Background

Indigenous Data Sovereignty is increasingly recognised as a foundation to ethical and effective public health research and data practices, yet there is limited evidence describing how these approaches can be implemented within Indigenous community-led health information systems. Yarrabah Counts was developed in response to community concerns regarding under-enumeration in national datasets, limited locally relevant indicators, and the need for evidence aligned with community priorities.

Methods

Partnerships and governance agreements were established with community leaders and groups to guide all stages of the project, including design, implementation and data use. Two data tools were implemented simultaneously: a community designed household census capturing population, housing and infrastructure data, and the national Mayi Kuwayu study, collecting individual-level data on culture, health and wellbeing. Local community researchers were employed and trained to support the data collection, sampling and culturally appropriate engagement. Data systems and analytical processes were developed to enable ongoing community governance and the use of data for planning and monitoring.

Results

The integrated system generated locally governed data across both household and individual levels, supporting monitoring of community defined priorities within the Yarrabah Leaders Forum Seven Pillar Community Development Strategy. Findings from the household census suggested higher population estimates than national census data, indicating likely under-enumeration, along with a high prevalence of housing and infrastructure issues, particularly in overcrowded households. When combined with Mayi Kuwayu data, these outcomes could be interpreted alongside broader measures of health, culture and wellbeing to support planning and policy making.

Conclusion

Yarrabah Counts shows how community-led health information systems can combine local and national data under community governance to produce more meaningful and actionable evidence. The project provides a practical model for strengthening data quality, supporting local decision-making and improving alignment between community needs, planning and investment.