Sociodemographic Factors and Presentation Features of Individuals Seeking Mental Health Care in Emergency Departments: A Retrospective Cohort Study.

Oliver Higgins, Rachel B Sheather-Reid, Stephan K Chalup, Rhonda L Wilson
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Abstract

Emergency Department (ED) presentations for Mental Health (MH) help-seeking have been rising rapidly, with EDs as the main entry point for most individuals in Australia. The objective of this retrospective cohort study was to analyse the sociodemographic and presentation features of people who sought mental healthcare in two EDs located in a regional coastal setting in New South Wales (NSW), Australia from 2016 to 2021. This article is a part of a broader research study on the utilisation of machine learning in MH. The objective of this study is to identify the factors that lead to the admission of individuals to an MH inpatient facility when they seek MH care in an ED. Data were collected using existing records and analysed using descriptive univariate analysis with statistical significance between the two sites was determined using Chi squared test, p < 0.05. Two main themes characterise dominant help-seeking dynamics for MH conditions in ED, suicidal ideation, and access and egress pathways. The main findings indicate that suicidal ideation was the most common presenting problem (38.19%). People presenting to ED who 'Did not wait' or 'Left at own risk' accounted for 10.20% of departures from ED. A large number of presentations arrived via the ambulance, accounting for 45.91%. A large proportion of presentations are related to a potentially life-threatening condition (suicidal ideation). The largest proportion of triage code 1 'Resuscitation' was for people with presenting problem of 'Behavioural Disturbance'. Departure and arrival dynamics need to be better understood in consultation with community and lived experience groups to improve future service alignment with the access and egress pathways for emergency MH care.

急诊科精神疾病患者的社会人口学因素和表现特征:回顾性队列研究
在澳大利亚,急诊室(ED)是大多数人寻求心理健康(MH)帮助的主要入口,而急诊室求助人数一直在迅速上升。这项回顾性队列研究旨在分析 2016 年至 2021 年期间在澳大利亚新南威尔士州(NSW)沿海地区的两家急诊室寻求精神医疗服务的患者的社会人口学特征和就诊特征。本文是机器学习在精神卫生领域应用的广泛研究的一部分。本研究的目的是确定在急诊室寻求 MH 治疗时导致 MH 住院病人入院的因素。研究人员利用现有记录收集数据,并采用描述性单变量分析方法对数据进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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