Indicator technical specifications
The information below provides technical specifications for the summary indicator data presented in the quick reference guide.
National Framework Indicator 3.4 Homelessness: Rate of children aged 0-17 years who receive assistance through homelessness services (accompanied and unaccompanied)
|
Definition |
Data source |
Numerator |
Number of children aged 0-17 years who received assistance through Specialist Homelessness Services (accompanied and unaccompanied) in the reference period |
Specialist Homelessness Services data collection |
Denominator |
Number of children aged 0-17 years at 30 June |
AIHW Population Database (sourced from ABS Australian Demographics Statistics) |
Explanatory notes
All children at risk of homelessness are not captured in the Specialist Homelessness Services (SHS) data collection; only those who sought and received assistance are included.
Assistance from SHS includes any service received (for example, shower or meal), not only accommodation. However, all SHS clients are either homeless, or at risk of homelessness, regardless of the service type they receive.
The national SHS data collection was implemented on 1 July 2011, replacing the Supported Accommodation Assistance Program (SAAP) data collection.
The COVID-19 pandemic and the resulting Australian Government closure of the international border from 20 March 2020, caused significant disruptions to the usual Australian population trends. This report uses Australian Estimated Resident Population (ERP) estimates that reflect these disruptions.
Accordingly, in the year July 2020 to June 2021, the overall population growth was much smaller than the years prior and in particular, there was a relatively large decline in the population of Victoria. ABS reporting indicates these were primarily due to net-negative international migration (National, state and territory population, June 2021 | Australian Bureau of Statistics (abs.gov.au)).
Please be aware that this change in the usual population trends may complicate your interpretation of statistics calculated from these ERPs. For example, rates and proportions may be greater than in previous years due to decreases in the denominator (population size) of some sub-populations.