes. A draft guidance document was shared with stakeholders for feedback in early 2020; however, implementation was delayed because of the COVID-19 pandemic.

Equity in data technical assistance

The COVID-19 pandemic demonstrated the need to develop more consistent, affirming demographic data collection practices across the agency. We provided a range of technical assistance during the COVID-19 response on documents related to equitably collecting and reporting demographic data. This included revising demographic data collection for the Citywide Immunization Registry, compiling best practices for collection of race/ethnicity, sex assigned at birth and gender identity, ability, nativity, language, and criminal-legal system involvement.

Social determinants of health survey

In 2017, using special one-time funding, the Health Department conducted a unique population-based survey focused on describing the social determinants of health in NYC.14 The recommendations included making this a routine surveillance tool for ongoing monitoring of social determinants of health (eg, material hardship, health care access, discrimination, community involvement and social support, housing) and their relationships with health indicators. The survey was subsequently resourced to be repeated in 2020 and early 2022 and is planned approximately biannually moving forward.

Reports on the health of historically marginalized populations

In late 2021, the Health Department published reports on the health of Asians and Pacific Islanders and Indigenous Peoples of the Americas living in NYC.15,16 Both reports were developed in collaboration with community partners who helped identify and prioritize metrics of interest to their communities. Data in the report on the health of Asians are disaggregated by as many as 13 ancestry groups, allowing for a more nuanced description of the health of API NYC residents. These reports happened alongside the data disaggregation project described previously, demonstrating the learnings as an agency as these principles were turned into practice.

All-staff survey

In late 2019, we conducted an all-staff survey to assess staff data-related equity skills agencywide, to provide information to create thoughtful and targeted tools and trainings, and to serve as a baseline upon which to evaluate the impact of future training efforts. The survey included questions to assess proficiency in applying equity principles when working with data throughout the complete data lifecycle. Results from the survey were shared with agency leadership in mid-2021.

Data for Equity work group

Data for Equity was officially made part of the agency Race to Justice infrastructure in early 2020. Because of the COVID-19 pandemic, reestablishment of the agencywide working group was delayed, although the planning team continued to respond to ad hoc requests and plan for the resumption of the working group. Through a blinded agencywide recruitment process, new work group members were identified, and the working group was launched in late 2021; in 2022, the working group will continue progress toward implementing the 2018 guidelines.

Health Data for NYC (HD4NYC)

Health Data for NYC was launched in 2019 with funding from the Robert Wood Johnson Foundation to bring together Health Department and academic investigators to conduct unique policy-relevant research to promote health equity (https://www.nyam.org/hd4nyc/). This program has increased the agency's ability to conduct and disseminate equity-focused research, as well as enhanced data sharing with community and academic partners.

Discussion

Given the paramount role of data in defining public health resource and policy decisions, addressing systematic oppression in how data are collected, analyzed, and shared must be an explicit part of intersectional and antiracist public health practice.17 Through a focused and collaborative process, we successfully established Data for Equity as an initial set of recommendations to guide equity in the data practices at the New York City Health Department. As momentum gathers nationally for this kind of transformative work in public health18–20 and data science more broadly,21–23 this agenda is a starting place that other health departments or organizations can use to assess the need for institutional reform, and serves as a model for thinking about how to build equity-focused data infrastructure.

A major lesson from this work is that embedding equity in data is not just about changing data processes, it is also about embedding guiding equity principles around racial and intersectional justice, combined with process change at all levels of the organization and across job functions. High-level leadership commitment is particularly essential to ensure that equity is embedded into actual planning and execution of analyses, report compiling, and other uses of data whether it be internal dissemination, program or resource allocation decisions, or publications. At the staff level, conventional practice is often simply to make changes to how data are collected or analyzed (eg, changes in demographic data collection), and staff who work with data (encompassing a range of titles and roles including analyst, epidemiologist, program evaluator, etc) are not often encouraged to think more broadly about how these changes contribute to antiracist public health practice. Conversely, staff who work directly with community members or who manage programs and may have a strong grounding in equity principles are often not included in data processes. Our recommendations recognize the multiple ways in which data are touched at all levels, including both the “typical” data work of public health (eg, collection and analysis) and areas less often considered when thinking about data (eg, a community's role and agency in data work), and attempt to create equitable and systematic processes that ensure that staff across all worksites and positions have the necessary resources and support to include equity in their practice.

Addressing racist practices in data work is often secondary to more public facing reforms. This results, at least in part, from the supposed neutrality of data, which itself is tied tightly to educational systems deeply rooted in White supremacy that privilege specific kinds of knowledge and credentialing.24 These systems create false and inequitable hierarchies between people who identify or are identified as “data people” and those who are not. Although a range of people contribute labor to produce data (eg, survey respondents, community engagement staff, administrators), it is uncommon for “nondata” contributions to be valued equitably and very common for much of the labor of people who work closely with the community to be erased or taken for granted. These omissions are often justified by seemingly neutral certification and training requirements (such as educational degrees) that are structurally less accessible for groups with fewer resources. Also, like in medical training, public health students must undergo training that perpetuates racist narratives to complete their requirements.25 Addressing these omissions and dismantling these hierarchies must be part of embedding equity in data practices within an institution.

The process of creating Data for Equity led to the identification of a series of ways of working that should be upheld in data-related activities (Table 2). These principles include developing structures and practices that build and nurture internal leaders by elevating the talent and honoring and supporting the leadership of Black, Indigenous, People of Color (BIPOC) individuals. In addition, by using the principles of consensus decision making and respect for others' points of view and lived experiences, this work has set a model for equitable work practices that will have a tangible impact on how the agency addresses and corrects inequities.

TABLE 2 - Principles for Working With Data in an Equitable Manner
Principle Rationale Challenges Example of How Data for Equity Implemented/Plans to Implement
Reframe “best practices” as “better practices.”
  • Recognizing that practices shift and change over time as we learn and unlearn.

  • People and communities grow and change and language shifts to include new ideas and identities. Categories and terminology will need to be continually evaluated and updated regularly to accommodate these changes.

  • Not a one-time effort.

  • Changing systems (eg, survey instruments, IT) can be resource intensive and expensive.

  • Resistance to changing the status quo.

  • Requires a commitment to continuous evolution.

  • Data for Equity created COVID-19 demographic data guidance that collapsed race and ethnicity into 1 category and added an ancestry category in acknowledgment of the well-documented limitations of OMB 1997 racial and ethnic group standards.26

Embed data reform within institutional reform and personal reform.
  • Each person's own lived experience and own position changes how we name and interpret oppression.27 The process of acknowledging our biases and need for growth is ongoing at the individual and institutional levels.

  • Resistance to changing the status quo.

  • Changes in government administrations can make it challenging to maintain institutional support over longer time periods.

  • We explicitly embedded values in our charter, recruitment materials, and ways of working agreements.

  • Embedded Data for Equity working group in the agency's overall racial and reform initiative, Race to Justice.

Present all data in a historical, political, and experiential context to understand any patterns illuminated by using this language.
  • Data are never collected outside of a context. For example, race/ethnicity categories not only add a sociopolitical lens to the data being collected but also reflect histories of privilege, racist social structures, and racist policies.

  • Space constraints in manuscripts and presentations and desire for succinct summaries.

  • Speed often takes precedence over the time needed to include appropriate context.

  • Learning to predict what narratives are going to be made from the data you create and share.

  • Health Department added context to the COVID-19 data page.28

  • Naming histories of colonialism, slavery, and disinvestment in public facing documents, such as the 2019 Community Health Profiles29 and neighborhood reports.30

Collect and analyze data intersectionally. Truly inclusive data are intersectional. The impact of racism on people of color cannot be fully understood unless we understand the specific impact on women of color and transgender, gender nonconforming, and nonbinary persons as well as people with disabilities, and so forth.31,32
  • Prohibitive costs associated with collecting samples large enough to describe small groups without compromising privacy.

  • “Representative” sampling is based on existing population measures that overlook many groups.

  • Methods to create more inclusive samples (eg, prospective sampling) are often considered biased.

  • Groups experiencing systemic oppression are often pitted against each other for notice and resources, creating the false notion that only 1 aspect of a person is important to collect (eg, advocating for race/ethnicity data over gender or sexuality data).

  • Conducted focus groups on how the agency deals with smaller groups.

  • Issued guidance on data disaggregation.

  • Obtained positions for building out qualitative data infrastructure.

  • Advocated for more comprehensive and inclusive demographic data collection in every system.

Value people's lived experience and treat as data rather than anecdote.
  • Lived experience is required for meaning and understanding.33 Identities captured in demographic data are social constructs that reflect complex and ever-evolving hierarchical power structures. Data collection instruments with poorly or broadly defined demographic categories often do not match people's lived experience, making it hard to accurately identify inequities with quantitative data.

  • Overemphasis on data standardization across times, across places, and across institutions.

  • Lived experience difficult to categorize and it often disrupts hegemonic, normative ideas of how health is produced.

  • Obtained positions for building out qualitative data infrastructure.

  • COVID-19 data standardization guidance recommended self-report as the standard for demographic data.

  • Began to collect data about experiences, not just identity. For example, asking questions about experiences of discrimination in health surveys instead of assuming demographic data represents a uniform exposure.

Be transparent and consultative in methods across the data life cycle.
  • Methodologic decisions (eg, reliability estimates) are largely inaccessible to people whose data are being analyzed. For example, decisions about how to aggregate small groups within a data set happen long after the data have been collected. This ends up creating categories that do not reflect lived experience accurately.

  • People who make analytic decisions about data are often not the people whose lived experience is represented in the data.

  • Although very important for communities, conversations about data processes can be dense and inaccessible.

  • Data disaggregation protocols recommend engaging with people who are represented in the data at every step along the data life cycle.

Always interrogate who is missing from data, why they are missing, and what that means for the analysis and dissemination.
  • When key populations are left out of data collection, it is rare that their absence is noted except for line in a limitations section. These absences have a compounding and inequitable effect.

  • Data are often described as representing the whole population, rather than the people who could be most easily counted. The needs of people who have been left out of the sampling frame have also been left out.

  • It can be costly to do outreach to groups who do not easily respond to our existing strategies.

  • There are no existing sampling frameworks to recruit people who are invisible to routine surveillance, making it difficult to assess representativeness.

  • Establishing trust is difficult—many groups have cultural, social, or historical reasons to avoid surveillance.

  • COVID-19 data standardization process began with assessing missingness of demographic data across data systems.


There have been many challenges to successfully accomplishing this work that can serve as lessons for other jurisdictions interested in planning similar initiatives. First, resources to implement the recommended actions, particularly the allocation of adequate staff time to learn and subsequently change data practices, have not always been available. Overt allocation of staff efforts in job descriptions and performance management tracking systems is needed. Second, decisions about what to prioritize have shifted with emerging public health crises and changing government administrations. Specifically, while the COVID-19 pandemic has made more apparent the impetus for this work, the allocation of staff to the emergency response has substantially slowed progress. Third, this work has also moved slowly because of the thought and extensive intraagency engagement required to do it and the longer time lines for implementation of more equitable processes. Fourth, as a large city agency with more than 6000 employees, we have observed immense variation among organizational units and data systems, making it difficult to develop systematic processes. Finally, even in jurisdictions such as New York, where the political context allows for this type of programming and discourse, the need for sufficient resources to adequately implement it remains a challenge that stymies advocacy of equitable practices and reform of structures and systems of oppression.

In the aftermath of the murder of George Floyd, widespread protests against racism, and the stark racial gaps in COVID-19 outcomes, the NYC Racial Justice Commission was formed to reckon with the ways that racism is embedded in City structures.34 As part of the Health Department's overall commitment to be publicly accountable for eliminating racism, in October 2021, Data for Equity was codified in the New York City Board of Health resolution declaring racism a public health crisis.35 At this time of heightened racial justice reckoning and increasing acknowledgment of such on behalf of all sectors in society, public health data hold immense power to shape narratives and make the invisible visible, which is a necessary step toward dismantling racism and oppression.

Data for Equity has helped establish clear expectations and a collaborative model for how a local health department can reform data work to address the structures and systems that lead to the collection and analysis of biased data. In addition, equity in data is an essential foundation of the national conversation and initiatives toward data modernization.36 This framework serves as a useful model for other jurisdictions to build upon in their own efforts to promote equitable health outcomes and become antiracist organizations.

Implications for Policy & Practice

  • To be a truly antiracist and intersectional public health agency and effectively eliminate health disparities requires recognition of the subjectivity of data and the power of data to dictate and reinforce narratives, accompanied by intentional reform of data practices.
  • Addressing systematic oppression in how data are collected, analyzed, and shared must be an explicit part of intersectional and antiracist public health practice. Equity in data is also an essential foundation of the national conversation and initiatives toward data modernization.
  • Although the public health community has made strides in foregrounding racial equity in public health rhetoric, fewer resources are available that address how to incorporate equity principles in the collection, analysis, and reporting of data that influence public health decisions. We created a framework to guide embedding equity in data practices at the New York City Health Department. Other health departments can use this model to design similar institutional reform initiatives.
  • In addition to staff training and reforms to data collection and analysis, high-level leadership commitment is essential to ensure that equity is embedded into actual planning and execution of analyses, report compiling, and other uses of data whether it be internal dissemination, program or resource allocation decisions, or publications.

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            Keywords:

            data disaggregation; health equity; racism; social determinants of health

            © 2022 The Authors. Published by Wolters Kluwer Health, Inc.