Cross-sectional study of area-level disadvantage and glycaemic-related risk in community health service users in the Southern.IML Research (SIMLR) cohort

Objectives. The aim of the present study was to determine the association between area-level socioeconomic disadvantage and glycaemic-related risk in health service users in the Illawarra-Shoalhaven region of New South Wales, Australia. Methods. HbA1c values recorded between 2010 and 2012 for non-pregnant individuals aged 18 years were extracted from the Southern.IML Research (SIMLR) database. Individuals were assigned quintiles of the Socioeconomic Indices for Australia (SEIFA) Index of Relative Socioeconomic Disadvantage (IRSD) according to their Statistical Area 1 of residence. Glycaemic risk categories were defined as HbA1c 5.0-5.99% (lowest risk), 6.0-7.49% (intermediate risk) and 7.5% (highest risk). Logistic regression models were fit with glycaemic risk category as the outcome variable and IRSD as the study variable, adjusting for age and sex. Results. Data from 29 064 individuals were analysed. Higher disadvantage was associated with belonging to a higher glycaemic risk category in the fully adjusted model (most disadvantaged vs least disadvantaged quintile; odds ratio 1.74, 95% confidence interval 1.58, 1.93; P < 0.001). Conclusion. In this geocoded clinical dataset, area-level socioeconomic disadvantage was a significant correlate of increased glycaemic-related risk. Geocoded clinical data can inform more targeted use of health service resources, with the potential for improved health care equity and cost-effectiveness.