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Quantifying the Missingness of Indigenous Status from Two Administrative Sources in Regional Australia Using Capture-Recapture Methods

2026/06/24 by Joanne Thandrayen, Luca Maestrini, Tamara Riley +4 · 1 voice
Mathematics · Medicine · Social Sciences · #Census and Population Estimation #Data-Driven Disease Surveillance #Survey Methodology and Nonresponse

paper · pdf · doi:10.1007/s11113-026-10011-9

openalex publication_date 2026/06/24 · openalex created_date 2026/06/25 · openalex updated_date 2026/07/23

Abstract

Abstract In Australia, official reporting of health and social measures is predominantly based on administrative data sources where all citizens are asked to volunteer their Indigenous status when completing administrative processes to receive services and programs. Due to historical, sociological and political factors, missing data on Indigenous status is common and varies widely, across many administrative databases. Algorithms and linkage across multiple databases are the primary method used to decrease missingness of Indigenous status in administrative data. We used a capture-recapture approach to quantify the overall missingness of Indigenous status in linked data from the admitted public hospital and emergency department of the regional jurisdiction of the Australian Capital Territory (ACT). We analyzed Indigenous status yearly data (2010–2022). That same approach was used to measure the missingness of Indigenous status by age and sex. Capture-recapture analyses indicated that the hospital and emergency department datasets may not have identified between 35 and 45% of people who are Indigenous over the years of the study period. Our study also found that missingness of Indigenous status was higher for those aged under 45 years and Indigenous males. Our study provides evidence that missingness of Indigenous peoples in administrative data collections in the ACT is significant which has implications for health and social outcome monitoring for Indigenous peoples in the ACT. The capture-recapture method used in this study offers an innovative approach to estimating missingness in administrative databases overall and by different demographic groups.

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