Working with Data

Data is central to the way that public health professionals work to ensure community health. However, when working with tribes and American Indian and Alaska Native (AI/AN) populations, it’s important to be aware of the U.S. government’s history of misusing tribal data and common data issues that might concern tribal citizens.

Data Sharing from a Tribal Perspective

Data is a resource that, as sovereign nations, tribes have the inherent right to govern and protect. When working with tribes, it is important to recognize and respect this right to data so as to build trust with tribal communities and support tribal efforts to use their data to meet their own needs.

Unfortunately, there is a long history of researchers and other health workers using tribal data in unethical ways. It’s important to be aware of this history and recognize the need for caution and respect when considering data issues.

For more information on conducting research with Native communities, see the document, “Walk Softly and Listen Carefully.”

Unethical Use of AI/AN Data

In the late 1980s, Arizona State University (ASU) researchers collected blood samples from the Havasupai Tribe for a study on Type II Diabetes. However, after completing their initial study, the researchers chose to use the samples without consent from the tribal members to conduct genetic studies, including culturally insensitive studies on migration and schizophrenia. The tribe only became aware of the violation in 2003, when a member of the tribe who had participated in the original study attended a lecture at ASU.

The Havasupai Tribe filed a lawsuit against the researchers, which was settled in 2010. The case brought much needed attention to the potential for research to perpetuate harmful stigmas against communities, the importance of protecting individuals’ privacy when using small data sources, and the lack of protection for tribal interests in current research practices.

AI/AN Data Challenges

In his Hot Topics in Practice webinar below, Adrian Dominguez, the Scientific Director at the Urban Indian Health Institute, discussed nine challenges of conducting epidemiological research in urban Indian communities. Many of the nine challenges could also be extended to conducting work with the AI/AN community more generally. They include:

Racial misclassification
Small population
Biomedical-epidemiological model
Limited sources that collect both race (AI/AN) and geography (urban)
Collapsing racial data into an ‘other’ category when sample sizes are too small
Variability in methods of collection, analysis, and presentation of data
High rates of missing data
Lack of cultural relevance

Webinar: Finding Strength in Small Numbers

Many of the challenges listed above lead to the erasure of AI/AN communities and the invisibility of the challenges facing them. However, there are strategies that can combat these challenges. A few strategies that Mr. Dominguez highlighted include:

Aggregating data across time to build larger samples
Using weighted sampling for small populations
Oversampling AI/AN populations when conducting a study
Limiting the stratification of data so as not to further reduce already small groups
Using data from linkage projects such as the IDEA-NW project
Clearly reporting the limitations of your methods in your work

Public health practitioners can also help counter the focus on disparities and the erasure of AI/AN communities in epidemiology by tying data to stories. For example, the Alaska Native Epidemiology Center’s The Health Portraits Project uses photography in an effort to balance discouraging statistics with community success stories. Learn more from We Are Public Health.

Additional Resources

Visit the following sites for more information on tribal and AI/AN data.

The following resources, from the Urban Indian Health Institute, relate specifically to data and equity for Urban AI/AN.