Anatomy of a demand shock: Quantitative analysis of crowding in hospital emergency departments in Victoria, Australia during the 2009 influenza pandemic
Identifieur interne : 000C65 ( Pmc/Curation ); précédent : 000C64; suivant : 000C66Anatomy of a demand shock: Quantitative analysis of crowding in hospital emergency departments in Victoria, Australia during the 2009 influenza pandemic
Auteurs : Peter Sivey [Australie] ; Richard Mcallister [Australie] ; Hassan Vally [Australie] ; Anna Burgess [Australie] ; Anne-Maree Kelly [Australie]Source :
- PLoS ONE [ 1932-6203 ] ; 2019.
Abstract
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.
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.
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.
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.
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.
Url:
DOI: 10.1371/journal.pone.0222851
PubMed: 31550288
PubMed Central: 6759189
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<author><name sortKey="Sivey, Peter" sort="Sivey, Peter" uniqKey="Sivey P" first="Peter" last="Sivey">Peter Sivey</name>
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<wicri:regionArea>Department of Education and Training, Australian Government, Canberra, ACT</wicri:regionArea>
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<front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Objective</title>
<p>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.</p>
</sec>
<sec id="sec002"><title>Design and setting</title>
<p>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.</p>
</sec>
<sec id="sec003"><title>Main outcome measures</title>
<p>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.</p>
</sec>
<sec id="sec004"><title>Results</title>
<p>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.</p>
</sec>
<sec id="sec005"><title>Conclusions</title>
<p>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.</p>
</sec>
</div>
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<subj-group><subject>Infectious Diseases</subject>
<subj-group><subject>Viral Diseases</subject>
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<subj-group><subject>Critical Care and Emergency Medicine</subject>
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<subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject>
<subj-group><subject>Health Care</subject>
<subj-group><subject>Health Care Facilities</subject>
<subj-group><subject>Hospitals</subject>
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</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject>
<subj-group><subject>Infectious Diseases</subject>
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</subj-group>
<subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject>
<subj-group><subject>Health Care</subject>
<subj-group><subject>Patients</subject>
</subj-group>
</subj-group>
</subj-group>
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<subj-group><subject>Mathematical and Statistical Techniques</subject>
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<title-group><article-title>Anatomy of a demand shock: Quantitative analysis of crowding in hospital emergency departments in Victoria, Australia during the 2009 influenza pandemic</article-title>
<alt-title alt-title-type="running-head">Quantitative analysis of crowding in hospital emergency departments during the 2009 influenza pandemic</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-3703-615X</contrib-id>
<name><surname>Sivey</surname>
<given-names>Peter</given-names>
</name>
<role content-type="http://credit.casrai.org/">Conceptualization</role>
<role content-type="http://credit.casrai.org/">Data curation</role>
<role content-type="http://credit.casrai.org/">Formal analysis</role>
<role content-type="http://credit.casrai.org/">Funding acquisition</role>
<role content-type="http://credit.casrai.org/">Investigation</role>
<role content-type="http://credit.casrai.org/">Methodology</role>
<role content-type="http://credit.casrai.org/">Project administration</role>
<role content-type="http://credit.casrai.org/">Supervision</role>
<role content-type="http://credit.casrai.org/">Validation</role>
<role content-type="http://credit.casrai.org/">Writing – original draft</role>
<role content-type="http://credit.casrai.org/">Writing – review & editing</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>McAllister</surname>
<given-names>Richard</given-names>
</name>
<role content-type="http://credit.casrai.org/">Data curation</role>
<role content-type="http://credit.casrai.org/">Formal analysis</role>
<role content-type="http://credit.casrai.org/">Writing – original draft</role>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Vally</surname>
<given-names>Hassan</given-names>
</name>
<role content-type="http://credit.casrai.org/">Conceptualization</role>
<role content-type="http://credit.casrai.org/">Funding acquisition</role>
<role content-type="http://credit.casrai.org/">Writing – review & editing</role>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Burgess</surname>
<given-names>Anna</given-names>
</name>
<role content-type="http://credit.casrai.org/">Conceptualization</role>
<role content-type="http://credit.casrai.org/">Writing – review & editing</role>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kelly</surname>
<given-names>Anne-Maree</given-names>
</name>
<role content-type="http://credit.casrai.org/">Conceptualization</role>
<role content-type="http://credit.casrai.org/">Supervision</role>
<role content-type="http://credit.casrai.org/">Writing – review & editing</role>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>School of Economics, Finance and Marketing, RMIT University, Melbourne, Victoria, Australia</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>Department of Education and Training, Australian Government, Canberra, ACT, Australia</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Department of Public Health, La Trobe University, Melbourne, Victoria, Australia</addr-line>
</aff>
<aff id="aff004"><label>4</label>
<addr-line>Department of Health and Human Services (Victoria), Melbourne, Victoria, Australia</addr-line>
</aff>
<aff id="aff005"><label>5</label>
<addr-line>Joseph Epstein Centre for Emergency Medicine Research at Western Health and School of Medicine-Western Clinical School, The University of Melbourne, Parkville, Victoria, Australia</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Lazzeri</surname>
<given-names>Chiara</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>Azienda Ospedaliero Universitaria Careggi, ITALY</addr-line>
</aff>
<author-notes><fn fn-type="COI-statement" id="coi001"><p><bold>Competing Interests: </bold>
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.</p>
</fn>
<corresp id="cor001">* E-mail: <email>peter.sivey@rmit.edu.au</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>24</day>
<month>9</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection"><year>2019</year>
</pub-date>
<volume>14</volume>
<issue>9</issue>
<elocation-id>e0222851</elocation-id>
<history><date date-type="received"><day>20</day>
<month>3</month>
<year>2019</year>
</date>
<date date-type="accepted"><day>8</day>
<month>9</month>
<year>2019</year>
</date>
</history>
<permissions><copyright-statement>© 2019 Sivey et al</copyright-statement>
<copyright-year>2019</copyright-year>
<copyright-holder>Sivey et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:href="pone.0222851.pdf"></self-uri>
<abstract><sec id="sec001"><title>Objective</title>
<p>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.</p>
</sec>
<sec id="sec002"><title>Design and setting</title>
<p>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.</p>
</sec>
<sec id="sec003"><title>Main outcome measures</title>
<p>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.</p>
</sec>
<sec id="sec004"><title>Results</title>
<p>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.</p>
</sec>
<sec id="sec005"><title>Conclusions</title>
<p>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.</p>
</sec>
</abstract>
<funding-group><funding-statement>PM and RM would also like to acknowledge funding from the La Trobe University “Building Healthy Communities” Research Focus Area. The funders played no role in the research; <ext-link ext-link-type="uri" xlink:href="https://www.latrobe.edu.au/research/research-focus-areas/building-healthy-communities">https://www.latrobe.edu.au/research/research-focus-areas/building-healthy-communities</ext-link>
.</funding-statement>
</funding-group>
<counts><fig-count count="4"></fig-count>
<table-count count="1"></table-count>
<page-count count="11"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>All relevant data are within the manuscript, Supporting Information files, and on Figshare: <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.9702851.v1">https://doi.org/10.6084/m9.figshare.9702851.v1</ext-link>
. Additionally, the full (raw) datasets are available on request from the Centre for Victorian Data Linkage: <email>cvdl@dhhs.vic.gov.au</email>
.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>All relevant data are within the manuscript, Supporting Information files, and on Figshare: <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.9702851.v1">https://doi.org/10.6084/m9.figshare.9702851.v1</ext-link>
. Additionally, the full (raw) datasets are available on request from the Centre for Victorian Data Linkage: <email>cvdl@dhhs.vic.gov.au</email>
.</p>
</notes>
</front>
</pmc>
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