| Title:
|
Modelling the transmission of healthcare associated infections: a systematic review |
| Abstract:
|
BACKGROUND: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. METHODS: MEDLINE EMBASE Scopus CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. RESULTS: In total 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%) variability in transmission routes (7%) the impact of movement patterns between healthcare institutes (5%) the development of antimicrobial resistance (3%) and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%) followed by vancomycin resistant enterococci (16%). Other common HCAIs e.g. Clostridum difficile were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries. The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon occurring in 35% and 36% of studies respectively but their application is increasing. Only 5% of models compared their predictions to external data. CONCLUSIONS: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved with an increased use of stochastic models and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models. |
| Published:
|
2013-06-28 |
| Journal:
|
BMC Infect Dis |
| DOI:
|
10.1186/1471-2334-13-294 |
| DOI_URL:
|
http://doi.org/10.1186/1471-2334-13-294 |
| Author Name:
|
van Kleef Esther |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/van_kleef_esther |
| Author Name:
|
Robotham Julie V |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/robotham_julie_v |
| Author Name:
|
Jit Mark |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/jit_mark |
| Author Name:
|
Deeny Sarah R |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/deeny_sarah_r |
| Author Name:
|
Edmunds William J |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/edmunds_william_j |
| sha:
|
06ebe6e32a2e7241e23d3fb51c4dffeed389861d |
| license:
|
cc-by |
| license_url:
|
https://creativecommons.org/licenses/by/4.0/ |
| source_x:
|
Medline; PMC |
| source_x_url:
|
https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/ |
| pubmed_id:
|
23809195 |
| pubmed_id_url:
|
https://www.ncbi.nlm.nih.gov/pubmed/23809195 |
| pmcid:
|
PMC3701468 |
| pmcid_url:
|
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701468 |
| url:
|
https://doi.org/10.1186/1471-2334-13-294
https://www.ncbi.nlm.nih.gov/pubmed/23809195/ |
| has_full_text:
|
TRUE |
| Keywords Extracted from Text Content:
|
vancomycin
enterococci
Clostridum difficile
individuals
€7
b c Figure 5
CINAHL (1937
VRE
hospital$
Escherichia coli
patients
HCAIs
human
urinary tract infections
LHS
CRE
12,13,24,27,33,34,38,39,45,46,51,58,60,64,66,68,69,74,77,78-,83-88,93,94,97,109
decolonisation
Foot-and-Mouth
MeSH
ARB
MRSA
E. coli
joint
bloodstream
EvK
Clostridium difficile
LTCFs
Vancomycin-resistant Enterococcus
SD and WJE
avian influenza A
C. difficile
H7N7
patient |
| Extracted Text Content in Record:
|
First 5000 Characters:Background: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. Methods: MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. Results: In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or cocolonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries. The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. Conclusions: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.
Healthcare-associated infections (HCAI) continue to cause a major burden on society, affecting more than 4 million patients annually in Europe alone, and causing an estimated 16 million additional bed-days responsible for €7 billion in direct medical costs [1] . In the United Kingdom, interventions such as improved hand hygiene, antibiotic stewardship and screening combined with decolonisation are believed to have set off a steep reduction in reported incidence of health care-associated methicillinresistant Staphylococcus aureus (MRSA) bacteraemia and Clostridium difficile infection with peak incidence in 2003/04 and 2007/08 respectively [2] . Further progress in reducing the burden of HCAI is hindered by uncertainty surrounding the role of asymptomatic carriers [3, 4] , environmental transmission [5] [6] [7] and the recent emergence of bacteria other than MRSA and C. difficile, such as enterobacteriaceae (e.g. Escherichia coli) [8] . Mathematical models are increasingly being used to obtain a deeper understanding of epidemiological patterns in hospital infections and to guide hospital infection control policy decisions, as is seen in other areas of infectious disease epidemiology [9] .
A previous review of the area provided insight into the type of models used for hospital epidemiology and highlighted their capacity to increase epidemiological understanding, and inform infection control policy [10] . This review, conducted in 2006, primarily aimed to explain the capacities of models and therefore was limited to a detailed description of a number of studies. Hence, the emerging trends in the area were not fully explored. Since 2006 the field has expanded considerably. We conducted a systematic review in order to establish how mathematical models have been applied in the field of HCAI, and how model methods have developed over time.
We searched Medline (1950 to present), EMBASE (1947 to present), Scopus (1823 to present), CINAHL (1937 to present) and Global health (1910 to present). Results were limited to peer-reviewed publications in English. Search terms and Medical Subject Headings (MeSH) for nosocomial organisms and antibiotic resistance were combined with search and MeSH terms for healthcare settings and mathematical models as follows:
Nosocomial infections in general (e.g."healthcareassociated infection$" or "hospital-acquired infection$") OR Nosocomial organisms (e.g. "C. difficile" or "Staphylococcus aureus") OR Antimicrobial resistance AND Nosocomial (e.g. "hospital$" or "healthcare") AND Mathematical modelling or economic evaluation model (e.g. "stochastic" or "deterministic" AND "model")
We decided not to use search terms for nosocomial infection types (e.g |
| Keywords Extracted from PMC Text:
|
VRE
[14,16,22,28,29,31,32,36],[40-42,45,49,52,53,55,63,65],[72,79,82,91,101,107,108
HCAIs
50,53
LTCFs
Foot-and-Mouth
decolonisation
C. difficile[58,60
bloodstream
MRSA
HCW
Extended-spectrum beta-lactamases
R-GNR
€7
H7N7
Vancomycin-resistant Enterococcus
individuals
E. coli
[14,32,36,40-42,48,50,57
[19,20,25,70,71,73]
Escherichia coli
CRE
[12,13,24,27,33,34,38,39],[45,46,51,58,60,64,66,68],[69,74,77,78,83-88,93,94,97],[109
LHS
~2,700
mote-based
Hybercube
Influenza-like
C. difficile
ESBL
Clostridium difficile
SD and WJE
ARB
EvK
patient
patients
human
joint
avian influenza A |
| Extracted PMC Text Content in Record:
|
First 5000 Characters:Healthcare-associated infections (HCAI) continue to cause a major burden on society, affecting more than 4 million patients annually in Europe alone, and causing an estimated 16 million additional bed-days responsible for €7 billion in direct medical costs [1]. In the United Kingdom, interventions such as improved hand hygiene, antibiotic stewardship and screening combined with decolonisation are believed to have set off a steep reduction in reported incidence of health care-associated methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia and Clostridium difficile infection with peak incidence in 2003/04 and 2007/08 respectively [2]. Further progress in reducing the burden of HCAI is hindered by uncertainty surrounding the role of asymptomatic carriers [3,4], environmental transmission [5-7] and the recent emergence of bacteria other than MRSA and C. difficile, such as enterobacteriaceae (e.g. Escherichia coli) [8]. Mathematical models are increasingly being used to obtain a deeper understanding of epidemiological patterns in hospital infections and to guide hospital infection control policy decisions, as is seen in other areas of infectious disease epidemiology [9].
A previous review of the area provided insight into the type of models used for hospital epidemiology and highlighted their capacity to increase epidemiological understanding, and inform infection control policy [10]. This review, conducted in 2006, primarily aimed to explain the capacities of models and therefore was limited to a detailed description of a number of studies. Hence, the emerging trends in the area were not fully explored. Since 2006 the field has expanded considerably. We conducted a systematic review in order to establish how mathematical models have been applied in the field of HCAI, and how model methods have developed over time.
Eligible studies had to fulfil the following criteria: 1) mathematical modelling of HCAI transmission and/or the dynamics of antimicrobial resistance; 2) dynamic transmission models only (i.e. a model which tracks the number of individuals (or proportion of a population) carrying or infected with a pathogen over time, while capturing the effect of contact between individuals on transmission [9]); 3) a primary focus on HCAI transmission in healthcare settings.
Studies were excluded if they did not involve: 1) human to human transmission; or did involve 2) within host transmission only; 3) pharmacodynamics and pharmacokinetics of drugs (e.g. the impact of antibiotic exposure, exploring antibiotic tolerance and investigating fitness), 4) animal transmission of HCAI; 5) community transmission of pathogens spread in the healthcare environment as well, where community spread was the focus of the paper (e.g. SARS epidemics); or 6) literature review without new primary studies. Moreover, no editorials or letters to editors were included, except if a new mathematical model was introduced.
Although HCAIs are often associated with antibiotic-resistant bacteria, HCAI models have involved antimicrobial susceptible pathogens as well. In this review, studies that did not specify a particular pathogen of concern, but that claimed to investigate antimicrobial resistant bacteria, were classified as antimicrobial resistant bacteria (ARB). Otherwise, the study was categorised as 'HCAI in general'. Moreover, as the majority of patients can carry HCAI such as MRSA and C. difficile asymptomatically, many mathematical models simulate the epidemiology of colonisation, however for brevity we have referred to all models as concerning the epidemiology of HCAI in the text.
Figure 3 shows that MRSA was the most common bacterial species studied (34%; 33 studies) [14-46], followed by Vancomycin-resistant Enterococcus (VRE) (or glycopeptide-resistant enterococci) (16%; 15 studies) [12,18,28,31,47-57] whereas C. difficile has rarely been the subject of a model (3%; 3 studies) [58-60]. As several studies investigated the dynamics of more than one pathogen, the total number of infection agents (N=102) listed in Figure 3 exceeds the total number of studies (N=96).
The first model of HCAI conceptualised the spread of antibiotic resistance in bacterial populations among hospital patients [13]. This was soon followed by models evaluating the effectiveness of interventions to reduce antibiotic resistance (e.g. antibiotic cycling or mixing). Since then, most HCAI models have aimed to quantify infection control effectiveness (64%; 62 studies). The infection control measures most frequently considered among these 62 papers have been: hand hygiene (37%; 23 studies), patient isolation (24%; 15 studies), HCW cohorting (23%; 14 studies), antibiotic stewardship (21%; 13 studies), and screening (18%, 11 studies). Figure 4 provides an overview of the main interventions modelled over time, emphasising that decolonisation and vaccination are more recent subjects of study. Moreover, a wider variability of interventions has been evaluated in the |
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