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

Appendix B
Figure B17 Estimated prevalence and number of 12–17 years at risk of a mental health disorder in
Inner Sydney
Table B8 displays the estimated number of young people with mental health disorders who live within
10 and 30 km of an existing, or allocated, headspace centre. As outlined above, this analysis makes
use of small area estimates of likely mental health burden based on YMM survey data.
These data suggest that the current allocation of headspace services produces a high level of
variability in service provision across states and between metropolitan and non-metropolitan areas.
In particular, although small in overall population, the Northern Territory appears to receive a lower
allocation of headspace centres relative to the needs of the youth population. This is due to both
socio-demographic composition of the population and the number of young people who live in rural
and remote areas, which both increase the likelihood of mental health disorders and increases
the distance required to travel to obtain services. These results suggest that alternative models
of allocation which are based on estimated population need and population dispersion may be
required to achieve more equitable mental health resource allocation. It is important to note that the
current model of centre expansion prioritises allocation to areas with high levels of socioeconomic
disadvantage and rural and remote areas. As these variables are correlated with mental health risk,
the current model does tend to allocate centres to areas with greater mental health service need,
and therefore a model using mental health prevalence data, as specified above, is likely to make a
modest difference in the allocation of most centres. A comparison of allocation based on the two
methods is included in Figure B19 and Figure B20.
Again, it is important to note that these small area estimates of mental health risk are not proposed
as an optimal method of centre allocation. Rather, the inclusion of this example aims to provide an
alternative weighting methodology, which may more closely reflect mental health service demand, for
consideration by the Department. The figures provided here are not actual observed counts, but are
estimates which are constrained by the available data, and should be interpreted accordingly.
As outlined above, the geographic definition of access impacts on the interpretation of current
population coverage and remaining need. Figure B18 displays SA1s within 10- and 30 km of an
existing centre. It also includes the estimated population at risk of a mental health disorder for
the same region. This figure indicates that those areas with higher at risk populations tend to fall
outside of the 10 km definition of geographic access; however, they are likely to be considered to
have access when the 30 km definition of access is applied. This highlights the importance of the
definition of access applied to the interpretation of current coverage, and the potential disparities in
service delivery.
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