Online tool for refining socio‐economic analyses better than postcodes: the example of cardiovascular disease in type 2 diabetes in the <scp>FIELD</scp> trial

BACKGROUND: Socio-economic status (SES) is strongly linked to health outcomes but remains difficult to measure accurately. Conventional approaches based on large geographical units (e.g. postcodes) may obscure SES effects evident at smaller scales. AIMS: We aimed to address key limitations of postcode-based socio-economic analyses by developing and demonstrating a more geographically precise small-area approach. METHODS: We developed an online platform (https://onlinecalc.app/SEIFA_SA1/) that integrates small-area SES data across multiple Australian Census periods and expresses SES as percentile rankings, preserving relative positionality over time and reducing inconsistencies introduced by Census method updates. To illustrate its utility, we analysed Australian participants from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study, linking each participant's residential collection district (~200 households) to Index of Relative Socio-economic Disadvantage (IRSD) and Index of Economic Resources (IER) percentiles 5-yearly from 1986 to 2006. RESULTS: In 5147 participants, lower area-level SES was consistently associated with prevalent cardiovascular disease (CVD) across all census years, while lower SES among participants who experienced new on-trial CVD events was evident in census data from the decade before trial entry (1986-1996) and not in 2001 or later. Among participants randomised to placebo, lower SES tertiles (2001-2006 Census) were associated with higher rates of new on-trial CVD (6.9% vs 9.8% and 7.0% vs 10.0% for upper vs lower IRSD and IER tertiles respectively; both P for trend = 0.027), whereas this association was not observed in fenofibrate-treated participants, in whom total CVD events were reduced overall by 11% (P = 0.035). CONCLUSION: Assessing SES at smaller geographical scales provides a more sensitive measure of socio-economic disparities influencing cardiovascular risk. This approach underscores the value of high-resolution census data for improving equity-focused health research and policy.