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EVIDENCE BASED RECOMMENDATIONSFOR NATIONAL HEALTHCARE-

ASSOCIATED INFECTION SURVEILLANCE

Philip L Russo BN, M.Clin.Epid.

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Public Health and Social Work

Faculty of Health

Queensland University of Technology

2016

Evidence based recommendations for national healthcare-associated infection surveillance i

Keywords

Key Words Infection prevention, infection control, nosocomial infection, surveillance, healthcare-associated infection, epidemiology, safety and quality, discrete choice experiment, public reporting

ii Evidence based recommendations for national healthcare-associated infection surveillance

Abstract

Background

Healthcare-associated infections (HAIs) cause significant morbidity and

mortality, and are the most common complication affecting patients. Most are

believed to be preventable, which requires an understanding of how, why and where

they are occurring. A HAI surveillance program informs such knowledge.

Surveillance of HAIs is fundamental to any infection prevention program and

provides data on which to develop an infection prevention program.

Australia is one of the few developed countries that does not have a national

HAI surveillance program. Several state-wide HAI surveillance programs based on

the National Health and Safety Network (NHSN) in the United States of America,

have developed independently. However, there has been no attempt to coordinate

surveillance activities to generate national data. As such, the national burden of HAIs

in Australia is unknown. This is important as it severely limits attempts to develop

national infection prevention policy based on evidence and implement best practice

across Australia.

This thesis aimed to develop evidence based recommendations for a national

HAI surveillance program through answering four research questions:

1. What are the similarities and differences between existing HAI surveillance

processes in Australia?

2. What level of agreement exists in the identification of HAI between those

participating in HAI surveillance, and are there any factors that influence

agreement level?

3. What are the key attributes of successful centrally coordinated HAI

surveillance programs?

4. What are the preferences and priorities of key stakeholders when considering

a national HAI surveillance program?

Method

This research was a multipart study comprising a scoping review, a cross-

sectional survey, qualitative interviews and a discrete choice experiment.

Evidence based recommendations for national healthcare-associated infection surveillance iii

First, the scoping review of statewide surveillance programs in Australia and

international programs was undertaken. This was done by reviewing information on

surveillance organisational websites and supporting resources, and through structured

discussions with representatives from these programs. This established the current

surveillance activities across Australia in the context of international programs, and

was used to inform the design of the first study.

The first study was a cross sectional online survey of those who undertake HAI

surveillance activities across Australia. The aim of this study was twofold; to

improve our understanding of current surveillance practices by identifying in detail

how surveillance is currently performed, and to measure agreement when identifying

HAIs through a series of clinical vignettes. Participants were recruited using a

snowballing method starting with an email to over 500 subscribers of the

Australasian College for Infection Prevention and Control list server. All infection

prevention staff in hospitals with more than 50 acute inpatient beds were invited to

complete an online survey.

The second study was a discrete choice experiment (DCE) that aimed to

identify key stakeholder preferences for a national surveillance program, and was

conducted in two parts. First, a series of seven semi-structured interviews with

leaders from international and state-wide Australian HAI surveillance programs

informed by a comprehensive literature review, was conducted to identify factors

that are influential in surveillance program implementation and success. The findings

enabled the identification of key characteristics of national surveillance programs,

which were then used to inform and construct the DCE. The DCE provided

quantitative evidence on which elements of a national HAI surveillance program key

stakeholders consider most important. A total of 184 clinical and non-clinical leaders

in infection prevention across Australia were purposively selected to participate in

the DCE.

Results

The scoping review highlighted many differences between statewide programs

in Australia such as the type of infections under surveillance, definitions and support

resources. These findings informed the design of the first study and have been

published in Australian Health Review.

iv Evidence based recommendations for national healthcare-associated infection surveillance

There were a total of 104 completed responses to the cross sectional study.

Large variation in surveillance methodology, definitions, reporting, staff skill and

support was identified across Australia highlighting the many gaps and issues

required to be addressed for a national program. These findings have been published

in the American Journal of Infection Control, and were further supported by the

results from the clinical vignettes, which identified only moderate agreement in HAI

identification (range 53%-75%, excluding the control vignette). Findings from the

clinical vignettes were published in Antimicrobial Resistance and Infection Control.

The outcomes from the scoping review and the cross-sectional study have

provided answers to research questions 1 and 2.

Data from the semi-structured interviews from the DCE were analysed to

identify main characteristics of national surveillance programs. This data identified

five distinct but related characteristics of large HAI surveillance programs; triggers,

purpose, data measurements, implementation and maintenance, and processes. These

findings have been accepted for publication in the American Journal of Infection

Control. The interview data also informed the construction of the DCE.

A total of 122 key stakeholders completed the DCE (response rate 66%). The

results identified key stakeholders strongest five preferences for national HAI

surveillance: 1) a mandatory program with continuous targeted surveillance on

specified HAIs, 2) a surveillance protocol which allows for risk adjustment of HAI

rates, 3) annual competency assessments of data collectors, 4) very accurate data, and

5) hospital level data publicly reported on a website but not associated with financial

penalties. These findings have been published the BMJ Open.

The results from the DCE provided answers to research questions 3 and 4.

Findings from this original research have provided a rich source of evidence on

which to base recommendations for a national surveillance program in Australia. The

recommendations include: a mandatory HAI surveillance program, standardised

national surveillance protocol, risk adjusted and publicly released hospital HAI data,

and regular competency assessments of surveillance staff. Success of the program

requires a comprehensive implementation strategy and central coordination with

regular evaluation and expansion.

Evidence based recommendations for national healthcare-associated infection surveillance v

Conclusion

Currently the true burden of HAIs in Australia remains unknown. The

recommendations within this PhD address the current surveillance gaps in Australia

identified from this research, reflect the key stakeholder preferences for a

surveillance program, and importantly, are in alignment with best practice. These

elements will also positively influence the likelihood of implementation and

sustainability

vi Evidence based recommendations for national healthcare-associated infection surveillance

Evidence based recommendations for national healthcare-associated infection surveillance vii

Table of Contents

Keywords .................................................................................................................................................. iAbstract ................................................................................................................................................... iiTable of Contents .................................................................................................................................. viiList of Figures ........................................................................................................................................ xiList of Tables ......................................................................................................................................... xiiList of Abbreviations ............................................................................................................................ xiiiStatement of Original Authorship ......................................................................................................... xvDeclarations of Interest ......................................................................................................................... xvAcknowledgements .............................................................................................................................. xviCHAPTER 1: INTRODUCTION ....................................................................................................... 11.1 Background .................................................................................................................................... 11.2 Context of the study ....................................................................................................................... 21.3 Background of the author and contribution ................................................................................... 41.4 Aim ................................................................................................................................................ 61.5 Thesis outline and significance ...................................................................................................... 61.6 Summary ........................................................................................................................................ 8CHAPTER 2:LITERATURE REVIEW ........................................................................................... 92.1 Surveillance programs ................................................................................................................. 10

2.1.1 Purpose of surveillance ................................................................................................... 112.1.2 Establishing a surveillance program ............................................................................... 13

2.2 Attributes of surveillance programs ............................................................................................ 152.3 A brief history of healthcare –associated infection surveillance ................................................. 192.4 Healthcare-associated infection surveillance methods ................................................................ 21

2.4.1 Automated surveillance systems ..................................................................................... 232.5 National healthcare-associated infection surveillance systems ................................................... 24

2.5.1 United States of America ................................................................................................ 262.5.2 United Kingdom ............................................................................................................. 272.5.3 Germany ......................................................................................................................... 282.5.4 France ............................................................................................................................. 292.5.5 Netherlands ..................................................................................................................... 292.5.6 ECDC .............................................................................................................................. 30

2.6 Effectiveness of large surveillance programs .............................................................................. 302.7 Healthcare-associated infection surveillance in Australia ........................................................... 33

2.7.1 Surveillance activity by Australian infection prevention staff ....................................... 352.8 Benchmarking, public reporting and financial penalties ............................................................. 372.9 Data Quality ................................................................................................................................. 41

2.9.1 Accuracy ......................................................................................................................... 412.9.2 Method Variation ............................................................................................................ 43

2.10 Discrete choice experiments ...................................................................................................... 442.10.1Identification of attributes and levels ............................................................................. 452.10.2Experimental design ....................................................................................................... 46

viii Evidence based recommendations for national healthcare-associated infection surveillance

2.10.3Data Collection ............................................................................................................... 472.10.4Data analysis ................................................................................................................... 48

2.11 Implementation Science ............................................................................................................ 482.12 Conclusion ................................................................................................................................. 50CHAPTER 3:THE RESEARCH QUESTIONS AND STUDY DESIGN ..................................... 533.1 Research Question 1 .................................................................................................................... 533.2 Research Question 2 .................................................................................................................... 543.3 Study 1 – Cross sectional survey: Current Australian hospital practices in healthcare-associated infection surveillance ............................................................................................................................ 54

3.3.1 Study 1 design ................................................................................................................. 553.4 Research Question 3 .................................................................................................................... 553.5 Research Question 4 .................................................................................................................... 563.6 Study 2 – Preferences for a healthcare-associated infection surveillance program using a discrete choice experiment .................................................................................................................................. 56

3.6.1 Study 2 design ................................................................................................................. 563.7 Ethics and Limitations ................................................................................................................. 58CHAPTER 4:HEALTHCARE-ASSOCIATED INFECTION IN AUSTRALIA ........................ 594.1 Introduction ................................................................................................................................. 594.2 Paper one: “Healthcare-associated infections in Australia: time for national surveillance” ....... 62

4.2.1 Abstract ........................................................................................................................... 624.2.2 Introduction ..................................................................................................................... 624.2.3 Methods .......................................................................................................................... 644.2.4 Results ............................................................................................................................. 654.2.5 Discussion ....................................................................................................................... 674.2.6 Conclusion ...................................................................................................................... 704.2.7 References ....................................................................................................................... 71

CHAPTER 5:VARIATION IN HAI SURVEILLANCE PRACTICES ....................................... 795.1 Introduction ................................................................................................................................. 795.2 Paper two: “Variation in healthcare-associated infection surveillance practices in Australia” .. 82

5.2.1 Abstract ........................................................................................................................... 825.2.2 Introduction ..................................................................................................................... 825.2.3 Method ............................................................................................................................ 835.2.4 Results ............................................................................................................................. 835.2.5 Discussion ....................................................................................................................... 845.2.6 References ....................................................................................................................... 86

CHAPTER 6:DIFFERENCES IN IDENTIFYING HEALTHCARE-ASSOCIATED INFECTIONS ................................................................................................................................ 916.1 Introduction ................................................................................................................................. 916.2 Paper three: “Differences in identifying healthcare-associated infections using clinical vignettes and the influence of respondent characteristics: a cross-sectional survey of Australian infection prevention staff” .................................................................................................................................... 94

6.2.1 Abstract ........................................................................................................................... 946.2.2 Introduction ..................................................................................................................... 956.2.3 Method ............................................................................................................................ 966.2.4 Results ............................................................................................................................. 986.2.5 Discussion ..................................................................................................................... 1006.2.6 Conclusion .................................................................................................................... 1026.2.7 References ..................................................................................................................... 103

CHAPTER 7:CHARACTERISTICS OF LARGE HEALTHCARE -ASSOCIATED INFECTION SURVEILLANCE PROGRAMS ............................................................................. 115

Evidence based recommendations for national healthcare-associated infection surveillance ix

7.1 Introduction ............................................................................................................................... 1157.2 Paper four: “Characteristics of national and statewide healthcare-associated infection surveillance programs: A qualitative study” ....................................................................................... 119

7.2.1 Abstract ......................................................................................................................... 1197.2.2 Introduction ................................................................................................................... 1207.2.3 The study ...................................................................................................................... 1207.2.4 Results ........................................................................................................................... 1227.2.5 Discussion ..................................................................................................................... 1307.2.6 Conclusion .................................................................................................................... 1327.2.7 References ..................................................................................................................... 133

CHAPTER 8:STAKEHOLDER PREFERENCES FOR A NATIONAL HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE PROGRAM .................................................... 1398.1 Introduction ............................................................................................................................... 1398.2 Paper five: “Novel application of a discrete choice experiment to identify preferences for a national healthcare associated infection surveillance programme: a cross-sectional study” .............. 142

8.2.1 Abstract ......................................................................................................................... 1428.2.2 Background ................................................................................................................... 1438.2.3 Methods ........................................................................................................................ 1448.2.4 Results ........................................................................................................................... 1518.2.5 Discussion ..................................................................................................................... 1558.2.6 Conclusions ................................................................................................................... 1578.2.7 References ..................................................................................................................... 1588.2.8 Supplementary Tables .................................................................................................. 162

CHAPTER 9:DISCUSSION ........................................................................................................... 1659.1 Introduction ............................................................................................................................... 1659.2 Answers to the Research Questions ........................................................................................... 1669.3 Purpose of a surveillance program ............................................................................................ 1689.4 Support for a surveillance program ........................................................................................... 1689.5 System ....................................................................................................................................... 169

9.5.1 Simplicity ...................................................................................................................... 1699.5.2 Flexibility ...................................................................................................................... 171

9.6 Data ............................................................................................................................................ 1729.6.1 Education and skill ....................................................................................................... 1729.6.2 Accuracy ....................................................................................................................... 1739.6.3 Consistency ................................................................................................................... 1749.6.4 Accuracy vs. Consistency ............................................................................................. 175

9.7 Utility ......................................................................................................................................... 1769.7.1 Reporting ...................................................................................................................... 1769.7.2 Timeliness ..................................................................................................................... 1779.7.3 Public Reporting ........................................................................................................... 1789.7.4 Financial Penalties ........................................................................................................ 1789.7.5 Summary ....................................................................................................................... 180

9.8 Investing in national healthcare-associated infection surveillance ............................................ 1809.9 Coordination, implementation and sustainability ...................................................................... 183

9.9.1 Coordinating role .......................................................................................................... 1839.9.2 Implementation and Sustainability ............................................................................... 1839.9.3 Summary ....................................................................................................................... 188

9.10 Limitations ............................................................................................................................... 1899.11 Recommendations for a national healthcare-associated infection surveillance program ........ 190CHAPTER 10: CONCLUSION ................................................................................................... 195REFERENCES .................................................................................................................................. 197

x Evidence based recommendations for national healthcare-associated infection surveillance

APPENDICES ................................................................................................................................... 217Appendix A: Key search terms and outputs for literature review ............................................ 217Appendix B: Ethics approval - Current Australian hospital practices in healthcare-

associated infection surveillance .................................................................................. 219Appendix C: Ethics approval - Key attributes of a healthcare-associated infection

surveillance program .................................................................................................... 221Appendix D: Ethics approval - Preferences for a healthcare-associated infection

surveillance program using a discrete choice experiment ............................................ 223Appendix E: Letter of Support from the Australasian College for Infection Prevention

and Control ................................................................................................................... 225Appendix F: Survey tool - Current Australian hospital practices in healthcare-associated

infection surveillance .................................................................................................... 226Appendix G: Current Australian hospital practices in healthcare-associated infection

surveillance: Frequency of access to other healthcare professionals – data not included in Chapter 5 .................................................................................................... 241

Appendix H: Current Australian hospital practices in healthcare-associated infection surveillance: Frequency of where HAI data is reported – data not included in Chapter 5 ....................................................................................................................... 242

Appendix I: Semi-structured interview guide for participants ................................................. 243Appendix J: Survey tool – Discrete choice experiment ........................................................... 245Appendix K Results of attitudinal questions in the discrete choice experiment not

included in the manuscript Chapter 8 ........................................................................... 265Appendix L: Normalisation process theory questions ............................................................. 266

As this is a thesis by publication, there are references in each publication that

are relevant for the individual publication and in the style of the journal to

which they were submitted. The references at the end of this thesis represent

the references for the unpublished sections of the document (Chapters 1, 2, 3,

9 and 10).

Evidence based recommendations for national healthcare-associated infection surveillance xi

List of Figures

Figure 1 - Funding sources of Australian hospitals ................................................................................. 3Figure 2 - The surveillance cycle .......................................................................................................... 11Figure 3 - Centrally coordinated (national) healthcare-associated infection surveillance

program................................................................................................................................. 25

xii Evidence based recommendations for national healthcare-associated infection surveillance

List of Tables

Table 1 - Common objectives of healthcare-associated infection surveillance .................................... 13Table 2 - Attributes of a surveillance program listed in CDC Guidelines ............................................ 16Table 3 - Attributes of a healthcare-associated infection surveillance program listed by NHSN ......... 17Table 4 - Healthcare-associated infection surveillance strategies ......................................................... 22Table 5 - Reductions in infection due to surveillance ........................................................................... 32Table 6 - Process and outcome measurements used in four high income countries ............................. 38

Evidence based recommendations for national healthcare-associated infection surveillance xiii

List of Abbreviations

ACD Administrative coding data

ACIPC Australasian College for Infection Prevention and Control

ACSQHC Australian Commission on Safety and Quality in Health Care

AMR Antimicrobial resistance

AU Antimicrobial usage

AURA Antimicrobial Use and Resistance in Australia

BSI Bloodstream infection

CAUTI Catheter associated urinary tract infection

CDC Centers for Disease Control (USA)

CDI Clostridium difficile infection

CFIR Consolidated Framework for Implementation Research

CLABSI

DCE

ECDC

FTE

HAI

HAUTI

Central line associated bloodstream infection

Discrete choice experiment

European Centre for Disease Control and Prevention

Full time equivalent

Healthcare-associated infection

Healthcare-associated urinary tract infection

HELICS Hospitals in Europe Link for Infection Control through Surveillance

ICU Intensive care unit

IHBI Institute of Health and Biomedical Innovation

IP Infection prevention

IPSE Improving Patient Safety in Europe

KISS Krankenhaus-Infektions-Surveillance-System (Germany)

MRSA Methicillin resistant Staphylococcus aureus

NHHI National Hand Hygiene Initiative

NHS National Health Service (UK)

NHSN National Health and Safety Network (USA)

NINSS Nosocomial Infection National Surveillance Scheme (UK)

NNIS National Nosocomial Infection Surveillance (USA)

xiv Evidence based recommendations for national healthcare-associated infection surveillance

NPT Normalisation process theory

NSQHSS National Safety and Quality Health Service Standards

OR Odds ratio

PPV Positive predictive value

QUT Queensland University of Technology

RR Risk ratio

SAB Staphylococcus aureus bacteraemia

SSI Surgical site infection

UK United Kingdom

USA United States of America

UTI Urinary tract infection

VAP Ventilator associated pneumonia

VICNISS Victorian Healthcare-associated Infection Surveillance System

VIF Variance inflation factor

Evidence based recommendations for national healthcare-associated infection surveillance xv

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date:

Declarations of Interest

I am a current member of the Board of Directors of the Australasian College

for Infection Prevention and Control, and Chair of its Research Committee.

I am also a member of the Healthcare Associated Infection Advisory

Committee of the Australian Commission for Safety and Quality in Health Care, a

member of the National Health and Medical Research Council’s Infection Control

Guidelines Advisory Committee, and previously Operations Director at the VICNISS

Coordinating Centre.

8th October 2016 _________________________

QUT Verified Signature

xvi Evidence based recommendations for national healthcare-associated infection surveillance

Acknowledgements

The very nature of nursing has meant that I have worked with many wonderful

people who have influenced my career. I would like to first acknowledge A/Professor

Denis Spelman who mentored me from very early beginnings. I am grateful to Denis

for many things, in particular the culture of continuous learning he encouraged

by asking at the end of every day, “What have you learnt today?”

I have also had the privilege of working closely with Professor Mike Richards

and Professor Lindsay Grayson for extended periods in my career and thank them

both for their support and guidance, and the extraordinary opportunity they provided

me to lead exciting initiatives.

The decision to undertake a PhD was a long time coming, and I sought the

advice of many. Thanks to A/Professor Brett Mitchell and Professor Ramon Shaban

who patiently played the roles of decision support systems during this time. They

have not only been great supports throughout this doctorate, they continue to inspire

me in their work and life.

My associate supervisors, Professor Mike Richards, Professor Allen Cheng,

and Professor Nick Graves have all been available when I needed them to be,

responded on short turn around, and always encouraging of my work. I thank them

for their support. I am of course grateful to Nick and his team at the Centre for

Research Excellence in Reducing Healthcare Associated Infections at Queensland

University of Technology (QUT) who provided me the opportunity to join their

collection of PhD candidates. Gratitude also to the other PhD candidates in this

cohort who always welcomed me into their various sessions even though I was often

present via a grainy screen and ad hoc audio! Their support has been much

appreciated.

My principal supervisor Dr Lisa Hall no doubt will be glad to see me off!

Weekly Skype sessions, phone calls, emails and frequent two day visits, Lisa has

provided me solid support. Her extraordinary ability to think a little left, right, above

and below was often the nudge I needed when my brain hit the wall. Lisa’s mixed

Evidence based recommendations for national healthcare-associated infection surveillance xvii

methods skill is unique. I am extremely grateful for her mentoring, guidance,

availability, and interest in my work and life.

I have been fortunate to receive financial support that has enabled my full time

studies. I wish to acknowledge the Rosemary Norman Foundation and the Nurses

Memorial Centre who awarded me the “Babe” Norman scholarship, the Centre for

Research Excellence in Reducing Healthcare-Associated Infection, QUT, Covidien

(Medtronic), and the Australian College of Nursing.

Undertaking a PhD is a completely selfish act, and three years is a long time in

the life of a family of five! Two sons finished school, the other landed his first full

time job. We celebrated an eighteenth, two twenty-firsts, two fiftieths, a twenty-fifth

wedding anniversary and my mother’s 80th! Regardless of work and study, family

life hurtles along, and I am grateful for their support and understanding throughout as

my study door was often closed. Special thanks to my wife Kate who not only

managed to complete a MPH during this time, but has encouraged and supported me

unconditionally.

I undertook this PhD for myself, and I have enjoyed it from the very start. I am

genuinely grateful to all those mentioned above, and many others who have played

their part in enabling me to take this once in a lifetime opportunity, albeit late!

xviii Evidence based recommendations for national healthcare-associated infection surveillance

“Systems awareness and systems design are important for health professionals,

but they are not enough. They are enabling mechanisms only. It is the ethical

dimensions of individuals that are essential to a system’s success. Ultimately, the

secret of quality is love. You have to love your patient, you have to love your

profession. If you have love, you can then work backward to monitor and improve

the system.”

Avedis Donabedian 2000

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 BACKGROUND

“Premum non nocere” is a guiding principle for medical personnel which,

when translated into English means, “first, do no harm”.1 Although dating back to

the early 1800’s, and despite its deficiencies as an absolute principle,1 it still has

relevance in todays healthcare setting. Patients seek out healthcare practitioners

generally expecting to gain some health benefit. Unfortunately there are some

patients who are the subject of harm, such as acquiring an infection.

A healthcare-associated infection (HAI) is defined as an infection that occurs

as a result of a healthcare intervention and may occur within, or after leaving, a

healthcare facility.2 Historically called a “nosocomial” infection, meaning “hospital

acquired”, the term “healthcare-associated” is now preferred, acknowledging that

today much healthcare is administered beyond the hospital walls. Various types of

infection can result from a healthcare intervention, such as pneumonia, urinary tract

infection, a bloodstream infection (BSI) caused by an intravenous device, or an

infected wound following a surgical procedure.

HAIs are the most common complication affecting patients in healthcare

facilities, and many result in significant morbidity and mortality.3 It is estimated that

in Europe and North America between 12%-32% of HAI BSIs result in death.4 In

developing countries, the burden of HAIs is significantly higher when compared to

developed countries, with the density of catheter related BSI estimated to be up to 19

times higher.5

In the field of safety and quality in healthcare, HAIs are considered preventable

adverse events, that is, a medical error resulting in injury.6 This places it alongside

other preventable adverse events such as fractures resulting from a patient fall, and

the adverse side effects following administration of an incorrect medication.

Logically, to prevent HAIs, it is important to know how often they are occurring,

why, where, how and to whom. A HAI surveillance program will deliver this

information.

2 Chapter 1: Introduction

Surveillance is the “ongoing and systematic collection, analysis and

interpretation of outcome specific data essential to the planning, implementation, and

evaluation of public health practice, closely integrated with the timely dissemination

of these data to those who need to know”.7 Surveillance has been likened to a nerve

cell, where an afferent arm receives information, data are analysed by the cell, and

the efferent arm then takes action.7

Surveillance of HAIs is the cornerstone of healthcare epidemiology and

infection prevention programs,8 and has been described as the single most important

factor in the prevention of HAIs.9 Surveillance is held in such esteem because it

provides the information on which an infection prevention program is planned, and

in a landmark study, has been shown to reduce HAI rates through the influence of

data on practices.10

Whilst many countries have well established national HAI surveillance

programs, Australia does not. This severely limits our understanding of the

epidemiology of HAIs in Australia, which in turn restricts our ability to implement

evidence based policy, and measure the real impact of any infection prevention

interventions. It has been suggested that in Australia, 175,000 HAIs occur annually,11

however this estimate was based on data from only two hospitals. The lack of a

national surveillance program means that a more precise estimate is unable to be

made. This is a significant gap in our knowledge of HAIs in Australia.

1.2 CONTEXT OF THE STUDY

Australia consists of six states and two territories with an estimated population

of 24 million.12 There are 1,359 hospitals, of which 55% are public. The state and

territory governments are the largest funders of the public hospitals, whilst health

insurance funds contribute most to funding private hospitals (Figure 1).

Approximately 80% of the public hospitals have less than 100 beds, only 3% over

500 beds, whilst 21% are considered remote.13


Figure 1 - Funding sources of Australian hospitals

Adapted from the Australian Institute for Health and Welfare 14

In 2012, the Australian Commission for Safety and Quality in Health Care

(ACSQHC) released the National Safety and Quality Health Service Standards

(NSQHSS).15 Standard 3 is specific to infection prevention and control, and lists as

actions required to meet the standard; “Surveillance systems for healthcare-

associated infections are in place” and “Healthcare-associated infections surveillance

data are regularly monitored by the delegated workforce and/or committees”.15 There

are no recommendations regarding the type, method or intensity of surveillance.

Although there is no national surveillance program, it would appear substantial

resources exist at a hospital level that are devoted to surveillance. A cross sectional

study undertaken across 152 hospitals in 2014 estimated the mean full time

equivalent (FTE) infection prevention nurses per 100 beds to be 0.66, or 1 FTE per

152 beds, and remained relatively constant when stratified by hospital size.16 From

the same study, infection prevention nurses estimated they spent 36% of their time

undertaking surveillance activities, of which 56% was spent on collecting data.17

The focal point of this PhD is the Australian healthcare setting, however part of

the second study required data collection outside of Australia, as it was deemed

crucial that existing national surveillance programs were explored. data were

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

Privatehospitals(n=612)

Publichospitals(n=747)

Stateandterritorygovernments AustralianGovernment

Individuals DeptofVeteranAffairs

Other Healthinsurancefunds


collected from four countries with populations ranging from 5 million to over 300

million. Three countries were English speaking. Despite differences in culture, size,

governance and funding structures, these countries all have well established national

surveillance programs that were explored in detail in the second study.

During the undertaking of this PhD, a large piece of work titled Antimicrobial

Use and Resistance in Australia (AURA) Project was commissioned by ACSQHC to

explore options for antimicrobial resistance (AMR) and antimicrobial usage (AU)

surveillance in Australia. Whilst acknowledging obvious synergies between a

national HAI surveillance program and national AMR and AU surveillance, it differs

from the focus of this PhD in that AMR and AU surveillance utilises population data

rather than patient level data to identify trends and distribution patterns. The final

report from the AURA project is not due for completion until late 2016, therefore

findings and recommendations are unable to be included in this thesis.

The scope of this PhD is directed towards outcome surveillance in acute care

health public and private facilities of greater than 50 beds. Larger acute care facilities

generally have a patient population at higher risk of acquiring a HAI as these

facilities are more likely to have sicker patients, intensive care units (ICUs) and

undertake complex procedures. It is in these facilities where surveillance is generally

thought to have the greatest impact with respect to reducing HAIs. However many of

the findings are generalisable to other healthcare facilities either directly or indirectly

by providing structure and context on how to approach surveillance of HAIs.

A point worth clarifying is the use of the terms “surveillance program” and

“surveillance system”. Frequently the terms are used interchangeably, and essentially

they are the same. For the purposes of consistency, I will be using the term

“surveillance program” except where a program describes itself in the literature as a

“system”.

1.3 BACKGROUND OF THE AUTHOR AND CONTRIBUTION

My familiarity with HAI surveillance reaches back several decades to my first

position in infection prevention at a large Melbourne hospital. Since this time I have

held positions as the inaugural Operations Director of the Victorian healthcare-

associated infection surveillance program, VICNISS, and the national project

manager for the National Hand Hygiene Initiative (NHHI). I also completed a


Masters in Clinical Epidemiology during this time. Through my roles at VICNISS

and with the NHHI, I have been on a number of state and national infection

prevention committees, including ongoing membership of the HAI Advisory

Committee of the ACSQHC.

HAI surveillance has been a crucial element in all my roles. Initially at a

hospital level, I was involved in the development and implementation of a surgical

site infection (SSI) surveillance program based on the National Nosocomial Infection

Surveillance (NNIS) System (now the National Health and Safety Network

[NHSN]). For the first time, risk adjusted, procedure specific, surgeon specific, SSI

rates were generated, analysed and importantly fedback to the surgeons and an

infection control committee. This surveillance activity expanded to include ICU

central line associated bloodstream infection (CLABSI) surveillance and also ad hoc

and point prevalence surveillance activities over the years.

At VICNISS, my team established and implemented a statewide HAI

surveillance program for all public acute care facilities in Victoria. The program

continues today and is arguably the most robust statewide HAI surveillance program

in Australia. My national role with the NHHI enabled me to work with hospitals and

health departments across all states and territories in Australia. It provided me with

an extraordinary and privileged national perspective of infection prevention.

Through my experience with HAI surveillance, it always struck me that a

major limitation was the inability to generate national data and make comparisons

with hospitals across Australia. Different to the United States of America (USA), the

United Kingdom (UK) and many European countries, who have well established

national HAI programs, Australia is small with fewer hospitals, yet no national

surveillance program. This means that where only state programs exist, context is

limited as they can only be compared with like facilities in that state, statewide

denominators are smaller, rates more variable, and data less robust. Over the years I

have networked with many international colleagues and been envious of their access

to national HAI data.

I have a firm belief that Australia has much to benefit from national HAI

surveillance data. This has led me to question how far away we are from a national

program, and explore what sort of surveillance program would best suit Australia.


I confirm my following contributions to both studies in this thesis: study

design, administration, data collection, data analysis and manuscript writing (study

design, and data analysis and manuscript writing was assisted by supervisors and

other authors listed in publications).

1.4 AIM

The overall aim of this research is to establish evidence based

recommendations for an Australian national HAI surveillance system.

The specific research questions are:

1. What are the similarities and differences between existing HAI

surveillance processes in Australia?

2. What level of agreement exists in the identification of HAI between

those participating in HAI surveillance, and are there any factors that

influence agreement level?

3. What are the key attributes of successful centrally coordinated HAI


4. What are the preferences and priorities of key stakeholders when

considering a national HAI surveillance program?

The answers to these questions were addressed by undertaking two studies. The

first was a cross sectional survey of infection prevention and control staff who are

currently involved in HAI surveillance across Australia working at acute care

hospitals with more than 50 beds. The second study was a discrete choice experiment

(DCE) involving a broader range of key stakeholders. These studies, and their

findings generated five papers - four published, with the fifth recently having been

accepted for publication.

1.5 THESIS OUTLINE AND SIGNIFICANCE

This thesis by publication comprises three main sections, Literature Review,

five papers from two studies, and the Discussion that includes the recommendations

for a national HAI surveillance program.

In Chapter 2, the literature review explores major historical studies on HAI

surveillance and key papers on national HAI surveillance programs demonstrating


the benefits of surveillance, the current gaps in Australian surveillance and the many

issues that are relevant to HAI surveillance. The last section of the literature review

describes the unique DCE method used in the second study, and its application in

health sciences.

Chapter 3 presents the research questions, and discusses why these questions

are considered important, and outlines the methods used in the two studies

undertaken. The following five chapters contain the publications that have been

generated from this research.

Chapter 4 provides the results of a broad overview of international surveillance

programs and a review of existing Australian surveillance activities. The findings

identified several well established international surveillance programs, and major

differences between surveillance activities and coordination between Australian

states and territories, contributing to answers for research question 1. This paper was

published in Australian Health Review.

The publication in Chapter 5 also provides answers to question 1 and explores

in detail the differences between surveillance practices currently undertaken across

Australia. This study identified broad variation in current surveillance practices

across Australia, and was published in the American Journal of Infection Control.

Chapter 6 presents the paper published in Antimicrobial Resistance and

Infection Control journal, and was also generated from the first study. Seven clinical

vignettes included as part of the first study demonstrated only moderate agreement

when identifying HAIs. Data from this analysis answers research question 2.

Such detailed analysis of Australian surveillance practices and measurement of

agreement has never previously been described in Australia.

Through a literature review and a series of semi-structured interviews with

international experts, qualitative analysis identified five key characteristics of HAI

surveillance programs, and are presented in Chapter 7. This work comprises the first

part of the second study, the DCE, and answers question 3. This manuscript has been

accepted for publication in the American Journal of Infection Control (June 2016).

The full results of the DCE are presented in Chapter 8, and identify key

stakeholder preferences for a national surveillance program. The findings from the

DCE answer question 4. This manuscript has been published in BMJ Open. This is


the first time in an Australian setting that stakeholder holder preferences for a

surveillance program have been described.

Chapter 9 provides a detailed discussion of the findings from the studies in the

context of current knowledge, and synthesis of the data. There is also a discussion on

a pragmatic implementation framework required for a new surveillance program. The

chapter concludes by providing recommendations for the establishment of a national

surveillance program. The recommendations listed in Chapter 9 have been based on

findings from the studies undertaken in this PhD, using both local and international

data.

The Conclusion within Chapter 10 then summarises this thesis.

The Appendices contain several items of interest, including ethics approvals,

the surveys used, survey results not included in the publications, letters of support

and other relevant material.

This is significant research both for Australia and for the international infection

prevention field. This work builds on current knowledge of HAI surveillance

programs, and adds new knowledge applicable for the development, implementation

and maintenance of a national HAI surveillance program.

1.6 SUMMARY

This chapter has provided an overview of the origins of this thesis, an

introduction to HAIs and surveillance, and an outline of the structure of this thesis. A

background of the author has also been provided to highlight the long association of

working in this topic area that has provided a rounded understanding of the many

challenges for those at both a local and national level. The next chapter provides a

literature review covering several important issues relating to HAI surveillance.

Chapter 2: Literature Review 9

Chapter 2: Literature Review

This literature review provides a narrative of key articles relevant to the topics

of interest included in this thesis. These topics include fundamental issues relating to

surveillance programs in general and specific to HAI surveillance.

As the second study undertaken as part of this doctorate included the unique

DCE method, which has not previously been described in infection prevention

literature, it was important to include literature discussing this novel method to

demonstrate its suitability for the study. Similarly, the review also presents key

papers on implementation science, which is a crucial element when considering

pragmatic, evidence based recommendations for a national surveillance program.

The literature was accessed through Pubmed using a number of different search

terms in a structured, systematic fashion. The key search terms used were

“healthcare-associated infection” and “surveillance”. As the term “healthcare-

associated infection” is reasonably new, a search was also conducted on the terms

“nosocomial infection” and “surveillance”. This produced a total of 144 articles. To

focus the search on national surveillance programs, their development and

implementation, key terms of “national”, “development”, “establishment” and

“implementation” were introduced individually. This resulted in a zero return

additional for each of these terms. The search terms of “public reporting” and “data

quality” were also added identifying 9 and 65 articles respectively.

A general search was then conducted using the term “discrete choice

experiment” and “implementation science” which generated 67 and 279 articles

respectively.

The topics of public reporting, data quality, discrete choice experiments and

implementation science are rapidly evolving areas, and to make the review more

manageable, the searches of these terms were limited to English, involving humans,

had an abstract and for the period 2005 to 2015.

10 Chapter 2: Literature Review

All article titles were reviewed for relevance to national surveillance or large

surveillance networks. Abstracts were reviewed for their relevance to the specific

topic under discussion.

Given the limited volume of articles specific to this work, grey literature, such

as government reports, recommendations and surveillance protocols were sourced

from organisation websites. This also included grey literature on public health

surveillance of which much of HAI surveillance is based. Many of the articles that

have been included in this review were sourced from references within the grey

literature.

A detailed description of the search terms used and articles identified are listed

in Appendix A.

2.1 SURVEILLANCE PROGRAMS

The origin of the word surveillance comes from early 19th century French,

translated from sur “over” and veillar “watch”, and is derived from the Latin, vigilare

which means to “keep watch”.18 Commonly used in the observation of suspect

persons, in healthcare, the term has been applied to observing individuals and or

diseases.19

Scientifically based healthcare surveillance programs first came to light with

the monitoring and isolation of people with serious communicable diseases in the

late 1800’s. American epidemiologist Alexander Langmuir is credited with shifting

the emphasis of surveillance from monitoring those with or at risk of communicable

diseases to the diseases themselves.7

Surveillance programs are designed to provide basic epidemiological

descriptive data such as the time, place and person involved in the particular event

under observation. Such basic information enables the monitoring of the event over

time.20

Surveillance can be viewed as an information cycle, typically commencing

with recognition of an event, data collection, data analysis, interpretation and

importantly, dissemination of results to enable action (Figure 2).20 It is this action

which differentiates surveillance from simply monitoring events.21


Figure 2 - The surveillance cycle

2.1.1 Purpose of surveillance

By its very existence, the science of “infection prevention” implies that HAIs

are preventable. Exactly what proportion of HAIs are preventable is unclear and

difficult to establish due to limitations with study designs. Most published literature

is derived from before and after studies which suffer from lack of randomisation and

control.22 It is also suggested that a possible publication bias exists in that studies

undertaken in this topic with negative outcomes may remain unpublished.22

In a recent systematic review looking at HAI reduction studies restricted to

USA and published in the previous 10 years, almost 5000 potentially relevant articles

were identified, however only 15 were included in the review to estimate the

proportion of preventable HAIs. Looking only at four types of HAI, seven “good

quality” studies demonstrated reductions in catheter associated blood stream

infections of between 18%-66%, two “good quality” studies and one “moderate

quality” study demonstrated reductions in ventilator associated pneumonia (VAP) of

between 46% and 55%, two “moderate studies” demonstrated reductions in catheter

associated urinary tract infection (CAUTI) of between 17%-69%, and three

“moderate quality” studies on SSI demonstrated reductions of 26%, 54% and 29%.23

DataCollection

DataAnalysis

Establishmentofratesanddissemination

Implementinterventions


Uschmeid et al.23 cautiously report their findings due to the general low quality

of the studies. They do however conclude that the study population with the highest

risk of infection were often those that demonstrated the greatest reductions when

compared to study populations with low risk of infection.23 This supports the notion

that those groups who are at greatest risk of infection should be targeted in

surveillance programs.

In a more recent study, Lambert24 estimated that 52% of VAP and 69% of BSIs

are preventable. Limitations of this study include its use of routine HAI surveillance

data that had not been rigorously validated, and participation bias in that ICUs

submitting data are likely to have lower rates than those that do not participate.24

Although it is challenging to quantify the preventable proportion of HAIs, there

is agreement that a significant proportion, and probably the majority of HAIs are

preventable.22,23 This underpins the purpose of HAI surveillance.

The stated purpose of the surveillance program should indicate why the

program exists.25 When establishing a surveillance program, the purpose and

objectives must be clearly understood. Thacker proposes that two questions be

considered to help clarify purpose: “What will be done with the data and analysis?”

and “What action will be taken?”7 Answers to these questions and specific, action

oriented commitment, will determine data requirements and analysis and avoid any

unnecessary data collection.7

It is suggested there is one simple purpose of public health surveillance, and

that is to “provide a scientific basis for appropriate policy decisions in public health

practice and allocation of resources”.7 The purpose of HAI surveillance is to

provide quality data that can act as an effective monitoring and alert system

and reduce the incidence of preventable infections.26,27

Accompanying the purpose should be a set of objectives, or goals of the

program, and include how data from the program can be used.25 Common objectives

of HAI surveillance programs are listed in Table 1.


Table 1 - Common objectives of healthcare-associated infection surveillance

• Establishbaselineandendemicratesofinfections

• Detectclusteringintimeandspaceandpotentialoutbreaks

• Alertkeypersonnelofexistenceofaproblem

• Assesseffectivenessofinfectionpreventionmeasures

• Generatehypothesesconcerningriskfactors

• Providedatatobeusedforqualityimprovementactivities

• Guidetreatmentandorpreventionstrategies

• Meetregulatoryrequirements

• Conductresearch

• Providedataforeducationofhealthcareworkers

• Makecomparisonswithinandbetweenhospitalsornetworks

• Benchmarkoutcomes

• Reducetheincidenceofhealthcare-associatedinfections

Adapted from Perl and Chaiwarth8 and Wilson 26

The purpose and objectives of the surveillance program are used to guide the

design of the program, and importantly are also used as a reference point for an

evaluation.25 Clarity of a surveillance programs purpose and its objectives are

therefore paramount.

2.1.2 Establishing a surveillance program

A common error when establishing surveillance programs is to attempt to

collect as much data as possible, even though its immediate purpose may not be

clear.20 Collecting data that is not required wastes scarce resources, and the

complexity of the data collected needs to be balanced between information needs and

available resources.20

One of the major challenges when commencing surveillance activities is to

clearly define the event under surveillance. The quest to find the perfect case

definition, or develop methodology to maximise sensitivity and specificity may not

always be achievable. Thacker suggests that at the sake of some misclassification, it

is more important to get a surveillance program started and capitalise on interest and

enthusiasm, with a view to refining the program at a later date.7 Once established,


surveillance programs should be subject to ongoing evaluation, including a review of

the sensitivity and specificity, so the extent of any misclassification will be

identified.7

Buehler20 highlights two essential elements of any surveillance program. First,

the case definition of the event under surveillance. The complexity of the definition

needs to take into consideration the objectives of the surveillance program and will

be balanced out by issues around sensitivity, specificity and feasibility. It is

important that case definitions are standardised and applied consistently to ensure

accurate measurement of the event.8,28 Consideration also needs to be given to those

who will be applying the definitions, requirements for training, availability of

supporting tests and the interpretation of results.20 The roles and responsibilities of

all those involved in the surveillance program must also be clearly understood.7

The second essential element identified by Buehler20 is defining the population

under surveillance. The population under surveillance may be defined by a specific

location (e.g. school, hospital) or broader such as residents in a specific geographical

location.20 In the case of HAI surveillance, it may mean all patients in a particular

ward (e.g. ICU) or all patients having a particular type of procedure (e.g. hip

replacement).

In a 1963 publication describing the uses of surveillance for the prevention and

control of malaria, poliomyelitis, influence and hepatitis, Langmuir19 concluded that

“The basis for effective surveillance is the current and accurate two way flow of

information among all those who need to know.”

To complete the loop and enable appropriate action, feedback of data to the

appropriate groups is critical to the success of any surveillance program.20 Unless the

information is provided to those who can implement change when required, efforts of

those involved in surveillance will be wasted. Feedback of data has also been found

to act as an incentive for ongoing participation.20

Thacker7 proposes that surveillance programs be evaluated at three levels.

Although specifically referring to public health programs, these can be modified to a

HAI program. First, the importance of the event being monitored. Second, the

usefulness and cost of the surveillance program. Third, an evaluation of surveillance


program attributes such as sensitivity, specificity, representativeness, timeliness

simplicity and acceptability.7

Finally, it is important to understand the clear distinction between surveillance

and research. Good surveillance will often generate research ideas and hypotheses,

but surveillance data is descriptive by nature and rarely provides detailed information

to test a hypothesis. In contrast, research is experimental in design, and tests

hypotheses by comparing and contrasting two groups.20,21

In summary, surveillance programs must be built on sound epidemiological

principles. Purpose and objectives must be clearly identified, case definitions

unambiguous, standardised and feasible, populations under surveillance well defined,

data analysed, and to complete the surveillance cycle, data must be disseminated to

those who need to know.

2.2 ATTRIBUTES OF SURVEILLANCE PROGRAMS

Surveillance programs comprise of “networks of people and activities” that

maintain the collection, management, analysis, interpretation and reporting of data.20

This may occur at a local or an international level, and is often reliant on long term

co-operation between different levels of staff in healthcare facilities and coordinating

agencies.20 It is therefore important to understand the elements that are fundamental

to a surveillance program.

In 2001 the Center for Disease Control and Prevention (CDC) released updated

guidelines for evaluating public health surveillance systems.25 Whilst some features

of HAI surveillance programs will differ from public health surveillance, there are

fundamental principles that can be applied to all surveillance programs, and the CDC

guide has been widely used for evaluation of various surveillance programs, though

occasionally with some modification.29

The detailed evaluation guide identifies ten attributes which it recommends be

used to assess a surveillance program. Within each attribute, the CDC also lists

measurable elements that could be considered when assessing each attribute. The ten

attributes are briefly defined in Table 2.


Table 2 - Attributes of a surveillance program listed in CDC Guidelines

Attribute Description

Usefulness Asystemisconsideredusefulifitcontributestothepreventionofadverseevents(i.eHAIs),includinganimprovedunderstandingoftheevent.

Simplicity Referringtoitsstructureandeaseofoperation,agoodsurveillancesystemshouldbeassimpleaspossiblewhilstmeetingitsobjectives.

Flexibility Aflexiblesurveillancesystemcanadapttochanginginformationoroperatingconditionswithlittlescalingupofresources.Flexiblesystemscanaccommodatenewevents,changesindefinitions,reportingandfunding.

DataQuality Thisreflectsthecompletenessandvalidityoftherecordeddata.

Acceptability Thisreflectsthewillingnessofpersonsandorganisationstoparticipateinthesurveillanceprogram.

Sensitivity Thisisdeterminedattwolevels.First,theabilitytodetectcasesoftheadverseevent.Second,theabilitytodetectoutbreaksandmonitorchangesovertime.

PredictivePositiveValue Thisreferstotheproportionofcasesidentifiedthatactuallyhavetheadverseeventofinterest.

Representativeness Thisistheextenttowhichthesystemaccuratelydescribestheoccurrenceoftheadverseeventinthepopulationbyplaceandperson.

Timeliness Thisisthespeedbetweenvariousstepsinthesurveillanceprocess.

Stability Thisreflectsthereliability(functionwithoutfailure)andavailability(operationalwhenneeded)ofthesystem.

Adapted from German et al.25 and Drewe et al.29

A systematic review on evaluation of public health surveillance systems

identified 99 articles appropriate for review, of which 73 were for surveillance

systems of human disease.29 The review did not include any articles on HAI

surveillance systems. The authors note that the majority of articles describe using the

CDC guidelines, and in particular, the attributes for the evaluations. However, the 99

studies reviewed revealed a further 13 attributes not included in the CDC guide; cost

effectiveness, specificity, portability, efficiency, negative predictive value,

coherence, consistency over time, efficacy, feasibility, interoperability, likelihood

ratio of positive test, relevance, and security. The most frequently assessed attributes

in the articles were all those listed by CDC, and further included cost effectiveness

and specificity.29


Not all attributes listed by CDC will be relevant or clearly identified in every

surveillance program, and some may be related to each other. For example,

simplicity and reliability may be reflected in the acceptability of the system. If a

system is not simple or stable (i.e. frequently breaks down or is offline), then this

will in turn affect its acceptability. Likewise, sensitivity, specificity and positive

predictive value (PPV), could all provide similar information, therefore not all

attributes would need to be assessed.

Drewe et al.29 suggest that sensitivity does not need to be high for a

surveillance program to be useful, but methodology must remain consistent over time

trends in sensitivity can be meaningful. This is an important point particularly when

considering the sensitivity reported from HAI validation studies.

More concise and specific to HAI surveillance, researchers from the NHSN

identify similar but fewer attributes of a HAI surveillance system.30 They list six

attributes, but provide only general suggestions on how they might be identified

(Table 3).

Table 3 - Attributes of a healthcare-associated infection surveillance program listed by NHSN

Attribute Description

Accuracy Aidedbytheuseofcasedefinitionsandaccuratedenominatordatabyensuringallthoseinthepopulationundersurveillanceareatriskofacquiringtheinfectionundersurveillance.Thepresenceandintensityofpostdischargesurveillancewillstronglyinfluencethenumeratordata.

Timeliness Prospectivesurveillanceisrecommendedtoenablequickidentificationandpromptinvestigation.Retrospectivesurveillanceisbestsuitedforissuesthathavelittleneedorinterventionduetothedelayindataanalysis.

Usefulness Surveillanceresourcesshouldonlybedirectedtowardsactionableissues.

Consistency Casedefinitionsmustbeapplieduniformly,surveillancemethodsanddatasourcesshouldbeconsistent,andeducationofthoseinvolvedinidentifyingcasesshouldbeuniform.Routinecross-checkingofcasedeterminationsshouldbeperformedregularly.

Practicality Surveillanceobjectivesmustbeachievablewithintheresourcesavailable.

Adapted from Allen-Bridson, Morrell and Horan.30


From a slightly different perspective, in 2001 before the creation of NHSN,

NNIS researchers listed three requirements they believed essential for a successful

multi centred surveillance program.31 First, the surveillance program must have a

clear purpose. Second, it must have standardised definitions, data fields and

protocols, and third, there must be a coordinating centre to standardise definitions

and surveillance protocols, receive, review quality, analyse and disseminate data, and

standardise risk adjustment approaches.31

The authors went on to list what they considered the seven “NNIS elements”

critical for the successful reduction of HAIs:

“1) Voluntary participation and confidentiality;

2) Standard definitions and protocols;

3) Defined populations at high risk (e.g., intensive care, surgical patients);

4) Site-specific, risk-adjusted infection rates comparable across institutions;

5) Adequate numbers of trained infection control practitioners;

6) Dissemination of data to health-care providers; and

7) A link between monitored rates and prevention efforts, where patient-carepersonnel relied on the data to alter their behaviour in ways that may have reduced

the incidence of nosocomial infections”.31

There is only one reported use of the CDC guidelines to evaluate a HAI

surveillance program. In a ten year review of the Krankenhaus-Infektions-

Surveillance-System (KISS), the HAI surveillance program program in Germany,32

Gastmeier et al.32 used the CDC guidelines25 to assess the KISS program. In terms of

simplicity and flexibility, Gastmeier notes that since its commencement in 1997, data

collection had become increasingly more electronic based improving simplicity, and

the recent inclusion of a new surveillance component for Clostridium difficile

demonstrated its flexibility.32 Acceptability and representatives was reflected in the

participation of over 500 hospitals of all sizes from across the country.32 KISS is now

a web based reporting system, this means that reports can be generated by hospitals

at any time, and the system is always available, demonstrating its timeliness and

stability.32


In conclusion, surveillance systems have many attributes, though it appears

from the literature these attributes aren’t always relevant or identifiable, and some

attributes may be more important than others. A successful HAI surveillance

program must be epidemiologically sound and balance attributes such as; accuracy,

timeliness, usefulness, consistency, and practicality.30

2.3 A BRIEF HISTORY OF HEALTHCARE –ASSOCIATED INFECTION SURVEILLANCE

The foundations of HAI surveillance can be traced back to Vienna in the mid

19th century to the work of physician Ignaz Semmelwies. Although not trained in

epidemiology, Semmelweis’ observations of mortality data and differences between

those who died and survived resulted in an infection prevention intervention with

dramatic effect. The Vienna Lying hospital was divided into two divisions, the first a

medical teaching service where women were delivered by physicians and students,

the second staffed by midwives. Semmelweis noted that the 1847 maternal death rate

in the first division was 10% compared to the second divisions of 3%.33 A thorough

epidemiological review of data and the coincidental death of a colleague from sepsis

following a needlestick injury during a post mortem, led Semmelweis to note a major

behavioural difference. Medical students undertook autopsies and often went to the

autopsy room to deliver women in the first division, in comparison to the midwives

who did not perform autopsies. Based on this observation Semmelweis hypothesised

that “cadaveric material was the cause of death”.33 Famously, Semmelwies

implemented a hand washing intervention on entry to the delivery suite, which

produced a dramatic and significant decrease in mortality rates.34 A failure to

effectively communicate his controversial findings led to Semmelweis fleeing to

Budapest, where his performance as a physician was criticised, and together with

reported episodes of psychosis, he was committed to an asylum where he eventually

died in 1865.35

Around the same time in the UK, William Farr and Florence Nightingale’s

shared interest in hospital mortality rates resulted in a collaboration that

demonstrated a relationship between hospital hygiene and infectious post operative

complications. Nightingale proposed that nursing staff collect and report data on

hospital mortality.33


In 1860, Scottish physician James Simpson reviewed mortality data following

amputation in country areas compared to metropolitan hospitals and found much

higher mortality rates in the hospitals. He also went on to demonstrate that the

mortality rates increased with the size of the hospital.33 Simpson recognised the

importance of cleanliness and the prompt containment of the excretions from

diseased patients.36

The first well documented report describing active SSI with routine reporting

was conducted by Brewer who provided systematic feedback to surgeons resulting in

a 95% reduction of SSIs in the early 1900’s.33,36,37

The most significant and comprehensive research into infection prevention

programs was undertaken in the USA by Robert Haley in the 1980’s.38 Clear benefits

of HAI surveillance programs were first demonstrated by Haley’s pioneering Study

on the Efficacy of Nosocomial Infection Control (SENIC) commissioned by the

CDC in response to rising criticism from hospitals of the resources required to

comply with their recommendations for infection prevention and surveillance.38 In a

retrospective multi-centred study, Haley set out to determine whether infection

control programs reduced rates of SSI, BSI, urinary tract infections (UTI) and

VAP.10 Over three phases using screening questionnaires, interview surveys, and

medical record reviews, Haley developed a surveillance index to measure the extent

to which each hospital conducted active surveillance, and a control index to measure

the intensity of efforts to reduce infections. After evaluating hospital infection

control programs over a 10 year period, and reviewing HAI rates of SSI, BSI, UTI

and VAP, Haley identified four essential components of an effective infection

prevention program;

• a structured surveillance program,

• one infection prevention nurse per 250 beds,

• an infection prevention physician, and

• a system for reporting infection rates to surgeons.10

Different combinations of these four factors reduced the rates of all four

infections, however the only factor that was present for each was an effective

surveillance program.8


This seminal work has been the foundation on which many HAI surveillance

programs have been established, and has also been used to estimate the impact of

infection prevention on rates for specific infections, to classify patients as either high

risk or low risk of infection, develop risk strata to predict patients probability to

develop indication, and to estimate the costs of infections.38

With the advancement of epidemiology, fundamental principles of good HAI

surveillance have been recognised. The methods used for HAI surveillance have

evolved over time, and this is explored in the next section.

2.4 HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE METHODS

Several methods for undertaking HAI surveillance have been described, and

can be categorised into two strategies; hospital wide surveillance and targeted

surveillance.39 Hospital wide surveillance involves prospective and continuous

surveillance of all areas of the hospital. Whilst being comprehensive, it is resource

intensive and costly. Although still requiring substantial resources, more efficient is

targeted surveillance, which also includes surveillance by objective or priority.

Typically this involves targeting high risk patients, or areas, for prospective

surveillance at the risk of missing clusters that may occur in other areas.8,30,38,39 A

comparison of the two surveillance strategies is provided in Table 4.


Table 4 - Healthcare-associated infection surveillance strategies

Strategy Advantages Disadvantages

Facilitywide Providesdataonallinfections

Establishesbaselineinfectionrates

Identifiesclusters

Recognisesoutbreaksearly

Identifiesriskfactors

Raisesprofileofinfectioncontrol

Amendabletosmallerfacilities

Expensive,resourceandtimeintensive

Yieldsexcessivedatawithlittletimetoanalyse

Detectsinfectionsthatarenotpreventable

Nodefinedobjectives;interventionsdifficult

Overallinfectionratesnotcomparable

Targeted Concentrateslimitedresourcestohighriskareas

Responsivetofindingoffacilityriskassessment

Focusonhighriskpatients/areas

Moreefficient,lessresourceintensive

Maymissclustersinotherareas

Collectsdataonlyfortargetedpatients/areas

Nobaselineratesinotherareas

Modified from Perl and Chaiwarth,8 Allen-Bridson, Morell and Horan,30 Perl,38 and Pottinger

et al.39

Targeted surveillance strategies are also supported by the work of Glenister40

from the UK in the early 1990’s. Acknowledging that hospital wide strategy was

resource intensive, Glenister40 conducted a single site, prospective continuous study

to determine the effectiveness of eight surveillance methods using sensitivity,

specificity and time for data collection as outcome measurements. Of the eight, a

combination of two methods, “laboratory based ward surveillance”, which involved

daily review of case records of those identified from positive microbiology reports,

and “ward liaison surveillance” comprising routine twice weekly ward visits,

discussions with nursing staff and review of records of patients reported to have an

infection, was found to have the highest sensitivity of all methods, and required one

third of the time of the reference method. This combination had the advantage of

identifying infections even when specimens were not taken or had negative results.

Although possibly not appropriate for all hospitals, this method was recommended to

increase efficiency and use of resources.40

Even though this method is efficient when compared to other methods, manual

medical record review of patients at risk of a HAI, which may also involve visiting

patents, reviewing microbiology results, discussions with ward staff and team


meetings remain resource intensive.41 Further, the application of definitions is

subject to interpretation and identification of cases is often dependent on effort.41 In

2009, results of a national infection control program survey in the USA reported that

infection prevention staff spend up to 45% of their time collecting, analysing and

interpreting HAI data, the largest percentage of time of any of their infection

prevention activities.42 The same study also noted that 35% of infection prevention

staff had assistance with data management, and 13% had statistical help.42 A recent

cross sectional study with infection prevention staff in Australian, it was identified

that 36% of their time is spent on surveillance, 56% of this time was used for

collecting data. 43

2.4.1 Automated surveillance systems

Acknowledging the burden of manual data collection, the move towards the

use of automated technology and electronic data as an aid to traditional HAI

surveillance methods is gathering momentum.

The use of automated technology and electronic data in HAI surveillance is

well described.44 In a large systematic review, Freeman et al.44 report that electronic

surveillance systems often produced higher sensitivity and specificity when

compared to traditional methods, however they are limited in that they are dependent

on the facility’s electronic information technology systems, and on occasion a HAI

detected by an electronic surveillance systems still requires confirmation by a

healthcare worker. Generally, automated systems ensure consistent application of

surveillance definitions, significantly reduce the burden of data management

associated with traditional methods, provide improved sensitivity and specificity, and

could be used as a tool by staff to enhance their surveillance programs.44

To improve the efficiency of surveillance resources, Perl and Chaiwarth8

believe that integration of rapidly developing surveillance technologies is essential. It

is estimated that electronic HAI surveillance systems reduce time spent on

surveillance by up to 65%, whilst also improving sensitivity and specificity.8

If electronic HAI surveillance systems are to play a bigger role in routine HAI

surveillance, then they need to be as good as, and less prone to subjectivity, than

existing methods. This is particularly important if HAI rates between hospitals are to

be compared or publicly reported. Inconsistent application of definitions and case


finding methods will influence the meaning of data and any comparisons made. A

recent study highlighted these advantages when using a computerised algorithm to

detect bloodstream infections compared to traditional methods.45 Researchers found

the differences in the outcome of these two methods significantly changed the order

of hospitals rankings when bloodstream infection rates were used as an indicator.45,46

Attempts at using administrative coding data (ACD) as a passive method to

detect HAIs is increasing, particularly in the USA where insurance claims are often

used by quality improvement programs and researchers.47 The use of ACD is

attractive because codes are often uniform across hospitals, they are stored

electronically and therefore convenient for applying algorithms.47 In a systematic

review of studies reporting the use of ACD for identifying HAIs, moderate

sensitivity and high specificity was found when detecting Clostridium difficile

infections (CDI) and orthopaedic SSIs. Evidence for other types of HAIs was limited

due to the small number of studies. The moderate level of sensitivity means that

using ACD as the only method to detect cases will result in some HAIs being missed,

and consequently HAIs rates reported using only ACD will be underestimated. Goto

et al.47 recommend that ACD may be useful as a factor within an algorithm, but

should not be used as the primary case finding method.

In summary, the increasing demands for more data in less time means that

current manual data collection methods are unsustainable. Although gradually more

hospitals are moving towards electronic records, Hebden48 reports that the uptake of

automated surveillance systems is low, and calls for more qualitative research to

explore the human factors associated with this poor uptake. Hebden48 implies that a

lack of implementation strategies could be partly to blame, as any automated process

requires an adjustment of workflow and roles, an understanding of how the data are

to be interpreted and then translated into knowledge to guide decision making.

2.5 NATIONAL HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE SYSTEMS

National HAI surveillance programs are characterised by two interrelated

cycles. At the micro level (hospital), surveillance is used to establish endemic rates

and to detect outbreaks, identify priorities and measure the effect of interventions. At

the macro level (state or national), data from participating sites is collated to provide

aggregated data that may be used for benchmarking and made available to


participating hospitals, policy makers and sometimes the public. Common to all

national surveillance programs is a central data coordination process, often

undertaken by a central body.49 The central body may also be responsible for

developing uniform definitions and methods, and provide education and support for

those involved in surveillance (Figure 3).

Figure 3 - Centrally coordinated (national) healthcare-associated infection surveillance program. Hospital activity in blue circles, central activity in rectangles.49

There are several well established national HAI surveillance programs. The

USA,50 Germany,32 the United Kingdom,51 Belgium,52 Switzerland,53 Spain54 and the

Netherlands55 are all well documented. Many European countries have further

collaborated to establish the European Centres for Disease Control and Prevention

(ECDC) HAI Surveillance network which prescribes uniform definitions and

methodology for participating countries, and facilitates a greater understanding of the

epidemiology of HAIs across Europe.56 A description of the national programs in

USA, Germany, France and UK, and an outline of the ECDC will now be provided.

Developcasedefinitionsandcasefindingmethods- Recruithospitals- Trainlocalsurveyors

DataCollection

DataAnalysis

Establishmentofendemicrates

Identifyoutbreaks

Implementinterventions

Establishsystemwiderates

Performinter-hospitalcomparisonsDisseminatefindingsValidatesurveillancemethodology

Modifymethodology


2.5.1 United States of America

The longest running national HAI surveillance program is the CDC NHSN.57

Originally known as the NNIS system, it commenced in 1970 with 62 hospitals

voluntarily participating. At this time, all participating hospitals conducted hospital

wide, prospective surveillance. Initially producing hospital wide rates, with time and

an improved understanding of the epidemiology of HAIs, it became clear that this

was epidemiologically unsound for comparing hospital data due to the difference

between hospitals. Surveillance moved away from hospital wide to targeted, and in

1986, NNIS created three surveillance components, ICU, high risk nursery and SSI.

Hospitals were able to choose which components they participated in. This, together

with data on device exposure and type of surgical procedures, facilitated comparable

risk adjusted data.8

By 1999, 285 hospitals across 42 States participated in NNIS. Voluntary

participation and confidentiality was listed as an essential element to the success of

surveillance.31 At this time, to facilitate application of standardised surveillance

methodology, CDC provided training for infection prevention staff and also

conducted a biennial conference which the infection prevention staff were

encouraged to attend.58

An increasing focus on healthcare safety and quality following the Institute of

Medicine’s 1999 report “To Err is Human” generated discussion on the use of HAI

data as a performance measure for hospitals.59 This resulted in several states

mandating participation in NNIS, a trend that continued during the 90’s.60 In 2005,

the NNIS program expanded to include co-existing healthcare worker exposure and

renal dialysis surveillance programs to create the NHSN,61 and by 2012, 4,444

healthcare facilities participated in the device-associated module of the program.62

Researchers at the NHSN believe the benefits of the surveillance program are

evident in the reduction of infections rates.31 In 2000, NNIS were able to demonstrate

reduction in UTIs, respiratory tract infections and BSIs in ICUs in participating

hospitals between 1990 and 1999, supporting the effectiveness of the national

surveillance program.58 Reductions in BSI rates varied from 31% in surgical ICUs to

44% in medical ICUs. The authors acknowledge that other explanations, such as a

national effort to reduce HAIs, and a shift of healthcare away from hospitals may


have also influenced these results.31 More recently, the NHSN report a 50% decrease

in CLABSI and a 17% decrease in SSI rates between 2008 and 2014.63

The NHSN has been criticised for accepting data that has not been validated or

is incomplete, and for not feeding back data in a timely fashion.64 However recent

improvements in data collection and reporting tools now provide immediate reports

once the data has been entered into the system.65 Currently NHSN is utilised by over

13,000 medical facilities to track HAIs. The range of participating facilities includes

hospitals, nursing homes, outpatient renal dialysis units and even psychiatric units

who access various components of the NHSN program. Enabling real time data

analysis through web portals, NHSN also provides extensive tools for surveillance

education and data interpretation, and includes an impressive array of online

instructional videos.66

The HAI definitions and surveillance methodology developed by the original

NNIS program and implemented across the USA could be considered the

international standard for HAI surveillance programs as identical or similar

definitions and methodology have been adopted by many countries.

2.5.2 United Kingdom

Based on the original NNIS system, the UK Government established the

Nosocomial Infections National Surveillance System (NINSS) in 1996.67 Like the

NNIS system, participation in NINSS was voluntary and confidential, and comprised

three surveillance modules, SSI, BSI and UTI.67 A user evaluation of the surgical site

infection module undertaken in 2000 reported that the program was highly valued,

participants utilised the data to compare hospitals, supported national uniform

protocols, and were eager to extend the range of surveillance activities.68

In the early 2000’s the emergence of methicillin resistant Staphylococcus

aureus in UK hospitals prompted the interest of the government.59 As a result,

mandatory reporting of all Staphylococcus aureus bloodstream (SAB) infections was

introduced in 2001, and this was extended to other resistant organisms during the

decade. At the same time, NINSS was not developed any further, and only the SSI

module was continued. In 2004, surveillance on all orthopaedic joint replacement

surgery became mandatory for all English National Health Service (NHS) Trusts.

Some refinement to methodology processes including active surveillance for


infections detected in patients readmitted following joint replacement surgery has

occurred since this time. The range of operative procedures has been expanded,

however only surveillance on joint replacements remain mandatory.69

Currently, all NHS trusts are required to perform a minimum of three months

surveillance on one of: hip prosthesis, knee prosthesis, repair of neck of femur or

reduction of long bone fracture, in each financial year.70 Since its commencement in

2004, there has been a 95% increase in the number of hospitals submitting data. In

2013/14, 198 hospitals participated.70 As well as the mandatory procedures,

participants can also voluntarily submit data on thirteen non-orthopaedic procedures,

commonly spinal surgery and coronary artery bypass grafts.70

In a review of six years of data from 2008/9 to 2013/14, no clear trend was

found in hip prosthesis or knee prosthesis infection rates. Decreases were identified

for repair of neck of femur and reduction of long bone fracture and for some non-

orthopaedic procedures. A significant increase was identified in spinal surgery

infection rates.70

The NHS has been criticised for the quality of its data. A recent survey of SSI

surveillance practices in participating trusts found variation in surveillance intensity,

data collection methods, application of definitions and national reporting amongst the

trusts resulting in unreliable benchmarking, and the likelihood of underreporting.71

The authors caution against expanding mandatory surveillance activity after finding

that surveillance methods for the mandatory procedures were less rigorous than those

for the non-mandatory procedures.71 These claims are refuted by the NHS,72 however

it must be acknowledged that any large surveillance programs are vulnerable to such

issues. Data from the UK is submitted to ECDC.

2.5.3 Germany

The German KISS HAI surveillance program was established in 1997, and was

also based on the original NNIS methodology. Commencing as a voluntary and

confidential program, its two first surveillance modules were ICU and SSI. Over the

next seven years these were followed by further modules; low birth weight neonates,

haematology/oncology, non ICU patients with devices, outpatients with ambulatory

operations and methicillin resistant Staphylococcus aureus (MRSA).32


Participation requires agreement to attend an introductory training course

conducted by the national reference centre, and attendance at least every two years at

a national reference centre event to exchange information, as well as agreeing to

subject data to a range of quality assurance processes.73

Annually, a series of clinical vignettes are sent out to all participating sites and

staff are required to determine the presence of a HAI. This allows the reference

centre to calculate the sensitivity and specificity to identify further educational

needs.74

The effect of the KISS program on HAI rates has been estimated to be between

a 20 to 30% reduction in infections.75 Recently researchers have demonstrated the

accuracy of data in the KISS program by measuring the sensitivity and specificity of

189 surveillance personal through a series of clinical vignettes presented over a

period of three years. The study also established that those with more surveillance

experience and higher education levels have higher diagnostic accuracy.74

Participation in KISS remains voluntary and confidential, however there

remains ongoing debate about the public release of hospital data.59 Data from KISS

is submitted to the ECDC.

2.5.4 France

France undertakes national surveillance through a collaborative effort of its

five regional infection control coordinating centres. The surveillance network, called

RAISIN, was established in 1998 and involves the infection control coordinating

centres, the national institute for public health surveillance, the Ministry of Health

and other associated public health bodies.76. RAISIN has been criticised because of

its voluntary participation,76 and clearly with five different centres coordinating local

surveillance activities the risk of variation exists. HAI data from RAISIN is also

submitted to the ECDC.

2.5.5 Netherlands

In the Netherlands, SSI surveillance commenced in 1996 as a component of the

new national HAI surveillance program.77 The PREZIES network extended to

catheter related BSI surveillance in 2000,78 and also provides a separate prevalence

surveillance module.79 The Netherlands also participates in the ECDC.


2.5.6 ECDC

The predecessor to the current ECDC surveillance program was the Hospitals

in Europe Link for Infection Control through Surveillance (HELICS) which aimed to

standardise HAI surveillance in Europe in the mid 1990’s.80 HELICS collected data

from national surveillance networks according to agreed protocols, and in 2005

became part of the Improving Patient Safety in Europe (IPSE) Network. In 2008,

coordination of surveillance was transferred form IPSE to the ECDC where it

remains today. Surveillance of HAIs following the ICU and SSI protocols continues,

however to broaden the scope of surveillance, a protocol for point prevalence

surveillance was developed in the late 2000s with a recommendation that it be

undertaken at least once every five years.81

The first European Union wide point prevalence survey was conducted in

2011/12 and collected data on HAIs and antimicrobial use in European acute care

hospitals from 29 member states. In 2011, 16 countries submitted data to the SSI

surveillance program, and included data on over 420,000 procedures.82

The ECDC is an example of how uniformity of national surveillance programs

can result in large international datasets that can be used by regions or countries in

need of support to prevent and control HAIs.

In summary, large national HAI surveillance networks that generate

meaningful data are achievable, but require investment in sound methodology,

coordination and education. Once these have been well established, the creation of

the ECDC demonstrates the potential to form multinational networks to provide

comparable national data and a greater understanding of the international

epidemiology on HAIs.

2.6 EFFECTIVENESS OF LARGE SURVEILLANCE PROGRAMS

There are several European studies that describe the effect of national

surveillance programs on HAI rates. To observe any effect of participating in the

surveillance program, Geubbels et al.77 retrospectively reviewed SSI data across five

years. As not all hospitals commenced the program at the same time, data

were stratified by the number of days between the start of surveillance in a hospital

and the day of surgery of a patient into five consecutive one year periods. In

the final multiple regression analysis that included over 21,000 procedures from 37

hospitals,


the adjusted risk of SSI in the fourth year was reduced by 31%, and in the fifth year

by 57%.77 Guebells et al.77 believes these findings suggest that hospitals can reduce

their SSI rates by participating in a surveillance network, though it is possible that

the reduction may be due to other factors including declining surveillance intensity

with time, meaning less infections would be detected.

Brandt et al.83 performed a retrospective multiple logistic regression on pooled

SSI data from 190,000 procedures from 86 hospitals participating in the KISS

program in Germany. Analysing the data across four years, and using year 1 as the

reference year, the odds ratio (OR) of acquiring a SSI was 0.84 in year 2, 0.75 in year

3, and 0.75 in year 4.83 The multiple logistic regression demonstrated that

participation in surveillance was a significant protective factor.83 Like Guebbels et

al.77 study, it is possible that the decrease was due to surveillance intensity, but on

the other hand it is argued that with more experience surveillance staff actually

become better at detecting infections. Another possibility is that over the four year

period hospital stays would have shortened resulting on SSIs presenting post

discharge and therefore not detected. The KISS program strongly recommend post

discharge surveillance, but it is not mandatory given the lack of an agreed, uniform

and efficient method.

In another study from the KISS group, Schwab84 undertook a study of BSIs and

pneumonias in neonatal intensive care unit comparing pooled data from the first year

of participation in the surveillance program to the third year. The risk ratio (RR)

demonstrated a significant reduction in BSI (RR:0.77, p=0.045) and central line

associated BSI (RR:0.76, p=0.009). Although a decline in the RR for pneumonia was

demonstrated, they were not statistically significant.84

In northern France, a large group of volunteer surgical wards participate in a

localised surveillance network called INCISO.85 In contrast to other surveillance

networks described in the literature, SSI data in the INCISO network is collected by

a surgical team with the assistance of infection prevention staff. Rioux revised SSI

data from the first six years to observe temporal trends as a result of undertaking

surveillance.85 The annual standardised infection ratio from pooled data from over

150,000 procedures decreased from 1.25 in 1998 to 0.74 in 2003. During this time

however many infection prevention interventions were introduced, notably regular

surgical antibiotic prophylaxis audits and preoperative surgical skin antisepsis


audits.85 Although data were not fed back to individual surgeon, a

competitive environment was encouraged between units who were able to compare

results. The authors also claim that the establishment of a coordinating centre was

crucial in the establishment of the surveillance network.85

In the southeast of France, the Mater Network group undertakes voluntary

localised surveillance in maternity units. A study by Vincent et al.86 reviewed the

impact of surveillance on post caesarean SSI and UTI rates. During the stay period,

over 37,000 caesarean deliveries were performed. The authors calculated adjusted

OR’s for SSI and UTI, and using both Pearson and Spearmans tests to observe for

correlation over time. Using the first year as the reference year, both SSI and UTI

decreased significantly over the study period as demonstrated in Table 5. Whilst the

authors acknowledge a reduction in HAIs being attributable to regular feedback, it

was unable to measure the significance of participation in a network. However they

state the evolution of improved infection prevention practices that were evident as a

result of comparing data and network meeting most likely had a role.86

Table 5 - Reductions in infection due to surveillance

Infectiontype Pearson(R) Spearman(p)

SSI -0.823(p=0.023) -0.786(p=0.036)

UTI -0.906(p=0.005) -0.926(p=0.011)

Adapted from Vincent et al.86

In another French study from the southern coordinating centre for nosocomial

infection control (southeast CCLIN), the temporal trend in SSI was found to be

equivalent to a 5% decrease every year for nine years OR 0.95 (p<0.0001) following

the commencement of surveillance in the network.87

Effective surveillance programs will deliver information to key stakeholders at

all levels that can be used to inform decisions. The simple act of collecting HAI data

will not in itself reduce HAIs, rather data must stimulate action.88 HAI surveillance

programs establish a baseline rate of infection which can then be used to detect

clusters or outbreaks, identify problems, evaluate prevention and control measures,

generate hypothesis concerning risk factors, guide treatment and prevention

strategies, make comparisons with other facilities, and ultimately, reduce the

incidence of HAIs.21,49,88


The evidence supports that participation in a national, or network, HAI

surveillance program is associated with a reduction in HAIs. An exact measurement

of the impact of surveillance data collection alone is difficult to estimate. HAI

surveillance should be considered as a package comprising many elements including:

• timely feedback of data,

• benchmarking and comparing of data,

• sharing of data and interventions,

• increased awareness amongst clinicians, executive and health

department staff.

2.7 HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE IN AUSTRALIA

In 1962 the Princess Alexandra Hospital in Brisbane appointed a nurse to the

position of Infection Control Sister. Part of this role was to follow every surgical

patient during the course of their hospital stay and record the incidence of infection.89

Data from this hospital wide surgical wound surveillance program was first

published in 1973. Procedure specific data were collected, and operations

classified into three groups, clean, potentially infected and frankly infected. On

follow up, surgical wounds were classified as either Grade 1 - absolutely clean,

Grade 2 - intermediate, or Grade 3 - discharging pus. The surgical wound

infection rate for clean procedures was calculated to be 4.6%, potentially infected

procedures 9.5%, and for frankly infected procedures (i.e. appendicectomy for

peritonitis) 25%. The authors then compare this data with reported rates

from the UK and USA. Acknowledging that the infection criteria and operation

categories were not identical, they concluded that the Australian rates were

favourable in comparison. When stratified by month, the rates for all

categories vary markedly, and the authors conclude that a monthly drop in

infection rates does not necessarily mean that the introduction of a preventive

measure is successful.90 In conclusion, it is noted that the Infection Control Sister

played a vital role in any large hospital by conducting this “continuing watch on

surgical infections”90

It is perhaps surprising that despite these pioneering surveillance

activities, knowledge gained from the data has not resulted in the establishment of

a national HAI surveillance program some 50 years later. However, some

statewide programs


have been implemented to a varying degree. In 1998 the New South Wales

Department of Health attempted a statewide HAI surveillance program and pilot

tested it until 2000.91 This was followed by Queensland,92 Victoria93 and Western

Australia94 who all implemented statewide programs between 2000 and 2005 using

infection definitions based on those developed by NNIS.

In December 2008 the Australian Health Ministers Conference endorsed

jurisdictional level surveillance of SAB and CDI. This was followed in 2009 by

further endorsement of the ACSQHC recommendation that hospitals routinely

monitor SAB and CDI. This has resulted in the development of implementation

guides for the surveillance of SAB and CDI produced by Technical Working Groups

under the auspices of the ACSQHC.95

The NSQHSS developed by the ACSQHC outline a set of standards for

“Preventing and Controlling Healthcare-associated Infections”.96 Whilst the standard

calls for surveillance to be in place, it is not specific about the type of surveillance, or

participation in any surveillance network. This is in contrast to the standard for hand

hygiene, where it stipulates that hand hygiene programs must be compliant with the

NHHI.96

Anecdotally, it is reported that many hospitals, networks or regions undertake

HAI surveillance above and beyond the mandatory requirements of their jurisdiction.

Examples include individual hospitals performing targeted surveillance in unique,

high risk populations (e.g oncology units, burns units) or in response to perceived

problems. The extent of this activity and the quality of data is unknown.

Unlike the international programs, there is only one study demonstrating the

effect of Australian state based programs on HAI rates over time. A recent

retrospective review of 11 years of HAI data in one state has identified a diminishing

rate of SSI since the program commenced.97 More longitudinal studies are needed to

support this finding.

Whilst concerns regarding the validity, lack of risk adjustment and differences

with inclusion and exclusion criteria for numerator and denominator data need to be

addressed in Australia,98-100 the work of the ACSQHC HAI program is gradually

bringing jurisdictions together and providing leadership and coordination for further

national HAI surveillance activity. Current ACSQHC strategies such as the National


Surveillance Initiative95 have promoted and supported increased jurisdictional

collaboration at both a health department and clinician level. The development of

national definitions for SAB have had good uptake, and identifiable hospital SAB

data are now published on the MyHospitals website (www.myhospitals.gov.au).

The recently released ACSQHC report on Antimicrobial Resistance and Antibiotic

Usage adds to the momentum for better national HAI data.101

It is worth noting here that the NHHI and collection and reporting of SAB data

are examples of national infection prevention activities that have been implemented

across healthcare facilities in all states and territories. They required strong national

leadership, broad support and cooperation between states and territories.95,102

Benefits of HAI surveillance are well described, and good evidence exists that

they can lead to a reduction of preventable HAIs. National HAI surveillance

programs have been long established in many countries that have implemented

uniform definitions and methods enabling comparison of hospital data whilst also

establishing benchmark rates and national comparators. Australia does not have a

formal national HAI surveillance program, and existing programs within Australia

are not centrally coordinated leading to the inability to facilitate meaningful

comparison nationally.

2.7.1 Surveillance activity by Australian infection prevention staff

A 1996 survey of 308 Australian infection prevention staff who conducted

surveillance identified that 46% undertake SSI surveillance, and 33% intravascular

device related bacteraemia surveillance, on a daily basis.103 Data from this study also

suggests that on average infection prevention staff spent approximately 4 hours per

week on surveillance activities, although this figure seems remarkably low given a

high proportion (76%) reported undertaking hospital wide surveillance.103

In a 2007 survey undertaken by the ACSQHC exploring surveillance activities

of infection prevention staff,104 49% of 276 respondents indicated they performed

SSI surveillance, however it is unclear if all respondents were from acute care

facilities performing surgical procedures. The most common procedures under

surveillance were joint replacement, lower segment caesarean sections and cardiac

surgery. Of the 276 respondents 52% indicated they performed surveillance on all

BSIs, 40% reported surveillance on CLABSI, with just over half of those reporting


ICU CLABSI. Less than half reported that data comparisons were made with a state

or national benchmark. Other types of analysis included the use of run charts and

control charts.104. Of the 98 full time staff in public facilities, 42 (43%) reported

surveillance activities took eight hours or less per week. When asked about barriers

to surveillance, 41% indicated time, 16% stated technology and computer issues,

14% suggested case finding and lack of institutional support. Limitations in this

survey included an over representativeness of hospitals with less than 60 beds and

design faults limited appropriate analysis. Nevertheless the survey identified many

differences in approaches to surveillance and acknowledged the lack of credible

national aggregation of data.104

In a large cross sectional survey on the roles and responsibilities of over 300

infection prevention staff across Australia from a variety of hospital sizes, Hall et

al.105 identified that 54% undertake surveillance activities on a daily basis, and that

those from public hospitals and larger facilities undertake surveillance more

frequently when compared to those from private hospitals.105 Interestingly there was

no association found between frequency of surveillance activities and years of

experience or qualifications. A further analysis of data from the same study reported

that infection prevention nurses estimated they spent 36% of their time undertaking

surveillance activities, of which 56% was spent on collecting data.17

In a recent case report from the US, it was reported that infection prevention

staff spend over 5 hours per day undertaking various surveillance activities to

comply with the HAI reporting requirements set out by the Centers for Medicare and

Medicaid Services.106 Whilst the use of automated surveillance processes have been

reported to reduce the amount of time spent on surveillance by up to 65%,107

increased demands for state and national regulatory requirements still demands

substantial resources.106

The extent to which the implementation of automated surveillance

methodologies in Australia is unknown. One state developed its own handheld

device application as a data collection tool for uploading into a relational database

prior to be transferred to a central state database.92 Whilst this has recently

transformed into a more advanced data collection and analysis process, other states

have not implemented a statewide application. In a sample of 40 infection prevention

units, 50% recently indicated they used some form of electronic HAI surveillance.17


Highlighting the desire for more electronic assistance, a recent survey by Hall et

al.108 identified the major priority for Australian infection prevention staff was access

to more information technology.108

In the absence of a coordinated national approach to HAI surveillance in

Australia, the range of variation between the surveillance programs, the type of

infections under surveillance, the quality of data collected, data analysis and

reporting is uncertain. There is increasing momentum from regulatory bodies for

more national activity, to date this as been slow to be implemented. Before any real

engagement can occur, we need to better understand the current situation of HAI

surveillance in Australia.

As noted, recent data indicates that infection prevention staff spend a large part

of their time undertaking various surveillance activities. At the same time, it is

unclear if this significant investment in resources can be justified. Futile surveillance

activities need to be identified and replaced with evidence based best practice to

ensure meaningful data are generated.

2.8 BENCHMARKING, PUBLIC REPORTING AND FINANCIAL PENALTIES

Benchmarking has been defined as the “process of making comparisons

between organisations with the aim to identify and implement best practice and

improve performance”.59 The purpose of publicly reporting health data is to enable

consumers to make informed choices about their healthcare, subsequently involving

them in the benchmarking process.59,109 It follows then that wherever public

reporting of data is facilitated, so too is benchmarking.

There are essentially two common measures used in healthcare for

benchmarking purposes, process and outcome. Process measures determine

compliance with evidence based practice, whilst outcome measures determine if

desired results have been achieved.110 Infection prevention related examples of

process measures used for benchmarking include hand hygiene, central line insertion

practices and compliance with surgical antimicrobial prophylaxis. Whilst common

outcome measures include incidence of SSI, BSI, CLABSI and VAP.59

Benchmarking and public reporting of HAI related process and outcome data is

now well embedded in the USA, UK and many European countries.59,111,112 A


summary of process and outcome measurements used in four high income countries

are listed in Table 6.

Table 6 - Process and outcome measurements used in four high income countries

Outcome USA England France Germany

IncidenceofallHCAIs P† .. .. ..

IncidenceofBSIduetoMRSA P‡ P,T T§ M

IncidenceofBSIduetospecificpathogensotherthanMRSA P‡ P .. ..

Incidenceofcentral-line-associatedBSI V,M¶,P .. V,T V,M||

RateofisolationofMRSAfromdiagnosticspecimens .. .. P,T ..

MRSAcolonisationrates .. .. .. V

IncidenceofClostridiumdifficileinfection P‡ P,T .. V

IncidenceofSSI V,M¶,P V,P,M** V,T V

Incidenceofcatheter-associatedUTI V,M¶,P .. V V

IncidenceofVAP V,M¶,P .. V V,M||

Incidenceofpost-procedurepneumonia V,P .. .. ..

Deviceutilisationratios V,P .. V V,M||

Prevalenceofantimicrobialresistance V,P V .. V

Processes USA England France Germany

Useofalcohol-basedhandrub .. .. P,T V

Compliancewithhead-of-bedelevationinventilatedpatients P†† .. .. ..

Adherencetocentral-lineinsertionpractices P§§ .. T ..

Compliancewithsurgicalantimicrobialprophylaxisorskindisinfection P§§ .. T ..

V=voluntary reporting. M=mandatory, confidential reporting. P=reported publicly. T=subject to a government target. HCAI=health-care-associated infection. BSI=bloodstream infection. MRSA=meticillin-resistant Staphylococcus aureus. SSI=surgical-site infection. UTI=urinary-tract infection. VAP=ventilator-associated pneumonia. *In at least one federal state. †Used in Pennsylvania until 2007.37 ‡California.85 §As part of a target related to the overall rate of isolation of MRSA from clinical specimens.77 ¶Nevada.86 ||For level 3 neonatal units only.81 **Orthopaedic SSI only.87 ††Missouri.43 ‡‡In England, NHS hospital trusts must show full adherence to a national code of practice,88 which includes an extensive framework of processes and practice. Compliance and inspection reports are made publicly available. §§New Hampshire.89

Modified from Haustein et al.59


In Australia, one HAI outcome measure, and one process measure are now

routinely reported. Following a broad consultative process, annual hospital

identifiable SAB rates have been publicly reported since 2012113,114 Hand hygiene

compliance rates by hospitals are also reported publicly.102 This process indicator

data has now become embedded in the Australian healthcare setting since

commencing in 2009, and has been associated with a reduction of SAB.115

Public reporting of HAI data attracts contrasting opinions. Proponents argue it

promotes transparency, motivates organisations to implement best practice, and

ultimately improves patient outcomes.116,117 It is also suggested that publicly reported

HAI data can be used by consumers to make informed choices when deciding which

hospitals to attend.118 Although there is little evidence of a direct effect on improved

patient outcomes, public reporting has been associated with organisational change

and increased awareness of infection prevention.59 Humphries119 argues that public

reporting of national data as a benchmark not only drives down infection rates in

hospitals within a country, but benchmarking between countries can also serve to

drive improvements.

Opponents argue that mandatory public reporting, particularly of outcome data,

is flawed due to the variability in measurements between hospitals, and the

competition it creates between hospitals places undue pressure on infection control

teams.111,112,120

In Australia, although reporting of SAB and hand hygiene compliance data is

now considered routine, there has been criticism of the lack of validation and

appropriate risk stratification of SAB data.100 Further, the resources required to

sustain the mandated volume of hand hygiene auditing has also been criticised.121

Aware of early concerns relating to public reporting of HAI data, in 2005 the

Healthcare Infection Control Practices Advisory Committee at the CDC, developed a

series of recommendations for policy makers when considering statewide public

reporting of HAIs.109 They included sound epidemiological methods, risk

adjustment, and suggested using a combination of process and outcome data for the

“production of useful reports for stakeholders”.109 Process measures are considered

ideal for public reporting and hospital performance measurement as they do not

require any risk adjustment.109 Outcome measures require appropriate risk

adjustment for comparison, without which they are prone to misinterpretation.109


In a review of public reporting across Europe, Martin et al.111 noted that debate

continues about the utility of public reporting, and doubt as to whether the public are

able to interpret HAI data appropriately. Kiernan122 notes that even if public

reporting is not particularly useful for the public, it captures the attention of

politicians and organisations, which can then translate into action.

Given the momentum of public reporting HAI data internationally, it is

reasonable to assume that it will also continue to expand in Australia. Therefore the

discussion now is not so much about whether or not HAI data should be publicly

reported, but rather how it should be reported, and which HAI data are suitable to

be used as a performance measurement.

In a survey of infection prevention leaders from 34 European member

countries, despite general support for public reporting of HAI data and it being

considered a major driver to strengthen infection prevention in hospitals, there was

strong disagreement about the benefits of public reporting, as well as the type of data

and format of the reports. The expert group conceded that benchmarking needed to

be accompanied by standardised methods and validation, and preferred reporting of

process indicators over outcome indicators.111

By comparison, a more recent review of healthcare performance measures

undertaken by Berenson, Pronovost and Krumholz110 recommended moving from

process measures to outcome measures. Whilst acknowledging the many challenges

of outcome measures as performance indicators, the authors state that process

measures do not always predict outcomes, and often require resource intensive,

manual data collection.110

Acknowledging the concerns when using HAI data as performance indicators,

namely lack of objectivity in applying infection definitions and insufficient risk

adjustment, the Healthcare Infection Control Practices Advisory Committee have

produced recommendations for public reporting of HAI data112 as an adjunct to their

2005 recommendations.109 Whilst not specifically addressing which infections are

suitable for public reporting, the recommendations highlight uniformity of

definitions, acknowledge the difference between surveillance and clinical definitions

may result in discordance, and that the final decision of determination must rest with

infection prevention teams. The recommendations then emphasise validation of

reported data, and recommend clear documentation of decision making in


determining presence of infection, external audit, and a review of any claims

regarding potential under-reporting.112

In summary, the demand for public reporting of HAI data appears to be

increasing. Regardless of any perceived informed decision making benefits for

patients, their use as a hospital performance indicator will likely continue. This

further emphasises the epidemiological surveillance principles for uniformity and

standardisation and appropriate risk adjustment that are key to any good HAI

surveillance program.

2.9 DATA QUALITY

2.9.1 Accuracy

Crucial to any surveillance program is the accuracy of the data. Determining

the accuracy of HAI surveillance data is commonly done using three measures,

sensitivity, specificity and PPV.28 Sensitivity provides a measure of the proportion of

people with true infection who are reported as having an infection, specificity refers

to the proportion of people without an infection who are reported as not having an

infection, and the PPV measures the proportion of people reported as having an

infection who do have a true infection.123

In practical terms, if a surveillance program reports a high sensitivity and a low

specificity, this means that most patients with an infection are captured, but the low

specificity means that many patients who don’t have in infection will be reported that

they do have an infection. The PPV is influenced by the sensitivity and specificity,

and the prevalence of the HAI. A low PPV will result in non HAIs being

investigated, meaning that surveillance resources are being wasted, but may also lead

to the implementation of unnecessary interventions.25

It is recommended that independent, trained observers be engaged to measure

the sensitivity, specificity and PPV of a surveillance program.28 However such

validation studies are expensive to conduct, have inherent methodological difficulties

and often tend to focus on one aspect of data collection.124

Nevertheless, the advent of public reporting, benchmarking, and the potential

for HAI outcome data to be linked to hospital funding, the importance of validity and

reliability of HAI data has increased.120,125,126


Emori127 first measured the accuracy of reporting ICU HAIs to the NNIS

program in 1998 and identified a sensitivity range of 30%-85% and a PPV range of

72%-87% for prospectively identified HAIs.127 A good specificity of over 98% was

reported for all HAIs. Their encouraging conclusion was that when an ICU HAI is

reported it is likely to be a true HAI, patients who do not acquire a HAI are identified

accurately, however because of the low sensitivity reported it is likely that some

HAIs were not being identified. To address this Emori127 recommended the need for

the training of data collectors to facilitate consistent application of infection criteria.

In a review of fourteen validation studies on HAI surveillance, Fabry et al.124

noted large variation in designs, studies were often limited to one or a small number

of facilities and were often focussed on one aspect of surveillance. In the studies

under review, many had similar findings to Emori127 with low to moderate sensitivity

and high specificity. When PPV was estimated they were generally high.124

Whilst large validation studies on national HAI programs are complex to

conduct, in the USA several states have undertaken their own validation studies.

Horan et al.65 from the NHSN reported that at May 2011, at least 15 states in the

USA had conducted validation studies, but again with variable results, supporting

findings from other similar validation studies.128-130

Comparable results have been found in Australian validation studies that have

been performed in two statewide HAI surveillance programs.94,131,132 In a review of

SSI reported from over 4,500 coronary artery bypass surgery procedures under

surveillance as part of the VICNISS, Friedman et al.131 found a PPV of 96%, but a

sensitivity of 55% and a specificity of 100%. When the review was limited to only

those infections that occurred in the sternum, (as opposed to sternum and graft site),

the PPV was 91%, sensitivity 62% and specificity 100%.131 These results implied

that not all SSIs, particularly those not at the sternal site, are identified.

Another study conducted by researchers from VICNISS on ICU CLABSI

surveillance estimated the sensitivity to be 35%, specificity 87% and PPV 59%.132

These findings revealed poor accuracy and consistency from those participating in

ICU CLABSI surveillance in the VICNISS program.132

In Western Australia, a review of all SAB events reported by public hospitals

identified 164 that were classified as healthcare-associated events during 2008.94 On


review of the medical records of each notified case, researchers estimated that the

overall sensitivity was 77% and specificity100%, and in hospitals that did not have

an on-site microbiology service, the sensitivity was only 40%.94

2.9.2 Method Variation

There are other influences that can affect the quality of surveillance data, such

as variation in methodology. In a review of HAI national surveillance programs in

ten countries and one multinational program (HELICS) which all report using the

CDC/NHSN definitions and methods, variation was identified in the type of surgical

procedures under surveillance and length of time of follow up.133 Further differences

were also found in data collection methods, category of staff performing

surveillance, prospective and retrospective data collection methods, data sources, and

the inclusion of routine post discharge surveillance as a routine part of case

finding.133 It was also noted that validation of data did not occur on a regular basis.

This, together with the differences identified between the programs, contributes some

uncertainty about the quality of the data and also limits the ability to make

comparison of rates between different programs, despite being based on the same

methodology.133

In a cross sectional study of 126 hospitals designed to characterise variation in

surveillance methods and application of HAI definitions, Keller et al.134 used a series

of clinical vignettes to measure variation amongst infection prevention staff. Despite

all sites participating in and following NHSN methods, only 61% responses correctly

identified a HAI. Interestingly, 24% of those collecting HAI data did not have a

clinical background, which on multivariate analysis was an independent predictor of

an incorrect application of the HAIs definitions.134

As mentioned earlier in the review of the German KISS program, researchers

recently demonstrated a sensitivity of 85.7% by measuring the sensitivity and

specificity of 189 surveillance personal through a series of clinical vignettes

presented over a period of three years.74 Accuracy was positively associated with

surveillance experience and higher education levels.74

In a large ethnographic study across 17 ICU’s all participating in the same HAI

surveillance program, Dixon-Woods et al.135 illustrated broad variation in data

collection systems and the application of infection criteria. Dixon-Woods et al.135


concluded that HAI data reported and used as hospital performance indicators clearly

misrepresented real infection rates. Rather than any deliberate attempt to game data,

the study identified that those involved in surveillance occasionally disagreed with

the standardised definitions and applied local interpretations largely because of

inequity aversion i.e. a dislike of unfair outcomes.135 This is an important finding

particularly if penalties are associated with infection rates.

In a cross sectional survey of 106 hospitals participating in mandatory

orthopaedic surgical site surveillance in the UK, Tanner et al.136 identified variation

in a number of areas including definitions applied, and the extent of post discharge

follow up. Not surprisingly it was noted that those who conducted inpatient

surveillance alone, and those who conducted inpatient surveillance and readmission

surveillance reported significantly lower SSI rates than those who also undertook

post discharge surveillance.136 Furthermore, the methods used to identify HAIs on

readmission and through post discharge surveillance varied enormously, also

affecting the reported rates.136

In an era of increased public reporting and performance measurement,

validation studies highlight the limitations in interpreting HAI data, and despite

participating in networked surveillance programs, variations in surveillance methods

between facilities continue. It is reasonable to expect similar findings amongst

Australian facilities, but studies are lacking that describe and measure this variation,

and if such variation has any impact on reported HAI rates.

Data accuracy and surveillance methods play a major role in the reliability of a

surveillance program. Misinterpretation of surveillance definitions and inconsistent

surveillance methods are the primary reasons for misclassification of infections.137

Also influencing the quality of the data include who collects the data, who applies

the definitions, the skill of those collecting the data, the data sources, the intensity of

case finding and the activities under surveillance.

It remains unclear what level of accuracy is acceptable, and what level of

resources and effort can be justified to provide high levels of accuracy.

2.10 DISCRETE CHOICE EXPERIMENTS

Data from HAI surveillance can serve several purposes, and will be used by

different stakeholders who possibly have different preferences on what they consider


to be a good HAI surveillance program. Discrete choice experiments are an emerging

method being applied in a variety of situations to identify preferences.

Discrete choice experiments (DCE) have their origins in mathematical

psychology, and have been used in marketing, transport, environmental economics

and more recently have been commonly used in health economics.138,139 DCEs are a

quantitative attribute based survey method, and can be used to elicit preferences for

healthcare products, interventions, services, policies or programs.140-142 They are a

form of stated preference measurement where participants say what they would

prefer rather than being observed what they prefer, such as occurs in revealed

preference measurements.143,144

Typically, DCEs offer participants hypothetical scenarios that vary along

several characteristics or attributes. The participants are required to choose one

scenario in favour of the other.145 The technique has been used to value health

outcomes, investigate trade-offs between these outcomes and recently to estimate

utility weights of quality adjusted life years.141

DCEs have been described as the simplest of choice techniques.146 The low

cognitive complexity required to participate in a DCE is considered a big advantage

when compared with other choice techniques.146 DCEs are considered superior to

ranking and rating methods as they provide quantitative data on the strength of

preferences and trade off, and probability of take up.140

There is general agreement that there are distinct stages, or components of a

DCE. Lancsar and Louviere142 identify three components; an experimental design

used to generate choice data, a discrete choice analysis to estimate preferences, and

use of the resulting model to obtain welfare measures and policy analysis. Others

propose five stages which include; identification of attributes, identification of levels,

the experimental design, data collection and data analysis.140,146 Each of these stages

are addressed below.

2.10.1 Identification of attributes and levels

The DCE is characterised by a proposal of alternatives of the state of the good

described. These descriptions are called attributes of the alternative.146 Each attribute

consists of levels, which offer variation in the alternatives of the choice sets.146 A

simple example can be demonstrated when deciding between two job choices based


on location, salary and opportunity (attributes). Job A is located 5 kms away, pays

$20/hr and offers good opportunity for promotion. Job B is located 20 kms away,

pays $27/hr and offers limited opportunity for promotion. So the levels for the

attribute of location are 5kms and 20kms, for salary the levels are $20 and $27, and

for opportunity the levels are good and limited.

Attributes and levels can be qualitative or quantitative and are usually

identified through literature reviews and qualitative research.142 Whilst there is no

one way to define an attribute, it is generally agreed attributes need to be relevant to

the requirements of the policy makers, plausible, meaningful and important to the

respondents.142,146

The number of attributes and levels are an important consideration when

designing a DCE. It is generally advised that the number of attributes be kept to a

minimum as the higher the number the more choices will be generated.140 For

example, if a DCE has 6 attributes each with 3 levels, the total number of choices

will be 36[3x3x3x3x3x3=729] resulting in 729 possible combinations. Although

there is no limit on the number of attributes, a 2012 review of 144 DCE studies

identified that 70% had 4-6 attributes.141 Even when attributes are kept to a

minimum, the number of choices can still be too many to present to respondents.

This issue is managed in the next stage.

2.10.2 Experimental design

Once the attributes and levels have been established, the choices with different

combinations of attributes and levels must be constructed. If all the combinations of

choices (also called the full factorial) are too great to present to respondents, a

statistical design theory is commonly used to draw independent samples of choices

from the full factorial.147 The resultant sample is called a fractional factorial. The aim

with a fractional factorial design is to ensure the properties of the full factorial are

maintained and the effects of interest can be estimated as efficiently as possible.146

Lanscar and Louviere142 argue that fractional factorial designs should be avoided and

the largest possible design should be implemented. They suggest putting

combinations into different blocks and randomly assigning respondents to different

blocks.142


Whilst acknowledging that a full factorial design has attractive statistical

properties, Kjaer explains that in reality the full factorial can only be use in very

small experiments, and the practical solution is to use fractional factorial technique,

even though there will be loss of statistical information.140,146 When using a

fractional factorial method, design efficiency principles; level balance, orthogonality,

minimal overlap and utility balance must be considered to optimise design

efficiency.140,146

In the 2012 review by De Bekker-Grob et al.,141 all 114 (100%) of the studies

reviewed used a fractional factorial design. This compared with 74% from an earlier

review of 34 studies in 2003.147

2.10.3 Data Collection

To administer a DCE, choice sets comprising two or more alternatives which

vary in attribute levels are presented to respondents who are required to select one

alternative.145 Standard pilot tests of the choice sets are required to test respondents

understanding of choices and levels, appropriateness, complexity and timing.142

When presenting the choice sets, careful consideration must be given to the

contextual introduction.145,146 A major consideration is if an “opt out” alternative is

provided. The omission of an “opt out’ alternative forces the respondent to select an

option that might not be a clear preference and so introduces bias.140 The inclusion of

an “opt out” alternative is appropriate in many situations but it is important that

respondents understand that selection of the “opt out” alternative indicates they are

happy with the current status.140 One clear issue with providing an “opt out”

alternative is that if the respondent feels that the choice task is too cognitively

demanding they may select the “opt out” to prevent making difficult choices,146

introducing another bias.

It is also recommended that “warm up” choices be offered to familiarise

respondents with the method. This also provides some internal consistency testing by

constructing choices where one alternative is clearly dominant over another.140

The survey can be delivered in a variety of ways; face to face interviews,

telephone interviews, mailed questionnaires, internet/email or a combination,146 each

with their own advantages. Hand delivered or mailed self completed questionnaires

have been used most commonly,147 however the use of computer based surveys is


expected to become more widespread given its ability to collect large amounts of

data at little expense.146

2.10.4 Data analysis

Analysis of data derived from the DCE is based on the random utility model.

To estimate the strength of the preferences and levels selected by respondents,

several models can be used including random effects probit and logit, conditional

logic and mixed logic.140 Random effects probit has been found to be the most

commonly used model.141 Variation between preferences based on respondents’

characteristics (e.g. profession, qualifications, work location) can also be estimated.

The analysis can be used to determine which attributes are most important and

preferred by the participants, and the strength of this preference in comparison to

another, and how willing respondents are to trade between attributes.148

Whilst use of DCE for eliciting patient and clinician preferences for healthcare

prevention, testing and treatment options is well described,141 the use of DCE in the

infection prevention setting has not previously been described.

2.11 IMPLEMENTATION SCIENCE

It is not within the scope of this PhD to comprehensively review literature on

implementation science, but rather explore its potential application in infection

prevention, particularly considering a new surveillance program, by introducing

some major concepts.

Despite research evidence to support specific practices in health care, many fail

to translate into improved patient outcomes.149 Examples of this include that despite

clear evidence on reducing HAIs, universally hand hygiene compliance rates remain

relatively low, and the uptake of infection prevention ‘bundles’ to prevent central

line associated blood stream infections remains low.150 To address the gap between

research and practice, healthcare is turning towards implementation strategies.

Implementation science is defined as “the study of methods to promote the

integration of research findings and evidence into healthcare policy and practice”,151

and aims to explore health care worker behaviour with respect to adoption and

maintaining interventions. Health researchers are now being encouraged to not only


examine the endpoint outcomes of their interventions, but to also consider the

implementation of any intervention.149

Specific to infection prevention, Pronovost et al.152 developed a model for

translating research into practice, which when applied successfully, resulted in a

large and sustained reduction in central line associated bloodstream infections.153 The

model focuses on systems, engagement and ownership, support, adaptation and

collaboration.152 It is acknowledged that successful application of the model requires

substantial resources and best suited to large scale projects.152 Pronovosts’ model

underpinned a landmark study in implementing an evidence based bundle which

resulted in a significant and sustained reduction of catheter related bloodstream

infections across 103 intensive care units.153

Various implementation theories have been published over the last decade,

however not all can be generalised and many have overlapping constructs.149 In order

to identify which frameworks may be suited to specific situations, Damschorder et

al.149 reviewed 19 different implementation theories described in the literature, and

from that established the Consolidated Framework for Implementation Research

(CFIR). The CFIR comprises five domains: intervention characteristics, outer setting,

inner setting, characteristics of the individuals involved, and the process of

implementation. Within each of these domains are a number of different constructs

that may or may not be applicable to certain interventions. It offers a pragmatic

foundation to assist in the understanding of the various influences that must be

considered in implementation.149

An emerging implementation theory being used in health is the Normalisation

Process Theory (NPT) developed by May et al.154 NPT comprises four constructs,

Coherence, Cognitive participation, Collective Action and Reflexive Monitoring.

The attraction of the NPT is its emphasis on complex healthcare interventions, and

its utility in the planning and development stages of the intervention, as well as

embedding and evaluating the intervention.155 It also has a strong emphasis on key

stakeholder engagement and relationships between stakeholders. 155,156

Clearly the development and implementation of a national HAI surveillance

program is a complex intervention. It involves many stakeholders including

consumers, healthcare workers, clinicians, executive staff, and government staff.

Issues regarding engagement, education, new or modified practices, and outcomes all


warrant careful consideration. Many of these aspects are potential barriers and

enablers of a new HAI surveillance program. It is therefore crucial to understand

exactly what these barriers and enablers are, and therefore include in an

implementation strategy.

2.12 CONCLUSION

This review has explored the literature on HAI surveillance programs and key

issues associated with them. Particular attention has focussed on the utility of HAI

surveillance and the benefits of national HAI surveillance in improving our

understanding of the epidemiology of HAIs. This ultimately informs infection

prevention interventions and reduces the incidence of HAI. Although it is difficult to

precisely measure the effect of surveillance alone, reductions in HAIs following

implementation of surveillance programs is evident from a broad range of literature.

The review has revealed a number of gaps that need to be addressed when

considering an Australian national surveillance program. These include uncertainty

regarding:

• exactly what surveillance activities and methods are being undertaken,

• suitability of any existing programs for expanding as a national program

• the extent to which current practices reflect best practice

• how well the data is being used to implement infection prevention strategy

• the accuracy of the existing data

• how much surveillance training is delivered

• agreement levels in identifying and classifying HAIs

• the suitability of data for comparing facilities and benchmarking

The literature relating to public reporting and data quality highlighted the

momentum towards publicly reporting of HAI data. This means it is imperative that

data quality be constantly monitored to add credibility to the surveillance program

particularly in light of HAI data being used for hospitals performance measurements.

Further, although the CDC guidelines for evaluation public health surveillance

programs highlight several attributes which provide a framework for evaluation, little

is known about barriers and enablers when it comes to developing and implementing


a surveillance program, and what the characteristics of well established programs are.

The fact that Australia currently doesn’t have a program can be used as an advantage

as it provides the opportunity to identify what stakeholders want from a national

program.

To assist in filling these gaps, literature about the proposed method for the

second study, a discrete choice experiment, was presented. Although DCEs have

been used in health settings previously, this will be the first time it will be applied in

an infection prevention setting.

To finish the review, the relevant literature regarding implementation science

and infection prevention was introduced as an important consideration in the

development of a national HAI surveillance program. An appropriate implementation

strategy is crucial to ensure appropriate translation of research to practice.

Chapter 3: The research questions and study design 53

Chapter 3: The research questions and study design

Four research questions and two studies form the basis of this work. The first

two questions were specific to the first study, and questions three and four were

specific to the second study.

Each of the questions are listed below with an explanation as to why they are

important. Following the questions for each study, I have provided a description of

the study design that was used to answer the questions.

The overall approach to answer the questions was a mixed methods design.

Mixed methods research involves both qualitative and quantitative approaches, either

in a single study or in multiple phases of a program of study.157 Mixed methods has

become increasingly popular as its strengths offsets the weaknesses of both

qualitative and quantitative methods,157 and provides a more rounded understanding

of the issue at hand than either qualitative and quantitative methods alone.

Whilst the first study in this PhD comprised a cross sectional survey generating

quantitative data, the discrete choice experiment, the method used in second study,

used a mixed methods approach where qualitative data were used to inform the

design of the experiment which was analysed using quantitative methods. This is

explained in more detail in Chapter 8.

A mixed methods approach was appropriate for this body of research as HAI

surveillance is a complex process. As well as collecting and analysing data, the

establishment of successful surveillance programs require an understanding of how

people collect the data, what resources are required, what are the enablers and

barriers, what level of support is required, how the data are used and how a new

or revised program is implemented.

3.1 RESEARCH QUESTION 1

What are the similarities and differences between existing HAI surveillance



The origins of this question come from the knowledge that not all states and

territories have coordinated surveillance programs, so it is important to understand

how similar the existing programs are, and what is occurring where there is no

coordinated program. If there are no differences in the coordinated programs, or if

differences are only minor, then a national program may only require coordination of

current activities. However, if the differences are broad and major, then

consideration would need to be given to a more comprehensive review and

development of a new program.

This question is specific to processes within a surveillance program including

definitions used, data sources, collection, analysis and reporting. The answer to this

question will identify any gaps in current practices and provide an understanding of

the effort required to develop a national program.


What level of agreement exists in the identification of HAI between those

participating in HAI surveillance, and are there any factors that influence agreement

level?

Several validation studies have described poor to moderate agreement amongst

those involved in surveillance when it comes to identifying HAIs in Australia. 94,131,132,158 The answer to this question will build upon knowledge gained from

question 1 that looks at processes, and attempts to quantify differences by using

clinical vignettes. Outcome from the vignettes will provide information on the effect

of any differences identified in answering question 1 has on outcome data. It will

improve our understanding on the quality of data currently being reported, and

further contribute knowledge in identifying what work needs to be done to develop a

national program.

3.3 STUDY 1 – CROSS SECTIONAL SURVEY: CURRENT AUSTRALIAN HOSPITAL PRACTICES IN HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE

The overall aim of this study was to improve our understanding of the current

status of HAI surveillance practices in Australia. To do this, a cross sectional survey

was conducted with infection prevention staff who undertook surveillance. Previous

surveys of Australian infection prevention staff have not provided the level of detail


this survey will collect, with the most recent survey being completed in 2008.103,104

Novel to this survey is the inclusion of clinical vignettes. The findings from this

study answered research questions 1 and 2.

3.3.1 Study 1 design

An online survey was constructed which sought data on the characteristics of

infection prevention staff who undertake surveillance, their surveillance practices

and the characteristics of the environment in which surveillance is undertaken.

Within the survey, a series of seven clinical vignettes describing potential HAIs were

included. The vignettes sought to explore agreement of HAI identification,

classification and calculation of rates. The vignettes were constructed in

collaboration with infection prevention experts from a jurisdictional surveillance

program. The final survey consisted a total of 88 items, however no respondents

were required to answer all as the logical design guided participants to questions that

were specific to their work environment and the type of surveillance they undertook.

Recruitment of participants used the snowballing method through the

Australasian College of Infection Prevention and Control (ACIPC) list server called

“Infexion Connexion”, which over 500 ACIPC members subscribe to. List server

subscribers received an email describing the study with a link to the survey.

Recipients were asked to forward the email on to all involved in surveillance.

Data were analysed using Stata, version 13 (Stata Corp, College Station,

Texas). The chi square test was performed to compare proportions between

groups, and Kruskall-Wallis to test for influence of State and Territory.

For the analysis of the vignettes, univariate logistic regression was used to

identify any characteristics that influenced agreement levels. A multivariable Poisson

model of the total number correct was developed from characteristics identified in

the Poisson univariate analysis. Further analysis was undertaken to assess any

multicollinearity.


What are the key components of successful centrally coordinated HAI



As I have identified from the literature, there are many well established

international HAI surveillance programs. When considering a new national program,

it is crucial to explore existing large surveillance programs to gain knowledge on a

broad range of issues such as how they commenced, implementation characteristics

associated with successful programs, barriers and enablers for engagement and

implementation, resource requirements, data usage, and strategies for long term

sustainability. Knowledge gained was used to inform recommendations for a national

program in Australia. Importantly, answers to this question were used in the

construction of the discrete choice experiment.


What are the preferences and priorities of key stakeholders when considering a

national HAI surveillance program?

The final question is specific to the Australian environment. There are many

elements involved in a national surveillance program including type of infections

under surveillance, data collection method, surveillance staff skill and competency,

and how the data are used. Even though an ideal program may be devised in

theory, practical success will largely depend on stakeholders belief in the value

of the program. Therefore this question attempts to identify what stakeholders want

from a national surveillance program, which elements of the program they

consider most important and those they consider least important.

3.6 STUDY 2 – PREFERENCES FOR A HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE PROGRAM USING A DISCRETE CHOICE EXPERIMENT

The overall aim of this study was to identify what type of national HAI

surveillance program stakeholders in Australia want. To do this we undertook a DCE

to elicit stakeholder preferences for a national HAI surveillance program. DCEs have

been used in health settings previously, however its use in an infection prevention

setting was novel. The outcomes of this study answered research questions 3 and 4.

3.6.1 Study 2 design

A crucial step in the development of the DCE was the identification of key

characteristics of a surveillance program. Identifying attributes for a DCE commonly

requires a literature review and the application of qualitative methods such as


interview or focus groups.142 For this part of the study, a review of the literature was

undertaken, and a series of semi-structured interviews were conducted with three

leaders from Australian statewide programs, and four from international HAI

surveillance programs. Participants were selected because of their leadership and

experience in developing, implementing and maintaining large surveillance

programs. Qualitative analysis of the data generated attributes and levels for

inclusion in the DCE.

To construct the DCE, advice was provided by Professor Julie Ratcliffe,

Professor in Health Economics, and Dr Gang Chen, Research Fellow, at Flinders

University, Adelaide. Professor Ratcliffe and Dr Chen have strong backgrounds in

DCEs and have conducted research using DCEs in a variety of settings.

The identification of five attributes and their corresponding levels resulted in a

total of over 23,000 possible choice questions. Clearly this was too many to include

in a survey. Therefore a D-efficient design was used to reduce the number of choice

scenarios into a more pragmatic number. A series of hypothetical surveillance

program scenarios were created where participants were required to choose one

surveillance program over another. The model consisted of two blocks of 12 pair

wise choice questions. To test for internal consistency, one choice question was

duplicated in each block resulting in 13 in each block. Participants were randomised

into one of the blocks.

To allow for subgroup analysis, demographic data such as gender, age,

qualifications and occupation was collected. A series of attitudinal questions relating

to HAI surveillance were also included.

A total of 184 participants were purposively selected to participate based on

their senior leadership role in infection prevention in Australia. Reminder emails

were sent out to encourage participants to complete the DCE over a five week period.

Data was analysed using Stata, version 13 (Stata Corp, College Station, Texas)

and a mixed logit models applied to identify and measure the strength of the

preferences by generating coefficients.

Although novel, the application of a DCE for constructing a HAI surveillance

system was favourable for a number of reasons:


- HAI surveillance systems have several attributes that have been

identified in the literature, though others may exist generating new

knowledge

- The influence of different attributes on the outcome may vary

- It is unknown which attributes of a HAI surveillance system are

considered more important than others, or if those using the HAI

surveillance system may be willing to trade off between different

attributes depending on their priorities

- DCEs have been used for priority setting frameworks where decision

makers are required to manage competing demands with limited

resources.159-161 Given that surveillance of HAIs is just one of the many

activities that must be resourced from the infection prevention budget,

which itself competes with other hospital services, the DCE provides

new knowledge in this setting.

By offering choices of attributes that make up a HAI surveillance system to

stakeholders, attributes that are considered most important were identified, the

strength of these weightings and the willingness of stakeholders to trade off attributes

in favour of others was also identified.

This has provided crucial information in constructing a HAI surveillance

program, and was used to support the evidence based recommendations for a national

HAI surveillance program.

3.7 ETHICS AND LIMITATIONS

Both studies were considered negligible/low risk research. Three ethics

approvals were granted. The first for the cross sectional survey, the second for the

semi-structured interviews, and third for the DCE. Ethics approval was granted from

the QUT University Human Research Ethics Committee (see Appendices B, C and

D)

Chapter 4: Healthcare-associated infection in Australia 59

Chapter 4: Healthcare-associated infection in Australia

4.1 INTRODUCTION

To help inform the aims and design of the first study on the current

surveillance practices of infection prevention staff in Australia (presented in

Chapters 5 and 6), a scoping review was undertaken of existing statewide Australian

surveillance activities, specifically observing the type of infections under

surveillance and the level of coordination of activities that occurs, and well

established international HAI surveillance programs

This scoping review identified disparity across many aspects of surveillance in

Australia. Some statewide surveillance programs have been introduced over a period

of time and evolved at different rates. Although some common factors were

identified, those that do have statewide programs mandate surveillance on a different

range of HAIs.

This review highlights the benefits of a national surveillance program as

demonstrated in international programs, identifies disparity in existing Australian

HAI surveillance, and outlines work necessary to establish a framework for a

national HAI surveillance program in Australia.

The findings of this review were published in the Australian Health Review

journal.


Statement of Contribution of Co-Authors for Thesis by Published Paper

The authors listed below have certified* that:

• they meet the criteria for authorship in that they have participated in the

conception, execution, or interpretation, of at least that part of the publication

in their field of expertise;

• they take public responsibility for their part of the publication, except for the

responsible author who accepts overall responsibility for the publication;

• there are no other authors of the publication according to these criteria;

• potential conflicts of interest have been disclosed to (a) granting bodies, (b)

the editor or publisher of journals or other publications, and (c) the head of

the responsible academic unit, and

• they agree to the use of the publication in the student’s thesis and its

publication on the Australasian Research Online database consistent with

any limitations set by publisher requirements.

In the case of this chapter:

Publication title and date of publication or status:

___________________________________________________________________

Contributor Statementofcontribution*

PhilipLRusso Study design, data collection, data analysis,

manuscriptwritingSignature

Date

AllenChengAdvisedonstudydesignandanalysisandmanuscript

preparation

MikeRichardsAdvisedonstudydesignandanalysisandmanuscript

preparation

NicholasGravesAdvisedonstudydesignandanalysisandmanuscript

preparation

LisaHallSupervisedstudydesign,administration,analysisand

manuscriptpreparation

11/7/2016


Principal Supervisor Confirmation.

I have sighted email or other correspondence from all Co-authors confirming

their certifying authorship.

Name

Signature

Date

Dr Lisa Hall

11/7/2016

QUT Verified Signature


4.2 PAPER ONE: “HEALTHCARE-ASSOCIATED INFECTIONS IN AUSTRALIA: TIME FOR NATIONAL SURVEILLANCE”

Russo PL, Cheng AC, Richards M, Graves N, Hall, L. Healthcare-associated

infections in Australia: time for national surveillance. Australian Health

Review, 2015; 39(1), 37-43

4.2.1 Abstract

Objective: Healthcare associated infection (HAI) surveillance programs are

critical for infection prevention. Australia does not have a comprehensive national

HAI surveillance program. The purpose of this paper is to provide an overview of

established international and Australian state wide HAI surveillance programs and

recommend a pathway for the development of a national HAI surveillance program

in Australia.

Methods: Examine existing HAI surveillance programs through a) literature

review, b) review of HAI surveillance program documentation such as websites,

surveillance manuals and data reports and c) direct contact with program

representatives.

Results: Evidence from international programs demonstrates national HAI

surveillance reduces the incidence of HAIs. However, the current status of HAI

surveillance activity in Australian States is disparate, variation between programs is

not well understood, and the quality of data currently used to compose national HAI

rates is uncertain.

Conclusions: There is a need to develop a well structured, evidence based

national HAI program in Australia to meet the increasing demand for validated

reliable national HAI data. Such a program could be leveraged off the work of

existing Australian and international programs.

4.2.2 Introduction

A healthcare associated infection (HAI) is an infection that occurs as a result of

a healthcare intervention.1 Historically called a “nosocomial” infection, meaning

“hospital acquired”, the term “healthcare” is now used in recognition that today

much healthcare occurs outside a hospital. Examples of HAIs are bloodstream


infections commonly caused by the presence of an intravenous device, or an infected

surgical wound following a surgical procedure. Many HAIs result in significant

morbidity and mortality.2 It is estimated that in Europe and North America between

12-32% of HAI bloodstream infections result in death.3 In Australia, it has been

suggested that 175,000 HAIs occur annually,4 but the exact figure is unknown.

Surveillance is defined as “the ongoing, systematic collection, analysis, and

interpretation of health data essential to the planning, implementation, and evaluation

of public health practice, closely integrated with the timely dissemination of these

data to those who need to know”.5 It is a fundamental component of modern

healthcare, demonstrated by the recently released National Safety and Quality Health

Service Standards for Australian Hospitals that include nineteen criteria on the

prevention and control of HAIs, and specifically mandate HAI surveillance.6

By its very existence, infection prevention implies that HAIs are preventable.

Whilst it is challenging to quantify the preventable proportion of HAIs, there is

agreement that a significant proportion, and probably the majority of HAIs are

preventable.7,8

The purpose of HAI surveillance is to provide quality data which can act as an

effective monitoring and alert system.9 The aim is to reduce the incidence of

preventable HAIs. A successful HAI surveillance program must be

epidemiologically robust, valid, accurate, timely, useful, consistent and practical. 5

Effective surveillance will deliver information to key stakeholders at all levels

to inform decisions. The simple act of collecting HAI data will not reduce HAIs,10

rather data must stimulate action and drive improvement. HAI surveillance systems

establish a baseline rate of infection which can then be used to detect clusters or

outbreaks, identify problems, evaluate prevention and control measures, generate

hypotheses concerning risk factors, guide treatment and prevention strategies, make

comparisons with other facilities, inform planning, and ultimately, reduce the

incidence of HAIs.11-14

Australia is one of the few developed countries without a national HAI

surveillance program. Unlike the United State of America, (USA), the United

Kingdom (UK) and many European countries who have supported and maintained

national HAI surveillance programs for decades, Australia lacks well structured


processes to produce high quality national HAI data. In the UK and some state in the

USA, reporting of some HAIs has been mandated by law.15,16 Such international

programs enable research on the epidemiology of HAIs, which also leads to

enhanced and refined surveillance processes improving the quality of HAI data now

commonly reported in the public domain.17,18 In the USA, hospitals are financially

penalised on the occurrence of events, many of them HAIs, which are deemed

preventable.19

Recent activity in Australia to develop national guides for the implementation

of surveillance on Staphylococcus aureus bloodstream (SAB) infection, Clostridium

difficile infection (CDI) and central line associated bloodstream infection

(CLABSI),20 is positive, but there is still much work to be done to improve our

knowledge on the epidemiology of HAIs across Australia.

The purpose of this paper is to review well established international HAI

surveillance programs and their impact on HAI rates, provide an overview of current

Australian HAI surveillance programs, and recommend a way forward to develop a

national HAI surveillance program. This review focuses on surveillance of infections

in large acute public healthcare facilities, where the risk and consequences of

infection is higher, due to the nature of the care that takes place.

4.2.3 Methods

A review of current literature on national HAI surveillance programs was

undertaken to identify existing national programs. The MEDLINE database from

1966 to 2013 was utilised by searching these key terms: cross infection, nosocomial

infection, nosocomial infection rates, healthcare associated infection, healthcare

associated infection rates, surveillance, infection prevention, infection control.

Australian jurisdictional and national programs from overseas that were best

described in the literature were then selected for review. To gain further information

on international programs a review of program websites, surveillance manuals,

annual reports and data reports (where available) was performed, and program

representatives Germany, UK, Spain, Scotland and the Netherlands were directly

contacted for clarification. For Australian surveillance activities, information was

sourced from program websites and manuals, and representatives from each program

were contacted for confirmation and clarification.


4.2.4 Results

International HAI Surveillance Programs and Impact

The longest running national HAI surveillance program is the Centers for

Disease Control’s National Healthcare Safety Network (NHSN) in the USA. 21

Originally called National Nosocomial Infection Surveillance (NNIS) system, it

commenced in 1970 with 62 hospitals voluntarily participating.21 In 2005, the

program expanded to include co-existing healthcare worker exposure and renal

dialysis surveillance programs to create the NHSN.22 The definitions and

methodology developed by the initial NNIS program have been largely adopted by

many programs internationally.18

In the USA, a review of HAI rates in hospitals participating in NNIS between

1990 and 1999 demonstrated decreases in urinary tract, respiratory tract and

bloodstream infections monitored in ICUs.23 Reductions in bloodstream infection

rates varied from 31-44%. The authors acknowledge that other explanations, such as

a national effort to reduce HAIs may have also influenced these results.24

Other well described national HAI surveillance programs include the

Krankenhaus-Infektions-Surveillance-System (KISS) in Germany,25 the UK,6

Spain27,28 France,29 Scotland,30 and the Netherlands.31

In Germany, Gastmeier demonstrated significant reductions in HAI of between

20-30% over a three year period in hospitals participating in the KISS program.

Significant reductions of 24-57% in surgical site infections (SSI) have been

demonstrated in the Netherlands and Denmark following the introduction of national

surveillance.32 A review of SSI in France over six years following the introduction of

surveillance demonstrated a 30% reduction in the first three years with an ongoing

decrease in infection rates over the next three years.29 In the Netherlands, SSI

surveillance commenced in 1996 as a component of the new national HAI

surveillance program “PREZIES”. Geubbels et al claim that surveillance led to a

decrease in risk of SSI of 31% when measured four years from the introduction of

the program, and of 57% in its fifth year.33

Current issues with international programs

A recent review of international surveillance programs noted that despite being

similarly structured and following international recommendations and standardised


definitions, widespread variation existed between programs.34 Grammatico-Guillon

et al identified variation in data collection methods and quality due to differences in

category of staff performing surveillance, variable data sources, prospective and

retrospective data collection, and the presence of routine post discharge

surveillance.34 It was also noted that validation of data did not occur on a regular

basis.34

Traditional surveillance methods are time consuming, application of definitions

is subject to interpretation and identification of cases is dependent on effort.35

Infection prevention staff spend up to 45% of their time undertaking surveillance.36

As Perl and Chaiwarth note, essential to the future of HAI surveillance is the

integration of rapidly developing surveillance technologies. Electronic HAI

surveillance systems, when compared to traditional surveillance methods, can reduce

time spent by up to 65%, and improved sensitivity or specificity can be

demonstrated.13 Recent studies have highlighted the advantages of using modern

technology such as increased accuracy of hospital rankings when computer

algorithms are used.37,38

Attempts have been made to use administrative code data (ACD) to identify

HAIs, but a recent systematic review found the use of ACD continues to demonstrate

only moderate sensitivity. Goto et al recommend that ACD may be useful as a factor

within an algorithm, but should not be used as the primary case finding method.3

The use of automated technology and electronic data as an aid to traditional

HAI surveillance methods is well described.39 Automated systems ensure consistent

application of surveillance definitions, significantly reduce the burden of data

management and provide improved sensitivity and specificity.39

The current situation in Australia

Of Australia's eight States and Territories, several States implemented HAI

surveillance programs during the 1990s and 2000s, using infection definitions based

on those developed by NNIS.40-43

In December 2008 the Australian Health Ministers’ Conference endorsed

jurisdictional level surveillance of SAB and CDI. This was followed in 2009 by

further endorsement of the Australian Commission on Safety and Quality in Health

Care (ASQHC) recommendation that hospitals routinely monitor SAB and CDI.


A comparison of surveillance components considered mandatory in existing

state wide programs is demonstrated in Table 1. There is consistency in Intensive

Care Unit CLABSI, and SSI surveillance of knee and hip replacement surgery across

the larger States. However there is inconsistency between mandatory surveillance

components, definitions, and post-discharge surveillance. Not included in the table

due to the large degree of variation, is inconsistency identified with regards to multi-

resistant, or significant, organism surveillance. Whilst some States report multi-

resistant, or significant, organism surveillance programs, others do not. Peculiar to

each jurisdiction is the intensity of surveillance undertaken with respect to the type

of organism, infection or colonisation, site, hospital onset or healthcare associated,

and requirements for the data to be notified at a State level. In Tasmania and Western

Australia, notification of SAB is mandated.

Anecdotally, it is reported that many hospitals, networks or regions undertake

HAI surveillance above and beyond the mandatory requirements of their jurisdiction.

Examples include individual hospitals performing targeted surveillance in unique,

high risk populations, or in response to perceived problems. The extent of this

activity and the quality of data is unknown.

4.2.5 Discussion

This review has identified well established international HAI surveillance

programs with evidence of a reduction of HAI rates, whilst highlighting some of the

major gaps in HAI surveillance activities undertaken across Australia.

The evolution of HAI surveillance programs in Australia has been fragmented.

Whilst some of the jurisdictional programs are now well established and embedded

into routine healthcare safety and quality processes, it could be argued that without

clear national direction, the programs evolved in a competitive environment. This

has resulted in variation among methods,44 duplication of effort and a limited ability

to collate and analyse data at a national level. Potential differences between

programs deserving of further research include level of training of those involved in

HAI surveillance, data analysis and reporting.

Unlike international programs, there is a lack of evidence demonstrating the

effect of these state wide programs on HAI rates over time, although two of these

programs have published validation studies.43,45-47


Current ASQHC strategies such as the National Surveillance Initiative 20 have

promoted and supported increased jurisdictional collaboration. The development of

national definitions for SAB and CDI have been followed by identifiable hospital

SAB data regularly published on the MyHospitals website.48 Whilst concerns

regarding the validity and lack of risk adjustment49-51 need to be addressed, the work

of the ACSQHC HAI program continues to provide direction for further national

HAI surveillance activity. The recently completed ACSQHC report on Antimicrobial

Resistance and Antibiotic Usage adds to the drive for better national HAI

surveillance processes.52

The Benefits of an Australian HAI surveillance program

As key stakeholders, consumers, healthcare workers and policy makers will all

benefit from a well constructed national HAI surveillance program. Consumers

clearly stand to gain from improved quality of care resulting in reduced risk of

acquiring a HAI. Healthcare workers will benefit from improved efficiency in

surveillance processes that could relieve the current burden of data collection, and

the development of national education programs for those undertaking HAI

surveillance to be uniformly accessible across Australia. The ready availability of

benchmarking data will assist hospitals in appropriately allocating resources to

infection prevention activities. Meaningful national comparisons of HAI rates by

hospital size, type, specialty and potentially by specific patient risk factors will

provide important contextual data across Australia. A comprehensive HAI

surveillance program will provide analysis and interpretation of data, and drive

investigation into unusual findings. This will lead to a sharing of information and

through informed policy making, will ultimately benefit patient care.

The ability to describe the epidemiology of HAIs will improve our

understanding of the difference between populations. Detailed data will enable the

identification of problem areas that may require more infection prevention resources,

and similarly highlight successful interventions which could act as role models and

inform policy on State and national infection prevention initiatives. It will provide

the foundation for local research initiatives to improve the safety and quality of

healthcare to patients.


Where to from here?

In 2010, major infection prevention bodies including the Association for

Professionals in Infection Control and Epidemiology, the Society for Healthcare

Epidemiology of America, the Infectious Diseases Society of America, and the

Centers for Disease Control and Prevention proposed four pillars for the elimination

of HAIs, the fourth of which was “data to target prevention efforts and measure

progress”.53 To deliver timely and high quality data, they recommended“(1)

reshaping standard definitions and surveillance methods to fit the new, emerging

information system paradigms (e.g. electronic health information records and data

mining); (2) creating national and global data standards for key HAI prevention

metrics; and (3) creating or refining the data analysis and presentation tools available

to prevention experts, clinicians, and policy makers at the local, state, national, and

international levels.”53 These will provide valuable direction for a national HAI

program in Australia.

There is much to be done in identifying a framework for a national surveillance

program, and the potential is exciting. First, we must take stock of the current

situation in Australia to understand precisely the what, how and why of HAI

surveillance currently being undertaken. To clearly identify, measure and describe

exactly how much variation exists between hospitals and States and how this

influences outcomes is necessary to inform future endeavours. Information

requirements need to be balanced against available resources and it is possible

current processes already exist which may be suitable to be extended into the

national arena, and that better use of current data may be achievable. Although SAB

data are currently being reported publicly, it is important that the data are validated

and appropriately risk adjusted for meaningful comparisons to be made.

Further, a meaningful way to report national CDI data that is currently collected

needs to be identified.

Second, resources, skill level and experience of those involved in current HAI

surveillance will influence the quality of the program, and an understanding of the

ideal mix of these characteristics is essential.

Third, we must explore the use of technology as an aid to efficient HAI

surveillance processes. Efficient data collection processes remain elusive. Current

manual data collection methods are unsustainable and impede wider surveillance


activity, so it is essential that the inclusion of automated electronic surveillance

systems be considered. Existing data that is readily accessible may inform efforts to

identify an agreed minimum level data set for some HAIs.

Fourth, we must identify the key components of successful programs. No

program will be perfect, but there are decades of lessons to be learnt from our

colleagues across the world. Similarly, we must also draw upon the experience of our

local experts and engage all key stakeholders to identify the barriers and enablers for

national HAI surveillance. For example, a model mapping out the influences on

reliable and valid HAI data has recently been developed by Australian researchers.54

4.2.6 Conclusion

Evidence clearly demonstrates that national HAI surveillance programs

provide meaningful, reliable and valid data that ultimately reduce the incidence of

HAIs. Whilst Australian jurisdictions continue to conduct disparate HAI surveillance

programs, utility of data at a national level remains limited. Centrally coordinated

international HAI surveillance programs can act as a model for an Australian system,

which can be further enhanced through the use of technology. The lack of a national

program in Australia presents a unique opportunity to construct a HAI surveillance

program based on the best available evidence.


4.2.7 References

1. National Health and Medical Research Council. Australian Guidelines for

the Prevention and Control of Infection in Healthcare: Commonwealth of

Australia; 2010.

2. Burke JP. Infection control - a problem for patient safety. N Engl J Med.

2003 February 2;348(7):651-6.

3. Goto M, Al-Hasan MN. Overall burden of bloodstream infection and

nosocomial bloodstream infection in North America and Europe. Clin

Microbiol Infect. 2013: 58(5); 688-96.

4. Graves N, Halton K, Paterson D, Whitby M. Economic rationale for

infection control in Australian hospitals. Healthcare Infection.

2009;14(3):81.

5. Allen-Bridson K, Morrell GC, Horan TC. Surveillance of Healthcare-

Associated Infections. In: Mayhall CG, editor. Hospital Epidemiology and

Infection Control. 4th ed. Philadelphia: Wolters Kluwer; 2012. p. 1329-43.

6. Australian Commission on Safety and Quality in Health Care. Standard 3.

Preventing and Controlling Hospital Acquired Infection. Sydney:

Commonwealth of Australia, 2012 October 10.

7. Umscheid CA, Mitchell MD, Doshi JA, Agarwal R, Williams K, Brennan

PJ. Estimating the proportion of healthcare-associated infections that are

reasonably preventable and the related mortality and costs. Infect Control

Hosp Epidemiol. 2011 Feb;32(2):101-14.

8. Harbarth S, Sax H, Gastmeier P. The preventable proportion of nosocomial

infections: an overview of published reports. J Hosp Infect. 2003;54(4):258-

66.

9. Wilson J, Wloch C, Saei A, McDougall C, Harrington P, Charlett A, et al.

Inter-hospital comparison of rates of surgical site infection following

caesarean section delivery: evaluation of a multicentre surveillance study. J

Hosp Infect. 2013 May;84(1):44-51.

10. Haley RW. The scientific basis for using surveillance and risk factor data to

reduce nosocomial infection rates. J Hosp Infect. 1995 June;30 Suppl:3-14.


11. Edmond MB. National and International Surveillance Systems for

Nosocomial Infections. In: Wenzel RP, editor. Prevention and Control of

Nosocomial Infections. 4th ed. Philadelphia: Lippincott Williams & Wilkins;

2003. p. 109-19.

12. Garcia-Abren A, Halperin W, Danel I. Public Health Surveillance Toolkit:

The World Bank; 2002.

13. Perl TM, Chaiwarth R. Surveillance: An Overview. In: Lautenbach E,

Woeltje KF, Malani PN, editors. Practical Healthcare Epidemiology. 3rd ed.

London: The University of Chicago Press; 2012. p. 111-42.

14. Reilly J, Stewart S, Allardice G, Cairns S, Ritchie L, Bruce J. Evidence-

based infection control planning based on national healthcare-associated

infection prevalence data. Infect Control Hosp Epidemiol. 2009

February;30(2):187-9.

15. Centers for Disease Conrtol and Prevention. Facilities in these states are

required by law to report HAI data to NHSN 2014 [accessed 2014 11 July].

Available from: http://www.cdc.gov/hai/stateplans/required-to-report-hai-

NHSN.html.

16. Public Health England. Mandatory Surveillance of Staphypococcus aureus

bacteraemia 2014 [accessed 2014 11 July]. Available from:

http://www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/Staphyloco

ccusAureus/EpidemiologicalData/MandatorySurveillance/.

17. Berrios-Torres SI, Mu Y, Edwards JR, Horan TC, Fridkin SK. Improved risk

adjustment in public reporting: coronary artery bypass graft surgical site

infections. Infect Control Hosp Epidemiol. 2012 May;33(5):463-9.

18. Haustein T, Gastmeier P, Holmes A, Lucet J-C, Shannon RP, Pittet D, et al.

Use of benchmarking and public reporting for infection control in four high-

income countries. Lancet Infect Dis. 2011 June;11(6):471-81.

19. Rosenthal VD, Ramachandran B, Villamil-Gomez W, Armas-Ruiz A,

Navoa-Ng JA, Matta-Cortes L, et al. Impact of a multidimensional infection

control strategy on central line-associated bloodstream infection rates in

pediatric intensive care units of five developing countries: findings of the


International Nosocomial Infection Control Consortium (INICC). Infection.

2012 August;40(4):415-23.

20. Australian Commission for Safety and Quality in Health Care. National

Surveillance Initiative. National Surveillance Initiative2013.

21. Richards C, Emori TG, Edwards J, Fridkin S, Tolson J, Gaynes R.

Characteristics of hospitals and infection control professionals participating

in the National Nosocomial Infections Surveillance System 1999. Am J

Infect Control. 2001;29(6):400-3.

22. Edwards JR, Pollock DA, Kupronis BA, Li W, Tolson JS, Peterson KD, et

al. Making use of electronic data: the National Healthcare Safety Network

eSurveillance Initiative. Am J Infect Control. 2008 Apr;36(3 Suppl):S21-6.

23. Centers for Disease Control and Prevention (CDC). Monitoring hospital-

acquired infections to promote patient safety--United States, 1990-1999.

MMWR Morb Mortal Wkly Rep. 2000 March 3;49(8):149-53.

24. Gaynes R, Richards C, Edwards J, Emori TG, Horan T, Alonso-Echanove J,

et al. Feeding back surveillance data to prevent hospital-acquired infections.

Emerg Infect Dis. 2001;7(2):295-8.

25. Gastmeier P, Sohr D, Schwab F, Behnke M, Zuschneid I, Brandt C, et al.

Ten years of KISS: the most important requirements for success. J Hosp

Infect. 2008 October;70 Suppl 1:11-6.

26. Cooke EM, Coello R, Sedgwick J, Ward V, Wilson J, Charlett A, et al. A

national surveillance scheme for hospital associated infections in England. J

Hosp Infect. 2000;46(1):1-3.

27. Gudiol F, Limón E, Fondevilla E, Argimon JM, Almirante B, Pujol M. The

development and successful implementation of the VINCat Program.

Enferm Infecc Microbiol Clin. 2012 June;30 Suppl 3:3-6.

28. Perez CD, Rodela AR, Monge Jodra V, Quality Control Indicator Working

G. The Spanish national health care-associated infection surveillance

network (INCLIMECC): data summary January 1997 through December

2006 adapted to the new National Healthcare Safety Network Procedure-

associated module codes. Am J Infect Control. 2009 Dec;37(10):806-12.


29. Rioux C, Grandbastien B, Astagneau P. Impact of a six-year control

programme on surgical site infections in France: results of the INCISO

surveillance. J Hosp Infect. 2007;66(3):217-23.

30. Reilly J, Cairns S, Fleming S, Hewitt D, Lawder R, Robertson C, et al.

Results from the second Scottish national prevalence survey: the changing

epidemiology of healthcare-associated infection in Scotland. J Hosp Infect.

2012 Nov;82(3):170-4.

31. Coello R, Gastmeier P, de Boer A. Surveillance of Hospital‐Acquired

Infection in England, Germany, and The Netherlands: Will International

Comparison of Rates Be Possible? Infect Control Hosp Epidemiol.

2001;22(6):393-7.

32. Gastmeier P. European perspective on surveillance. J Hosp Infect. 2007

June;65 Suppl 2:159-64.

33. Geubbels ELPE, Bakker HG, Houtman P, van Noort-Klaassen MA, Pelk

MSJ, Sassen TM, et al. Promoting quality through surveillance of surgical

site infections: five prevention success stories. Am J Infect Control. 2004

November;32(7):424-30.

34. Grammatico-Guillon L, Rusch E, Astagneau P. Surveillance of prosthetic

joint infections: international overview and new insights for hospital

databases. J Hosp Infect. 2013.

35. van Mourik MS, Troelstra A, van Solinge WW, Moons KG, Bonten MJ.

Automated surveillance for healthcare-associated infections: opportunities

for improvement. Clin Infect Dis. 2013 Jul;57(1):85-93.

36. Gaynes RP. Surveillance of nosocomial infections: a fundamental ingredient

for quality. Infect Control Hosp Epidemiol. 1997 July;18(7):475-8.

37. Hota B, Lin M, Doherty JA, Borlawsky T, Woeltje K, Stevenson K, et al.

Formulation of a model for automating infection surveillance: algorithmic

detection of central-line associated bloodstream infection. J Am Med Inform

Assoc. 2010 January;17(1):42-8.


38. Lin MY, Hota B, Khan YM, Woeltje KF, Borlawsky TB, Doherty JA, et al.

Quality of traditional surveillance for public reporting of nosocomial

bloodstream infection rates. JAMA. 2010 November 11;304(18):2035-41.

39. Freeman R, Moore LS, Garcia Alvarez L, Charlett A, Holmes A. Advances

in electronic surveillance for healthcare-associated infections in the 21st

Century: a systematic review. J Hosp Infect. 2013 Jun;84(2):106-19.

40. McLaws ML, Taylor PC. The Hospital Infection Standardised Surveillance

(HISS) programme: analysis of a two-year pilot. J Hosp Infect. 2003

April;53(4):259-67.

41. Morton A, Clements AC, Doidge SR, Stackelroth J, Curits M, Whitby M.

Surveillance of Healthcare-Acquired Infections in Queensland, Australia:

Data and Lessons From the First 5 Years. Infect Control Hosp Epi.

2008;29(8):695-701.

42. Russo PL, Bull A, Bennett N, Boardman C, Burrell S, Motley J, et al. The

establishment of a statewide surveillance program for hospital-acquired

infections in large Victorian public hospitals: a report from the VICNISS

Coordinating Centre. Am J Infect Control. 2006 September;34(7):430-6.

43. VanGessel H, McCann, RL., Peterson, AM., Goggin, LS. Validation of

healthcare associated Staphylococcus aureus bloodstream infection

surveillance in Western Australia. Healthcare Infection. 2010;15:21-5.

44. Richards MJ, Russo PL. Surveillance of hospital-acquired infections in

Australia--One Nation, Many States. J Hosp Infect. 2007 June;65 Suppl

2:174-81.

45. Friedman ND, Russo PL, Bull AL, Richards MJ, Kelly H. Validation of

coronary artery bypass graft surgical site infection surveillance data from a

statewide surveillance system in Australia. Infect Control Hosp Epidemiol.

2007 July;28(7):812-7.

46. Goggin LS, van Gessel H, McCann RL, Peterson AM, Van Buynder PG.

Validation of surgical site infection surveillance in Perth, Western Australia.

Healthcare Infection. 2009;14(3):101.


47. McBryde ES, Brett J, Russo PL, Worth LJ, Bull AL, Richards MJ.

Validation of statewide surveillance system data on central line-associated

bloodstream infection in intensive care units in Australia. Infect Control

Hosp Epidemiol. 2009 Nov;30(11):1045-9.

48. Australian Institute for Health and Welfare. MyHospitals [15/03/2013].

Available from: http://www.myhospitals.gov.au.

49. Cheng AC. How should we interpret hospital infection statistics? MJA. 2014

December 12;199(11):735-6.

50. Worth L, Thursky, K.A., Slavin, M.A. Public disclosure of health care-

associated infections in Australia: quality improvement or parody? MJA.

2012;197(1):29.

51. Worth LJ, Bull, Ann L., Richards, MJ. Public reporting of health care-

associated infection data in Australia: time to refine. MJA.

2013;198(5):252-3.

52. Shaban RZ CM, Christiansen K & the Antimicrobial Resistance Standing

Committee, . National Surveillance and Reporting of Antimicrobial

Resistance and Antibiotic Usage for Human Health in Australia. 2013.

53. Cardo D, Dennehy PH, Halverson P, Fishman N, Kohn M, Murphy CL, et

al. Moving toward elimination of healthcare-associated infections: a call to

action. Infect Control Hosp Epidemiol. 2010 Nov;31(11):1101-5.

54. Mitchell BG, Gardner A. A model for influences on reliable and valid health

care-associated infection data. Am J Infect Control. 2014 Feb;42(2):190-2.


Table 1. Comparison of mandatory healthcare associated infection surveillance components in acute care public facilities by state

All states and territories in Australia undertake surveillance for Staphylococcus aureus bloodstream (SAB) infection and Clostridium difficile infection (CDI).

✔, surveillance performed; Ó, surveillance not performed; 1, with modifications; 2, including neonatal intensive care unit (NICU); 3, NICU only; 4, if >50

procedures per year; 5, Royal Women’s hospitals and Women’s Mercy Hospital only; 6, infections only; ICU, intensive care unit; MRSA, methicillin-resistant

Staphylococcus aureus; MRAB, multi-resistant Acinetobacter; NHSN, National Health and Safety Network; BSI, bloodstream infection; NA, not applicable.

StatewideHAIsurveillanceprogram

Centrallineassociated

bloo

dstreamin

fectionsin

ICU

(includ

esperiphe

rallyin

serted

)

Acqu

isition

ofM

RSAinIC

UB

Acqu

isition

ofM

RABinIC

UB

Corona

ryArteryBy

passgraft

Hipprosthe

sis

Knee

prosthe

sis

LowerCaesarean

sectio

n

SSIP

ostD

ischargeSurveillan

ce

includ

ed–excep

tfor

read

mission

s

NHSN

definition

s

AllM

RSAinfections

Haemod

ialysisaccessassociated

bloo

dstreamin

infection

Hospitalw

ideBS

I

NSWHealthcareAssociatedInfectionsProgram

✔ ✔ ✔ ✔ ✔ ✔ ✖ ✖ ✔ (1) ✖ ✖ ✖

78 Chapter 4: Healthcare-associated infection in Australia

QLDCentreforHealthRelatedInfectionSurveillanceandPrevention(CHRISP)(mediumtolargehospitals)

✔ ✔ ✔ ✔ ✔ ✔ ✖ ✔A ✔ (1) ✔ ✔ ✔

SASouthAustralianHAIsurveillanceprogram

✔ ✔ ✔ ✖ ✖ ✖ ✖ ✖ ✔ ✔ ✖ ✔

TASTasmanianInfectionPreventionandControlUnit(TIPCU)

✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ NA ✖ ✖ ✖VICVictorianHealthcareAssociatedInfectionSurveillanceSystem(VICNISS)

✔ (2) ✖ ✖ ✔ ✔(4) ✔ (4) ✔ (5) ✔A ✔ ✖ ✖ ✖

WAHealthcareInfectionSurveillanceWesternAustralia(HISWA)

✔ ✔(6) ✔ (6) ✖ ✔ ✔ ✖ ✔A ✔ (1) ✔ ✔ ✖

AOptionalBIncludescolonisationandinfection

Chapter 5: Variation in HAI surveillance practices 79

Chapter 5: Variation in HAI surveillance practices

5.1 INTRODUCTION

As established in the scoping review, not all states and territories have a

statewide coordinated approach to surveillance, and those that do, undertake

surveillance of different infections. Although there are overall similarities between

the existing surveillance programs, this isolated statewide approach has meant that

they have evolved at different rates and direction.

Before recommendations for a national HAI surveillance program can be

developed, it is crucial to identify and understand in more detail the current status

of HAI surveillance, identify any gaps and clearly describe how much variation

exists between the programs. It is possible that current surveillance practices are

suitable to be scaled up nationally avoiding the need to establish a formal national

program.

A cross sectional study was designed with two main aims:

1. Identify variation between surveillance activities and characteristics of

those undertaking surveillance

2. Measure agreement in HAI identification, classification and

calculation of HAI rates, and if differences amongst those undertaking

surveillance influenced their responses.

The paper presented in this chapter addresses the first aim, and identified

widespread variation amongst surveillance practices both between different states

and territories, and between facilities within the same state and territory. Major

gaps identified included deficits in education and training of surveillance staff, and

a lack of best practice surveillance methodology.

These findings were published in the American Journal of Infection Control.

The results of the study specific to the second aim are presented in the next

Chapter.

80 Chapter 5: Variation in HAI surveillance practices




conception, execution, or interpretation, of at least that part of the

publication in their field of expertise;

• they take public responsibility for their part of the publication, except for

the responsible author who accepts overall responsibility for the publication;










___________________________________________________________________




Date


preparation


preparation


preparation

11/7/2016

Chapter 5 Variation in HAI surveillance practices 81






Name

Signature

Date

Dr Lisa Hall

11/7/2016


5.2 PAPER TWO: “VARIATION IN HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE PRACTICES IN AUSTRALIA”

Russo PL, Cheng AC, Richards M, Graves N, Hall L. Variation in health

care-associated infection surveillance practices in Australia. Am J Infect

Control 2015; 43(7): 773-5

5.2.1 Abstract

In the absence of a national healthcare associated infection (HAI) surveillance

program, differences between existing state-based programs were explored using an

online survey. Only 51% of respondents who undertake surveillance have been

trained, less than half perform surgical site infection surveillance (SSI)

prospectively, and only 41% indicated they risk adjust SSI data. Widespread

variation of surveillance methods highlights future challenges when considering the

development and implementation of a national program in Australia.

5.2.2 Introduction

Many countries have well established coordinated national healthcare

associated infection (HAI) surveillance programs, but Australia does not. Separate

evolution of Australia’s eight States and Territories surveillance programs during

the 1990’s and 2000’s1 has led to differences that are poorly understood.1-3

Recently HAI surveillance has been mandated in the National Safety and Quality

Health Service Standards for Australian Hospitals.4

Surveillance data have traditionally been used for internal purposes, but the

advent of reporting to external agencies at a State, Territory and national level5 has

underlined issues relating to variation in processes, resources and training between

hospitals. Reducing such variation is a logical step towards providing valid and

consistent information.

A few HAI surveillance validation studies have been done within States and

Territories of Australia that have demonstrated moderate sensitivity.6-8 No studies

have been done to explore variation among States and Territories to show national

variability. With an estimated 175,000 HAIs occurring annually9 variability among

surveillance inhibits understanding of the true epidemiology of HAIs in Australia,


limiting our ability to measure the impact of nationally organised infection

prevention interventions.

The purpose of this study is to identify variation between HAI surveillance

practices among Australian hospitals in the eight States and Territories.

5.2.3 Method

An online survey was administered to infection prevention staff from both

public (government funded) and private acute care facilities with more than 50 beds

who undertake HAI surveillance. The survey sought information on infection

prevention staff and team demographics, surveillance training, definitions, data

sources, collection processes, analysis and reporting. Four current and two former

infection prevention staff piloted the survey.

Recruitment of participants was through an open invitation email distributed

through the list server of the Australasian College of Infection Prevention and

Control (ACIPC). Recipients were also asked to pass it on to others. Coordinators

of State and Territory surveillance programs, where they existed, were contacted

and requested to encourage those in their region to complete the survey. Members

of the Australian Commission on Safety and Quality in Health Care HAI Advisory

Committee were requested to overtly support completion of the survey to their

peers and colleagues.

No identifying details of participants or their facilities were requested. Ethics

permission was granted by the University Human Research Ethics Committee,

Queensland University of Technology (1400000339).

Data was analysed using Stata, version 13 (StataCorp). The chi square test

was performed to compare proportions between groups, and Kruskall-Wallis to test

for influence of State and Territory.

5.2.4 Results

A total of 104 completed responses were received over a five week period.

Due to the logical design of the survey, respondents were not required to answer

every question, therefore the number of responses varied for different questions.

Characteristics of the respondents and their surveillance practices are listed in Table

1.


When stratified by hospital size, several statistically significant differences

were identified and are listed in Table 2. Other findings included: respondents

working in public hospitals were more likely to be part of a team (79% v

50%;p=0.010) and be trained in surveillance (58% v 20%:p=0.002). Those from

private hospitals with less that 200 acute beds were more likely to be working as

sole practitioners (90% v 54%;p=0.040) and work part time (80% v 39%;p=0.027).

Respondents who had received surveillance training were significantly more

likely to undertake prospective SSI surveillance (69% v 29%: p<0.001) and risk

adjust their SSI data (61% v 24%: p=0.001). These factors were also significantly

influenced by State and Territory, p=0.007 and p<0.001 respectively (Kruskall-

Wallis test).

When questioned how confident they were that their HAI data was accurate,

60% (n=78) believed their SSI data was accurate and 79% (n=57) believed their

CLABSI data was accurate.

5.2.5 Discussion

Widespread variation among HAI surveillance was found for States and

Territories, public and private and different sized facilities. Important disparities

between States and Territories such as definitions1 and other items mean that until

the adoption of national uniform protocol, any attempt to compare State and

Territory level data or aggregate for use at a national level will be flawed.

This study identified that just over half of the respondents who undertake

HAI surveillance have been trained. This is an important finding given that many of

the criteria in the National Health and Safety Network based HAI definitions

require interpretation. We also found that those who have been trained were more

likely to undertake prospective surveillance and risk adjust SSI data. This indicates

a poor understanding of basic HAI surveillance principles and the dangers of not

risk adjusting data.10 This finding suggests that the benefits of training extend

beyond the application of definition criteria, but also assist in appropriate methods

and analysis. The lack of training in Australia places uncertainty about the validity

of data currently collected by the various programs.

We found that reporting to those who have the ability to implement change,

such as hospital executive, was inconsistent. This means not all data collected is


being used to drive improvement, implying precious resources are being wasted on

redundant activities.

There are limitations in this study. A true response rate was unable to be

calculated as the number of infection prevention staff involved in HAI surveillance

is unknown.11 Approximately 500 ACIPC members subscribe to the list server,

(personal communication, ACIPC secretary June 2014), but not all would undertake

HAI surveillance, nor are all infection prevention staff members of ACIPC. It is

estimated there are approximately 215 acute public hospitals with more than 50

beds in Australia,12 and our respondents were from all States and Territories with a

broad range of experience working in different sized hospitals, and so we are

confident this is representative of those undertaking HAI surveillance. It is possible

that there may have been a respondent bias in that those that responded to the

survey may be systematically different to those that did not.

The findings from this study highlight the future challenges when considering

the purpose and usefulness of any potential national HAI surveillance program in

Australia. This work supports previous recommendations for further training and

standardization to allow external comparisons to be made in a national surveillance

system.13

The effect of this widespread variation has on data quality, and appropriate

identification of HAIs has not been described. To quantify the significance of this

variation, we intend to evaluate the assessment of a series of clinical vignettes by

infection prevention staff.


5.2.6 References

1 Russo PL, Cheng AC, Richards M, Graves N, Hall L. Healthcare-

associated infections in Australia: time for national surveillance. Aust

Health Rev. 2014;39:37-43.

2. Cruickshank M, Ferguson J. Reducing harm to patients from health care

associated infection: the role of surveillance: Australian Commission on

Safety and Quality in Health Care; 2008.

3. Richards MJ, Russo PL. Surveillance of hospital-acquired infections in

Australia – One Nation, Many States. J Hosp Infect. 2007;65:174-81.

4. Australian Commission on Safety and Quality in Health Care. Safety and

Quality Improvement Guide Standard 3: Preventing and Controlling

Healthcare Associated Infections (October 2012). Sydney. ACSQHC,

2012.

5. National Health Performance Authority. MyHospitals. Retrieved 23rd

February 2015 from www.myhospitals.gov.au




2007 July;28(7):812-7.

7. McBryde ES, Brett J, Russo PL, Worth LJ, Bull AL, Richards MJ.

Validation of statewide surveillance system data on central line-associated

bloodstream infection in intensive care units in Australia. Infect Control

Hosp Epidemiol. 2009 Nov;30(11):1045-9.




9. Graves N, Halton K, Paterson D, Whitby M. Economic rationale for

infection control in Australian hospitals. Healthcare Infection.

2009;14(3):81.


10. O'Neill E, Humphreys H. Use of surveillance data for prevention of

healthcare-associated infection: risk adjustment and reporting dilemmas.

Curr Opin Infect Dis. 2009 August;22(4):359-63.

11. Hall L, Halton K, Macbeth D, Gardner A, B M. Roles, responsibilities and

scope of practice: describing the ‘state of play’ for infection control

professionals in Australia and New Zealand. Healthcare Infection. 2015 (in

press)

12. Australian Institute for Health and Welfare. Australian hospital statistics

2012–13. Health services series no. 54. Cat. no. HSE 145. Canberra:

AIHW. 2014.

13. Murphy CL, McLaws ML. Methodologies used in surveillance of surgical

wound infections and bacteremia in Australian hospitals. Am J Infect

Control. 1999;27(6):474-81.


Table 1 – Characteristics of survey respondents

Characteristic Value n

Age – mean (IQR) 48.9 (43-55) 104

Years in Infection Control – mean (IQR) 11.8 (5-17)

Masters degree or higher 28%

State or Territory

• Australian Capital Territory

• Northern Territory

• Tasmania

9%

• New South Wales 19%

• Queensland 20%

• South Australia 8%

• Victoria 29%

• Western Australia 15%

Work in hospital > 200 beds 65%

Work in the Public sector 80%

Work less than 38 hours per week 35%

Hours per week doing surveillance - mean 7.6 (range 1-40)

Part of an infection control team 73%

Trained in HAI surveillance 51%

SSI surveillance 81


IQR – Interquartile range

SSI – surgical site infection

CLABSI – Central line associated bloodstream infection

NHSN – National Health and Safety Network

VAP – Ventilator associated pneumonia

CAUTI – Catheter associated urinary tract infection

• Use NHSN definitions with no

modifications

64%

• Do prospective surveillance 47%

• Risk adjust rates 41%

• Report data to Hospital Executive 84% 63

CLABSI 66

• Use NHSN definitions with no

modifications

67%

• Do prospective surveillance 60%

• Report data to Hospital Executive 82% 55

Report VAP data to Hospital Executive 15% 20

Report CAUTI data to Hospital Executive 30% 20


Table 2 – Differences in characteristics of HAI surveillance practices by

hospital size

Characteristic Less than 200

beds (n)

More than 200 beds

(n)

P value

Chi2

Work as part of a

team 36% (38) 94% (66) < 0.001

Daily access to IDP 26% (38) 77 (65) < 0.001

Rare or never have

access to another

ICP

61% (38) 32% (66) 0.004

Trained in HAI

surveillance 34% (38) 61% (66) 0.010

Prospective SSI

surveillance 31% (29) 56% (52) 0.032

Use surveillance

software 32% (28) 65% (49) 0.005

Risk adjust SSI data 26% (29) 48% (52) 0.072

SSI – surgical site infection

IDP – Infectious Disease Physician

ICP – Infection Control Professional

HAI – healthcare associated infection

See Appendix H for further data not included in published article.

Chapter 6: Differences in identifying healthcare-associated infections 91

Chapter 6: Differences in identifying healthcare-associated infections

6.1 INTRODUCTION

With widespread variation amongst surveillance practices established, and gaps

relating to education and best practice identified, it was important to investigate any

effect these may have on outcome data. In the same cross sectional study, a series of

clinical vignettes were presented to respondents specific to the type of surveillance

they undertook. Participants were required to respond applying their usual

surveillance practice and method.

The aim of this aspect of the study was to measure agreement in HAI

identification, classification and calculation of HAI rates, and if differences amongst

those undertaking surveillance influenced their responses.

The study established that there is only moderate agreement in HAI

identification, classification and calculation of HAI rates amongst those currently

involved in surveillance in Australia. Whilst there were no statistically significant

factors that influenced the overall agreement level, the findings suggest that those

from smaller facilities with fewer resources were less likely to correctly identify

HAIs, and that the current national data on SAB may not be reliable.

The findings specific to the aim above were published in the Antimicrobial

Resistance and Infection Control journal.

92 Chapter 6: Differences in identifying healthcare-associated infections

















___________________________________________________________________




Date

AdrianBarnettAdvised on statistical analysis and manuscript

preparation


preparation


preparation


preparation

11/7/2016

Chapter 6 Differences in identifying healthcare-associated infections 93






Name

Signature

Date

Dr Lisa Hall

11/7/2016


6.2 PAPER THREE: “DIFFERENCES IN IDENTIFYING HEALTHCARE-ASSOCIATED INFECTIONS USING CLINICAL VIGNETTES AND THE INFLUENCE OF RESPONDENT CHARACTERISTICS: A CROSS-SECTIONAL SURVEY OF AUSTRALIAN INFECTION PREVENTION STAFF”

Russo PL, Barnett AG, Cheng AC, Richards M, Graves N, Hall L. Differences

in identifying healthcare-associated infections using clinical vignettes and the

influence of respondent characteristics: a cross-sectional survey of Australian

infection prevention staff. Antimicrob Resist Infect Control 2015; 4(29): 1-7.

6.2.1 Abstract

Background

Australia has commenced public reporting and benchmarking of healthcare

associated infections (HAIs), despite not having a standardised national HAI

surveillance program. Annual hospital Staphylococcus aureus bloodstream (SAB)

infection rates are released online, with other HAIs likely to be reported in the future.

Although there are known differences between hospitals in Australian HAI

surveillance programs, the effect of these differences on reported HAI rates is not

known.

Objective

To measure the agreement in HAI identification, classification, and calculation

of HAI rates, and investigate the influence of differences amongst those undertaking

surveillance on these outcomes.

Methods

A cross-sectional online survey exploring HAI surveillance practices was

administered to infection prevention nurses who undertake HAI surveillance. Seven

clinical vignettes describing HAI scenarios were included to measure agreement in

HAI identification, classification, and calculation of HAI rates. Data on

characteristics of respondents was also collected. Three of the vignettes were related

to surgical site infection and four to bloodstream infection. Agreement levels for

each of the vignettes were calculated. Using the Australian SAB definition, and the

National Health and Safety Network definitions for other HAIs, we looked for an


association between the proportion of correct answers and the respondents’

characteristics.

Results

Ninety-two infection prevention nurses responded to the vignettes. One

vignette demonstrated 100% agreement from responders, whilst agreement for the

other vignettes varied from 53% to 75%. Working in a hospital with more than 400

beds, working in a team, and State or Territory was associated with a correct

response for two of the vignettes. Those trained in surveillance were more commonly

associated with a correct response, whilst those working part-time were less likely to

respond correctly.

Conclusion

These findings reveal the need for further HAI surveillance support for those

working part-time and in smaller facilities. It also confirms the need to improve

uniformity of HAI surveillance across Australian hospitals, and raises questions on

the validity of the current comparing of national HAI SAB rates.

6.2.2 Introduction

Despite the absence of a standardised national healthcare associated infection

(HAI) surveillance program in Australia, public reporting of HAI rates has

commenced. Annual hospital level HAI Staphylococcus aureus bloodstream (SAB)

infection rates have been reported publicly since 2012–13 [1]. Although national

safety and quality health service standards mandate HAI surveillance [2], there is a

large variation in HAI surveillance processes across Australia’s eight State and

Territories [3, 4]. Although a national definition for SAB does exist [5], a major

difference is the varying use of the National Health and Safety Network (NHSN)

definitions [6] with or without local modifications to identify other HAIs [4]. It is

unclear how much this variation influences the interpretation and application of

definitions and subsequent HAI rates.

Whilst benchmarking and public reporting of HAI is new to Australia, it has

been common in several countries for some time, including the USA, England, and

France [7]. Nevertheless, there remains significant concern regarding the use of HAI


data as performance indicators, particularly in light of insufficient standardisation of

events being monitored [8, 9].

If HAI rates are used as quality indicators, data must be robust and reliable

[10]. A recent study by Keller et al identified low inter-rater reliability between those

performing HAI surveillance and concluded that such discordance could

“dramatically affect not only hospital reputations but also hospital reimbursement”

[11]. Despite the lack of evidence demonstrating a reduction of HAI rates using

financial incentives [12, 13], one Australian State has recently implemented financial

penalties for preventable HAI bloodstream infections [14].

If Australia is to commence public reporting of other HAI data, it is important

to be assured the data are robust and reliable. The objective of this study was

to measure agreement in HAI identification, classification, and calculation of HAI

rates amongst those undertaking HAI surveillance in Australian hospitals using a

series of clinical vignettes. We also investigated if differences amongst those

undertaking surveillance influenced their responses

6.2.3 Method

Study Instrument

A total of seven vignettes representing HAI surveillance situations that may

occur in the acute care setting were developed as part of a larger cross-sectional

survey which explored HAI surveillance practices in Australian hospitals [4]. The

vignettes were based on those published in similar studies and in a local

implementation guide [15-17], and were further developed in collaboration with

infection prevention experts from a jurisdictional surveillance program. As not all

hospitals undertake surveillance on the same type of inception, the survey was

designed so that participants only answered those vignettes on which they undertook

surveillance. For example, if a respondent indicated they did not perform

surveillance on central line associated bloodstream infections (CLABSI), they were

not presented with a vignette describing a potential CLABSI.

The vignettes were categorised into either a surgical site infection (SSI) or

bloodstream infection. These types of infection were included as they represent the

most common types of HAI surveillance undertaken. The first was specific to those

undertaking SSI surveillance on coronary artery bypass graft surgery (CABG) to


identify how they calculated an infection rate if more than one wound site was

involved. A gastrointestinal surgery vignette was designed to be a straightforward

case and therefore considered a positive control. The other SSI vignette was slightly

more challenging in that it sought clarification as to whether or not the SSI was an

organ space or deep SSI.

The SAB vignette asked respondents to indicate if they would classify it as

healthcare associated. Three central line associated bloodstream infection (CLABSI)

vignettes sought to identify differences regarding local modifications of the NHSN

definitions, and the application of either 48 hours or 2 calendar days as the marker of

hospital acquisition.

For each vignette, participants were instructed to answer applying their “usual

definitions and methods”.

The survey was constructed using a secure online tool and piloted by four

current and two former infection prevention staff. The pilot participants provided

feedback on clarity, simplicity, flow and logic of the survey. After further

amendments, the survey was further piloted by two of the six involved in the initial

pilot.

Population and recruitment

The survey was administered to infection prevention nurses who undertake

HAI surveillance from both public (government funded) and private acute care

facilities with more than 50 beds. This size facility was targeted as they were

considered more likely to undertake HAI surveillance on a routine basis.

Recruitment was through an open invitation email distributed through the

Australasian College of Infection Prevention and Control (ACIPC) list server.

Coordinators of State and Territory surveillance programs, where they existed, were

contacted and requested to encourage those in their State and Territory to complete

the survey. Members of the Australian Commission on Safety and Quality in Health

Care HAI Advisory Committee were requested to overtly support completion of the

survey to their peers and colleagues. The email requested all recipients to forward on

to others who may not have received it.


No identifying details of participants or their facilities were requested. Ethics

permission was granted by the University Human Research Ethics Committee,

Queensland University of Technology (1400000339).

Statistical analysis

Agreement for the SSI and CLABSI vignettes was calculated as the proportion

of responses considered correct using NHSN definitions [6], and for the SAB

vignette according to the Australian SAB definition [5]. Data was analysed using

Stata, version 13 (Stata Corp, College Station, Texas).

Single variable predictors of correct answers

For each vignette, univariate analysis using logistic regression was used to

generate an odds ratio of answering correct depending on the participants’

characteristics. To examine all vignettes combined a Poisson regression was used to

analyse the total number correct across all vignettes with an adjustment to the

denominator as participants only answered those vignettes on which they undertook

surveillance. The results are presented as risk ratios and 95% confidence intervals,

where a risk ratio above 1 means a greater ‘risk’ of a correct answer. To make these

results comparable with the logistic regression model using individual vignettes, the

odds ratios from the logistic regressions were converted to risk ratios [18].

To explore the influence of the location (i.e. State or Territory of respondent), a

Kruskall–Wallis test was used for each individual vignette and the combined analysis

of the total number correct.

Multivariable predictors of correct answers In an attempt to identify independent predictors of answering correct, a

multivariable Poisson model of the total number correct was developed from

characteristics identified in the Poisson univariate analysis that had a p-value under

0.5. A high p-value threshold was used to ensure that all potentially important

variables were considered. To check for multicollinearity, the variance inflation

factor (VIF) of each variable was explored. Variables with a VIF of 5 or above

indicating high collinearity were removed from final multivariable model.

6.2.4 Results

A total of 92 responses to the vignettes were received. All respondents were

registered nurses with an average age of 49 and a mean of 12 years of experience


working in infection prevention. There was representation from each of the eight

States and Territories in Australia. The majority of respondents worked as part of a

team (73%) and in public facilities (80%). Only 51% reported having been trained in

HAI surveillance. The median number of vignettes answered was 5 out of a

maximum of 7. (Table 1)

A summary of each vignette, response options and response rates are listed in

Table 2 The number of respondents varied from 23 for Vignette 1 to 85 for Vignette

5. This reflects the usual type of infections participants performed surveillance on,

and so those vignettes not answered were not missing values but correctly not

answered. The control vignette was correctly answered by all respondents, however

the correct response rates for the other vignettes varied from 53% to 75%. (Table 2)

Predictors of correct answers

Univariate analysis identified three factors that were statistically significantly

associated with the outcome of two of the vignettes (Table 3). For Vignette 3, which

challenged the responder with the difference between classifying a SSI as either an

organ space infection or a deep infection, those who worked in a team were more

than twice as likely to respond correctly (RR=2.16, [95%CI:1.14, 2.97]) The State or

Territory of the respondents was also statistically significantly associated with a

correct answer (p=0.045, Kruskall–Wallis test).

Vignette 5 explored the difference between the current NHSN criteria for

CLABSI against 2008 criteria. Working in a hospital with over 400 beds more than

doubled the likelihood of a correct answer (RR=2.42, [95%CI:1.09, 3.45]), but those

who have had surveillance skills assessed were less likely to have a correct answer

(RR=0.32, [95%CI;0.09, 0.98]). There was evidence that the proportion answering

correctly varied between State or Territory (Kruskal–Wallis test: p=0.043).

Those characteristics that were more frequently associated with a correct

response across all vignettes were: working in a hospital over 400 beds, having been

formally trained in surveillance, being trained by a central organisation, working in a

team, and having daily access to an epidemiologist. The characteristic most

commonly associated with an incorrect response was working part-time.


No statistically significant factors were identified for the total number correct,

but characteristics most strongly associated with a correct response were working in

a team RR=1.15 (95% CI: 0.89, 1.49) and daily access to an epidemiologist RR=1.15

(95%CI: 0.81, 1.62). Working part-time was most strongly associated with an

incorrect answer RR=0.89 (95%CI: 0.69, 1.14).

Multivariable analysis

Two multivariable models were developed. (Table 4) Characteristics from the

univariate analysis that had a p-value < 0.5 were included in the first model (Model

A). The variable “Work in a Team” was found to have a VIF of 5. Therefore, a

second multivariate model (Model B) was generated following the omission of

“Work in a Team”.

For both models, the probability of getting a correct answer increased by 12%

if the respondent had daily access to an epidemiologist, and 8% if they had an

academic degree or higher. For Model A the probability increased by 11% if they

worked as part of a team. Both models also identified that incorrect answers were

more common for respondents who were part-time or with less than five years

experience. No statistically significant factors were identified.

6.2.5 Discussion

This study has identified disparity in HAI identification, classification, and

calculation of HAI rates using clinical vignettes in large acute care Australian

hospitals. Although one vignette returned an encouraging result of 100% correct

response rate, it was included as a positive control. The range of responses of 53% to

75% for the other six vignettes follows on from recent findings describing the broad

variation amongst surveillance practices in Australia [4], and infer that comparison

between hospitals, States and Territories, and any aggregation of existing data will be

flawed. This is implicit from the following findings.

First, aggregation of SSI rates following CABGs will result in an

underestimation of the true rate whilst some hospitals, States and Territories persist

in using each incision as the denominator to calculate a rate. Second, the inability to

distinguish between organ space and deep space means that any aggregated SSI data

reported by type of infection will likely be unreliable and incomparable. Third, the

present use of both 48 hours or 2 calendar days as criteria for CLABSI acquisition


clearly affects the CLABSI rate reported. Fourth, even though a national definition

for SAB exists (unlike the potential HAIs described in other vignettes) when

presented with a complex SAB event the ability to correctly identify it is moderate.

This is important as current SAB rates, that are publicly reported on a safety and

quality website in Australia encouraging hospital comparisons [1], could be

misleading.

The univariate analysis findings suggest that those from larger hospitals and in

States with established programs are more likely to be in agreement with current

NHSN HAI definitions. This could be explained by the team environment of larger

hospitals which may provide improved knowledge from greater learning

opportunities, and the training provided by the established programs.

Although no statistically significant predictors were identified in the

multivariable analysis, the results from both models indicate that those with less

experience and those who work part-time require increased support and training to

identify HAIs.

Daily access to an epidemiologist was positively associated with a correct

answer for all vignettes and also both models of the multivariable analysis. Given

that only 1% of respondents have daily access to an epidemiologist, this may be a

proxy for other factors (e.g., a thriving research culture) that have not been identified

in this study and is worthy of further exploration.

The results of this study are consistent with recent international studies that

have identified broad variation in the identification of both SSI and CLABSI within

and between HCW groups [19, 15, 20-22, 16]. Similar to Keller’s study [15], we

attempted to identify characteristics that may act as independent predictors of a

correct response. Keller identified that those with a clinical background were more

likely to identify a HAI correctly. All the respondents to this study were infection

prevention nurses with a clinical background and like Keller, no other significant

predictors were identified in a multivariable model.

Unlike a recent study using clinical vignettes [23], we were unable to estimate

sensitivity and specificity for this study. Although most hospitals use HAI definitions

based on NHSN, there is no uniform national definition for surgical site infection or

CLABSI in Australia, and so there is no gold standard available to measure


sensitivity and specificity. Also, the emphasis and main objective of this study was to

measure agreement, rather than sensitivity and specificity amongst participants.

There are limitations to this study. Selection bias and small numbers may

influence the results. Despite the small number of responses, variation in agreement

is clearly evident. A survey response rate was unable to be calculated as the number

of infection prevention staff in Australia is unknown [24], and we are uncertain how

many received the survey. Approximately 500 ACIPC members subscribe to the list

server, (personal communication, ACIPC secretary June 2014), but not all undertake

HAI surveillance, nor are all infection prevention staff members of ACIPC. It is

estimated there are approximately 215 acute public hospitals with more than 50 beds

in Australia [25], and our respondents were from all States and Territories with a

broad range of experience working in different sized hospitals, and so we are

confident this is representative of those undertaking HAI surveillance. Not all

participants answered each vignette, as they were only required to answer vignettes

relevant to the type of surveillance they usually perform, therefore some vignettes

were correctly not answered. Completing vignettes online does not represent reality,

and many infection prevention staff will discuss potential HAIs before making a

decision, particularly those who work in teams.

A major strength of this study is its anonymity in that there was no pressure

influencing the respondents if they had any uncertainty. This in fact may represent a

more accurate reflection of infection prevention staff true understanding.

6.2.6 Conclusion

The results of this study have been derived from those who are currently

charged with collecting HAI data, and indicate that training and support resources for

those in smaller facilities who work part-time needs to be strengthened.

Before national reporting can be established, robust standardised surveillance

processes need to be implemented. Presently, the validity of existing SAB data is

questionable, and the temptation to aggregate any existing HAI rates to generate

national data must be avoided.


6.2.7 References

1. National Health Performance Authority. MyHospitals. In: MyHospitals.

2015. http://www.myhospitals.gov.au. Accessed 9th March 2015 2015.

2. Australian Commission on Safety and Quality in Healthcare. Standard 3.

Preventing and Controlling Hospital Acquired Infection. Sydney:

Commonwealth of Australia2012 October 10.


wound infections and bacteremia in Australian hospitals. Am J Infect

Control. 1999;27(6):474-81.

4. Russo PL, Cheng AC, Richards M, Graves N, Hall L. Variation in health


Control. 2015. doi:10.1016/j.ajic.2015.02.029.

5. Australian Commission on Safety and Quality in Healthcare. National

definition and calculation of HAI Staphylococcus aureus bacteraemia 2014.

http://www.safetyandquality.gov.au/our-work/healthcare-associated-infection/

national-hai-surveillance-initiative/national-definition-and-caluculation-of-

hai-staphylococcus-aureus-bacteraemia/. Accessed 18 September

2014.

6. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of

health care–associated infection and criteria for specific types of infections in

the acute care setting. Am J Infect Control. 2008;36(5):309-32.

doi:http://dx.doi.org/10.1016/j.ajic.2008.03.002.

7. Haustein T, Gastmeier P, Holmes A, Lucet J-C, Shannon RP, Pittet D et al.

Use of benchmarking and public reporting for infection control in four high-

income countries. Lancet Infect Dis. 2011;11(6):471-81.

8. Cheng AC, Bass P, Scheinkestel C, Leong T. Public reporting of infection

rates as quality indicators. Med J Aust. 2011;195(6):326-7.

doi:10.5694/mja11.10778.

9. Haut ER, Pronovost PJ. Surveillance bias in outcomes reporting. JAMA.

2011;305(23):2462-3. doi:10.1001/jama.2011.822.


10. Leaper D, Tanner J, Kiernan M. Surveillance of surgical site infection: more

accurate definitions and intensive recording needed. J Hosp Infect.

2013;83(2):83-6. doi:http://dx.doi.org/10.1016/j.jhin.2012.11.013.

11. Keller SC, Linkin DR, Fishman NO, Lautenbach E. Variations in

identification of healthcare-associated infections. Infect Control Hosp

Epidemiol. 2013;34(7):678-86. doi:10.1086/670999.

12. Calderwood MS, Kleinman K, Soumerai SB, Jin R, Gay C, Platt R et al.

Impact of Medicare's payment policy on mediastinitis following coronary

artery bypass graft surgery in US hospitals. Infect Control Hosp Epidemiol.

2014;35(2):144-51. doi:10.1086/674861.

13. Lee G, Kleinman K, Soumerai S, Tse A, Cole D, Fridkin SK et al. Effect of

Nonpayment for Preventable Infections in U.S. Hospitals. The New England

Journal of Medicine. 2012;367(15):1428-37.

14. Runnegar N. What proportion of healthcare-associated bloodstream infections

(HA-BSI) are preventable and what does this tell us about the likely impact of

financial disincentives on HA-BSI rates? Australasian College for Infection

Prevention and Control 2014 Conference; 23-26 November 2014; Adelaide,

Australia 2014.

15. Wright M-O, Hebden JN, Allen-Bridson K, Morrell GC, Horan TC. An

American Journal of Infection Control and National Healthcare Safety

Network data quality collaboration: A supplement of new case studies. Am J

Infect Control. 2012;40(5, Supplement):S32-S40. doi:http://

dx.doi.org/10.1016/j.ajic.2012.03.010.

16. Australian Commission on Safety and Quality in Healthcare. Implementation

Guide for Surveillance of Staphylococcal aureus bacteraemia. 2013.

http://www.safetyandquality.gov.au/wp-

content/uploads/2012/02/SAQ019_Implementation_guide_SAB_v10.pdf.

Accessed 18 Septmeber 2014.


17. Grant RL. Converting an odds ratio to a range of plausible relative risks for

better communication of research findings. BMJ. 2014;348:f7450.

doi:10.1136/bmj.f7450.

18. Birgand G, Lepelletier D, Baron G, Barrett S, Breier AC, Buke C et al.

Agreement among healthcare professionals in ten European countries in

diagnosing case-vignettes of surgical-site infections. PLoS One.

2013;8(7):e68618. doi:10.1371/journal.pone.0068618.

19. Lepelletier D, Ravaud P, Baron G, Lucet J-C. Agreement among Health Care

Professionals in Diagnosing Case Vignette-Based Surgical Site Infections.

PLoS One. 2012;7(4):e35131. doi:10.1371/journal.pone.0035131.

20. Mayer J, Greene T, Howell J, Ying J, Rubin MA, Trick WE et al. Agreement

in classifying bloodstream infections among multiple reviewers conducting

surveillance. Clin Infect Dis. 2012;55(3):364-70.

21. Rich KL, Reese SM, Bol KA, Gilmartin HM, Janosz T. Assessment of the

quality of publicly reported central line-associated bloodstream infection data

in Colorado, 2010. Am J Infect Control. 2013;41(10):874-9.

doi:10.1016/j.ajic.2012.12.014.

22. Schröder C, Behnke M, Gastmeier P, Schwab F, Geffers C. Case vignettes to

evaluate the accuracy of identifying healthcare-associated infections by

surveillance persons. The Journal Of Hospital Infection. 2015.

doi:10.1016/j.jhin.2015.01.014.

24. Hall L, Halton K, Macbeth D, Gardner A, Mitchell BG. Roles,

responsibilities and scope of practice: describing the ‘state of play’ for

infection control professionals in Australia and New Zealand. Healthcare

Infection. 2015;doi:http://dx.doi.org/10.1071/HI14037.

24. Australian Institute for Health and Welfare Australian hospital statistics

2012–13. Health services series no. 54. Cat. no. HSE 145. Canberra:

AIHW2014.


Table 1 – Number of vignettes answered by respondents

Number of

vignettes

completed

Percentage of 104*

participants

completing

0 12%

1 6%

2 4%

3 21%

4 8%

5 2%

6 31%

7 (maximum) 17%

* 104 responses represent all those who completed the online survey, 12 did not

complete any vignettes.


Table 2 – Summary of Vignettes and responses (responses in bold indicate correct

response)

Vignette Summary (n=responses) Response

options

Response rate

(95% CI)

1) CABGS patient with 2 SSI and 3

incisions (n=23)

1 SSI from 1

procedure

2 SSI from 1

procedure

2 SSI from 3

procedures

17% (5%-39%)

74% (52%-90%)

9% (1%-28%)

2) Straightforward SSI following hip

replacement (n=81)

Yes SSI

No SSI

100% (96%-

100%)*

0%

3) SSI following bowel resection

with collection requiring surgical

drainage (n=81)

Organ space

SSI

Deep SSI

72% (60%-81%)

28% (19%-40%)

4) Presentation with infected leg

ulcer with subsequent SAB during

admission (n=84)

Yes HAI SAB

No HAI SAB

53% (42%-64%)

47% (36%-58%)

5) CLABSI if applying pre 2008

NHSN criteria 2b (n=57)

Yes CLABSI

No CLABSI

25%(14%-38%)

75% (62%-86%)

6) ICU attributable CLABSI (n=56) Yes CLABSI 63% (49%-75%)


No CLABSI 38% (25%-51%)

7) CLABSI if using 2 calendar days

but not 48 hours (n=55)

Yes CLABSI

No CLABSI

60% (46%-73%)

40% (27%-54%)

95%CI = 95% Confidence Intervals

*exact 95% confidence interval

CABGS – Coronary artery bypass surgery

SSI - Surgical site infection

HAI – Healthcare associated infection

SAB – Staphylococcus aureus bloodstream bacteraemia

CLABSI – Central line associated bloodstream infection


Table 3 – Univariate logistic regression analysis of vignette and respondent characteristics, with the Kruskall–Wallis test of

influence of State or Territory.

Variable

(proportion of

respondents)

n=92

Vignette 1

RR

(95%CI)

Vignette 3

RR

(95%CI)

Vignette 4

RR

(95%CI)

Vignette 5

RR

(95%CI)

Vignette 6

RR

(95%CI)

Vignette 7

RR

(95%CI)

Hospital over 200 beds

(64%)

n/a 1.15

(0.47, 2.10)

1.00

(0.58, 1.36)

0.94

(0.30, 2.15)

0.56

(0.14, 1.41)

1.13

(0.44, 1.90)

Hospital over 400 beds

(38%)

0.95

(0.11, 3.07)

1.50

(0.71, 2.42)

1.10

(0.72, 1.41)

2.42 ^

(1.09, 3.45)

1.02

(0.46, 1.74)

1.07

(0.51, 1.72)

Academic degree or

higher (72%)

0.95

(0.01, 3.24)

1.41

(0.58, 2.41)

1.33

(0.91, 1.59)

1.02

(0.33, 2.27)

0.56

(0.14, 1.41)

1.36

(0.59, 2.05)

Public hospital

(79%)

1.40

(0.14, 3.30)

0.97

(0.29, 2.04)

0.76

(0.32, 1.25)

1.27

(0.37, 2.72)

1.74

(0.71, 2.46)

1.31

(0.44, 2.12)


Less than 5 years

infection control

experience (23%)

1.07

(0.92, 3.15)

0.50

(0.16, 1.35)

0.66

(0.27, 1.13)

1.02

(0.19, 2.53)

0.63

(0.18, 1.56)

1.86

(0.85, 2.42)

Formal surveillance

training (48%)

1.76

(0.29, 3.44)

1.23

(0.54, 2.20)

0.70

(0.35, 1.11)

1.02

(0.19, 2.53)

1.25

(0.54, 2.02)

1.22

(0.56, 1.91)

Trained by central

organisation (21%)

1.07

(0.20, 2.82)

1.53

(0.58, 2.66)

1.02

(0.52, 1.44)

2.27

(0.53, 3.68)

1.04

(0.37, 1.92)

1.00

(0.35, 1.82)

Surveillance skills

assessed (17%)

n/a 0.99

(0.32, 2.34)

0.72

(0.27, 1.25)

0.32 *

(0.09, 0.98)

1.94

(0.85, 2.60)

1.05

(0.37, 1.92)

Work in a team (73%) 2.04

(0.04, 3.81)

2.16 #

(1.14, 2.97)

1.02

(0.58, 1.40)

1.02

(0.33, 2.27)

0.85

(0.26, 1.75)

1.69

(0.86, 2.24)

Daily access to Infectious

Diseases Physician

(59%)

1.73

(0.18, 3.49)

0.89

(0.35, 1.77)

1.05

(0.64, 1.39)

0.53

(0.14, 1.55)

0.58

(0.16, 1.38)

1.17

(0.48, 1.90)


Daily access to

Epidemiologist (1%)

1.35

(0.14, 3.73)

1.14

(0.25, 2.99)

1.39

(0.68, 1.71)

1.45

(0.23, 3.37)

1.63

(0.32, 2.67)

1.20

(0.27, 2.29)

Daily access to

Microbiologist (64%)

1.73

(0.18, 3.49)

0.90

(0.34, 1.81)

0.82

(0.44, 1.23)

1.39

(0.51, 2.65)

1.00

(0.35, 1.87)

0.91

(0.31, 1.72)

Effective full time staff

>3 (27%)

0.49

(0.05, 2.34)

0.84

(0.23, 2.11)

0.69

(0.29, 1.19)

0.76

(0.24, 1.82)

0.95

(0.30, 1.90)

0.39

(0.08, 1.19)

Rarely or never have

access to an ICP with

more experience (43%)

0.49

(0.08, 1.91)

1.51

(0.69, 2.49)

1.07

(0.66, 1.41)

0.66

(0.23, 1.60)

0.93

(0.36, 1.74)

1.04

(0.43, 1.78)

Work part time (34%) 0.26

(0.04, 1.40)

0.57

(0.21, 1.34)

0.83

(0.44, 1.25)

0.72

(0.21, 1.86)

0.63

(0.18, 1.56)

1.05

(2.46, 1.92)

Kruskall–Wallis test for

State/Territory (P-value) 0.0875 0.0454 0.4163 0.0427 0.2826 0.3389

# p=0.011 ^ p=0.033 * p= 0.049


RR = Risk Ratio

95%CI = 95% Confidence Interval


Table 4 – Multivariable analysis of respondent characteristics using Poisson regression of the number of correct answers.

Model A - includes “Work in a team” Model B - excludes “Work in a team”

Variable Risk ratio (95%

CI) P value

Risk ratio (95%

CI) P value

Hospital over 400 beds 0.99 (0.76, 1.31) 0.963 1.04 (0.81, 1.32) 0.766

Academic degree or higher 1.08 (0.83, 1.39) 0.583 1.08 (0.84, 1.40) 0.545

Less than 5 years infection

control experience 0.96 (0.71, 1.31) 0.808 0.96 (0.71, 1.30) 0.806

Daily access to Epidemiologist 1.12 (0.78, 1.61) 0.548 1.12 (0.77, 1.61) 0.555


Work part time 0.92 (0.69, 1.22) 0.555 0.91 (0.69, 1.20) 0.503

Work in a team 1.11 (0.82, 1.50) 0.509 - -

95% CI = 95% Confidence Interval

A risk ratio above 1 indicates an increased chance of a correct answer.

Chapter 7: Characteristics of large healthcare -associated infection surveillance programs 115

Chapter 7: Characteristics of large healthcare -associated infection surveillance programs

7.1 INTRODUCTION

The current status of HAI surveillance in Australia is now understood and gaps

have been identified. The next step was to explore existing large or national

programs to identify key factors that contributed to their development,

implementation and sustainability. There are several well established national HAI

surveillance programs. Arguably the best known, and largest, surveillance program is

conducted across the USA from the CDC/NHSN based in Atlanta, Georgia. Many

European countries also have well established programs, and many countries

included in the European Union also contribute HAI data to the ECDC, generating

data from across Europe.

Typically these national programs are embedded in the various health care

systems, and are relied upon to generate data to inform infection prevention policy at

a national level. Validation studies have been undertaken from within some of these

programs to quantify the sensitivity and specificity, however these studies are

complex and expensive, and don’t provide any qualitative information on

surveillance programs. Whilst the CDC have published guidelines in evaluating

public health surveillance systems and identify key attributes of a surveillance

program, there is a lack of information on the characteristics of HAI surveillance

programs.

To improve our understanding of these large surveillance programs,

specifically barriers and enablers, issues around data quality, and how data are used,

a series of semi-structured interviews were conducted with senior leaders from

well established international and state based Australian HAI surveillance programs.

This is the focus of this chapter.

116 Chapter 7: Characteristics of large healthcare -associated infection surveillance programs

Analysis of the semi-structured interview data study identified five

characteristics of HAI surveillance programs: triggers, purpose, data measures,

processes and implementation and maintenance. The findings from this study can be

used to guide the development of a new surveillance program, and also has the

potential to be used alongside existing quantitative tools to review existing programs.

The semi-structured interviews also served another important purpose. That

was to assist in the identification of attributes for the construction of the discrete

choice experiment presented in Chapter 8.

This manuscript has been accepted for publication in the American Journal for

Infection Control (June 2016).

Chapter 7 Characteristics of large healthcare -associated infection surveillance programs 117

Statement of Contribution of Co-Authors for Thesis by Published Paper.
















___________________________________________________________________




Date

SallyHavers Advisedondataanalysisandmanuscriptpreparation


preparation


preparation


preparation

11/7/2016







Name

Signature

Date 11/7/2016

Dr Lisa Hall


7.2 PAPER FOUR: “CHARACTERISTICS OF NATIONAL AND STATEWIDE HEALTHCARE-ASSOCIATED INFECTION SURVEILLANCE PROGRAMS: A QUALITATIVE STUDY”

Russo PL, Havers S, Cheng AC, Richards M, Graves N, Hall L. Characteristics

of national and statewide healthcare associated infection surveillance

programs: A qualitative study. Am J of Infect Control 2016 (accepted for

publication 24 June 2016).

7.2.1 Abstract

Background: There are many well established national healthcare associated

infection surveillance programs (HAISP). Although validation studies have described

data quality, there is little research describing important characteristics of large

HAISPs. The aim of this study was to broaden our understanding and identify key

characteristics of large HAISPs.

Methods: Semi-structured interviews were conducted with purposively selected

leaders from national and state based HAISPs. Interview data was analysed

following an interpretive description process.

Results: Seven semi structured interviews were conducted over a six month

period during 2014-15. Analysis of the data generated five distinct characteristics of

large HAISPs: 1) Triggers: surveillance was initiated by government or a cooperative

of like minded people, 2) Purpose: a clear purpose is needed and determines other

surveillance mechanisms, 3) Data measures: consistency is more important than

accuracy, 4) Processes: a balance exists between the volume of data collected and

resources, 5) Implementation and maintenance: a central coordinating body is crucial

for uniformity and support.

Conclusions: National HAISPs are complex and affect a broad range of

stakeholders. Whilst the overall goal of HAI surveillance is to reduce the incidence

of HAI, there are many crucial factors to be considered in attaining this goal. The

findings from this study will assist the development of new HAISPs, and could be

used as an adjunct to evaluate existing programs..


7.2.2 Introduction

Background

Surveillance of healthcare associated infections (HAIs) is the cornerstone of

healthcare epidemiology and infection prevention programs.1,2 Whilst Australian

hospitals are expected to perform HAI surveillance,3 there is no nationally

coordinated HAI surveillance program. Despite vast amounts of resources being used

for HAI surveillance in Australia,4,5 there remains a lack of uniformity and an

inability to generate national data.6,7 This contrasts with many other countries,

including the USA,8 Germany,9 and England,10 where national surveillance programs

are well established.

Although there have been a number of validation studies of national HAI

surveillance programs to measure data quality,11 there is a lack of research exploring

basic characteristics typical of large HAI surveillance programs. Whilst key

attributes of public health surveillance programs have been identified,12 there

remains a gap in understanding of how these would apply in a healthcare setting.

More information is needed on a number of issues, in particular, barriers and

enablers in implementing and maintaining surveillance programs, factors that

influence data quality, how HAI data are used and the upstream affect this can have

on those involved in managing the data.

This is important because once these characteristics have been identified and better

understood, this knowledge could then be used in program evaluation, and

importantly in this situation, to inform the development and implementation of an

Australian HAI surveillance program.

To address this knowledge gap, we undertook a series of in depth semi-structured

interviews with experts involved in the development and implementation of large

(national and statewide) HAI surveillance programs.

7.2.3 The study

Aim

The aim of this study was to broaden our understanding of the key characteristics of

large HAI surveillance programs, specifically barriers and enablers to


implementation, data quality, and how data are used, in order to inform the design of

a national surveillance strategy in Australia.

Design

A qualitative study design characterised by semi-structured interviews was used to

explore key characteristics of large HAI surveillance programs through describing

the viewpoints of surveillance experts who have had key roles in designing and

implementing such programs.

Participants

Participants were purposively selected because of their expertise in HAI surveillance

and experience in developing, implementing and maintaining large surveillance

programs. All international participants were from developed countries with national

surveillance programs. Not all countries had publicly funded healthcare.

Data collection

Semi-structured interviews were undertaken between October 2014 and March 2015.

All interviews were conducted by an author (PLR) with sound knowledge of HAI

surveillance to allow for in depth discussion with the experts. All interviews were

conducted in English, either in person, by phone, or via online video discussion.

A general interview guide was developed based on key surveillance literature

(Appendix).12-14 Topics focussed on the key attributes of surveillance identified from

the CDC guidelines for public health surveillance,12 other factors relating to the

barriers and enablers during development, implementation and maintenance of the

program, participation requirements and incentives, the role of a central coordinating

body, data usage and reporting processes.

Ethical considerations

The study was approved by the Queensland University of Technology Human

Research Ethics Committee (approval number 1500000304). Written consent was

obtained from each expert, and participants were de-identified to ensure anonymity

and confidentiality.

Data analysis

All interviews were digitally recorded and transcribed verbatim. Content analysis

was conducted by two authors (PLR, SH) who coded all transcripts and

independently generated lists of major and minor themes. Both authors are


experienced infection prevention (IP) professionals, however, neither have had an

association with a formal national HAI surveillance program.

A process of interpretive description was undertaken whereby the themes were

critically examined.15 The process of interpretive description relies on intellectual

inquiry where the researchers constantly explore and question the findings. In a

workshop situation, three authors (PLR, SH, LH) reviewed the codes - renaming,

merging or eliminating where appropriate until consensus was reached on a final set

of themes that were believed to appropriately categorise and describe the phenomena

being studied. Importantly it was intended for the themes to have practical

application potential by providing a structure that could aid a review of existing

programs or be applied to the planning of new programs.

Rigour

The integrity of this research can be demonstrated by addressing the criteria of

credibility, dependability, confirmability and transferability.16 Credibility relates to

the accuracy and appropriateness of the data.17 To achieve this, all interviews were

transcribed verbatim and transcripts were provided back to each expert for review of

accuracy. An audit trail of methods, data analysis and decisions made was

maintained to support the dependability of the work. Confirmabilty was achieved by

maintaining notes of discussions from researcher meetings and throughout the

interpretative description process. Whilst this study is novel in its purpose, the

findings are transferrable to any HAI surveillance program given they share the

overall purpose of reducing the incidence of infection. The study described in this

manuscript was also assessed against the Critical Appraisal Skills Programme

(CASP) checklist for qualitative studies to ensure the reporting is of high quality.18

7.2.4 Results

Semi-structured interviews were conducted with seven participants and lasted

between 40 to 80 minutes. Four were current or former leaders from different

national HAI surveillance programs from countries with populations ranging from 5

million to 300 million, two were leaders of different Australian statewide

surveillance programs and one from a national health agency. Interviews with further

participants were not conducted due to data saturation.


Our analysis identified five distinct characteristics; triggers, purposes, data measures,

processes, and implementation and maintenance. The key characteristics are

summarised in Table 1 and described in more detail below.

Table 1 – Summary of key characteristics of large HAI Surveillance programs

Key Characteristic Features

Triggers Top down - government initiated

Bottom up – Cooperative of like minded people

Purpose Clear and well communicated

Determine mechanics of other surveillance processes

Data measures Data quality

Consistency more important than accuracy

Processes Balance between volume of data collected and resources available

Data use influences surveillance processes

Implementation and

maintenance

Central coordinating body with specific expertise

Mandatory participation

Triggers

The term triggers relates to the reasons why surveillance programs commenced. Two

different types of triggers were identified, “top down” and “bottom up”. Top down

triggers have been related to a governmental response to an outbreak, the sudden

realisation of a paucity of reliable HAI data to direct policy, or the appreciation of

the burden of HAIs. One government’s response to an outbreak was labelled a “call

to action” and described by the expert as an overt demonstration that government

was actively addressing the issue.

“a large outbreak…resulted in a number of deaths, and it was a large

hospital…and it resulted in a government, ministerial action plan. So this

outbreak was the catalyst for them taking healthcare associated infection …

seriously as a political issue. And because it was a political issue it created the

healthcare associated infection taskforce...” Expert 1.


The bottom up trigger emerges from the opposite direction, where a collective of

like-minded experts who understand the benefits of coordinated surveillance

collaborate to establish a network of hospitals applying the same methodology

enabling comparison of data.

“a cooperative which is run separately from the Health Department, that

determines what surveillance we’re going to have” Expert 2.

One expert believed that a major strength in the bottom up approach was that those

who participated were doing so because they understood the value of HAI

surveillance. It was stated that surveillance participants have a strong vested interest

in ensuring the quality of the data. Experts believed that with no externally applied

mandate and no threat of negative consequences, there was strong buy in and

enthusiasm for the program that greatly enhanced likelihood of uptake and success.

“It was done by people who were really interested and wanted to do it” Expert

2.

Characteristic of the voluntary programs described by the experts was the

confidentiality of the data. Experts stated that the assurance of hospital anonymity

was favourable to hospital executive and IP staff and added to the likelihood of

uptake as well as the quality of the data.

Another attraction described by the experts was their autonomy from health

departments. However one expert described that this also meant without the

imprimatur of a governmental body, some clinicians and executive staff may have

been less likely to value the data.

The success of cooperative programs has led to health departments providing

resources to centralise and expand surveillance programs. Whilst this has enhanced

some programs, the experts noted that it also meant compromising on autonomy,

voluntary participation and confidentiality of the data.

“and that’s when the State decided it [surveillance] was going to be a branch

of the Health department because they were funding it. “Expert 2.

Triggers may influence some of the start up activities of the surveillance program,

and in particular, what key stakeholders see as the purpose of the program.


Purpose

Surveillance programs need to have a clear, well communicated purpose that is

understood by all key stakeholders.1,12 Whilst there was general consensus amongst

the experts that the overall purpose of a HAI surveillance program is to reduce the

incidence of HAI, it was noted that commonly data are used to make comparisons

and facilitate benchmarking.

“They wanted to be able to see how they were doing compared to other people.

So there’s undoubtedly a need to do that. The second thing they said was, we

really value having a standardised system we can participate in because that

enables us to benchmark.” Expert 3.

There are often competing demands from a range of stakeholders in how data are

used. Some experts expressed concern that data generated from HAI surveillance

programs are being used for unintended purposes. One expert noted that initially

clinicians used the data to generate infection rates, then safety and quality bodies

wanted data for clinical performance indicators, and now health departments

want data as a hospital performance indicator, and possibly to financially penalise a

hospital. “the one source of truth if you like for all healthcare associated infections …

and that’s the data we use then for doing our safety and quality reporting,

executive dashboard reporting and then the more detailed epidemiological

reports…”Expert 4.

So although the initial purpose of a surveillance program relates to reducing HAIs,

once data are available it becomes used for multiple purposes.

Data measures

Core to the success of all surveillance programs is data quality.1,12,13 In an ideal

surveillance program, data would be completely accurate, and consistency

guaranteed. In reality neither are likely to be completely achievable due largely to the

amount of resources required.

Highly accurate HAI data infers that all HAIs under surveillance are identified. Such

extensive case finding is resource intensive, and must be balanced against available

resources and the priorities of the IP program. One expert noted that their

surveillance program captured 90% of its surgical site infection data, which was

considered to be adequately accurate. The amount of effort that was required to


identify the remaining 10% was considered an inefficient use of resources. The

majority of experts supported this view.

Several experts stated that consistent application of definitions and case finding

methodology is vital for valid comparison of data between facilities. One expert

described that their program had made minor amendments to a definition many years

after commencement, acknowledging that their ability to compare data before and

after the introduction of these amendments would be compromised. However this

contrasted to several recent changes to definitions over a reasonably short period,

which led the expert doubting the validity of current data.

National surveillance programs rely on many different personnel to collect data

across all the participating hospitals. Whilst absolute consistency is impossible to

guarantee, some programs provide regular assessments as a form of ‘proxy’

validation processes. Experts described providing refresher sessions, workshops,

conferences and online vignettes for surveillance personnel in an attempt to maintain

consistency.

Even though not optimal, one expert considered this type of validation important for

the credibility of the surveillance program.

“So validation takes money and one of the things that, because its so

important…whether you have a confidential system or a mandatory system it’s

even more important I think with a mandatory system because you don’t want

people to game the system. But either way if you want to believe the data and to

be able to make it actionable you need to validate it.” Expert 5.

Two experts commented that accuracy was considered to be more important for the

clinicians whose performance may be reflected in the HAI data (e.g. surgeons).

However strong consensus amongst the experts was that consistency is more

important than accuracy.

“When you run a national surveillance system you understand it’s about

consistency, not perfection.” Expert 3.

The quality of surveillance data is also influenced about how the data are

collected, and what happens to the data afterwards. This could be viewed as the ‘data

journey’, which is core to the next theme, processes.


Processes

Surveillance is a cyclical process. Data collection, risk adjustment, analysis and

reporting processes contribute to the simplicity of the overall program, which

determines acceptance and timeliness.12

Some surveillance processes described in the interviews were quite prescriptive.

However, flexibility in data management processes was favoured by those who

acknowledged the differences between hospital resources. One expert described that

they weren’t particularly concerned how the data are managed at a hospital, so long

as it arrived into their central database in the correct format.

One expert noted that the collection of routine epidemiological data doesn’t require

highly skilled professionals. However the application of surveillance definition

criteria requires objectivity, training and skill, and several experts agreed that the IP

teams are best placed to do this. Two experts believed IP staff would be able to teach

these skills to others.

“You actually need well trained but low level staff to collect the data and the

decision as to whether its an infection or not is independent because its based

on a set of criteria, its not the same as a clinical ‘I’m going to treat this patient

because they’ve got an infection’. If it meets the criteria, then it’s an infection.”

Expert 3.

However, one expert expressed concern over the recent introduction of an automated

process that provided the health department with real time access to laboratory based

infection data at participating hospitals. Hospitals participating in this mandatory

surveillance system are subject to financial penalty, and the ability for the health

department to access such data without any expert interpretation was of great

concern to the expert who feared a misinterpretation of the data.

There was general consensus that if data is used for comparisons then risk adjustment

is required. Risk adjustment requires collection of data beyond the basic

epidemiological data, adding to the complexity of surveillance. Views differed as to

how much risk adjustment is necessary. One expert described the use of complex

algorithms built from a range of patient factors requiring access to more data, though

these are better suited to hospitals with electronic data feeds. Without electronic data

feeds, the balance of data required for the algorithms and resources available would

be a major consideration.


“putting a whole bunch of risk factors together…to me that just exacerbates the

problem, it means you have to collect risk factor data on a whole bunch of

things, you’ll have missing data, which means you have to exclude those

records and your estimate is then reduced.” Expert 3.

Despite differences amongst the experts as to how much risk adjustment was

necessary, there was general consensus that some type of risk adjustment should be

attempted, particularly if data are to be publicly reported for comparison

and benchmarking.

“Well you know I would say its better than nothing. Anything is better than

nothing, anything is better than a crude SSI rate for the hospital.” Expert 5.

Looming large at the end of the data journey are the potential consequences for the

hospitals in an era of public reporting. On a cautious note, several experts expressed

concern about an unintended consequence of associating HAI data with financial

penalties for hospitals with higher rates.

“once you start putting penalties in you start getting people game the data.”

Expert 2.

Two expressed concern about the influence financial penalties had on IP teams. It

was described that IP teams undertaking surveillance are frequently placed in

difficult situations when the data they report results in a financial penalty to the

hospital. The purpose of a financial penalty is to incentivise the hospital into

improving performance. However as one expert noted, a hospital that reports a high

rate may already suffer from a lack of resources which may have led to the high

infection rate in the first place, and to penalise them further will only place more

patients at risk.

“when you have disincentives to report like targets, financial penalties, you

know it results in all sorts of perverse behaviour sometimes in surveillance.”

Expert 1.

Despite the lack of evidence supporting the use of financial penalties, there was a

general sense amongst the experts that it was inevitable that HAI data would be

associated with them.


Implementation and Maintenance

Common to any large surveillance program is a central coordinating body. Typically,

the roles of the central body are to establish and communicate surveillance goals,

develop protocols, provide education and support to participating facilities, collate

and analyse national data and provide reports to key stakeholders. Two experts stated

that ideally staff of a central body would have expertise in surveillance,

epidemiology, IP, infectious diseases, microbiology and implementation.

Experts described the necessity to ‘sell’ the program to hospital executive and

clinicians, which can be resource intensive. Two experts described personally

visiting all the hospitals that were considering participation, explaining the program

and convincing them of the benefits of surveillance.

“So I sit down and have a cup of coffee and say, ‘between you and me, they’re

going to make you do it so why don’t you say yes, you know me and trust me so

let’s do it’. So there was a fair degree of personal contact that was involved in

that originally.” Expert 2.

The level of ongoing funding was seen to ebb and flow over time in one program and

there was a sense of loss described from those involved in voluntary programs as

they evolved into mandatory government programs. One expert described

disappointment with this type of transition.

“it stopped being a cooperative of interested hospitals outside the Health

Department to being a Health Department initiative, where the data was

basically more Health Department collected data.” Expert 2.

One expert reported that IP staff expressed concern that with the transition from a

voluntary to a mandated government program, the quality of the existing data would

be diluted. They also described the close relationship the central body had with the

participants of the voluntary program. This meant they knew which hospitals

required more support, and provided them with an understanding of the quality of the

data from each hospital. Concern regarding the dilution of data quality in mandatory

surveillance programs was supported by another expert who stated that rather than IP

staff thinking about the purpose of surveillance, and planning their surveillance

activities appropriately, they do the minimum required to meet the mandatory

demands.


“But actually infection control in this country now is so driven by the

Department of Health that people do what the Department of Health tell them

to do. They don’t necessarily, they’re not innovative and actually ‘I'm going to

do this because it makes a difference’ because there are so many instructions

about data you have to collect and provide, most of which isn’t surveillance,”

Expert 3.

There was general support for mandating surveillance. One expert believed that

mandating surveillance forces dissenters into participation. Several experts indicated

that although mandating surveillance came with its own issues, it at least acted as a

stimulus for surveillance activities to commence, and eventually with time, provided

HAI data with which to base interventions.

Mandating participation does not guarantee engagement. In one program where

surveillance was mandated, hospital IP staff did not believe that surveillance was

their role. This situation led the expert to express concerns about the quality of the

data if those collecting it didn’t believe in that role. In contrast, in the setting of a

voluntary program, another expert described that IP staff from hospitals were

constantly requesting that they be able to participate because they believed HAI

surveillance added credibility to their overall program.

Flexibility and choice was seen as important by an expert who described that even

though their program did have some mandatory aspects, it also offered a choice of

surveillance activities for hospitals. Another expert stated that in their program,

hospitals were mandated to do surveillance, but they weren’t mandated to participate

in the national surveillance program, so long as they participated in a program.

7.2.5 Discussion

This study has identified five distinct but related characteristics of large HAI

surveillance programs; triggers, purpose, data measurements, implementation and

maintenance, and processes. Data obtained from the interviews has provided a

unique insight into the broad and complex issues that must be considered in the

development, implementation and maintenance of surveillance programs.

Regardless of the triggers for surveillance, purpose is crucial at the outset and

remains important throughout the lifetime of a surveillance system. The purpose

needs to be clear and well understood, and needs to take into consideration of how


the data should be used. This study identified strong concerns that data from HAI

surveillance programs is being used by external agencies to measure overall hospital

and healthcare worker performance. These concerns are consistent with recent

reports in the literature that describe the predicament often faced by IP staff as a

result of this added purpose.19-21 If the data are to be used for multiple purposes, this

needs to be clearly identified and communicated.

Consistency of surveillance processes is considered more important than accuracy,

and the volume of data collected must be balanced against the resources available.

To support the credibility of a surveillance program, some level of validation needs

to be demonstrated. The study also identified that the central coordinating bodies

with specific expertise, together with some form of mandatory yet flexible

participation are characteristic of strong well established national programs. The

crucial role of a central body to ensure standardisation and provide support is well

described.14,22,23

HAI surveillance programs are complex interventions that affect a wide variety of

healthcare workers in different ways. Although the surveillance programs referred to

in this study are well established, several of the issues described were specific to

implementation, which points to the value of an implementation framework in the

planning or evaluation stage.

The normalisation process theory (NPT)24 has been used across a variety of health

settings as an implementation framework for a range of interventions, and in

particular is specific to complex health interventions.25 NPT is distinguished by its

focus on stakeholder engagement, acknowledges the role of opinion leaders, and

addresses the roles and relationships of stakeholders.25 A major strength of the NPT

is that it can be used in the design phase of the intervention to support the various

interactions between the stakeholders required for implementation.26 The NPT

consists of four major constructs; coherence, cognitive participation, collective action

and reflexive monitoring,24 and can be identified in the data from this study. The

issue described where IP staff did not believe surveillance was their role is consistent

with collective action; skill set workability domain of NPT which refers to the

division of labour of an intervention.24 Purpose was one of the main themes

identified from this study, and is included within the coherence domain of NPT,

which explores the clarity of purpose and the shared sense of purpose.26


Whilst it was not a specific aim for this study, these findings suggest that the

implementation of a HAI surveillance program would clearly benefit from the

application of an implementation framework.

Limitations

The study has some limitations. The views of the person representing a surveillance

program may not be representative of all those working in the program. Although

each participant was provided with transcripts of the interview to check for accuracy,

the analysis of themes was not provided to participants.

A strength of this study is that four of the participants were from four different

countries each with well established surveillance programs, and each of the

international experts had key roles at the start up and long term maintenance of the

program.

7.2.6 Conclusion

Large HAI surveillance programs are complex, and the development, implementation

and maintenance of a surveillance program presents many challenges. This study

identified the key characteristics of national and statewide surveillance programs

through the use of an interpretative descriptive analysis of the rich data acquired

from in depth semi-structured interviews with experts from a range of large

surveillance programs.

The findings from this study are relevant and meaningful to stakeholders considering

the development of new surveillance programs particularly by highlighting the

barriers and enablers that would need to be addressed through an implementation

strategy.


7.2.7 References


Woeltje KF, Malani PN, eds. Practical Healthcare Epidemiology. 3rd ed.

London: The University of Chicago Press; 2010:111-142.

2. Scheckler WE, Brimhall D, Buck AS, et al. Requirements for Infrastructure

and Essential Activities of Infection Control and Epidemiology in Hospitals:

A Consensus Panel Report. Infect. Control Hosp. Epidemiol. 1998;19(2):114-

124.


Preventing and Controlling Hospital Acquired Infection. Safety and Quality

Improvement Guide. 2012. http://www.safetyandquality.gov.au/wp-

content/uploads/2012/10/Standard3_Oct_2012_WEB.pdf. Accessed 10

October 2013.

4. Mitchell BG, Hall L, Halton K, MacBeth D, Gardner A. Time spent by

infection control professionals undertaking healthcare associated infection

surveillance: A multi-centred cross sectional study. Infection, Disease &

Health. 2015;21(1):36-40.

5. Mitchell BG, Hall L, MacBeth D, Gardner A, Halton K. Hospital infection

control units: Staffing, costs, and priorities. Am. J. Infect. Control.

2015;43(6):612-616.

6. Havers SM, Russo PL, Mitchell B, Hall L. Health-care-associated infections.

The Lancet Infectious Diseases. 2015;15(7):763-764.


care-associated infection surveillance practices in Australia. Am. J. Infect.

Control. 2015;43(7):773-775.

8. Dudeck MA, Edwards JR, Allen-Bridson K, et al. National Healthcare Safety

Network report, data summary for 2013, Device-associated Module. Am. J.

Infect. Control. 2015;43(3):206-221.

9. Schroder C, Schwab F, Behnke M, et al. Epidemiology of healthcare

associated infections in Germany: Nearly 20 years of surveillance. Int. J.

Med. Microbiol. 2015;305(7):799-806.


10. England PH. Guidance: Surgical site infection surveillance service (SSISS).

2016; https://www.gov.uk/guidance/surgical-site-infection-surveillance-

service-ssiss. Accessed 31 May, 2016.

11. Fabry J, Morales I, Metzger M-H, Russell I, Gastmeier P. Quality of

information: a European challenge. J. Hosp. Infect. 2007;65, Supplement

2(0):155-158.

12. German RR, Lee LM, Horan JM, et al. Updated guidelines for evaluating

public health surveillance systems: recommendations from the Guidelines

Working Group. MMWR Recomm. Rep. 2001;50:1-35.

13. Drewe JA, Hoinville LJ, Cook AJ, Floyd T, Stark KD. Evaluation of animal

and public health surveillance systems: a systematic review. Epidemiol.

Infect. 2012;140(4):575-590.

14. Gastmeier P, Sohr D, Schwab F, et al. Ten years of KISS: the most important

requirements for success. J. Hosp. Infect. 2008;70 Suppl 1:11-16.

15. Thorne S, Kirkham SR, O'Flynn-Magee K. The Analytic Challenge in

Interpretive Description. International Journal of Qualitative Analysis.

2004;3(1):1-20.

16. Lincoln YS, Guba EG. Naturalistic Inquiry. Newbury Park, CA: Sage

Publications; 1985.

17. Denscombe M. The Good Research Guide. 5th Edition ed. Berkshire,

England.: Open University Press; 2014.

18. Critical Appraisal Skills Programme [CASP]. CASP Checklists. 2014;

http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf

. Accessed 18th November 2015.

19. Dixon-Woods M, Leslie M, Bion J, Tarrant C. What counts? An ethnographic

study of infection data reported to a patient safety program. Milbank Q.

2012;90(3):548-591.

20. Horowitz HW. Infection control: Public reporting, disincentives, and bad

behavior. Am. J. Infect. Control. 2015;43(9):989-991.

21. Talbot TR, Bratzler DW, Carrico RM, et al. Public reporting of health care-

associated surveillance data: recommendations from the healthcare infection


control practices advisory committee. Ann. Intern. Med. 2013;159(9):631-

635.

22. Desenclos J-C, Raisin Working Group. RAISIN - a national programme for

early warning, investigation and surveillance of healthcare-associated

infection in France. Euro Surveillance: Bulletin Europeen sur les Maladies

Transmissibles = European Communicable Disease Bulletin. 2009;14(46).

23. Russo PL, Bull A, Bennett N, et al. The establishment of a statewide

surveillance program for hospital-acquired infections in large Victorian

public hospitals: a report from the VICNISS Coordinating Centre. Am. J.

Infect. Control. 2006;34(7):430-436.

24. May C, Finch T. Implementing, Embedding, and Integrating Practices: An

Outline of Normalization Process Theory. Sociology. 2009;43(3):535-554.

25. McEvoy R, Ballini L, Maltoni S, O'Donnell CA, Mair FS, MacFarlane A. A

qualitative systematic review of studies using the normalization process

theory to research implementation processes. Implementation Science.

2014;9:2.

26. Murray E, Treweek S, Pope C, et al. Normalisation process theory: a

framework for developing, evaluating and implementing complex

interventions. BMC Med. 2010;8:63.


Appendix - Semi Structured interview guide – HAI surveillance

• Can you describe your association with the surveillance program?

• What was the trigger for the establishment of your program?

• When establishing the program, what were the enablers and barriers?

• Thinking back to the early days of the program, can you tell me about any

barriers you faced during implementation?

• And how were these addressed?

• Do you still face the same barriers today?

• What was the funding source of the program, has it remained the same?

• Has ongoing funding been threatened, and how has this been addressed?

• What role does the central coordinating centre play, and with what resources?

• What role, how crucial, is the role of a central coordinating centre?

• What would you say are the strengths of the program?

• What about the weaknesses?

• Of those (CDC) attributes do you consider any to be more important than

others?

• Which of those attributes do you think is the most important?

• Can you reveal any other attributes that are important?

• If Australia was to develop a national program, which of these attributers do

you think it should focus on? Initially? Implementation phase?

The potential DCE attributes (based on the CDC Guidelines) and sample questions

include:

• Simplicity

⁃ What does the ‘simplicity’ of a program mean to you?

⁃ Does data flow through the system easily?


⁃ Does the program integrate with other systems?

⁃ Does the program require staff to be trained?

⁃ What is the method for collecting, collating, analysing and reporting

data?

• Flexibility

⁃ What does the ‘flexibility’ of a program mean to you?

⁃ Can the program be scaled up for new infections without requiring too

many new resources?

⁃ Can the program be used to collect data for local use only?

• Data quality

⁃ What does the ‘data quality’ mean to you?

⁃ Does the program accept incomplete data?

⁃ Does the system have data quality validation rules built in?

⁃ Have any validation studies been undertaken?

• Acceptability

⁃ What does the ‘acceptability’ of a program mean to you?

⁃ Is there a high participation rate?

⁃ Do policy makers use the data?

• Sensitivity

⁃ What does the ‘sensitivity’ of a program mean to you?

⁃ Does it detect real changes in trends?

⁃ Has the sensitivity been assessed over time?

• Representativeness

⁃ What does the ‘representativeness’ of a program mean to you?

⁃ Are the features of the population of interest reflected in the data?

• Timeliness


⁃ What does the ‘timeliness’ of a program mean to you?

⁃ Is the time between surveillance steps acceptable and are reports

generated in a timely manner?

To complete the interview, open ended question regarding the leaders general

opinion on success and future improvement will be asked.

• What has made this program successful?

• How could this program be improved?

Chapter 8: Stakeholder preferences for a national healthcare-associated infection surveillance program 139

Chapter 8: Stakeholder preferences for a national healthcare-associated infection surveillance program

8.1 INTRODUCTION

Following on from identifying characteristics of surveillance program detailed

in the previous chapter, the final stage of this research was to identify what type of

surveillance program would be best suited to Australia. Given that Australia does not

have a national program, an ideal opportunity existed to engage key stakeholders to

identify what they considered most important. This type of engagement has not been

reported previously.

To identify priorities, a discrete choice experiment (DCE) was conducted with

key stakeholders from across Australia. Although novel in this setting, the DCE was

chosen because it has been found to be appropriate in settings where there are

competing demands for limited resources, and also because it can provide

quantitative data on the strength of preferences.

Data from the semi-structured interviews described in Chapter 7 were used to

assist the construction of the DCE. Several attitudinal questions regarding

surveillance were also included in the DCE to improve our understanding of

participant views towards surveillance.

The findings have established that stakeholders believe a national surveillance

program would be beneficial, and prefer mandatory participation with publicly

reported outcome data. It is also suggested the DCEs may also be applicable to other

infection prevention initiatives.

This is the first time such data has been identified in Australia, and has

provided a clear understanding on which elements of a surveillance program are

preferred. This also provides important information that can be used to increase the

likelihood of successful implementation.

140 Chapter 8: Stakeholder preferences for a national healthcare-associated infection surveillance program

This manuscript has been published in BMJ Open.

















___________________________________________________________________




Date

GangChenAdvised on experiment design, data analysis and



preparation


preparation

11/7/2016



preparation

JulieRatcliffeAdvised on experiment design, data analysis and







Name

Signature

Date 11/7/2016

Dr Lisa Hall


8.2 PAPER FIVE: “NOVEL APPLICATION OF A DISCRETE CHOICE

EXPERIMENT TO IDENTIFY PREFERENCES FOR A NATIONAL

HEALTHCARE ASSOCIATED INFECTION SURVEILLANCE

PROGRAMME: A CROSS-SECTIONAL STUDY”

Russo PL, Chen G, Cheng AC, Richards M, Graves N, Ratcliffe J, Hall L.

Novel application of a discrete choice experiment to identify preferences for a

national healthcare-associated infection surveillance programme: a cross-

sectional study. BMJ Open 2016; 6(5): e011397.

8.2.1 Abstract

Objective

To identify key stakeholder preferences and priorities when considering a

national healthcare associated infection (HAI) surveillance programme through the

use of a discrete choice experiment (DCE).

Setting

Australia does not have a national HAI surveillance programme. An online

web based DCE was developed and made available to participants in Australia.

Participants

A sample of 184 purposively selected healthcare workers based on their senior

leadership role in infection prevention in Australia.

Primary and Secondary Outcomes

A discrete choice experiment requiring respondents to select one HAI surveillance programme over another based on five different characteristics (or

attributes) in repeated hypothetical scenarios. Data was analysed using a mixed logit

model to evaluate preferences and identify the relative importance of each attribute.

Results

A total of 122 participants completed the survey (response rate 66%) over a

five week period. Excluding 22 who mismatched a duplicate choice scenario,

analysis was conducted on 100 responses. The key findings included: 72% of

stakeholders exhibited a preference for a surveillance programme with continuous

mandatory core components (mean coefficient 0.640 [p<0.01]), 65% for a

standard surveillance protocol where patient level data are collected on both

infected and not infected patients, (mean coefficient 0.641 [p<0.01]), and 92% for

hospital level data


that is publicly reported on a website and not associated with financial penalties

(mean coefficient 1.663 [p<0.01]).

Conclusions

The use of the DCE has provided a unique insight to key stakeholder priorities

when considering a national HAI surveillance programme. The application of a DCE

offers a meaningful method to explore and quantify preferences in this setting.

Strengths and weaknesses

• This study is the first reported use of a discrete choice experiment in the area

of healthcare associated infection surveillance

• The results offer a unique insight into the priorities of stakeholders when

considering healthcare associated infection surveillance programmes

• Not all healthcare associated infection surveillance stakeholder groups

participated

8.2.2 Background

A healthcare associated infection (HAI) is an infection that occurs as a result of

a healthcare intervention.1 Common HAIs include a bloodstream infection after the

insertion of an intravenous catheter, or a wound infection following surgery.

Preventing HAIs requires a multimodal approach.2 Although surveillance of HAIs is

acknowledged as crucial to HAI prevention,3 Australia is yet to develop a national

HAI program, and existing State and Territory programs are known to have broad

variation of practices and a lack of agreement in identifying HAIs.4,5

There are many stakeholders in HAI surveillance, these include clinicians,

hospital executives, governing and regulatory bodies, funders and of course

consumers. Ideally data should be used by clinicians to drive infection prevention

efforts and reduce the incidence of HAIs.6 Data has also been used to measure

hospital performance and, despite a lack of evidence as a driver to reduce infection,

hospitals have been financially penalised based on this data.7,8 As such, there are

competing demands from a surveillance program.

A national HAI surveillance program designed to meet the needs of all

stakeholders may not be possible. This study sought to employ discrete choice


experiment (DCE) methodology to identify the most important considerations for

those involved in HAI surveillance and to assess the degree of convergence or

otherwise in the preferences of key stakeholder groups.

DCEs are a quantitative attribute based survey method, used to elicit

preferences for healthcare products, interventions, services, policies or programs.9-11

Typically, DCEs offer participants a series of hypothetical choice scenarios

comprising two or more scenarios that vary according to several key characteristics

or attributes, where the participants are required to indicate their preferred scenario.12

A form of stated preference, DCEs are able to provide information on the relative

importance of the attributes presented in the hypothetical scenarios.13

DCEs may be considered as more cognitively challenging for participants than

other ordinal approaches to preference elicitation e.g. ranking and rating methods.14

However, the main advantages of DCEs are they present choices in a manner that is

potentially more relevant to the participants and they provide more information as

they generate quantitative data on the strength of preferences and trade offs, and the

probability of take up.9,13

Extensively used in health economics DCEs have recently been used to assist

in developing priority setting frameworks and clinical decision making.10 In public

health settings, DCEs have been used for priority setting frameworks where decision

makers are required to manage competing demands with limited resources.15-17 DCEs

have also been used to predict uptake of new policies or programs.18

The main objective of the study was to identify key stakeholder preferences for

a national surveillance program. This will provide crucial information on potential

acceptance of a surveillance program, and provide insight into how stakeholders

consider certain elements of surveillance. This data will be vital for informing the

future design and implementation of a national HAI surveillance program in

Australia.

8.2.3 Methods

Identification of attributes and levels

There several key stages in the development of a DCE. The first step in the

construction of a DCE is the identification of attributes and levels of the intervention

being valued. The chosen attributes and their respective levels are the key factors that


will influence the choice of one surveillance program over another.14 Hence, it is

important that the chosen attributes and levels for the DCE are realistic and salient to

the participants within the context in which the DCE is being applied.9,11,19

To identify the attributes and levels we used two methods commonly described

in the literature.11 First, a review of the literature was undertaken which identified

key articles describing health related surveillance systems and their attributes.20-22

Second, seven semi-structured interviews were conducted with experts in HAI

surveillance. Participants were purposively selected because of their expertise in HAI

surveillance and experience in developing, implementing and maintaining large

surveillance programs. Four interviews were with leaders from four different

international HAI surveillance programs, two with leaders of different state

surveillance programs in Australia and one interview with an expert from a national

body representing national surveillance policy. Using attributes identified from the

literature review, an interview guide was constructed for the purpose of

corroborating these attributes or identifying new ones. Content analysis using

interpretive description was conducted on the transcripts of the semi-structured

interviews to identify major themes, which were then compared to the attributes

identified in the literature. Themes that did not align with those from the literature

were used to construct questions about potential new attributes.

Initially fourteen potential attributes were identified. Following review, some

of these attributes were collapsed to form six major attributes. Through a series of

discussions between the researchers (PLR, LH, JR, GC,) the attributes were further

refined to five (Figure 1). The attributes deemed to be most important in the initial

design and implementation of a national HAI program were: 1) mandatory

participation requirements, 2) the type of surveillance protocol, 3) frequency of

competency assessments of those collecting data, 4) the overall accuracy of the data,

and finally, 5) how the data were to be reported.

The levels for each attribute were selected based on a number of

considerations. In accordance with best practice guidance for the design and conduct

of DCEs in health care they needed to be plausible, actionable and provide a range of

options without being too extreme.23 The final levels selected largely reflected a

variety of current practices from existing international and local, state based


surveillance programs. The final attributes and levels are described in more detail in

Table 1.

Figure 1 – Development of attributes for the discrete choice experiment

Legend

a. Resources required to undertake surveillance

b. Cost effectiveness of the HAI surveillance programme

c. Simplicity of the surveillance programme. e.g. amount of data required, ease of access to

data

d. Efficiency of surveillance processes (commonly related to resources and simplicity)

e. Comparisons of HAI data with other like facilities or a benchmark

f. Flexibility of the programme. e.g. is it able to be tailored to meet individual needs, does it

require all infections or is it targeted?

Flexibilityf

Automa0onk

Consistencyl

DataQualityh

Validityi

Training&skillj

Costeffec0veb

Mandatoryg

Resourcesa

Simplicityc

Efficiencyd

Comparisonse

Accuracyofdata

Accuracy,Se,Spm

Valida0onofdata

Breadthofprogram

Intensityofsurveillance

Repor0ngofdata

RiskAdjustment

Accuracy

Par)cipa)onrequirements

SurveillanceProtocol

Competency

Repor)ng

PR,PM&FPn

FinalA:ributes


g. Mandatory components required for participation

h. Data quality such as completeness and sense, and related to validity, accuracy and skill of

data collectors

i. Validity of the data, related to quality, accuracy and skill of data collectors

j. Training and skill of those involved in collecting, analysing and reporting data. Is there a

formal training programme, are skills assessed?

k. Automation of surveillance e.g. electronic data systems, automated surveillance

programmes.

l. Consistency of surveillance e.g. consistent methods applied, definitions, analysis, risk

adjustment. Related to training and skill if those involved in surveillance

m. Accuracy, sensitivity and specificity of the surveillance programme identified through

formal studies.

n. Public reporting, performance measures and financial penalties associated with HAI data.

This relates to how data is used.

Table 1– Final attributes and levels for the discrete choice experiment

Participation requirements (mandatory)

- Targeted 12 months / Other 3 months - Continuous 12 months targeted

surveillance on specified healthcare associated infections with choice of others for

minimum three months/year.

- Targeted 3 months / Other 3 months - minimum three months targeted

surveillance on specified healthcare associated infections with choice of others for

minimum three months/year.

- Complete choice 3 months - minimum three months surveillance on your own

choice of healthcare associated infections.

Surveillance Protocol

- Light protocol -patient level data on infected patients only, and aggregated

numbers of denominator is collected. Fewer resources required. Does not allow for risk

adjustment of HAI rates. Limited ability to compare data externally.

- Standard protocol – patient-level data are collected on both infected and non-

infected patients. More resources required. Allows for risk adjustment of healthcare

associated infection rates. Good ability to compare data externally.

Competency

After the initial surveillance training, surveillance staff are required to undergo regular

assessment to ensure skills are maintained.

- Every data submission period – (e.g. quarterly) supports high consistency of


surveillance processes.

- Annually – supports reasonable consistency of surveillance processes.

- Every 2 years – does not support high consistency of surveillance processes.

Accuracy

It is unlikely that all data will be completely accurate all the time. In general terms there will

be an error margin with the HAI rates.

- Very accurate - approximately 1-5% error range

- Reasonably accurate – approximately 6-10% error range

- Less accurate – approximately 11 -15% error range

Reporting

The reporting of HAI rates and their use as a performance measure associated with financial

penalties for the hospital within a national surveillance programme.

- Public with no penalty – Data publicly reported on website and not associated

with financial penalties.

- Public and with penalty - Data publicly reported on website and associated

with financial penalties

- Not public but with penalty – Data not publicly reported but is associated

with financial penalties.

- Not public and with no penalty – Data not publicly reported and not

associated with financial penalties.

Experimental design

The five attributes and their corresponding levels resulted in 216 profiles (=

33*41*21), and a total of 23,220 possible pair wise choice scenarios (=(216*215)/2).

A D-efficient design (NGENE Manual 1.1.1 [computer program]. Choice Metrics,

2012)24 with no prior parameters information (which minimise the Dz-error) was

used to reduce the number of choice scenarios into a more pragmatic number of 24

choice scenarios for presentation using the Ngene version 1.1.2 DCE design software

(www.choice-metrics.com). Ngene was also employed to divide the resulting DCE

design into two blocks, each containing 12 pair wise choice scenarios to reduce the

size of the questionnaire presented to participants. In each block, one choice

scenarios was duplicated to form a test of internal consistency. This resulted in a total

of 13 choice scenarios in each block.


The Survey

The survey was constructed using an online survey tool (Key Survey

[computer program]. MA: Braintree, 2015). Prior to answering the choice questions,

participants were required to respond to five Likert scale attitudinal questions

regarding HAI surveillance. This was followed by a detailed description of each of

the attributes and levels (Table 1). A sample choice scenario was then presented

A hypothetical scenario was presented which informed the participants a

mandatory national HAI surveillance programme was to be implemented, and

assuming their existing level of resourcing, they were requested to indicate which of

the two surveillance programmes presented they would consider most beneficial to

their existing infection prevention programme (Table 2).

Table 2 – Example of a choice scenario

Attributes Surveillance programme A Surveillance programme B

Participation

requirements

(mandatory)

Targeted 12 months / Other 3

months Complete choice 3 months

Surveillance

protocol Light protocol Standard protocol

Competency Annually Every 2 years

Accuracy Very accurate Less accurate

Reporting Not public but with penalty Public and with penalty

Which

would you

prefer?

(tick)

Surveillance

programme A

Surveillance

programme B

Each choice scenario consisted of the same five attributes but with differing

levels. Participants were then randomised into one of two choice blocks. For each

choice question, participants were forced to choose one or the other, there was no opt


out option available. To assist the participants understanding, a full definition of each

attribute and level was made visible using a hover tool.

The final section of the survey comprised five demographic questions

regarding age, occupation, years of experience in infection prevention, size of the

hospital they worked in (if applicable), State or Territory of employment, and an

open general comments question.

The survey was piloted by eight infection prevention experts. Pilot participants

indicated they found the DCE easy to understand and complete. All eight correctly

matched the duplicate questions.

DCE participants

In total 184 participants were purposively invited to undertake the DCE over a

5-week period during June and July 2015. These participants were selected because

they met at least one of the following criteria, they were:

• Coordinators of infection prevention programmes of a network of acute care

hospitals or at a single site with more than 100 beds (there were 147 of these

hospitals identified in Australia25);

• Infectious diseases physicians or microbiologists attached to infection

prevention programmes at large acute care hospitals;

• Senior health department employees or advisors whose role influences

national/state/territory infection prevention policy;

• Key stakeholders on national representative committees involved in national

HAI surveillance initiatives;

• Considered by the research team (PLR and LH) to be opinion leaders in

infection prevention in Australia.

Potential participants identified included 146 attached to acute care hospitals,

and another 38 non-hospital based stakeholders. Potential participants received a

personalised email inviting them to undertake the survey.

Data analysis

The DCE data were analysed using a random utility model,26 which could be

specified empirically as:


𝑈!"# = 𝑥!"#! 𝛽! + 𝜀!"#

where Uitj is the utility individual i derives from choosing alternative j in

choice scenario t, xitj is a vector of explanatory variables (i.e. observed attributes), βi

is a vector of coefficients reflecting the desirability of the attributes, and εitj is a

random error. Conditional on βi, it is assumed that εitj is independent and identically

distributed (iid) extreme value type 1.

The conditional logit model is a classical method to estimate the utility

function.14 However, it assumes that all respondents have the homogeneous

preference for the attributes (i.e. βi = β). Allowing for the potential preference

heterogeneity among respondents, the mixed logit (MIXL) model has gained

popularity recently.27-29 The MIXL model estimates both the mean and distribution

for each attribute level (i.e. βi = β + ηi, ηi is a vector of individual-specific deviations

from the mean). In this study, it was assumed that all coefficients of attribute levels

are random with normal distribution. The Akaike information criterion (AIC) was be

used to compare the overall fit of DCE models. Data were analysed using Stata,

version 13 (Stata Corp, College Station, Texas, USA).

Ethics

The study was approved by the Queensland University of Technology Human

Research Ethics Committee (approval number 1500000304).

8.2.4 Results

A total of 122 completed responses were received over a 5-week period

(response rate 66%). Of the 122 respondents, 98 (79%) were clinicians (infection

prevention nurses, infectious diseases physician and microbiologists), and others

were health department representatives or had acted in a health department advisory

role. There was proportionate representation from all State and Territories, 76% had

>10 years experience in infection prevention and 66% were aged over 50 years. Of

the 93 respondents whose primary employment was in a hospital, 43 (46%) worked

in a hospital with >400 beds. Further details of respondent characteristics are listed in

Table 3


Table 3 – Respondent characteristics

Characteristic Percent

(n=122)

Age Bracket Less than 30 0.8

30 - 39 9.0

40 - 49 24.6

50 - 59 46.7

More than 59 18.9

Occupation Health Department representative 10.7

Infection prevention nurse 65.6

Infectious diseases physician 13.1

Other 10.7

Years experience

in infection

prevention

Less than 5 4.9

5 to 10 17.2

11 to 15 27.9

16 to 20 19.7

More than 20 27.9

n/a 2.5

Number of acute

beds

Less than 100 2.5

100 - 199 13.1

200 - 400 25.4

More than 400 35.3

n/a 23.8

State or Territory Australian Capital or Northern Territory 4.9

New South Wales 27.1

Queensland 17.2

South Australia 7.4

Tasmania 5.7

Victoria 27.9

Western Australia 9.8

n/a, not applicable.


A total of 22 (18%) respondents mismatched the duplicate choice scenario.

Analysis of the DCE output was undertaken on both the full data set (with

mismatches) and the data set with the mismatches excluded. The results of both data

sets were very similar; however, it was decided to present results excluding the

mismatched respondents on the basis that it could not be assumed that these

respondents fully understood the DCE, providing a useable response rate of n=100

for data analysis (see Supplementary Table 1 for results on full data set).

Results of the MIXL estimates are presented in Table 4. It can be seen that all

attributes were found to have a statistically significant influence on the preferences

for a HAI surveillance programme.

Table 4 – Mixed logit estimates for sample excluding participants who

mismatched duplicate question

Mean coefficient Standard deviation

Attribute Level Coefficient Standard

error Coefficient

Standard

error

Participation

requirements

(mandatory)

Targeted 12

months / Other

3 months

0.640** 0.198 1.083** 0.268

Targeted 3

months / Other

3 months

0.331* 0.158 0.619* 0.281

Complete

choice 3 months Reference

Surveillance

Protocol

Standard

protocol 0.641** 0.204 1.698** 0.240

Light protocol Reference

Competency

Every data

submission

period

0.546** 0.202 1.325** 0.243

Annually 0.778** 0.170 0.044 0.367

Every two years Reference


Accuracy

Very accurate 1.132** 0.204 1.031** 0.229

Reasonably

accurate 0.977** 0.201 0.754** 0.260

Less accurate Reference

Reporting

Public with no

Penalty 1.663** 0.277 1.163** 0.274

Not Public but

with Penalty 0.467* 0.194 0.971** 0.337

Not Public and

with no Penalty 0.725** 0.232 1.453** 0.258

Public and with

Penalty Reference

N 100

Observations 2400

** p<0.01, * p<0.05

Log likelihood -674.968

All attributes were dummy coded

The results identify key stakeholders strongest preferences were for a

surveillance programme that has:

• A mandated continuous targeted surveillance on specified HAIs with

choice of others for a minimum 3 months/year (followed by minimum 3

months targeted surveillance on specified HAIs with choice of others for

minimum 3 months/year);

• The standard surveillance protocol where patient level data are

collected on both infected and non-infected patients;

• Annual competency assessments of data collectors (followed by

competency assessments every data submission period);

• Very accurate data (followed closely by reasonably accurate data);

and


• Hospital-level data publicly reported on a website and not associated

with financial penalties (followed by hospital level data not publicly reported

and not associated with financial penalties).

The statistical significance of the standard deviation coefficients for all but one

of the attribute levels (annual competency) confirms the existence of preference

heterogeneity for the majority of the attributes. As all coefficients for attribute levels

are assumed to be normally distributed, the mixed logit estimates relating to the

mean coefficient and standard deviation for each attribute level were used to

calculate the distribution of preference heterogeneity.30 For example, the coefficient

(s.d) for the level of targeted 12 month with choice of three month surveillance is

0.640 (1.083) indicates 72% of the respondents exhibited a preference for targeted 12

months with choice of three month surveillance over a complete choice of

surveillance for three months. Similarly 65% of respondents had a preference for

Standard protocol over light, and 86% preferring very accurate data over less

accurate and 92% demonstrated a preference for data to be reported public with no

penalty over publicly reported with penalty.

Sub group analyses were conducted using conditional logit model and reported

in Supplementary Tables 2a and 2b. However, owing to the small sample size in the

sub groups, the results should be interpreted with caution. One interesting finding

worth highlighting here is that when occupation was divided into clinician and non–

clinician, it was found that clinicians preferred very accurate data (p<0.01), non

clinicians preferred mostly accurate data (p<0.05; full results included in

Supplementary file Tables 2a and 2b).

8.2.5 Discussion

This novel application of a DCE has identified the preferences of key

stakeholders for a national HAI surveillance programme.

This study indicates key stakeholder preference for a national HAI surveillance

programme that has mandatory continuous surveillance on targeted infections with

an option to choose surveillance in other areas, a protocol that facilitates risk

adjustment for meaningful comparisons, and annual competency assessments of

those who undertake the surveillance. The preference is for HAI data to be highly

accurate and publicly reported, but not to be associated with any financial penalties.


A surprising result was the preference for annual competency assessments over

the more frequent every data submission (quarterly). One explanation may be that

competency assessments every data submission may have been considered too

resource intensive when compared against an annual assessment.

There are several important points in this study. First, the DCE was constructed

based upon the findings from a literature review and a series of semi-structured

interviews with experts in HAI surveillance. This means that the attributes and levels

were relevant and meaningful to participants. Second, an attractive feature of a DCE

is its ability to provide information about the acceptability (or otherwise) of different

characteristics of programs not yet available in practice.31 This is a crucial point,

particularly when considering issues around implementation. Third, the results

provide a unique insight into HAI surveillance issues not previously demonstrated in

Australia. This study provides evidence identifying the specific characteristics of a

HAI surveillance program that are acceptable to key stakeholders, which, if they are

included in a national program, will increase the likelihood of successful

implementation. And finally, given the multimodal approach to infection prevention,

and the competing interests of multiple stakeholders, we suggest that DCEs have the

potential to clearly identify priority frameworks in this setting given competing

demands and limited resources.

A potential limitation of DCEs is that there is some evidence to indicate that

respondents tend to make their choices on the basis of familiarity, that is they tend to

express a preference for the status quo,32 and this may explain some of the preference

choices observed in this study. Twenty two respondents mismatched the duplicate

choice scenario. This could mean that some found the DCE challenging, alternatively

it may be that some respondents changed their preferences as they worked through

the DCE. Nevertheless, analyses of data both with and without these mismatches

indicated very similar results and did not alter the main findings. Another potential

limitation is that the not all key stakeholder groups were able to be included in this

study for practicality reasons, in particular hospital executive and quality and safety

staff. However major strengths of this study are the inclusion of attributes identified

through qualitative research methods that are relevant and meaningful, its specific

targeting of leaders in infection prevention programs, the national sample frame, and

a high response rate.


Our study is the first application of a discrete choice analysis to identify key

stakeholder preferences and priorities for HAI surveillance. Given the multimodal

approach to infection prevention, and the competing interests of multiple

stakeholders, DCEs have the potential to clearly identify priority frameworks in this

setting, where competing demands and limited resources have been clearly

demonstrated.33,34

8.2.6 Conclusions

This paper describes the novel application of a DCE to identify stakeholder

preferences for a national HAI surveillance programme that can be used to inform

evidence based recommendations.

In HAI prevention where there are many key stakeholders from a variety of

settings with differing and competing priorities, the application of a DCE has the

potential to explore and quantify preferences in this setting.


8.2.7 References

1. National Health and Medical Research Council. Australian Guidelines for the

Prevention and Control of Infection in Healthcare. Commonwealth of

Australia; 2010.

2. Mitchell BG, Gardner A. Addressing the need for an infection prevention and

control framework that incorporates the role of surveillance: a discussion

paper. J. Adv. Nurs. 2014;70:533-542.



A Consensus Panel Report. Infect. Control Hosp. Epidemiol. 1998;19:114-

124.

4. Russo PL, Barnett AG, Cheng AC, Richards M, Graves N, Hall L.

Differences in identifying healthcare associated infections using clinical

vignettes and the influence of respondent characteristics: a cross-sectional

survey of Australian infection prevention staff. Antimicrob Resist Infect

Control. 2015;4:1-7.


care-associated infection surveillance practices in Australia. Am. J. Infect.

Control. 2015;43:773-775.


reduce nosocomial infection rates. J. Hosp. Infect. 1995;30 Suppl:3-14.

7. Lee TB, Montgomery OG, Marx J, et al. Recommended practices for

surveillance: Association for Professionals in Infection Control and

Epidemiology (APIC), Inc. Am. J. Infect. Control. 2007;35:427-440.

8. Runnegar N. What proportion of healthcare-associated bloodstream

infections (HA-BSI) are preventable and what does this tell us about the

likely impact of financial disincentives on HA-BSI rates? Australasian

College for Infection Prevention and Control 2014 Conference; 23-26

November, 2014; Adelaide, Australia.

9. WHO Library Cataloguing-in-Publication Data. How to Conduct a Discrete

Choice Experiment for Health Workforce Recruitment and Retention in

Remote and Rural Areas: A User Guide With Case Studies. 2012.


http://www.who.int/hrh/resources/DCE_UserGuide_WEB.pdf. Accessed 26 June

2015.

10. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in

health economics: a review of the literature. Health Econ. 2012;21:145-172.

11. Lancsar E, Louviere J. Conducting discrete choice experiments to inform

healthcare decision making: a user's guide. Pharmacoeconomics.

2008;26:661-677.

12. Ryan M, Gerard K, Currie G. Using discrete choice experiments in health

economics. In: Jones AM, ed. The Elgar Companion to Health Economics.

Second ed. Cheltenham, UK: Edward Elgar Publishing Limited; 2012:437-

446.

13. Louviere J, Hensher DA, Swait J. Stated choice methods: analysis and

applications. Cambridge: Cambridge University Press; 2000.

14. Ryan M, Gerard K, Amaya-Amaya M. Using Discrete Choice Experiments to

Value Health and Health Care. The Netherlands: Springer; 2008.

15. Baltussen R, Stolk E, Chisholm D, Aikins M. Towards a multi-criteria

approach for priority setting: an application to Ghana. Health Econ.

2006;15:689-696.

16. Baltussen R, ten Asbroek AHA, Koolman X, Shrestha N, Bhattarai P,

Niessen LW. Priority setting using multiple criteria: should a lung health

programmeme be implemented in Nepal? Health Policy Plan. 2007;22:178-

185.

17. Green C, Gerard K. Exploring the social value of health-care interventions: a

stated preference discrete choice experiment. Health Econ. 2009;18:951-976.

18. Hall J, Kenny P, King M, Louviere J, Viney R, Yeoh A. Using stated

preference discrete choice modelling to evaluate the introduction of varicella

vaccination. Health Econ. 2002;11:457-465.

19. Ryan M, Gerard K, Amaya-Amaya M. Discrete Choice Experiments in a

Nutshell. In: Ryan M, Gerard K, Amaya-Amaya M, eds. Using Discrete

Choice Experiments to Value Health and Health Care. Vol 11: Springer

Netherlands; 2008:13-46.


and public health surveillance systems: a systematic review. Epidemiol.

Infect. 2012;140:575-590.



requirements for success. J. Hosp. Infect. 2008;70 Suppl 1:11-16.




23. Ryan M. A role for conjoint analysis in technology assessment in health care?

Int. J. Technol. Assess. Health Care. 1999;15:443-457.

24. Johnson FR, Lancsar E, Marshall. D., et al. Constructing Experimental

Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint

Analysis Experimental Design Good Research Practices Task Force. Value

Health. 2013;16:3-13.

25. National Health Performance Authority. MyHospitals. MyHospitals 2015;

http://www.myhospitals.gov.au. Accessed 9th March, 2014.

26. McFadden D. Conditional logit analysis of qualitative choice behavior. In:

Zarembka P, ed. Frontiers in Econometrics. New York: Academic Press;

1973:105-142.

27. Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete

Choice Experiments in Health Economics: A Review of the Literature.

Pharmacoeconomics. 2014;32:883-902.

28. Hole AR. Fitting mixed logit models by using maximum simulated

likelihood. Stata Journal. 2007;7:388-401.

29. McFadden D, Train K. Mixed MNL models for discrete response. J Appl

Econ. 2000;15:447-470.

30. Bessen T, Chen G, Street J, et al. What sort of follow-up services would

Australian breast cancer survivors prefer if we could no longer offer long-

term specialist-based care? A discrete choice experiment. Br. J. Cancer.

2014;110:859-867.

31. Ratcliffe J, Laver K, Couzner L, Crotty M. Health Economics and Geriatrics:

Challenges and Opportunities. In: Atwood C, ed. Geriatrics. 2012:209-234.

32. Salkeld G, Ryan M, Short L. The veil of experience: Do consumers prefer

what they know best? Health Econ. 2000;9:267-270.

33. Haustein T, Gastmeier P, Holmes A, et al. Use of benchmarking and public

reporting for infection control in four high-income countries. Lancet Infect

Dis. 2011;11:471-481.


34. Zingg W, Holmes A, Dettenkofer M, et al. Hospital organisation,

management, and structure for prevention of health-care-associated infection:

a systematic review and expert consensus. Lancet Infect. Dis.;15:212-224.


8.2.8 Supplementary Tables

Supplementary Table 1 - Mixed logit estimates for sample including participants who

mismatched duplicate question

** p<0.01, * p<0.05

Log likelihood = -897.518

All attributes were dummy coded

Mean coefficient Standard deviation

Attribute Level Coefficient Standard error Coefficient Standard

error

Participation requirements (mandatory)

Targeted 12mth / Other 3mth 0.824** 0.200 1.351** 0.236

Targeted 3mth / Other 3mth 0.390** 0.146 -0.810** 0.249

Complete choice 3mth Reference

Surveillance Protocol

Standard protocol 0.627** 0.173 1.644** 0.193

Light protocol Reference

Competency

Every data submission period

0.403* 0.168 1.245** 0.187

Annually 0.604** 0.145 -0.193 0.208

Every two years Reference

Accuracy

Very accurate 1.113** 0.172 0.940** 0.190

Reasonably accurate 0.878** 0.156 -0.533* 0.215

Less accurate Reference

Reporting

Public with no Penalty 1.330** 0.224 1.314** 0.258

Not Public but with Penalty 0.433* 0.180 0.993** 0.266

Not Public and with no Penalty 0.498* 0.200 1.522** 0.255

Public and with Penalty Reference

N 122

Observations 3172


Supp

lemen

taryfile–Tab

le2a-C

onditio

nallog

itestim

atesbysubg

roup

**p<

0.01

*p<

0.05

Agegrou

pOccup

ationcatego

ry

Yearse

xperiencein

infectionpreven

tion

Num

bero

finp

atientbed

s

Attribute

Level

<50

>5

0Clinician

Non

clinician

<15

>1

5<20

020

0to400

>4

00

Participation

requ

iremen

ts

Targeted

12m

th/

Other3mth

0.25

40.39

1**

0.32

8*

0.47

50.19

10.54

0**

0.40

40.27

20.28

6

Targeted

3mth/Other

3mth

0.17

00.17

60.19

50.12

40.08

30.27

3*

0.18

20.13

00.18

9

Surveillance

Protocol

Stan

dardprotocol

0.49

4**

0.28

5*

0.29

7**

0.65

20.51

4**

0.24

10.41

20.39

5*

0.28

1

Compe

tency

Everyda

tasu

bmiss

ion

perio

d0.05

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0.32

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0.38

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0.38

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0.28

20.36

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0.41

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0.27

60.42

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0.37

8*

Accuracy

Veryaccurate

0.72

0**

0.55

7**

0.65

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60.79

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0.46

5**

0.67

4*

0.52

7**

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Reason

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0.71

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0.36

9**

0.48

5**

0.50

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0.58

8**

0.42

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0.47

60.55

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Repo

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50.27

2

Observatio

ns

816

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01,22

41,10

438

467

279

2

Chapter 9: Discussion 165

Chapter 9: Discussion

9.1 INTRODUCTION

The main focus of this thesis has been to identify evidence based

recommendations for a national HAI surveillance program in Australia. To meet this

objective, I undertook two distinct studies that have produced data that contribute to

the body of knowledge regarding HAI surveillance programs in general, and

specifically, to the Australian setting.

The first study set out to improve our understanding of the current landscape in

Australia with regards to existing surveillance practices and the quality of those

practices. This involved undertaking a cross sectional online survey of those

currently involved in HAI surveillance activities across Australia. The findings from

this study provided detailed information about current surveillance practices. Whilst

some commonality was identified, broad variation and major gaps, in particular with

methodology, training, and support were evident. These findings were further

supported when it was revealed that there is only moderate agreement in identifying

and classifying many common HAIs. This means that interventions based on this

data will be misguided. Furthermore, findings from this study casts doubt over

reliability of the current SAB data, which has been publicly reported in Australia

since 2012.

The second study was conducted in two parts. The first involved exploring well

established large HAI surveillance programs to identify factors that are influential in

their implementation and success. This was achieved through a literature review and

a series of semi-structured interviews with leaders of HAI surveillance programs in

Australia and overseas. Qualitative analysis of the data from the interviews was then

used to inform the second part of the study, a discrete choice experiment (DCE) that

provided quantitative evidence on which elements of a national HAI surveillance

programs key stakeholders consider most important.

Data from both studies can now be used to construct a national program that

will address current gaps, and be comprised of elements based on the evidence from

166 Chapter 9: Discussion

the studies, best practice identified from international programs and the literature.

Importantly, the recommendations will be acceptable to key stakeholders, which will

facilitate appropriate implementation.

There are major strengths in this work. First, infection prevention staff, who

generally are both the drivers of HAI surveillance and those charged with

implementing interventions, have been represented in the design of these studies, and

make up a large proportion of study participants. Second, there has been proportional

representation from each state and territory for both studies. Third, leaders from

major international and Australian statewide HAI surveillance programs contributed

to the identification of surveillance program attributes, which provided crucial data

for the construction of the DCE. Fourth, key stakeholders, including policy makers at

both a state and national level participated in the DCE. Fifth, the findings from the

DCE have identified a number of practical elements of HAI surveillance that will be

acceptable to key stakeholders in a national program.

There are many complexities and related issues in this discussion, and the

sections of this chapter will synthesise the information in a considered manner. First

I will provide a summary answer to each of the research questions that form the basis

of this thesis. The discussion will then address purpose and attitudes, followed by a

detailed section which focuses on three broad concepts; System, Data and Utility.

Within each of these, some of the key attributes identified in the CDC guidelines for

evaluation of surveillance programs will be referred to.25 To conclude, issues around

implementation and sustainability will be addressed, before presenting a series of

recommendations for national HAI surveillance in Australia.

9.2 ANSWERS TO THE RESEARCH QUESTIONS

Research Questions

1 - What are the similarities and differences between existing HAI surveillance


Broad variation has been identified in surveillance activities and methodology.

Best practices with regards to prospective surveillance, risk adjustment of data and

reporting of HAI data to hospital executive are commonly not followed. Major gaps

have been identified with regards to surveillance training and education across


Australia with half of those who undertake surveillance reporting they have never

received surveillance training.

2 - What level of agreement exists in the identification of HAI between those

participating in HAI surveillance, and are there any factors that influence agreement

level?

There is clear disparity in HAI identification, classification, and calculation of

HAI rates amongst those currently undertaking surveillance in Australia. This raises

concern about the existing SAB data currently reported at a national level. Working

in a hospital with more than 400 beds, working in a team, and state or territory was

associated with a correct HAI identification, classification, and calculation. Those

trained in surveillance were more commonly associated with a correct response,

whilst those working part-time were less likely to respond correctly.

3 - What are the key components of successful centrally coordinated HAI


Five distinct characteristics of large HAI surveillance programs have been

identified: 1) Triggers: surveillance was initiated by government or a cooperative of

like minded people, 2) Purpose: a clear purpose is needed and therefore determines

other surveillance mechanisms, 3) Data measures: consistency is more important

than accuracy and efforts to validate data add credibility to the program, 4)

Processes: a balance exists between the volume of data collected and resources

available, and how data are used influences earlier surveillance processes and 5)

Implementation and maintenance: a central coordinating body is crucial for

uniformity and support, and mandatory participation is supported with some degree

of flexibility.

4 - What are the preferences and priorities of key stakeholders when

considering a national HAI surveillance program?

Key stakeholder preference is for a national HAI surveillance program that has

mandatory continuous surveillance on targeted infections with an option to choose

surveillance in other areas, a protocol that facilitates risk adjustment for meaningful

comparisons, and annual competency assessments of those who undertake the

surveillance. The preference is for HAI data to be highly accurate and publicly

reported, but not to be associated with any financial penalties.


9.3 PURPOSE OF A SURVEILLANCE PROGRAM

Data from the literature8,20,25,31 and the semi-structured interviews emphasise

the importance of defining the purpose of a national surveillance program.

It must be understood that surveillance alone will not reduce HAIs, it must be

used to drive action and infection interventions.31,86 Data from this work revealed

that although the overall purpose of a HAI surveillance program is to reduce the

incidence of HAI, this is prone to being overlooked by various stakeholders in their

endeavours to meet benchmarks and targets. This means that when establishing the

purpose of a national HAI surveillance program, it should be kept in mind that data

may be used for a variety of purposes not originally intended.

A clear purpose must be established and understood by all stakeholders. The

purpose will guide surveillance activities, methods and utility, and will also be

influential in the implementation and maintenance of the program.

9.4 SUPPORT FOR A SURVEILLANCE PROGRAM

Despite the evidence supporting the benefits of national HAI surveillance

programs,80-83 it cannot be assumed that a national surveillance program would be

automatically embraced by key stakeholders in Australia. This is quite plausible

given that some states already have established programs.

As noted in Chapter 4, the existing state surveillance programs have evolved

independently over time resulting in variation of surveillance activities. The reason

for this is likely due to the historical funding structure of public hospitals where

states and territory governments are the largest funders (Figure 1). Furthermore,

given there has never been a national central body to encourage, coordinate and

develop incentives for national activity, states and territories have taken up the

initiative to implement local activity in response to their specific environment and

resources.

Another barrier to the implementation of a national program would be if

stakeholders of current statewide programs do not see any benefit in participating in

a national program. Therefore it is crucial to engage key stakeholders and understand

attitudes to national surveillance as a crucial element of an implementation strategy.


There are precedents of similar initiatives requiring broad support in Australia

that will inform the implementation of national surveillance program. First, the

NHHI demonstrated how strong national leadership and vision resulted in the support

and cooperation of states and territories to implement a large infection prevention

intervention.100 Second, following support from all Australian Health Ministers,

since 2012 all public hospitals have been required to submit SAB data to their

jurisdictional health departments, which is then collated and published as national

data.

The first section of the DCE contained a series of questions that explored the

attitudes of respondents towards surveillance. The results identify overwhelming

support for national HAI surveillance (Appendix K). Whilst this does not guarantee

successful implementation, strong belief in an intervention increases the likelihood of

its successful implementation.

This work has generated new knowledge identifying broad and strong

stakeholder support for a national HAI surveillance program, and identified key

barriers and enablers for consideration in an implementation strategy.

9.5 SYSTEM

The surveillance System refers to the specific mechanics of the surveillance

program such as the types of infection under surveillance, the specific data being

collected, and the analysis undertaken on the data. Some elements discussed in this

section are also relevant to points discussed in the Data and Utility sections of the

discussion. In discussing the System, it is helpful to also consider the CDC attributes

of simplicity, flexibility and acceptability.

Surveillance programs should be as simple as possible whilst ensuring the

program objectives are met.25 Simplicity is reflected in elements such as the amount,

volume and type of data required, the number and accessibility of data sources,

integration with other systems, available tools for collection and analysis, data

cleaning and mapping needs, and training requirements.

Flexibility refers to a system that is adaptable to meet the needs of

participants.25 An example in this case may be a scalable surveillance program that

provides for tailoring of surveillance activities in response to acute changes in

infection prevention priorities e.g. in an outbreak situation.


The attributes of simplicity and flexibility strongly influence the acceptability

of the overall program. Acceptability is a crucial component to implementation, and

not only relates to the participants belief in the value of the surveillance activity, but

is also influenced by any barriers placed by a lack of simplicity and flexibility.

9.5.1 Simplicity

A major influence on the simplicity of HAI data collected is related to the data

requirements for appropriate risk adjustment. If data are publicly reported, and hence

used for benchmarking, risk adjustment is fundamental.107 This is important as not all

patients are at equal risk of acquiring a HAI, and the comparison of hospital data

without risk adjustment is a critical flaw.

It is also important to consider the complexity of the risk adjustment. The level

of the risk adjustment is influenced by several factors, and needs to be balanced

against the complexity of data required and the resources available.

For SSI, the basic Risk Index developed by predecessors of NHSN considers

the patients American Society of Anaesthesiology score, the type of wound and the

duration of procedure, which is calculated into a simple risk index ranging from 0-

3.160 In recent times, complex algorithms developed by researchers from NHSN

based on more detailed patient level have demonstrated better prediction.161,162

NHSN recommend participating hospitals use the algorithms, even though they

require the collection of more patient level data. The increased data requirements

would favour facilities with electronic medical records, which may partially explain

why they are yet to be adopted internationally.

In Australia, a novel risk stratification model has been developed by the

National Health Performance Authority to report annual hospital SAB rates. Rather

than stratify at a patient level, hospitals are stratified into four peer groups according

to the hospital size, type of services provided and the proportion of patients

considered more at risk.111 This simple type of stratification is appealing in that it

does not require collection of data at a patient level. Importantly though, it is yet to

be validated, and it is uncertain if it would be applicable to other types of HAIs.

An important finding from this work has identified stakeholder’s preference for

a protocol that enables risk adjustment is consistent with their belief that national

surveillance with benchmarking would be beneficial. This contrasts with findings


that less than half of those who undertake SSI surveillance risk adjust their SSI data,

though this was significantly associated with never having received surveillance

education.

This work has identified new knowledge in that the preference of key

stakeholders for a HAI surveillance program is one that enables comparison of

hospital infection rates with other like facilities and against a benchmark through

the use of standardised risk adjustment.

9.5.2 Flexibility

Data from the semi-structured interviews established the importance of

flexibility in the system. In one national program, although mandatory activities

exist, participants are offered a choice of how intensive the surveillance is, and also

choice regarding duration of surveillance. This was designed to meet the varying

needs of the facilities’ different size and resources.

The outcomes of the DCE have informed us that key stakeholders prefer a

mandatory surveillance program targeting specific HAIs with the option to conduct

surveillance on other HAIs. The preference for mandatory surveillance on specific

infections implies that stakeholders acknowledge the morbidity and mortality

associated with certain HAIs, and see the benefit of benchmarking data, which would

provide some contextual framework for their own data. The combination of

mandatory surveillance activities with an option for other surveillance is consistent

with a flexible system.

In considering the recommended CDC attributes relating to simplicity and

flexibility, and the preferences of key stakeholders, I have identified key elements

that should be included in a national HAI surveillance program, these include;

• Data to allow for basic risk adjustment,

• Specific HAIs should be targeted under a mandatory program, and

• Optional surveillance activities be made available which would allow

facilities to tailor a surveillance program best suited to their needs.

A system consisting of the above elements meets the criteria required for

simplicity and flexibility. As a consequence, the program will also be highly


acceptable as it consists of those features that were preferred by key stakeholders.

This in turn will increase the likelihood of successful implementation.

This work has clearly established that key stakeholders prefer a HAI

surveillance program that comprises mandatory surveillance of core infections

combined with optional surveillance that can be adapted to meet individual

infection prevention program priorities. This new knowledge can guide the

establishment of a national HAI surveillance program in Australia.

9.6 DATA

The second major concept to be discussed relates to data, specifically, the

factors that influence the overall quality of the data. The CDC highlight the

importance of representativeness, sensitivity and positive predictive value (PPV) in

surveillance systems.25 The following sections will discuss methodology influences

on data quality, and in alignment with the data from the semi-structured interviews, I

will then discuss the issues relating to sensitivity and PPV under the banner of

accuracy and consistency.

9.6.1 Methodology

As I have established, there is broad variation in surveillance practices across

Australia. Furthermore, I have identified large gaps in surveillance education, skill

level, reporting, and access to expert support.

The issue of surveillance education and training is important. Data from this

work clearly establishes a link between surveillance education and best practice.

It is a major concern that this research identified that only half of those who

currently perform HAI surveillance have ever received any education in surveillance.

It is not clear how those who have never received education gained their surveillance

knowledge. Education in surveillance is not just about understanding a definition, but

also needs to provide a basic understanding of epidemiology, including current

knowledge of best practices relating to case finding, analysis and reporting.

It has already been established that those educated in surveillance were

significantly more likely to follow best practice, such as risk adjusting data and

prospectively collecting data. Prospective data collection enables identification of

HAIs as they occur, providing better quality data than that which can be collected


retrospectively. The effect of prospective data collection on timeliness will be

discussed in the next section of this discussion.

HAI definitions often include clinical, laboratory and objective criteria. This

means that those whose role it is to detect HAI must have clinical knowledge, an

understanding of laboratory reports, and importantly, the ability to objectively apply

definition criteria. Given the gap in education, and the identification of broad

variation in practices and methods in Australian hospitals, it is not surprising that

only moderate agreement levels in HAI identification, classification, and calculation

of HAI rates amongst those undertaking HAI surveillance has been established.

Other predictors of data quality were identified from the responses to a series

of clinical vignettes that revealed differences in identifying, classifying and counting

infections and calculating infection rates. Size of the hospital, state and territory, and

working in a team were found to be significant predictors for two of the vignettes.

Whilst there were no significant predictors found when all the vignettes were

analysed together, there were associations found with access to expert resources, part

time infection prevention staff and experience in infection prevention.

Although the finding of only moderate agreement may be expected in the

absence of a national program, variation has also been described in well established

national surveillance networks.122,132,163 This means that the quality of HAI data

needs to be constantly monitored during the life of a surveillance program.

Despite evidence of variable agreement when identifying HAIs in large

surveillance programs,122 recently Schroder et al74 demonstrated high sensitivity and

specificity of case ascertainment of surveillance staff from 218 intensive care units

participating in the KISS program in Germany using clinical vignettes. This study

provides optimism for acceptable agreement levels achievable in a national program

built upon expertise, central coordination and support. The German HAI surveillance

program requires introductory education for all staff undertaking surveillance and

annual updates.32

The scant surveillance education currently available in Australia clearly affects

current practices in relation to prospective surveillance, risk adjustment, and the

ability to correctly and uniformly identify, classify and calculate HAIs, consequently

affecting overall data quality. The findings from this research provide new


information for the Australian setting, and add to the knowledge that education and

experience influence the accuracy of correct HAI identification.74

9.6.2 Accuracy

The DCE also explored preferences around the crucial issues of data accuracy

and consistency.

Results of the DCE identified that the overall key stakeholders’ preference was

for highly accurate data. Of note, when this finding is stratified by occupation group,

non clinicians had a significant preference for reasonably accurate data compared to

clinicians preference for highly accurate data.

Highly accurate data implies that all infections under surveillance are captured

equating to excellent representativeness. Capturing all HAIs is extremely challenging

and resource intensive, particularly as hospital length of stay decreases. Evidence

demonstrates that many SSIs manifest after discharge, however valid and efficient

methods to conduct post discharge surveillance are yet to be identified.71 Public

Health England require that patients readmitted to hospital with an SSI must be

included as a case, however it is optional to include those captured in outpatient

clinics or via a patient questionnaire.164

Period prevalence post discharge surveys have been used to estimate the

proportion of SSIs that develop after discharge, and can inform surveillance

programs on the accuracy of the data.165

Data from the semi-structured interviews revealed that patients with SSIs who

present back to hospital and re-admitted are likely to be those which are most

serious, and worthy of surveillance resources, whilst those that do not require

readmission are much less serious, and hence do not warrant resources to capture.

Clearly, the decision to include infections captured post discharge can affect the

accuracy and representativeness of the overall program.

9.6.3 Consistency

Surveillance consistency requires uniformity. For HAI surveillance, much of

the issue around uniformity relates to the ongoing consistent application of

definitions by those involved in surveillance. In any national surveillance program,

the reality is that possibly thousands of staff will be involved in the identification of


HAIs. Naturally, where subjective application of infection criteria is required, the

potential for variation will exist. Providing those who apply the definitions with the

skills and tools to maximise uniformity is a crucial to a national program.

Whilst absolute consistency cannot be guaranteed, there are a number of ways

consistency can be improved and supported. Apart from providing uniform training

and the use of data quality assurance mechanisms to all those involved in

surveillance,72 other activities which support skill attainment and consistency have

been implemented. These have taken the form of online assessments and attendance

at specialist workshops or conferences.32,74

9.6.4 Accuracy vs. Consistency

The quest for high data accuracy and consistency places pressure on

surveillance resources. The results from the DCE indicated that the stakeholders

preferred moderate consistency achievable from annual assessments of those who

undertake surveillance, when compared to the high consistency possible with

assessment every time data are submitted. In contrast, the surveillance experts who

participated in the semi-structured interviews all remarked that they believed

consistency to be more important than accuracy in surveillance programs. This view

is also supported in the literature.29

The choice from the stakeholders for the moderately consistent option may

have been influenced by concerns about resources expended for a quarterly

assessment, whereas an annual assessment might have been considered more

feasible. The preference for high accuracy could simply be a result of the competing

pressures faced by infection prevention staff in an era of performance measurement

and public reporting, or perhaps confusion about the purpose of surveillance and the

role of surveillance data.

Clearly, a surveillance program’s credibility is enhanced if it can demonstrate

that HAI data are reported by those who have undergone training and regular

competency assessment.

This will also provide clinicians and consumers with confidence when

considering data generated from the program. The assurance of data quality is

particularly important during implementation planning when it becomes necessary to

convince sceptics on the merits of national surveillance.


In this section regarding Data, I have discussed the findings from this work and

the gaps that must be addressed in a national surveillance program. The quality of the

data are influenced by many factors, crucial to this is the education and competency

of those involved in collecting data. Further to this, the tension between data

accuracy and consistency not only relates to the skill of the staff, but also the

available resources. Although the stakeholder preference identified in the DCE was

for highly accurate data, contrasting evidence suggests that the resources required to

capture every HAI cannot be justified and that consistency, achieved through

uniformity and competency, is more important in a national HAI surveillance

program.

In noting these findings, it is clear that a national HAI surveillance program

should have a strong emphasis on uniform surveillance education for all surveillance

staff at commencement of surveillance activity, and provide regular refresher and

update sessions, including an annual competency assessment. National surveillance

protocols and tools would further enhance consistency.

This work has established the key stakeholder choice for satisfactorily

accurate national data, possible within existing resources, is a standard

surveillance protocol which includes the collection of basic risk factor data.

Furthermore, the importance of consistency is recognised. The preference

for annual competency assessments of surveillance staff was acceptable to ensure

sufficient consistency.

9.7 UTILITY

The third major concept to be discussed is that relating to Utility. For the

purposes of this discussion, Utility includes issues around reporting of the HAI data,

how the data are used and by whom.

A commonly used term for surveillance these days is “data for action” In 2001,

Gaynes et al31 identified a number of critical elements that a surveillance system

must have for successful reductions of HAIs. Two of those elements are the

dissemination of data to healthcare providers, and a link between HAI data and

prevention efforts, i.e. using the data to drive improvement. The intent of providing

data to “those who need to know”,19 such as clinical and executive staff, is that they

are the ones who can influence and authorise change to drive improvement.8


9.7.1 Reporting

For data to be actionable it must be reported. As described in Chapter 5, the

first study identified that currently HAI data are not always reported to hospital

executive, in fact it was revealed that while just over 80% of respondents reported

SSI and CLABSI data to their executive, 30% reported CAUTI, and only 15%

reported VAP data.

The reasons for this low frequency of reporting are not clear. It is possible that

those involved in surveillance may not have confidence in the data, don’t believe that

the HAI rate is of interest (particularly if there is no change from previous data

periods) or simply they are not aware that it needs to be reported. Perhaps of greater

concern is that they are merely undertaking surveillance to meet regulatory

requirements and otherwise have no interest in the data. This is possibly symptomatic

of a surveillance program that is not flexible in meeting individual hospital’s needs.

Clearly if data are not being reported to the hospital executive, then it is likely

that it is not being used to drive improvement, which ultimately leads us to question

the purpose and value of undertaking surveillance.

9.7.2 Timeliness

To enable appropriate action to drive improvement, infections need to be

identified in a timely manner. The CDC defines timeliness as the speed between the

various steps along the surveillance process.25 Allen-Bridson, Morrell and Horan30

note that timeliness of reporting HAI data are largely dependent on whether data

collection is prospective or retrospective. Prospective data collection facilitates

timely reporting, and subsequent prompt action when required. This means that

issues such as a sudden increase in infections are identified as they occur, triggering

investigation and intervention. Prospective data collection is recommended by

NHSN, and so it follows that many of the national HAI surveillance programs based

on NHSN also recommend prospective data collection.

If the objective of a HAI surveillance program is to reduce HAIs, clearly

timeliness of surveillance processes, enabled by prospective data collection, is a

crucial factor. This is an important point, particularly with respect to the findings

from the first study that identified that only 47% of respondents reported undertaking


prospective SSI surveillance, whilst 60% reported undertaking prospective CLABSI

surveillance.

This indicates that currently data are not reported in a timely manner. This

means that vulnerable patients are at risk of infection whilst data is not being seized

upon to design and implement interventions. Again the importance of education is

recognised by the finding that those who undertook prospective surveillance were

significantly more likely to have received surveillance education.

9.7.3 Public Reporting

Timely reporting is particularly important at a hospital level because it is at the

hospital where immediate interventions can take place. Similarly, data reported

externally, such as public reporting, is also believed to translate into action.120

The review of the literature relating to public reporting noted several countries

now have routine reporting of HAI rates, including Australia which now reports

hospital SAB rates annually, however controversy remains as to whether or not data

are valid and if the public find HAI data useful.98,109

Even though the DCE identified strong support for public reporting (Appendix

K), this should only be considered a trigger to explore how public reporting can be

achieved. In consultation with key stakeholders including the public, major issues to

be considered include which data would be reported, the frequency of reporting, the

most appropriate format and the detail of explanatory text that might be required to

accompany the data. Ideally surveillance processes and data quality would be

established and validated prior to the release of HAI data to the public.

9.7.4 Financial Penalties

Closely related to the issue of public reporting, is that of financial penalties for

hospitals reporting high, or above threshold HAI rates. Financial penalties associated

with HAIs are already common in the USA.166 In Australia, one state has instituted

financial penalties for preventable bloodstream infections in the absence of public

reporting.167 Recently, an Australian private health fund announced the introduction

of non payments to hospitals for “hospital acquired complications” including HAI.168

The DCE explored the attitudes of stakeholders around financial penalties and

identified only 47% of the 122 respondents believed that financial penalties


associated with high HAI rates would be beneficial to their infection prevention

program. Not surprisingly, the overall findings from the DCE demonstrated that

stakeholder’s preference was for public reporting when it was not associated with

financial penalties.

The rejection of financial penalties by stakeholders could be due to a number

of factors. First, stakeholders may believe that in general, HAIs should not be used as

performance indicators. Second, there may be concern that data is not accurate

enough. Third, financial penalties will place infection prevention staff in awkward

situations knowing that data they report will financially affect the hospital. Fourth,

financial penalties may result in perverse behaviour by infection prevention staff or

others who may be inclined to game data, or overrule the classification of an

infection to ensure penalties are avoided. The last two points are important and

worthy of further exploration.

Now that public reporting and financial penalties have been in place in the

USA for some time, issues have been identified that may serve to caution the

introduction of such a system in Australia. The situation in the USA, according to

Horowitz,169 has resulted in “A destructive triangulation…between administrators,

clinicians and infection control departments.” Horowitz169 describes the scenario

where hospital administrators, fearful for their hospitals’ reputation and the threat of

financial penalty, place pressure on infection control teams to “revise” data reports.

This issue was also identified in the semi-structured interviews where it was noted

that HAI data are now being used for purposes it was never intended.

Data from the interviews corroborated Horowitz’s concern that infection

prevention teams were being placed in difficult situations when reporting HAI data

that would ultimately penalise their hospital. Interview data also identified that this

situation could lead to perverse behaviour including the underreporting of infection.

This issue has also been raised by Talbot et al110 who describe the use of

“clinical adjudication panels”. This is where a panel external to the infection control

team make the final determination of a HAI. The panel however often apply clinical

rather than epidemiological definitions to the situation and therefore will not always

be in agreement with the infection control team. To overcome this, Talbot et al110

have developed a number of recommendations for public reporting of HAI data,


which includes that authority for final decision making to “individuals with specific

content expertise and training in healthcare epidemiology and infection prevention”.

Despite this, and conflicting literature about the effect of financial penalties on

HAI data,170,171 Kiernan172 firmly believes financial penalties have contributed to the

significant decline in MRSA infections in the UK. The momentum towards financial

disincentives appears to be building, and ultimately, it is likely to be the funders, and

not the clinicians who will decide to apply financial penalties.

This section has noted that currently in Australia there appears to be much

surveillance activity that is not warranted given that data is not always being reported

to appropriate stakeholders. This is crucial to the purpose of undertaking surveillance

and the flexibility of the program, and needs to be explored further. Public reporting

is now routine in many countries, and has commenced in Australia, as has financial

penalties in one state. Although there may be a sense of inevitability in Australia,

lessons from overseas experience tell us that financial penalties can place undue

pressure on infection control teams.

Regardless, transparency of data fosters a strong safety culture.173 Regularly

published hospital level data informs consumers about their local health facility, and

can be used to compare performance.

Stakeholders have indicated their support for a program that enables risk

adjusted comparisons with like facilities and against a national benchmark. Once an

appropriate number of facilities are participating, and risk adjustment processes have

been implemented, publicly reported, hospital level HAI data should become

normalised.

Surveillance experts would be able to provide explanatory commentary to

maximise elucidation and advise on appropriate use of the data. During the

development of the surveillance program, and in the implementation planning stages,

consideration needs to be given to ensuring that hospital infection prevention teams

are not vulnerable to the influences of other parties when reporting data.

This work has identified that routine public reporting of hospital HAI data

would be highly acceptable to key stakeholders as a component of a national HAI

surveillance program in Australia.


9.8 INVESTING IN NATIONAL HAI SURVEILLANCE

The unique situation in Australia of combined government funding of public

hospitals presents complex challenges when looking to fund national initiatives. That

the state/territory governments provide more funding to public hospitals than the

Australian government could act against the adoption of a national surveillance

program unless clear benefits are demonstrated. Several important points can be

made to support the funding of a national initiative.

It can be argued that the only true measurement of an infection prevention

intervention is the infection outcome. Clearly, if this infection outcome measurement

is unreliable due to flawed HAI surveillance, then the effectiveness, both from a

quality and economic point of view, will never be truly known.

The state/territory and Australian governments have already invested

significantly in national infection prevention initiatives including national SAB

reporting, the National Hand Hygiene Initiative, the Antimicrobial Use and

Resistance in Australia project, the National Safety and Quality Health Service

Standards, and the National Infection Control Guidelines.174 Logically, it can be

argued that without reliable national HAI data, the effectiveness of these activities

will remain unknown. This is a crucial point, particularly when attempting to

demonstrate health benefit for money spent, which historically has not been well

measured.

Many studies examining the cost of HAIs have overestimated the real cost

generally because accounting methods rather than economic methods have been

used, 175 thereby misrepresenting benefits of infection prevention interventions. A

HAI incurs many costs, not the least, extra hospital stay, and it is the increased

number of bed days experienced by the patient that accounts for the majority of the

costs associated with a HAI.175 Rather than identify a dollar cost, attributable costs

are best expressed in the number of extra bed days that a HAI causes, or alternatively

the number of bed days that are released by a reduction in HAIs.102,175 In a health

system where there is a demand for hospital beds, such as in Australia, the freeing up

of bed days means that more patients can be treated.

We can demonstrate how a reduction would affect healthcare at a national level

using recent data. An Australian cohort study estimated that 1.73% of patients


admitted to hospitals acquire a healthcare associated urinary tract infection (HAUTI)

and that the expected extra length of stay due to the HAUTI was four days. Data

from this study estimate this equates to over 380,000 extra bed days per year across

Australia. A 10% reduction (i.e to 1.56% of patients) in this rate would free up over

38,000 extra bed days per year nationally.176

These figures from one type of HAI indicate that thousands of patients on

waiting lists could be treated sooner, and logically more if this is generalisable for

other HAIs. This provides an economic advantage through avoiding costs that are

incurred whilst waiting for admission, such as maintenance treatment, general pain

and discomfort, and loss of productivity and income.

There are also two other fundamental economic arguments for a national

program. First, considering the economies of scale, it can be suggested there are

gains to be made from national approaches to interventions rather than the states and

territories, or hospitals, developing their own local interventions where the risk of

duplication of effort is likely. Second, findings from this work have identified that

surveillance currently being performed is inconsistent with best practice, not being

reported to those who need to know and consequently not used to reduce incidence.

This means that precious and costly infection prevention resources are currently

being wasted in producing outcome data that is flawed. Current surveillance

practices need to be re-aligned to a standardised national best practice. This could

only be achieved through a coordinated national surveillance program.

Once established, further efficiencies could also be achieved with the

introduction of semi automated surveillance programs. Manual surveillance is

resource intensive, however evidence is emerging regarding the benefits of semi-

automated processes. In a single centre study in the Netherlands, a semi automated

surveillance program implemented to identify deep SSIs following hip and knee

replacement reduced the number of medical records required to be reviewed from

over 2500 to just 76. The semi-automated program was shown to have 100%

sensitivity and reduced the workload by 95%.177

Current investment in national initiatives could be diverted to the establishment

of a national surveillance program. An evidence based national HAI surveillance

program will provide a platform for stakeholders to identify real infection issues.

Reliable outcome data will enable economists to demonstrate the cost effectiveness


of various interventions, and thereby informing administrators and clinicians to

develop and implement interventions that return the greatest health outcomes.

This, together with the redirecting of current investment into flawed

surveillance activities towards a national standard, would clearly be a good economic

decision for both state/territory and Australian governments.

9.9 COORDINATION, IMPLEMENTATION AND SUSTAINABILITY

9.9.1 Coordinating role

Naturally, a national HAI surveillance program requires central coordination.

In the USA this is undertaken by the NHSN, in Germany by the Institute of Hygiene

and Environmental Medicine in Berlin and in the UK, by the NHS.

Given Australia’s disparate situation with regards to HAI surveillance, clearly

a central coordinating role is warranted. Data from the literature review and the semi-

structured interviews indicate that typically the role of the central agency is to

establish and communicate surveillance goals, develop protocols, provide education,

training and support to participating facilities, ensure the robustness of the data,

collate and analyse national data and provide reports to key stakeholders including

governmental bodies. Data from the interviews also identified that ideally staff of a

central agency would have expertise in surveillance, epidemiology, infection

prevention, infectious diseases, microbiology and implementation.

The important role of a central agency was identified from the data described

in Chapter 7. Whether or not this role is undertaken by a health department body or

independently was initially shown to be associated with the trigger for surveillance.

Contrasting arguments for either an independent agency or a government

agency include that a surveillance program coordinated independently would

produce more reliable data given that there is no threat of punitive action if high rates

are reported. On the other hand, it could be argued that in the favour of a government

associated agency is that hospitals executive are more likely to be compliant with

surveillance requirements, and stakeholders may place more faith in the overall

program if it is endorsed by the government.

Nevertheless, the data indicated that regardless of the initiating body, after

some time, and with the advent of HAI data being used for performance


measurement, inevitably health departments will demand more of a say in how

surveillance activities are conducted and reported, particularly if government funding

is required to support the program.

A major challenge for the central agency would be to leverage off existing state

surveillance activities where feasible to minimise the introduction of new processes.

This work would comprise an important part of an implementation strategy, which is

discussed in the next section.

9.9.2 Implementation and Sustainability

When considering a new national program, it is important to acknowledge the

role of appropriate implementation planning, and explore issues around

sustainability. Failure to adequately implement change is often the reason why

research findings fail to translate into improved patient outcomes.147 Despite strong

evidence supporting infection prevention interventions, the implementation of best

practice remains a challenge.148 When considering the implementation of infection

prevention practices, success is influenced by the characteristics of the practice and

organisation, and the environmental context.178

A national HAI surveillance program is a complex intervention given that it

will affect numerous stakeholders, including: infection prevention teams, hospital

clinicians and executive staff, state, territory and commonwealth government

department staff, health professional colleges and organisations, accrediting bodies,

private health funders, and not the least, individual patients and consumer

organisations. Adding to the challenge of implementation is the differences between

hospitals such as size, resources, skill, and patient mix, implying the intervention

may need to be tailored for each site.

To inform and guide the implementation process, the application of an

appropriate implementation framework strategy is crucial. Chapter 7 introduces one

such frameworks which could be used in this setting, the Normalisation Process

Theory (NPT)152. Not every element of the constructs in either of these frameworks

will be applicable for a national HAI surveillance program, however data from the

semi-structured interviews with international surveillance experts, described in

Chapter 7, highlighted several implementation and maintenance issues that would


have been detected and mitigated by the application of an implementation

framework.

The NPT framework has been used across a variety of health settings for a

range of interventions, and in particular is specific to complex health interventions.154

Murray et al153 suggests the strength of the NPT is that it can be applied not only to

assist implementation, but also in developing, embedding and evaluating the

intervention.

NPT is distinguished by its focus on stakeholder engagement, acknowledges

the role of opinion leaders, and addresses the roles and relationships of

stakeholders.154 A major strength of the NPT is that it can be used in the design phase

of the intervention to support the various interactions between the stakeholders

required for implementation.153 These qualities would seem particularly relevant to

the implementation of a national HAI surveillance program. For these reasons, I will

discuss the NPT in the context of implementing a national surveillance program.

The NPT consists of four major constructs; coherence, cognitive participation,

collective action and reflexive monitoring.152 In considering the design and

implementation of a national surveillance program, I will discuss each of the four

constructs of the NPT thinking mostly about the infection prevention teams who are

commonly charged with implementing and maintaining the HAI surveillance

programs. To guide this discussion, questions to consider in the implementation on

complex interventions proposed by Murray et al153 have been used as a guide to step

through the considerations in each of the constructs (Appendix L).

Coherence

Coherence relates to the sense making of the intervention to individuals and as

a collective.153,179 Specific to a HAI surveillance program, coherence relates to the

purpose of the program, how clearly stakeholders understand what the intervention

is, how different it is to existing practice and what the benefits will be. The

importance of a HAI surveillance program having a clear purpose has already been

established and demonstrated in Chapter 7, and the need for clear purpose is further

supported by the NPT as crucial to implementation.

As identified in the first study of this work and described in Chapters 4 and 5,

the range of existing surveillance activities varies between hospitals. Therefore,


when considering how different a national surveillance program may be from current

surveillance activities, those charged with implementing the program nationally

(central agency) will need to understand that for some infection prevention teams, the

change to a national program may represent a big departure from current practice,

whilst for others, only minimal, if any, changes would be required.

Whilst a major change to existing practice may potentially be a barrier to

implementation, it could also act as an enabler. The facilities that will have the most

to gain from a national program are those that currently do not follow best practice.

Therefore participation in a national program would mean adopting best practice for

these facilities, and even though it may require changes to their current practices,

ultimately it will result in the biggest gains, that being more meaningful data.

The benefits of a national surveillance program have previously described.

These benefits should not only be valued by the infection prevention teams, but

should also be brought to the attention of other stakeholders, specifically clinicians

and executive staff, so they can embrace the national program. Given that the

national surveillance program would be improving patient safety and quality, it will

also clearly align with each facility’s goals.

Cognitive participation

Cognitive participation is about ‘belief’ in the intervention, the level of

enrolment and legitimation of stakeholders. Cognitive participation seeks to explore

if all stakeholders believe the intervention to be valuable, and if their support will be

maintained over time through the investment of resources.153,179

Data from the attitudinal questions in the second study (Appendix K)

demonstrate the stakeholders overwhelming believe that not only surveillance, but a

national surveillance program which reports hospital identifiable data, would be

beneficial to their own infection prevention programs. Given this, it would seem that

key stakeholders believe that national surveillance is valuable and would be eager to

participate.

Nevertheless, there may be those who are less certain. As identified from the

semi-structured interviews, stakeholder “buy in” was crucial to the establishment of a

surveillance program, and interviewees described the investment they made in

visiting facilities specifically to engage and enlist their support for the program that


was being implemented. The identification of champions and opinion leaders among

the stakeholder groups to persuade dubious colleagues on the value of national

surveillance is another strategy to assist enrolment.

Of course, enrolment may be automatic if a system of mandatory surveillance

activities is introduced. However it must not be assumed this will overcome any

barriers to implementation. As was identified from the semi-structured interviews

and described in Chapter 7, the risk of a mandated surveillance program which staff

don’t actually believe in is that their participation would merely be a process that

allows them to “tick the box”, with little concern about the quality of the data or the

purpose of their surveillance. At the very least this runs the risk of producing dubious

data whilst also wasting precious resources.

Collective action

Collective action relates to the actual work that is required and seeks to clarify

the relationships between those doing the work and the appropriate allocation and

understanding of the specific roles. It also points to an understanding of how any new

work will impede current work, and whether or not the intervention aligns with

existing practices.153,179

In a national surveillance program, the bulk of the “work” will most likely be

undertaken by infection prevention teams. Data indicates this may require major

changes to the current work for some facilities, but very little for others. As well as

clarifying the roles of the workers, it also extends to the roles and relationships with

a central coordinating agency, and clearly understanding what the central agency

requires from participating facilities. Likewise, the role of the agency with regards to

data analysis, collation and support to participating sites would need to be clearly

understood.

A major challenge to the implementation process would be to fill the current

gap in education by providing a uniform program for all surveillance staff. Even for

facilities whose programs are currently following best practice surveillance, a

national program would require some amount of education. At the very least, training

on data submission processes and interpretation of national data would be warranted.

Of course the introduction of any new technology, such as data collection or analysis

tools would also require education of all those involved.


The introduction of a national program based on best practice would be

expected to promote and enhance the work of infection prevention teams. It would

lead to greater support by clinician and executive staff if confidence in the data were

strong, ultimately resulting in better infection prevention programs.

Reflexive monitoring

Reflexive monitoring is about participants looking back over the new

intervention, attempting to identify its affect on practices, any perceived benefits or

disadvantages, and whether or not the intervention may be improved. This could be

through either communal or individual appraisal processes.153,179

In a surveillance program, reflexive monitoring could be facilitated in a

number of ways. For the infection prevention teams this could be through a review of

the resources required to participate in the national surveillance program and

observing for any infection trends associated with the introduction of the program.

Those involved in collecting data would need to consider how the new program has

affected their current work, and if processes are suitably efficient. Whilst there may

not be any change in infection rates, it could be expected that a reduction of

surveillance resources, and more meaningful data, should be evident following the

implementation.

Those in charge of the surveillance programs at a hospital level would also

need to consider how the data are being used, and if the surveillance program is

meeting their own needs. If the infection prevention teams perceive that the data are

being used to punish them, or their facility, this may result in a loss of support for the

national program.

Similarly, clinical and executive hospital staff would need to review what

impact they believe the new surveillance program has had, and whether or not

improvements could be made. Even a formal validation and cost benefit study could

be considered, however as indicated previously, this would be an expensive

undertaking.

The central agency should have constant feedback systems in place so users

can log issues as they arise, and establish processes to measure if the purpose is

being met. Although such feedback may recommend changes to improve the

program, it must be remembered that in a surveillance program, any changes to


definitions or methods will likely diminish the ability to analyse data already

submitted.

Whilst improvements to some aspects of the surveillance process could be

adopted readily, it would be unwise to make changes to some elements of the

program. Given this, a national program may consider extensive piloting of the

program with feedback sought prior to commencement, and that changes to the

program can only be made infrequently (e.g. every 5-10 years).

In summarising this section, it is clear that the implementation strategy for a

complex national HAI surveillance program is crucial to success. Therefore, as well

as the proposed coordinating agency having expertise in clinical areas of infection

prevention and epidemiology, there is a strong case to be made for engagement of

expertise in program implementation.

9.10 LIMITATIONS

There are limitations in this work. Whilst the respondent sample of both studies

was representative nationally, a precise response rate for the first study was unable to

be determined as the exact number of infection prevention staff in Australia is

unknown. In the second study, not all key stakeholder groups were invited to

participate. This was largely due to practical reasons in that the language that was

required to represent the attributes of a surveillance program in the DCE would only

be familiar with those closely involved in HAI surveillance. Therefore groups such

as executive staff, safety and quality staff and patients did not participate. This

should be a focus of future research.

Although not specifically a limitation, whilst the outcome of the DCE has

identified stakeholder preferences, it does not necessarily represent best practice.

Further, it is important to keep in mind that respondents participated in a DCE about

a hypothetical national HAI surveillance program of which they have no substantial

experience. The DCE may yield different findings if it was undertaken with

participants who have had the experience of participating in a national program.

I have had considerable long term involvement in HAI surveillance at hospital,

state and national levels, which could be considered both a weakness and a strength.

My professional experience, training in epidemiology, understanding of national

infection prevention issues, and my network with international colleagues has led me


to support the development of a national HAI surveillance program. Whilst this

would have had minimal influence in the results from the first study, it could have

potentially influenced the direction of the conversation in the semi-structured

interviews and some of the interpretation of this data. However, the interpretive

description process undertaken by myself supported by fellow researchers would

have minimised this potential bias. On the other hand, my in depth knowledge of the

topic could also have resulted in a more informative interview, and the identification

of issues not apparent to others.

9.11 RECOMMENDATIONS FOR A NATIONAL HAI SURVEILLANCE PROGRAM

The recommendations below address the current surveillance gaps in Australia

identified from this research, reflect the key stakeholder preferences for a

surveillance program, and importantly, are in alignment with best practice. These

elements will also positively influence the likelihood of implementation and

sustainability.

It is recommended that a national HAI surveillance program in Australia

should comprise the following key elements.


Recommendation Rationale

1. Mandatory

surveillanceofcore

infections.

Thiswouldensurethatinfectionsconsideredtobeof

nationalimportanceareincludedinthesurveillance

program.Whilstparticipationcouldinitiallybeoffered

voluntarily,itshouldbebroadcastthatatanominated

pointintime,participationinthenationalprogramwill

becomemandatoryaspartofupdatedNationalSafetyand

QualityHealthServiceStandards.

2. Optionalsurveillance

forotherinfections.

Thisflexibleoptionacknowledgesdifferentinfection

preventionprioritiesexistacrossparticipatinginstitutions

whilstmaintaininguniformityandconsistencynationally.

3. Standardiseduniform

surveillanceprotocol

comprisingofdata

specificationstofacilitate

riskadjustment.

Thisiscrucialtoenablecomparativeoutcomedatabetween

hospitals,andhospitalsagainstabenchmark.Initiallybasic

riskadjustmentwouldbestandardwhilstmorecomplex

riskadjustmentandalgorithmsaredeveloped.

4. Regularuniform

competencyassessments

ofsurveillancepersonnel.

Priortocommencementofsurveillanceactivities,hospital

staffinvolvedinsurveillancewouldundergoanintroductory

courseandberequiredtomeetminimumcompetency

standards.Theeducationprogramwouldinvolveallaspects

ofHAIsurveillanceincludingbasicepidemiology,

surveillancedefinitionsandmethods,riskadjustmentand

reporting.Toensureongoingcompetency,allstaffwould

berequiredtoparticipateinanannualsurveillanceskill

competencyassessment.


5. HospitalidentifiableHAI

datatoberoutinelypublicly

reported.

AprocessforreleasingHAIdatainthepublicforum

wouldbeestablishedinconsultationwithstakeholders

toaddressappropriatereportingformat,frequencyand

explanatoryinformation.Thefinalgoalistohaverobust,

riskadjusted,hospitallevelHAIdatapubliclyreleasedon

aregularbasisenablinghospitalcomparisonsand

benchmarking.

6. Centralcoordination

withexpertadvice

Thoseassumingresponsibilitytodevelopand

implementanationalprogramshouldhaveexpertisein

surveillance,epidemiology,infectionprevention,

infectiousdiseases,microbiologyandimplementation.

7. Acomprehensive

implementationstrategy.

Thestrategywouldbeestablishedalongsidethe

developmentofthesurveillanceprogram,andbea

constantframeofreferenceforthedevelopment,roll

outandmaintenanceoftheprogram.

8. Regularevaluationof

thesurveillanceprogram.

Toensuresustainability,constantmonitoringand

evaluationisrequired.ElementsoftheCDCevaluation

guidelines,reflexivemonitoringfromtheNPT,andthe

characteristicsofsuccessfullargesurveillanceprograms

identifiedinthisworkcouldbestructuredtoguide

ongoingreviewoftheprogram.Furthermore,regular

nationalpointprevalencesurveyscouldbeusedto

monitortheburdenofHAIsovertimeandobservefor

emerginginfections.Datafromalltheseactivities

woulddetermineifthesurveillanceprogramwas

fulfillingitspurposeandinformongoingimprovements

totheprogram.


9. Constantstrengthening

andexpansionofthe

program.

Keyareasforfutureresearchinclude:

• Theuseofelectronicmedicalrecordsandautomated

surveillancetechnologytorelievesurveillanceburden,track

patientspostdischarge,anddevelopinfectionriskalgorithms

formorecomplexandimprovedriskadjustmentmethods

• Validationofasurveillancemethodforidentifying

communityonsetHAIs

• SurveillanceofHAIsinnonacutefacilities

• Consumeruseofpubliclyreportedinfectiondata

Chapter 10: Conclusion 195

Chapter 10: Conclusion

National HAI surveillance programs reduce the incidence of HAI. This is

achieved by benchmarking, identifying problem areas and implementing best

practice.

National surveillance and benchmarking also instils stakeholders including

consumers with confidence that facilities are measuring safety and quality in a

uniform manner, underpinning the expectation that the quality of patient care should

not be dependent on the location or type of facility.

Large surveillance programs have been successfully implemented

internationally and generate data that is used for multiple purposes, including priority

setting and informing policy at both local and national levels. Australia is lagging

behind its international colleagues due to the absence of a national HAI surveillance

program, and as such, we do not understand the epidemiology of HAIs in Australia.

The present situation of separate state-based surveillance programs has been

demonstrated to have varying methodology and measurement.182-184 This means that

current HAI data cannot be collated to generate national data.

The advantages of knowing such data at a national level cannot be

underestimated. The dearth of current information presents unreasonable challenges

to those at a hospital, state and national level seeking evidence on which to base

infection prevention policy. It also severely limits local and national infection

prevention research initiatives. Importantly it raises doubt on patient safety and

quality in infection prevention on a national scale.

Such data not only directs policy, but is also used to measure the impact of

HAI interventions and programs. A recent description of interim data from the

Agency for Healthcare Research and Quality which uses data from the NHSN,

indicated that over 15,000 deaths from CAUTI, CLABSI, SSI and VAP had been

averted between 2011 and 2014, resulting in an estimated cost saving of over 2

billion dollars.185 Such detailed level of information is simply unable to be generated

in Australia.

196 Chapter 10: Conclusion

Although attempts have been made to establish national SAB surveillance,

concerns regarding the robustness of this data have been raised.99,100,182 Despite

concerted efforts by the ACSQHC towards establishing national definitions for

CLABSI and SSI,95 there remains no mandate, coordination or support to undertake

such surveillance nationally.

There is no incentive for hospitals to adopt uniform definitions or contribute to

any potential national database. Whilst undertaking surveillance is listed as a

criterion in the NSQHSS, it is ultimately a state, territory or facility decision as to the

type of surveillance that is performed.

The research findings from this PhD have given rise to new knowledge on HAI

surveillance in Australia. The recommendations contained within this PhD outline an

evidence based framework for a national HAI surveillance program, which are

realistic, pragmatic, achievable, and acceptable to stakeholders.

Two triggers for the establishment of national surveillance programs have been

identified; bottom up, a collaborative of like minded experts initiating surveillance

within a network, and top down, a direction from a government, often in response to

adverse findings.

It remains to be seen which will be the trigger for a national surveillance

program in Australia. Nevertheless, the findings from this work provide crucial

guidance for the development, implementation and sustainability of an evidence

based national HAI surveillance program.

References 197

References

1. Smith CM. Origin and Uses of Primum Non Nocere—Above All, Do No

Harm! J Clin Pharm. 2005;45(4):371-377.

2. National Health and Medical Research Council. Australian Guidelines for the

Prevention and Control of Infection in Healthcare. Commonwealth of

Australia; 2010.

3. Burke JP. Infection control - a problem for patient safety. N Engl J Med.

2003;348(7):651-656.

4. Goto M, Al-Hasan MN. Overall burden of bloodstream infection and

nosocomial bloodstream infection in North America and Europe. Clin

Microbiol Infect. 2013;19:501-509.

5. Allegranzi B, Nejad, SB., Combescure, C., Graafmans, W., Atar, H.,

Donaldson, L., Pittet, D. Burden of endemic health-care associated infection

in developing countries: systematic review and meta-analysis. Lancet.

2011;377(9761):228-241.

6. Kohn L, Corrigan J, Donaldson M. To err is human : building a safer health

system. Washington: National Academy Press; 2000.

7. Thacker SB. Surveillance. In: Gregg MB, ed. Field Epidemiology. New York:

Oxford University Press; 1996:16-32.


Woeltje KF, Malani PN, eds. Practical Healthcare Epidemiology. 3rd ed.

London: The University of Chicago Press; 2010:111-142.



A Consensus Panel Report. Infect Control Hosp Epidemiol.

1998;19(2):114-124.

10. Haley RW, Culver DH, White JW, et al. The Efficacy of Infection

Surveillance and Control Programs in Preventing Nosocomial Infections in

US Hospitals. Am J Epidemiol. 1985;121(2):182-205.

198 References

11. Graves N, Halton K, Paterson D, Whitby M. Economic rationale for infection

control in Australian hospitals. Healthcare Infection. 2009;14(3):81.

12. Australian Bureau of Statistics. Population clock. 2016;

http://www.abs.gov.au/ausstats/abs%40.nsf/94713ad445ff1425ca2568200019

2af2/1647509ef7e25faaca2568a900154b63?OpenDocument. Accessed 13

February, 2016.

13. Australian Institute of Health and Welfare. Australian hospital statistics

2012–13. Health services series no. 54. Cat. no. HSE 145. Canberra: AIHW.

2014.

14. Australian Institute for Health and Welfare. Hospital Resources. 2016;

http://www.aihw.gov.au/haag13-14/hospital-resources/ - t5. Accessed 13

February, 2016.


Safety and Quality Health Service Standards. Commonwealth of Australia;

2011.

16. Mitchell BG, Hall L, MacBeth D, Gardner A, Halton K. Hospital infection

control units: Staffing, costs, and priorities. Am J Infect Control.

2015;43(6):612-616.

17. Mitchell BG, Hall L, Halton K, Macbeth D, Gardner A. Time spent by


surveillance: a multi-centred cross sectional study. Infection, Disease and

Health. 2016;Accepted for publication.

18. Oxford University Press. Oxford Dictionary. 2015;

http://www.oxforddictionaries.com/definition/english/surveillance. Accessed

13 August, 2015.

19. Langmuir AD. The Surveillance of Communicable Diseases of National

Importance. N. Engl. J. Med. 1963;268(4):182-192.

20. Buehler JW. Surveillance. In: Rothman KJ, Greenland S, eds. Modern

epidemiology. 3 ed: Lippincott-Raven Philadelphia, PA, USA:; 2008.

21. Garcia-Abren A, Halperin W, Danel I. Public Health Surveillance Toolkit.

The World Bank; 2002.

References 199

22. Harbarth S, Sax H, Gastmeier P. The preventable proportion of nosocomial

infections: an overview of published reports. J Hosp Infect.

2003;54(4):258-266.

23. Umscheid CA, Mitchell MD, Doshi JA, Agarwal R, Williams K, Brennan PJ.

Estimating the proportion of healthcare-associated infections that are

reasonably preventable and the related mortality and costs. Infect Control

Hosp Epidemiol. 2011;32(2):101-114.

24. Lambert M-L, Silversmit G, Savey A, et al. Preventable proportion of severe

infections acquired in intensive care units: case-mix adjusted estimations

from patient-based surveillance data. Infect Control Hosp Epidemiol.

2014;35(5):494-501.




26. Wilson J. Surgical site infection: the principles and practice of surveillance:

Part 2: analysing and interpreting data. Journal of Infection Prevention.

2013;14(6):198-202.

27. Damani N. Manual of Infection Prevention and Control. Third ed. Oxford:

Oxford University Press,; 2012.

28. Arias KM. Surveillance. In: Carrico R, ed. APIC Text of Infection Control

and Epidemiology. 3rd ed. Washingotn DC: APIC; 2009.


and public health surveillance systems: a systematic review. Epidemiol

Infect. 2012;140(4):575-590.

30. Allen-Bridson K, Morrell GC, Horan TC. Surveillance of Healthcare-

Associated Infections. In: Mayhall CG, ed. Hospital Epidemiology and

Infection Control. 4th ed. Philadelphia: Wolters Kluwer; 2012:1329-1343.

31. Gaynes R, Richards C, Edwards J, et al. Feeding back surveillance data to

prevent hospital-acquired infections. Emerg Infect Dis. 2001;7(2):295-298.


requirements for success. J. Hosp Infect. 2008;70 Suppl 1:11-16.

200 References

33. LaForce MF. The Control of Infections in Hospitals: 1750 to 1950. In:

Wenzel RP, ed. Prevention and Control of Nosocomial Infections. 2nd

Edition ed. Baltimore: Williams and Wilkins; 1993.

34. Noakes T D, Borresen J, Hew-Butler T, Lambert M I, Jordaan E.

Semmelweis and the aetiology of puerperal sepsis 160 years on: an historical

review. Epidemiol Infect. 2008;136(1):1-9.

35. Stewardson A, Pittet D. Ignác Semmelweis—celebrating a flawed pioneer of

patient safety. Lancet.378(9785):22-23.

36. Selwyn S. Hospital Infection the first 2500 years. J Hosp Infect.

1991;18(Supplement A,):5-64.

37. Brewer GE. Studies in Aseptic Technique. JAMA. 1915;17:1369-1372.

38. Perl TM. Surveillance, Reporting and the Use of Computers. In: Wenzel RP,

ed. Prevention and Control of Nosocomial Infections. 3rd ed. Philadelphia:

Lippincott Williams & Wilkins; 1997.

39. Pottinger JM, Herwaldt LA, Perl TM. Basics of Surveillance - An Overview.

Infect Control Hosp Epidemiol. 1997;18(7):513-527.

40. Glenister HM, Taylor LJ, Bartlett CLR, Cooke EM, Sedgwick JA,

Mackintosh CA. An evaluation of surveillance methods for detecting

infections in hospital inpatients. J Hosp Infect. 1993;23(3):229-242.

41. van Mourik MSM, Troelstra A, van Solinge WW, Moons KGM, Bonten

MJM. Automated Surveillance for Healthcare-Associated Infections:

Opportunities for Improvement. Clin Infect Dis. 2013;57(1):85-93.

42. Stone PW, Dick A, Pogorzelska M, Horan TC, Furuya EY, Larson E.

Staffing and structure of infection prevention and control programs. Am J

Infect Control. 2009;37(5):351-357.

43. Mitchell BG, Hall L, Halton K, MacBeth D, Gardner A. Time spent by


surveillance: A multi-centred cross sectional study. Infection, Disease &

Health. 2015;21(1):36-40.

References 201

44. Freeman R, Moore LSP, García Álvarez L, Charlett A, Holmes A. Advances

in electronic surveillance for healthcare-associated infections in the 21st

Century: a systematic review. J Hosp Infect. 2013;84(2):106-119.

45. Hota B, Lin M, Doherty JA, et al. Formulation of a model for automating

infection surveillance: algorithmic detection of central-line associated

bloodstream infection. J Am Med Inform Assoc. 2010;17(1):42-48.

46. Lin MY, Hota B, Khan YM, et al. Quality of traditional surveillance for

public reporting of nosocomial bloodstream infection rates. JAMA.

2010;304(18):2035-2041.

47. Goto M, Ohl ME, Schweizer ML, Perencevich EN. Accuracy of

administrative code data for the surveillance of healthcare-associated

infections: a systematic review and meta-analysis. Clin Infect Dis

2014;58(5):688-696.

48. Hebden JN. Slow adoption of automated infection prevention surveillance:

Are human factors contributing? Am J Infect Control. 2015;43(6):559-562.

49. Edmond MB. National and International Surveillance Systems for

Nosocomial Infections. In: Wenzel RP, ed. Prevention and Control of

Nosocomial Infections. 4th ed. Philadelphia: Lippincott Williams & Wilkins;

2003:109-119.

50. Dudeck MA, Edwards JR, Allen-Bridson K, et al. National Healthcare Safety

Network report, data summary for 2013, Device-associated Module. Am J

Infect Control. 2015;43(3):206-221.

51. Cooke EM, Coello R, Sedgwick J, et al. A national surveillance scheme for

hospital associated infections in England. J Hosp Infect. 2000;46(1):1-3.

52. Mertens R, Jans B, Kurz X. A computerised Nationwide Network for

Nosocomial Infection Surveillance in Belgium. Infect Control Hosp.

Epidemiol. 1994;15(3):171-179.

53. Sax H, Uckay I, Balmelli C, et al. Overall burden of healthcare-associated

infections among surgical patients. Results of a national study. Ann Surg.

2011;253(2):365-370. doi: 310.1097/SLA.1090b1013e318202fda318209.

202 References

54. Perez CD, Rodela AR, Monge Jodra V, Quality Control Indicator Working G.

The Spanish national health care-associated infection surveillance network

(INCLIMECC): data summary January 1997 through December 2006

adapted to the new National Healthcare Safety Network Procedure-associated

module codes. Am J Infect Control. 2009;37(10):806-812.

55. Mannien J, van den Hof S, Brandt C, Behnke M, Wille JC, Gastmeier P.

Comparison of the National Surgical Site Infection surveillance data between

The Netherlands and Germany: PREZIES versus KISS. J Hosp Infect.

2007;66(3):224-231.

56. European Centre for Disease Control and Prevention. Surveillance of

Surgical Site Infections in European Hospitals - HAISSI protocol version

1.02. Stockholm: European Centre for Disease Control and Prevention;2012.

57. Richards C, Emori TG, Edwards J, Fridkin S, Tolson J, Gaynes R.

Characteristics of hospitals and infection control professionals participating

in the National Nosocomial Infections Surveillance System 1999. Am J

Infect Control. 2001;29(6):400-403.

58. Centers for Disease Control and Prevention (CDC). Monitoring hospital-

acquired infections to promote patient safety--United States, 1990-1999.

MMWR Morb Mortal Wkly. Rep. 2000;49(8):149-153.

59. Haustein T, Gastmeier P, Holmes A, et al. Use of benchmarking and public

reporting for infection control in four high-income countries. Lancet

Infect Dis. 2011;11(6):471-481.

60. Weinstein RA, Siegel JD, Brennan PJ. Infection-Control Report Cards —

Securing Patient Safety. N Engl J Med. 2005;353(3):225-227.

61. Edwards JR, Pollock DA, Kupronis BA, et al. Making use of electronic data:

the National Healthcare Safety Network eSurveillance Initiative. Am J

Infect Control. 2008;36(3 Suppl):S21-26.

62. Dudeck MA, Weiner LM, Allen-Bridson K, et al. National Healthcare Safety

Network (NHSN) report, data summary for 2012, Device-associated module.

Am J Infect Control. 2013;41(12):1148-1166.

References 203

63. Progress Report March 2014. National and State healthcare associated

infections: Progress Report 2014;

http://www.cdc.gov/hai/surveillance/progress-report/index.html. Accessed 2

March, 2016.

64. Anderson DJ, Miller BA, Chen LF, et al. The network approach for

prevention of healthcare-associated infections: long-term effect of

participation in the Duke Infection Control Outreach Network. Infect Control

Hosp Epidemiol. 2011;32(4):315-322.

65. Horan TC, Arnold KE, Rebmann CA, Fridkin SK. Network approach for

prevention of healthcare-associated infections. Infect Control Hosp

Epidemiol. 2011;32(11):1143-1144.

66. Centers for Disease Control and Prevention (CDC). About NHSN. 2015;

http://www.cdc.gov/nhsn/about.html. Accessed 24 June, 2015.

67. Cooke EM, Coello R, Sedgwick J, et al. A national surveillance scheme for

hospital associated infections in England. Team of the Nosocomial Infection

National Surveillance Scheme. J Hosp Infect. 2000;46(1):1-3.

68. Wilson JA, Ward VP, Coello R, Charlett A, Pearson A. A user evaluation of

the Nosocomial Infection National Surveillance System: surgical site

infection module. J Hosp Infect. 2002;52(2):114-121.

69. Public Health England. Protocol for the Surveillance of Surgical Site

Infection - Version 6. London: Public Health England; June 2013.

70. Public Health England. Surveillance of surgical site infections in NHS

hospitals in England, 2013/14. London: Public Health England; December

2014.

71. Tanner J, Padley W, Kiernan M, Leaper D, Norrie P, Baggott R. A

benchmark too far: findings from a national survey of surgical site infection


72. Lamagni T, Wilson J, Wloch C, Elgohari S, Harrington P, Johnson A.

Improving patient safety through surgical site infection surveillance: response

to Tanner et al. J Hosp Infect. 2013;84(3):269-270.

204 References

73. National Reference Centre for the Surveillance of Nosocomial Infections.

Protocol: Surgical site infection surveillance. 2011; http://www.nrz-

hygiene.de/en/surveillance/hospital-infection-surveillance-

system/participation/. Accessed 29 July, 2015.

74. Schröder C, Behnke M, Gastmeier P, Schwab F, Geffers C. Case vignettes to

evaluate the accuracy of identifying healthcare-associated infections by

surveillance persons. J Hosp Infect. 2015;90(4):322-326.

75. Gastmeier P, Geffers C, Brandt C, et al. Effectiveness of a nationwide

nosocomial infection surveillance system for reducing nosocomial infections.

J Hosp Infect. 2006;64(1):16-22.

76. Desenclos J-C, Raisin Working Group. RAISIN - a national programme for

early warning, investigation and surveillance of healthcare-associated

infection in France. Euro Surveillance: Bulletin Europeen sur les Maladies

Transmissibles = European Communicable Disease Bulletin. 2009;14(46).

77. Geubbels ELPE, Nagelkerke NJD, Mintjes-De Groot AJ, Vandenbroucke-

Grauls CMJE, Grobbee DE, De Boer AS. Reduced risk of surgical site

infections through surveillance in a network. Int J Qual Health Care.

2006;18(2):127-133.

78. van der Kooi TI, Wille JC, van Benthem BH. Catheter application, insertion

vein and length of ICU stay prior to insertion affect the risk of catheter-

related bloodstream infection. J Hosp Infect. 2012;80(3):238-244.

79. van der Kooi TII, Manniën J, Wille JC, van Benthem BHB. Prevalence of

nosocomial infections in The Netherlands, 2007–2008: results of the first four

national studies. J Hosp Infect. 2010;75(3):168-172.

80. Mertens R, van den Berg JMJ, Fabry J, OB J. HELICS: a European project to

standardise the surveillance of hospital acquired infection, 1994-1995.

Eurosurveillance. 1996;1(4). http://www.eurosurveillance.org/

ViewArticle.aspx?ArticleId=154. Accessed 29 July 2015.

References 205

81. European Centre for Disease Prevetion and Control. Point prevalence survey

of healthcare associated infections and antimcrobial use in European acute

care hospitals. Stockholm: ECDC;2013.

82. European Centre for Disease Prevention and Control. Annual

Epidemiological Report 2013. Reporting on 2011 surveillance data and 2012

epidemic intelligence data. Stockholm, ECDC: European Centre for Disease

Prevention and Control;2013.

83. Brandt C, Sohr D, Behnke M, Daschner F, Ruden H, Gastmeier P. Reduction

of surgical site infection rates associated with active surveillance. Infect

Control Hosp Epidemiol. 2006;27(12):1347-1351.

84. Schwab F, Geffers C, Barwolff S, Ruden H, Gastmeier P. Reducing neonatal

nosocomial bloodstream infections through participation in a national

surveillance system. J Hosp Infect. 2007;65(4):319-325.

85. Rioux C, Grandbastien B, Astagneau P. Impact of a six-year control

programme on surgical site infections in France: results of the INCISO


86. Vincent A, Ayzac L, Girard R, et al. Downward trends in surgical site and

urinary tract infections after cesarean delivery in a French surveillance

network, 1997-2003. Infect Control Hosp Epidemiol. 2008;29(3):227-233.

87. Couris CM, Rabilloud M, Ecochard R, et al. Nine-year downward trends in

surgical site infection rate in southeast France (1995–2003). J Hosp Infect

2007;67(2):127-134.


reduce nosocomial infection rates. J Hosp Infect. 1995;30 Suppl:3-14.

89. Davis N, Fielding G, Garlick F, Rao A. The Infection Control Sister: Her

Role in a Large Hospital. Lancet. 1963:1321=1322.

90. Davis N, Cohen J, Rao A. The Incidence of Surgical Wound Infection: A

Prospective Study of 20,822 Operations. Aust NZ J Surg. 1973;43(1):75-80.

206 References

91. McLaws ML, Taylor PC. The Hospital Infection Standardised Surveillance

(HISS) programme: analysis of a two-year pilot. J Hosp Infect.

2003;53(4):259-267.

92. Morton A, Clements AC, Doidge SR, Stackelroth J, Curits M, Whitby M.

Surveillance of Healthcare-Acquired Infections in Queensland, Australia:

Data and Lessons From the First 5 Years. Infect Control Hosp

Epidemiol. 2008;29(8):695-701.

93. Russo PL, Bull A, Bennett N, et al. The establishment of a statewide

surveillance program for hospital-acquired infections in large Victorian

public hospitals: a report from the VICNISS Coordinating Centre. Am J

Infect Control. 2006;34(7):430-436.





Surveillance Initiative. National Surveillance Initiative 2013;

http://www.safetyandquality.gov.au/our-work/healthcare-associated-infection/

national-hai-surveillance-initiative/. Accessed 6 October, 2013.


Preventing and Controlling Hospital Acquired Infection. Safety and Quality

Improvement Guide. 2012. http://www.safetyandquality.gov.au/wp-

content/uploads/2012/10/Standard3_Oct_2012_WEB.pdf. Accessed 10

October 2013.

97. Worth LJ, Bull AL, Spelman T, Brett J, Richards MJ. Diminishing surgical

site infections in Australia: time trends in infection rates, pathogens and

antimicrobial resistance using a comprehensive Victorian surveillance

program, 2002-2013. Infect Control Hosp Epidemiol. 2015;36(4):409-416.

98. Cheng AC, Woolnough E, Worth LJ, Pilcher DV. How should we interpret

hospital infection statistics? Med J Aust. 2013;199(11):735-736.

References 207

99. Worth L, Thursky, K.A., Slavin, M.A. Public disclosure of health care-

associated infections in Australia: quality improvement or parody? Med

J Aust. 2012;197(1):29.

100. Worth LJ, Bull, Ann L., Richards, MJ. Public reporting of health care-

associated infection data in Australia: time to refine. Med J Aust.

2013;198(5):252-253.

101. Shaban RZ CM, Christiansen K & the Antimicrobial Resistance Standing

Committee. National Surveillance and Reporting of Antimicrobial

Resistance and Antibiotic Usage for Human Health in Australia. 2013.

102. Grayson ML, Russo PL. The national hand hygiene initiative. Med J Aust.

2009;191(8):420-421.


wound infections and bacteremia in Australian hospitals. Am J Infect.

Control. 1999;27(6):474-481.

104. Cruickshank M, Ferguson J. Reducing harm to patients from health care

associated infection: the role of surveillance. Australian Commission on

Safety and Quality in Health Care; 2008.




Infection. 2015;http://dx.doi.org/10.1071/HI14037.

106. Parrillo S. The Burden of National Healthcare Safety Network (NHSN)

Reporting on the Infection Preventionist: A Community Hospital Perspective

Paper presented at: APIC 2015 Annual Conference, 2015; Nashville,

Tennessee.

107. Wright M-O. Automated surveillance and infection control: Toward a better

tomorrow. Am J Infect Control. 2008;36(3, Supplement):S1-S6.




Infection. 2015;20:29-35

208 References

109. McKibben L, Horan T, Tokars JI, et al. Guidance on public reporting of

healthcare-associated infections: recommendations of the Healthcare

Infection Control Practices Advisory Committee. Am J Infect Control.

2005;33(4):217-226.

110. Berenson RA, Pronovost PJ, HM K. Achieving the Potential of Health Care

Performance Measures. 2013. http://www.rwjf.org/en/library/

research/2013/05/achieving-the-potential-of-health-care-performance-

measures.html?cid=xem_hcpm5-21-13A&cid. Accessed 19 December 2015.

111. Martin M, Zingg W, Hansen S, et al. Public reporting of healthcare-

associated infection data in Europe. What are the views of infection

prevention opinion leaders? J Hosp Infect. 2013;83(2):94-98.

112. Talbot TR, Bratzler DW, Carrico RM, et al. Public reporting of health care-

associated surveillance data: recommendations from the healthcare infection

control practices advisory committee. Ann Intern Med.

2013;159(9):631-635.

113. National Health Performance Authority. MyHospitals. MyHospitals 2015;

http://www.myhospitals.gov.au. Accessed 9th March, 2014.

114. Collignon P, Cruickshank M, Dreimanis D. Staphylococcus aureus

bloodstream infections: an important indicator for infection control. Chapter

2: Bloodstream infections – an abridged version. Healthcare Infection.

2009;14(4):165-171.

115. Grayson ML, Russo PL, Cruickshank M, et al. Outcomes from the first 2

years of the Australian National Hand Hygiene Initiative. Med J Aust.

2011;195(10):615-619.

116. Hibbard JH, Stockard J, Tusler M. Does publicizing hospital performance

stimulate quality improvement efforts? Health Affairs (Project Hope).

2003;22(2):84-94.

117. Werner RM, Asch DA. The unintended consequences of publicly reporting

quality information. JAMA. 2005;293(10):1239-1244.

References 209

118. Haut ER, Pronovost PJ. Surveillance bias in outcomes reporting. JAMA.

2011;305(23):2462-2463.

119. Humphreys H, Cunney R. Performance indicators and the public reporting of

healthcare-associated infection rates. Clin Microbiol Infect

2008;14(10):892-894.

120. Fridkin SK, Olmsted RN. Meaningful measure of performance: a foundation

built on valid, reproducible findings from surveillance of health care-

associated infections. Am J Infect Control. 2011;39(2):87-90.

121. Page K, Barnett AG, Campbell M, et al. Costing the Australian National

Hand Hygiene Initiative. J Hosp Infect. 2014;88(3):141-148.

122. Kiernan MA. Public reporting of healthcare-associated infection: professional

reticence versus public interest. J Hosp Infect. 2013;83(2):92-93.

123. Akobeng AK. Understanding diagnostic tests 1: sensitivity, specificity and

predictive values. Acta Pædiatrica. 2007;96(3):338-341.

124. Fabry J, Morales I, Metzger M-H, Russell I, Gastmeier P. Quality of

information: a European challenge. J Hosp Infect. 2007;65, Supplement

2(0):155-158.

125. Klazinga N, Fischer C, ten Asbroek A. Health services research related to

performance indicators and benchmarking in Europe. J. Health Serv. Res.

Policy. 2011;16 Suppl 2:38-47.

126. Perla RJ, Peden CJ, Goldmann D, Lloyd R. Health care-associated infection

reporting: The need for ongoing reliability and validity assessment. Am J

Infect Control. 2009;37(8):615-618.

127. Emori TG EJ, Culver DH et al. Accuracy of reporting nosocomial infections

in intensive-care-unit patients to the National Nosocomial Infections

Surveillance System: a pilot study. Infect Control Hosp Epidemiol.

1998;19(5):308-316.

210 References

128. Backman LA, Melchreit R, Rodriguez R. Validation of the surveillance and

reporting of central line-associated bloodstream infection data to a state

health department. Am J Infect Control. 2010;38(10):832-838.

129. Kainer MA, Mitchell J, Frost BA, MM S. Validation of central line

associated blood stream infection [CLABSI] data submitted to the National

Healthcare Safety Network [NHSN]: a pilot study by the Tennessee

Department of Health [TDH]. Paper presented at: 5th Decennial International

Conference on Health care-Associated Infections:; March 3, 2010; Atlanta.

130. Soe MM, Kainer MA. Sustainable, cost-effective internal data validation of

healthcare associated infections surveillance reported to the National

Healthcare Safety Network [NHSN]. Paper presented at: 5th Decennial

International Conference on Healthcare-Associated Infections:; March 3,

2010; Atlanta.




2007;28(7):812-817.

132. McBryde ES, Brett J, Russo PL, Worth LJ, Bull AL, Richards MJ. Validation

of statewide surveillance system data on central line-associated bloodstream

infection in intensive care units in Australia. Infect Control Hosp Epidemiol.

2009;30(11):1045-1049.

133. Grammatico-Guillon L, Rusch E, Astagneau P. Surveillance of prosthetic

joint infections: international overview and new insights for hospital

databases. J Hosp Infect. 2013.

134. Keller SC, Linkin DR, Fishman NO, Lautenbach E. Variations in

identification of healthcare-associated infections. Infect Control Hosp

Epidemiol. 2013;34(7):678-686.

135. Dixon-Woods M, Leslie M, Bion J, Tarrant C. What counts? An ethnographic

study of infection data reported to a patient safety program. Milbank Q.

2012;90(3):548-591.

References 211

136. Tanner J, Padley W, Davey S, Murphy K, Brown B. Patient narratives of

surgical site infection: implications for practice. J Hosp Infect.

2013;83(1):41-45.

137. Rich KL, Reese SM, Bol KA, Gilmartin HM, Janosz T. Assessment of the

quality of publicly reported central line-associated bloodstream infection data

in Colorado, 2010. Am J Infect Control. 2013;41(10):874-879.

138. Hensher DA, Louviere JJ, Swait J. Stated Choice Methods: Analysis and

Application. Cambridge: University Press; 2000.

139. McNamara A, Chen G, George S, Walker R, Ratcliffe J. What factors

influence older people in the decision to relinquish their driver's licence? A

discrete choice experiment. Accid Anal Prev. 2013;55:178-184.

140. WHO Library Cataloguing-in-Publication Data. How to Conduct a Discrete

Choice Experiment for Health Workforce Recruitment and Retention in

Remote and Rural Areas: A User Guide With Case Studies. 2012.

http://www.who.int/hrh/resources/DCE_UserGuide_WEB.pdf. Accessed 26

June 2015.

141. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in

health economics: a review of the literature. Health Econ. 2012;21(2):145-

172.

142. Lancsar E, Louviere J. Conducting discrete choice experiments to inform

healthcare decision making: a user's guide. Pharmacoeconomics.

2008;26(8):661-677.

143. Bridges JFP, Hauber AB, Marshall D, et al. Conjoint analysis applications in

health--a checklist: a report of the ISPOR Good Research Practices for

Conjoint Analysis Task Force. Value Health. 2011;14(4):403-413.

144. Louviere JJ, Flynn TN, Carson RT. Discrete Choice Experiments Are Not

Conjoint Analysis. Journal of Choice Modelling. 2010;3(3):57-72.

145. Ryan M, Gerard K, Currie G. Using discrete choice experiments in health

economics. In: Jones AM, ed. The Elgar Companion to Health Economics.

Second ed. Cheltenham, UK: Edward Elgar Publishing Limited; 2012:437-

446.

212 References

146. Kjaer T. A review of the discrete choice experiment-with emphasis on its

application in health care. Syddansk Universitet; 2005.

147. Ryan M, Gerard K. Using discrete choice experiments to value health care

programmes: current practice and future research reflections. Appl Health

Econ Health Policy. 2003;2(1):55-64.

148. Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete

Choice Experiments in Health Economics: A Review of the Literature.

Pharmacoeconomics. 2014;32(9):883-902.

149. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC.

Fostering implementation of health services research findings into practice: a

consolidated framework for advancing implementation science. Implement

Sci. 2009;4:50.

150. Saint S, Howell J, xa, et al. Implementation Science: How to Jump Start

Infection Prevention. Infect Control Hosp Epidemiol. 2010;31(S1):S14-S17.

151. Eccles MP, Mittman BS. Welcome to Implementation Science.

Implement Sci. 2006;1(1).

152. Pronovost PJ, Berenholtz SM, Needham DM. Translating Evidence into

Practice: A Model for Large Scale Knowledge Translation. BMJ.

2008;337(7676):963-965.

153. Pronovost P, Needham D, Berenholtz S, et al. An intervention to decrease

catheter-related bloodstream infections in the ICU. N Engl J Med.

2006;355(26):2725-2732.

154. May C, Finch T. Implementing, Embedding, and Integrating Practices: An

Outline of Normalization Process Theory. Sociology. 2009;43(3):535-554.

155. Murray E, Treweek S, Pope C, et al. Normalisation process theory: a

framework for developing, evaluating and implementing complex

interventions. BMC Med. 2010;8:63.

156. McEvoy R, Ballini L, Maltoni S, O'Donnell CA, Mair FS, MacFarlane A. A

qualitative systematic review of studies using the normalization process

theory to research implementation processes. Implement Sci. 2014;9:2.

References 213

157. Cresswell JW, Plano Clark VL. Designing and conducting mixed methods

research. 2nd ed. Los Angeles: Sage publications; 2011.

158. Goggin LS, van Gessel H, McCann RL, Peterson AM, Van Buynder PG.

Validation of surgical site infection surveillance in Perth, Western Australia.

Healthcare Infection. 2009;14(3):101.

159. Baltussen R, Stolk E, Chisholm D, Aikins M. Towards a multi-criteria

approach for priority setting: an application to Ghana. Health Econ.

2006;15(7):689-696.

160. Baltussen R, ten Asbroek AHA, Koolman X, Shrestha N, Bhattarai P,

Niessen LW. Priority setting using multiple criteria: should a lung health

programme be implemented in Nepal? Health Policy Plan.

2007;22(3):178-185.

161. Green C, Gerard K. Exploring the social value of health-care interventions: a

stated preference discrete choice experiment. Health Econ.

2009;18(8):951-976.

162. Anderson DJ, Chen LF, Sexton DJ, Kaye KS. Complex surgical site

infections and the devilish details of risk adjustment: important implications

for public reporting. Infect Control Hosp Epidemiol. 2008;29(10):941-946.

163. Berrios-Torres SI, Mu Y, Edwards JR, Horan TC, Fridkin SK. Improved risk

adjustment in public reporting: coronary artery bypass graft surgical site

infections. Infect Control Hosp Epidemiol. 2012;33(5):463-469.

164. Mu Y, Edwards JR, Horan TC, Berrios-Torres SI, Fridkin SK. Improving

risk-adjusted measures of surgical site infection for the national healthcare

safety network. Infect Control Hosp Epidemiol. 2011;32(10):970-986.

165. Tanner J, Kiernan M, Leaper D, Baggott R. Reliable surgical site infection

surveillance with robust validation is required. J Hosp Infect.

2013;84(3):270.

166. Harrington. P, Wloch. C, Elgohari. E, T L. Protocol for the Surveillance of

Surgical Site Infection - Surgical Site Infection Surveillance Service (Version

6). In: England PH, ed. London: Public Health England; 2013.

214 References

167. Reilly J, Allardice G, Bruce J, Hill R, McCoubrey J. Procedure-specific

surgical site infection rates and postdischarge surveillance in Scotland. Infect

Control Hosp Epidemiol. 2006;27(12):1318-1323.

168. Lee G, Kleinman K, Soumerai S, et al. Effect of Nonpayment for Preventable

Infections in U.S. Hospitals. N Engl J Med. 2012;367(15):1428-1437.

169. Runnegar N. What proportion of healthcare-associated bloodstream

infections (HA-BSI) are preventable and what does this tell us about the

likely impact of financial disincentives on HA-BSI rates? Australasian

College for Infection Prevention and Control 2014 Conference; 23-26

November, 2014; Adelaide, Australia.

170. Binstead T. Medibank forces hospitals to offer surgery 'warranty'. Australian

Financial Review. 23 November, 2015.

171. Horowitz HW. Infection control: Public reporting, disincentives, and bad

behavior. Am J Infect Control. 2015;43(9):989-991.

172. Farmer S, Black B, Bonow R. Tension Between Quality Measurement, Public

Quality Reporting, and Pay for Performance. JAMA. 2013;309(4):349-350.

173. Lee TB, Montgomery OG, Marx J, et al. Recommended practices for

surveillance: Association for Professionals in Infection Control and

Epidemiology (APIC), Inc. Am J Infect Control. 2007;35(7):427-440.

174. Kiernan M. "Top down" approaches to infection prevention and control.

Paper presented at: Australasian College for Infection Prevention and Control

2015 Conference; 22-25 November, 2015; Hobart, Tasmania.

175. National Patient Safety Foundation. Free from Harm: Accelerating Patient

Safety Improvement Fifteen Years after To Err Is Human. National Patient

Safety Foundation.;2015.

176. Australian Commission for Safety and Quality in Health Care. Healthcare

Associated Infection. Healthcare Associated Infection 2013;

http://www.safetyandquality.gov.au/our-work/healthcare-associated-

infection/. Accessed 8 September, 2015.

References 215

177. Graves N, Harbarth S, Beyersmann J, Barnett A, Halton K, Cooper B.

Estimating the cost of health care-associated infections: mind your p's and q's.

CID. 2010;50(7):1017-1021.

178. Mitchell BG, Ferguson JK, Anderson M, Sear J, Barnett A. Length of stay

and mortality associated with healthcare-associated urinary tract infections: a

multi-state model. J Hosp Infect. 2016;93(1):92-99.

179. van Mourik MS. Automated monitoring of nosocomial infections.

European Congress of Clinical Microbiology and Infectious Diseases; 9-12

April, 2016; Amsterdam, Netherlands.

180. Krein SL, Olmsted RN, Hofer TP, et al. Translating infection prevention

evidence into practice using quantitative and qualitative research. Am J

Infect Control. 2006;34(8):507-512.

181. May C. Towards a general theory of implementation. Implement Sci.

2013;8(1):18.

182. Russo PL, Barnett AG, Cheng AC, Richards M, Graves N, Hall L.

Differences in identifying healthcare associated infections using clinical

vignettes and the influence of respondent characteristics: a cross-sectional

survey of Australian infection prevention staff. Antimicrob Resist Infect

Control. 2015;4(29):1-7.



Control. 2015;43(7):773-775.

184. Russo PL, Cheng AC, Richards M, Graves N, Hall L. Healthcare-associated

infections in Australia: time for national surveillance. Aust Health Rev.

2015;39(1):37-43.

185. Agency for Healthcare Research and Quality. Saving Lives and Saving

Money: Hospital-Acquired Conditions Update 2015;

http://www.ahrq.gov/professionals/quality-patient-safety/pfp/interimhac2014-

ap1.html. Accessed 7 December, 2015.

216 References

Appendices 217

Appendices

Appendix A: Key search terms and outputs for literature review

Topic Keytermsandlimits Articles PubmedSearchhistory

Surveillanceofhealthcare-associatedinfection

Healthcare-associatedinfectionORnosocomialinfectionANDsurveillance

144 Search((healthcare-associatedinfection[MeSHMajorTopic])ORnosocomialinfection[MeSHMajorTopic])ANDsurveillance[MeSHMajorTopic]

Nationalhealthcare-associatedinfectionsurveillanceprograms

Healthcare-associatedinfectionORnosocomialinfectionANDsurveillanceANDnational

0 Search((healthcare-associatedinfection[MeSHMajorTopic])ORnosocomialinfection[MeSHMajorTopic])ANDsurveillance[MeSHMajorTopic]))ANDnational[MeSHMajorTopic]

Developmentofhealthcare-associatedinfectionsurveillanceprograms

Healthcare-associatedinfectionORnosocomialinfectionANDsurveillanceANDdevelopment

0 Search((healthcare-associatedinfection[MeSHMajorTopic])ORnosocomialinfection[MeSHMajorTopic])ANDsurveillance[MeSHMajorTopic]))ANDdevelopment[MeSHMajorTopic]

Establishmentofhealthcare-associatedinfectionsurveillanceprogram

Healthcare-associatedinfectionORnosocomialinfectionANDsurveillanceANDestablishment

0 Search((healthcare-associatedinfection[MeSHMajorTopic])ORnosocomialinfection[MeSHMajorTopic])ANDsurveillance[MeSHMajorTopic]))ANDestablishment[MeSHMajorTopic]

Implementationofhealthcare-associatedinfectionsurveillanceprograms

Healthcare-associatedinfectionORnosocomialinfectionANDsurveillanceANDimplementation

0 Search((healthcare-associatedinfection[MeSHMajorTopic])ORnosocomialinfection[MeSHMajorTopic])ANDsurveillance[MeSHMajorTopic]))ANDimplementation[MeSHMajorTopic]

Publicreportingofhealthcare-associated

Healthcare-associatedinfectionORnosocomialinfectionANDpublic

9 "Search(healthcare-associatedinfection[Title/Abstract])ANDpublicreporting[Title/Abstract]Filters:Abstract;Publicationdatefrom

218 Appendices

infectiondata reportingHuman20050101-20151231abstract

2005/01/01to2015/12/31;Humans",9,01:07:32

Validationofhealthcare-associatedinfectiondata

Healthcare-associatedinfectionORnosocomialinfectionANDsurveillanceANDdataqualityHuman20050101-20151231abstract

65

“Search(((((healthcare-associatedinfection[Title/Abstract])ORnosocomialinfection[Title/Abstract])ANDsurveillance[Title/Abstract])ANDhasabstract[text]AND(""2005/01/01""[PDat]:""2015/12/31""[PDat])ANDHumans[Mesh]))ANDdataqualityFilters:Abstract;Publicationdatefrom2005/01/01to2015/12/31;Humans",65,01:08:13

Discretechoiceexperiments

DiscretechoiceexperimentANDsurveillanceHuman20050101-20151231abstract

67

"Search(discretechoiceexperiment)ANDsurveillanceFilters:Abstract;Publicationdatefrom2005/01/01to2015/12/31;Humans",67,01:17:21

Implementationscience

ImplementationscienceORnormalisationprocesstheoryHuman20050101-20151231abstract

279

"Search(implementationscience[Title/Abstract])ORnormalisationprocesstheory[Title/Abstract]Filters:Abstract;Publicationdatefrom2010/01/01to2015/12/31;Humans",279,01:24:18

Appendices 219

Appendix B: Ethics approval - Current Australian hospital practices in healthcare-associated infection surveillance

Subject:EthicsApplicationApproval--1400000339Date: Thursday,26June2014at10:58:59AustralianEasternStandardTimeFrom: QUTResearchEthicsUnitTo: LisaHall,NicholasGraves,PhilRussoCC: JanetteLamb

DearDrLisaHallandMrPhilRusso

ProjectTitle:CurrentAustralianhospitalpracticesinhealthcare-associatedinfectionsurveillance

EthicsCategory: Human-LowRiskApprovalNumber:1400000339ApprovedUntil: 26/06/2015(subjecttoreceiptofsatisfactoryprogressreports)

WearepleasedtoadvisethatyourapplicationhasbeenreviewedandconfirmedasmeetingtherequirementsoftheNationalStatementonEthicalConductinHumanResearch.

IcanthereforeconfirmthatyourapplicationisAPPROVED.

Ifyourequireaformalapprovalcertificatepleaseadviseviareplyemail.

CONDITIONSOFAPPROVALPleaseensureyouandallotherteammembersreadthroughandunderstandallUHRECconditionsofapprovalpriortocommencinganydatacollection:

Standard:Pleaseseeattachedorgotowww.research.qut.edu.au/ethics/humans/stdconditions.jspSpecific:Noneapply

DecisionsrelatedtolowriskethicalreviewaresubjecttoratificationatthenextavailableUHRECmeeting.YouwillonlybecontactedagaininrelationtothismatterifUHRECraisesanyadditionalquestionsorconcerns.

WhilstthedatacollectionofyourprojecthasreceivedQUTethicalclearance,thedecisiontocommenceandauthoritytocommencemaybedependentonfactorsbeyondtheremitoftheQUTethicsreviewprocess.Forexample,yourresearchmayneedethicsclearancefromotherorganisationsorpermissionsfromotherorganisationstoaccessstaff.Thereforetheproposeddatacollectionshouldnotcommenceuntilyouhavesatisfiedtheserequirements.Pleasedon'thesitatetocontactusifyouhaveanyqueries.Wewishyouallthebestwithyourresearch.

Appendices 221

Appendix C: Ethics approval - Key attributes of a healthcare-associated infection surveillance program

Subject:EthicsApplicationApproval--1400000679Date: Monday,22September2014at16:50:22AustralianEasternStandardTimeFrom: ResearchEthicsTo: LisaHall,NicholasGraves,PhilRussoCC: JanetteLamb

DearDrLisaHallandMrPhilipRusso

ProjectTitle:Keyattributesofahealthcare-associatedinfectionsurveillanceprogram

EthicsCategory: Human-LowRiskApprovalNumber:1400000679ApprovedUntil: 22/09/2017(subjecttoreceiptofsatisfactoryprogressreports)




CONDITIONSOFAPPROVALPleaseensureyouandallotherteammembersreadthroughandunderstandallUHRECconditionsofapprovalpriortocommencinganydatacollection:

Standard:Pleaseseeattachedorgotowww.research.qut.edu.au/ethics/humans/stdconditions.jspSpecific:Noneapply


WhilstthedatacollectionofyourprojecthasreceivedQUTethicalclearance,thedecisiontocommenceandauthoritytocommencemaybedependentonfactorsbeyondtheremitoftheQUTethicsreviewprocess.Forexample,yourresearchmayneedethicsclearancefromotherorganisationsorpermissionsfromotherorganisationstoaccess

222 Appendices

staff.Thereforetheproposeddatacollectionshouldnotcommenceuntilyouhavesatisfiedtheserequirements.Pleasedon'thesitatetocontactusifyouhaveanyqueries.Wewishyouallthebestwithyourresearch.KindregardsJanetteLambonbehalfofChairUHRECOfficeofResearchEthics&IntegrityLevel4|88MuskAvenue|KelvinGrovep:+61731385123e:[email protected]:http://www.orei.qut.edu.au

Appendices 223

Appendix D: Ethics approval - Preferences for a healthcare-associated infection surveillance program using a discrete choice experiment

Subject:Ethicsapplication-approved-150000030Date: Monday,1June2015at12:24:40AustralianEasternStandardTimeFrom: QUTResearchEthicsUnitTo: LisaHall,NicholasGraves,PhilRusso,PhilipRussoCC: JanetteLamb

DearDrLisaHallandMrPhilipRusso

ProjectTitle:Preferencesforahealthcare-associatedinfectionsurveillanceprogramusingadiscretechoiceexperiment

EthicsCategory: Human-LowRiskApprovalNumber:1500000304ApprovedUntil: 1/06/2017

(subjecttoreceiptofsatisfactoryprogressreports)




CONDITIONSOFAPPROVALPleaseensureyouandallotherteammembersreadthroughandunderstandallUHRECconditionsofapprovalpriortocommencinganydatacollection:Standard:Pleaseseeattachedorgotohttp://www.orei.qut.edu.au/human/stdconditions.ispSpecific: Noneapply


WhilstthedatacollectionofyourprojecthasreceivedQUTethicalclearance,thedecisiontocommenceandauthoritytocommencemaybedependentonfactorsbeyondtheremitoftheQUTethicsreviewprocess.Forexample,yourresearchmayneedethicsclearancefromotherorganisationsorpermissionsfromotherorganisationstoaccessstaff.Thereforetheproposeddatacollectionshouldnotcommenceuntilyouhavesatisfiedtheserequirements.Pleasedon'thesitatetocontactusifyouhaveanyqueries.Wewishyouallthebestwithyourresearch.Kindregards

224 Appendices

JanetteLambonbehalfofChairUHRECOfficeofResearchEthics&IntegrityLevel4|88MuskAvenue|KelvinGrovep:+61731385123e:[email protected]:http://www.orei.qut.edu.au

Appendices 225

Appendix E: Letter of Support from the Australasian College for Infection Prevention and Control

13May2014MrPhilRussoPHDStudent,SchoolofPublicHealth&SocialWorkInstituteofHealthandBiomedicalInnovationQueenslandUniversityofTechnology

ViaEmail:[email protected]

DearPhilPHD:NationalHealthcare-associatedInfectionSurveillance

I am pleased to extend this letter of support for the Australasian College for InfectionPrevention andControl's (ACIPC) involvement in the above research. Youhave requestedaccesstoourDiscussionList,InfexionConnexion,withvoluntaryinvolvement;thedatawillbede-identified(meetingchangestothePrivacyAct2014)andensureconfidentiality.

TheCollegeExecutiveCouncilunderstandthatthisimportantresearchhasthepotentialtosupportICPsintheireverydaysurveillancework.ItisalsoimportantthatthisinformationissharedatourNationalConferenceandwearepleasedthatyouareaninvitedspeakerandwillbepresentingtheresultsofthisresearch.

Welookforwardtothepositiveoutcomesfromyourresearch.

MARIJAJURAJA

PresidentAustralasianCollegeforInfectionPreventionandControlLtd

Australasian College for Infection Prevention and Control Ltd GPO Box 3254 Brisbane Qld 4001 . ABN 61 154 341 036

P + 61 7 3211 4695 F + 61 7 3211 4900 E [email protected] W www.acipc.org.au

Kindregards

226 Appendices

Appendix F: Survey tool - Current Australian hospital practices in healthcare-associated infection surveillance

Hello and welcome to the HAI surveillance survey!

Description This project is being undertaken as part of a PhD for Philip Russo The purpose of this project is to to identify and describe the differences between the healthcare associated infection (HAI) surveillance programs in Australia, measure agreement between clinicians when identifying HAI's and identify factors that may influence agreement levels. You are invited to participate in this project because you are an infection prevention and control professional involved in HAI surveillance.

Participation Participation will involve completing an 88 item, anonymous questionnaire with likert scale answers (strongly agree – strongly disagree), multiple choice answers, and a series of clinical vignettes that will take approximately 20 minutes of your time. Questions will include “Do you undertake post discharge surgical site infection surveillance?”, “Which surgical site infection definitions do you use?”. Your participation in this project is entirely voluntary. If you agree to participate, please note some questions must be answered before you can progress to the next. You do not have to have to complete any question(s) or the survey if you are uncomfortable answering. Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT. If you do agree to participate it will not be possible to withdraw, once you have submitted your responses.

Expected benefits It is expected that this project will directly benefit you. Data from this study will be used to inform the larger Research Project to identify evidence based practices for national HAI surveillance. A national HAI surveillance program will result in uniform methodology and reporting. This will close the current gap we have with current systems and ensure we are measuring infections the same way across Australia. This will improve our understanding of the epidemiology of HAIs in Australia and enable meaningful national comparisons of HAI rates by hospital size, type, specialty and potentially by specific patient risk factors. Detailed data will enable us to identify problem areas that require more infection prevention resources and target interventions. Successful interventions could act as role models and also inform State and national policy

Risks There are no risks beyond normal day-to-day living associated with your participation in this project.

Privacy and Confidentiality All comments and responses are anonymous and will be treated confidentially unless required by law. You are not asked to provide your name or any contact details. Any data collected as part of this project will be stored securely as per QUT’s Management of research data policy. Consent to Participate Submitting the completed online questionnaire is accepted as an indication of your consent to participate in this project.

Questions / further information about the project Ifyouhaveanyquestionsorrequirefurtherinformationpleasecontactoneoftheresearchteammembersbelow.

Appendices 227

Name–PhilipRusso,PhDStudentName–LisaHall,SeniorResearchFellow

Phone–0411659486 Phone0731386425Email:[email protected] Email [email protected]

Concerns / complaints regarding the conduct of the project QUT is committed to research integrity and the ethical conduct of research projects. However, if you do have any concerns or complaints about the ethical conduct of the project you may contact the QUT Research Ethics Unit on [+61 7] 3138 5123 or email [email protected]. The QUT Research Ethics Unit is not connected with the research project and can facilitate a resolution to your concern in an impartial manner. This study has been approved by the QUT Human Research Ethics Committee (approval number 1400000339).

Thankyouforhelpingwiththisresearchproject.Pleasekeepthissheetforyourinformation.

Byagreeingtoparticipateinthisstudy,youareagreeingthatyou:• havereadandunderstoodtheinformationprovidedintheInformationto

Participantssection.• havehadanyquestionsansweredtoyoursatisfaction.• agreetoparticipateinthisonlinesurvey• understandthatonceyouhavesubmittedyourresponses,thesecannotbe withdrawn.

• agreethatresearchdatacollectedforthisstudymaybepublishedormaybeprovidedtootherresearchers

NB:thelastquestionofthissurveyprovidesyouwithanopportunitytoprovideanygeneralfeedbackregardingthissurveyorHAIsurveillance.

228 Appendices

Pleaseindicateyourresponsetothestatementsaboveandconsenttoparticipate:• Idonotagreeanddonotgivemyconsent• Idoagreeandgivemyconsent

Section1-DEMOGRAPHICDATA

Whatisyourage?

Gender?• Male• Female

Whatpositionareyoucurrentlyemployedin?

• RegisteredNurse• EnrolledNurse• Other

Pleasespecify.

InwhichStateorTerritoryareyouemployed?• ACT• NSW• NT• QLD• SA• TAS• VIC• WA

Approximatelyhowmanyovernightbedsdoesyourfacilityhave?• Lessthan50• 51-100• 101-200• 201-300• 301-400• Morethan400

Pleaseindicateyourqualifications:(tickallthatapply)• PhD• MastersdegreeinInfectionControl• MastersdegreeinPublicHealthorEpidemiology• Mastersdegree–Other• BachelorofNursing• DiplomainNursing• Diploma–Other• CertificateinInfectionControl

Appendices 229

• CertificateinSterilisation• CertificateinPublicHealthorEpidemiology• Certificate–Other

OtherHowmanyhoursareyoucontractedorallocatedtoworkoninfectioncontrolactivitiesperweek(onaverage)Isyourmainplaceofemploymentapublicsectororprivatesectorfacility?

• PublicSector• PrivateSector

Howmanyyearsofexperienceininfectionpreventionandcontroldoyouhave?Section2-YOURHEALTHCAREASSOCIATEDINFECTION(HAI)SURVEILLANCEDoyouundertakeHAIsurveillanceatyourhospital?

• Yes• No

WhatelementsofHAIsurveillanceareyouinvolvedin?(tickallthatapply)

• DataCollection• DataAnalysis• DataReporting

HowmanyyearsofexperienceinHAIsurveillancedoyouhave?ThinkingaboutalltheHAIsurveillanceactivitesyouareinvolvedin,onaverage,howmanyhoursperweekdoyouthinkthiswouldaddupto?DidyoureceiveanyHAIsurveillancetrainingpriortocommencingHAIsurveillance?

• Yes• No

WhoprovidedtheHAIsurveillancetraining?

• Mysupervisor/boss/teamleader• Acolleague• Acentralagency(e.gVICNISS,CHRISP,HISWAetc)

Other(pleasespecify)

230 Appendices

HaveyouundergoneanyformalassessmentofyourHAIsurveillanceskills?

• Yes• No

WhoperformedtheassessmentofyourHAIsurveillanceskills?

• Mysupervisor/boss/teamleader• Acolleague• Acentralagency(e.gVICNISS,CHRISP,HISWAetc)

Other(pleasespecify)Thinkingaboutthepastfiveyearsonly,approximatelyhowmanytimeshaveyourHAIsurveillanceskillsbeenassessed?(pleaseenternumber)Doyouworkwithotherinfectionpreventionnursesinyourfacility?

• Yes• No–Iworkasasolepractitioner

Excludingyourself,whatistheaverageweeklytotalnumberofhoursworkedbyinfectionpreventionstaffatyourfacility?ThinkingaboutyourteamenvironmentandconfirmingthepresenceofaHAIinapatient,pleaseindicatewhichresponsemostaccuratelyreflectsyourteampractice?Asateam,wediscusseverypossibleHAIbeforeconfirming

• Always• Often• Sometimes• Rarely• Never

Atmyfacility,Ihaveroutineaccess(eitherfacetofaceorphone)to: Daily Weekly Lessthan

weeklyRarely Never

IDPhysician Epidemiologist Statistician Microbiologist Colleagueswhohavemoresurveillanceexperience

Administrativesupport

Appendices 231

Section3-SURGICALSITEINFECTIONSURVEILLANCE(SSI)

Doyouundertakesurgicalsiteinfectionsurveillance?• Yes• No

Whichsurgicalsiteinfectiondefinitionsdoyouuse?• NationalHealthandSafetyNetwork(NHSN)WITHNOmodifications• NationalHealthandSafetyNetwork(NHSN)WITHmodifications


OnwhichproceduresdoyouundertakeSSIsurveillance?(tickallthatapply)

• Abdominalaorticaneurysmrepair• Abdominalhysterectomy• Appendixsurgery• Breastsurgery• Cardiacsurgery• Coronaryarterybypassgrafts• Gallbladdersurgery• Colorectalsurgery• Craniotomy• Caesareansection• Femero-poplitealoffemero-tibialbypasssurgery• Gastricsurgery• Hernoirrhaphy• Hipprostheses• Kneeprosthesis• Laminectomy• Pacemakersurgery• Smallbowelsurgery• Spinalfusion• Vaginalhysterectomy• Ventricularshunt

WouldyoudescribeyourmethodforcollectingSSIdataasbeingmostly• Prospective(i.e.whilstthepatientisinhospital)• Retrospective(i.e.afterthepatienthasbeendischarged)

WhenundertakingSSIsurveillance,wouldyousaymostofthedataiscollectedusing• Paperbasedmanualsystems• Electronicsystems• Acombinationofboth


CLINICALVIGNETTEAftercoronaryarterybypassgraftsurgeryapatienthasthreesurgicalincisionsites,sternal,leftsaphenousveinandrightsaphenousvein.A

232 Appendices

surgicalsiteinfectionisconfirmedinthesternalwoundandleftsaphenousveinsite.Thinkingaboutcalculatinginfectionrates,andhowthisscenarioisreported,wouldyouconsiderthistobe:

• Onesurgicalsiteinfectionfromoneprocedure• Twosurgicalsiteinfectionsfromoneprocedure• Twosurgicalsiteinfectionsfromthreeprocedures

CLINICALVIGNETTEA55y.o.maleundergoestotalhipreplacementonthe1stFebruary,andisdischargedwellfromhospitalonthe6thFebruary.Onthe21stFebruaryhebecomesfebrile(38.5oC)andhiswoundedgesbegintoseparateandisdischargingcloudyooze.Onthe22ndFebruaryhepresentstoemergencydepartmentunwell,febrile(39oC)thewholelengthofhiswoundhasdehiscedandisdischargingpurulentfluid.HeisadmittedtohospitalandcommencedonIVantibiotics.Thereisnosignofinfectionelsewhere.ApplyingyourusualHAIdefinitions,doesthismanhaveasurgicalsiteinfection?

• Yes• No

CLINICALVIGNETTETwoweeksafterabowelresection,a35yofemalepresentstotheEmergencyDepartmentwithsevereabdominalpainandfever(39.2oC).Ultrasounddemonstratesacollectionoffluidintheabdomen.Shewastakentotheatrewherethecollectionwasdrainedandaculturewastaken,whichlatergrewE.coli.ApplyingyourusualHAIdefinitionsdoesthisfemalehave

• Anorganspacesurgicalsiteinfection?• Adeepsurgicalsiteinfection?

DoyouroutinelycompareyourSSIdataagainst:(tickallthatapply)

• Anotherhospital• Aggregatedstatedata• Nationaldata• NHSNrate• Idon'tcompareourHAIdata

Other(pleasespecify)DoyouroutinelyriskadjustSSIrates?

• Yes• No

DoyouroutinelyuseaStandardisedInfectionRatio(SIR)foranyproceduresinyourSSIdataanalysis?

• Yes• No

Doyouroutinelyreport

• Alldeep,organspaceandsuperficialSSIinfections• OnlydeepandorganspaceSSIinfections• OnlydeepSSIinfections

Appendices 233

Other(pleasespecify)Dosurgicalstaffatyourfacilityreviewthedatapriortoreportinginfectionrates?

• Yes• No

Doesthisrevieweverresultinchangestotheinfectionrates?

• Always• Often• Sometimes• Rarely• Never

IfapatientisdeterminedtohaveacquiredaSSIfollowingaprocedureconductedatanotherfacility,doyouroutinelynotifythefacilitywheretheprocedurewasconducted?

• Yes• No

IfapatientisdeterminedtohaveacquiredaSSIfollowingaprocedureconductedatanotherfacility,wouldyouincludethisSSIinyourfacilitySSIdata?

• Yes• No

Doyouundertakepostdischargesurgicalsiteinfectionsurveillance?

• Yes• No

Whatmethoddoyoupredominantlyusetocollectpostdischargedata?(selectone)

• Telephonethepatient• Mailtopatientsandrequesttocompleteformandreturn• Homevisitsbyclinician• Outpatientclinicvisits

Other(pleasespecify)Doyouroutinelyincludeinfectionsdetectedpostdischargeinyourreports?

• Yes• No

DoyouroutinelyreportthenumberorproportionofHAIsthatweredetectedpostdischargeversusthosethatweredetected'in-house'?

• Yes• No

234 Appendices

AreyouconfidentyourSSIratesareanaccuratereflectionofthetrueSSIrate?• Yes• No

Ifno,whynot?

Section4-BLOODSTREAMINFECTIONSURVEILLANCE(BSI)

Doyouundertakebloodstreaminfectionsurveillance?• Yes• No

IsBSIsurveillanceconductedhospitalwide?• Yes• No

WouldyoudescribeyourmethodforcollectingBSIdataasbeingmostly• Prospective• Retrospective

WhenundertakingBSIsurveillance,wouldyousaythemostofthedataiscollectedusing(selectone)

• Paperbasedmanualsystems• Electronicsystems• Acombinationofboth

Other

CLINICALVIGNETTEA72yomaleisadmittedtohospitalwithaninfectedlegulcer.AcultureofthelegulcertakenbyhisGeneralPractitioner5dayspriortoadmissionidentifiesStaphylococcusaureus.Fourdaysafteradmissiontohospitalbloodculturesaretakenduetoongoingfever>39.0oC.Staphylococcusaureusisisolatedfromthebloodcultures.ApplyingyourusualHAIdefinitions,doesthismalehaveahealthcareassociatedbloodstreaminfection?

• Yes• No

Doyouundertakecentrallineassociatedbloodstreaminfection(CLABSI)surveillance?

• Yes• No

IsCLABSIsurveillanceperformed• OnlyinIntensiveCareUnits• Onlyinnon-IntensiveCareUnit• Hospitalwide

Other

Appendices 235

Whichdefinitionsdoyouuse?

• NationalHealthandSafetyNetwork(NHSN)WITHNOmodifications• NationalHealthandSafetyNetwork(NHSN)WITHmodifications

Other(pleasespecify)WhencalculatingCLABSIrates,doyouusecentrallinedaysasthedenominator?

• Yes• No

Ifnotusingcentrallinedays,whichdenominatordoyouuse?Arecentrallinedayscollecteddaily?

• Yes• No

Howmanytimesaweekarecentrallinedayscollected?

• 6• 5• 4• 3• 2• 1

Arecentrallinedayscollectedatthesametimeeveryday?

• Yes• No

PriortoreportingIntensiveCareUnitCLABSIrates,dotheICUPhysiciansatyourfacilityreviewthedata?

• Yes• No

DoesthisrevieweverresultinchangestotheCLABSIrates?

• Yes• No

DoyouroutinelycompareyourCLABSIdataagainst:(tickallthatapply)

• Anotherhospital• Aggregatedstatedata• Nationaldata• NHSNrate• Idon'tcompareourHAIdata


236 Appendices

DoyouroutinelyuseaStandardisedInfectionRatio(SIR)inyourCLABSIdataanalysis?

• Yes• No

AreyouconfidentyourCLABSIratesareanaccuratereflectionofthetrueCLABSIrate?

• Yes• No

Ifno,whynot?CLINICALVIGNETTEA63yofemaleisadmittedtoICUfollowingmyocardialinfarctionon16Juneandhasacentrallineinserted.Onthe17June,shebecomesfebrile(38.8oC)andhasbloodculturestaken.Resultsonthe19thJuneshowonebloodculturegrewStaphylococcusepidermisandsheiscommencedonIVvancomycin.ApplyingyourusualHAIdefinitions,doesthisfemalehaveacentrallineassociatedbloodstreaminfection?

• Yes• No

CLINICALVIGNETTEApatientpresentsunconscioustotheEmergencyDepartmentat10:00ontheDecember20,isresuscitated,hasacentrallineinserted,andistransferredtoICUat13:00.OnDecember21,thepatientisrecordedashavingatemperatureof39oC.OnDecember22at22:30bloodculturesaretakenduetoongoingtemperaturesover39oC,butwithnoobviousfocus.ThebloodculturesgrewStaphylococcusaureus.ApplyingyourusualHAIdefinitions,doesthispatienthaveanICUattributablecentrallineassociatedbloodstreaminfection?

• Yes• No

CLINICALVIGNETTEApatientistransferredfromamedicalwardtoICUat1400onJuly11andhasacentrallineinserted.OnJuly13at04:30bloodculturesaretakenduetoongoingtemperaturesover39oC,butwithnoobviousfocus.ThebloodculturesgrewStaphylococcusaureus.ApplyingyourusualHAIdefinitions,doesthismalehaveacentrallineassociatedbloodstreaminfection?

• Yes• No

Section5-OTHERHAISURVEILLANCEDoyouundertakeurinarytractinfectionsurveillance?

• Yes• No

Doyouundertakecatheterassociatedurinarytractinfectionsurveillance?

Appendices 237

• Yes• No

Doyouundertakeventilatorassociatedpneumoniasurveillance?

• Yes• No

Doyouundertakeventilatorassociatedeventsurveillance?

• Yes• No

DoyouundertakeanyotherHAIsurveillance?

• Yes• No

PleaselistothertypesofHAIsurveillanceyouundertake.Section6-DATACOLLECTIONANDREPORTINGWhichofthesemethodsdoyouroutinelyusetoidentifyaHAI? Daily Every

secondday

Twiceaweek

Weekly Everysecondweek

Monthly Lessthanmonthly

Never

Reviewmicrobiologyresults

Undertakewardrounds

Contactwardstaff

Ofthese,whichdoyouconsiderthemostvaluablesourceofinformationforidentifyingHAIs?(selectallthatapply)

• Microbiologyresults• Wardrounds• Contactwithwardstaff

Doyouuseanysoftwareprogramstoassistinsurveillance?

• Yes• No

Wasthesoftwaredevelopedin-houseorisitacommercialproduct?

238 Appendices

• Developed'in-house'• CommercialProduct(pleasename)

AndhowmanyyearshaveyoubeenusingthesoftwareforHAIsurveillancepurposes?WheredoesyourHAIdatagetreported?(tickallthatapply) Clinicians

InfectionControlcommittee

SafetyandQualityCommittee

HospitalExecutive

Surgicalsiteinfectiondata

Centrallinebloodstreaminfectiondata

Ventilatorassociatedpneumoniaoreventdata

Catheterassociatedurinarytractinfectiondata

OtherHAIdata OfalltheseHAIsurveillanceactivities,whichthree(3)doyoubelievearethemostimportant?

• Procedurespecificsurgicalsiteinfection• Intensivecareunitcentrallineassociatedbloodstreaminfection

surveillance• Catheterassociatedurinarytractinfection• Bloodstreaminfection• Ventilatorassociatedpneumonia• Ventilatorassociatedevent/complications• Multi-resistantorganism(includingClostridiumdifficileinfection)• Dialysisrelatedinfection

Pleasespecify

Appendices 239

Section7-FUTURESURVEILLANCEPRIORITIESThinking about conducting surgical site infection surveillance, of the surgicalprocedureslistedbelow,selectuptoamaximumoffive(5)whichyoubelievearethemostimportantprocedurestoundertakesurveillanceforyourfacility?

• Abdominalaorticaneurysmrepair• Abdominalhysterectomy• Appendixsurgery• Breastsurgery• Cardiacsurgery• Coronaryarterybypassgrafts• Gallbladdersurgery• Colorectalsurgery• Craniotomy• Caesareansection• Femero-poplitealoffemero-tibialbypasssurgery• Gastricsurgery• Hernoirrhaphy• Hipprostheses• Kneeprosthesis• Laminectomy• Pacemakersurgery• Smallbowelsurgery• Spinalfusion• Vaginalhysterectomy• Ventricularshunt

240 Appendices

IfyoucouldimproveanyelementofyourHAIsurveillanceprocess,fromthelistbelow,pleaserankallelementsinorderofprioritythatyouwouldliketoseeimproved?(draganddrop,mostimportantatthetop) 1 2 3 4 5 6 7 8 9 10 11 12HAIdefinitions HAIsurveillancetraining

Electronicsurveillancetools

Reportingtools Comparativedatareports(e.g.nationalrateorratefromasimilarhospital)

Riskadjustmentofdata

Moretimetoundertakesurveillance

Accesstomedicalexpertise

Accesstomicrobiologicalexpertise

Accesstoinfectionpreventionexpertise

Accesstostatisticalexpertise

WouldyouliketomakeanygeneralcommentsaboutHAIsurveillanceorthissurvey?Endofsurvey

Appendices 241

Appendix G: Current Australian hospital practices in healthcare-associated infection surveillance: Frequency of access to other healthcare professionals –

data not included in Chapter 5

StaffCategoryn=104 Daily Weekly Lessthan

weekly Rarelyornever

InfectiousDiseasesPhysician

58% 15% 15% 13%

Microbiologist 63% 8% 12% 17%

IPstaffwithmoreexperience

44% 6% 8% 43%

Epidemiologist 11% 3% 4% 83%

Statistician 9% 5% 4% 83%

Administrativeassistance 36% 4% 3% 57%

242 Appendices

Appendix H: Current Australian hospital practices in healthcare-associated infection surveillance: Frequency of where HAI data are

reported – data not included in Chapter 5

Infectiontype N Clinicia

ns ICC S&Q HospitalExec

HospitalBoard

Consumers

Statebody

SSI 63 83% 100% 78% 84% 49% 25% 67%

CLABSI 55 78% 96% 80% 82% 47% 40% 69%

VAP 20 65% 40% 10% 15% 20% 5% 5%

CAUTI 20 70% 50% 30% 30% 25% 0% 0%

Appendices 243

Appendix I: Semi-structured interview guide for participants

Semi-structuredinterviewguide.Thankyouforagreeingtoparticipateinaninterviewformystudytitled“Keyattributesofahealthcare-associatedinfectionsurveillanceprogram”Iwillbeaskingaseriesofsemi-structuredquestionsexploringcomponentsofhealthcare-associatedinfection(HAI)surveillanceprogramssuchas:

- simplicity- accuracy- flexibility- acceptability- dataquality- representativeness- timeliness- stability

Iaminterestedinfindingouttheextenttowhichthesecomponentsmayexistinourprograms,howyoumightmeasurethem,andifthereareotherkeycomponentsthatyoucanidentify.IfyouwereinvolvedinthedevelopmentandimplementationoftheHAIsurveillanceprogram,Iwouldalsoliketoaskyouaboutsomeoftheenablersandbarriersyouexperienced.Finally,IwanttoexplorewhatotherfactorsneedtobeconsideredintheestablishmentofnationalHAIsurveillanceinAustralia.Someexamplesofthequestionsare:

- Canyoutellmehowimportantdataqualityistoyourprogram?- Whatsortoflengthsdoyougotomeasurethequalityofyourdata?- Apartfromdataaccuracy,canyoulistanyotheranotherelementsthatyou

believehaveledtothesuccessofyourprogram?- Lookingbacktowhenyouimplementedtheprogram,isthereanythingyou

woulddodifferently?Ilookforwardtospeakingwithyou.Pleasedonothesitatetocontactmeifyourequirefurtherinformationpriortotheinterview.

244 Appendices

KindregardsPhilipRusso,PhDScholar,Phone–0731386425

[email protected]

DrLisaHall,SeniorResearchFellowPhone-0731386425

[email protected]

SchoolofPublicHealthandSocialWork,InstituteofHealth&BiomedicalInnovation,QueenslandUniversityofTechnology,Brisbane,QLD

Appendices 245

Appendix J: Survey tool – Discrete choice experiment

Helloandwelcometothehealthcare-associatedinfectionsurveillancesurvey.DescriptionThisprojectisbeingundertakenaspartofaPhDforPhilipRusso.Thepurposeofthisprojectistoidentifywhichattributesofahealthcare-associatedinfection(HAI)surveillanceprogramyouvaluemost.YouareinvitedtoparticipateinthisprojectbecauseyouareaninfectionpreventionandcontrolprofessionalinvolvedinHAIsurveillance.ParticipationParticipationwillinvolvecompletingfiveattitudinalmultiplechoicequestions,thirteenstatedchoicepreference(oneortheother)questions,threedemographicmultiplechoicequestions,onequestionaboutthesurvey,andyouwillthenbeinvitedtoprovideanygeneralcomments.Yourparticipationinthisprojectisentirelyvoluntary.Ifyouagreetoparticipate,pleasenoteeveryquestionmustbeansweredbeforeyoucanprogresstothenext.Youdonothavetocompleteanyquestion(s)orthesurveyifyouareuncomfortableanswering.YourdecisiontoparticipateornotparticipatewillinnowayimpactuponyourcurrentorfuturerelationshipwithQUT.Ifyoudoagreetoparticipateitwillnotbepossibletowithdraw,onceyouhavesubmittedyourresponses.ExpectedbenefitsItisexpectedthatthisprojectwilldirectlybenefityouandyourpatients.DatafromthisstudywillbeusedtoinformthelargerResearchProjecttoidentifyevidencebasedpracticesfornationalHAIsurveillance.AnationalHAIsurveillanceprogramwillresultinuniformmethodologyandreporting.ThiswillclosethecurrentgapwehavewithcurrentsystemsandensurewearemeasuringinfectionsthesamewayacrossAustralia.ThiswillimproveourunderstandingoftheepidemiologyofHAIsinAustraliatoenableappropriateinfectionpreventioninterventions.RisksTherearenorisksbeyondnormalday-to-daylivingassociatedwithyourparticipationinthisproject.PrivacyandConfidentialityAllcommentsandresponsesareanonymousandwillbetreatedconfidentiallyunlessrequiredbylaw.Youarenotaskedtoprovideyournameoranycontactdetails.AnydatacollectedaspartofthisprojectwillbestoredsecurelyasperQUT’sManagementofresearchdatapolicy.ConsenttoParticipateSubmittingthecompletedonlinequestionnaireisacceptedasanindicationofyourconsenttoparticipateinthisproject.Questions/furtherinformationabouttheprojectIfyouhaveanyquestionsorrequirefurtherinformationpleasecontactoneoftheresearchteammembersbelow.

246 Appendices

Name–PhilipRusso,PhDStudent Name–LisaHall,SeniorResearchFellow

Phone–0731386425 Phone-0731386425

[email protected] [email protected]

Concerns/complaintsregardingtheconductoftheprojectQUTiscommittedtoresearchintegrityandtheethicalconductofresearchprojects.However,ifyoudohaveanyconcernsorcomplaintsabouttheethicalconductoftheprojectyoumaycontacttheQUTResearchEthicsUniton[+617]31385123oremailethicscontact@qut.edu.au.TheQUTResearchEthicsUnitisnotconnectedwiththeresearchprojectandcanfacilitatearesolutiontoyourconcerninanimpartialmanner.ThisstudyhasbeenapprovedbytheQUTHumanResearchEthicsCommittee(approvalnumber1500000304).

Thankyouforhelpingwiththisresearchproject.Pleasekeepthissheetforyourinformation.Byagreeingtoparticipateinthisstudy,youareagreeingthatyou:

• havereadandunderstoodtheinformationprovidedintheInformationtoParticipantssection

• havehadanyquestionsansweredtoyoursatisfaction• agreetoparticipateinthisonlinesurvey• understandthatonceyouhavesubmittedyourresponses,thesecannotbe

withdrawn• agreethatresearchdatacollectedforthisstudymaybepublishedormaybe

providedtootherresearchersPleaseindicateyourresponsetothestatementsaboveandconsenttoparticipate:Yes/NoNB:thelastquestionofthissurveyprovidesyouwithanopportunitytoprovideanygeneralfeedbackregardingthissurveyorHAIsurveillance.

Appendices 247

SECTIONA-Attitudinalquestions

Inthissectionofthequestionnaireweareinterestedinobtainingyourviewsaboutanumberofstatementsrelatingtohealthcare-associatedinfection(HAI)surveillance.Wewouldlikeyoutoreadthrougheachstatementcarefullyandindicatetheextenttowhichyouagreeordisagree.

Pleasenotetherearenowrongorrightanswerstothesequestions.Weareinterestedinyourviews.

TowhatdegreedoyoubelievethatHAIsurveillanceisbeneficialtoyourinfectionpreventionprogram?

HighlyModeratelySlightlyNotatallUnsure

TowhatdegreedoyoubelieveaNationalHAIsurveillanceprogramwouldbebeneficialtoyourinfectionpreventionprogram?


DoyoubelieveitwouldbebeneficialtoyourinfectionpreventionprogramtocompareHAIdatawithsimilarhospitals?


TowhatdegreedoyoubelievethatpublicreportingofallhospitalHAIrateswouldbebeneficialtoyourinfectionpreventionprogram?


TowhatdegreedoyoubelievethatimplementingfinancialpenaltiesforhighHAIrateswouldbebeneficialtoyourinfectionpreventionprogram?


248 Appendices

SECTIONB–SurveyIntroduction

IfyouwereaskedtochoosebetweentwohypotheticalnationalHAIsurveillanceprogramswithdifferentcharacteristics,wewouldliketoknowwhichoptionyouwouldprefer.Intherestofthissectionwepresentpairsofhypotheticalsurveillanceprogramsforyoutochoosebetween.Thepossibledifferencesintheprogramare:

Participationrequirements(mandatory)

• Targeted12mth/Other3mth-Continuous12monthstargetedsurveillanceonspecifiedhealthcare-associatedinfectionswithchoiceofothersforminimumthreemonths/year.

• Targeted3mth/Other3mth-Minimumthreemonthstargetedsurveillanceonspecifiedhealthcare-associatedinfectionswithchoiceofothersforminimumthreemonths/year.

• Completechoice3mth-Minimumthreemonthssurveillanceonyourownchoiceofhealthcare-associatedinfections.

SurveillanceProtocol

• Lightprotocol-Patientleveldataoninfectedpatientsonly,andaggregatednumbersofdenominatoriscollected.Fewerresourcesrequired.Doesnotallowforriskadjustmentofhealthcare-associatedinfectionrates.Limitedabilitytocomparedataexternally.

• Standardprotocol–Patientleveldataiscollectedonbothinfectedandnotinfectedpatients.Moreresourcesrequired.Allowsforriskadjustmentofhealthcare-associatedinfectionrates.Goodabilitytocomparedataexternally.

Competency

Aftertheinitialsurveillancetraining,surveillancestaffarerequiredtoundergoregularassessmenttoensureskillsaremaintained.

• Everydatasubmissionperiod–(e.g.quarterly)Supportshighconsistencyofsurveillanceprocesses.

• Annually–Supportsreasonableconsistencyofsurveillanceprocesses.

• Everytwoyears–Doesnotsupporthighconsistencyofsurveillanceprocesses.

Accuracy

Itisunlikelythatalldatawillbecompletelyaccurateallthetime.IngeneraltermstherewillbeanerrormarginwiththeHAIrates.

• Veryaccurate-Approximately1%-5%errorrange

• Reasonablyaccurate–Approximately6%-10%errorrange

• Lessaccurate–Approximately11%-15%errorrange

Reporting

ThereportingofHAIratesandtheiruseasaperformancemeasureassociatedwithfinancialpenaltiesforthehospitalwithinaNationalsurveillanceprogram.

Appendices 249

• PublicwithnoPenalty–Datapubliclyreportedonwebsiteandnotassociatedwithfinancialpenalties.

• PublicandwithPenalty-Datapubliclyreportedonwebsiteandassociatedwithfinancialpenalties

• NotPublicbutwithPenalty–Datanotpubliclyreportedbutisassociatedwithfinancialpenalties.

• NotPublicandwithnoPenalty–Datanotpubliclyreportedandnotassociatedwithfinancialpenalties.

Belowisanexampleofastatedchoicepreferencequestion.Feelfreetoanswerit.TrialQuestion

Attributes SurveillanceprogramA SurveillanceprogramB

Participationrequirements

Targeted3mth/Other3mth Completechoice3mth

SurveillanceProtocol Standardprotocol Lightprotocol

Competency Annually Everytwoyears

Accuracy Veryaccurate Reasonablyaccurate

Reporting NotPublicbutwithPenalty PublicwithnoPenalty

Whichnationalsurveillanceprogramwouldyouprefer?(pleasetick)ProgramA ProgramB

250 Appendices

ThisisnowthecommencementoftheSurvey.Youwillbepresentedwiththirteenpairsofchoices.Whenansweringthefollowingquestions,pleaseconsiderthefollowingscenario.ScenarioYouareadvisedofaproposalforaNationalHAIsurveillanceprogramwithmandatoryparticipation.Workinginyourcurrentenvironmentwithyourexistingresources,considerwhichoptionyouwouldfindmostbeneficialtoyourinfectionpreventionprogram.ChoosewhichnationalHAIsurveillanceprogramyouwouldprefertoparticipateinfromthefollowingchoices.

Pleasecommencethesurvey.

BlockA

PLEASEPLACEATICKINYOURPREFERREDPROGRAM

1 SurveillanceprogramA SurveillanceprogramB

ParticipationrequirementsTargeted3mth/Other3mth Targeted12mth/Other3mth


Competency Everydatasubmissionperiod Annually

Accuracy Reasonablyaccurate Lessaccurate

Report PublicwithnoPenalty NotPublicbutwithPenalty


Appendices 251

1Scenario6


ParticipationrequirementsCompletechoice3mth Targeted3mth/Other3mth

SurveillanceProtocol Lightprotocol Standardprotocol

Competency Annually Everydatasubmissionperiod

Accuracy Veryaccurate Lessaccurate

Report NotPublicandwithnoPenalty PublicandwithPenalty


BLOCK1Scenario10


ParticipationrequirementsTargeted3mth/Other3mth Completechoice3mth


Competency Everytwoyears Everydatasubmissionperiod


Report NotPublicbutwithPenalty PublicwithnoPenalty


252 Appendices

BLOCK1Scenario14




Competency Everytwoyears Annually

Accuracy Lessaccurate Veryaccurate

Report NotPublicbutwithPenalty PublicandwithPenalty


BLOCK1Scenario15






Report PublicwithnoPenalty PublicandwithPenalty

Whichnationalsurveillanceprogramwouldyouprefer?(pleasetick)ProgramA ProgramB BL

Appendices 253

OCK1Scenario16


Participationrequirements Targeted3mth/Other3mth Targeted12mth/Other3mth




Report NotPublicandwithnoPenalty PublicwithnoPenalty


BLOCK1Scenario17






Report PublicandwithPenalty NotPublicandwithnoPenalty

Whichnationalsurveillanceprogramwouldyouprefer?(pleasetick)ProgramA ProgramB BLOC

254 Appendices

K1Scenario19






Report PublicandwithPenalty PublicwithnoPenalty


BLOCK1Scenario20

9 SurveillanceprogramA SurveillanceprogramBParticipationrequirementsCompletechoice3mth Targeted12mth/Other3mth



Accuracy Reasonablyaccurate Veryaccurate

Report NotPublicandwithnoPenalty NotPublicbutwithPenalty


Appendices 255

BLOCK1Scenario22








BLOCK1Scenario23








256 Appendices








BLOCK1Scenario24








Appendices 257

BlockB



Surveillanceprotocol Lightprotocol Standardprotocol


Accuracy Lessaccurate Reasonablyaccurate

Report PublicwithnoPenalty NotPublicandwithnoPenalty







Report NotPublicbutwithPenalty PublicandwithPenalty


BLOC

K2Scenario4

258 Appendices




Competency Everydatasubmissionperiod Everytwoyears


Report NotPublicbutwithPenalty PublicwithnoPenalty


BL

OCK2Scenario5








BLOCK2S

Appendices 259

enario7








BLOCK2Scenario8



Surveillanceprotocol Standardprotocol Lightprotocol





BLOCK2Sc

260 Appendices

enario9






Report NotPublicandwithnoPenalty NotPublicbutwithPenalty


BLOCK2Scenario11








BLO

Appendices 261

CK2Scenario12






Report NotPublicbutwithPenalty NotPublicandwithnoPenalty


BLOCK2Scenario13






Report PublicandwithPenalty NotPublicbutwithPenalty


BLOCK2

262 Appendices

Scenario18






Report NotPublicbutwithPenalty NotPublicandwithnoPenalty









BLOCK2

Appendices 263

Scenario21






Report PublicandwithPenalty NotPublicbutwithPenalty


264 Appendices

SECTIONC–Aboutyou Pleaseselectwhichjobtitlebestdescribesyourmainoccupation?

Infectioncontrol/preventionnurseInfectiousDiseasesPhysicianHealthDepartmentrepresentativeOther(pleaselist)

Whichagebracketdoyoubelong? <30

30-3940-4950-59>59

Howmanyyearshaveyouworkedininfectionprevention?

<55-1011-1516-20>20n/a

Howmanyinpatientacutecarebedsatyourfacility?

50-99100-199200-400>400n/a

Whereareyoulocated?

ACTorNTNSWQLDSATASVICWA

Howdifficultdidyoufindthissurvey? VeryEasy

EasyNeutralDifficultVerydifficult

Doyouhaveanyothercommentsthatyouwouldliketomakeaboutthisquestionnaire?

Thankyouforparticipatinginthissurvey.

Appendices 265

Appendix K Results of attitudinal questions in the discrete choice experiment not included in

the manuscript Chapter 8

QuestionTowhatdegreedoyoubelievethefollowingarebeneficialtoyourHAIpreventionprogram:(n=122)

Highly%

Moderately%

Slightly%

Notatall%

Unsure%

HAIsurveillance 86.9 12.3 0.8 0.0 0.0

ANationalHAIsurveillanceprogram 70.5 24.6 0.8 3.3 0.8

CompareHAIdatawithsimilarhospitals 61.5 30.3 8.2 0.0 0.0

PublicreportingofallhospitalsHAIrates 35.3 41.0 7.4 15.6 0.8

FinancialpenaltiesforhighHAIrates 17.2 30.3 27.9 17.2 7.4

266 Appendices

Appendix L: Normalisation process theory questions

Normalisation process Theory constructs Questions to consider when applying NPT

Coherence - i.e meaning and sense making be participants • Is the intervention easy to describe? • Is it clearly distinct from other interventions? • Does it have a clear purpose for all relevant participants? • Do participants have a shared sense of its purpose? • What benefits will the interventions bring and to whom? • Are these benefits likely to be valued by potential participants? • Will it fit with overall goals and activity of the organisation?

Cognitive participation - i.e. commitment and engagement by participants

• Are target user groups likely to think the intervention is a good idea? • Will they see the point easily? • Will they be prepared to invest time, energy and work in it?

Collective action - i.e the work participants do to make the trial function

• How will the intervention affect the work of the user groups? • Will it promote or impede their work? • What effect will it have on consultations? • Will staff require extensive training before they can use it? • How compatible is it with existing work practices? • What impact will it have on division of labor, resources, power and

responsibility between different professional groups? • Will it fit with the overall goals and activity of the organisation?

Reflexive monitoring - i.e. participants reflect on or appraise the trial

• How are the users likely to perceive the intervention once it has been in use for a while?

• Is it likely to be perceived advantageous for patients and staff? • Will it be clear what effects the intervention has had? • Can users/staff contribute feedback about the intervention once it is in use? • Can the intervention be adapted/improved on the basis of experience?

Adapted from Murray et al155

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