Ethical Data Linkage with Indigenous Communities: The Manitoba Experience

Main Article Content

Alan Katz
Kathi Avery Kinew
Leona Star


Indigenous populations are known to have poor health and health outcomes in many countries. Indigenous peoples continue to be the subjects of unethical research. Research that is undertaken without their consent, involvement in the design, delivery and interpretation of results that perpetuates negative stereotypes ignoring the historical and ongoing impacts of colonialism.

Objectives and Approach
In order to understand the health status and health system use of First Nations people in Manitoba Canada we developed a partnership between the First Nations Social Secretariat of Manitoba and researchers to link First Nations identifiers with administrative data. This partnership was based on long-standing relationships with researchers who were affiliated with the Manitoba Centre for Health Policy.

A tripartite data sharing agreement set out the parameters of sharing data that supported the linkage of the Federal Registered First Nations database to the Manitoba Population Research Data Repository. The DSA facilitated direct First Nations input into the indicators chosen, the reporting cohorts, the interpretation of results and the language of the report.

Conclusion / Implications
DSAs can be used as a tool to facilitate partnerships with Non-indigenous researchers and Indigenous Nations that lead to meaningful partnerships and lay the foundation for respectful and ethical research. The research presents findings the health of First Nations and shines a light on the underlying colonialism and racism that contributes to the health inequities. These findings have the potential to influence health and well-being of First Nation peoples in Manitoba. This model of collaboration can be used a model in other jurisdictions.

Article Details

How to Cite
Katz, A., Kinew, K. A. and Star, L. (2020) “Ethical Data Linkage with Indigenous Communities: The Manitoba Experience”, International Journal of Population Data Science, 5(5). doi: 10.23889/ijpds.v5i5.1473.

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