Identification of Aboriginal children using linked administrative data: Consequences for measuring inequalities

Area of Science:

• Public Health
• Data Linkage
• Child Development

Background:

• Accurate identification of Aboriginal children in administrative datasets is crucial for understanding health and developmental disparities.
• Existing methods may undercount the Aboriginal population due to data source limitations.

Purpose of the Study:

• To examine how identifying Aboriginal children across multiple administrative datasets affects health and development estimates.
• To assess the impact of different data linkage algorithms on population counts and outcome measures.

Main Methods:

• Linked administrative data (birth registrations, perinatal statistics, Australian Early Development Census, school enrolments) for South Australian children (born 1999-2005).
• Six algorithms were used to derive Aboriginal ethnicity.
• Developmental outcomes (literacy, numeracy, birthweight) were compared across identification algorithms.

Main Results:

• The proportion of identified Aboriginal children varied from 1.9% to 4.7% based on the algorithm used.
• Developmental outcome estimates were significantly altered; for instance, reading proficiency increased by 12 percentage points when using linked data.
• Consistent differences in outcomes were observed across all measures based on the identification algorithm.

Conclusions:

• The number of Aboriginal children identified and associated developmental inequalities are dependent on the number and type of data sources used.
• Linking multiple administrative datasets provides a more comprehensive identification of Aboriginal children, essential for informing policy and service delivery to close developmental gaps.