Epidemiology of healthcare-associated infections and implementation of infection prevention and control in acute care hospitals in Mainland China

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Thesis

Reference

Epidemiology of healthcare-associated infections and implementation of infection prevention and control in acute care hospitals in Mainland

China

WANG, Jiancong

Abstract

Healthcare-associated infection (HAI) and antimicrobial resistance (AMR) are major public health threats. The aims of this research were to estimate HAI prevalence, AMR incidence, as well as to assess adoption and implementation of infection prevention and control (IPC) in Mainland China. In 2015, the pooled HAIs prevalence of all hospitals in Dongguan was 2.9%.

Between 2006 and 2016, the weighted HAI prevalence in Mainland China, as identified in the systematic review, was 3.1%. Incidence proportion of AMR in Dongguan was lower than average incidence proportion in mainland China. Most of the ECDC key and WHO core components were reported to be implemented by acute care hospitals in China, but significant gaps in effective IPC were identified. This is the first multilevel research on HAI and AMR in the context of implementing key elements for effective IPC. These outcomes serve as a reference for future IPC strategies in Mainland China.

WANG, Jiancong. Epidemiology of healthcare-associated infections and

implementation of infection prevention and control in acute care hospitals in Mainland China. Thèse de doctorat : Univ. Genève, 2020, no. Sc. Bioméd. - S. Glob. 9

DOI : 10.13097/archive-ouverte/unige:150291 URN : urn:nbn:ch:unige-1502914

Available at:

http://archive-ouverte.unige.ch/unige:150291

Disclaimer: layout of this document may differ from the published version.

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Epidémiologie des infections associées aux soins de santé et mise en œuvre de la prévention et du contrôle des infections dans les hôpitaux de soins

de courte durée en Chine

Thèse

présentée à la Faculté de Médecine de l’Université de Genève

pour obtenir le grade de Docteur en Santé Globale par

Jiancong Wang

Superviseurs PD MD Walter Zingg Professeur Didier Pittet Professeur Stephan Harbarth

Thèse n° ___________

Genève

2020

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PhD thesis

Title: Epidemiology of healthcare-associated infections and implementation of infection prevention and control in acute care hospitals in Mainland China

PhD candidate: Jiancong Wang

Institute of Global Health, Faculty of Medicine, University of Geneva

Supervisors

PD MD Walter Zingg Professor Didier Pittet Professor Stephan Harbarth

Submitted to the steering committee of the Global Health Institute, University of Geneva, Geneva (CH)

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ACKNOWLEDGMENTS

First of all, I would like to express my sincere thanks to my doctoral committee, Dr Walter Zingg, Professor Didier Pittet, and Professor Stephan Harbarth for their guidance, support, and continuing encouragement throughout the duration of my study. They granted me the opportunity to succeed in the PhD programme of the institute of Global Health at the University of Geneva. Without their contributions, this project would not have been possible.

Secondly, I thank all my Chinese colleagues who collaborated and supported this project; special thanks go to Dr Mouqing Zhou from the Dongguan Nosocomial Infection Control and Quality Improvement Centre, and to Dr Fangfei Liu from the Department of Nosocomial Infection Management, Second Affiliated Hospital, Xi’an Jiaotong University. Furthermore, I sincerely thank all hospitals participating in my projects for providing me with access to their datasets

Thirdly, I express my thanks to all my colleagues in the Infection Prevention and Control Unit at the University of Geneva hospitals for their input and advice with the statistical analyses and clinical outcome interpretations. I have learned a lot from all my colleagues during the past four years. Futhermore, I also thank Alexandra Peters for English editing; Julien Sauser for statistical support; and Ermira Tartari and Romain Martischang for comments on this PhD thesis.

Fourthly, I also thank the coordinating committee, Professor Antoine Flahault, Dr Nathalie Bot, and Ms Lemlem Girmatsion of the Global Health Institute for their support and administrative coordination.

Finally, I would like to thank my parents, Xiaohao Wang and Anna Qi for their understanding, encouragement, and support of my studies in Geneva.

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LIST OF PUBLICATIONS

Scientific publications

(1) Wang J, Hu J, Harbarth S, Pittet D, Zhou M, Zingg W. Burden of healthcare- associated infections in China: results of the 2015 point prevalence survey in Dong Guan City. J Hosp Infect 2017;96:132-138. (Impact factor=3.70).

(Citations = 16)

(2) Wang J, Liu F, Tartari E, Huang J, Harbarth S, Pittet D, Zingg W. The Prevalence of Healthcare-Associated Infections in Mainland China: A Systematic Review and Meta-analysis. Infect Control Hosp Epidemiol 2018;39:701-709. (Impact factor=2.86). (Citations = 18)

(3) Wang J, Liu F, Tan JBX, Harbarth S, Pittet D, Zingg W. Implementation of infection prevention and control in acute care hospitals in Mainland China - a systematic review. Antimicrob Resist Infect Control 2019;8:32. (Impact factor=3.68). (Citations = 10)

(4) Wang J, Zhou M, Huang G, Guo Z, Sauser J, Metsini A, Pittet D, Zingg W.

Antimicrobial resistance in Southern China: Results of prospective surveillance in Dong Guan City, 2017. J Hosp Infect 2020;105:188-196. (Impact factors = 3.70).

(5) Wang J, Zhou M, Hesketh T, Kritsotakis EI. Clinical impact of third generation cephalosporin-resistance in Enterobacteriaceae infections: a multicenter cohort study in Southern China. (Impact factor=1.97) (Under review by Am J Infect Control)

COVID-19 publications

(6) Wang J, Zhou M, Liu F. Reasons for healthcare workers becoming infected with novel coronavirus disease 2019 (COVID-19) in China. J Hosp Infect

2020;105:100-101. (Impact factors = 3.70). (Citations = 109)

(7) Wang J, Zhou M, Liu F, Lee YF. Will the status of infection prevention and control (IPC) professionals be improved in the context of COVID-19? Am J Infect Control 2020;48:729-730. (Impact factors = 1.971). (Citation = 1)

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(8) Wang J, Lee YF, Liu F, Zhou M. To relax restrictions: are communities ready to deal with repeated epidemic waves of COVID-19? Infect Control Hosp Epidemiol 2020 DOI: https://doi.org/10.1017/ice.2020.228. (Impact factors = 2.86)

(9) Wang J, Zhou M. Issues to be considered when planning sero-epidemiological studies in regions with a low incidence of SARS-CoV-2. Am J Infect Control 2020 DOI: 10.1016/j.ajic.2020.06.176. (Impact factors = 1.97)

(10) Wang J, Lee YF, Zhou M. What is the best timing for healthcare workers infected COVID-19 to return to work? Am J Infect Control 2020 (Impact factors = 1.97) (Accepted)

(11) Zhao J, Jia J, Zhong L, Wang J, Cai Y, Qian Y. COVID-19 in Shanghai: IPC policy exploration in support of work resumption through System Dynamics modeling. (Impact factors = 2.28) (Under review by Risk Management and Healthcare Policy)

Other scientific publications

(12) Wang J. The challenges of antimicrobial resistance surveillance in China. Am J Infect Control 2019; 47:1403-1404. (Impact factor=1.97). (Citations = 1) (13) Wang J, Zhou M, Liu F. Effect of antibiotic stewardship programmes on

reduction of antimicrobial resistance in China. Am J Infect Control 2020;

48:233-234. (Impact factor=1.97).

(14) Neofytos D, Metsini A, Gardiol C, Wang J, Widmer A, Pittet D, Zingg W.

Systematic review on costs and hospital charges associated with healthcare- associated infections. (In preparation)

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Conference publications

(1) Wang J, Liu F, Tartari E, Zingg W. Burden of healthcare-associated infections in China: a systematic review (P145). Published at the Fourth International

Conference on Prevention and Infection Control on June 21 2017, in Geneva, Switzerland.

(2) Wang J, Gasalla M, Zingg W. Bloodstream infection associated with peripherally inserted central catheters: a systematic review and meta-analysis (N° O15).

Published at the Fourth International Conference on Prevention and Infection Control on June 22 2017, in Geneva, Switzerland.

(3) Wang J, Liu F, Zingg W. Hospital organization, management, and structure for infection prevention and control of healthcare-associated infection in Chinese hospitals: systematic reivew (POS1-47). Published at the 7^th Geneva Health Forum, Precision Global Health in the Digital Age on April 10 2018, in Geneva, Switzerland.

(4) Wang J, Zhou M, Guo Z, Zingg W. Antimicrobial resistance in Southern China:

results of prospective surveillance in Dongguan City, 2017(P325). Published at the Fifth International Conference on Prevention and Infection on September 12 2019, in Geneva, Switzerland.

(5) Liu F, Suo Y, Wang J, Zingg W. Antibiotic use and antimicrobial resistance in Northwestern China in 2017 (P0569). Accepted as poster session at the 30th European Congress of Clinical Microbiology & Infectious Diseases (ECCMID), on 18 April 2020, in Paris France.

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ABSTRACT IN ENGLISH

Background: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are a major public health threat. Few data is available about the epidemiology of HAI and AMR and implementation of infection prevention and control (IPC) in acute-care hospitals in Mainland China. The objectives of this thesis were to estimate HAI prevalence, AMR incidence, as well as to assess adoption and implementation of IPC elements in Mainland China.

Methods: We summarised the results of a point prevalence survey on HAI in

Dongguan city; performed a systematic review and meta-analysis on HAI prevalence in Mainland China; analysed data from prospective AMR surveillance in Dongguan city; and performed a systematic review on adoption and implementation of IPC elements in Mainland China.

Results: In 2015, the pooled HAIs prevalence of the 51 secondary- and tertiary care hospitals in Dongguan city was 2.9% (95%CI: 2.6% - 3.1%). Between 2006 and 2016, the weighted HAI prevalence in Mainland China, as identified in the systematic review, was 3.1% (95%CI: 2.9% - 3.3%). The three most common HAI-types were lower respiratory tract infection (47.2%), urinary tract infection (11.2%) and upper respiratory tract infection (10.1%). In Dongguan city, the incidence proportion of Escherichia coli and Klebsiella pneumoniae non-susceptible to 3^rd generation cephalosporins (3GC) (43.9% and 30.2%, respectively) as well as Pseudomonas aeruginosa and Acinetobacter baumannii non-susceptible to carbapenems (29.5%

and 50.9%, respectively) were high. The incidence proportion of carbapenem-

resistant Enterobacteriaceae was low (2.6%). The incidence density of HAI due to E.

coli non-susceptible to 3GC and fluoroquinolones combined was 0.09 (95% CI: 0.07- 0.11) per 1,000 patient-days. To variable degrees, most of the ECDC key and WHO core components were reported to be implemented by acute care hospitals in China, but significant gaps in effective IPC were identified, particularly in organization and management of IPC, postgraduate education and training activities in IPC, and both outcome and process indicator surveillance.

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Conclusion: This is the first multilevel research on HAI and AMR in the context of implementing key elements for effective IPC in Mainland China. These outcomes serve as a reference to prioritise and plan future IPC strategies in Mainland China.

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ABSTRACT IN FRENCH

Contexte: Les infections associées aux soins de santé (IAS) et la résistance aux antimicrobiens (RAM) constituent une menace majeure pour la santé publique. On dispose de peu de données sur l'épidémiologie des IAS et des RAM et la mise en œuvre de la prévention et du contrôle des infections (PCI) dans les hôpitaux de soins de courte durée en Chine continentale. Les objectifs de cette thèse étaient d'estimer la prévalence des infections associées aux soins de santé et l'incidence de la

résistance aux antimicrobiens, ainsi que d'évaluer l'adoption et la mise en œuvre d'éléments défis de la prévention et du contrôle des infections (PCI) en Chine continentale.

Méthodes: Nous avons résumé les résultats d’une enquête de prévalence ponctuelle sur les infections nosocomiales à Dongguan; mené une analyse systématique et une méta-analyse de la prévalence des IAS en Chine continentale; analysé les données de surveillance prospective de la résistance aux antimicrobiens dans la ville de Dongguan; et procédé à une revue systématique de l'adoption et de la mise en œuvre d'éléments de la PCI en Chine continentale.

Résultats: En 2015, la prévalence globale des IAS au sein des 51 hôpitaux de soins secondaires et tertiaires de la ville de Dongguan était de 2,9% (IC à 95%: 2,6% à 3,1%). Entre 2006 et 2016, la prévalence pondérée des IAS en Chine continentale identifiée dans la revue systématique, était de 3,1% (IC à 95%: 2,9% à 3,3%). Les trois types d'infection les plus courants étaient les infections des voies respiratoires inférieures (47,2%), des voies urinaires (11,2%) et des voies respiratoires

supérieures (10,1%). Dans la ville de Dongguan, les proportions d’incidence d’Escherichia coli et de Klebsiella pneumoniae non sensibles aux céphalosporines de 3e génération (3GC) (respectivement 43,9% et 30,2%), ainsi que de

Pseudomonas aeruginosa et Acinetobacter baumannii non sensibles aux carbapénèmes (29,5% et 50,9 %, respectivement) étaient élevés. La proportion d'incidences d'Enterobacteriaceae résistantes au carbapénème était faible (2,6%).

La densité d'incidence des IAS dues à E. coli non sensible au 3GC et aux

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fluoroquinolones combinées était de 0,09 (IC à 95%: 0,07-0,11) pour 1000 jours- patients. À des degrés divers, la plupart des composantes clés de PCI proposés par l’ECDC et l’OMS ont été mises en œuvre par des hôpitaux de soins de courte durée en Chine, mais des lacunes importantes ont été constatées dans l’efficacité de la PCI, en particulier dans l’organisation et la gestion de la PCI, les activités de formation postdoctorale et de formation à la PCI, et surveillance des indicateurs de résultats et de processus.

Conclusion: Il s'agit de la première recherche multiniveau sur les IAS et la RAM dans le contexte de la mise en œuvre d'éléments clés pour une PCI efficace. Ces résultats servent de référence pour hiérarchiser et planifier les futures stratégies PCI en Chine continentale.

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ABBREVIATIONS

AMR Antimicrobial resistance

BSI Bloodstream infection

CAI Community-associated infection

CAUTI Catheter-associated urinary tract infection CDC US Centers for Disease Prevention and Control CDI Clostridium difficile infection

CHINET China Antimicrobial Resistance Network CLABSI Central line-associated bloodstream infection CNKI China National Knowledge Infrastructure CRE Carbapenem-resistant Enterobacteriaceae

DNICC Dongguan city Nosocomial Infection Control and Quality Improvement Centre

DNISS Dongguan city Nosocomial Infection Surveillance System EARS-Net European Antimicrobial Resistance Surveillance Network ECDC European Centre for Disease Prevention and Control

GDP Gross domestic product

GI Gastrointestinal tract infection

HAI Healthcare-associated infection

ICROMS Integrated Quality Criteria for the Review of Multiple Study Designs

IPC Infection prevention and control

LOS Length of stay

LRTI Lower respiratory tract infection

MRSA Methicillin-resistant Staphylococcus aureus

MDR Multidrug resistant

NCLSI US National Clinical and Laboratory Standards Institute NHAISS Chinese National Healthcare-Associated Infection

Surveillance System

NHCPRC National Health Commission of the People’s Republic of China

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PPS Point prevalence survey

PRISMA Preferred Reporting Items for Systematic Reviews and Meta-analysis

SIGHT Systematic Review and Evidence-based Guidance on Organization of Hospital Infection Control

SSI Surgical site infection

STROBE Strengthening the Reporting of Observational Studies in Epidemiology

UTI Urinary tract infection

VAP Ventilator-associated pneumonia

WHO World Health Organization

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OUTLINE

ACKNOWLEDGMENTS ... 1

LIST OF PUBLICATIONS ... 2

ABSTRACT IN ENGLISH ... 5

ABSTRACT IN FRENCH ... 7

ABBREVIATIONS ... 9

OUTLINE ... 11

GENERAL INTRODUCTION ... 13

1.1 Healthcare-associated infections ... 13

1.1.1 Epidemiology of healthcare-associated infections ... 13

1.1.2 Surveillance of healthcare-associated infections ... 13

1.2 Antimicrobial resistance ... 14

1.2.1 Epidemiology of antimicrobial resistance ... 14

1.2.2 Surveillance of antimicrobial resistance ... 15

1.3 Implementation of infection prevention and control ... 16

1.4 Rationale of the present thesis ... 16

1.5 Research hypotheses ... 18

1.6 Specific aims of this thesis project ... 18

MATERIALS AND METHODS ... 19

2.1 Demographic and socioeconomic information... 19

2.1.1 Mainland China ... 19

2.1.2 Dongguan city, Guangdong Province ... 19

2.2 Structure of the research plan ... 21

2.3 Article 1 – Dongguan city HAI PPS study ... 21

2.3.1 Settings ... 21

2.3.2 Study design ... 21

2.3.3 Data collection ... 22

2.3.4 Statistical analysis ... 22

2.3.5 Ethics ... 22

2.3.6 Roles and contributions of the candidate ... 22

2.4 Article 2 – HAI prevalence in China: a systematic review ... 23

2.4.1 Search strategy ... 23

2.4.2 Inclusion/exclusion criteria ... 23

2.4.3 Data abstraction ... 24

2.5 Article 3 – AMR prospective surveillance in Dongguan city ... 25

2.5.1 Settings ... 25

2.5.2 Databases... 25

2.5.3 Antimicrobial resistance ... 25

2.5.4 Data analysis ... 26

2.5.6 Ethics ... 27

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2.6 Article 4 – Implementation of IPC in China: a systematic review ... 27

2.6.1 Search strategy ... 27

2.6.2 Inclusion/exclusion criteria ... 28

2.6.3 Data extraction ... 28

RESULTS ... 29

3.3 Article 3 – AMR prospective surveillance in Dongguan city 2017 ... 34

GENERAL DISCUSSION ... 46

4.3 Article 3 – AMR prospective surveillance in Dongguan city 2017 ... 48

4.5 Limitations of the research work ... 52

4.6 Conclusion ... 52

4.7 Future perspectives ... 52

4.7.1 Infection control research in Mainland China ... 52

4.7.2 Infection control research in Dongguan city... 53

4.8 Implications for global health ... 54

BIBLIOGRAPHY ... 56

ANNEXES ... 66

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GENERAL INTRODUCTION

1.1 Healthcare-associated infections

1.1.1 Epidemiology of healthcare-associated infections

Healthcare-associated infections (HAIs) are a major public health threat, causing increased healthcare costs, increased morbidity and mortality, and prolonged hospitalisation (1-6). The European Centre for Disease Prevention and Control (ECDC) estimated the HAI prevalence in Europe at 5.7% in 2011/2012 (7), with more than 2.6 million new HAI cases occurring every year (8). The US Centres for Disease Control and Prevention (CDC) estimated the HAI prevalence in the USA at 4.0% in 2011 (9). Compared to high income countries, the HAI prevalence in developing countries has been reported to be substantially higher (10.1%) (2). Hospitalisations of patients undergoing an HAI are 2.5 times longer, equivalent to an excess length of stay (LOS) of 11 days (10). From an economic perspective, attributable costs due to HAIs have been estimated at approximately ₤3000 (10), about 2.8 times higher compared to patients without HAI. However, given the significant number of attributable LOS and economic burden, this number is rather an underestimation.

The Asia-Pacific region has been described as a geographic source for emerging infectious diseases and antimicrobial resistance (AMR) (11, 12). As a consequence, the Global Infection Prevention and Control (IPC) priorities 2018 - 2022 by the WHO has highlighted the burden of HAI due to AMR pathogens in Asia (8). Ling and colleagues estimated the HAI prevalence in Southeast Asia at about 9.0% (4). The situation of China is not well known, although the People’s Republic of China is the largest economic body in the Asian region and faces similar healthcare problems towards HAI as its neighbouring countries (1, 11). Li and colleagues published a conference paper estimating a pooled HAI point prevalence of 5.2% in 127 hospitals in China by collating results from published PPS reports between 1990 and 2011 (13).

1.1.2 Surveillance of healthcare-associated infections

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Healthcare-associated infection surveillance is indispensable for both estimating the burden of HAI and prioritizing IPC strategies (14). Prospective incidence surveillance is the gold standard in understanding and controlling HAIs, but it is time-consuming and costly. Prospective HAI incidence surveillance is used in most countries for estimating the burden of HAI in selected high-risk settings such as intensive care or neonatal units (15). On the other hand, point prevalence surveys can be performed with reasonable resources, can be conducted hospital-wide, and provide an in-depth snapshot of the burden of HAI (14, 15). However, HAI estimation is limited by size, and large sample sizes are required to detect statistically significant differences (15).

PPSs produce valid data on national level; and thus countries and regions have started to conduct national and international PPSs for HAI estimation (1, 15).

Globally, large national and multi-national PPSs have been organised by the ECDC and the US CDC, using standardised PPS surveillance protocols (1, 2, 7, 9). These surveillance systems have become the reference and benchmark to organise surveys and compare outcomes (7, 9).

The first HAI PPS in China was organised in 2001 by the Ministry of Health of the People's Republic of China (16), using a standardised national HAI PPS

surveillance protocol (1, 14, 17). Prevalence surveys were then recommended to be conducted on regional levels. In 2009, HAI PPSs have become a mandatory

surveillance activity in China. The “Standard for nosocomial infection surveillance in China” (WS/T312-2009) (18), and the Chinese National Healthcare-Associated Infection Surveillance System (NHAISS) organise such surveys on a yearly basis since 2009 (1, 14).

1.2 Antimicrobial resistance

1.2.1 Epidemiology of antimicrobial resistance

Emerging AMR has become a challenge in many regions worldwide. HAIs are more often due to antimicrobial resistant pathogens compared to community-acquired infections (CAIs), and thus, have been recognized as one of the leading threats to modern healthcare by the United Nations and the WHO (2, 19, 20). In Europe, the

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incidence proportion of bloodstream infections (BSIs) due to 3^rd generation

cephalosporin- (3GC-) resistant Escherichia coli has increased from 14.2% in 2014 to 14.9% in 2017 (21). The proportions of BSIs due to carbapenem resistant-

Pseudomonas aeruginosa and Acinetobacter baumannii are high (17.4% and 33.4%, respectively), particularly in Southern Europe (21). Although the proportion of BSIs due to methicillin-resistant Staphylococcus aureus (MRSA) decreased from 19.6% in 2014 to 16.9% in 2017, infections due to this pathogen remain a public health priority in Europe (21). Stewardson and colleagues estimated the incidence densities of hospital-onset BSI and community-acquired BSI due to MRSA at 0.026 per 1,000 patient-days, and 0.01 per 100 admissions in 10 European hospitals in 2010 and 2011 (22). Rohde and colleagues estimated the incidence densities of HAI and CAI due to 3GC-resistant Enterobacteriaceae at 0.52 per 1,000 patient-days, and 0.28 per 100 admissions in 6 German university hospitals between 2014 and 2015 (23). In China, Zhang and colleagues assessed the population burden of all infections due to carbapenem-resistant Enterobacteriaceae (CRE) in 25 Chinese university hospitals in 2015 (24); the incidence density of all infections due to CRE was 0.04 per 1,000 patient-days.

1.2.2 Surveillance of antimicrobial resistance

Global AMR surveillance networks, such as the Global Antimicrobial Resistance Surveillance System (GLASS) (25) or the European Antimicrobial Resistance Surveillance Network (EARS-Net) (21, 26), report on current status and trends of AMR on a yearly basis. The summary reports from both AMR surveillance systems, as open sources, were published on both the WHO and the ECDC official websites (21, 25).

A major driver to step up surveillance on infectious diseases in Mainland China was the severe acute respiratory syndrome outbreak in 2003 (27, 28). Two national AMR surveillance networks have been established since 2005: the China

Antimicrobial Resistance Surveillance System (CARSS) (29) and the China

Antimicrobial Surveillance Network (CHINET) (30). Summary reports of CARSS and

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CHINET are published annually on the official website of the National Health Commission of the People’s Republic of China (NHCPRC) (29) and in the Chinese Journal of Infection and Chemotherapy (30).

However, as other non-high-income countries, conducting prospective incidence surveillance on HAI and AMR was very difficult because of limited financial resources and limited laboratory capacity (2, 25).

1.3 Implementation of infection prevention and control

The prevention of HAI and AMR is a first priority for patient safety in acute-care hospitals worldwide (1, 2, 6, 9, 14). Implementation of the evidence-based core components for the prevention of HAI and AMR, issued by both the ECDC and the WHO, is not known for most countries (31-33). The United Nations Sustainable Development Goals highlighted the importance of IPC as a contributor to safe and effective high-quality health service delivery (32). This is echoed by the WHO recommending to strengthen IPC at the global level (8).

In 2006, the National Health Commission of People’s Republic of China

(NHCPRC) published the “Nosocomial Infection Management Methods” (Decree No.

48). These are guidelines defining elements on the organisation of IPC at the hospital level (16). In 2018, with the “Accreditation regulation of control and prevention of healthcare-associated infection in hospitals” (WS/T 592 - 2018), hospital

accreditation was linked to the elements issued by the NHCPRC (34). The NHCPRC decree targets three broad areas of IPC: 1) structure, organization and management of IPC; 2) education and training in IPC; and 3) outcome and process indicator surveillance in IPC.

1.4 Rationale of the present thesis

There are strong global interests to prevent HAI and control the spread of AMR and to improve IPC practices in developing countries (2, 32). To understand challenges, positions and contributions of China in the prevention of HAI and AMR, we must know the epidemiology of HAI and AMR and the level IPC standards implemented in

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Chinese hospitals. This overarching rationale represented the scientific framework of the research activities and outputs summarized below.

Original Scientific Article 1:

- Given the fact that HAI PPS have become mandatory surveillance activities in Mainland China in 2009 (18), we analysed data of a HAI prevalence survey in Dongguan city with a population of around 8 million.

- To better understand variations of HAI prevalence in the different provinces in China, we compared prevalence data published in the Chinese literature with the national PPS report published by the National Healthcare-Associated Infection Surveillance System (NHAISS) (35);

- Infections due to AMR pathogens are considered a problem in acute care hospitals in Mainland China with multidrug-resistant pathogen transmission in healthcare being of particular concern;

- From an IPC perspective, there are gaps in how AMR is reported in Mainland China: 1) most publications on AMR in China focus on molecular characteristics rather than epidemiology (36); 2) pooled data on AMR are not de-duplicated (26);

3) where available, data on infections due to AMR pathogens are not stratified into HAI and CAI (23); and 4) data analysis does not look at AMR combinations (21, 30, 36);

- To fill this gap, we combined microbiology data of Dongguan city with information from the local Nosocomial Infection Surveillance System to estimate the burden of AMR in HAI and CAI.

- There is no information about how and if at all hospitals prevent HAIs and control AMR in China; after ECDC has published the key components on effective IPC, the WHO also recommended eight of these components to prevent HAI and control the spread of AMR (32); however, it is not yet known if hospitals in China have adopted and implemented these components.

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1.5 Research hypotheses

First, we hypothesized that the HAI prevalence in Dongguan city was similar compared to the findings of other Chinese PPS studies. Second, we hypothesized that the HAI prevalence published in the Chinese literature was similar compared to the reports of NHAISS. Third, we hypothesized that the incidence proportion of AMR in Dongguan city was similar to the numbers reported by the China Antimicrobial Resistance Network (CHINET) and neighbouring countries. Forth, we hypothesized that the implementation of the WHO core/ECDC key components is limited in China.

1.6 Specific aims of this thesis project Article 1:

- To analyse data on HAI and antibiotic use obtained during the 2015 PPS in Dongguan city;

Article 2:

- To assess the prevalence of HAI in mainland China by collating results from reports, either in English of Chinese, published between January 2006 and August 2016;

Article 3:

- 1) to summarise incidence proportions of pooled AMR data from Southern China in 2017 and to compare the results with the 2017 CHINET report (30); 2) to calculate incidence densities of HAI and CAI due to AMR pathogens and to compare the results on HAI with data from Germany (23); and 3) to calculate incidence proportions of bloodstream infections due to AMR pathogens in tertiary- care hospitals and to compare the results with the 2017 EARS-Net report (21);

Article 4:

- To assess adoption and implementation of elements of the three “National Health Commission of the People’s Republic of China” (NHCPRC) areas (i.e. “structure, organization and management of IPC”, “education and training in IPC”, and

“surveillance of outcome and process indicators in IPC”) by acute care hospitals in Mainland China, and to compare the findings with the ECDC and WHO core

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components for facilitating effective IPC.

MATERIALS AND METHODS

2.1 Demographic and socioeconomic information 2.1.1 Mainland China

China currently has a population of 1.34 billion, which is increasing and at the same time rapidly aging (37). The medical system is complex, with imbalances between the provinces and between rural and urban areas. The average length of stay (LOS) in acute care facilities was 10.0 days in 2012, which is significantly longer than the average LOS in the United States (4.5 days) and the European Union (5.1 days) (7, 38, 39). The average cost for one inpatient is approximately $1,738 USD (40).

According to the WHO Global Health Expenditure database, only 5% of the gross domestic product (GDP) was spent on health in 2016 (41), which equates to

approximately $398 USD per capita per year (42). In 2013, the rates of doctors and nurses per 1,000 population in Mainland China were 1.7 and 2.0, respectively (37, 43).

2.1.2 Dongguan city, Guangdong Province

Dongguan city has a population of 8.2 million and is located in the Guangdong province in Southern China. With a GDP of $ 14,030 USD per capita in 2018, Dong Guan City is a high-income area according to the World Bank. Its GDP ranked at the 22^nd position (approximately $ 93.3 billion USD) of the larger cities in China in 2015.

In 2016, the allocated total budget for healthcare was $ 57.3 million USD (44). In 2010, the number of doctors, registered nurses, and acute care beds per 1,000 population were 1.7, 2.0 and 3.1, respectively (45).

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Illustration 1 Map of People’s Republic of China

Red pushpin represents the location of Dongguan city

Illustration 2 Map of Dongguan city

Sources: Originated from Google Map (2019).

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2.2 Structure of the research plan

2.3 Article 1 – Dongguan city HAI PPS study 2.3.1 Settings

All secondary-and tertiary-care hospitals of Dongguan city took part in the PPS in 2015. Most hospitals had a mix of different clinical specialities: internal medicine, surgery, gynaecology and obstetrics, ear-nose-throat (ENT) diseases, paediatrics, neonatology, adult intensive care, and other departments.

2.3.2 Study design

The PPS was organised by the Dongguan Nosocomial Infection Control and Quality Improvement Centre (DNICC). One week before the survey, workshops were

PhD research project

Antimicrobial resistance (Dongguan city)

In-hospital mortality associated with Enterobacteriaceae infection (competing risk survival models)

Healthcare associated infection (Dongguan city)

Prevalence of healthcare associated infections in

Mainland China

Implementation of infection prevention and control in acute care hospitals in Mainland China

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organized for local IPC data collectors on how to apply the standardised protocol.

The surveys were performed on one single calendar day in all 51 hospitals. The Dongguan PPS protocol followed the Chinese standard for nosocomial infection surveillance (18). All patients present in the hospital at 00:01 on the day of survey were included in the PPS. HAI case definitions were based on the definition criteria established by the Ministry of Health of the People’s Republic of China (46-48). HAIs were mainly categorized into lower respiratory tract infection (LRTI), urinary tract infection (UTI), surgical site infection (SSI), bloodstream infection (BSI),

gastrointestinal tract infection (GI), and other types of infections. An infection was defined as HAI if diagnosed > 48 hours after admission and if the specific clinical HAI criteria were met (46, 47). Antimicrobial use was documented if administered on the survey day.

2.3.3 Data collection

All patient data were collected on standardised case-related forms, which were provided by the DNICC. The hospitals submitted summary data stratified by medical department to the DNICC and to the National Healthcare-Associated Infection Surveillance System (NHAISS).

2.3.4 Statistical analysis

The HAI prevalence (with 95% confidence intervals) for each participating hospital was analysed based on aggregated data, and stratified by clinical departments.

Statistical analysis was performed using STATA version 13.0 (Stata Corporation, College Station, Texas, USA).

2.3.5 Ethics

This survey was part of a mandated quality improvement initiative and did not require ethical approval.

2.3.6 Roles and contributions of the candidate

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JW and WZ established the study protocols. JH and MZ provided the PPS dataset from all the hospitals. JW cleaned and structure the database. Data analysis was done by JW and WZ. JW and WZ wrote the first draft of the manuscript. All the authors reviewed and contributed to subsequent drafts. All authors had full access to the original study data.

2.4 Article 2 – HAI prevalence in China: a systematic review 2.4.1 Search strategy

We followed the Preferred Reporting Items for Systematic Reviews and Meta- Analysis (PRISMA) guidelines (49). We searched PubMed, the China National Knowledge Infrastructure (CNKI), and the Chinese Wan Fang digital database. The medical subject heading (MeSH) terms and key words for PubMed were: “Cross infection” [MeSH Terms], “nosocomial infection”, “hospital acquired infection”,

“hospital-acquired infection”, “health care associated infection”, “healthcare associated infection”, “healthcare-associated infection”, “infection control” [MeSH Terms], “prevalence” [MeSH Terms], “point prevalence survey”, “cross-sectional studies” [MeSH Terms], “surveillance”, “epidemiological monitoring” [MeSH Terms],

“epidemiology” [MeSH Terms], “population surveillance” [MeSH Terms], “China”

[MeSH Terms]. The search terms for CNKI and Wan Fang digital database were:

“healthcare-associated infection”, “nosocomial infection”, “prevalence”, and “cross- sectional study”. Similar terms in Chinese were used to search the Chinese databases.

2.4.2 Inclusion/exclusion criteria

The following inclusion criteria were applied: 1) any PPS report performed in acute care hospitals in Mainland China and published between January 2006 and August 2016; 2) publications reporting on two or more hospitals; and 3) study language being either English or Chinese. The following exclusion criteria were applied: 1) conference papers, editorials/letters, case-control studies, or review articles; 2) duplication of studies; 3) publications reporting on antimicrobials only; and 4) national

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reports (duplication of data). If published in both Chinese and English, the English report was selected.

2.4.3 Data abstraction

Title, abstract and full text reviewing, as well as data extraction were performed independently by two individual researchers (JW, FL). Disagreements were discussed and resolved by a third researcher (WZ). The following data were

extracted systematically from eligible full text articles: title, authors, publication year, geographical region, healthcare setting, number of hospitals, sample sizes, patients with HAI, number and types of HAI, infection sites, pathogens, and proportion of multidrug resistant organisms. Data were verified by cross-checking (JW, FL, JH). All studies were assessed for quality using the Strengthening the Reporting of

Observational Studies in Epidemiology (STROBE) checklist (50). Three levels of quality were defined: high quality (>75% criteria met), moderate quality (50-75%

criteria met), and low quality (≤50% criteria met).

Meta-analysis was performed using the STATA metaprop command in a random- effects model to calculate weighted HAI prevalence along with the corresponding 95% confidence intervals, and to produce forest plots. Results were stratified by type of healthcare setting (general hospitals, children hospitals, maternal and child health hospitals, and oncology hospitals), and by provinces and municipalities. Association of HAI prevalence with GDP per capita, was tested using linear regression analysis.

Statistical analysis was performed using STATA version 14.0 (Stata Corporation, College Station, Texas, USA).

JW and WZ established the study protocols. JW and FL preformed literature search and data extraction independently. JW, FL and JH did data verification and cross- checking. Descriptive and statistical analysis was done by JW and WZ. JW and WZ

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wrote the first draft of the manuscript. All authors reviewed and contributed to subsequent drafts. All authors had full access to the original study data.

2.5 Article 3 – AMR prospective surveillance in Dongguan city 2.5.1 Settings

Seven public/not-for-profit hospitals, two secondary- and five tertiary-care hospitals provided data on AMR incidence of 2017. All institutions are general hospitals with a mixed patient population.

2.5.2 Databases

Antimicrobial susceptibility data were obtained from microbiology laboratories of the seven hospitals. Clinical data were obtained from Dongguan city Nosocomial

Infection Surveillance System (DNISS) (51): 1) patient identifier, age, gender, date of hospital admission, department of admission, date of hospital discharge, LOS, and in-hospital mortality; 2) presence of healthcare/community-associated infection or colonisation, infection type, date of infection/colonisation; 3) type and date of microbiology sampling, identified pathogen, date of microbiological reporting, and microbiological identifier. The definitions of HAI included temporal (>48 hours after admission), clinical and microbiological criteria (1, 14, 46, 47). HAIs and CAIs due to AMR pathogens were obtained by merging the microbiology database with the DNISS clinical database, using anonymous patient identifiers (the same for both databases), dates of microbiological sampling, dates of microbiological reporting, pathogens, and microbiological identifiers.

2.5.3 Antimicrobial resistance

The following HAI-relevant indicator pathogens were addressed: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, Enterococcus faecalis, and Enterococcus faecium (7, 21, 30). Identification and susceptibility testing was performed using VITEK® 2

(BioMérieux, France) in all hospitals. Breakpoints for minimal inhibitory

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concentrations were based on the US National Clinical and Laboratory Standards Institute (NCLSI) guidelines (modified version based on M100 – 28^th edition in 2017) (52). Primary outcome was non-susceptibility to antimicrobial agents of

epidemiologically significant antimicrobial categories, termed AMR markers (21, 26).

Multidrug-resistant (MDR) pathogens were defined as pathogens with non- susceptibility to at least one agent of three or more relevant antimicrobial classes (53).

2.5.4 Data analysis

Incidence proportions of pooled bacterium-antimicrobial (“bug-drug”) combination data as used by the ECDC protocol were calculated (26), with the corresponding 95% confidence intervals. Results were compared to the 2017 national CHINET report (30). Data were de-duplicated using the criteria of the EARS-Net surveillance protocol and taking into account all pathogens in context with the first infection during hospitalization. Incidence proportions of CAI as well as incidence densities of HAI due to AMR pathogens were calculated (26). Healthcare-associated infections due to E. coli and K. pneumoniae non-susceptible to 3^rd-generation cephalosporins,

fluoroquinolones, carbapenems, or any combination of these agents, were compared with a multi-centre surveillance report, performed 2014/2015 in Germany (23).

Bloodstream infections due to AMR pathogens were calculated following the EARS- Net protocol for pathogen-resistance combinations (26). Incidence proportions of BSI due to MDR pathogens were compared to the 2017 EARS-Net report (21).

Healthcare-associated infections due to antibiotic-resistant E. coli and K.

pneumoniae were compared to the 2014/2015 German AMR surveillance data (23) using Pearson’s Chi-Square test. Bloodstream infections due to MDR pathogens were compared to the 2017 EARS-Net data (21) using the Fisher’s exact test. Two- sided P-values <0.05 were considered statistically significant. All statistical analyses and graphs were performed using STATA 15.1 version (Stata Corporation, College

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Station, Texas, USA).

2.5.6 Ethics

The Chinese Ethics Committee of Registering Clinical Trials waived patient informed consent and approved data analysis for this study (ChiECRCT20190134).

JW and WZ established the study protocol. MZ, GH and ZG provided the microbiology and clinical data. JW cleaned and structured the database. Data analysis was done by JW, JS and WZ. JW and JS did data verification and cross- checking. JW, MZ and WZ wrote the first draft of the manuscript. All the authors reviewed and contributed to subsequent drafts. All authors had full access to the original study data.

2.6 Article 4 – Implementation of IPC in China: a systematic review 2.6.1 Search strategy

This systematic review followed the “Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA)” guidelines (49). We searched PubMed, the Chinese National Knowledge Infrastructure database, and the Cochrane library for any relevant documents. In addition, we looked for guidelines on the official websites of the National Health Commission of the People’s Republic of China (NHCPRC) and the regional ministries of health.

Primary outcomes were adopting or implementing (having) elements of the three NHCPRC areas. Secondary outcomes were the change of outcome indicators (e.g.

HAI or AMR or hand hygiene) or process indicators by applying IPC practices. The search terms addressed the three IPC areas specified by the NHCPRC for acute care hospitals: 1) structure, organization and management of IPC; 2) education and training of IPC; and 3) surveillance of process and outcome indicators relevant to IPC.

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2.6.2 Inclusion/exclusion criteria

Any article was eligible for inclusion when all of the following criteria were met: 1) use of a quantitative, qualitative or combined (mixed-methods) method; 2) reporting on one of the primary and/or secondary outcomes; 3) publication between January 2012 and October 2017; and 4) publication either in English or Chinese. Articles were excluded if they met one of the following criteria: 1) conference papers, editorials, or letters; 2) duplicated results; 3) risk factor analysis without information on the use of any IPC practice; 4) non-acute healthcare setting; or 5) outbreak investigations.

2.6.3 Data extraction

Title, abstract and full text review were performed by two individual researchers (JW, FL). Disagreements were resolved by consensus, and, when necessary, discussed with a third researcher (WZ). Data extraction was stratified by two hospital categories (primary care and secondary/tertiary care hospitals). Articles were further categorised as survey reports, observational studies or interventional studies. The following data were extracted from survey reports: title, authors, publication year, province, total number of hospitals, and the number of hospitals applying specific elements of the three NHCPRC areas. The following data were extracted from observational studies:

title, author, publication year, province, study aim, setting, surveillance protocol, sample size, study duration, methodology, and outcome. The following data were extracted from interventional studies: title, authors, publication year, province, study aim, population, intervention, comparison, study design and outcome. Data extraction for interventional studies followed the “PICO” (population – intervention – comparison – outcome) concept (49). Data were verified by cross-checking (JW, FL and JBXT).

Survey reports and observational studies were quality assessed by using the

“Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) checklist (50). Interventional studies were quality assessed by using the “Integrated quality Criteria for the Review of Multiple Study designs” (ICROMS) checklist (54).

Findings were stratified by the three NHCPRC areas, and compared with the ECDC key components (31), and the WHO core components (32).

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Frequencies of NHCPRC elements mentioned in the survey reports were calculated on hospital level (with the corresponding 95% confidence interval), and stratified by hospital categories (primary- and secondary/tertiary care hospitals). The difference of each identified element between hospital categories was tested by Pearson’s Chi- Square test. Statistical analysis was performed using STATA version 14.0 (Stata Corporation, College Station, Texas, USA). Results of the observational and interventional studies were summarized descriptively.

JW and WZ established the study protocol. JW and FL preformed literature search and data extraction independently. JW, FL and JBXT did data verification and cross- checking. Data analysis was done by JW and WZ. JW, JBXT and WZ wrote the first draft of the manuscript. All authors reviewed and contributed to subsequent drafts. All authors had full access to the study data.

RESULTS

3.1 Article 1 – Dongguan city HAI PPS study

In the 2015 Dongguan PPS, 37 secondary-care and 14 tertiary-care hospitals assessed a total of 9679 and 11,641 patients, respectively. A large proportion of patients were hospitalised in surgery (8105, 38.0%), followed by internal medicine (5924, 27.8%) and gynaecology and obstetrics (2714, 12.7%).

A total of 616 patients had 681 HAIs. The pooled HAI prevalence in secondary care, tertiary care, and all hospitals together was 2.3% (95%CI: 2.0-2.6%), 3.4%

(95%CI: 3.0-3.7%), and 2.9% (95%CI: 2.6-3.1%), respectively. There was significant variation among the hospitals (Figure 1). Together, LRTI, UTI, SSI, and BSI

accounted for 73.1% of all HAIs. LRTI (35.5%) was the most frequently diagnosed HAI, followed by UTI (17.0%) and SSI (15.1%).

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Figure 1 Prevalence of healthcare-associated infections – Dongguan city point prevalence survey 2015

Note: •: Pooled HAI prevalence; −: 95% confidence interval

The pooled HAI point prevalence was highest in adult intensive care (29.4%, 95%CI: 23.4-35.5), followed by surgery (3.5%, 95%CI: 3.1-3.9), internal medicine (3.5%, 95%CI: 3.0-4.0), and neonatology (3.3%, 95%CI: 1.7-4.9).

A total of 533 microorganisms were identified. Gram-negative bacteria were most frequently isolated (363/533, 68.1%), followed by Gram-positive bacteria (103/533, 19.3%). E. coli was the most frequent single pathogen (79/533, 14.8%), followed by P. aeruginosa (74/533, 13.9%), K. pneumonia (59/533, 11.1%), and A. baumannii (58/533, 10.9%). In LRTI patients, P. aeruginosa was the most frequent pathogen (42/210, 20%), followed by A. baumannii (38/210, 18.1%). A total of 175 isolates (32.8%) were multidrug-resistant organisms.

Thirty-five secondary and 12 tertiary hospitals provided information about

antimicrobial use in 19,445 patients. A total of 6759 patients (34.8%) received one or more antimicrobials, 4581 (23.6%) for treatment and 2178 (11.2%) for prophylaxis. A total of 5350 (79.2%) patients received a single antimicrobial, 1327 (19.6%) received

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a combination of two antimicrobials, and 82 (1.2%) received a combination of three or more antimicrobials.

3.2 Article 2 – HAI prevalence in China: a systematic review

In total, 115 studies were eligible for quality assessment and data extraction: 42 reports from general hospitals, 19 from children’s hospitals, 27 from maternal and child health hospitals, and 27 from oncology hospitals (Figure 2). Seventeen (14.78%), 84 (73.04%) and 14 (12.17%) reports were of high, moderate and low quality, respectively.

Figure 2 Systematic review profile – HAI prevalence in China: a systematic review

Healthcare-associated infections in different healthcare settings

In total, 53,642 patients had 57,479 HAIs. Pooled and weighted prevalence were 2.98% (95% CI: 2.96-3.01) and 3.12% (95% CI: 2.94-3.29), respectively (Table 1).

Children hospitals had the highest prevalence (4.43%, 95%CI: 3.39-5.47), followed by oncology hospitals (3.96%, 95%CI: 3.12-4.79), general hospitals (3.02%, 95%CI:

2.79-3.26), and maternal and child health hospitals (1.88%, 95%CI: 1.47-2.29).

In general hospitals, the highest weighted prevalence was reported in intensive care (26.07%, 95%CI: 23.03-29.12), followed by surgery (3.26%, 95%CI: 2.96-3.57), and internal medicine (3.06%, 95%CI: 2.67-3.46).

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Table 1 Pooled and weighted prevalence of healthcare-associated infections in general hospitals, children hospitals, maternal and child health hospitals, and oncology hospitals

Types of healthcare-associated infections

LRTI was the most frequent type of HAI (24,185 infections, 47.28%), followed by UTI (5773 infections, 11.29%), upper URTI (5194 infections, 10.15%), and SSI (5044 infections, 9.86%). Together, LRTI, UTI, SSI, and BSI accounted for 71.33% of all HAIs. The three most frequent HAIs in general hospitals were LRTI, UTI, and SSI.

The three most frequent HAIs in children’s hospitals were LRTI, URTI, and gastrointestinal infection (GI) (Table 2).

Table 2 Types of healthcare associated infections in Mainland China, 2006 - 2016

Healthcare-associated infections in different provinces of China We observed marked variation in HAI prevalence among the provinces and municipalities of China (Figure 3). The range of weighted prevalence in different provinces and municipalities in China was 1.73% to 5.45%.

The distribution of HAI prevalence and the number of reports varied across

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provinces and municipalities in Mainland China for 2006-2016 (Figure 4). No eligible survey had been performed in more than one province. The GDP of the different provinces and municipalities was significantly associated with the weighted HAI prevalence (coefficient=-0.022, p<0.001, 95%CI, -.035 to -.008); this difference represents an HAI reduction of 2.2% per 1000 CNY increase of GDP (Figure 5).

Figure 3 Weighted prevalence of healthcare-associated infections in the different provinces and municipalities of China

Note: Boxes show prevalence of HAIs and their 95% confidence intervals. The reference lines show the 95% CI of the prevalence of healthcare associated infections for the entire population.

Figure 4 Prevalence of healthcare associated infections and number of reports in the different provinces of Mainland China in Mainland China, 2006-2016

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Note: GH: General hospitals; CH: Children hospitals; MCH; Maternal and child health hospitals; OH:

Oncology hospitals; HAI: healthcare-associated infection

Note: The size of circles represents the number of publications; the shade of colour represents the prevalence of healthcare-associated infection. Provinces without colour did not contribute to this systematic review either because there were no reports or because publications did not fulfil the inclusion criteria.

Figure 5 Association of healthcare-associated infection prevalence and gross domestic product per capita in Mainland China, 2006-2016

Note: The HAI prevalence decreases by 2.2% with an increase of 1,000 Chinese Yuan (CNY) in GDP per capita.

Distribution of pathogens

The two most common microorganisms isolated in general hospitals were P.

aeruginosa (15%) and E. coli (13%), respectively. K. pneumoniae (19%) and E. coli (10%) were the two most common microorganisms in children’s hospitals,

respectively. K. pneumoniae (24%) and E. coli (18%) were the two most common microorganisms in maternal and child health hospitals, respectively. In oncology hospitals, E. coli (19%) and K. pneumonia (15%) were the two most common microorganisms.

3.3 Article 3 – AMR prospective surveillance in Dongguan city 2017

Two secondary and 5 tertiary care hospitals provided 16,548 microbiology results on

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bacteria. Together, they represented a total of 246,266 admissions accumulating 2,218,861 patient-days in 2017.

Among gram-negative bacteria, non-susceptibility to 3^rd generation cephalosporins (3GC) in E. coli and K. pneumoniae were 43.9% and 30.2%, respectively. Non-susceptibility to carbapenems in E. coli and K. pneumoniae were 0.9% and 4.2%, respectively. On average, 29.5% and 19.7% of P. aeruginosa were non-susceptible to carbapenems and ceftazidime, respectively; 50.9% and 53.6% of A. baumannii were non-susceptible to carbapenems and cefepime, respectively.

Among gram-positive bacteria, 26.3% of Staphylococcus aureus were non- susceptible to oxacillin; 0.7% of Enterococcus faecalis were non-susceptible to vancomycin; but no E. faecium was found to be vancomycin-resistant.

Figure 6 compares AMR data of Dongguan city to CHINET. E. coli, K.

pneumoniae, and A. baumannii were consistently and significantly more susceptible in Dongguan city compared to the national data. However, P. aeruginosa were significantly less susceptible to piperacillin-tazobactam (29.9% vs. 13.4%), imipenem (29.5% vs. 23.6%), gentamicin (17.5% vs. 10.7%), and ciprofloxacin (22.2% vs.

14.8%) in Dongguan city compared to CHINET data. With the exception of S. aureus, non-susceptibility was consistently higher in tertiary care hospitals compared to secondary care hospitals.

Figure 6 Comparison of non-susceptibility of indicator pathogens to antimicrobial resistance markers between Dongguan city and the China Antimicrobial Surveillance Network

A. B.

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C. D.

E. F.

G.

A total of 1508 and 4090 pathogens were allocated to HAI and CAI, respectively.

Non-susceptibility was consistently higher in HAI compared to CAI, although statistically significant differences were identified only for P. aeruginosa, and for E.

coli and K. pneumoniae non-susceptible to both ceftriaxone and ciprofloxacin (Fig 7).

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Figure 7 Comparison of non-susceptibility of indicator pathogens to antimicrobial resistance markers between healthcare-associated and community-acquired infections

A. B.

C. D.

E.

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Table 3 Incidence density of healthcare-associated and community-acquired infections due to indicator pathogens, non-susceptible to one or more antimicrobial resistance markers

HAIs CAIs

Categories N

HAIs per 1,000 patient days

95% CI N CAIs per 100

admission 95% CI

Escherichia coli 3GC 66 0.05 0.04 - 0.06 192 0.14 0.12 - 0.17

3GC+FQ 120 0.09 0.07 - 0.11 321 0.24 0.22 - 0.27

3GC+FQ+CAR 0 0 N/A 1 0.001 0.000 - 0.004

Klebsiella pneumoniae

3GC 24 0.02 0.01 - 0.03 47 0.04 0.03 - 0.05

3GC+FQ 28 0.02 0.01 - 0.03 57 0.04 0.03 - 0.06

3GC+FQ+CAR 11 0.008 0.004 - 0.014 7 0.005 0.002 - 0.011

Pseudomonas aeruginosa CAR 8 0.006 0.003 - 0.012 25 0.02 0.01 - 0.03

CAR+FQ 6 0.004 0.002 - 0.010 4 0.003 0.001 - 0.008

CAR+FQ+AMG 34 0.02 0.02 - 0.03 7 0.005 0.002 - 0.011

Acinetobacter baumannii CAR 0 0 N/A 1 0.001 0.000 - 0.004

CAR+FQ 0 0 N/A 1 0.001 0.000 - 0.004

CAR+FQ+AMG 47 0.03 0.03 - 0.05 27 0.02 0.01 - 0.03

Staphylococcus aureus

OXA 18 0.01 0.01 - 0.02 74 0.06 0.04 - 0.07

OXA+FQ 4 0.003 0.001 - 0.007 6 0.005 0.002 - 0.010

OXA+FQ+RMP 4 0.003 0.001 - 0.007 1 0.001 0.000 - 0.004

Note: AMG: aminoglycoside; CAI: community-associated infection; CAR: carbapenem; FQ:

fluoroquinolone; HAI: healthcare-associated infection; OXA: oxacillin; RMP: rifampicin; 3GCR: 3^rd generation cephalosporin

Table 3 summarises incidence densities of HAI and CAI due to indicator

pathogens non-susceptible to one or more antimicrobial combinations. No HAI due to carbapenem non-susceptible E. coli was reported. The incidence density of HAI due to E. coli non-susceptible to 3GC and fluoroquinolones combined was 0.09 (95%CI:

0.07-0.11) per 1000 patient-days. The incidence proportion of CAI due to E. coli non- susceptible to 3GC and fluoroquinolones combined was 0.24 (95%CI: 0.22-0.27) per

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100 admissions. The incidence density of HAI due to K. pneumoniae non-susceptible to 3GC, fluoroquinolones and carbapenem combined was 0.008 (0.004-0.014) per 1000 patient-days.

Incidence densities of HAI due to E. coli and K. pneumoniae non-susceptible to 3GC and fluoroquinolones combined were significantly lower compared to the 2014/2015 multi-centre surveillance study in Germany (Figure 8). No significant differences in incidence densities of HAI due to MDR K. pneumoniae were identified between Dongguan city and Germany.

Figure 8 Incidence densities of healthcare-associated infections due to resistant Escherichia coli and Klebsiella pneumoniae – Differences between Dongguan City, 2017 and Germany, 2014 and 2015

CAR: carbapenem; ESCCOL: Escherichia coli; FQ: fluoroquinolone; KLEPNE: Klebsiella pneumoniae;

IRR: Incidence rate ratio; 3GC: 3^rd generation cephalosporin

Note: No healthcare-associated infection due to E. coli non-susceptible to 3^rd generation cephalosporin, fluoroquinolones and carbapenem combined were reported.

Ten percent (562/5598) of the pathogens in the combined dataset were isolated from blood. Among these, 41.4% (65/157) of E. coli were non-susceptible to 3GC, but all E. coli were susceptible to carbapenems; 19.6% (10/51) and 2.0% (1/51) of K.

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pneumoniae were non-susceptible to 3GC and carbapenems, respectively; all of 15 isolated P. aeruginosa were susceptible to carbapenems; 28.6% (4/14) of A.

baumannii non-susceptible to carbapenems; 21.3% (10/47) of S. aureus were non- susceptible to oxacillin; but all 14 isolated E. faecalis and 4 isolated E. faecium were susceptible to vancomycin.

Incidence proportions of BSI due to MDR E. coli were significantly higher

compared to the 2017 EARS-Net report (14.9% vs. 4.6%, P<0.001) (Figure 9). There were no significant differences for MDR K. pneumoniae, or MDR A. baumannii, or MDR S. aureus, respectively.

Figure 9 Incidence proportions of bloodstream infections due to multidrug resistant indicator pathogens between Dongguan city, 2017 and the 2017 EARS-Net report

ACIBAU: Acinetobacter baumannii; AMG: aminoglycoside; BSI: bloodstream infections; CAR:

carbapenem; EARS-Net: European Antimicrobial Resistance Surveillance Network; ESCCOL:

Escherichia coli; FQ: fluoroquinolone; KLEPNE: Klebsiella pneumoniae; MDR: multidrug resistant; OXA:

oxacillin; PEN: penicillin; PSEAER: Pseudomonas aeruginosa; RMP: rifampicin; STAAUR:

Staphylococcus aureus; 3GCR: 3^rd generation cephalosporin

3.4 Article 4 – Implementation of IPC in China: a systematic review

In total, 56 articles were eligible for data extraction and analysis: 27 survey reports on structure, organisation and management of IPC; 17 observational studies

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measuring outcome and process indicators; 5 interventional studies applying education and training; and 7 interventional studies testing the effectiveness of IPC strategies (Figure 10). They embraced three broad areas in IPC, issued by the

“National Health Commission of the People’s Republic of China (NHCPRC)”: 1) structure, organization and management of IPC; 2) education and training of IPC;

and 3) surveillance of process and outcome indicators relevant to IPC).

Figure 10 Systematic review profile – Implementation of IPC in China: a systematic review

NHCPRC area “structure, organisation and management of IPC”

The search terms addressing the NHCPRC area on “structure, organisation and management of IPC” identified 27 survey reports (Table 4):

- Element of “structure, organisation and management, guideline provision”

o Most primary care hospitals had an IPC committee (71%), a formal IPC programme (61%), and provided IPC guidelines (57%). Most

secondary/tertiary care hospitals had an IPC committee (98%), performed feedback on IPC indicators (93%), and provided IPC guidelines (85%).

o No information on feedback, allocated IPC funding/budget, and IPC research was identified for primary care hospitals.

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o The frequencies of the elements were significantly different between hospital types, in favour for secondary/tertiary care hospitals.

- Element of “education and training”

o Significantly more secondary/tertiary care hospitals offered regular, postgraduate IPC training compared to primary care hospitals (75% vs.

53%, P<0.001).

o The survey reports did not describe details on target population, training content, or frequency of training activities.

- Element of “indicator and outcome surveillance, auditing”

o Surveillance of antimicrobial consumption (55%) was the most reported surveillance element in primary care hospitals, followed by HAI point prevalence surveys (39%), and incidence surveillance of surgical site infection (38%).

o Waste management (62%) was the most frequently audited element in primary care hospitals, followed by sterilization and medical device decontamination (58%), and environmental culturing (57%).

o Incidence surveillance of SSI (71%) was the most reported surveillance element in secondary/tertiary care hospitals, followed by HAI point prevalence surveys (67%), and surveillance of AMR (64%).

o Environmental culturing (92%) was the most frequently audited element in secondary/tertiary care hospitals, followed by waste management (57%), and sterilization and medical device decontamination (55%).

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Table 4 NHCPRC areas and elements of infection prevention and control identified by 27 survey reports