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

English version

Autoři: Peter Sivey ^aff001; Richard McAllister ^aff002; Hassan Vally ^aff003; Anna Burgess ^aff004; Anne-Maree Kelly ^aff005
Působiště autorů: School of Economics, Finance and Marketing, RMIT University, Melbourne, Victoria, Australia ^aff001; Department of Education and Training, Australian Government, Canberra, ACT, Australia ^aff002; Department of Public Health, La Trobe University, Melbourne, Victoria, Australia ^aff003; Department of Health and Human Services (Victoria), Melbourne, Victoria, Australia ^aff004; Joseph Epstein Centre for Emergency Medicine Research at Western Health and School of Medicine-Western Clinical School, The University of Melbourne, Parkville, Victoria, Australia ^aff005
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222851

Souhrn

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.

Klíčová slova:

Critical care and emergency medicine – Hospitals – Patients – Infectious diseases – Epidemiology – Influenza – Regression analysis – Time series analysis

Zdroje

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