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Using Integrated Administrative Data to Identify Youth Who Are at Risk of Poor Outcomes as Adults

6.5 Target populations 15 to 19 years and 20 to 24 years (pages 3 to 6)

Targeting investment toward those who need it most based on an individualised risk measure is often difficult to accomplish, either due to practical considerations, such as the efficiency of focusing efforts at a distinct geographic area or community, or due to data limitations that could restrict the ability to calculate individualised risk. As such, it may be necessary for investment to be targeted at specific target populations identified through a smaller set of identifiable characteristics. Some potential target populations (five for people aged 15 to 19 and five for people aged 20 to 24) were identified based on regression modelling, clustering and descriptive analysis using the approach outlined above.

These populations are described in pages 3 to 6 of the A3 document - with pages 3 and 5 describing the demographic characteristics and outcomes for each population at ages 15 to 19 and 20 to 24 respectively and pages 4 and 6 indicating their geographic location.

This represents one potential way the model could be used to target services. Numerous other approaches will be equally valid, depending on the nature of investment being considered. Although an attempt was made to make the target populations as separate as possible while still being highly predictive of risk, there is some overlap between target populations, especially in the 15 to 19 age range, where the construction of the target populations relies on a wide range of characteristics.

In broad terms, the five target populations at ages 15 to 19 are constructed around a mixture of age and gender criteria alongside childhood indicators of risk such as contact with CYF or youth justice and, in the late teens, benefit receipt. Target populations in the 20 to 24 age range are largely specified on a smaller number of criteria defined largely by a history of benefit receipt and corrections sentences. This is both consistent with the fact that only corrections and welfare outcomes are able to be modelled for most of these ages and that benefit type is related to other domains, such as ill health and disability.

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