Abstract
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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 524-530 |
| Number of pages | 7 |
| Journal | Journal of Affective Disorders |
| Volume | 277 |
| DOIs | |
| Publication status | Published - 1 Dec 2020 |
| Externally published | Yes |
