Elevating the Care Experiences of Pregnant People in Indian Country through Equity-Centered Data Visualization

Abstract Information: Data visualizations serve as a powerful evaluation and storytelling tool, transforming complex data into clear, compelling narratives. Historically, data suppression of American Indian and Alaska Native (AI/AN) populations have stifled their ability to advocate for systems change, limiting their access to the very data that could empower their communities and inform policy changes. Storytelling through data visualization can uplift Indigenous perspectives within data. In making information more accessible, these visual tools not only highlight but contextualize health outcomes in American Indian/Alaska Native (AI/AN) populations. Rooted in historical trauma, medical mistrust, limited care access, structural racism, and Indigenous disenfranchisement, systemic inequities lead to adverse mental, sexual, and reproductive AI/AN health outcomes. This session seeks to demonstrate the value of mapping technologies in highlighting opportunities to address historical, systemic, structural, and institutional issues impacting the health and well-being of pregnant people in Indian Country. By leveraging existing public-facing data sources within a geospatial context, we explore how socio-economic factors and geography impact AI/AN reproductive health outcomes. In our approach to data visualization, we are committed to ensuring that we do not perpetuate descriptions of data that misalign with the realities of Indigenous communities, particularly in relation to state and county boundaries which may not reflect their territorial integrity or governance structures. Our maps prioritize the ten states with the largest American Indian/Alaska Native (AI/AN) populations, incorporating layers such the Purchased/Referred Care Delivery Areas (PRCDA) that reflect reservation territories rather than county boundaries. We highlight data from nationwide datasets, where AI/AN population data is often suppressed due to the small sample sizes. By examining state-level data in instances where county-level AI/AN populations are too small for individual reporting, we ensure that the data is not omitted from our work due to small sample size. We concentrate on health outcomes that disproportionately affect AI/AN communities, to address underlying factors contributing to these health disparities and suggest actionable solutions. In alignment with the Indigenous HIV/AIDS Syndemic Strategy (Syndemic Strategy), our intention is to share tools created through this project with Indigenous communities and their partners across the US. Our hope is that these tools will support the development of policy within IHS, Tribal, and Urban Indian health systems and supportive policies at the national, state, and local levels, inspiring solutions grounded in core values common across Indigenous communities to promote the health and well-being of Indigenous people.