Is headspace making a difference to young people’s lives?

Appendix B
and less access to mainstream services, and consequently, a greater need for headspace services.
However, additional factors may be associated with the prevalence of mental health disorder in young
people, and identification of such factors, which can be incorporated into the model of future centre
allocation, could result in more effective and efficient resource allocation. It is important to note that
this modelling is constrained by data availability. Rather than providing an optimal weighting strategy
for centre allocation, this analysis aims to provide an alternative methodology for consideration by the
Department.
Small area estimates of prevalence of mental health disorders
Method
YMM wave one data were used to determine socio-demographic factors associated with the
prevalence of mental health disorders in young people. A Poisson regression model was fitted to the
YMM data at the SA1 level to predict the prevalence of mental health disorder by socio-demographic
variables which were individually associated with prevalence of disorder. Variables included in the
model were limited to those which are available for all small areas across Australia and that were
collected in YMM and could be matched to census data. This model was applied to census data
at the small area level (SA1) to allow for estimation of the prevalence of mental health disorders in
young people across the whole of Australia.
In order to asses current and likely access for young people with a mental health disorder, the
number of young people within 10 and 30 km of headspace centres for existing Rounds 1-8 and
hypothetical Rounds 9-14, using the current model of allocation, was calculated using the method
described above. It is important to note that implicit in this model, and any extrapolation based on it,
is the assumption that the demographic characteristics of 4-17 year olds are similar to those of 18-25
year olds.
Results
Socio-demographic factors which were identified as being individually associated with the prevalence
of mental health disorders in young people were:
• SEIFA
• income
• family type
• Indigenous status
• housing tenure
• language spoken at home
• born overseas.
This approach represents a potential methodological improvement over an approach which allocates
resources on the basis of SEIFA and remoteness. Although SEIFA and remoteness are intended as
proxies for disadvantage and potential service need, the weights applied are not clearly justified. In
contrast, the small area estimation process empirically derives the association between risk factors
and mental health disorders. The small area estimation process includes a mix of household and
individual level factors (such as income, family type) and SEIFA, which is an area-level estimate.
There are substantial differences in the prevalence of mental health disorders in young people across
geographic areas in Australia, at geographic levels germane to service delivery. For example, Figure
B16 indicates substantial differences in the prevalence of mental health disorders in the Sydney
metropolitan area at the SA1 level. Figure B16 displays the prevalence and number of young people
in Inner Sydney who are estimated to be at risk of a mental health disorder. This figure displays the
catchments of five headspace centres. These centres include headspace Camperdown, headspace
Ashfield, headspace Chatswood, headspace Brookvale and headspace Bondi Junction. These
figures aim to demonstrate the differences between the two indicators of demand for any defined
service catchment area, those indicators being the prevalence of risk of mental health disorder
and number of young people residing in the area. For example, many small areas (SA1s) within the
central Sydney area have a low prevalence of disorder, relative to other areas in Australia. However,
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