COVID-19 Hospital-Acquired Infections Among Patients in ...

COVID-19 Hospital-Acquired Infections Among Patients in Victorian Health Services - March 2021

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COVID-19 Hospital-Acquired Infections

Among Patients in Victorian Health

Services (25 January 2020-15

November 2020)

Final Report


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To receive this document in another format, phone +61 (03) 9096 0000, using the National

Relay Service 13 36 77 if required, or email [email protected].

Authorised and published by the Victorian Government, 1 Treasury Place, Melbourne.

© State of Victoria, Australia, Department of Health, April 2021.

Suggested citation: Veale, H. J.1, Dale, K. 1, Ampt, F. 1,10, Kalman, T. 1, Kaufman, C. 1, Gibson, E. 1,6,9,

Carville, K. 1,5, Harper, C.1,12, Ahmed, H. 1, Pehm, M. 1,13, Bull, A. 2, Brett, J. 2, Worth, L. 2, Sherry, N. L. 3,11,

Leeb, K. 1, Cheng, A.1,7,8, Rowe., S. L. 1 (2021). COVID-19 Hospital-Acquired Infections Among Patients in

Victorian Health Services (25 January 2020- 15 November 2020). Victorian Department of Health.

* Authors Veale and Dale contributed equally to this report.

This work was guided by the COVID-19 Hospital-Acquired Infections Working Group:

Prof Brett Sutton1

Prof Allen Cheng1, 7, 8

Nicola Quin1 Ben Fielding1 Kira Leeb1 Dr Frances Ampt1, 10

Stacey Rowe1

Tali Kalman1 Hilary Veale1 Kylie Carville1,5 Claire Boardman1,7 Judy Sutherland1 Dr Ann Bull2

Judy Brett2

A/Prof Leon Worth2

Dr Norelle Sherry3,11

Angela Nolan4

Affiliations:

1 Victorian Department of Health, Melbourne, Australia

2 Victorian Healthcare Associated Infections Surveillance System, Melbourne, Australia

3 Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology &

Immunology, University of Melbourne at the Peter Doherty Institute for Infection & Immunity,

Melbourne

4 St Vincent’s Hospital, Melbourne, Australia

5 Doherty Institute for Immunity and Infection, Melbourne, Australia

6 Geelong Centre for Emerging Infectious Disease, Geelong, Australia

7 Monash University, Melbourne, Australia

8 Alfred Health, Melbourne, Australia

9 Deakin University, Geelong, Australia

10 Burnet Institute, Melbourne, Australia

11 Austin Health, Melbourne, Australia

12 Queensland University of Technology, Queensland, Australia

13 The Royal Women’s Hospital, Melbourne, Australia

mailto:[email protected]


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Contents

Abbreviations & Acronyms ........................................................................................................................ 5

Key Findings ............................................................................................................................................. 6

Introduction................................................................................................................................................ 7

Purpose ..................................................................................................................................................... 8

Methods..................................................................................................................................................... 8

Results .................................................................................................................................................... 12

Acquisition in hospitalised COVID-19 patients .................................................................................... 12

Characteristics and outcomes of patients with C19-HAIs ................................................................... 16

Hospitals with C19-HAIs ..................................................................................................................... 18

Genomic analysis of C19-HAI patients ............................................................................................... 22

Key strengths, challenges and lessons learnt ..................................................................................... 27

Case study 1: The risk of ‘unrecognised cases’...................................................................................... 34

Case study 2: PPE use is not always perfect ......................................................................................... 35

Discussion ............................................................................................................................................... 36

Acknowledgements ................................................................................................................................. 41

References .............................................................................................................................................. 42

Appendix 1: Definitions of C19-HAI diagnosed during hospital stay....................................................... 45

Appendix 2: Definitions of C19-HAI diagnosed post-discharge .............................................................. 46

Appendix 3: Facilitated discussion attendee brief ................................................................................... 48

Appendix 4: Victorian Hospitals with C19-HAIs ...................................................................................... 52

https://dhhsvicgovau.sharepoint.com/sites/PublicHealthIntelligence-DHHS-GRP-COVID-19v2/Shared%20Documents/COVID-19%20v2/Situation%20awareness/Healthcare/3.%20HAI%20patients/HAI%20patient%20-%20Project%20work,%20reports/DO%20NOT%20UPDATE_COVID-19%20Healthcare-Associated%20Infections%20Among%20Patients%20Hospitalised%20in%20Victorian%20Health%20Services%20Report.docx#_Toc67908517

https://dhhsvicgovau.sharepoint.com/sites/PublicHealthIntelligence-DHHS-GRP-COVID-19v2/Shared%20Documents/COVID-19%20v2/Situation%20awareness/Healthcare/3.%20HAI%20patients/HAI%20patient%20-%20Project%20work,%20reports/DO%20NOT%20UPDATE_COVID-19%20Healthcare-Associated%20Infections%20Among%20Patients%20Hospitalised%20in%20Victorian%20Health%20Services%20Report.docx#_Toc67908518


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List of figures

Figure 1: Selection process for patients with definite and probable C19-HAIs ........................................... 13 Figure 2: Timeline of COVID-19 cases in Victoria (25 January 2020- 15 November 2020), stratified by

hospitalisation and healthcare acquisition .................................................................................................. 15 Figure 3: Age distribution of COVID- 19 cases, by hospitalisation and C19-HAI status ............................ 16 Figure 4: Prevalence of COVID-19 infections per 100,000 people/km2 and location of hospitals with C19-

HAIs in metropolitan Melbourne .................................................................................................................. 20 Figure 5: C19-HAI cases by genomic cluster and hospital ......................................................................... 24

List of tables

Table 1: Results of assessment process for patients with C19-HAIs diagnosed between 25 January 2020

and 15 November 2020 ............................................................................................................................... 14 Table 2: C19-HAIs by hospitalisation status and timing of diagnosis ......................................................... 14 Table 3: Demographic characteristics and outcomes of patients with C19-HAIs and patients hospitalised

with community-acquired COVID-19; number (column per cent) unless specified .................................... 17 Table 4: Definite and probable C19-HAIs in patients, by hospital and health service (25 January to 15

November 2020; ordered by number of C19-HAI cases per health service). ............................................. 19 Table 5: Definite and probable C19-HAIs, by Department of Health region and hospital type, 25 January

2020 to 15 November 2020......................................................................................................................... 20 Table 6: Characteristics of hospitals unaffected, affected and highly affected by C19-HAIs, Victoria 2020;

median (range) unless specified ................................................................................................................. 21 Table 7: Genomic clustering for C19-HAI patients, by hospital (25 January to 15 November 2020) ......... 23 Table 8: Examples of possible chains of transmission for C19-HAI patients ............................................. 25 Table 9: Patient factors relevant to C19-HAIs............................................................................................. 27 Table 10: Infrastructure limitations relevant to C19-HAIs ........................................................................... 29 Table 11: Resource/technology factors relevant to C19-HAIs .................................................................... 30 Table 12: Staff factors ................................................................................................................................. 31 Table 13: Broader pandemic, social and political context ........................................................................... 32 Table 14: Summary of instrumental strategies and 'lessons learnt' by participating health services ......... 33 Table 15: Summary of suggestions by health services for improvement in State responses .................... 33


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Abbreviations & Acronyms

AGB Aerosol Generating Behaviours

AV Ambulance Victoria

C19-HAI COVID-19 Hospital- Acquired Infections

CALD Culturally and Linguistically Diverse

CHO Chief Health Officer

COVID-19 Coronavirus Disease 2019 (viral disease caused by SARS-CoV-2)

CQV COVID-19 Quarantine Victoria

The department Department of Health

ICD International Classification of Disease

IPC Infection Prevention and Control

ICU Intensive Care Unit

HEPA High- Efficiency Particle Air Filters

HH Hand Hygiene

IQR Inter- Quartile Range

HSIMT Health Services Incident Management Team

MDU- PHL Microbiological Diagnostic Unit Public Health Laboratory

N/A Not Applicable

MET Medical Emergency Team

PHESS Public Health Event Surveillance System

PITSTOP Patient Injury Time-Out STOP

PPE Personal Protective Equipment

RACF Residential Aged Care Facility

RT-PCR Reverse transcription-polymerase chain reaction

SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus-2

SCOVID-19 Suspected COVID-19

UK United Kingdom

VAED The Victorian Admitted Episodes Dataset

VEMD Victorian Emergency Minimum Dataset

VICNISS Victorian Nosocomial Infection Surveillance System


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Key Findings

• A retrospective investigation and analyses were undertaken to identify and describe COVID-19

infections acquired among patients admitted to Victorian hospitals.

• The analyses drew on data relating to COVID-19 notifications made to the State Government

Department of Health between 25 January and 15 November 2020. Cases assessed as being

hospital acquired (probable, definite or indeterminant) were verified by the health services

concerned.

• The review identified 277 patients as having COVID-19 hospital-acquired infections (C19-HAIs) in

Victorian hospitals in the period of interest.

• During the same period, 2,492 cases were hospitalised with COVID-19 in Victoria, equating to

around one hospital-acquired infection for every nine patients hospitalised with COVID-19.

• Of the 2,492 cases hospitalised with COVID-19, 266 (11%) were hospital-acquired, and an

additional 11 cases were diagnosed with COVID-19 following discharge from hospital.

• Thirty hospitals were identified as having patients with C19-HAI, with a median of two cases per

hospital. Eight hospitals had 10 or more cases each, and together accounted for three quarters of

C19-HAI cases.

• Most C19-HAI cases occurred in hospitals in the North and West Metropolitan Region of Melbourne

reflecting areas of highest community prevalence. Almost all cases occurred in public hospitals

(90%), with slightly more cases observed in acute than sub-acute/non-acute facilities.

• C19-HAI patients were older and had lower rates of ICU admission and ventilation compared with

hospitalised patients with community-acquired COVID-19. Risk of death among C19-HAI patients

was greater than in hospitalised community-acquired COVID-19 patients, however this was

accounted for by the older age of C19-HAI patients.

• Hospitals with 10 or more C19-HAI cases had more patients hospitalised with community-acquired

COVID-19, accepted more transfers from residential aged care facilities (RACF), and had more

patients whose COVID-19 status may have been unrecognised upon admission, than hospitals

with no or few C19-HAIs.

• Genomic sequencing was available for 219 of the 277 (76%) C19-HAIs. There were 31 distinct

genomic clusters and 15 hospitals had C19-HAIs in a single genomic cluster. Eleven hospitals with

C19-HAIs had genomic clusters with links to one or more RACFs.

• Although an association was found between the number of cases transferred from RACFs and

C19-HAIs, the extent and nature of this relationship remains unclear.

• Genomic and epidemiologic investigation of several C19-HAIs showed unrecognised cases in

patients and staff to be the source of infection. However, data limitations mean the relative

contribution of different sources to C19-HAI incidence could not be quantified.

• Facilitated discussions were held with affected hospitals, and the main transmission risks reported

included limitations in existing hospital infrastructure (lack of single rooms and limitations of

ventilation systems), ‘unrecognised’ asymptomatic or pre-symptomatic infections, patient transfers

and ‘high risk’ patient behaviours including wandering and ‘aerosol generating behaviours’.


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• Key strategies reported by health services included optimising patient flow and cohorting of high-

risk patients, frequent asymptomatic testing of patients and staff, strengthening contact tracing

systems, and establishing or strengthening rapid response multi-disciplinary incident management

teams.

• Health services identified a need for increased consideration of infection prevention and control in

health infrastructure design, increased access to rapid testing, and support for effective and

consistent personal protective equipment (PPE) for staff.

• C19-HAIs represented a minority all COVID-19 hospitalisations. Effective infection prevention and

control policies and practices are in place across Victorian health services. Lessons have been

learnt from the COVID-19 experience that will continue to strengthen systems and preparedness

for future epidemics, improving safety and outcomes for patients, staff and the community.

Introduction

Victoria has experienced two waves of COVID-19, with 20,343 cases diagnosed in Victoria between 25

January and 15 November 2020, inclusive. A total of 2,492 cases were hospitalised with COVID-19 during

this time, and 4,170 hospital staff1 were infected. Transmission of COVID-19 in hospital settings

puts patients, particularly those who are older or immunocompromised, at risk of severe illness and death

(Wake et al. 2020; Wang et al. 2020). An understanding of how SARS-CoV-2 (the virus that causes COVID-

19) has spread in healthcare settings is essential to protecting staff and patients now and into the future.

Hospital-acquired infections (also known as healthcare-acquired or healthcare-associated infections) are

infections that are acquired in hospital. They may become evident during admission or after a person leaves

the healthcare facility (Australian Commission on Safety and Quality in Health Care, 2017).

Hospital-acquired infections are the most common complication among hospitalised patients in Australia,

with over 165,000 cases in acute healthcare facilities reported annually (National Health and Medical

Research Council, 2019). A study conducted in 2018 found that 9.9% of patients hospitalised in Australia

who have an infection, acquired that infection in hospital (Russo et al. 2019).

Acquisition of COVID-19 by patients in hospital has been described in many international settings. An

early study in the United Kingdom (UK) found that at least 12.5% of all COVID-19 infections in hospitalised

patients were hospital-acquired (Carter et al. 2020). More recent estimates arising from the UK suggest

that 17.6% are hospital-acquired (Heneghan et al. 2020). A study in China found that 12.3% of COVID-19

infections in patients were hospital-acquired (Wang et al. 2020).

To better understand COVID-19 transmission in Victorian hospitals, the State Government Department of

Health (the department), together with the Victorian Healthcare Associated Infection Surveillance

Coordinating Centre (VICNISS), the Microbiological Diagnostic Unit Public Health Laboratory (MDU- PHL)

and selected Victorian hospitals, undertook a retrospective investigation and analysis to identify and

describe COVID-19 hospital-acquired infections (C19-HAI) occurring among patients in Victoria. We

examined genomic linkages among cases in hospitals, and explored key lessons learnt by highly affected

health services.

1 Hospital staff includes workers who provide direct clinical care to a patient or client, and those who work in a healthcare setting but do not provide clinical care to a patient or client.


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Purpose

To describe the epidemiology of C19-HAIs among hospitalised patients in Victoria between 25 January

2020 to 15 November 2020, and explore key lessons learnt by hospitals regarding factors contributing to

transmission risk and interventions to minimise transmission.

Methods

1. Case definitions

Definitions for definite, probable, indeterminate and non-healthcare-acquired COVID-19 infections

were developed for patients diagnosed during hospital admission (Appendix 1) and following

discharge (Appendix 2) based on existing definitions of C19-HAIs (Victorian Department of Health

and Human Services, 2020; European Centre for Disease Prevention and Control, 2020; Health

Protection Surveillance Centre, 2020; Khan et al. 2020; Rhee et al. 2020; Rickman et al. 2020).

International definitions often do not acknowledge post-discharge diagnoses of C19-HAI, however

the definition used in this report includes these cases.

2. Data sources

Four key data sources were used for analysis:

i. Public Health Event Surveillance System (PHESS)

PHESS is the population-wide Victorian surveillance system capturing all conditions notifiable under the Public Health and Wellbeing Act 2008, including COVID-192. Variables such as hospitalisation status, symptom onset date, diagnosis date and links to outbreaks are documented in PHESS as part of routine public health surveillance and investigation.

ii. The Victorian Admitted Episodes Dataset (VAED)

The VAED provides a comprehensive dataset of the causes, effects and nature of illness, and the use of hospitals in Victoria. All Victorian public and private hospitals, including rehabilitation centres, extended care facilities and day procedure centres, report a minimum set of data for each admitted patient episode to the department via the VAED.

iii. Victorian Healthcare Associated Surveillance System (VICNISS) database

VICNISS have collated and reported data on hospital-acquired infections in Victoria since 2003. Data collation and reporting occurs via a secure online portal accessed by all Victorian health services. Results are analysed and reported quarterly to the department (SCV) and individual health services. In April 2020, the department engaged VICNISS to implement an enhanced hospital-based surveillance system. As part of this system, all Victorian hospitals were required to notify the department via the VICNISS reporting platform, of key data relating to hospitalised COVID-19 patients, including patients’ COVID-19 status at admission.

iv. Microbiological Diagnostic Unit Public Health Laboratory (MDU-PHL)

MDU- PHL is a public health laboratory in the Department of Microbiology & Immunology at the University of Melbourne, located at the Doherty Institute. It has served the community since 1897, providing analytic services and technical advice in public health microbiology to inform public health policy and practice. MDU- PHL has a strong focus on

2 PHESS has since been superseded for surveillance of COVID-19 but was in place during the period of interest of this investigation.


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microbial genomics, using this information for public health and clinical microbiology practice in Australia, and providing leadership and training in this field. MDU- PHL has performed genomic analysis on more than 18,000 SARS-CoV-2 samples for the department throughout the COVID-19 epidemic.

3. Screening and assessment of hospital acquisition

Cases with possible C19-HAIs diagnosed between 25 January 2020 and 15 November 2020, inclusive, were extracted from PHESS. Cases were included if they met one of the following criteria:

• epidemiological link to an acute or sub-acute hospital outbreak

• symptom onset more than 2 days after hospital admission (no upper limit)

• first positive specimen collected within 14 days following discharge.

Cases were excluded if their diagnosis date3 occurred prior to their date of hospital admission.

VICNISS data was reviewed to identify additional cases not captured in the PHESS extract.

Cases were then matched to VAED data to confirm dates of hospital admission and discharge. To maximise the capture of potential C19-HAI cases, all patients in the VAED data flagged as ‘nosocomial coronavirus’ (by International Classification of Disease (ICD) code and a nosocomial flag “C” (for complicating diagnosis) on the record) were also investigated.

The likelihood of hospital acquisition was assessed for each case against specific C19-HAI case

definitions. This assessment was conducted by both VICNISS and the department, and different

definitions were used for cases diagnosed during hospital admission (Appendix 1) and those

diagnosed following discharge (Appendix 2). Cases were categorized as having either a definite,

probable, indeterminate, or non-healthcare-acquired COVID-19 infection.

Hospital infection prevention and control personnel were then consulted to confirm C19-HAI status

and attributing hospitals for all patients and to validate the data. Cases determined to have definite

or probable C19-HAIs were assigned attributing hospitals where possible.

4. Epidemiological analysis of C19-HAIs

Demographic data, hospital admission (excluding Emergency Department presentations without

subsequent admission) and outbreak data were extracted from PHESS for all COVID-19

notifications, including those with C19-HAI. Reasons for patient admission (principal diagnosis)

were obtained from the VAED dataset where VAED admission details could be linked to PHESS

records. The number of hospital transfers from residential aged care facilities (RACFs) was

obtained from Ambulance Victoria (AV). This dataset only includes non-emergency hospital

transfers from RACFs managed by AV. Data from the VAED and Victorian Emergency Minimum

Dataset (VEMD) provided enhanced information regarding whether a patient was culturally and

linguistically diverse (CALD) and their country of birth. Data on the characteristics of Victorian

Hospitals was obtained from Department of Health records and, hospital bed occupancy was

obtained from the Department of Health COVID-19 Daily Capacity and Occupancy Register from

Health Collect.

Descriptive and statistical analyses were used to examine the demographic profile, disease severity

and outcomes of patients with a C19-HAI and possible associations. Statistical analyses included

multiple logistic regression.

3 Diagnosis date refers to the date the case was recorded as ‘confirmed’ in PHESS. This corresponded to the date the department was notified of the positive case or the first positive test date.


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A COVID-19 death was defined as a death in a confirmed COVID-19 case, where there was no

clear alternative cause of death that could not be related to COVID-19 (e.g. trauma), and no period

of complete recovery from COVID-19 between illness and death. Where a Coroner’s report was

available, these findings were observed (Victorian Department of Health and Human Services

2021).

For descriptive analysis, all Victorian hospitals were grouped into three categories of C19-HAI

severity: unaffected by C19-HAIs, affected (1-9 cases of C19-HAI) or highly affected (>10 cases of

C19-HAI). Considered hospital characteristics included the number of COVID-19 admissions

(excluding C19-HAIs), and the number of patients with potentially unrecognised community-

acquired COVID-19 on admission. This latter group was defined as all admitted non-HAI COVID-

19 cases whose first positive swab was taken after theirn first day of hospital admission. The

rationale for including this group was that their COVID-19 status may have been unsuspected and,

hence, they may have posed an infection risk. Analyses were performed using Stata, version 15.1,

R, version 3.6.3 (Boston, MA), and Microsoft Excel, version 2008.

To map the geographic distribution of COVID-19 prevalence in the Melbourne metropolitan area,

two density surfaces representing the number of individuals per square kilometre were constructed

using a Gaussian kernel smoothing function: the first (numerator) was based on all COVID-19

cases diagnosed between 25 January 2020 and 15 November 2020 in the Melbourne metropolitan

area (cases in hotel quarantine were excluded as they did not acquire their infections in Victoria).

The second (denominator) was based on all individuals estimated to be present at the start of the

period and considered at risk. Details of the population at risk comprised counts of individuals

resident at the meshblock level from the 2016 Census of Population and Dwellings (Australian

Bureau of Statistics, 2016). Location details for COVID-19 cases were obtained by geocoding the

listed home address for each confirmed COVID-19 case. The ratio of the density surface of COVID-

19 positive individuals to the density surface of the population of individuals at risk provided a relief

map of the prevalence of COVID-19 expressed as the number of individuals COVID-19 positive per

100,000 per square kilometre (Bithell, 1990; Lawson and Williams, 1994). This map provided a

means to identify areas of relatively high COVID-19 risk, corrected for the irregular geographic

distribution of the population at risk. Bandwidth parameters for the kernel functions (used to control

the amount of smoothing applied to each of the estimated density surfaces) were calculated by

cross validation (Bowman and Azzalini, 1997).

5. Genomic Analysis

Genomic data were used to explore how closely related C19-HAI cases were within hospitals, to

describe the number and size of distinct clusters within hospitals, and to investigate transmission

links along with epidemiologic data. MDU-PHL conducted genomic sequencing and phylogenetic

analysis of cases classified as having probable or definite C19-HAIs and other cases with an

epidemiological link to the attributing hospitals (including patients and staff). Methods used were

as described in Seemann et al. (2020). Genomic links between cases and to outbreaks in RACFs

were then investigated and compared with epidemiological data held by the department to

determine if C19-HAI allocations were supported, and the direction of transmission (from RACF to

hospital or vice versa and between patients or between patients and staff) where possible.

6. Facilitated discussions with hospitals and case studies

To explore the context of C19-HAI within hospitals, four health services (representing eight public

hospitals and three sub-acute facilities), were invited to share key challenges and key lessons learnt

in a facilitated group discussion. Sessions were held between 5 and 15 January 2021 and were


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conducted as 90-minute remote meetings (via Microsoft Teams). Participants discussed the

following pre-defined questions, notified in advance via a meeting attendee brief (Appendix 3):

i. What were the ‘top 5’ key factors or challenges contributing to C19-HAIs within your health service?

ii. What strategies or strengths worked well in preventing and managing this? Why? iii. What strategies didn’t work as well, or what other conditions were exacerbating? Why? iv. What were the key lessons learnt, and what would you recommend for the future?

Participants were asked to reflect on the key strengths or strategies within the organisation which

were most protective or instrumental in the prevention and control of C19-HAIs, and the greatest

‘lessons learnt’. Meeting notes were taken by departmental staff, and distributed to participants

after the meeting, for review and approval. Notes from all sessions were synthesised and common

themes extrapolated.


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Results

Acquisition in hospitalised COVID-19 patients

A total of 601 cases were assessed against the C19-HAI case definitions. Of these, 576 were identified

through PHESS, and an additional 25 from VICNISS data (Error! Reference source not found.). There

were 277 cases assessed as definite or probable C19-HAI (Error! Reference source not found.), 34 of

which were diagnosed following discharge from the hospital of acquisition. Of these 34 patients, 23 were

subsequently readmitted to hospital with COVID-19 (either to the same or a different facility) while 11 were

not readmitted to a Victorian hospital and therefore were never hospitalised with COVID-19 in Victoria

(Table 1: Results of assessment process for patients with C19-HAIs diagnosed between 25 January 2020

and 15 November 2020

C19-HAI category Number of cases

Definite C19-HAI 187

Probable C19-HAI 90

Subtotal (C19-HAI) 277

Indeterminate 14

Not C19-HAI 208

Total cases assessed 499

Table 2). For a further 14 patients, the source of infection was unable to be determined (Error! Reference

source not found.).

Between 25 January and 15 November, 2,492 COVID-19 cases were hospitalised in Victoria. Because 11

C19-HAI cases diagnosed with COVID-19 following discharge were not readmitted to a Victorian hospital,

266 (11%) definite or probable C19-HAIs were counted among the 2,492 cases hospitalised in Victoria with

COVID-19 (Table 1: Results of assessment process for patients with C19-HAIs diagnosed between 25

January 2020 and 15 November 2020



Probable C19-HAI 90


Indeterminate 14

Not C19-HAI 208


Table 2).


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Figure 1: Selection process for patients with definite and probable C19-HAIs


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Table 1: Results of assessment process for patients with C19-HAIs diagnosed between 25 January 2020 and 15 November 2020



Probable C19-HAI 90


Indeterminate 14

Not C19-HAI 208


Table 2: C19-HAIs by hospitalisation status and timing of diagnosis

Diagnosis and hospitalisation category All

C19-HAIs C19-HAIshospitalised

with COVID-19 in Victoria

Diagnosed during the admission in which acquisition occurred 243 243

Diagnosed post-discharge

Subsequently admitted to a Victorian hospital with COVID-19^

23 23

Never readmitted 10# -

Transferred to an interstate hospital (never notified in Victoria)

1 -

Total 277 266*

* 2,226 non-C19-HAI cases were hospitalised in Victoria giving a total of 2,492 COVID-19 cases hospitalised in Victoria ^ Some patients were readmitted to their hospital of acquisition and some were admitted to another service # This includes one patient diagnosed post-mortem

The earliest diagnosis date for a patient with a definite or probable C19-HAI was 22 March 2020, and the

latest was 12 October 2020. Most patients with a C19-HAI were diagnosed during the second wave of the

pandemic, with the majority in July and August 2020. A small number of C19-HAIs were observed during

the second half of April, when there were low numbers of overall COVID-19 infections (Error! Reference

source not found.). Eight of these nine cases occurred in a single hospital (Albert Road Clinic).


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Figure 2: Timeline of COVID-19 cases in Victoria (25 January 2020- 15 November 2020), stratified by hospitalisation and healthcare acquisition


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Characteristics and outcomes of patients with C19-HAIs

The median age of patients with C19-HAIs was 82 (interquartile range [IQR] 69-87) years, which was

significantly older (p < 0.001) than hospitalised community-acquired COVID-19 patients (median age of 70

years, IQR 50-85) and other COVID-19 cases (median age 35 years, IQR 24-54) (Figure 3, Table 3). Twelve

(4%) C19-HAI patients were epidemiologically linked to an outbreak at an RACF, compared to 1031 (46%)

hospitalised community-acquired COVID-19 patients.

Figure 3: Age distribution of COVID- 19 cases, by hospitalisation and C19-HAI status

There were slightly more female patients with C19-HAIs (53%) than male (47%), which was similar to other

hospitalised community-acquired COVID-19 cases, and both groups also had the same proportion who

identified as Aboriginal and/or Torres Strait Islander (0.4% of those with known status in each group) (Error!

Reference source not found.). However, patients with C19-HAIs were significantly less likely than

hospitalised community-acquired COVID-19 cases to be born overseas (p < 0.001, 45% versus 57%) or be

culturally and linguistically diverse (CALD)4 (p < 0.001, 29% versus 46%).

Principal diagnosis5 data was available from the VAED for 168 of the 277 patients with C19-HAIs (61%).

For other C19-HAI cases, data matching was unsuccessful for their relevant admissions. Assessment of

the PHESS and VAED data matching process for all COVID-19 cases (not only C19-HAIs) revealed some

systematic bias. Cases admitted to private hospitals, those born in certain countries, and non-English

speakers were slightly less likely to be matched. However, given the demographic of C19-HAIs this is likely

to have had little impact on our findings.

4 Born in a non-English speaking country and/or speaks a language other than English at home (Victorian Department of Health, 2020b) 5 The diagnosis chiefly responsible for occasioning the patient’s episode of care in hospital (Australian Institute of Health and Welfare, 2016)


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Principal diagnoses for matched C19-HAI cases varied, but the most common were bone fracture (18%),

cancer (10%), dementia or delirium (9%), infection (7%), and joint pain (6%). Principal diagnosis data was

not obtained for community-acquired COVID-19 cases. Patients with C19- HAIs had a significantly longer

length of stay (median, 26 days, IQR 14-40) than hospitalised community-acquired COVID-19 patients

(median 7 days, IQR 3-17, p < 0.001).

When compared with hospitalised community-acquired COVID-19 cases, the C19-HAI group were less

likely to be admitted to ICU (7% versus 13%) or ventilated (3% versus 6%) but had a longer length of stay

(median 17 versus 9 days) (Table 3). Risk of death among C19-HAI patients was greater than in community-

acquired, hospitalised COVID-19 patients (31% versus 20%) (Table 3), however this was attributable to the

older age of C19-HAI patients (the difference was not statistically significant when adjusting for age,

p=0.29).

Table 3: Demographic characteristics and outcomes of patients with C19-HAIs and patients hospitalised with community-acquired COVID-19; number (column per cent) unless specified

Patients with hospital-acquired COVID-19

Patients with community-acquired COVID-19#

Demographic characteristics

Male* 129 (46.6%) 1026 (46.1%)

Female 148 (53.4%) 1199 (53.9%)

Age, years; median (IQR) 82 (69-87) 70 (50-85)

CALD 79 (28.5%) 1018 (45.7%)

Born overseas 124 (44.8%) 1273 (57.2%)

Aboriginal and/or Torres Strait Islander 1 (0.4%) 10 (0.4%)

Epidemiological links

Linked to an outbreak at an RACF 11 (4.0%) 1028 (46.2%)

Outcomes

Length of stay, days^; median (IQR) 17 (9-26) 9 (5-19)

Deaths 85 (30.7%) 447 (20.1%)

ICU 20 (7.2%) 282 (12.7%)

Ventilated 7 (2.5%) 143 (6.4%)

Cases 277 2226 # The total number of patients admitted to Victorian hospitals with COVID-19 was 2,492, including 266 cases C19-HAI (see Table 2).

* One patient hospitalised without C19-HAI had a designated sex of “other”. ^ Calculated as the total number of days spent in hospital from COVID-19 diagnosis (including the diagnosis date). This includes all hospital admissions entered into PHESS.


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Hospitals with C19-HAIs

Thirty hospitals had definite or probable C19-HAIs in patients. The median number of C19-HAI cases was

two, ranging from one to 78 (Table 4). Eight hospitals had 10 or more cases in each; together they

accounted for three-quarters of C19-HAI cases (Royal Melbourne Hospital - Royal Park Campus, Royal

Melbourne Hospital - City Campus, St Vincent's Hospital Melbourne, St George's Health Service,

Hazeldean Transition Care, Footscray Hospital, Golf Links Road Rehabilitation Centre and Brunswick

Private Hospital). The remaining cases occurred across 22 hospitals.

Most hospitals with definite or probable C19-HAIs were in the North and West Metropolitan Region (77%)

and were located near areas that had a prevalence of more than 50 COVID-19 infections per 100,000

people/km2 (Figure 1: Selection process for patients with definite and probable C19-HAIs4). Seventy-seven

percent of C19-HAI cases were also residents of the North and West Metropolitan Region. There were no

patients with C19-HAIs identified in regional Victorian hospitals. Almost all C19-HAI cases occurred in public

hospitals (90%), with slightly more cases observed in acute than sub-acute/non-acute facilities (Table 5,

see Appendix 4 for hospital types).

Factors associated with C19-HAI

Of the 2,226 cases hospitalised with community-acquired COVID-19, 572 had their first positive COVID-19

swab taken during their hospital admission, 160 of which (28%) were taken after the first day of admission.

Given the delay in testing, it is possible that these cases were not suspected as having COVID-19 on

admission (‘potentially unrecognised’ cases). Hospitals highly affected by C19-HAI had higher numbers of

potentially unrecognised COVID-19 cases, as well as more community-acquired COVID-19 hospitalisations

overall, than hospitals with few or no C19-HAIs (Table 6).

Hospitals with a higher C19-HAI caseload had higher numbers of community-acquired COVID-19

hospitalisations and accepted more transfers of residents from RACFs than hospitals with few or no C19-

HAIs. However there was no clear relationship between C19-HAI caseload and the number of RACF

residents admitted with potentially unrecognised COVID-19 (Table 6). One quarter of all COVID-19 cases

in Victoria (regardless of hospitalisation) were linked to RACF outbreaks during the period of this study

(5,049 of 20,343), and close to half of all hospitalised community-acquired cases (46%; 1,028 of 2,226).

Similarly, of the 160 potentially unrecognised cases, 48% (77) were linked to RACF outbreaks.

Several sub-acute and non-acute hospitals saw relatively high numbers of C19-HAIs despite having few

COVID-19 admissions, potentially unrecognised COVID-19 cases or direct aged care transfers. These

hospitals commonly had links to, and received transfers from, larger acute hospitals (e.g. Royal Melbourne

Hospital Royal Park Campus, St Georges Health Service, Golf Links Road Rehabilitation Centre and

Hazeldean Transitional Care).

Conversely, some hospitals had few or no C19-HAIs despite experiencing significant numbers of COVID-

19 admissions, potentially unrecognised cases and RACF transfers, and over forty Victorian hospitals that

received COVID-19 admissions had no C19-HAIs.


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Table 4: Definite and probable C19-HAIs in patients, by hospital and health service (25 January to 15 November 2020; ordered by number of C19-HAI cases per health service).

C19-HAI cases Additional contextual information (all figures exclude C19-HAIs)

Hospital C19-HAIs diagnosed during admission in the attributable hospital

C19-HAIs diagnosed post-discharge from the attributable hospital

Total C19-HAIs

Community-acquired COVID-19 hospitalisations*

Community-acquired COVID-19 hospitalisations, potentially unrecognised on admission**

COVID-19 cases admitted from RACF outbreaks***

Other planned Ambulance Victoria transfers from RACF outbreaks^^

Melbourne Health

Royal Melbourne Hospital - Royal Park Campus

73 4 77 38 1 29 1

Royal Melbourne Hospital - City Campus

12 6 18 243 9 70 2

St Vincent’s Health

St Vincent's Hospital Melbourne 27 10 37 120 8 32 2

St George's Health Service 22 0 22 1 0 1 0

Western Health

Hazeldean Transition Care 19 0 19 0 0 0 0

Footscray Hospital 15 1 16 128 10 29 3

Sunshine Hospital 6 2 8 230 18 54 8

Williamstown Hospital 2 0 2 1 0 0 2

Peninsula Health

Golf Links Road Rehabilitation Centre 12 4 16 31 0 31 0

Frankston Hospital 5 2 7 61 6 22 2

Rosebud Hospital 1 0 1 4 0 4 1

Northern Health

Bundoora Extended Care Centre 2 0 2 1 0 1 3

Broadmeadows Hospital 1 0 1 1 0 0 0

Epworth Health Care

Epworth Richmond 2 0 2 85 3 55 10

Epworth Hawthorn 1 0 1 1 0 0 0

St John of God Health Care Inc.

St John of God Berwick Hospital 1 0 1 24 0 24 25

St John of God Frankston Rehabilitation Hospital

1 0 1 2 2 0 0

Other health services

Brunswick Private Hospital 9 1 10 21 5 18 22

Albert Road Clinic 5 3 8 0 0 0 0

The Alfred Hospital 7 0 7 107 2 19 0

Werribee Mercy Hospital 7 0 7 147 7 60 2

Dandenong Hospital 3 0 3 77 2 13 1

Box Hill Hospital 2 0 2 80 5 14 0

Royal Children's Hospital 2 0 2 23 4 0 0

St Vincent's Private Hospital – East Melbourne

1 0 1 38 16 33 43

Austin Hospital 1 0 1 194 8 57 5

Cabrini Hospital - Malvern 1 0 1 31 4 13 4

Essendon Private Clinic 1 0 1 1 1 0 0

Northpark Private Hospital 0 1 1 1 0 0 0

Peter MacCallum Cancer Centre 1 0 1 0 0 0 0

Facility Unknown^ 1 0 1 N/A N/A N/A 0

Total 243 34 277 1691 111 579 136

* Includes admissions to multiple hospitals per patient but excludes readmissions to the same hospital. Admitting hospital missing for 17 COVID-19 cases.

** First positive swab taken after day of admission

*** COVID-19 positive aged care residents linked to an outbreak in an RACF. Some residents were transferred to more than one hospital; hence total reflects number of admissions rather than number of individuals. Readmissions to the same hospital are excluded.

^ For one patient, there were two possible attributing facilities: Peninsula Health Golf Links Road Rehabilitation Centre and St John of God Frankston Rehabilitation Hospital.

^^ This excludes C19-HAI cases, COVID-19 cases admitted from RACF outbreaks and excludes transfers made with private vehicles and emergency transfers.


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Table 5: Definite and probable C19-HAIs, by Department of Health region and hospital type, 25 January 2020 to 15 November 2020.

C19-HAI classification: n (%)

Definite Probable Total

Hospital location

Metropolitan 187 (100%) 90 (100%) 277 (100%)

North and West 148 (79.1%) 65 (72.2%) 213 (76.9%)

Eastern 20 (10.7%) 5 (5.6%) 25 (9.0%)

Southern 19 (10.2%) 20 (22.2%) 39 (14.1%)

Rural 0 0 0

Hospital type

Public (% of Total) 174 (93.5%) * 75 (83.3%) 249 (90.2%)

Large acute metropolitan (% of Public) 40 (23.0%) 44 (58.7%) 84 (33.7%)

Other acute metropolitan (% of Public) 13 (7.5%) 15 (20%) 28 (11.2%)

Sub-acute, non-acute and un-peered (% of Public) 121 (69.5%) 16 (21.3%) 137 (55%)

Private (% of Total) 12 (6.5%) * 15 (16.7%) 27 (9.8%)

Large acute metropolitan (% of Private) 3 (25%) 2 (13.3%) 5 (18.5%)

Other acute metropolitan (% of Private) 5 (41.7%) 5 (33.3%) 10 (37%)

Sub-acute, non-acute and un-peered (% of Private) 4 (33.3%) 8 (53.3%) 12 (44.4%)

*One case is not included as acquisition could not be determined between a private and public facility

Figure 4: Prevalence of COVID-19 infections per 100,000 people/km2 and location of hospitals with C19-HAIs in metropolitan Melbourne


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Table 6: Characteristics of hospitals unaffected, affected and highly affected by C19-HAIs, Victoria 2020; median (range) unless specified.

‘Unaffected’

No C19-HAIs

‘Affected’

1 to 9 C19-HAIs

‘Highly affected’

10+ C19-HAIs

COVID-19 caseload

Total hospitals 200 22 8

C19-HAIs 0(0-0) 2 (1 - 8) 19 (10 - 77)

Community-acquired COVID-19 hospitalisations* 0 (0 - 195) 24 (0 - 230) 35 (0 - 243)

Community-acquired COVID-19 hospitalisations, potentially unrecognised on admission (excluding C19-HAIs)

0 (0 - 9) 2 (0 - 18) 3 (0 - 10)

Admissions from RACFs

COVID-19** cases admitted from RACF outbreaks (excluding C19-HAIs)

0 (0 - 67) 9 (0 - 67) 30 (0 - 78)

COVID-19** cases admitted from RACF outbreaks, potentially unrecognised on admission (excluding C19-HAIs)

0 (0 - 8) 0 (0 - 16) 1 (0 - 5)

Other planned AV transfers from RACF outbreaks^ 0 (0 - 65) 1 (0 - 43) 2 (0 - 22)

Facility characteristics

Median occupied bed number^^ (range) 39 (0 - 579) 113 (24 - 586) 61 (20 - 510)

Public facility (%) 131 (65.5%) 13 (59.1%) 7 (87.5%)

Provides acute care (%) 169 (84.5%) 20 (90.9%) 3 (37.5%)

Provides sub-acute care (%) 31 (15.5%) 2 (9.1%) 5 (62.5%)

Emergency department (%) 30 (15.0%) 10 (45.5%) 3 (37.5%)

Intensive care unit (%) 19 (9.5%) 7 (31.8%) 3 (37.5%)

* Includes admissions to multiple hospitals per patient but excludes readmissions to the same hospital. Admitting hospital missing for 17 COVID-19 cases.

** COVID-19 positive aged care residents linked to an outbreak in an RACF. Some residents were transferred to more than one hospital; hence total reflects number of admissions rather than number of individuals. Readmissions to the same hospital are excluded.

^ This excludes C19-HAI cases, COVID-19 cases admitted from RACF outbreaks and excludes transfers made with private vehicles and emergency transfers. ^^ For each facility the number of occupied beds was taken as the mean number of occupied beds from 26 August until 15 November 2020 (study end). This data was only available for 214 facilities. Source: COVID-19 Daily Capacity and Occupancy Register from Health Collect. For Hazeldean Transition Care the value was assumed to be 30, the number of operational beds.


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Genomic analysis of C19-HAI patients

Genomic clustering for C19-HAI patients and within affected hospitals

Genomic sequencing was available for specimens from 219 of the 277 patients with C19-HAI, occurring

across 24 of the 30 hospitals with C19-HAI patients (MDU-PHL received no sample for 22 patients and

sequencing failed for 36).

During the study period, there were 613 distinct genomic clusters in the Victorian population. Of these, 31

clusters included C19-HAI cases: GC.001 – GC.031. In fifteen hospitals, all C19-HAI patients were part of

the same single genomic cluster. The remaining nine hospitals each had between two and five clusters

represented among their C19-HAI patients. The number of genomic clusters within a hospital can be

understood as the minimum number of separate introductions of COVID-19 to that hospital.6 Overall, the

number of C19-HAI patients in each genomic cluster ranged from one to 36 individuals (Table 7 and Figure

5).

Of the 31 genomic clusters that included C19-HAI patients, nine (29%) were present across multiple

hospitals. This was more commonly seen in hospital networks with multiple campuses, such as Western

Health, which encompasses Sunshine Hospital, Footscray Hospital, Williamstown Hospital, and Hazeldean

Transition Care. Genomic clusters more frequently identified in the community were also more likely to be

present in more than one hospital (GC.001, GC.004, GC.007, GC.011). One cluster (GC.004) was observed

among C19-HAI patients in four hospitals: Frankston Hospital, Royal Melbourne Hospital (Royal Park

Campus), Sunshine Hospital and Hazeldean Transition Care (Figure 5). This cluster had the largest number

of community cases in Victoria with 3,120 infections, originating from the outbreak in the Rydges Hotel on

Swanston Street (a quarantine hotel). Given the large number of community infections within this genomic

cluster, multiple separate and unrelated introductions to each facility are likely.

Eleven of the 31 clusters had genomic links to outbreaks in aged care facilities, and 29 included hospital

staff (Table 7).

6 For example, for a hospital with one genomic cluster, one patient admitted with COVID-19 may be the original source of all C19-HAIs in the hospital. Alternatively, there may be multiple source patients who share the same genomic sequence, simply because some genomic sequences are more common in the community than others.


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Table 7: Genomic clustering for C19-HAI patients, by hospital (25 January to 15 November 2020)

Hospital C19-HAIs

Distinct genomic clusters (C19-HAIs per cluster)

Cluster/s include hospital staff#

Cluster/s have genomic links to cases in RACFs

Melbourne Health


77 4 (36,18,1,1) Yes Yes

Royal Melbourne Hospital - City Campus 18 3 (6,6,1) Yes Yes

St Vincent’s Health

St Vincent's Hospital Melbourne 37 4 (31,1,1,1) Yes Yes

St George's Health Service 22 1 (16) Yes Yes

Peninsula Health

Golf Links Road Rehabilitation Centre 16 1 (11) Yes No

Frankston Hospital 7 5 (1,3,1,1,1) Yes No

Rosebud Hospital 1 1 (1) Unavailable No

Western Health

Hazeldean Transition Care 19 2 (12,1) Yes Yes

Footscray Hospital 16 2 (11,5) Yes Yes

Sunshine Hospital 8 3 (1,1,4) Yes Yes

Williamstown Hospital 2 1 (1) Yes No

Northern Health

Bundoora Extended Care Centre 2 Unavailable Unavailable Unavailable

Broadmeadows Hospital 1 1 (1) Unavailable Yes

St John of God Health Care Inc.

St John of God Berwick Hospital 1 Unavailable Unavailable Unavailable


1 Unavailable No Unavailable

Epworth Health Care

Epworth Richmond 2 1 (1) Unavailable Yes

Epworth Hawthorn 1 1 (1) No No

One facility per health service

Brunswick Private Hospital 10 4 (1,1,3,1) Yes No

Albert Road Clinic 8 1 (7) Yes No

The Alfred Hospital 7 1 (7) Yes No

Werribee Mercy Hospital 7 1 (7) Yes Yes

Dandenong Hospital 3 2 (2) Yes No

Box Hill Hospital 2 1 (2) Yes No

Royal Children's Hospital 2 Unavailable Unavailable Unavailable

St Vincent's Private Hospital – East Melbourne

1 1 (1) Yes No

Austin Hospital 1 1 (1) Unavailable No

Cabrini Hospital - Malvern 1 Unavailable Unavailable Unavailable

Essendon Private Clinic 1 Unavailable Unavailable Unavailable

Northpark Private Hospital 1 1 (1) Yes No

Peter MacCallum Cancer Centre 1 1 (1) Yes Yes

Facility Unknown^ 1 N/A N/A N/A

^Acquisition for this case could not be determined between a private and public facility


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Figure 5: C19-HAI cases by genomic cluster and hospital*

* Box around genomic cluster name indicates genomic link7 to cases in aged care facilities. Size of each bubble is proportional to the number of C19-HAI cases.

7 This does not show direction of transmission (from RACFs to hospitals or vice versa).


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Genomic and epidemiological investigation

Genomic and epidemiological data was used to investigate chains of transmission that led to C19-HAIs. In

a healthcare setting, a patient may acquire their infection from another patient, from a staff member or a

visitor who, in turn, may have acquired their infection in a hospital or elsewhere in the community, including

in aged care settings. Genomic data and detailed epidemiological data were not available for all COVID-19

cases, so it was not possible to determine the most common chains of transmission. Despite this, chains

of transmission for some C19-HAI patients were elucidated and are provided in Table 8: Examples of

possible chains of transmission for C19-HAI patients.

Table 8: Examples of possible chains of transmission for C19-HAI patients

Possible sources of infection for C19-HAIs, with specific examples.

Attributing hospital type

Genomic cluster^

Suspected transmission between settings

C19-HAI patients in cluster at attributing hospital*

Staff cases in cluster at attributing hospital*

Patient-to-patient transmission

A patient acquired COVID-19 after sharing a room with an unrecognised infectious patient who had been transferred from another hospital. The latter had had close contact with a COVID-19 positive nurse but was not identified during contact tracing. See below (staff-to-patient) for further details on this chain.

Other acute metropolitan

D Hospital-to-hospital

1 1

An RACF resident acquired their infection as an inpatient after sharing a room with another patient who wasn’t recognised to be infectious. The RACF resident was only identified as a close-contact three days after being transferred back to their RACF and tested positive on the same day.


C Hospital-to-RACF

5 8

A patient acquired their infection after sharing a room with another patient. This patient acquired their infection from a nurse, who unknowingly worked while infectious.

Large acute metropolitan

H NA 2 2

A patient acquired their infection following contact with another unrecognised infectious patient. The latter was admitted following surgery and had an initial negative test, followed by a positive test. He was noted as the source of the outbreak.

Sub/ non-acute

A Community-to-hospital

11 0

A patient was discharged from hospital to an RACF after sharing a room with a presumably unrecognised case.


G Hospital-to-RACF

1 0

A resident admitted from an RACF with delirium and presumed sepsis initially tested negative. The patient was tested again after the RACF informed the hospital it had COVID-19 cases. The patient, who had acquired their infection at the RACF, tested positive six days after admission, seeding a ward outbreak.


C RACF-to-hospital

5 8

Staff-to-patient transmission

A patient acquired their infection from a nurse. How the nurse acquired their infection is uncertain. However, another patient they cared for around a week earlier tested positive a few days after their contact, after being advised their spouse had tested positive in the community, although neither were sequenced. The first patient was then missed as part of contact tracing and was transferred to another hospital, where one further patient was infected (see patient to patient section above).


D NA 4 8


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Possible sources of infection for C19-HAIs, with specific examples.

Attributing hospital type

Genomic cluster^

Suspected transmission between settings

C19-HAI patients in cluster at attributing hospital*

Staff cases in cluster at attributing hospital*

A patient likely acquired their infection from a nurse, who unknowingly worked while infectious. It is unclear where the nurse acquired their infection.


E NA 1 0

Patients likely acquired their infection from a doctor, who unknowingly worked while infectious. It is unclear where the doctor acquired their infection, but their spouse, who wasn’t a healthcare worker, tested positive at a similar time.


B Community-to-hospital

7 1

A patient acquired their infection from a nurse who unknowingly worked while infectious. The nurse acquired their infection from a patient on the COVID ward.


H Community to hospital

2 2

* Number of patients or staff identified in the genomic cluster only, not based on epidemiology. Not all cases had specimens sequenced, hence these figures may be underestimates.

^Coded cluster reference number; hospital names and cluster numbers not disclosed to preserve confidentiality.

In all the examples provided, hospital-acquired infections occurred because of unrecognised infections in

either patients or hospital staff.

These specific examples may not be representative of all C19-HAIs. Chains of COVID-19 transmission are

easier to establish in small rather than large genomic clusters, and many C19-HAI cases were seen in large

clusters. For example, the GC.001 cluster seen at the Royal Melbourne Hospital Royal Park Campus was

a large outbreak with at least 93 staff and patients8 diagnosed within a relatively short period, so the

specifics of transmission are difficult to determine. There is epidemiological evidence to support the

possibility that some patients at Royal Park Campus were infected by a visitor who had also visited an

exposure site during their acquisition period. However, no genomic data is available to support this.

Some chains of transmission spanned more than one facility (Table 8). Following investigation of the eleven

genomic clusters that had links to outbreaks in RACFs, one instance of transmission from an aged care

facility resident to a hospital setting was found, and two occasions of hospital to RACF transmission.

However, incomplete capture of epidemiological links between individual patients and staff prevented a

comprehensive investigation, so it is possible that other transmission links exist between RACFs and

hospitals.

8 Genomic sequencing was unavailable for 29 C19-HAI patients and staff at RMH Royal Park Campus


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Key strengths, challenges and lessons learnt

Facilitated discussions were held with four health services: Peninsula Health, Western Health, St Vincent’s

Hospital and Melbourne Health. These health services represent eight public hospitals and three sub-acute

facilities within metropolitan Melbourne, accounting for 70 percent of the total 277 cases with C19-HAIs.

Central to the discussions undertaken with health services was the acknowledgement of the key strengths

within hospitals which were able to prevent greater numbers of C19-HAIs, as have been reported

internationally (Zhou, 2020). Key strengths identified included the strong baseline risk mitigation by staff

well-trained in the prevention of hospital-acquired infections and the use of personal protective equipment

(PPE) and hand hygiene (HH).

Hospital-based incident management and contact tracing teams were crucial to effectively respond to C19-

HAIs, undertaking rapid epidemiological investigation and establishing up and down-stream contacts to

manage transmission risks. The professionalism, hard work and dedication demonstrated by healthcare

staff throughout Victoria’s first and second ‘waves’ was noted to be a strong line of defence against the

challenges encountered.

Several common themes were identified in relation to the main challenges and factors contributing to C19-

HAIs that were identified by health services. The most common and significant issues raised involved

patient care needs and behaviours and limitations of hospital infrastructure, resources and technology.

Underlying these contributing factors and challenges was the rapidly evolving pandemic, highly infectious

nature of SARS-CoV-2 and the potential extent of aerosol transmission risk, which was largely

unrecognised in the early stages of the pandemic. The World Health Organization noted the role of aerosol

transmission in July 2020 (World Health Organization, 2020b). The key contributing factors identified by

health services are summarised below in Tables 9 to 13, followed by the key strategies and

recommendations identified.

Patient factors

Services considered that most cases with C19-HAIs occurred as a result of patient-to-patient transmission,

although patient-to-staff and staff-to-patient transmission was also reported. The impact of unrecognised

cases, high levels of community transmission, patient movement and transfers in from RACF as well as

patient behaviours were all noted to be important contributing factors as detailed in Table 9.

Table 9: Patient factors relevant to C19-HAIs

Contributing Factor

Description Mitigating actions and strategies

Unrecognised cases

These could include patients admitted from the community/other facility who were asymptomatic on admission, patients with atypical symptoms (e.g. falls, functional decline or delirium) or patients unable to communicate symptoms. Onwards transmission was suspected to have occurred in some cases before diagnosis, particularly if the patient was not able to be adequately isolated or they were not recognised as ‘high risk’ early in the pandemic. Identification of infection in unrecognised cases was complicated by increasing community transmission, negative test results during incubation and delays in external laboratory results for some hospitals.

“Community transmission within our region was so intense that there was an ever-present risk of

Frequent, repeated testing of asymptomatic patients in high-risk wards and on-site “testing blitzes”

Management of suspected COVID-19 (sCOVID-19) patients with negative results as infected for duration of infectious period

On-site laboratory capability (in facilities with this capacity) allowing rapid results

Use of GeneXpert rapid tests (results within hours) – however supplies were very limited at that time


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patients testing negative whilst incubating COVID-19 on admission.”

High levels of community transmission

With increasingly high community transmission, the “sheer number” of cases, often asymptomatic and unrecognised upon admission, increased “virus entry” and “viral load” within wards and further strained the ability to isolate positive patients and manage patient flow. Participating health services identified with the concept described internationally of a “critical mass” of cases beyond which health systems struggle to cope.

Cancellation of many elective procedures

More conservative assessment of risk of incoming patients

Broadened asymptomatic testing practices across hospital

Patient transfers between wards and sites

While attempts were made to minimise patient transfers within hospitals, patient care needs and recognition of new infections meant that there was frequent, unavoidable need for patient transfers between wards, increasing patient contacts and risk of transmission. Transfers of unrecognised/ asymptomatic patients from acute to sub-acute wards was recognised as a source of outbreaks for two health services, with transmission occurring more easily in subacute wards given the physical layout of the wards and nature of patient behaviour and activity.

Patient screening prior to transfer, frequent asymptomatic testing

Use of masks for patients during transfer

Ensuring balanced, integrated expertise within incident management teams to manage IPC and patient needs.

Transfers from RACFs

Some hospitals received large numbers of residents (including unrecognised, asymptomatic cases) decanted from RACFs. In the very early stages of RACF outbreaks, hospitals receiving transfers were not made aware of the level of risk associated with transferred residents. Wandering, confusion, aerosol generating behaviours (AGB) and high nursing care needs of the aged care cohort complicated hospital IPC.

Other facilities received only smaller numbers of known cases transferred from RACFs, who were managed in COVID-19 wards and presumed not to be associated with C19-HAIs.

Testing upon admission, frequent asymptomatic testing

Cohorting of positive patients

‘Aerosol Generating Behaviours’ (AGBs)

AGBs were typically displayed by aged care or other patients with delirium, cognitive impairment or agitation. AGBs included shouting, singing or bursts of physical exertion e.g. kicking. AGBs were exacerbated by the confusion and disorientation associated with transfers, social isolation and the exclusion of social visitors, and use of full personal protective equipment (PPE) for staff. Many of the strategies staff would usually use to settle agitated patients could not be used for IPC reasons.

Training staff to prevent, recognise and manage AGBs

Early intervention with integrated, multidisciplinary teams to de-escalate behaviours

Facilitating contact with family/friends

Provision of single rooms where possible

Increased use of N95 masks for staff and cessation of high-risk activities where safe e.g. bed washes instead of showers.

Wandering

Wandering behaviours were recognised as a contributing factor by all health services. Without (controversial and potentially harmful) physical or pharmacological restraint, patients with impaired cognition could wander between rooms, increasing risks to patients in other rooms and common areas, and complicating cleaning and contact tracing. Furthermore, the physical layout of some aged care wards was such that wandering patients could easily move between wards (detailed in Table 10).

Integrated patient management to address patient needs and behaviours

Cohorting of aged care positive patients in one ward.


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Infrastructure

Limitations of existing infrastructure was identified as a significant contributor to C19-HAIs. Key

contributors noted were difficulties effectively isolating many suspected or confirmed cases and managing

patient and staff ‘flow’, and the suspected issues around ventilation (Table 10).

Table 10: Infrastructure limitations relevant to C19-HAIs

Contributing Factor


Limited single rooms, transmission risk in shared rooms

Facilities lacked enough single rooms to isolate patients wherever required. Some facilities/wards were more affected than others, having mostly 4 to 6-bed rooms, sometimes lacking complete division between rooms. There was greater risk of transmission in shared rooms given shared air, lower nurse to patient ratios and aerosol generating behaviours. The high demand for beds to meet delayed care needs, and high community transmission was also noted to increase the risk. Asymptomatic, unrecognised infections and different clinical requirements complicated attempts to cohort patients in shared rooms.

“It was a significant challenge to manage the surge in demand for single rooms.”

Frequent asymptomatic testing of patients (and staff) – up to every 3 days.

Immediate transfer of suspected or confirmed cases into COVID-19 and sCOVID-19 wards established in wards best designed/equipped for IPC measures.

Density limits on room occupancy

Risk management for shared bathrooms (one-person limit +/- ‘rest’ period or cleaning between subsequent users.

Facility lay-out and design

The design of wards, particularly in older buildings, did not allow for the ‘flow’ and space required for optimal delineated donning/doffing stations, clinical waste and linen management. Existing surfaces and furnishings in older and rehabilitation wards tended to favour comfort over amenability to frequent cleaning.

Optimising space for donning and doffing and waste management.

Non-essential activity moved off wards, e.g. tea rooms and administration.

Increased use of ‘spotters’ to enforce correct and purposeful use of space and PPE.

Unidirectional flow through clinical spaces and minimising staff and patient movement.

Ventilation

Existing ventilation systems, particularly in older buildings, were not designed to manage the high viral load associated with large numbers of patients and level of aerosol transmission risk. Ventilation issues ranged from limited availability of negative pressure rooms, air exchange systems operating below expected function and unexpected exhaustion to common areas, to shared air space across rooms.

Investigation and re-balancing of existing ventilation systems.

Use of portable high-efficiency particle air filters (HEPA) and air scrubbers in some facilities.

Maximising use of wards with the best available ventilation.

One facility trialled the use of ventilation hoods for use in ICU to manage aerosol transmission risks in COVID-19 patients.


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Resources and technology

A number of logistical or technical challenges were encountered including laboratory testing, PPE and

contact tracing systems, summarised in Table 11.

Table 11: Resource/technology factors relevant to C19-HAIs

Contributing Factor


Delays in test results

The timeliness of test results was variable. Facilities with internal diagnostic laboratory capability could obtain swab results within 24 hours. However, facilities without this capability could face delays up to several days. Given criticality of time, laboratory delays were reported to significantly hamper IPC and outbreak responses.

In one hospital without laboratory capability, rapid tests (GeneXpert) were “instrumental” in rapidly containing an outbreak. Supply was very limited however, and staff were concerned that supply was not prioritised to meet their significant need.

Rapid tests were critical in managing outbreaks for facilities without in-house laboratory capability.

Diagnostic laboratory capability on-site circumvented this issue in some facilities.

Isolating where possible or cohorting of high risk/sCOVID-19 patients.

Test reliability during incubation

Unrecognised, incubating patients could repeatedly test negative before a confirmatory result was obtained, as existing PCR tests do not detect cases during the incubation period. This complicated the identification, isolation and clearance of asymptomatic patients.

Repeated and frequent testing.

Range of precautionary IPC measures and management of sCOVID-19 patients.

PPE

Low feasibility of PPE compliance among inpatients remains a challenge.

Health services faced logistical challenges initially, requiring time to source increased supply and scale-up waste management for increased(tripled) clinical waste.

High workload, fatigue, frequent donning/doffing, imperfect fit, comfort/pressure sores, PPE failure in steamy environments and difficulty administering ‘refresher’/updated training created opportunities for momentary lapses in PPE compliance.

Constantly changing or conflicting guidance on PPE usage also complicated hospital policies, logistics, and staff communications, particularly regarding the use of N95 masks.

Increased use of designated ‘spotters’ to monitor and remind staff of optimal use.

Implementing more extensive PPE use (e.g. N95 masks) pre-dating formal state guidelines.

Some implemented ‘buddy’ systems to minimise risks to patients, and staff risks during don/doff.

Contact tracing systems

“We didn’t have any electronic capability”. Health services were responsible for performing contact tracing at their facilities. Existing contact tracing systems, adequate under normal circumstances, were not designed to cope with the “sheer volume” and rapid increase in cases. Most relied upon the use of spreadsheets and quickly became “overwhelmed”. Contact tracing, while more extensive for staff infections, was “very labour intensive” and the time required put significant strain on the workforce.

Rapid expansion of contact tracing teams utilising available/re-deployed staff.

Some providers implemented purpose-built data systems.


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Staff

While staff to patient transmission was not identified as a significant source of C19-HAIs (more relevant in

staff-staff transmission), factors relating to hospital staff were identified as potential contributing factors,

as summarised in Table 12.

Table 12: Staff factors

Contributing Factor


Staff Fatigue

Staff fatigue was well recognised. The pressures of dealing with very frequent PPE changes, significant changes to personnel, duties, policies and procedures across the hospital, the impact of rostering changes and furloughing of staff, managing increased patient care needs, and keeping up with the rapidly changing evidence environment all contributed to staff fatigue. It was noted that fatigue could lead to momentary breaches in IPC measures.

Implementing strong staff support systems, ‘buddy’ systems, and spotters.

Strengthening staff wellbeing support programs including enhanced communication and support for furloughed and infected staff.

Clear and consistent modes of communication around changing policies, procedures, guidelines and the broader COVID-19 context.

Staff common areas

Staff tea rooms were identified as a potential transmission risk among staff (less applicable to C19-HAIs.)

Movement of tea rooms off wards, density limits, staff training.

Staff mobility

COVID-19 wards often had patients with diverse clinical and support needs necessitating the movement of specialist staff between wards.

“Very tricky to solve….you can’t really lock all relevant staff to a hot ward”

Furthermore, functions such as Medical Emergency Teams (MET) and security continued to operate across the hospital as it was not feasible to have these services designated for COVID-19 wards. Workforce dynamics also meant some staff worked across multiple health services and sites, e.g. specialist consultants, nursing staff when shortages required it.

“Locking down wards” to designated, multidisciplinary teams as far as possible.

Rostering changes and establishment of ‘A’ and ‘B’ teams or “team silos” to prevent interaction among groups of staff and reduce the impact of furlough.


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Broader pandemic, social and political context

Global factors associated with a rapidly developing pandemic were implicit in all challenges discussed by

participating health services, and a number of other political and contextual factors were identified as

having complicated the ability of health services to respond in real-time (Table 13).

Table 13: Broader pandemic, social and political context

Contributing Factor


Rapidly evolving epidemiology, global lack of knowledge

(implicit in all discussions)

Amidst constantly changing knowledge, guidelines and evidence, hospital IPC teams often had to develop their own protocols, and it was difficult to know if interventions were “going too hard or too fast…. It all came down to capacity”. One participant commented that early on, “like so many others, the health services were operating in an evidence-free environment…it’s an evolving science”.

Establishment of integrated, multidisciplinary incident management teams with IPC expertise to “scale-up” hospital responses.

Constant monitoring of facility, local and global situation, rapid action.

Changing and conflicting guidance

Health services looked to the department as a “beacon” for guidance, however guidelines frequently changed, and it was difficult to keep up with shifting advice. In some cases, guidance was “not easy to decipher”, and there was little guidance around testing on wards and management of patient transfers.

Having internal website as a ‘single source of truth’.

Daily COVID newsletter/bulletin for staff.

Clear highlighting of changes made to guidelines.

Establishment of forums for health services to provide input into the development of guidelines, and feedback on drafts.

Communication with the department

Communication with the department for contact tracing and outbreak management could be “problematic” and very time-consuming. Hospital staff lacked a single contact point with the department and were corresponding with multiple different staff across divisions, with a lack of continuity. In some cases, staff or patients received advice from Department of Health staff which conflicted with advice provided by health services.

Website used as a ‘single source of truth’.

Clear highlighting of changes made to guidelines.

Media environment

Conflicting and contradictory advice in public media and “scare campaigns” created anxiety among staff and patients and complicated the implementation of consistent, co-ordinated IPC measures.

Premier, Minister and CHO provided daily media conferences.


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Key strategies and recommendations from health services

Participating health service providers reflected on the key strategies in responding to challenges and to

prevent and manage C19-HAIs. These ‘lessons learnt’ are summarised in Table 14. In addition,

participating health service providers reflected on broader issues and identified opportunities for

improvement in broader State responses, summarised in Table 15.

Table 14: Summary of instrumental strategies and 'lessons learnt' by participating health services

“Act fast”: rapid intervention (immediate action) is critical. Develop clear, rapid outbreak response protocols for facilities/wards which define the action to be taken within hours

Conduct frequent, repeated asymptomatic testing among patients and staff to enable early detection of unrecognised infections.

Strengthen incident management teams to increase scale/capacity of responses and include multi-disciplinary infectious disease, allied health, operational, patient care, data and systems expertise. ‘HSMIT’ (Health Services

Incident Management Teams) and ‘PITSTOP’ (Patient Injury Time-Out STOP) approaches were successfully

adapted and utilised by one provider, enabling rapid responses alongside epidemiological investigations to contain outbreaks.

Implement stringent PPE protocols and support including N95 masks early, PPE ‘spotters’ or ‘buddies’ and pay attention to risks associated with unrecognised cases in ‘cold’ wards.

Prepare to meet diverse patient needs and COVID-19 presentations with a multi-disciplinary approach, to minimise transfers and behaviours associated with transmission risk.

Designate dedicated sCOVID-19 and COVID-19 wards, utilising the best available infrastructure most amenable to stringent IPC needs, including adequate ventilation.

Strengthen existing staff support systems and maintain regular contact with unwell or furloughed staff. A holistic approach to staff wellbeing was emphasised to support staff wellbeing under prolonged pressure.

Conduct regular ‘walk-arounds’ by IPC staff to observe ward function and IPC issues “on the ground”. “Usually it is not about people doing the wrong thing, but about how the wards work in a day to day sense”.

Table 15: Summary of suggestions by health services for improvement in State responses

Recommended that the definition for an outbreak in health services should be based on single case (rather than 2 cases), triggering immediate outbreak response

Identified a need for IPC requirements (particularly single room availability, ventilation and lay-out) to be considered in the design of future infrastructure. However, health services qualified that IPC design factors should be balanced with other needs of patients, particularly patients hospitalised for prolonged periods or undergoing rehabilitation.

Identified a need for improved communication of current, centrally located State guidance for health services, particularly around PPE use (e.g. establish a single site online for health services where real-time current advice is maintained rather than having real time changes communicated to hospital CEOs).

Identified the need for easier access to Department of Health case, contact and outbreak management staff, e.g. ‘a single point of contact’.

Identified a need for clearer, evidence-based guidance for health services regarding a range of critical IPC measures including PPE use, managing transfers, testing in the hospital setting.

Identified a need for a broader conversation around management of outbreaks in RACFs, and whether transfer of aged care residents into the hospital environment is in the best interests of patients or the best approach for the community more broadly

Identified a need for supply of rapid testing kits and platforms to be prioritised for facilities without on-site diagnostic laboratory capability

Identified a need for an ‘early warning’ system for health services regarding local risks in the community and transferring facilities.


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Case study 1: The risk of ‘unrecognised cases’

“We can’t always detect those ‘unrecognised cases’ that come in while incubating. They don’t have

symptoms and don’t report any exposures. They pose a significant risk.”

Mr B was 83 years old and living alone. Lately he had started to have more falls. His neighbour found

him on the ground by his letterbox one day, unable to get up. The neighbour called an ambulance

and Mr B was brought to hospital.

On arrival to the Emergency Department, Mr B was slightly disorientated and in acute pain. He did not

report any shortness of breath, fever or a runny nose. Nor did he report any known exposure to COVID-

19, but he did often catch the bus and go shopping in his local area that had a high rate of COVID-

19 community transmission and several active outbreaks. Mr B had a history of heart disease and was

an ex-heavy smoker with a daily cough. A hip x-ray showed no fracture. A COVID swab was taken given

his cough and uncertainty about exposures and he was moved to a single room with precautions for

suspected COVID.

Later that day the test result came back negative and Mr B was moved to the General Medical ward to

a shared, four-bed room. On day two his oxygen levels were adequate, and he did not have a fever.

He did have some ‘crackles’ in his lungs that were not considered abnormal for an 83-year-old ex-

smoker.

On day three Mr B remained afebrile and his oxygen levels were good. His pain was settling well.

However, he was becoming more confused and somewhat agitated. He was found at the bed side of

his roommates (Mr K and Mr G) several times, and later in another room, very unsteady and so one-

on-one nursing was arranged to reduce the risk of falls and wandering. He was started on antibiotics

for a presumed urinary infection causing confusion and falls.

On day four, his respiratory status was stable however another x-ray showed some fluid on the lungs

and other lung changes so another COVID test was ordered and Mr B was moved to a single room. Mr

B had not been exposed to any COVID-positive patients or staff during his admission. Later that day, a

positive result was returned. Mr B was immediately transferred to a COVID ward and contact tracing

and testing of staff and patients commenced. There had been potentially three days of Mr B being

infectious on the ward and in a shared room.

Unfortunately, several staff and patients, including Mr K and Mr G had been infected, as well as family

members of staff. Many staff were furloughed as the hospital acted to contain the outbreak.

Mr B remained stable and was discharged to a transition care unit on day 15 once he was cleared of

COVID-19. The hospital now requires two negative swabs at least 48 hours apart before transfer out of

isolation or between wards.

NB. This case study is not based on actual events but is indicative of common challenges faced

by hospitals dealing with COVID-19. The strategies implemented are real examples of how

hospitals have responded to similar cases.


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Case study 2: PPE use is not always perfect

“Staff were exhausted from hundreds of PPE changes every week, on top of the increased pressure

and stress. Unfortunately, it’s not always one hundred percent perfect every time.”

Mrs M was 74 years old had been on the geriatric evaluation and management ward for several

weeks, recovering from shoulder surgery and a small stroke. Many hospital staff had been

furloughed recently because of other ward outbreaks, and new protocols for personal protective

equipment (PPE) had been introduced following a directive from the Department of Health for the

need to wear a mask and eye protection continuously in client facing roles.

Lisa, one of the ward nurses, had been asked to assist Mrs M in the shower before finishing her night

shift because of staff shortages. Lisa had worked with Mrs M over the last week. Lisa lived in a house

with three others – another nurse, an aged care worker and a student. Lisa had heard late the

previous evening that her housemate had been ordered to isolate and get tested because the aged

care facility he worked in had declared a COVID outbreak. Lisa’s housemate had been working all

week and the housemates had been sharing evening meals. Lisa planned to get tested later that

afternoon. The current rules were that she was still allowed to work even though her housemate was

a close contact.

Lisa put on her PPE (mask, gloves, gown, protective eyewear), and got assistance to move Mrs

M onto the commode and into the shower. The PPE made Lisa hot and uncomfortable in the small

bathroom and the goggles fogged. She had been wearing ill-fitting facemasks all week and she had

developed small wounds across her nose and cheeks from the pressure of the masks; she had applied

small dressings to these.

During the shower, Mrs M dropped her face washer and leaned over to reach it, losing

balance. Lisa just managed to right Mrs M. She momentarily removed her mask and eyewear to

catch her breath and wipe her face. She couldn’t reach the hand santiser and did not want to leave

Mrs M, so she re-adjusted the goggles and the mask, that had become very damp, and completed

her task of helping Mrs M to wash, dry and dress herself before helping her back to her chair.

After her shift, Lisa caught the bus home and then slept. That evening she got a COVID

test and cancelled her next night shift. The next day, three of the four housemates received

positive COVID test results. Lisa called the hospital and gave details of all the rooms and wards she

had worked in over the past two weeks. Mrs M also tested positive the next day.

The hospital immediately reassessed all ward activities and assisted showers were deemed too high

risk given the extended time, close contact, humidity and lack of ventilation. Bed baths were

implemented instead. More regular asymptomatic testing of staff on the aged care wards was

instituted and a PPE buddy system was put in place, along with extra on-line training for the new

protocols. Face shields and fit-testing of masks was also implemented. More regular breaks

and closer monitoring of staff fatigue and prolonged PPE use were put in place.

NB. This case study is not based on actual events but is indicative of common challenges

faced by hospitals dealing with COVID-19. The strategies implemented are real examples

of how hospitals have responded to similar cases.


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Discussion

Magnitude of C19-HAIs in Victoria and in other jurisdictions

This study identified 277 COVID-19 hospital-acquired infections among patients in Victorian hospitals from

25 January 2020 to 15 November 2020. Of these, 266 were hospitalised during their COVID-19 infection,

accounting for 11% of all COVID-19 hospitalisations in Victoria during the study period. This is similar to

the 10% of all (non–COVID 19) infections in Australia estimated to be hospital acquired (Russo et al. 2019).

There are few published studies on hospital-acquired COVID-19 in patients, but our finding is comparable

to the 12.5% reported in hospitals in the UK and Italy in their first wave (Carter et al. 2020), slightly lower

than the 15% and 16.2% reported in single-centre studies in the UK over shorter time periods (Rickman et

al. 2020, Marago et al. 2020), and considerably lower than a more recent estimate of 25% in the North

West region of the UK (Heneghan et al. 2020). However, comparisons must be interpreted with caution

given differences in definitions of C19-HAIs, methods and setting. For example, only two other reviewed

studies included patients with C19-HAIs diagnosed following discharge from hospital (Rhee et al. 2020;

Marago et al. 2020). Other studies generally included much smaller sample sizes, and, to our knowledge,

no other study has investigated C19-HAIs over a period greater than five months.

Characteristics and outcomes of C19-HAIs compared with non-healthcare-acquired COVID-19

Patients with C19-HAIs were hospital inpatients when they acquired their COVID-19 infections. Therefore,

their characteristics reflect those of the admitted patient population in Victoria, and the hospital settings in

which exposures occurred. Unsurprisingly, this differed from hospitalised community-acquired COVID-19

patients, whose characteristics more closely reflect the broader Victorian COVID-19 population. C19-HAI

patients were older and less likely to be culturally or linguistically diverse than hospitalised community-

acquired COVID-19 patients. A high number were seen in facilities which primarily serve an older population

(sub-acute, rehabilitation and transitional care) and two non-acute hospitals alone accounted for over one

third (36%) of C19-HAI cases (Royal Melbourne Hospital Royal Park Campus and St George's Health

Service).

The lower proportion of C19-HAI patients admitted to ICU and ventilated may also reflect their advanced

age, given active treatment options for life-threatening illness would be less likely to be considered

appropriate in this cohort. More information on comorbidities and disease severity could shed light on this

hypothesis. A study conducted in the United Kingdom found that patients with C19-HAI had more

comorbidities, had a longer total length of stay, and were significantly frailer than those patients who

acquired COVID-19 through community transmission (Marago et al. 2020). Length of stay was similarly

longer for Victorian C19-HAI patients.

Mortality was higher among C19-HAI patients than those admitted with community acquired COVID-19,

however the difference was largely accounted for by age. Older age is known to be a key risk factor for

COVID-19 mortality (Sepandi et al. 2020; World Health Organization, 2021; Mehraeen et al. 2020;

Department of Health, 2021c). One UK multi-centre study found that there was no greater risk of mortality

for hospital-acquired COVID-19, despite this group being older and frailer than the community acquired

group (Carter et al. 2020). The authors concluded that this was likely to reflect timely supportive treatment

for the hospital-acquired group, versus later presentation and more severe symptoms in the community-

acquired group. This occurred in the context of very high community incidence and delayed care seeking,

unlike the situation in Victoria.


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Hospitals with cases of C19-HAI

Hospitals with C19-HAIs reflect the geographical distribution of COVID-19 cases in the community. Eight

public hospitals (both large acute and sub-acute) together accounted for three quarters of all C19-HAI

cases. These included large tertiary hospitals with high overall COVID-19 patient loads (Royal Melbourne

Hospital City Campus, St Vincent’s Hospital and Footscray Hospital) smaller hospitals affiliated with larger

health services (Hazeldean Transition Care, St George’s Health Service, Royal Melbourne Hospital Royal

Park Campus and Golf Links Road Rehabilitation Centre) and Brunswick Private Hospital.

Potential factors related to C19-HAI

i) COVID-19 caseload and ‘unrecognised’ cases

Hospitals with 10 or more C19-HAI cases had more patients hospitalised with community-acquired COVID-

19, accepted more transfers from aged care, and had more patients whose COVID-19 status may have

been unrecognised following their admission, than hospitals with no or few C19-HAIs. Unrecognised

infections in either patients or hospital staff was also determined to be the cause of hospital-acquired

infections in the small number of cases for which genomic and epidemiological data was available. The

health services interviewed also identified ‘unrecognised’ cases as a key transmission risk, particularly if

full IPC precautions were not in place, if patient transfers happened in this group, and if test results were

delayed or rapid testing was unavailable (a particular issue for hospitals without on-site pathology). Health

services reported that they were not always aware of whether patients were admitted from high-risk settings

or following potential exposure, and that this was compounded by limitations of existing infrastructure to

achieve isolation and effective management of suspected and confirmed cases.

The number of COVID-19 patients admitted to hospitals is influenced by the hospital type and level of

service provided. Hospitals equipped with intensive care units and emergency departments can accept

patients with severe acute respiratory illness. Furthermore, large tertiary referral hospitals (the Royal

Melbourne Hospital and the Royal Children’s Hospital) are designated to receive suspected cases of listed

human diseases from ports of entry (Victorian Department of Health, 2017) while the Alfred Hospital, Royal

Melbourne Hospital, St Vincent’s Hospital, the Royal Women’s and Royal Children’s Hospitals are

designated to receive patients through the COVID-19 Quarantine Victoria system (CQV) (Victorian

Department of Health, 2021b). Thus, the large tertiary referral hospitals saw the greatest caseloads of

COVID-19.

Almost a third of the genomic clusters found in C19-HAI patients were present in more than one hospital.

This was more commonly seen for clusters with a large number of community cases, and amongst hospital

networks with multiple campuses, potentially reflecting patient transfers and/or staff mobility within

networks, although multiple independent introductions from different community sources cannot be ruled

out without more detailed epidemiologic data.

ii) Transfers from Residential Aged Care Facilities

Interviewed health services noted the potential impact of RACF resident transfers on C19-HAIs. During the

pandemic response, some COVID-19 outbreaks occurring in RACFs impacted operational capacity and

safety requiring COVID-19-positive residents – and in some instances, asymptomatic residents – to be

transferred to hospital (Victorian Department of Health, 2020a). Our analysis confirmed a crude association

between the number of COVID-19 cases transferred from RACFs and the number of C19-HAIs in each

hospital, but the relationship is complex and difficult to quantify.


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Cases linked to RACFs were more likely to be hospitalised than non-RACF cases. This may simply reflect

the age and comorbidities of aged care residents, but could also have resulted from the practice of

hospitalising RACF residents implicated in COVID-19 outbreaks regardless of their clinical need,

contributing to the overall COVID-19 caseload in hospitals and hence the risk of C19-HAI.

While cases linked to RACFs were disproportionately hospitalised, they were not over-represented among

potentially ‘unrecognised’ cases. This suggests that RACF transfers contributed to C19-HAI risk because

of the additional COVID-19 load they placed on hospitals, rather than a specific issue with lack of detection

of cases among those transferred from RACFs.

Nonetheless, this patient population does present significant transmission risk given the potential for

wandering and ‘aerosol-generating behaviours’ typical of patients with delirium and dementia, regardless

of whether their COVID-19 status is known or suspected on admission. Health services also indicated that

the wards in which aged care patients were accommodated were multi-bed rooms, in low-acuity settings

where staff may have been less familiar with full PPE use, and therefore not settings where COVID-19

outbreaks were anticipated. These anecdotal observations could not be confirmed by quantitative analysis.

A number of other factors would have influenced the relationship between aged care transfers and C19-

HAIs that could not be captured, for example the circumstances in which residents were transferred, the

accuracy and timing of information provided to the accepting facility, existing capacity and availability of

single rooms at the hospital, and the timing of the transfers.

Some methodological limitations affected our ability to determine the full extent of RACF-to-hospital

transmission. First, complete data on epidemiological linkages between individual staff and patients were

not available. Second, the challenge of assigning acquisition status to cases that share exposures prior to,

as well as during, hospitalisation may have led to an underestimate of C19-HAIs transmitted from RACF

residents. For example, if an RACF resident sharing a hospital room with a COVID-19 case from the same

facility tests positive on day 3 to 7 of admission, they are not classified as probable C19- HAI (Appendix 1)

because of the reasonable suspicion that they contracted the virus prior to admission. Third, it is possible

that ‘unrecognised’ cases from RACFs were overestimated. For example, some of those that did not test

positive on day-one of admission were nonetheless treated as high-risk by the hospital. Finally, while

genomic links between hospitals and RACFs were common, these links may have represented

transmission from RACFs to hospitals, shared genomic strains circulating in other RACFs, health facilities,

and the broader community, as well as transmission from hospitals to RACFs.

iii) Patient and staff transmission/factors

Investigations revealed specific examples of staff-to-patient and patient-to-patient transmission, however

limited epidemiologic and genomic data meant that the predominant mode of transmission and reasons for

all C19-HAIs (e.g. specific issues around IPC or contact tracing) could not be identified. Health services

interviewed raised PPE breaches (due to staff fatigue, high workload, frequent donning/doffing, imperfect

fit, comfort/pressure sores) as a potential contributing factor, as well as staff mobility across wards and

instances of contact tracing resources being overwhelmed by explosive outbreaks.

iv) Mode of transmission

Discussions with health services highlighted limitations in the ventilation systems and layout of some older

wards, which were thought to contribute to higher risk of transmission through aerosols. Current evidence

suggests that SARS-CoV-2 can be spread through aerosol transmission in specific settings, particularly in

crowded, indoor environments that do not have adequate ventilation (Lelieveld et al. 2020; Santarpia et al.

2020; World Health Organization, 2020a). It is understood that air exchanges per hour above a certain


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standard do not, in and of themselves, ensure adequate mitigation of aerosol transmission. Other issues

related to overall dynamics of airflow, configuration of spaces and location of suspected and confirmed

cases and staff on wards are important considerations in managing risk. The impact of poor ventilation on

COVID-19 transmission was not further investigated in this study.

Health services also acknowledged the possible role of fomite transmission in the form of shared equipment

and facilities and staff movements between wards. While no studies have directly demonstrated fomite

transmission of SARS-CoV-2, it is considered a likely mode of transmission (World Health Organization,

2020b).

Data on other factors that may influence the likelihood of hospitals having hospital-acquired infections were

not available at the time of this analysis, for example, PPE use, availability of single rooms, aerosol

generating procedures/behaviours and ventilation factors.

Limitations

This study had some limitations. Cases who presented to emergency departments without subsequent

admission were excluded, and we could only include patients diagnosed outside of Victoria if we had been

notified by the relevant jurisdiction. Despite this, our definition of C19-HAI was more inclusive than others

internationally that may underestimate C19-HAIs by not considering those diagnosed post-discharge.

Incomplete epidemiological and genomic data precluded any quantification regarding the most likely causes

of C19-HAIs. A comprehensive assessment of all hospital-based outbreaks, which was not possible with

currently available data, would have allowed us to allocate C19-HAIs to specific outbreaks, determine the

source of more C19-HAI cases, and quantify those resulting from transfers between hospitals versus other

introductions. Similarly, this would allow a clearer understanding of the impact of aged care transfers. The

new COVID-19 surveillance system implemented in early 2021 allows for more detailed epidemiologic data

collection and will assist with this prospectively.

Healthcare staff may play an important role in transmission to patients, as demonstrated by the genomic

clusters that include staff and the interaction between staff and patients described in Table 8. However,

quantifying cases of transmission between staff and patients was out of scope for this study and would

require a comprehensive analysis of outbreaks as detailed above.

Comorbidity data was limited, and for VAED linkage, data was missing in approximately 39 percent of

cases. However, few sources of systematic bias were found on assessment, and these are not expected

to have significantly affected the results. Transfers from RACFs to hospitals were estimated using linkage

to RACF outbreaks among COVID-19 cases and AV data for planned non-emergency transfers. However,

these methods may have underestimated unrecognised cases transferred out of the context of a known

outbreak, in an emergency or by private vehicle.

Our analysis did not examine the change in COVID-19 patient admissions over time, and some hospitals

may have seen an influx of COVID-19 hospitalisations over a short period, coinciding with an increase in

C19-HAIs. More in-depth analysis of these dynamics at the health service level would be required to

understand this relationship.

Health service strategies and recommendations

Although many challenges were identified by health services in terms of preventing and managing C19-

HAIs, only a minority of those in hospital with COVID-19 acquired their infection in hospital. Health services

identified key strategies and strengths that were thought to have been protective in preventing potentially

larger numbers of C19-HAIs, not least of which was the professionalism, hard work and expertise of hospital

staff under high and sustained pressure.


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Recommendations from affected health services included clearer and more consistent communication from

state government and guidelines for health services, particularly around PPE use, managing transfers and

asymptomatic testing. Health services identified a need for: supply of rapid tests to be prioritised for facilities

without on-site diagnostic laboratory capability; an ‘early warning’ system regarding local risks in the

community and in transferring facilities; and consideration of the best way to manage outbreaks in RACFs.

The department has implemented measures to address several key issues identified by health services as

part of this investigation. These include recommended patient densities to reduce the number of patients

in multi-bed wards, daily declarations by staff to confirm they are free of COVID-19 symptoms and active

surveillance testing performed in all hospitals with confirmed and high risk suspected COVID-19 patients.

PPE guidance has been strengthened for all staff providing care to suspected and confirmed COVID-19

patients in recognition of the risk of airborne transmission and aerosol generating behaviours. Guidance

has also been provided on cleaning procedures for COVID-19 wards and rostering of staff working in

COVID-19 wards, including their deployment to other wards and facilities. Personal protective equipment

‘spotters’ have been introduced to provide support and guidance to healthcare staff when donning and

doffing PPE. Engineering assessments of ventilation systems have been performed in 2 health services

and further health technical advice on heating, ventilation and air-conditioning systems has been

developed. The department has also created a policy on infection control measures to optimise ventilation

in healthcare settings (Victorian Department of Health 2021a). All health services have Respiratory

Protection Programs in place and 27 have commenced fit-testing of masks, with over 22,000 workers tested

across 40 public institutions.

Based on this study, the department is investigating the feasibility of establishing a prospective surveillance

system in partnership with VICNISS, to rapidly identify future cases of C19-HAI. Comprehensive analyses

of hospital-based outbreaks and cases in healthcare staff, following the validation of existing data in

partnership with key health services, are also planned, allowing the investigation of some of the outstanding

questions raised by this analysis.

Conclusion

While a considerable number of COVID-19 cases were acquired in hospitals in 2020, they represent a small

percentage of COVID-19 cases overall, and a small percentage of COVID-19 hospitalisations (11%) – lower

than has been reported internationally. Overall, effective infection prevention and control policies and

practices are in place across Victorian health services. Valuable lessons have been learnt from the COVID-

19 experience that will continue to strengthen systems and preparedness for future epidemics, improving

safety and outcomes for patients, staff and the Victorian community.


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Acknowledgements

We would like to acknowledge Peninsula Health, Melbourne Health, St Vincent’s Health and Western

Health for their contribution to this report, and all Victorian hospitals and laboratories for their

professionalism, dedication, expertise and hard work throughout 2020.

We would also like to acknowledge VICNISS and the IPC staff at hospitals with whom they closely work,

for their ongoing efforts to monitor and address hospital-acquired infections, not just COVID-19.

We would like to thank MDU-PHL for their genomic analyses completed for this report and their guidance

and feedback on our interpretation of results. We acknowledge their ongoing support of the COVID-19

response through the provision of genomic analyses, investigation, and reporting – providing valuable

insights for the prevention and control of COVID-19.

In addition, we would like to acknowledge Daneeta Hennessy, Stephanie Curtis and Rebecca Gang, who

assisted in setting up the VICNISS enhanced patient monitoring surveillance system, and Dennis

Wollersheim, Katie Walker and Jessie Goldsmith, who provided technical advice and input.

This work was guided by the COVID-19 Hospital-Acquired Infections Working Group (with DH and health

service representation) whose support and feedback helped to shape the report and ensure its relevance

to all stakeholders.


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Appendix 1: Definitions of C19-HAIs diagnosed during hospital stay

• Definite hospital-acquired COVID-19 1. Confirmed positive reverse transcription-polymerase chain reaction (RT-PCR) test OR

symptom onset* on day >14 of hospital stay**

• Probable hospital-acquired COVID-19 1. Confirmed positive RT-PCR test OR symptom onset* on day 8-14 of hospital stay**

And No known exposure or risk factors prior to hospitalisation.

2. Confirmed positive RT-PCR test OR symptom onset* on day 3-7 of hospital stay**

And Strong suspicion of healthcare transmission (e.g. known confirmed case on same ward during hospital admission), And No known exposure or risk factors prior to hospitalisation.

3. Confirmed positive RT-PCR test OR symptom onset* within 14 days of an exposure to a

confirmed COVID-19 case during a previous hospitalisation And No known exposure or risk factors in the community

• Indeterminate acquisition 1. Confirmed positive RT-PCR OR symptom onset* on day 3-14 of hospital stay** with

insufficient information on the source of infection to assign to another category

• Non- hospital acquired COVID-19 1. Confirmed positive RT-PCR test OR symptom onset* on day 1 or 2 of hospital stay**

2. Confirmed positive RT-PCR test OR symptom onset* on days 3-14 of hospital stay** And Strong suspicion of community transmission (e.g. contact of a confirmed case in household or other community setting) And No known exposures in healthcare

*When testing of a symptomatic individual has been performed, the earliest known date (symptom onset or PCR test) should be applied for case-definition. **If patient has been directly transferred from other facility/s date of initial hospitalisation is regarded as the admission date.


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Appendix 2: Definitions of C19-HAIs diagnosed post-discharge

• Definite hospital-acquired COVID-19 1. Confirmed positive RT-PCR test OR symptom onset within 2 days following discharge from hospital

And Patient was admitted to hospital at least 14 days prior to symptom onset

• Probable hospital-acquired COVID-19 1. Confirmed positive RT-PCR test OR symptom onset on day 3-14 following discharge from hospital

And Strong suspicion of healthcare transmission (e.g. known confirmed case on same ward during hospital admission) And No known exposure or risk factors after discharge or prior to hospitalisation (where admission occurred less than 14 days prior to symptom onset)

2. Confirmed positive RT-PCR test OR symptom onset within 2 days following discharge from hospital

And Patient was admitted to hospital less than 14 days prior to symptom onset And Strong suspicion of healthcare transmission (e.g. known confirmed case on same ward during hospital admission) And No known exposure or risk factors after discharge or prior to hospitalisation (where admission occurred less than 14 days prior to symptom onset)

• Indeterminate acquisition

1. Confirmed positive RT-PCR test OR symptom onset on day 3-7 following discharge from hospital, with insufficient information on the source of infection to assign to another category

• Non-hospital acquired COVID-19 1. Confirmed positive RT-PCR test OR symptom onset on day 3-7 following discharge from hospital

And Strong suspicion of community transmission (e.g. contact of a confirmed case in household or other community setting) And No known exposures in healthcare

2. Confirmed positive RT-PCR test OR symptom onset on day 8-14 following discharge from hospital, without strong suspicion of healthcare transmission (e.g. known confirmed case on same ward during hospital admission)

3. Confirmed positive RT-PCR test OR symptom onset within 2 days following discharge from hospital

And Patient was admitted to hospital less than 14 days prior to symptom onset And Strong suspicion of community transmission (e.g. contact of a confirmed case in household or other community setting) And


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No known exposures in healthcare NOTE:

• When testing of a symptomatic individual has been performed, the earliest known date (symptom onset or PCR test) should be applied for case-definition.

• If patient has been directly transferred from other facility/s date of initial hospitalisation is regarded as the admission date.


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Appendix 3: Facilitated discussion attendee brief

Department of Health Data, Intelligence, Modelling & Epidemiology (DIME)

Background

Between 25 January to 15 October 2020, 20,345 COVID-19 cases have been notified to the Victorian

Department of Health and Human Services (the department). An understanding of how SARS-CoV-2 can

spread in healthcare settings is essential for protecting staff, patients, and the broader community.

In recent months, healthcare organisations across the globe have demonstrated a great generosity of

spirit in sharing case studies, lessons learnt, and experiences with various challenges and strategies in

rapid, real-time publications and forums to enhance global efforts in responding to the pandemic.

In this same spirit, the Department is working with Victorian Healthcare Associated Infection Surveillance

(VICNISS) to identify and describe COVID-19 hospital-acquired infections (C19-HAI) among hospital

patients. A state-wide report quantifying and describing identified C19-HAI at a health service level will be

completed in early 2021. It is anticipated that the report will be publicly released. As part of this work,

VICNISS and the Department are inviting Hospital infection, prevention and control (IPC) personnel to

contribute to a review of key C19-HAI ‘lessons learnt’ throughout 2020. These sessions will follow a rapid,

abbreviated form of the process recommended by the World Health Organisation to review actions taken

in response to a public health event.9

Gathering insights from the most affected health services will provide essential context to C19-HAIs, as

well as make an important contribution to prevention and control efforts.

9 World Health Organisation. (2019). GUIDANCE FOR AFTER ACTION REVIEW (AAR).

https://www.who.int/ihr/publications/WHO-WHE-CPI-2019.4/en/

COVID-19 Hospital- acquired infections in patients –capturing lessons learnt by

Victorian health services

https://www.who.int/ihr/publications/WHO-WHE-CPI-2019.4/en/


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Lessons learnt – Brief for Attendees

Scope C19-HAI ‘Lessons learnt’ sessions are intended as an open discussion,

focused on identifying practical experience with challenges, strategies and

lessons learnt in the prevention and management of C19-HAI in Victoria.

Purpose To provide an opportunity for hospital IPC personnel to identify, discuss and

document key lessons learnt regarding C19-HAI.

To assist organisational leaders to identify challenges, and opportunities for

continuous improvement, strengths and future preparedness.

To provide an opportunity for other healthcare organisations to learn and

benefit from the experience and knowledge of others.

To serve as a useful resource for all healthcare organisations into the future.

To provide valuable context for the state-wide report quantifying and

describing identified C19-HAI.

Attendees Co- Facilitator

C19-HAI Lessons learnt

Judy Brett (VICNISS Senior Infection Control

Consultant)

Co- Facilitator(s)/Note taker

C19-HAI Lessons learnt

Claire Kaufman (Victorian Department of

Health Epidemiology support),

Hilary Veale (Victorian Department of Health

Epidemiologist)

Infectious Disease personnel

Hospital IPC team

IPC Co-ordinator(s), Infectious Disease

personnel, other relevant staff

Output

A written summary of key observations, reflections and lessons learnt will be

collated by the C19 HAI Lessons learnt project team, and presented

thematically in aggregated form. The C19-HIA Lessons learnt summary will be

incorporated within the broader state-wide C19-HAI written report, anticipated

for public release.

Any documents volunteered by contributing Hospitals (eg written case studies)

may also be incorporated into the report, to illustrate the summarised themes

and provide further context.

Dissemination

A copy of the final approved report will be provided to Health Services for prior

approval before being made public.

Ground Rules:

- Meeting attendees should contribute to, and experience the benefits of an open discussion within

a ‘safe space’

- Maintain focus on:

- Productive discussions that will yield lessons learnt within the defined time and scope


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- Issues, strategies or resources, rather than individual behaviours or performance metrics

- Be kind, to ourselves and others. Exercise self-care. 2020 has been a tremendously challenging

year, and you may not have had a lot of time to reflect yet. The conversations may stir up

emotions, which is completely natural. Let’s make this a supportive and constructive discussion.

Framework for discussion

The sessions are intended to facilitate open and productive discussions yielding key lessons learnt within

the defined scope and time, and will focus on issues, strategies and resources rather than individual

behaviours or performance metrics.

To facilitate consistency and a focused discussion, the sessions will explore key

factors/issues/challenges contributing to HAIs with the facility, strategies which did or didn’t

work, strengths and recommendations using flexible discussion questions. Attendees will be

encouraged to identify the issues and experiences of greatest significance with the greatest potential

for valuable lessons learnt, and to consider factors across three broad ‘domains’ as represented below:

Key Questions:

Note – given time restraints, we will try to focus in each meeting on the ‘top 5’ factors that the IPC staff

feel were the most significant challenges encountered in the context of HAIs.

1. What were the ‘top 5’ key factors or issues contributing to HAIs within the facility?

2. What strategies or strengths worked well in preventing and managing this? Why?

3. What strategies didn’t work as well, or what other conditions were exacerbating? Why?

4. What were the key lessons learnt, and what would you recommend for the future?

Personneleg staffing, furloughing,

training, communication, handover, staff rooms.

Resources, Tech,

Infrastructureeg PPE, IT systems,

ventilation, water, food and waste management,

built environment.

Patient Care/ Procedureseg patient transfers,

identifying infections, meals, personal care,

dementia patient care, cohorting, cleaning


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Reporting of Lessons Learnt

• Sessions will not be recorded.

• With permission, de-identified quotations may be captured in some instances to illustrate

observations.

• A written summary of the discussion will be sent to Hospital attendees and management within

~48 hours of the meeting for review and approval, prior to inclusion in the state-wide report.

• Secondary sources (e.g. de-identified case studies, reviews) may be volunteered by


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Appendix 4: Victorian Hospitals with C19-HAIs

Victorian hospitals with definite and probable C19-HAI cases from 25 January 2020 to 15 November 2020

Hospital Public/ Private

Health Service

Hospital Category Department of Health Region

Albert Road Clinic Private Ramsay Health Care

Other Acute Metropolitan Southern Metropolitan

Alfred Hospital Public Alfred Health Large Acute Metropolitan North and West Metropolitan

Austin Hospital Public Austin Health Large Acute Metropolitan North and West Metropolitan

Box Hill Hospital Public Eastern Health

Large Acute Metropolitan Eastern Metropolitan

Broadmeadows Hospital Public Northern Health

Other Acute Metropolitan North and West Metropolitan

Brunswick Private Hospital Private Health Care Sub/Non-Acute North and West Metropolitan

Bundoora Extended Care Centre

Public Northern Health

Sub/Non-Acute North and West Metropolitan

Cabrini Hospital - Malvern Private Cabrini Health Ltd

Large Acute Metropolitan Southern Metropolitan

Dandenong Hospital Public Monash Health


Epworth Hawthorn Private Epworth Health Care

Sub/Non-Acute Eastern Metropolitan

Epworth Richmond Private Epworth Health Care

Large Acute Metropolitan North and West Metropolitan

Essendon Private Clinic Private IPHoA Management


Footscray Hospital Public Western Health


Frankston Hospital Public Peninsula Health


Hazeldean Transition Care Public Western Health


Golf Links Road Rehabilitation Centre

Public Peninsula Health

Sub/Non-Acute Southern Metropolitan

Northpark Private Hospital Private Healthscope Other Acute Metropolitan North and West Metropolitan

Peter MacCallum Cancer Centre

Public Peter MacCallum Cancer Institute


Rosebud Hospital Public Peninsula Health

Other Acute Metropolitan Southern Metropolitan

Royal Children's Hospital Public Royal Children's Hospital


Royal Melbourne Hospital - City Campus

Public Melbourne Health



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Hospital Public/ Private

Health Service

Hospital Category Department of Health Region


Public Melbourne Health


St George's Health Service Public St Vincent’s Health

Sub/Non-Acute Eastern Metropolitan

St John of God Berwick Hospital

Private St John of God Health Care Inc



Private St John of God Health Care Inc

Sub/Non-Acute and Southern Metropolitan

St Vincent's Hospital Public St Vincent’s Health


St Vincent's Private Hospital - East Melbourne

Private St Vincent’s Private Hospital Limited


Sunshine Hospital Public Western Health


Werribee Mercy Hospital Public Mercy Hospitals Victoria Ltd


Williamstown Hospital Public Western Health


Hospital Category Definitions

These categories were based on the VICNISS hospital peer groups (VICNISS, 2020), hospital size and

type of services provided.

Large acute metropolitan: hospitals in metropolitan Melbourne that provide inpatient medical care and

other related services for surgery, acute medical conditions or injuries (usually for a short-term illness or

condition) with more than 200 beds.

Other acute metropolitan: hospitals in metropolitan Melbourne that provide inpatient medical care and

other related services for surgery, acute medical conditions (including psychiatric care) or injuries (usually

for a short-term illness or condition) with less than 200 beds.

Sub-acute/Non-acute: includes hospitals with a primary clinical purpose of rehabilitation, palliative care,

geriatric evaluation and management or psychogeriatric care. Non-acute facilities provide ‘maintenance

care’ or support for a patient with impairment, activity limitation or participation restriction due to a health

condition.

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