Abstract
This paper presents trends and characteristics for 32,094 suicidal presentations to two Emergency Departments (EDs) in a large health service in Australia across a 10-year period (2009–2018). Prevalence of annual suicidal presentations and for selected groups of consumers (by sex, age groups, and ethnicity) was determined from a machine learning diagnostic algorithm developed for this purpose and a Bayesian estimation approach. A linear increase in the number of suicidal presentations over 10 years was observed, which was 2.8-times higher than the increase noted in all ED presentations and 6.1-times higher than the increase in the population size. Females had higher presentation rates than males, particularly among younger age groups. The highest rates of presentations were by persons aged 15–24. Overseas-born persons had around half the rates of suicidal presentations than Australian-born persons, and Indigenous persons had 2.9-times higher rates than non-Indigenous persons. Of all presenters, 70.6% presented once, but 5.7% had five or more presentations. Seasonal distribution of presentations showed a peak at the end of spring and a decline in winter months. These findings can inform the allocation of health resources and guide the development of suicide prevention strategies for people presenting to hospitals in suicidal crisis.
Highlights
Suicide is a global, complex phenomenon that results in an annual loss of more than 800,000 lives worldwide [1]
While many countries have national systems that record, collect and process information related to suicide, very few have equivalent systems dedicated to non-fatal suicidal behaviors [3]
These variables included a range of diagnostic codes and presenting problems assigned at the point of Emergency Departments (EDs) triage such as Indigenous/non-Indigenous status of the consumer, score according to the Australian Triage Scale (5-point clinical tool used to establish the maximum waiting time for medical assessment and treatment of a patient), discharge destinations, and 52 different keywords indicative of the presentations being suicide-related
Summary
Complex phenomenon that results in an annual loss of more than 800,000 lives worldwide [1]. To overcome the above noted limitations inherent to the use of ED administrative data and to reduce high costs associated with manual identification of relevant cases, a machine learning algorithm was developed to identify suicidal presentations using presentation descriptions in the Emergency Department Information System (EDIS) database [23]. This algorithm was used to obtain data on suicidal presentations between 2009 and 2018 which were analyzed in this paper.
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