Changes in monthly unemployment rates may predict changes in the number of psychiatric presentations to emergency services in South Australia

Abstract

BackgroundTo determine the extent to which variations in monthly Mental Health Emergency Department (MHED) presentations in South Australian Public Hospitals are associated with the Australian Bureau of Statistics (ABS) monthly unemployment rates.MethodsTimes series modelling of relationships between monthly MHED presentations to South Australian Public Hospitals derived from the Integrated South Australian Activity Collection (ISAAC) data base and the ABS monthly unemployment rates in South Australia between January 2004–June 2011.ResultsTime series modelling using monthly unemployment rates from ABS as a predictor variable explains 69 % of the variation in monthly MHED presentations across public hospitals in South Australia. Thirty-two percent of the variation in current month’s male MHED presentations can be predicted by using the 2 months’ prior male unemployment rate. Over 63 % of the variation in monthly female MHED presentations can be predicted by either male or female prior monthly unemployment rates.ConclusionsThe findings of this study highlight that even with the relatively favourable economic conditions, small shifts in monthly unemployment rates can predict variations in monthly MHED presentations, particularly for women. Monthly ABS unemployment rates may be a useful metric for predicting demand for emergency mental health services.

Trend of emergency department presentations with a mental health diagnosis in Australia by diagnostic group, 2004–05 to 2016–17

“…The largest diagnostic group in Australia and all jurisdictions throughout the study period was stress‐related disorders (F40–F49) with over a quarter of presentations, consistent with findings from WA (Perth) during the period 2002–03 to 2016–17, SA during the period 2004–11 and earlier findings from NSW (1999–2006) and the high prevalence of this condition in the community . However, low prevalence disorders, including psychotic disorders (F20–F29), comprised 13.6% of presentations on average, and experienced a 14.6% increase in the number of presentations per 10 000 population over the study period.…”

“…Further, coding practises within EDs, particularly the ability of non-clinicians 49,50 or nontreating clinicians to assign diagnostic codes and a general lack of understanding of the potential usefulness of administrative data and thus the importance of robust coding practises are also of concern. The largest diagnostic group in Australia and all jurisdictions throughout the study period was stress-related disorders (F40-F49) with over a quarter of presentations, consistent with findings from WA (Perth) during the period 2002-03 to 2016-17, 16 SA during the period 2004-11 15 and earlier findings from NSW (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) 7 and the high prevalence of this condition in the community. 51 However, low prevalence disorders, including psychotic disorders (F20-F29), comprised 13.6% of presentations on average, and experienced a 14.6% increase in the number of presentations per 10 000 population over the study period.…”

“…[11][12][13][14] In the USA, mood disorders, anxiety and alcohol-and drug-related mental disorders have been identified as the major diagnostic groups among MHrelated ED presentations between 1992 and 2001. 3 In Australia, neurotic, stress-related and somatoform disorders predominated within MHrelated ED presentations in New South Wales (NSW) between 1999 and 2006 7 and South Australia (SA) between 2004 and 2011, 15 with stress and anxiety-related diagnoses and substance-use, particularly alcohol-related presentations driving an increase in presentations in metropolitan Perth, Western Australia (WA) between 2002-03 and 2016-17. 16 In Victoria (VIC), depression, self-harm ideation, self-harm overdose then psychosis were identified as the primary (non-mutually exclusive) MH reasons for visit across five EDs in 2004.…”

Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City

“…To predict the number of febrile ED visits using the forecast model, we used the Autoregressive Integrated Moving Average (ARIMA) modeling method. ARIMA modeling has been used for forecasting time series variables, such as patient volume, in medical science [16,17]. We developed an ARIMA model optimized to forecast febrile ED visits using number of febrile ED visits during the past few days.…”

A procura pelos cuidados de urgência de saúde mental em Portugal em tempos de crise

“…Ainda assim, a lite-ratura sobre o impacto do desemprego na procura de serviços de saúde mental reporta um efeito retardatário de apenas 1 a 2 meses até se começar a verificar um aumento de utilização destes serviços, particularmente entre homens em idade laboral, o que é consistente com os nossos dados (Kiernan et al, 1989;Gili et al, 2012;Bartoll et al, 2014;Bidargaddi et al, 2015).…”