Caution: Some people may find parts of this content confronting or distressing.
Please carefully consider your needs when reading the following information about suicide and self-harm. If this material raises concerns for you contact Lifeline on 13 11 14, or see other ways you can seek help.
The information included here places an emphasis on data, and as such, can appear to depersonalise the pain and loss behind the statistics. The AIHW acknowledges the individuals, families and communities affected by suicide each year in Australia.
Aboriginal and Torres Strait Islander readers are advised that information relating to Indigenous suicide and self-harm is included.
The AIHW supports the use of the Mindframe guidelines on responsible, accurate and safe suicide and self-harm reporting. Please consider these guidelines when reporting on statistics on the monitoring of suicide and self-harm.
There is growing evidence that social factors, including education, employment status, income level and wealth, play an important role in determining the risk of suicide in high income countries (Blakely et al, 2003).
A combination of factors contribute to someone considering suicide. Although some social factors may be associated with an increased risk of suicide, they are not a direct cause.
Understanding how social factors affect the risk of suicide is important to better inform strategies to reduce suicide in Australia and may help in the planning of more effective evidence-based prevention and intervention programs.
Using linked data from the Multi-Agency Data Integration Project (MADIP), the AIHW conducted two studies to identify social characteristics associated with greater risk of death by suicide. While these two pieces of work are distinct, together they add to the growing understanding of population-level influences on suicide deaths in Australia.
The MADIP is a partnership among Australian Government agencies to link administrative and survey data. These studies used de-identified Australian Census of Population and Housing (2011) data linked with 7 years of Death Registrations (2011 to 2017). For more detailed information on the MADIP and linkage methods used, see Technical notes.
Data linkage combines information from multiple sources, while preserving privacy. All linked data sets used for analysis at the AIHW comply with legislative and regulatory standards, are securely stored and accessed, and meet ethical standards and community expectations. Protocols are in place to prevent privacy breaches or the unauthorised identification of individuals, and to ensure data security and restricted access to information.
The initial analysis, Educational attainment, employment and deaths by suicide, found that the cumulative risk of suicide in Australia is higher in those with fewer years of education and is lower among those who are employed. These results have been reported previously on Suicide and self-harm monitoring.
A further analysis, Association between socioeconomic factors and deaths by suicide: a modelling study, reported here for the first time, developed statistical regression models to examine the association between the 10 identified predictive socioeconomic factors from the 2011 Census and deaths by suicide in Australia. The difference between this approach and the previous cumulative risk analysis, is that regression allows for adjustment for the various risk factors for suicide, which may make estimates more precise.
The multivariate (multiple variables) regression model showed that the strongest associations with deaths by suicide (relative to respective reference groups, and after adjusting for other variables in the model) included:
Results for other variables are reported below.
Univariate and multivariate competing risk models were used (Fine and Gray, 1999). Results of sex stratified models are also reported, these are multivariate models split by males and females to investigate the interactions within the sex.
See Technical notes for further information on the data and methods used.
Generally, results from the modelling show important differences in the relationship between deaths by suicide and the different socioeconomic factors, relative to comparison groups, as seen in the forest plot below.
Estimates presented are hazard ratios for the group of interest compared with a reference group. Reference group values are indicated as the dotted line at 1. A hazard ratio (HR) indicates how many times higher the probability of an event is in one group of people with a particular characteristic than in another group without that characteristic, after adjusting for other factors in the model. The size of the reported hazard ratio indicates the strength of the relationship a social factor has to deaths by suicide, relative to the reference group.
Ninety-five per cent (95%) confidence intervals are also presented to indicate the statistical precision and significance. The result is interpreted as having a statistically significant impact (that is, not due to chance) if the interval does not cross the value of 1.
This chart shows the output from competing-risks regression models to explore the association between socioeconomic factors and deaths by suicide. For simplicity and ease of understanding, the model estimates are reported as hazard ratios.
Results from four models: univariate, multivariate, stratified: males and stratified: females can be displayed in this chart. The univariate model does not adjust for the other socioeconomic factors, while multivariate model adjusts for all other factors. The stratified: males and stratified: females models are multivariate models for only males and females, respectively.
The modelling carried out includes only a subset of known factors that may influence deaths by suicide. Results from this analysis need to be interpreted with caution and within the context of the information provided. For example, due to data quality and availability, known associated factors such as mental health status and past-history of self-harm are not included in this modelling.
Results of the multivariate analysis showed that from September 2011 to December 2017, when adjusting for other factors in the model:
When separated by sex and adjusting for other factors, important differences were:
The key datasets used in MADIP modelling were:
This analysis was carried out in consultation with the Australian National University, the University of Melbourne and the University of Western Sydney.
Biddle N & Marasinghe D 2021. Using census, social security and tax data from the Multi-Agency Data Integration Project (MADIP) to impute the complete Australian income distribution. Tax and Transfer Policy Institute – Working Paper 8/2021.
Fine J & Gray R 1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association 94(446): 496-509.
Hernan MA 2010. The hazards of hazard ratios. Epidemiology 21(1): 13–15.
The estimated suicide risk (measured as the age-adjusted cumulative incidence (risk) from 2011–2017) is higher among those with fewer years of education, as reported at Census 2011.
Among males with only secondary school or no education the cumulative suicide risk is 2.6 times higher than among males with a university degree (Table 1).
The education gradient in female suicide mortality was consistent with that seen for males, but the ratio is smaller (1.6 times) between the highest and lowest levels of educational attainment. These estimates are the first for Australia, and like other countries, show a strong relationship between educational attainment and the risk of suicide.
The estimated suicide risk is higher among males than females at all levels of educational attainment.
Among males with only secondary school or no education the cumulative suicide risk is 3.5 times higher than among females with the same level of educational attainment (Table 2).
The gap is smallest for those with a university degree with the suicide risk for males 2.2 times higher than females.
Estimated suicide risk, by highest educational attainment and labour force status, by sex, aged 25–54 years, Australia, 2011 to 2017.
The vertical bar chart shows age-adjusted cumulative risk of suicide for males and females by highest level of educational attainment (secondary school or lower; diploma, certificate; and bachelor degree or higher). Users can also choose to view age-adjusted cumulative risk or proportion by labour force status. The lowest age-adjusted cumulative suicide risk among males was in those with a bachelor degree or higher while the highest age-adjusted cumulative suicide risk was seen in those with secondary school or lower as their highest level of educational attainment.
Estimated suicide risk (measured as the age-adjusted cumulative incidence from 2011–2017) is lower among those with a job, as reported at Census 2011.
Among males of prime working age (25–54 years) who were not in the labour force (people who are neither working nor looking for work):
For males who were not in the labour force the cumulative suicide risk was actually a little higher (rate ratio of 1.3) than for males who were unemployed at the time of the 2011 Census. This reminds us that in thinking about the relationship between labour force status and suicide, it is important to focus on people of workforce age who are not employed, regardless of whether they are classified as being unemployed.
Among females, employment is also associated with the lowest suicide risk but did not vary greatly between those not in the labour force and those unemployed.
The cumulative suicide risk for females not in the labour force was:
Year 12 and below : Bachelor degree and higher
A social gradient describes a spectrum from high to low socioeconomic position and shows that, in general, the lower an individual’s socioeconomic position the worse their health (WHO, 2020). While a social gradient was evident in both male and female employment circumstances, the estimated suicide risk is considerably higher for males than females across all 3 labour force categories (Table 2).
Among males not in the labour force the cumulative suicide risk is 4.4 times higher than among females not in the labour force.
Year 12 and below
Diploma or Certificate
Bachelor degree and higher
Monitoring and analysing suicide risk by education and employment status can be very informative. Sweden regularly publishes data on suicide rates by level of education (e.g. Sweden; Socialstyrelsen National Board of Health and Welfare, 2017) and Case and Deaton (2015, 2017, 2020) and Phillips and Hempstead (2017) have shown that rises in suicide rates in the US are being driven by rises among people with a high school or lower level of education. That said, care is required in drawing causal inferences from the data. Education and employment are clearly associated; for example—adults of working age with a degree or higher level of education are considerably more likely to be employed than those with a high school or lower level of education. This means that some of the apparent association between education and suicide risk is explained by the association between education and employment status. These associations will be drawn out in data modelling.
Blakely et al. (2003) found similar associations between the risk of death by suicide by labour force status for New Zealand using linked data. However, they argue that while being unemployed was associated with a 2- to 3-fold increased relative risk of death by suicide compared with being unemployed, around half of this association might be explained by confounding mental illness.
Addressing socioeconomic inequalities in mortality within countries is a key public health priority globally (WHO 2008). Analyses of education inequalities in Australia in both chronic disease mortality (AIHW 2019) and all-cause mortality (Korda et al. 2019) reveal a clear gradient across differing levels of education with the probability of dying in 2011–12 decreasing as education levels increase. Quantifying inequalities for specific mortality causes will provide a broader understanding of the experience of population groups, the relationships between health and welfare, and insights into underlying reasons for these inequalities.
Australian Institute of Health and Welfare (AIHW) 2019. Indicators of socioeconomic inequalities in cardiovascular disease, diabetes and chronic kidney disease. Cat. no. CDK 12. Canberra: AIHW.
Blakely TA, Collings SCD, and Atkinson J 2003. Unemployment and Suicide. Evidence for a causal association. Journal of Epidemiology and Community Health, 57 (8): 594–600.
Case A & Deaton A 2015. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. PNAS. 112(49):15078–15083.
Case A & Deaton A 2017. Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity 397.
Case A & Deaton A 2020. Deaths of Despair and the Future of Capitalism. Princeton: Princeton University Press.
Korda RJ, Biddle N, Lynch J, Eynstone-Hinkins J, Soga K, Banks E, Priest N, Moon L, Blakely T 2020. Education inequalities in adult all-cause mortality: first national data for Australia using linked census and mortality data. International Journal of Epidemiology. 49(2): 511–518. https://doi.org/10.1093/ije/dyz191
Phillips JA, Hempstead K 2017. Differences in U.S. Suicide Rates by Educational Attainment, 2000-2014. Am J Prev Med. 2017;53(4): e123–e130. doi:10.1016/j.amepre.2017.04.010
Socialstyrelsen National Board of Health and Welfare. 2017. Regional comparisons 2016; Six questions about Swedish healthcare. Viewed 28–07–2020.
World Health Organisation (WHO) 2008. Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health. Geneva: The World Health Organization and Commission on Social Determinants of Health.
WHO 2020. Social determinants of health. Viewed 28–07–2020.
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