368 Ethnic differences in heart failure incidence in New South Wales, Australia: a novel approach using Census-data-linkage

OP 37: Health Status 3, B210 (FCSH), September 5, 2025, 11:30 - 12:30 Aims Ethnic inequalities in heart failure in Australia have not been described due to lack of routine collection of ethnicity data in Australia’s non-Indigenous population. We have linked health datasets to the Census to examine heart failure incidence by ethnicity in New South Wales (NSW), Australia. Methods We created a population-based cohort study of all residents of NSW aged ≥ 25 years who responded to the 2016 Census and could be linked to the Person Linked Integrated Data Asset (PLIDA) (n = 4,609,869). To estimate incident heart failure, we excluded those with a documented history of heart failure in the past 10 years. Incident cases of heart failure were identified from hospital data or deaths using relevant ICD-10 codes. Ethnicity was measured by self-reported ancestry in the 2016 Census with categorisations informed by an expert and community panel. Results In preliminary results, the age standardised incidence of heart failure from 2016 to 2022 was 203 per 100 000 person-years in females and 285 per 100,000 person-years in males. The incidence of heart failure was higher in those of Māori (females=420 per 100,000; males=746 per 100,000), Pasifika (females=371 per 100,000; males=566 per 100,000), North African and Middle Eastern (females=285 per 100,000; males=366 per 100,000) and Central Asian ethnicity (females=256 per 100,000; males=336 per 100,000). All other ethnic groups had heart failure incidence that was equivalent to or lower than the population average. Conclusion We found substantial ethnic inequalities in the incidence of heart failure in a whole-population data linkage study. These inequalities by ethnicity in Australia’s non-Indigenous population have not been previously described and highlight groups in the population experiencing cardiovascular health disadvantage. Further analyses will explore how these inequalities intersect with measures of socioeconomic position and migration factors, such as visa class.