Webinar Talk-Back: Racial Equity Throughout Data Integration

By: Susan Martinez, Program Associate, Data Across Sectors for Health & Sallie Milam, Deputy Director of the Mid-States Region at the Network for Public Health Law.

Regardless of intent, data use can lead to harmful outcomes for vulnerable populations. Responses to the COVID-19 pandemic (including some featured in All In Data for Community Health’s Lessons From the First Wave) highlighted the impact of inequitable data practices, most notably in the disparate health outcomes for communities of color. From these discussions within the All In Network came a three-part series hosted by All In, the Network for Public Health Law (NPHL), and Actionable Intelligence for Social Policy (AISP) on Racial Equity throughout Data Integration. Part 1 served as an introduction to data integration and how it differs from data sharing. Part 2 delved into AISP’s Toolkit for Centering Racial Equity as well as the work of Baltimore’s Promise.  A racial equity lens in data integration can lead to implementations that are high risk and low benefit, further impeding trust (between those collecting data and the data subjects) which is a critical element in building equitable data systems.

Source: AISP, Toolkit on Centering Racial Equity in Data Integration

As a learning collaborative, All In is committed to sharing and learning from one another’s lessons and challenges to equitable data sharing. It’s important for us to meet organizations where they are in their journey. We asked the audience to rank where they felt their organization was in terms of how they’ve dealt with issues of racial equity. As evidenced below, responses reflect the wealth of knowledge and opportunity within the network.  

One of our presenters, Amy Hawn Nelson of AISP, stated during the second webinar, “Data moves at the speed of trust.” We took some time to speak with our moderator Sallie Milam, Deputy Director of the Mid-States Region at NPHL, to hear her perspective on why Racial Equity matters and where trust-building can occur in the data life cycle, including her work with Tribal communities.

All In: Sallie, thanks for sitting down with us as we reflect on these last two webinars. What practices and resources can we look to if we are interested in incorporating the concepts from the webinars into our practices?

Sallie Milam (SM): Of course, I want to begin with a big shout-out to AISP and their new toolkit Centering Racial Equity Throughout Data Integration. AISP provides information and resources to integrate racial equity throughout the entire data lifecycle as data are shared and linked by the individual. Because centering racial equity is a continuous process, this toolkit provides questions to ask at each stage, for example, lists of positive and problematic practices and work in action. Finally, activities are included to provide additional guidance around who should be at the table, mapping assets and engaging the community, and identifying root causes through factor analysis.

To prepare for the conversation around centering racial equity through data sharing and integration, the first webinar in this series highlighted AISP’s Introduction to Data Sharing & Integration. This intro offers a comprehensive and accessible overview of useful definitions, frameworks, privacy laws, data sharing agreements, use cases, and nuts and bolts guidance for the variety of considerations encompassed with beginning data sharing. 

All In: Throughout these past two webinars, the subject of legal issues (data privacy and the like) have emerged. Could you talk about how you see public health law interacting with this issue (of racial equity in data sharing)?

SM: Law and policies define all aspects of the data life cycle, including collection, use, sharing, and destruction. Data owners may not be able to share data needed to address racial disparities due to restrictions or ambiguities within law or policy. The Network for Public Health Law offers an equity assessment framework for public health laws and policies. This framework assists in identifying issues in the drafting, design, or implementation of a law or policy that could have a disproportionate impact on different population groups. The framework is meant to guide a discussion around how equity is considered in both process and outcomes and can help identify opportunities for improvement. This equity assessment tool is a critical tool at a critical time.

Law may also protect racial equity by prohibiting data sharing in certain situations. Concern over immigration enforcement can prevent immigrants from obtaining needed health care. Many immigrants worry that health workers will share their undocumented status with immigration authorities. Removing barriers to immigrants’ utilization of preventive and other health care services is important for public health. This Network for Public Health Law issue brief explores relevant federal and state health privacy laws and how they apply to undocumented immigrants and provides information on health care providers’ rights and responsibilities when providing health care to immigrants.

Emerging frameworks to enable data sharing across the social determinants of health hold promise to improve racial equity. In this report, SIREN illuminates where health care organizations share personal information outside of healthcare with other sectors – such as housing programs, school health programs, and social service programs – while protecting individual privacy in compliance with federal and state law. De-identification is also a data sharing strategy that advances health equity where it is useful for a community and population where individual-level data are not needed. 

All In: Another emerging topic throughout the past two webinars has been ethical considerations to data sharing, particularly how data practices impact Tribal communities. What are some strategies to center racial equity with Tribal Nations and their peoples?

SM: It is essential to begin with honoring and respecting Tribal data sovereignty. Tribal nations are separate and sovereign jurisdictions. As sovereign nations, Tribes have inherent authority to protect their Tribal citizens’ health and wellness and provide public health services as they determine best. Read more about Tribal public health law and Tribal self-determination. To govern public health service delivery to their people, Tribal nations have the authority to administer the collection, ownership, and application of their own data, which is known as indigenous data sovereignty.

Where Tribes do not have the capacity to collect data on Tribal citizens themselves, they may partner with other jurisdictions, such as state governments. Data sharing between state governments, Tribal Epidemiology Centers, and Tribes should be grounded in a strong data governance program. Data Governance Strategies for States and Tribal Nations. A first step in establishing a data governance program is the adoption of a principle-based framework that aligns with the organization’s mission, vision, and values. 

State governments and Tribes might evaluate starting with the NCVHS Stewardship Framework which identifies eight elements:

  1. Openness, transparency, and choice – what information is being collected and why, consent options
  2. Purpose specification – the initial purpose of the data collection and its downstream uses are defined and made explicit at the point of collection
  3. Community engagement and participation – whether and how communities should be involved in decision-making about data
  4. Data integrity and security – evaluation of confidentiality, integrity and availability risks to the data and a plan to address those risks 
  5. Accountability – identification of a person or entity responsible for data governance at each stage of the data lifecycle
  6. Protecting de-identified data – ensuring that data are de-identified, as appropriate, and have administrative safeguards, as needed
  7. Attending to the risks of “enhanced” data sets – ensuring that re-identification risks are appropriately managed when data sets are merged
  8. Stigma and discrimination – ensuring that data uses don’t stigmatize or result in negative attitudes towards communities

This Fact Sheet provides considerations for state governments and Tribes’ evaluation of a data governance framework, meaningful Tribal partnership and consultation in data sharing, and best practices for data sharing between Tribes and state governments. All In’s Lessons from the First Wave offers real-world examples of successes in Tribal, Tribal Epidemiology Center and state government collaboration and communication around data sharing. Honoring and respecting Tribal data sovereignty is critical for Tribes to protect the health and wellness of their citizens and to achieve health equity.

All In: Finally, in your own words, what do you think is lost if we don’t commit to equitable data practices?

SM: If we don’t commit to equitable data practices now then we continue to perpetuate systemic racism. Existing policies, practices and laws prevent BIPOC from having the same access to conditions needed to be healthy as White people while creating advantages for White people. National Institute for Health Care Management (NIHCM) Foundation. These needed conditions are social determinants of health and include socioeconomic status, education, neighborhood, and the built environment, employment, social support networks and access to health care. Beyond Health Care: The Role of Social Determinants in Promoting Health and Health Equity. Systemic racism is not new and exists within the fabric of systems designed to support our most vulnerable individuals. 

The net worth of a White family is typically 10X greater than a Black family. This disparity underscores the impact of 246 years of slavery, violence against Black individuals, and racial discrimination. Today in the US, black children are 2X as likely to live in poverty as white children. While designed to provide cash assistance for low-income families, Temporary Assistance to Needy Families (TANF) contributes to the Black-White poverty gap. The higher the percentage of African Americans in a state, the lower the percentage of money is actually spent on helping them with basic expenses. A number of states spend federal TANF money on ancillary programs instead of providing direct cash assistance to individuals. These ancillary programs include support for marriage formation, reduction of out-of-wedlock births, and attendance at a Christian summer camp. Welfare Money Is Paying for a Lot of Things Besides Welfare

State governments are building large integrated eligibility systems to better administer benefits to vulnerable individuals. Some estimate that these systems cost approximately $6.5B per year to operate and improve. Integration of health and human services programs promises a client-centered administrative culture and a more seamless customer experience across programs. While these massive data integration engines are new, many perpetuate the systemic racism already built into stand-alone agency-specific data systems and agency policy. 

Equitable data practices are needed to guide governments to steward their resources so that everyone living within their boundaries counts and has the same opportunities to be healthy. Black lives matter.

All In: Thanks again for taking the time to speak with us. We’re looking forward to checking out Part 3 of this series!

Recordings and materials for Parts 1 and 2 of the All In and NPHL Webinar Series on Racial Equity Throughout Data Integration are available on the All In Online Community (create a profile here. Register here to watch Part 3 of this webinar series, live on October 14th, 3 pm ET. 

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