Indigenous epidemiology: can new methods and new data science really avoid data colonisation? — The Association Specialists
  1. Schedule
  2. Oral Presentations - Indigenous and community-led and governed data collection, measurement and reporting
  3. Indigenous epidemiology: can new methods and new data science really avoid data colonisation?

Indigenous epidemiology: can new methods and new data science really avoid data colonisation? (23931)

Andrew Sporle 1 , Nicole Satherley 1 , Tori Diamond 1 , Gemma Ngari 1 , Ruby Pankhurst 1 , Tom Elliott 1
  1. Inzight Analytics Limited, Mount Eden, AUCKLAND, New Zealand

Background

Epidemiology is increasingly global. Data gathering and analysis tools are readily available from multinational companies, while data storage and management tools are increasingly cloud-based and accessible. Research funding is increasingly international and requiring diversity in participation. Concurrently, the emergence of AI tools has resulted in pressure to access data that are new or different to inform performance improvement of proprietary LLMs .

Methods

This presentation considers the potential contributions of new methods and open source tools to indigenous epidemiology in the context of the global provision of readily accessible data gathering, storage and analysis tools. 

Results

New statistical methods and data science tools do provide the means to describe and investigate outcomes for indigenous populations at a level of precision not previously possible. This includes demonstrating the limitations of current official statistics data sources and data management methods, while creating new opportunities for data collection. Emerging open-source tools can provide solutions to data storage and analysis in ways that reduce but not remove the technical barriers to data use. The increased use of common medical record tools in the Pacific creates opportunities for coordinated and consistent methods, but all these opportunities are constrained by data storage and management limitations that can also compromise data sovereignty.

Conciusion.

Realising the potential of new epidemiological methods and technology requires consideration of the audience for our work as epidemiologists. These new tools could empower new audiences beyond central government decision making, but that would requires commitment to building local data gathering and information implementation systems that meet the needs of those new audiences. These developments need to based on data sovereignty principles and open source tools to limit the potential of data colonisation by big tech. This requires a focus on building Pacific indigenous data governance, sovereign data systems and workforce capability.