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18 Equity Frameworks

Toolkit for Centering Racial Equity Within Data Integration

“Building data infrastructure without a racial equity lens and understanding of historical context will exacerbate existing inequalities along the lines of race, gender, class, and ability….In the push to increase data sharing and integration, agencies often gloss over the very real effects of racial bias in system design — with serious consequences for the populations they serve. But intentionally centering racial equity from the ground up can result in data integration that’s both effective and ethical.” https://www.aecf.org/resources/a-toolkit-for-centering-racial-equity-within-data-integration

Toolkit for Centering Racial Equity Within Data Integration

A national group of civic data stakeholders developed the Toolkit for Centering Racial Equity Within Data Integration. Their work seeks to help agencies acknowledge and compensate for the harms and bias baked into data and data structures, into practice, and cultural understandings and perceptions of populations served by government agencies. The toolkit encourages centering racial equity and community voice throughout the data lifecycle of planning, data collection, data access, algorithms/ use of statistical tools, data analysis, and reporting and dissemination. The toolkit helps researchers embed questions of racial equity throughout the data life cycle, includes exercises and examples, and encourages a communityengaged framework. The framework provides strategies, stories, resources, and activities to aid researchers and organizations in applying an equity lens when using data. It also takes a deeper dive into the phases of the data life cycle, highlighting positive and problematic practices using real-world examples of work taking place at each stage.

 

Racial Equity in Data Analysis: Positive & Problematic Practices POSITIVE PRACTICE PROBLEMATIC PRACTICE Using participatory research to bring multiple perspectives to the interpretation of the data Describing outcomes without examining larger systems, policies, and social conditions that contribute to disparities in outcomes (e.g., poverty, housing segregation, access to education) Engaging domain experts (e.g., agency staff, caseworkers) and methods experts (e.g., data scientists, statisticians) to ensure that the data model used is appropriate to examine the research questions in local context Applying a “one size fits all” approach to analysis (i.e., what works in one place may not be appropriate elsewhere) Correlating place to outcomes (e.g., overlaying redlining data to outcomes) Leaving out the role of historical policies in the interpretation of findings Using appropriate comparison groups to contextualize findings Making default comparisons to White outcomes (e.g., assuming White outcomes are normative) Employing mixed methods approaches when developing the analytic plan, including purposefully seeking out qualitative data (interviews, focus groups, narrative, long- form surveys) in conjunction with quantitative administrative data to better understand the lived experience of clients Using one-dimensional data to propel an agenda (e.g., use of student test scores in isolation from contextual factors such as teacher turnover, school-level demographics) Disaggregating data and analyzing intersectional experiences (e.g., looking at race by gender) Disregarding the individual or community context in the method of analysis and interpretation of results Empowering professionals and community members to use data to improve their work and their communities Analyzing data with no intent to drive action or change that benefits those being served
Positive and Problematic (Data Equity) Practices during the planning phase. https://assets.aecf.org/m/resourcedoc/aisp-atoolkitforcenteringracialequity-2020.pdf

National Institute on Minority Health and Health Disparities Research Framework

The NIMHD Minority Health and Health Disparities Research Framework reflects an evolving conceptualization of factors relevant to understanding and promoting minority health and to understanding and reducing health disparities.

The framework is a vehicle to encourage NIMHD- and NIH-supported research that addresses the complex and multi-faceted nature of minority health and health disparities, including research that spans different domains of influence (biological, behavioral, physical/built environment, sociocultural environment, health care system) as well as different levels of influence (individual, interpersonal, community, societal) within those domains.

The framework also provides a classification structure that facilitates analysis of the NIMHD and NIH minority health and health disparities research portfolios to assess progress, gaps, and opportunities. Examples of factors are provided within each cell of the framework (e.g., family microbiome within the interpersonal-biological cell). These factors are not intended to be exhaustive. Populations experiencing health disparities, as well as other features of this framework, may be adjusted over time.

A conceptual framework from the National Institute on Minority Health and Health Disparities (NIMHD) showing how health outcomes are shaped by interactions across four levels of influence—Individual, Interpersonal, Community, and Societal—and five domains: Biological, Behavioral, Physical/Built Environment, Sociocultural Environment, and Health Care System.
The NIMHD Research Framework maps how multiple levels and domains of influence—from biology and behavior to healthcare systems and social environments—interact to shape health disparities and outcomes.

Citation: National Institute on Minority Health and Health Disparities (2017). NIMHD Research Framework. Retrieved from https://nimhd.nih.gov/researchFramework. Accessed on (September 25, 2024).

We All Count’s Data Equity Framework

The Data Equity Framework recognizes that although data is objective, human biases can impact every stage of its handling. The framework provides researchers with tools to acknowledge and address these biases in their work, promoting fairer data practices and decisions. The framework is a discipline-agnostic tool that breaks down any kind of data work (social sector, government, academia, etc) into seven stages.

Seven Universal Stages of the Data Lifecycle

  1. Funding
  2. Motivation
  3. Project Design
  4. Data Collection and Sourcing
  5. Analysis
  6. Interpretation
  7. Communication and Distribution

We All Count’s Data Equity Framework recognizes key equity decision points at each stage and provides simple, practical tools to help make decisions that result in more equitable data outcomes. The Data Equity Framework doesn’t exist to support one specific group of people or type of identity. It provides the tools to help define equity goals, priorities, and the lived experiences you desire to center. We All Count encourages using their Data Equity Framework to break up your data work into manageable parts and go through an intentional, equity-oriented process to make the key decisions along the way.

Racial Equity and Policy (REAP) Framework

Public policy is a powerful determinant of racial inequity in health, but we lack tools for examining how the entire policymaking process impacts racial inequities. The Racial Equity and Policy (REAP) Framework (REAP) is a structured approach designed to address racial disparities and advance equity through policy decisions. The framework helps policymakers, organizations, and advocates analyze and reform policies historically contributing to or perpetuating racial inequities. The REAP framework seeks to ensure that racial equity is embedded in decision-making processes and outcomes.

The REAP framework considers key insights about the policy process and offers a series of questions for consideration as a starting point to aid policymakers, analysts, and academics, in examining the racial equity impacts of policies. The framework relies on three themes that are believed to reflect the structural mechanisms that contribute to the emergence of racial inequities in policy.

  1. Disproportionality refers to the way policies differentially allocate benefits and burdens to racial groups
  2. Decentralization concerns the level of government through which a given policy benefit or burden is designed or implemented
  3. Voice relates to the ability of communities of color to shape the policy environment

The REAP framework emphasizes questions around disproportionality, decentralization, and voice, which are thought to be important when evaluating racial equity within a policy environment. However, it is important to note that these factors alone do not definitively label a policy as “racist.” They are designed to highlight potential pathways through which racism may operate.

https://www.commonwealthfund.org/publications/issue-briefs/2022/jan/racial-equity-framework-assessing-health-policy

A Racial Equity Framework for Assessing Health Policy | Commonwealth Fund

Source: Jamila Michener, A Racial Equity Framework for Assessing Health Policy (Commonwealth Fund, Jan. 2022). https://doi.org/10.26099/ej0b-6g71

Quantitative Critical Analysis or QuantCrit

Critical race theory (CRT) in education centers, examines, and seeks to transform the relationship that undergirds race, racism, and power. CRT scholars have applied a critical race framework (Quantitative Critical Analysis) to advance research methodologies, namely qualitative interventions.

Quantitative Critical Analysis or QuantCrit is an approach that combines traditional quantitative research methods with critical (race) theory to examine and address social inequalities, power structures, and systemic biases within data and research. It challenges the notion that numbers and statistical analyses are objective, and recognizes that the processes of data collection, interpretation, and presentation can reflect social and political influences.

Traditional quantitative research methods avoid the need to look beyond numbers and see the flaws in systems that promote structural racism. QuantCrit centers on oppression and acknowledges the impact of structural racism on economic, political, and educational systems. Quantcrit introduces a framework for using quantitative research in race-conscious ways to challenge racist ideologies, give voice to marginalized groups, and provide proper context to data to avoid further harm to marginalized groups.

Quantitative Critical Analysis merges traditional data analysis with a critical framework that questions how numbers are used, whose voices are represented or missing, and how research can contribute to a more just and equitable society.

References and Further Reading

  1. https://stemequity.net/what-is-quantcrit/
  2. Suzuki, S., Morris, S. L., & Johnson, S. K. (2021). Using QuantCrit to Advance an Anti-Racist Developmental Science: Applications to Mixture Modeling. Journal of Adolescent Research, 36(5), 535-560. https://doi.org/10.1177/07435584211028229
  3. Garcia, N. M., López, N., & Vélez, V. N. (2017). QuantCrit: rectifying quantitative methods through critical race theory. Race Ethnicity and Education, 21(2), 149–157. https://doi.org/10.1080/13613324.2017.1377675
  4. Pearson MI, Castle SD, Matz RL, Koester BP, Byrd WC. Integrating Critical Approaches into Quantitative STEM Equity Work. CBE Life Sci Educ. 2022 Mar;21(1):es1. doi: 10.1187/cbe.21-06-0158. PMID: 35100005; PMCID: PMC9250366.
  5. Priddie, C., & Renbarger, R. (2023). Connecting QuantCrit to Gifted Education Research: An Introduction. Gifted Child Quarterly, 67(1), 80-89. https://doi.org/10.1177/00169862221116636

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Perspectives on Race and Racism in Health and Healthcare Copyright © 2025 by Tiffany J. Grant, PhD, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, except where otherwise noted.

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