big data: the power and importance of accurate surveillance › roundtableone... · data linkage...
TRANSCRIPT
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Big Data: The Power and Importance of Accurate Surveillance
Professor Alan Johnson Department of HCAI & AMR National Infection Service
Public Health England
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Principles of Surveillance
2 Big data
Data need to be: • Collected (Source) • Stored • Analyzed • Made available • Acted upon Information for action!
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Data on Antimicrobial Resistance
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Hospital laboratories routinely: • Identify bacteria • Undertake AST Data stored in LIMS
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Sources of Microbiology Data
• LabBase/SGSS (collects routinely-generated laboratory data)
• MOLIS (Reference Lab Database)
• Mandatory surveillance programmes (Trust data) • Bacteraemia due to MRSA, MSSA, E. coli • C. difficile infection • Surgical site infection (SSI)
• GRASP (Gonococcal Resistance to Antimicrobials Surveillance Programme)
• BSAC Sentinel surveillance (collects isolates) • Bacteraemia • Respiratory
4 Big data
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MRSA and E. coli Bacteraemia in England
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CEPH
CIP
MRSA E. coli
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Making Use of AMR Data • Inform national guidance for treatment • Highlight burden of antibiotic-resistant infections • Strategic planning
• Need for new antibiotics • Rapid diagnostics • Antibiotic stewardship
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What the Data Doesn’t Tell us
Procedures?
Device related?
Underlying Infections?
Antibiotic Treatment?
Co-morbidities? Nosocomial?
Speciality?
Big data 7
Clinical outcomes?
Transmission?
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Sources of Clinical and Other Data • Hospital Episode statistics
• Death registrations (ONS)
• Renal registry
• National joint registry
• Prescribing data
• Patient Reported Outcome Measures (NHS)
• Paediatric Intensive Care Audit Network
• Neonatal Data Analysis Unit (Imperial College)
• Intensive Care National Audit & Research Centre
• Clinical Practice Research Datalink
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Data Linkage
• Linkage of disparate but complementary data sets provides a much more comprehensive picture
• Requires patient-level record linkage • Deterministic linkage (e.g. NHS number) • Probabilistic linkage (multiple identifiers)
• Highly robust information governance and secure IT systems essential to ensure patient confidentiality
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10 Big data
LabBase Microbiology surveillance
data Positive blood cultures Patients aged 1m-18y
(N=8,718)
HES Hospital administrative
in-patient data Individual patient records
Patients aged 1m-18y (N=1,834,300)
Example of Data Linkage
Linked PHE & HES data
Study to investigate the incidence, epidemiology and impact (mortality; LoS) of paediatric HA-bacteraemia
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11 Big data
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Health Protection Research Units (Funded by NIHR)
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DATA GENOMICS INTERVENTIONS
Antimicrobial use, assessment and impact
Key PHE priority healthcare-associated pathogens and target infections
Exploiting database technologies
Applying WGS to increase insights into resistance transfer
Public and patient involvement
Strain archives
Imperial College University of Oxford
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UK Five Year Antimicrobial Resistance Strategy 2013 to 2018
1. Improving infection prevention and control practices
2. Optimising prescribing (through antimicrobial stewardship)
3. Improving professional education, training and public engagement
4. Developing new drugs, treatments and diagnostics
5. Better access to and use of surveillance data
6. Better identification and prioritization of AMR research needs
7. Strengthened international Collaboration
UK 5-Year AMR Strategy 7 Key Areas for Future Action
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PHE Fingertips Web Portal
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15 Big data
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16 Big data
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Summary
Surveillance
Big data 17
• Surveillance epidemiology • Disparate data sources • Link to combine information • “Big data” gives the “Big Picture”