Answers are grounded in this paper and cite their sources.
This analysis generates spatially consistent socio-economic indexes using census data, suggesting improvements for longitudinal studies.
National census data are frequently used as a source of area-level socioeconomic information. However, this data is less suitable for longitudinal analysis due to infrequent data collection and changes to administrative boundaries of census areas. This study aims to address these limitations by generating annual, geographically consistent Socio-Economic Indexes for Areas (SEIFA) from the Australian Bureau of Statistics (ABS) for 1996 - 2021. Geographic Information Systems (GIS) were used to update historic spatial data to align with a set of contemporary boundaries. SEIFA estimates for non-census years were generated using linear interpolation. The final dataset includes 59,421 Statistical Area 1 s (SA1) after excluding areas with no SEIFA data for any census year. The methodology resulted in annual, spatially, and temporally consistent SEIFA data from 1996 to 2021 standardised to the 2021 SA1 boundaries. Creating annual SEIFA data at a small geographic scale addresses key challenges associated with tracking area-level socioeconomic factors over time. By standardising data across multiple years, this approach maintains consistency in geographic units, to overcome potential limitations of using census data in longitudinal research.
No takes yet. Share an insight, caveat, or question.
Reid et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: