Suicide & self-harm monitoring
Australian Youth Self-Harm Atlas: Suicidality and self-harm among young people by region
On this page In this section
Background 12-month suicidality and self-harm prevalence among 12 to 17-year-olds Associations between risk and protective factors, and 12-month self-harm prevalence among 12 to 17-year-olds Study limitations and important data interpretation considerations Download data tablesBackground
The Australian Youth Self-Harm Atlas study investigated regional variability in suicidality and self-harm, as well as risk and protective factors, for young people aged 12 to 17 years. The full Australian Youth Self-Harm Atlas study included both quantitative (Hielscher et al. 2022; Hielscher et al. 2024) and qualitative components (Hielscher et al. 2022). Aspects of the quantitative component of the study are presented here.
Strengthening suicide prevention
The National Mental Health and Suicide Prevention Agreement (Commonwealth of Australia, 2022) identifies the importance of strengthening regional planning and evaluation of suicide prevention initiatives. To do this, detailed regional data are needed.
The Australian Youth Self-Harm Atlas study:
- Is the first national Australian study to estimate the variability of youth self-harm and suicidality, across small areas of geography (Hielscher et al. 2022; Hielscher et al. 2024).
- Distinguishes between self-harm without suicidal intent, suicidal ideation/planning, and suicide attempt. This differentiation has service and program planning implications but is not often available within administrative datasets.
- Data are representative of whole communities, rather than being limited to the experience of those using hospital (or other healthcare) services.
While identifying communities whose residents are not faring as well as others may be seen as stigmatising, the purpose for doing so is to provide evidence upon which community members and decision-makers can rely.
Data sources
The Australia Youth Self-Harm Atlas study generated synthetic estimates of youth suicidality and self-harm using:
- Young Minds Matter (YMM) survey. A nationally representative household survey about the health and wellbeing of children and young people conducted between 2013 and 2014.
- 2016 Census of Population and Housing.
- 2019 Australian Bureau of Statistics Estimated Resident Population data.
Generating synthetic estimates
The Australian Youth Self-Harm Atlas study generated synthetic estimates to enable measurement of suicidality and self-harm prevalence, and related risk and protective factors for small areas.
The Young Minds Matter (YMM) survey data holds information about suicidality and self-harm among young people that completed the survey. While the sampling strategy used for the survey was scientifically robust, not every community across Australia was invited to participate (Hafekost et al., 2016). As such, it is not possible to use YMM data to directly measure suicidality and self-harm among the young people within each community across Australia. To solve this problem, small area estimation methods were used to produce synthetic prevalence estimates of youth suicidality and self-harm for Statistical Area level 1s (SA1) across Australia. SA1s are a standardised measure of geography and part of the Main Structure of the Australian Statistical Geography Standard (ASGS), developed by the Australian Bureau of Statistics. SA1s generally have a population of 200 to 800 people, and an average population of about 400 people.
The small area estimation undertaken involved linking Young Minds Matter survey data with 2016 Census data. Noting that 2016 Census data are available for all SA1 areas, whereas the survey data are only available for those SA1 areas that were invited to participate. Patterns in responding for those who completed both the Young Minds Matter (YMM) survey and the 2016 Census were then used to extrapolate responses to the youth suicidality and self-harm YMM survey questions for communities that were not actually invited to complete the survey. Data generated in this way, using sophisticated statistical models, are referred to as synthetic estimates. Synthetic estimates generated for SA1 areas were then summed together and presented at broader areas of geography. Synthetic estimates were presented in this publication at SA3, SA4 and Primary Health Network (PHN) areas.
Australian Bureau of Statistics Estimated Resident Population data for 2019 were used to calculate suicidality and self-harm prevalence estimates for geographic areas.
As a means of external validation, synthetic suicidality and self-harm prevalence estimates were compared to rates of death by suicide. At an SA2 level, each of the suicidality and self-harm measures used within the study were positively correlated with the average annual rate of death by suicide between 2010-2019.
Synthetic estimates based on small numbers of young people were suppressed to maintain confidentiality and avoid publishing statistics of low reliability.
The Australian Youth Self-Harm Atlas study includes the following suicidality and self-harm outcomes:
- Self-harm (regardless of intent): self-injurious behaviour irrespective of intent or motivation, including behaviours with either suicidal or non-suicidal intent, or where intent is ambiguous. This was the primary outcome of this study (inclusive of non-suicidal self-harm and suicide attempt behaviour).
- Non-suicidal self-harm: self-injurious behaviour for which there is evidence that the person did not intend to kill themselves.
- Suicidal ideation/plans: thoughts of engaging in or planning suicide-related behaviour; without engaging in suicidal behaviour.
- Suicide attempt: non-fatal, self-directed, potentially self-injurious behaviours with an intent to die.
- Suicidality: suicidal thoughts or behaviours, including ideation, plans, and attempts.
Study limitations and important data considerations
The information provided by the Australian Youth Self-Harm Atlas Study may be the best available small area data for youth suicidality and self-harm.
Even so, there are important limitations to consider included within the Study limitations and important data considerations section of this publication.
The study team
The Australian Youth Self-Harm Atlas study was undertaken by a team of researchers and clinicians, and at the heart of the project was a partnership between Queensland Institute of Medical Research (QIMR) Berghofer and Roses in the Ocean. Roses in the Ocean is a lead Australian organisation for lived experience of suicide.
The AIHW has worked in collaboration with Youth Self-Harm Atlas study authors, Dr Emily Hielscher (formerly of Queensland Institute of Medical Research (QIMR) Berghofer) and Professor David Lawrence (Curtin University), to integrate quantitative findings of the study into the AIHW Suicide and Self-Harm Monitoring website.
12-month suicidality and self-harm prevalence among 12 to 17-year-olds
About these maps
These maps visualise synthetic estimates of 12-month prevalence of suicidality and self-harm (for 2019) among 12 to 17-year-olds. Twelve-month prevalence refers to the prevalence of having experienced suicidality and self-harm at some point during the preceding twelve-month period. Each of the study suicidality and self-harm outcomes are visualised within a separate map.
Interpreting these maps
For these maps, variation in synthetic estimates has been visualised using percentiles. For example, those areas with the darkest colouring fall within the >90th percentile group, meaning that the 12-month prevalence of suicidality and self-harm was higher in those areas than 90% of all other areas in Australia.
Synthetic estimates are available in these maps at SA3, SA4 and PHN areas.
Darker colouring indicates that an area has a higher estimated prevalence of suicidality and self-harm, while lighter colouring indicates lower estimated prevalence. Grey colouring indicates that an estimate was not generated for that area due to insufficient data. This is mostly areas where few or no people live.
The data can be viewed at different geographies using the ‘Geographic View’ or the ‘Zoom View’. More detailed instructions on using the ‘Geographic View’ are included below.
These maps show
- Large variability across the country for self-harm (regardless of intent), non-suicidal self-harm, suicide attempt, suicidal ideation/plan, and suicidality.
- The Northern Territory had the highest prevalence of self-harm (regardless of intent).
- A possible trend towards increasing prevalence with increasing remoteness. However, a sparsity of Young Minds Matter (YMM) survey data for more remote areas meant that estimates for some more remote areas were not able to be generated. As such, the possible relationship between the suicidality and self-harm prevalence and remoteness cannot be fully assessed.
The ‘Geographic View’ allows users to select the type of area (that is SA3, SA4, PHN, State) that data are aggregated to, as well as the type of area for which geographic boundaries are displayed. For example, a user may choose to view data for a suicidal or self-harm prevalence outcome aggregated to SA3 areas, but with PHN boundaries displayed. In this view, the user can visually inspect the variability between SA3 areas within each PHN.
To do this the user selects only SA3 from the ‘Geographical Boundary’ menu and only ‘PHN Level’ from the ‘Suicidality and Self-Harm Prevalence’ menu within the mapped product. Click the triangle next to the menu title to expand and collapse the options for each menu. Then ensure only ONE option is selected from each menu. When the eye icon is open the option is selected. When the eye icon has a diagonal line through it, the option is not selected. Click the eye icon to select and deselect options.
The eye icon directly next to both the menu titles should be open.
Associations between risk and protective factors, and 12-month self-harm prevalence (irrespective of intent) among 12 to 17-year-olds
About these maps
These maps visualise area level co-occurrence of 12-month self-harm prevalence (irrespective of intent) and a limited number of risk and protective factors. This means looking at the total prevalence of youth self-harm (irrespective of intent) within an area and the prevalence of a risk or protective factor within the same area.
Protective factors are those hypothesised to be associated with lower self-harm prevalence. Risk factors are those hypothesised to be associated with higher self-harm prevalence.
Australian Youth Self-Harm Atlas study authors selected risk and protective factors based on a literature review and expert knowledge. However, the selected factors do not encompass all those potentially relevant to youth suicidality and self-harm. Only three of the eight risk and protective factors included within the Australian Youth Self-Harm Atlas study are presented in this publication.
The co-occurrence of 12-month self-harm prevalence (irrespective of intent) and three of the risk and protective factors are each visualised within separate maps.
The Australian Youth Self-Harm Atlas study risk and protective factors included within this publication are:
Major depression and anxiety disorders among 12 to 17-year-olds:
Area level proportion of young people, aged 12 to 17 years, who experience anxiety or depression over a 12-month period. Synthetic estimates generated from combining Young Minds Matter (YMM) survey data and 2016 Census data.Socio-economic decile:
The Area-level Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) decile. Derived from 2016 Census data. Higher IRSAD scores (and deciles) indicate relatively low financial disadvantage and high financial advantage.Percentage of 12 to 17-year-olds that are male:
Area level proportion of 12 to 17-year-olds that are male. Derived from 2016 Census data.
Interpreting these maps
It is important to remember that these maps do not provide evidence that a risk or protective factor caused lower or higher youth self-harm in an area.
Conclusions can only be drawn about experiences of young people at the level of the geographic area under consideration. No inferences can be made about the experiences of individual young people. See the Data interpretation considerations section of this publication for more information.
On these maps dark blue colouring represents areas where both self-harm and the factor of interest have higher prevalence (that is a strong positive association).
Dark pink colouring represents areas where self-harm prevalence is high, and the risk/protective factor of interest is low (that is a strong negative association).
The strong aqua colouring represents areas where self-harm prevalence is low, and the risk/protective factor of interest is high.
The off-white colouring represents areas where both self-harm and the risk/protective factor of interest have lower prevalence.
Grey colouring indicates that an estimate was not generated for that area due to insufficient data.
Both self-harm and the risk or protective factors are visualised using quantiles cut-offs. Synthetic estimates are available in these maps at SA3 areas only.
These maps show:
- There is geographic variation in the relationships between youth self-harm and risk and protective factors across Australia.
- There were differences in the pattern of relationships between youth self-harm and risk and protective factors between regional and metropolitan areas. Generally, there was greater diversity in relationship size and direction among metropolitan areas.
Major Depression and Anxiety Disorders among young people aged 12 to17 years:
Particularly across remote areas of Western Australia, Northern Territory, South Australia, and Far North Queensland, higher youth self-harm prevalence is associated with higher prevalence of major depression and anxiety disorders among people aged 12 to17 years. However, there are areas where higher self-harm prevalence is associated with lower depression and anxiety prevalence, and vice versa. Predominately within Victoria and Tasmania, there are outer regional and remote areas where lower self-harm prevalence is associated with lower depression and anxiety prevalence.
Socio-economic Advantage and Disadvantage:
Broadly, lower socio-economic advantage (lower socio-economic decile) was associated with higher youth self-harm prevalence. However, mostly (but not exclusively) within major capital cities, there were areas in which higher socio-economic advantage (higher socio-economic decile) was associated with higher youth self-harm. There are also areas, predominately across western, central, and far north-eastern New South Wales, western Victoria, and Tasmania, where lower socio-economic advantage is associated with lower youth self-harm prevalence.
Males aged 12 to 17 years:
Most areas in which youth self-harm prevalence is lower and the proportion of 12 to17-year-olds that are male is higher are concentrated across Tasmania, Victoria, and New South Wales. There are also areas, largely across remote Western Australia, South Australia, Far North Queensland, and in proximity to the east coast, where higher youth self-harm is associated with lower proportion of young males, aged 12 to 17 years.
Study limitations and important data interpretation considerations
Study limitations
The nature of synthetic small area estimates
The synthetic small area estimates modelled may be different to the number of actual cases of youth self-harm and suicidality with communities. As with all statistical models, the model used for the Youth Self-Harm Atlas study have underlying assumptions, which if violated, may adversely impact the accuracy of estimates.
The model used for the Youth Self-Harm Atlas study assumes rates of youth self-harm and suicidality of small areas can be determined on the basis the socio-demographic characteristics of the area. Further, that the relationship between youth suicidality and self-harm, and socio-demographic characteristics does not vary substantially between areas.
Areas with limited data
Young Minds Matter survey data was sparse for remotes areas of Western Australia and the Northern Territory, which may adversely impact the accuracy of self-harm and suicidality estimates for these areas.
The timeliness of data used
Data sources used for the study were the Young Minds Matter survey collected during 2013-2014, the 2016 Census, and 2019 Estimate Resident Populations. It is unknown whether the prevalence of youth self-harm and suicidality reported in 2013-14 and the socio-demographic characteristics of young people in 2016, accurately reflect the contemporary experience of young people.
Nature of the associations visualised within the ‘risk and protective factors' maps
Area level relationships between self-harm and risk and protective factors are likely interrelated and complex in nature. The risk and protective factors for self-harm prevalence maps cannot account for this complexity and instead display only the relationship between a single measured risk or protective factor and a single measure of self-harm. Therefore, the true association between the risk or protective factor and self-harm may be smaller or larger than what is presented in the maps.
Data interpretation considerations
Use of separate statistical models for self-harm and suicidality outcomes
The Youth Self-Harm Atlas used separate statistical models to generate estimates for each self-harm and suicidality outcome included within the study. Each model generated best estimates for each of the study outcomes separately. In addition, some outcome variables had more data available to generate estimates compared to others. This is because Young Minds Matter survey participants can choose to respond that they “prefer not to answer” a question.
Due to this modelling design, there may be small inconsistencies where outcome estimates do not exactly add to the total estimate. For example, in South Western Sydney PHN, the estimated 12-month non-suicidal youth self-harm was 10%, and the total estimated self-harm (regardless of intent) was 9.4%.
Area level and individual person level data
When interpreting the numeric data of the Youth Self-Harm Atlas, inferences can only be drawn about experiences of young people at the level of the geographic area under consideration. Just as there is variation between area level outcomes, there is also variation, and sometimes substantial variation, within areas. This means that area level findings do not apply to every individual living within the area. It is not appropriate to use data or information generated by the Youth Self-Harm Atlas to make inferences about the experience of individual young people. This is regardless of whether the inference is made about a specific young person or about individual young people in general terms. Erroneously drawing conclusions about individual people based on aggregated data for a group of people, is known as the ecological fallacy (Firebaugh, 2015).
Aggregating data to different types of geography
Analysis of the same dataset about individual people may provide different results depending on the size and shape of the geographic areas used to aggregate the data. This problem, which effects all spatial analysis of aggregated data, is referred to as the modifiable areal unit problem (Wong, 2009; Lloyd, 2014; Tuson et al., 2019). The Youth Self-Harm Atlas data are presented using different types of standardised geographic geographies commonly used by governments and healthcare providers.
Co-occurrence is not causation
The Australian Youth Self-Harm Atlas study investigates regional variability in suicidality and self-harm, as well as risk and protective factors, for young people aged 12–17 years of age. The study also explores area level co-occurrence of self-harm and risk and protective factors. The study does not investigate or provide evidence that the risk or protective factors may be causing self-harm (or vice versa).
Download data tables
Supplementary tables
Young people – Suicidality and self-harm among young people by region - Australian Youth Self-Harm Atlas
The Commonwealth of Australia (2022) The National Mental Health and Suicide Prevention Agreement. The Federal Financial Relations website, accessed 3 March 2023.
Wong, D. (2009). The Modifiable areal unit problem (MAUP). In A. S. Fotheringham, & P.A. Rogerson, The SAGE handbook of spatial analysis (pp. 105-120). SAGE Publications, Limited. https://doi.org/10.4135/9780857020130.n7
Firebaugh, G. (2015). Ecological fallacy, statistics of. In J. D. Wight (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed., vol. 6, pp. 865–867). Elsevier Ltd. https://doi.org/10.1016/B978-0-08-097086-8.44017-1
Hafekost, J., Lawrence, D., Boterhoven De Haan, K., Johnson, S. E., Saw, S., Buckingham, W. J., Sawyer, M. G., Ainley, J., & Zubrick, S. R. (2016). Methodology of Young Minds Matter: The second Australian child and adolescent survey of mental health and wellbeing. Australian & New Zealand Journal of Psychiatry, 50(9), 866–875. https://doi.org/10.1177/0004867415622270
Hielscher, E., Chang, I., Hay, K., McGrath, M., Poulton, K., Giebels, E., Blake, J., Batterham, P., Lawrence, D., and Scott, J. (2022). Australian Youth Self Atlas – Summary Report. QIMR Berghofer Medical Research Institute: Brisbane, Australia.
Hielscher, E., Hay, K., Chang, I., McGrath, M., Poulton, K., Giebels, E., Blake, J., Batterham, P., Scott, J. G. and Lawrence, D. (2024) Australian Youth Self-Harm Atlas: Spatial modelling and mapping of self-harm prevalence and related risk and protective factors to inform youth suicide prevention strategies. Epidemiology and Psychiatric Sciences, 33, e34, 1-14. https://doi:10.1017/S2045796024000301
Lloyd, D. (2014). Exploring spatial scale in geography. John Wiley & Sons. https://doi.org/10.1002/9781118526729.ch3
Tuson, Yap, M., Kok, M. R., Murray, K., Turlach, B., & Whyatt, D. (2019). Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem. International Journal of Health Geographics, 18(1), 6–6. https://doi.org/10.1186/s12942-019-0170-3