1. SHAIPI: Future Collaboration and Capacity Building
10th March 2017

2. Lucy Gozdzielewska, Glasgow Caledonian University
Evaluating the effectiveness of the World Health Organisation’s 6-step handrubbing technique for healthcare staff
Lucy Gozdzielewska, Glasgow Caledonian University

3. Deconstructing the effectiveness of the World Health Organisation’s 6 step handrubbing technique for healthcare staff

4. Debbie Waddell, Glasgow Caledonian University
Investigating and analysing the factors which lead to the successful implementation and embedding of catheter associated urinary tract infections (CAUTI) care bundles in the acute care setting of NHS Scotland
Debbie Waddell, Glasgow Caledonian University

5. “It is only a catheter, pop it in!”
Let’s see now…was that insert catheter, inflate balloon…or inflate balloon, insert catheter?

6. Stephen Fox, University of Glasgow
An ETT2 infection system in E. coli that cause urine infections to go bad?
Stephen Fox, University of Glasgow

7. An ETT2 infection system in E
An ETT2 infection system in E. coli that cause urine infections to go bad?
ETT1
E. coli Type 3 Secretion System 1
ETT2
E. coli Type 3 Secretion System 2 ?
E. coli Bacteria
ETT2
System
Intestine
Urinary Tract
ST131
ST69
Stephen Fox

8. Jiafeng Pan, University of Strathclyde
Data linkage and statistical modelling to provide stratified risk assessment for HAI
Jiafeng Pan, University of Strathclyde

9. Data linkage and statistical modelling to provide stratified risk assessment for HAI
Jiafeng Pan, Kim Kavanagh, Chris Robertson, Marion Bennie, Charis Marwick, Colin McCowan
ECOSS: Laboratory confirmed CDI cases
Aug 2010 – July 2013
All CDI cases with linked hospital records
CA-CDI
matched population based controls
SMR01:
Hospital Admissions
SMR01 – comorbidity markers
PIS: Prescriptions 1) Antimicrobials
2) Proton pump inhibitors/H2 antagonists
3) Drug counts
Risk factors
Predictive model
1439 CA-cases
43% at least one stay
Average 13 different items
25% ≥1 Comorbidities
9% resident in care home
7945 controls
17% resident in care home
14% at least one stay
Average 6 different items 4C
22% exposed
3% >=29 DDDs
4% exposed
0.1% >=29 DDDs
59% exposed
20% >=29 DDDs
7% ≥1 Comorbidities
CA-CDI cases
Model performance:
Cut point 0.88
Sensitivity 68.9%
Specificity 75.6%
AUC 0.791
Missed CDI cases
Missed cases:
Younger
Mean age 53 vs.72
Healthier
1.3 days in hospital vs. 13.8
5.8 different items vs. 16.3
Less antibiotic exposure
3.1 vs. 30 DDDs
67% no antibiotic vs. 23%
39% no risk factors
Controls
Correctly classified
as controls
Correctly classified as CDI
OR = 4.4 ( )
Prediction probability
OR = 17.9 ( )

10. Cosmika Goswami, University of Glasgow
Linking WGS and Clinical data for Escherichia coli bacteraemia from Scotland
Cosmika Goswami, University of Glasgow

11. Linking WGS and Clinical data for Scottish E. coli bacteraemia
1. Clinical isolates from Glasgow and Dumfries
HAI
Gender(F)
Catheter(On)
p < ***
p < *
p < ***
p < 0.858
p < 0.258
2. Mortality: Hazard ratio with 95% CI
Age(+1yr)
Dominant Strain 1 2 3 4 5 6
Values
Total 162 isolates
HAI 59%(n=96), CAI 41%(n=66)
Catheter on 16% (n=25)
Deceased 36%(n=59)
Strain types:
3. NMDS plot for virulence genes:
4. PanGWAS significant genes for HAI-CAI:
toxin/antitoxin gene
Bonferroni corrected threshold
Association threshold
-log10(p)
NMDS1
NMDS2
ST69
ST131
ST95
ST73
Origin
Community
Hospital
Genes (No particular order)

12. Characterisation of risk factors associated with antibiotic resistance in urinary isolates in the community
Eilidh Fletcher, Public Health and Intelligence, NHS National Services Scotland

13. Identified Risk Factors
Characterisation of risk factors associated with antibiotic resistance in urinary isolates in the community
Care home
Increasing age
More hospital stays
More drug classes
Different antibiotics
Male gender
Increasing comorbidites
Identified Risk Factors
How much antibiotics?
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