Beyond Access: Reclaiming Data Sovereignty to Strengthen Public Health Outcomes

Katlin T Wilson, MS (1); Alec J Calac, PhD (2); and Samuel Kohn, JD (3)
1:University of Minnesota Medical School, Minneapolis, MN USA and Rincon Band of Luiseño Indians, Valley Center, CA USA 2:UC San Diego School of Medicine, La Jolla, CA USA and Pauma Band of Luiseño Indians, Pauma Valley, CA USA 3:Kaplan Kirsch LLP, San Francisco, CA USA and Apsáalooke Nation (Crow Tribe of Montana), Crow Agency, MT USA
DOI: https://doi.org/10.5281/zenodo.18488898
February 5, 2026

Abstract: 

Public health data are the foundation of responsible governance, shaping decisions about funding, policy, and disease surveillance and prevention. Yet, for American Indian and Alaska Native (AI/AN) communities, public health systems have often underreported the true burden of disease on tribal lands leading to chronic underinvestment in workforce and infrastructure. These data gaps promote invisibility of AI/AN health disparities and delay timely intervention for emerging public health issues as well as hamper surveillance of communicable diseases and toxic exposures, including syphilis, human immunodeficiency virus, and heavy metal contamination in groundwater. This Commentary examines how structural, methodological, and sovereignty-based inequities limit access to equitable public health data for AI/AN communities. We first trace the colonial legacy of data extraction and development of the nation’s public health infrastructure, then describe how racial misclassification and underinvestment worsens AI/AN health disparities. We close by challenging the nation’s public health leaders to operationalize Indigenous Data Sovereignty through the recognition of tribal public health authority and use of the CARE Principles for Indigenous Data Governance.  

Why Data Matters

Persistent gaps in American Indian and Alaska Native (AI/AN) public health data reflect structural and methodological choices shaped by colonial public health infrastructure, misclassification, and fragmented jurisdiction that undermine Tribal decision-making and require an Indigenous Data Sovereignty approach grounded in Tribal public health authority and the CARE Principles.

Public health data is collected and monitored to guide prevention, surveillance, and health policy1. These data inform how resources are distributed, which issues are prioritized, and how success is quantified1. While health data are often treated as objective and universal, the reality is that this data is shaped by the individuals and systems that design, collect, and interpret data. Thus, it is a reflection of who is counted, how categories are defined, and whose realities are made visible. 

For AI/AN communities, the invisibility and misclassification of our identities and associated health outcomes within national datasets (e.g., National Cancer Institute Surveillance, Epidemiology, and End Results Database) furthers a colonial legacy of erasure, which we define as a systematic process, rather than conscious action(s) undertaken by an individual2–4. When AI/AN individuals are misclassified as members of another racial or ethnic group or omitted entirely due to low sample sizes, AI/AN leaders and advocates are unable to justify their requests for public health funding, workforce development, and policy and program development rooted in culture and community values. Misclassification and underreporting of AI/AN public health crises shifts the burden to communities experiencing disparities who then must invest more time in recollecting data to redemonstrate the existence of longstanding health disparities5–7. This is compounded by methodological issues in epidemiology and biostatistics which often posit that no meaningful interpretations can be made from small samples, or that demographic variables only have value if grouped together, which only negates the utility and value of these interpretations for AI/AN communities.

Although Indigenous Data Sovereignty has been widely articulated, implementation gaps persist in public health practice. This commentary contributes by (1) connecting data erasure to specific operational features of U.S. public health surveillance and intergovernmental data custody, (2) clarifying that Tribal Epidemiology Centers and Tribes function as public health authorities in ways that should trigger routine, rapid data sharing, (3) identifying methodological alternatives to small-population suppression that preserve privacy while improving interpretability, and (4) proposing concrete governance terms—beyond HIPAA compliance—that protect against secondary use, proxy inference, and AI-enabled misuse of Tribal data.

Structure of US Public Health System and Tribal Public Health Authority

The US public health system is composed of local, state, tribal, territorial, and federal entities that share responsibility for population health surveillance and response. Tribal public health authorities, including 12 regional Tribal Epidemiology Centers and 575 federally-recognized AI/AN governments, should hold the same public health responsibilities to AI/AN people as their state and federal counterparts to the general population8. 

Given the ongoing data inequities and profound health disparities in AI/AN communities, Tribal Epidemiology Centers (TECs) were established by Congress through the Indian Health Care Improvement Act (IHCIA) to support Tribal public health surveillance and data-driven intervention in partnership with Tribal nations and organizations9. Under federal law, TECs are formally designated to collect, analyze, and dissemination public health data to address health priorities identified by Tribes9. They function as Public Health Authorities for purposes of public health surveillance and response. As such, TECs are legally permitted to request and use federal public health data, including protected health information, in accordance with applicable privacy laws9–12. 

TECs are uniquely equipped to minimize bias, racial misclassification, and underreporting in public health datasets, thereby providing a more representative and actionable assessments of disease burden aligned with Tribal priorities13,14. Despite this statutory framework, data collection for AI/AN populations remains constrained by undersampling, inadequate representation, and inconsistent data sharing with TECs15,16. Federal and state agencies often withhold or delay data, citing bureaucratic or privacy concerns, even though TECs possess legal authorities comparable to those of state and territorial health department16. These barriers were particularly apparent at the height of the COVID-19 pandemic, and may have delayed timely medical and public health responses on AI/AN reservations. For example, the Government Accountability Office reported that during COVID-19, at least one Tribal Epidemiology Center requested CDC COVID-19 case surveillance data in May 2020, but CDC initially declined to share it based on a misunderstanding that the TEC lacked authority to receive protected health information; CDC later acknowledged the miscommunication and provided access in late June, nearly six weeks after the request—delaying timely situational awareness for Tribal public health response planning16–19.This persistent inequity is inconsistent with the federal government’s trust and treaty commitments to support Tribal health and self-determination, and prevents Tribes from making timely, data-driven decisions essential to public health preparedness and sovereignty. 

Structural Challenges: Colonial Infrastructure and Fragmented Authority 

Many challenges facing Tribal health data stem from structural inequities embedded in infrastructure and governance. These include structural and interpersonal obstacles that keep state and federal public health information from tribes such as misinterpretation of data-sharing policies, inconsistent processes, or outdated/poor quality data20. Public health surveillance systems, electronic health records, and interoperability standards were designed without representation or partnership of Tribal communities or public health authorities. Consequently, Tribal health departments operate with limited funding, outdated facilities and technology, and inconsistent access to national data reporting systems maintained by federal and state government or public health authorities15,16,20. Federal and state agencies routinely control or store data collected from Tribal citizens without clear agreements on ownership or governance21. In many jurisdictions, Tribes contribute data to state-operated systems (e.g., immunization registries, reportable disease surveillance, and vital records), yet access to those same datasets is governed by state-controlled processes and data-use agreements that can delay or limit Tribal access—even for aggregate, de-identified public health reporting. per the US Department of Health and Human Services Tribal Data Access Policy Guidelines22. This dynamic reinforces colonial hierarchies of knowledge that prevents Tribes from the ability to make timely, data driven decisions for their communities. 

Federal public health law, regulation, and policy further entrench these barriers by structuring data governance around agency-specific mandates, funding streams, and administrative discretion. Public health datasets are routinely siloed according to federal and state institutional uses (e.g. compliance reporting, internal analytics, and program evaluation), rather than governed as shared public health infrastructure responsive to Tribal priorities. As a result, access to the same data is often permitted or denied based on how agencies interpret jurisdiction, ownership, and risk within their own regulatory and political frameworks. This siloing reflects governance choices embedded in federal data systems, limiting Tribal access despite clear statutory authority.

Racial Misclassification: Erasure of Individual and Community Hardship

Despite persistent disparities in life expectancy, mental health, cancer incidence, and chronic disease, national datasets, including the National Vital Statistics System (NVSS), often understate or omit these disparities6,23. This is not a reflection of reality and demonstrates the flaws in design. Broad racial categories, inconsistent data standards between local, state, federal, and Tribal public health authorities, and decentralized reporting systems perpetuate incomplete and misleading statistics. 

This invisibility and erasure is not new. In the 19th and early 20th centuries, federal enumeration practices frequently excluded or inconsistently counted Indigenous people, producing official statistics that distorted Native population realities and facilitated policy-making without accurate Tribal representation.”Federal and state agencies have yet to develop consistent mechanisms that ensure full inclusion and respect for Tribal sovereignty and health surveillance. Digital health systems in national registries perpetuate this legacy by not only excluding or misclassifying AI/AN people, but also in failing to share data equitably with Tribal public health authorities, including Tribal governments, Tribal epidemiology centers, and Tribal health programs. This results in a persistent gap in our understanding of Native health outcomes and limits the effectiveness of public health interventions, thereby perpetuating inequities that influence disease morbidity and mortality. From pandemic reporting to maternal mortality surveillance, Native people are often left out of the data that drive national public health decisions. As Indigenous scholars and citizens, we view this as both a health equity crisis and a breach of the federal government’s trust and treaty responsibilities.

Data for Responsible Governance

Tribal governance refers to the sovereignty of federally recognized tribes and the right to self govern, per government to government relationship established via treaties with the US Federal government. Data governance is the right for a nation to own the data or control the information. Having data sovereignty and the right to ownership and responsible practices, especially in data analysis, are necessary for informed decision making, especially within the context of healthcare and public health. 

As described by Rainie and colleagues, data functions as a strategic resource that both enables and reflects self-determination3. When Tribes rely on data collected or interpreted by external entities, they risk ceding control over how their people, priorities, and conditions are defined. Data sovereignty therefore becomes foundational to governance in that it is about both access to information and the ability to determine what questions are asked, what indicators are measured, and how results are used to guide Nation-specific policy and programming3,21,24.

Importantly, Indigenous Data Sovereignty does not require the elimination of all data silos. Rather, it affirms the sovereign right of Tribes to determine when data should be linked, and when it should remain separated. Tribes may appropriately choose to silo public health surveillance data from basic research datasets (e.g. genomic, exposomic, or other high-dimensional data) to prevent unauthorized secondary use or analytic practices that exceed the original public health purpose24,25. In this context, purposeful Tribal siloing functions as a protective governance mechanism, in contrast to externally imposed federal or state siloing that restricts Tribal access and control.

Mechanisms that support indigenous data governance include the following: 

  • Tribal Institutional Review Boards (IRB) that ensure research aligns with community ethics, benefits, and priorities. 
  • Data sharing agreements and Memoranda of Understanding that define ownership confidentiality and use. 
  • Tribal research codes and laws that codify consent and establish penalties or mechanisms of recourse for misuse of Tribal data.
  • Community directed research that prioritizes Tribal-defined indicators of wellness rather than Western-centric metrics. 
  • Tribally directed data siloing from basic research datasets as a protective governance mechanism. 

Responsible data governance transforms research from extraction to reciprocity. It empowers Tribes to decide what health means, what metrics matter, and how findings are used to support wellness. 

Methodological Challenges in Classification, Standardization and Quality 

As mentioned above, many epidemiologic conventions treat small AI/AN or Tribal populations as analytically insignificant, leading to suppression of AI/AN data due to “unstable estimates.” Many surveillance systems suppress AI/AN counts or rates below a minimum cell size (for example, withholding estimates when case counts fall below a specified threshold), which may protect privacy but also removes AI/AN communities from the metrics that drive response and funding decisions. Alternative approaches, such as multi-year pooling, Bayesian hierarchical models, small-area estimation, and controlled-access reporting, can preserve privacy while producing interpretable estimates with transparent uncertainty24,26,27. Ultimately, even when data exists, methodological flaws perpetuate invisibility3. This reflects a methodological choice rather than a scientific limitation as approaches such as the Bayesian modeling or small area estimation28. Other methods include longitudinal Tribal comparisons and database crossreferencing24,26,27. Racial misclassification of AI/AN individuals, especially those with multiracial identities, is one of the most persistent problems in public health data surveillance and analysis5,6,29. Most notably, AI/AN descendants are racially misclassified on death certificates resulting in an underestimation of actual AI/AN deaths by almost 30%,  usually due to coding as White, Hispanic, or other30. This is for a multitude of reasons including, but not limited to, narrow definitions of racial identity, stereotypes of AI/AN people, and misunderstanding AI/AN identity as a phenotype rather than a political status6,29,30. This systematically results in gross underestimation of disease burden, morbidity, and mortality rates30. Federal and state datasets often use incompatible racial and ethnic categories, while omitting Tribal affiliation entirely14,15,31. Even the U.S. Office of Management and Budget’s updated race and ethnicity standards are inconsistently applied in data reporting, not only in public health surveillance, but especially in the datasets utilized by researchers for publications that are critical in evidence based medicine. Without consistent enforcement of abiding by OMB standards and establishing standardized categories that reflect Tribal identity, AI/AN data cannot be meaningfully compared or tracked longitudinally. 

Public health reporting delays, often weeks or months, hinder rapid response to emerging threats as evidenced by response during the COVID-19 pandemic and the congenital syphilis epidemic16,32–34.  Outdated Electronic health records (EHR) systems and limited internet access in rural areas further prevent real-time data sharing32,35. During COVID-19, several Tribes, including the Yurok Tribe, faced delayed case reporting due to state-level bottlenecks, forcing them to develop their own community-based dashboards. Similarly, Urban Indian Health Organizations (UIHOs) faced challenges in accessing local COVID-19 data, despite serving a significant proportion of the AI/AN population. UIHOs were often excluded from state and county data systems, resulting in incomplete reporting and undercounting of Native cases, further marginalizing a vulnerable community with increased morbidity and mortality36–38. The Urban Indian Health Institute coined this as data genocide, reiterating the ongoing colonial agenda and emphasizing that missing or misclassified data translates into missed funding and resources (e.g. delayed vaccine access and personal protective equipment)36,38. In response, both Tribes and UIHOs demonstrated innovation and resilience by developing independent reporting tools and community-based testing partnerships35,36,39. These efforts underscore the importance of Tribal systems that are accountable to Indigenous communities.

These structural and methodological barriers result in Indigenous Data Erasure, which is the systemic exclusion or distortion of Native identity in datasets2,35. Misclassification and omission are technical flaws demonstrating the manifestation of ongoing colonialism in data form.

Indigenous Data Sovereignty: a Non-Issue with HIPAA 

Tribal sovereignty can be undermined not only through overt denial of authority, but also through indirect practices such as feigned ignorance of Tribal public health status, proxy inference of Tribal identity, and AI-enabled reidentification. One of the most frequent occurrences includes the misapplication of HIPAA to justify both data withholding and ungoverned secondary use.

Indigenous Data Sovereignty is not in tension with HIPAA’s public health framework; rather, HIPAA is frequently misinterpreted and operationalized in ways that obstruct lawful sharing with Tribal public health authorities, including Tribal Epidemiology Centers. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a US federal law designed to protect the confidentiality of personal health information (PHI), including increased patient control over access to their own PHI40. While HIPAA provides privacy protections and safeguards for individual patients, it also permits disclosure of health data to public health authorities for disease surveillance, prevention, and response40. Despite HIPAA’s explicit inclusion of data disclosure for public health authorities, HIPPA is frequently misinterpreted and misapplied as a barrier to data sharing with Tribes and TECs17,41. This is in part due to HIPAA’s design which is not meant to address the collective rights of sovereign Nations. Under HIPAA, Tribes are often treated as data sources rather than data owners and limits the ability of Tribes to exercise control over their data, even that of their citizens. As described earlier in this article, this was especially prominent during the pandemic and further disclosed in the Office of Government Accountability Report on Tribal Epidemiology Centers.  Indigenous data sovereignty requires a shift in the collective framework that recognizes that data about Tribal citizens inherently belong to the Nation as a whole4,42. Because HIPAA is the dominant baseline privacy framework in U.S. health systems, the most immediate leverage point is consistent, correct application of its public health pathways—paired with Tribal data governance instruments (e.g., data-sharing agreements, Tribal IRB review, and Tribal codes) that operationalize Indigenous Data Sovereignty.  Reiterating and institutionalizing IDS principles throughout the data-sharing process, even in contexts not directly restricted by HIPAA, is essential to ensuring that Tribal sovereignty is recognized and respected across all levels of data governance. Strengthening Tribal IRBs and adopting data-sharing agreements that explicitly recognize Tribal ownership are essential steps toward reconciling these frameworks. This approach moves beyond the limitations of federal privacy law and affirms that data related to Tribal citizens and Nations must be governed according to Tribal authorities, not merely federal or state misinterpretations of HIPAA.

Editors and agencies often conflate HIPAA permission with governance sufficiency. Where the request is purely for public health practice (e.g., surveillance line lists, case investigation support, outbreak response), a Tribal IRB review may not be required; however, a data-sharing agreement remains critical to operationalize Indigenous Data Sovereignty by specifying governance terms that HIPAA does not supply, such as ownership and stewardship expectations, permitted uses, linkage and reidentification safeguards, redisclosure limits, retention/destruction, audit rights, and prohibitions on secondary use (including AI training or commercial analytics) without Tribal authorization. These guardrails become especially necessary when public health datasets are later combined with ‘basic research’ or biomedical data (e.g., genomic or exposomic research) or used for cross-jurisdictional analytics, where the boundary between public health practice and research can blur. 

Tribally Informed Solutions 

Despite systemic challenges, Indigenous Nations are leading innovative and sovereign models of data governance that center reciprocity, accountability, and the wellbeing of our communities. 

Standardizing and enforcing recognition of TECs as public health authorities through federal guidance, standardized data-use agreements, and grant conditions for state and federal data stewards would reduce recurring delays and inconsistent access. Within the federal landscape, HHS has implemented Tribal consultations to inform the development of a comprehensive Tribal Data Sharing Policy that clarifies data access for Tribes and TECs16. This policy effort was enacted in response to documented inconsistencies in Tribe and TEC access to data systems.

Tribally controlled data repositories, such as Native BioData Consortium, demonstrates a paradigm shift from extractive research toward Indigenous governance and stewardship. These repositories ensure that data are collected, stored, and analyzed in ways that align with community values and self-determination. 

The CARE Principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) shift the focus from open access to ethical stewardship. When federal and state agencies adopt CARE Frameworks, they move toward genuine partnership with Tribal Nations and away from extractive research models. The basis of CARE principles is to recognize that Western-centric research frameworks are often not compatible with traditional research and knowledge of Indigenous communities, especially when it comes to centering purpose and people. The application of the CARE principles ensures that data governance is designed to benefit Indigenous communities and support innovation, effective governance, citizen engagement, and equity. This means that public health data initiatives must be aligned with tribal priorities, accessible to Tribes, and oriented toward improving community-defined outcomes rather than solely institutional metrics43.  This has been successfully employed in ecology and biodiversity research, but must be applied to epidemiology and public health research to appropriately and equitably address Native health outcomes44. 

There have been several community wins and innovations. The Tribal wastewater monitoring projects and community led COVID-19 surveillance efforts demonstrate how Indigenous public health innovation thrives when communities have control over their own data32,45. 

Policy Recommendations: 

Congressional inaction in updating data governance frameworks to reflect emerging technologies is more than a regulatory gap. It is a structural condition that allows existing power inequities over data and resources to persist, disproportionately burdening Tribal Nations. Addressing Indigenous Data Sovereignty therefore requires not only better implementation of existing law, but proactive congressional oversight that recognizes data governance as a determinant of public health equity and Tribal self-determination.

  • Mandate Federal and State Recognition of Tribal Data Authority: Require agencies to honor TEC and Tribal public health authority in all data-sharing policies.
  • Standardize Racial and Tribal Identifiers: Ensure federal datasets include accurate AI/AN and Tribal affiliation fields consistent with OMB standards.
  • Invest in Tribal Data Infrastructure: EHR interoperability and long-term funding for TECs and Tribal health departments.
  • Institutionalize the CARE Principles: Embed CARE into federal and academic data governance frameworks to align with sovereignty and reciprocity.
  • Support Indigenous Data Workforce Development: Fund training programs for Tribal epidemiologists, data scientists, and analysts.
  • Recognize and Resource Tribally Controlled Repositories: Support models as essential infrastructure for national data equity.

Conclusion: 

Data, more than numbers, are mirrors of our existence, history and future. For too long, Indigenous health data have been collected about us, not for us or by us. These inequities are not only technical, but are structural and immoral. Reclaiming data sovereignty is the path toward justice and self determination. It restores our right to define wellness, measure progress, and protect our communities on our own terms. When Tribes govern their own data, they reclaim their narratives, strengthen their sovereignty, and shape their futures. 

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Conflicts of Interest: The authors have no conflicts of interest to disclose.