Anatomy of a demand shock: Quantitative analysis of crowding in hospital emergency departments in Victoria, Australia during the 2009 influenza pandemic

Peter Sivey, Richard McAllister, Hassan Vally, Anna Burgess, Anne-Maree Kelly, Peter Sivey, Richard McAllister, Hassan Vally, Anna Burgess, Anne-Maree Kelly

Abstract

Objective: An infectious disease outbreak such as the 2009 influenza pandemic is an unexpected demand shock to hospital emergency departments (EDs). We analysed changes in key performance metrics in (EDs) in Victoria during this pandemic to assess the impact of this demand shock.

Design and setting: Descriptive time-series analysis and longitudinal regression analysis of data from the Victorian Emergency Minimum Dataset (VEMD) using data from the 38 EDs that submit data to the state's Department of Health and Human Services.

Main outcome measures: Daily number of presentations, influenza-like-illness (ILI) presentations, daily mean waiting time (time to first being seen by a doctor), daily number of patients who did-not-wait and daily number of access-blocked patients (admitted patients with length of stay >8 hours) at a system and hospital-level.

Results: During the influenza pandemic, mean waiting time increased by up to 25%, access block increased by 32% and did not wait presentations increased by 69% above pre-pandemic levels. The peaks of all three crowding variables corresponded approximately to the peak in admitted ILI presentations. Longitudinal fixed-effects regression analysis estimated positive and statistically significant associations between mean waiting times, did not wait presentations and access block and ILI presentations.

Conclusions: This pandemic event caused excess demand leading to increased waiting times, did-not-wait patients and access block. Increases in admitted patients were more strongly associated with crowding than non-admitted patients during the pandemic period, so policies to divert or mitigate low-complexity non-admitted patients are unlikely to be effective in reducing ED crowding.

Conflict of interest statement

Anna Burgess and Anne-Maree Kelly are employed by the Department of Health and Human Services (Victoria) which is responsible for running public hospitals in Victoria. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1. Total presentations per day and…
Fig 1. Total presentations per day and total ILI presentations per day (seven-day moving average).
The increase in ILI presentations observed in May and June 2009 can be divided into patients who were admitted into the hospital (including short stay wards and ICU) and those not admitted (Fig 2). This division shows two distinct patterns: the non-admitted patients peak on 6 June at 722 per day whereas the admitted presentations peak on 24 June at 180 per day.
Fig 2. Admitted and non-admitted ILI presentations…
Fig 2. Admitted and non-admitted ILI presentations per day (seven-day moving average).
Both average waiting times and the number of patients who did not wait increased markedly during the pandemic period (Fig 3). From 41.1 minutes on 15 May (before the pandemic), the seven day average waiting time rose to 43.7 minutes on 6 June (the non-admitted presentations peak) then to 52 minutes on 24 June (the admitted patients peak). Average waiting times remained elevated after the end of the pandemic, only declining gradually through the rest of the winter of 2009.
Fig 3. Average waiting time and number…
Fig 3. Average waiting time and number of did-not-wait presentations (seven-day moving average).
The seven-day average number of patients experiencing access block (Fig 4) increases from 285 per day on 15 May up to 296 per day on 6 June and then further to 377 per day (total increase of 32%) on 24 June at the peak of admitted presentations period.
Fig 4. Number of access-block presentations (seven-day…
Fig 4. Number of access-block presentations (seven-day moving average).

References

    1. Schull M.J., Mamdani M.M. and Fang J., Community influenza outbreaks and emergency department ambulance diversion. Ann Emerg Med, 2004. 44(1): p. 61–7. 10.1016/j.annemergmed.2003.12.008
    1. Costello B.E., et al., Pandemic H1N1 influenza in the pediatric emergency department: a comparison with previous seasonal influenza outbreaks. Ann Emerg Med, 2010. 56(6): p. 643–8. 10.1016/j.annemergmed.2010.03.001
    1. Lindstrom S.J., Nagalingam V. and Newnham H.H., Impact of the 2009 Melbourne heatwave on a major public hospital. Intern Med J, 2013. 43(11): p. 1246–50. 10.1111/imj.12275
    1. Andrew E., et al., Stormy weather: a retrospective analysis of demand for emergency medical services during epidemic thunderstorm asthma. BMJ, 2017. 359: p. j5636 10.1136/bmj.j5636
    1. Lambe S., et al., Waiting times in California's emergency departments. Ann Emerg Med, 2003. 41(1): p. 35–44. 10.1067/mem.2003.2
    1. Hoot N.R. and Aronsky D., Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med, 2008. 52(2): p. 126–36. 10.1016/j.annemergmed.2008.03.014
    1. Fatovich D.M., Nagree Y. and Sprivulis P., Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia. Emerg Med J, 2005. 22(5): p. 351–4. 10.1136/emj.2004.018002
    1. Harris A. and Sharma A., Access block and overcrowding in emergency departments: an empirical analysis. Emerg Med J, 2010. 27(7): p. 508–11. 10.1136/emj.2009.072546
    1. Goodacre S. and Webster A., Who waits longest in the emergency department and who leaves without being seen? Emerg Med J, 2005. 22(2): p. 93–6. 10.1136/emj.2003.007690
    1. Baker A.W., et al., Local influenza-like illness surveillance at a university health system during the 2009 H1N1 influenza pandemic. American Journal of Infection Control, 2012. 40(7): p. 606–610. 10.1016/j.ajic.2011.12.009
    1. Jeng K., et al., Monitoring seasonal influenza A evolution: Rapid 2009 pandemic H1N1 surveillance with an reverse transcription-polymerase chain reaction/electro-spray ionization mass spectrometry assay. Journal of Clinical Virology, 2012. 54(4): p. 332–336. 10.1016/j.jcv.2012.05.002
    1. Lum M.E., et al., Impact of pandemic (H1N1) 2009 influenza on critical care capacity in Victoria. Med J Aust, 2009. 191(9): p. 502–6.
    1. Louie J.K., et al., Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA, 2009. 302(17): p. 1896–902. 10.1001/jama.2009.1583
    1. Kumar A., et al., Critically ill patients with 2009 influenza A(H1N1) infection in Canada. JAMA, 2009. 302(17): p. 1872–9. 10.1001/jama.2009.1496
    1. Lau K., Hauck K. and Miraldo M., Excess influenza hospital admissions and costs due to the 2009 H1N1 pandemic in England. Health Economics, 2019. 28(2): p. 175–88. 10.1002/hec.3834
    1. Sugerman D., et al., A survey of emergency department 2009 pandemic influenza A (H1N1) surge preparedness—Atlanta, Georgia, July-October 2009. Clin Infect Dis, 2011. 52 Suppl 1: p. S177–82.
    1. Investigators A.I., et al., Critical care services and 2009 H1N1 influenza in Australia and New Zealand. N Engl J Med, 2009. 361(20): p. 1925–34. 10.1056/NEJMoa0908481
    1. Gaudette, E., Health Care Demand and Impact of Policies in a Congested Public System. University of Southern California CESR-Schaeffer Working Paper 2014. 2014–005.
    1. Sivey P., Should I Stay or Should I Go? Hospital Emergency Department Waiting Times and Demand. Health Economics, 2018. 27(3): p. e30–e42. 10.1002/hec.3610
    1. Martin S., et al., The market for elective surgery: Joint estimation of supply and demand. Journal of Health Economics, 2007. 26(2): p. 263–285. 10.1016/j.jhealeco.2006.08.006
    1. Richardson D.B. and Mountain D., Myths versus facts in emergency department overcrowding and hospital access block. Medical Journal of Australia, 2009. 190(7): p. 369-+.
    1. Schull M.J., Kiss A. and Szalai J.P., The effect of low-complexity patients on emergency department waiting times. Ann Emerg Med, 2007. 49(3): p. 257–64, 264 e1. 10.1016/j.annemergmed.2006.06.027
    1. Nichol K.L., Heterogeneity of influenza case definitions and implications for interpreting and comparing study results. Vaccine, 2006. 24(44–46): p. 6726–8. 10.1016/j.vaccine.2006.05.064
    1. Moore K., et al., Syndromic surveillance for influenza in two hospital emergency departments. Relationships between ICD-10 codes and notified cases, before and during a pandemic. BMC Public Health, 2011. 11: p. 338 10.1186/1471-2458-11-338
    1. Gunasekara F.I., et al., Fixed effects analysis of repeated measures data. Int J Epidemiol, 2014. 43(1): p. 264–9. 10.1093/ije/dyt221
    1. StataCorp, Stata Statistical Software: Release 15. 2017, StataCorp LLC: College Station, TX.
    1. Stang A.S., et al., Crowding measures associated with the quality of emergency department care: a systematic review. Acad Emerg Med, 2015. 22(6): p. 643–56. 10.1111/acem.12682
    1. Guttmann A., et al., Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ, 2011. 342: p. d2983 10.1136/bmj.d2983

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