LAUSR

BACKGROUND Comorbid depression and type 2 diabetes (T2D) is an important risk factor for dementia. This study investigates the factors associated with, the spatial variation and spatial convergence of diagnosed cases of these conditions. This approach may identify areas with unmet needs. METHODS We used cross-sectional data (2010 to 2014) from 16 general practices in west Adelaide, Australia. Multi-level modelling accounting for individual-level characteristics nested within statistical area level 1 (SA1) determined covariate associations with these three diseases. Getis-Ord Gi method was used to investigate spatial variation, hot spots and cold spots of these conditions. RESULTS 1.4% of active patients in west Adelaide aged 45 and above were diagnosed with dementia, 9.6% with depression and 13.3% with T2D. Comorbidity was significant across all three diseases. Elderly age (65+ years) was significantly associated with diagnosed dementia and T2D. Hyperlipidemia or hypertension diagnosis and belonging to lower socioeconomic status were significantly associated with diagnosed T2D and depression. The spatial distribution of each disease varied across west Adelaide. Spatial convergence of the three diseases was observed in two large hot spot clusters and one main cluster of cold spots. LIMITATIONS Due to underreporting, potentially significant covariates like alcohol intake were unable to be assessed. There may be a bias towards health-conscious individuals or patients managing diagnosed diseases that actively visit their general practice. CONCLUSIONS Patterns of spatial convergence and the shared associations in dementia, depression and diabetes enable policymakers to tailor interventions to the areas where risk of these conditions are greater.