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PW 2158 Examining the geographical variation of discrete and aggregate drowning events using ambulance victoria attended cases
  1. Robert Andronaco1,
  2. Bernadette L Matthews1,
  3. Emily Andrew2,
  4. Karen Smith2,3,4
  1. 1Life Saving Victoria, Victoria, Australia
  2. 2Ambulance Victoria, Victoria, Australia
  3. 3Monash University, Victoria, Australia
  4. 4University of Western Australia, Western Australia, Australia


Background Drowning has a major impact on public health, and as such is investigated using various analytical and/or environment specific approaches. Spatial analysis approaches offer an opportunity to investigate drowning with emphasis on ‘place’. As drowning can be geographically represented as individual points or as event counts or summaries, there is a need to demonstrate how different spatial data types can be used to visualise how drowning relative risk vary geographically.

Methods The study used 10 years of individually georeferenced Ambulance Victoria (AV) attended cases (January 1 st 2007 to 31 st December 2016). Being recorded as individual drowning events, they were first used to construct whole area continuous relative risk maps. By assigning the individual events to areal units and counting the events, relative risk maps were also devised for discrete areal unit maps. Additionally, as event cases were attributed a fatal and/or non-fatal descriptor, analysis was extended to examine geographical differences of the relative risk of each drowning type for both continuous and discrete areal unit maps.

Findings and conclusions Using the same underlying data source to construct both continuous and discrete form maps allows comparison of the mapped outputs. Although some inherent methodological differences are associated with each data type, they enabled comparison of not only the relative risk of drowning but a comparison of fatal and non-fatal drowning events and how they vary geographically. While visualising the relative risk of drowning is an insightful preliminary step, these methods are more often used for advanced spatial exploratory analysis and modeling tasks. The benefits and limitations of the different outputs from an injury prevention perspective will be discussed.

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