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Updated: Feb 26, 2026

The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice
Published on: November 1, 2015
オーストラリアにおける全身性エリテマトーデスの有病率、2010-2022年:連結された全国行政健康データを用いた人口ベース研究
Lucinda Roper1,2, Rachel Black3,4,5, Joanna Tieu3,4,5
1Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Objectives:
Systemic lupus erythematosus (SLE) is a heterogenous inflammatory condition, with widely varying global prevalence estimates. Frequency of SLE in the general population of Australia has been reported to be notably lower than contemporary estimates in countries such as the US or UK at 19-39 per 100,000 as opposed to 65-97 per 100,000. This study aimed to develop a national SLE cohort using linked administrative datasets and to estimate prevalence using this approach.
Methods:
We developed an algorithm to identify SLE cases using the National Health Data Hub, a linked national administrative data asset, including data on medications dispensed and medical services subsidised under Australia's universal subsidised coverage schemes, hospital admissions and deaths. These classification criteria were developed with an expert panel of rheumatologists. Individuals were classified as 'certain', 'uncertain', or 'no SLE'. Cohort characteristics were described, and period prevalence (2010-2022) was calculated.
Results:
A cohort of 26,788 individuals with SLE were identified, consisting of 16,294 certain cases and an additional 10,494 uncertain cases. The period prevalence of SLE in Australia from 2010-2022 was 77-127/100,000 (certain cases or overall cases). Among the certain cases, just over half had received care for SLE only in an outpatient setting.
Conclusion:
This is the first Australian SLE study using comprehensive linked administrative health data and identified higher prevalence than previously reported, more closely aligning with international estimates. This algorithm may serve as a foundation for future Australian and international studies seeking to identify SLE in administrative health datasets.
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