improved surveillance of bugs and drugs that include...

Improved Surveillance of Bugs and Drugs that include patient data

Susan Hopkins Clinical Director Infection Services, Royal Free London Lead Healthcare Epidemiologist, AMR Programme, Public Health England

ESPAUR 2012

• CMO Report and book • Highlight scale of problem

2013 •  AMR Strategy • Development of ESPAUR

2014 •  1st ESPAUR report: Resistance, Consumption and Stewardship • Development of Antibiotic Guardian; HPRUs; AST toolkits

2015 •  2nd ESPAUR report – UTI; granularity in prescribing; NHS Atlas; One Health UK • Quality Premium developed; National risk register; CPE surveillance

2016 •  3rd ESPAUR report; MDR displays; CPE ERS outputs; reduction in prescribing •  Fingertips AMR local indicators; O’Neill report; CQUIN introduced; PPS

2017 •  Focus on Big Ambitions: inappropriate prescribing and GNBSI •  Big data development – patient level prescribing and AMR data linkage

ESPAUR 2010-2014: Year 2 Report

Data Linkage

DeepMind

Options for data access

Section 251 statue

Patient and their data

Minimise risk

Privacy

Maximise public benefit

Maintain public trust

Consent, studies

Pseudonymisation

Big Data or Big Headache

Need to protect patient confidentiality Maintain public trust Data protection Freedom of Information Information Governance Development of data linkage

techniques ‒ probabilistic versus deterministic

Principles of Data Protection Act

•  Fair and Lawful •  Purpose for processing •  Adequate, Relevant and not excessive •  Accuracy •  Retention •  Rights •  Security •  Transfer overseas

Working definition of pseudonymisation

•  Technical process applied to identifiers which replaces them with pseudonyms

•  Enables us to distinguish between individual without enabling that individual identified

•  Either reversible or irreversible •  Part of de-identification

Challenges in data linkage

•  Modern cohorts/registries (NHS Number) •  Huge amounts of data but different database structures/sizes •  Major challenges when creating cross/cohort/platform analyses

•  Semantic interoperability /data harmonisation issues •  Original metadata - standards •  Variable definitions from rad/lab results/GP(readcodes)/inpatient •  Difficult to move very large and complex data

•  Privacy protection essential •  threat from re-identification scientists

•  World-wide shortage of skills and expertise •  No single person with all necessary skills •  Build upon existing expertise, developments and investments

Data linkage and AMR Ecological Linkage

PHE Local AMR Indicators; Public Health Profiles; GP Profile

Individual linkage Mortality data : survival and cause of death GP and hospital activity: health service impact/comorbidity Laboratory and imaging systems: severity of condition/comorbidity Deprivation

Family/household linkage Impact on the wider family

Improve drug-bug surveillance

Increased coverage from NHS laboratories from 30% to 98% Increased daily reporting from 10% to 82% Increased automated reporting from 0% to 78%

Laboratory text files(preferred method)

SGSS LaboratoryData Import

Legacy CoSurv/Amsurv files

Bespoke Excel format (CDR and

AMR)

sFTP

Web Upload

Email

Web Data Entry

Laboratory Person

SGSS Data

Warehouse

ReportingWeb

Business Intelligence Layer (BI)

Mining and Analysis

APIHPZone

External networks PHE network

BI Security layerRole based model based

on requirement / permission to view patient

level or aggregate data

SGSS Operational Database

Research in Progress Pharmacy systems

GPs, CCGs Hospitals

Deprivation & E. coli BSI rates, CCG, 2012/13-2015/16

~ 40% variation explained by deprivation

PHE mandatory surveillance team

Does antimicrobial stewardship explain the reduction in CDI?

Dingle et al. Lancet Infect Dis 2017 in press.

Mortality Number of deaths within 30 days of specimen collection by infection

Data Linkage Mandatory data and Mortality ONS More deaths within 30 days of E. coli BSI from S. aureus BSI and CDI combined. CFR highest for MSSA CFR E. coli = CDI

Mandatory Surveillance Team, PHE

Patients from LTCF have higher AMR in urinary tract isolates

Simple Graphics Data linkage, Postcode matching LTCF almost double trimethoprim and ciprofloxacin resistance in urines

Rosello, JAC, 2017

Factors associated with iGAS deaths in community onset cases

Data linkage for HES and SGSS, postcode matching, ONS mortality records

Saavedra Campos Epid Inf 2017

E. coli Resistance & Hospitals

Carbapenems

Gentamicin

Piperacillin/tazobactam Third generation cephalosporins

Fluoroquinolones

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

Fluoroquinolones 3rdg cephalosporins Piperacillin/tazobactam Gentamicin Carbapenems

Community onset 17.4% 9.3% 8.7% 8.6% 0.2%>2 Days 22.1% 14.4% 15.1% 13.5% 0.3%>7 Days 24.1% 15.9% 16.5% 14.8% 0.4%

E. coli BSI

Data Linkage 1. HES 2. ONS 3. HCAI DCS 4. SGSS

E. coli BSI

Data Linkage 1. HES 2. ONS 3. HCAI DCS 4. SGSS

Naylor BioRxIV 2017

Financial case for action

AMR Local Indicator

Sample Hospital with 464 patients in previous year: Excess costs = £605,000 Excess deaths = 60

https://improvement.nhs.uk/resources/preventing-gram-negative-bloodstream-infections/

E. coli bloodstream infections

Linkage of patient-level antibiotic prescriptions to laboratory reports

E. coli bacteraemia rate, England, 2011/12-Q2 2017/18 (PHE mandatory surveillance)

Incidence of Escherichia coli bacteraemia is still increasing

•  For reports including risk factor data, about 50% of patients had a UTI reported as underlying source of infection

•  Need to investigate prescribing and resistance pattern in the community

E. coli urine isolates susceptible or resistant to recommended UTI treatment options, England 2016

0%

20%

40%

60%

80%

100%

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Nitrofurantoin Trimethoprim Co-Amoxiclav Quinolones* Mecillinam Fosfomycin

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Not tested Susceptible Non-susceptible *99% ciprofloxacin

ESPAUR Report 2017

How appropriate are antibiotic prescriptions for UTI in the community?


Linkage of patient-level antibiotic prescribing data to laboratory records

6 months before 1 Aug 2013 – 31 Jan 2014

6 months after 1 May 2014 – 31 Oct 2014

3 months 1 Feb – Apr 30 2014

PHE laboratory surveillance data All NHS numbers, all samples (e.g. urine, blood, sputum etc.)

NHS Business Services Authority Prescribing data

3 months 1 Feb 2014 – Apr 30 2014

Electronic prescribing system (EPS) only – captures about 20% of all prescriptions (does not include FP10 (green) forms that capture about 70% of all prescriptions)


15,417 NHS no. 22,461 antibiotic prescriptions

(tablet only) AND bacterial isolate from URINE/KIDNEY

8,593 NHS no. 10,928 prescriptions

7,328 Trimethoprim prescriptions 3,600 Nitrofurantoin prescriptions

74% susceptibility to Trimethoprim

89% susceptibility to Nitrofurantoin 60% resistance to Trimethoprim

Trimethoprim prescription 3,000 +ve urine isolates identified in time relation of +/-

14 day prescription

Nitrofurantoin prescription 1,200 +ve urine isolates identified in time relation

of +/- 14 day prescription

*InitialoutputLinking English antibiotic prescribing and microbiology data

Linkage results

Antibiotic prescription combination Row

Oct '13 Oct '14Trim R T T T T T T T T T T T RNitro S R

Trim T T R T T T T T T T T T TNitro S

Trim T R T T T T T T T T T T TNitro S

Oct '13 Aug '14 Oct '14Trim R S SNitro S N N N N N N N N N N R S

Trim R T TNitro N S N N N

Jul '14Trim T T T RNitro N N S

Trim T RNitro N S N

Dec '13Trim S T TNitro S N N N

Dec '13Trim S T T TNitro R N N

Sep '14Trim T T T RNitro N N S

1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Key: T Trimethoprim prescriptionN Nitrofurantoin prescriptionR Resistant urine isolateS Sensitive urine isolate

d)

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a)

j)

i)

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f)

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T T T T T T T T T T T

T N T N T

T N T N T

N N T T N

T N T T N

T N T T N

N N N N T N T

N N N N N N N N N N

T T T T T T T T T T T T


6

T

AfterBefore February 2014 March 2014 April 2014

Example patients (n=10):Timing of prescription/testing

Linking English antibiotic prescribing and microbiology data Linkage of patient-level antibiotic prescriptions to laboratory reports

Antibiotic prescription combination Row

Oct '13 Oct '14Trim R T T T T T T T T T T T RNitro S R

Trim T T R T T T T T T T T T TNitro S

Trim T R T T T T T T T T T T TNitro S

Oct '13 Aug '14 Oct '14Trim R S SNitro S N N N N N N N N N N R S

Trim R T TNitro N S N N N

Jul '14Trim T T T RNitro N N S

Trim T RNitro N S N

Dec '13Trim S T TNitro S N N N

Dec '13Trim S T T TNitro R N N

Sep '14Trim T T T RNitro N N S

1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Key: T Trimethoprim prescriptionN Nitrofurantoin prescriptionR Resistant urine isolateS Sensitive urine isolate

d)

c)

b)

a)

j)

i)

h)

g)

f)

e)

T T T T T T T T T T T

T N T N T

T N T N T

N N T T N

T N T T N

T N T T N

N N N N T N T

N N N N N N N N N N



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AfterBefore February 2014 March 2014 April 2014

1,742 antibiotic prescriptions had a +MSU with AST in the 30 days prior to the prescription date

1,002 trimethoprim prescriptions: •  57% of isolates were susceptible to trimethoprim •  43% of isolates were resistant to trimethoprim

740 nitrofurantoin prescriptions: •  81% of isolates were susceptible to nitrofurantoin •  19% of isolates were resistant to nitrofurantoin

X

30 day window

Linking English antibiotic prescribing and microbiology data

Antibiotic susceptibility test result available prior to prescription

The future is bright

•  Data linkage capabilities and projects increasing

•  Capacity is a major issue

•  Amount of work needed is often underestimated

•  Public engagement and Privacy concerns

Acknowledgements Rebecca Guy, Katherine Henderson, Sarah Gerver, Emma Budd, Alex Bhattacharya,

Alicia Roseelo Dean Ironmonger, Richard Puleston, Anne-Marie O’Connell, Katie Hopkins, Rachel Freeman, Neil Woodford, Russell Hope, Graeme Rooney, David Ladenheim, Miroslava Mihalkova, Katherine Henderson, Chris Fuller, Peter Stephens, Mehdi Minaji, Diane Ashiru-Oredope, Berit Muller-Pebody, Alan Johnson

Members of the ESPAUR oversight group, PHE AMR delivery board and ARHAI

Health Protection Research Units, PHE staff, NHS BSA, Rx info, Quintiles IMS, NHS Digital, NHS Trusts, GPs, CCGs, DH, CMO, NHS England, NHS Improvement.

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