Chart 1: Who Is Dying? Suicide Deaths by Occupation Group

First-ever breakdown of suicide deaths by occupation, Australia 2023–2024 combined. Males extend left; females extend right.

Source: Australian Bureau of Statistics (2025). Deaths from external causes, 2024. Note: Data combines 2023 and 2024 registrations. Occupation coding is experimental and subject to revision.

Chart 3: The Most Dangerous Industries — Injury Rates Over 10 Years

Workers’ compensation injury frequency rate by industry. Hover to compare. Click legend to filter.

Source: Safe Work Australia (2025). Workers’ Compensation Injury Frequency Rate detailed data file, 2023–24 — Table 2.1. Rate = lost time claims per million hours worked.

Chart 4: The Regional Divide — Suicide Rates by Location, 2024

Age-standardised death rates per 100,000 population. Compare capital cities to regional areas by state.

Source: Australian Bureau of Statistics (2025). Causes of Death, Australia, 2024 — Table 11.15. Age-standardised rates per 100,000 population, 2024. Gap percentages show regional rate relative to capital city rate.

Chart 5: The Double Burden — Injury Rate vs Suicide Deaths by Occupation

Each bubble is an occupation group. Size = total suicide deaths. X-axis = workplace injury rate. Hover for details.

Sources: Australian Bureau of Statistics (2025). Deaths from external causes, 2024; Safe Work Australia (2025). Workers’ Compensation Injury Frequency Rate detailed data file, 2023–24. Bubble size proportional to total suicide deaths. Dashed lines show occupational averages. Male workforce share derived from ABS occupation suicide data.

The data demands a rethink. Workplace mental health policy has focused on office environments — flexible work, employee assistance programs, mindfulness. The workers most likely to die by suicide are not at standing desks. They are on building sites, in paddocks, behind the wheel. If this crisis is to be addressed, policy must follow the data.

Acknowledgements

I acknowledge the use of three publicly available government datasets to support this assignment: the Australian Bureau of Statistics Deaths from External Causes, 2024 (occupation suicide data), the ABS Causes of Death, Australia, 2024 (Tables 11.1 and 11.15, covering national trends and regional rates), and the Safe Work Australia Workers’ Compensation Injury Frequency Rate detailed data file, 2023–24. These sources formed the empirical foundation for all five data visualisations and the narrative analysis presented in this article.

I referred to RMIT course module materials on data journalism practice, including guidance on narrative visualisation, interactivity, accessibility, responsible colour use, and avoiding misleading visual representations.

R, RStudio, and R Markdown were used to clean, wrangle, and prepare the data. The plotly and dplyr/tidyr packages were used to build the interactive visualisations and perform data transformations respectively. The final article was published as a self-contained HTML document via RPubs.

ChatGPT (OpenAI, 2025) was used as a generative AI assistant during this assignment to support R code development, chart design decisions, and editorial feedback on visualisation clarity. All analytical interpretations, narrative framing, and editorial judgements are my own. AI use is acknowledged in accordance with RMIT Library’s Artificial Intelligence (AI): Acknowledgement and Referencing Guidelines.

References

Australian Bureau of Statistics. (2025). Causes of death, Australia, 2024 (Cat. no. 3303.0). https://www.abs.gov.au/statistics/health/causes-death/causes-death-australia/latest-release

Australian Bureau of Statistics. (2025). Deaths from external causes, 2024. https://www.abs.gov.au/statistics/health/causes-death/deaths-external-causes/latest-release

OpenAI. (2025). ChatGPT (June 2025 version) [Large language model]Retrieved June 10, 2025, from. https://chat.openai.com

Safe Work Australia. (2025). Workers’ compensation injury frequency rate detailed data file, 2023–24. https://www.safeworkaustralia.gov.au/data-and-research/workers-compensation-statistics/workers-compensation-injury-frequency-rates