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547 Detecting cases of antidepressant poisoning in Victorian hospital emergency department data
  1. Jane Hayman1,
  2. Zakary Doherty2,3,
  3. Anselm Wong4,
  4. Shaun Greene4
  1. 1Victorian Injury Surveillance Unit, Monash Accident Research Centre, Clayton, Australia
  2. 2School of Rural Health, Monash University, Bendigo, Australia
  3. 3Alfred Health, Melbourne, Australia
  4. 4Victorian Poisons Information Centre, Austin Health, Heidelberg, Australia


Background It is estimated that one in seven Australians will experience depression in their lifetime. Data from the Australian Pharmaceutical Benefits Scheme shows that 72% of mental-health related prescriptions filled in Australia in 2019/20 were for antidepressant medications, which equates to 29.4 million prescriptions. The large number of antidepressants prescribed could be associated with harm from these medications with regard to unintentional or intentional poisoning. Routinely collected Emergency Department (ED) data can provide a wealth of information on incidence, risk factors and outcomes of antidepressant poisoning. However, in the Victorian ED data collection system, antidepressant poisoning is not broadly captured in ICD10-AM diagnostic codes and cases can only be identified using the narrative text field in the ED records.

Aims To describe a text search method to identify cases of antidepressant poisoning in emergency department data.

Method The Victorian Emergency Minimum Dataset (VEMD) was examined for the period July 2016 to June 2021. These data are de-identified and along with demographic and clinical information they contain a ‘description of event’ text field. Searches of this text field included key terms such as names of antidepressant medication, including brand names, as well as terms indicating a poisoning or self-harm.

Results Identified cases had a wide range of diagnosis codes that were mostly unrelated to antidepressants. This demonstrates the utility of the text search method in emergency department data, rather than relying on diagnosis codes alone.

Conclusions and Learning outcomes In the absence of diagnosis codes to indicate antidepressant poisonings, comprehensive text searching methods are needed. Improvements to case identification will enable monitoring of emerging trends in antidepressant use requiring hospital care, which in turn will help to better target prevention efforts.

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