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Background The Australian Bureau of Statistics (ABS) presently produces health data for small population groups using a Generalised Linear Mixed Model (GLMM) method. Although this method is highly effective at producing reliable local level health data, it takes several months to compile data once it’s collected. The Stratified Reweighting Method (SRM) was investigated as an innovative efficient method for producing local level health data. Methods The SRM harnesses information from both health survey and Census data. A cluster analysis of 12 Census data items creates 13 area groups with similar population demographics. A replicated survey data set is then created where each small area is bolstered by the other small areas within its area group. The survey weights from this dataset are adjusted to match Census data of each small area across several demographic variables. A final survey weight adjustment ensures consistency of the small area predictions with national survey estimates. Results Health statistics were produced for over 20 health outcomes in the latest ABS National Health Survey; and the ABS Survey of Disability, Ageing and Carers. It was found that, compared to the GLMM method: the models had lower, but still acceptable quality; the errors of prevalence estimates were similar magnitude; and the data compilation time was reduced to within two weeks. Conclusions The SRM is an efficient approach for producing acceptable quality official local health statistics. Key messages The SRM is an innovative and efficient weight-based method using health survey and population Census data to produce official local health statistics.

817Official small area health statistics using the stratified reweighting method