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Spatial epidemiology is aimed at identifying patterns in the geographical distribution of data and can detect irregularities such as clusters of a disease or disadvantage. Where the data is available and sufficiently robust, analysis of data at small level areas such as suburbs can provide specific information to also inform these place-based initiatives [9] and specific organisational management interventions [10]. The spatial epidemiology study reported here was undertaken in 2015 as part of a translational social epidemiology study [11] to inform the design and implementation of an integrated care initiative for vulnerable families and their children in Sydney Local Health District [12], with a focus on the spatial distribution of perinatal and family social disadvantage and adversity.
Determination of an appropriate spatial scale of analysis is heavily influenced by data availability and ethical considerations; it requires an area large enough for sound quantitative analysis but not so large that extensive internal diversity could dilute (and therefore render statistically invisible) any significant spatial concentration of disadvantage [25]; and not so small that it may identify individuals. While in areas of low population density SA1 data may potentially identify individuals, this is the best level of aggregation in urban areas as it provides sufficient granularity to prevent the identification of individual subjects while avoiding the ecological effect in the representation of aggregated data [26]. 2b1af7f3a8