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 29064 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. What is known about the topic? The rapid increase in the prevalence of Type 2 diabetes (T2D), both globally and nationally within Australia, is a major concern for the community and public health agencies. Individual socioeconomic disadvantage is a known risk factor for abnormal glucose metabolism (AGM), including T2D. Although small-area-level socioeconomic disadvantage is a known correlate of AGM in Australia, less is known of the association of area-level disadvantage and glycaemic-related risk in individuals with AGM. What does this paper add? This study demonstrates a robust association between small-area-level socioeconomic disadvantage and glycaemic-related risk in regional New South Wales. The study demonstrates that it is feasible to use geocoded, routinely collected clinical data to identify communities at increased health risk. What are the implications for practitioners? The identification of at-risk populations is an essential step towards targeted public health policy and programs aimed at reducing the burden of AGM, its complications and the associated economic costs. Collaboration between primary care and public health in the collection and use of data described in the present study has the potential to enhance the effectiveness of both sectors.