Big data, e health and the Farr Institute

Liam Smeeth

London School of Hygiene and Tropical Medicine

Thanks to: Harry Hemingway, Emily Herrett, Harriet Forbes, Ian Douglas, Krishnan Bhaskaran, Tjeerd van Staa, Ben Goldacre, Iain Chalmers and many others

Funding: Wellcome Trust, MRC, BHF, HTA

Plan

• Big data and e health

• Examples of research

• Data quality

• The Farr Institute

UK Government: big data

Universities and Science Minister

Chancellor of the Exchequer

Prime Minister

Health Minister

Big data: is it something new?

Two answers:

• No

• Yes

Big data: is it something new?

Yes

Computers mean that more health related data are available and can be linked together

Genomic and metabolomic data are available at a new scale and new level of detail

The computerisation of health related data and the -omic revolution extraordinary opportunities for research

• Better research

• More efficient research

• Research that couldn’t otherwise be done

Examples

Measles mumps rubella vaccination and autism

• 1998 Lancet paper: MMR vaccination might cause autism

• MMR vaccine coverage fell internationally

• Measles outbreaks occurred

MMR coverage by time of 2nd birthday, England NHS Immunisation Statistics, HSCIC

Study raises concerns

Measles mumps rubella vaccination and autism

• United Kingdom Medical Research Council funded case-control study

• Similar large studies in USA and Denmark

• Only possible because of electronic health records (big data)

Effect.5 .75 1 1.25 1.5 2

Combined

Current study

DeStefano et al

Madsen et al ASD

Madsen et al autism

Effect size (95% CI)

0.92 (0.68 – 1.24)

0.83 (0.65 – 1.07)

0.93 (0.66 – 1.30)

0.86 (0.68 – 1.09)

0.87 (0.76 to 1.001)

Decreased risk Increased risk

Smeeth et al, Lancet 2004;354;963-9

MRC study

72.0

74.0

76.0

78.0

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0%

MM

R c

ove

rage

Autism risk published

MMR coverage by time of 2nd birthday, England NHS Immunisation Statistics, HSCIC

Our study published

• Cohort study within the Clinical Practice Research Datalink (CPRD)

• 5.2 million people

• 33.9 million person-years of follow-up included

• 184,594 people (3.5%) experienced one of the 21 commonest cancers

Body mass index and cancer

1980 1984 1988 1992 1996 2000 2004 2008 2012 2013

Age-standardised prevalence of overweight and obesity ages ≥20 years, by sex, 1980–2013

Ng M et al Lancet 2014

Bhaskaran K et al Lancet in press

Body mass index and cancer: a cohort study of 5.2 million people

Bhaskaran K Lancet in press

Different causes

Bhaskaran K et al Lancet in press

Data quality: myocardial infarction as an example

Capture of acute myocardial infarction events in primary care, hospital admission, disease registry and national mortality records

Emily Herrett, Anoop Dinesh Shah, Rachael Boggon, Spiros Denaxas, Liam Smeeth, Tjeerd van Staa, Adam Timmis, Harry

Hemingway

BMJ 2013; 346; f2350

Herrett E et al. BMJ 2013;346:bmj.f2350

Incidence

Incidence

Herrett E et al. BMJ 2013;346:bmj.f2350

Diagnostic validity • Around 90% of patients with an ST elevation

myocardial infarction recorded in the national registry (MINAP) had raised cardiac enzymes or characteristic EKG findings, but….

• Registry (an audit) incomplete

• Hospital Episode Statistics more complete

• Primary care clinical record much more complete: but all three together best

• Cross validation suggested primary care diagnosis had a high validity

Electronic health data and evaluation

• Generalisability or external validity

– adherence to intervention

– clinical care received

– co-morbidities

– co-prescriptions

– selected groups of participants

– absolute risks and benefits different

Poor guides to clinical practice and policy

Challenges for randomised trials 1

• Recruitment: inadequate sample size

– review of all 114 multicentre trials from two major UK public funders over seven years

– only 31% of trials achieved their recruitment target

– over half had to be awarded an extension Campbell MK et al Health Technol Assess 2007

• Loss to follow-up: leading to bias

• Costs: up to $10,000 per participant not unusual

Challenges for randomised trials 2

Can electronic health records help with randomised trials?

• recruitment

• generalisable

• outcomes

• costs

incorporate evaluation into everyday care?

Electronic health data and evaluation

What to do in the absence of evidence?

What to do in the absence of evidence?

randomise

Are the patient and the doctor or the policy maker and manager

happy to randomise?

Option A Option B

100% follow-up: totally electronic records based

Is there an absence of clear evidence?

Results included in the evidence base

Text messaging reminders for influenza vaccine in primary care (TXT4FLUJAB)

A randomised controlled trial using electronic health records

Emily Herrett, Tjeerd van Staa, Liam Smeeth

• Targets for the elderly are reached

• Targets for patients under 65 at risk are missed

• Last year 51.6% of eligible patients were vaccinated compared to a 75% target

Influenza vaccine uptake Vaccine uptake, 2011/12

0

10

20

30

40

50

60

70

80

% vaccinated

UK government target: 75%

SMS text message reminders

• Widely used by practices

• Effective for appointment reminders

• High mobile phone usage (93% for age <60, 70% for age 60+)

TXT4FLUJAB methods

• Design: cluster randomised trial using English primary care electronic health records

• Intervention: text message vaccine reminder to patients under 65 in risk groups:

– “Hello Fernanda, to reduce your risk of serious health problems from flu we recommend vaccination. Call 0207 927 2837 to book. The London medical practice”

Consenting practices

randomised

Text messaging group:

60 practices

≈ 600,000 people

SMS reminder to patients

under 65 at risk

Standard care group:

60 practices

≈ 600,000 people

Seasonal flu campaign as

planned

Practices invited to

trial

Researchers ascertain exposure and outcome data

remotely from practice records

TXT4FLUJAB costs

• Total costs to date: £50,000

• Cost per clinic: £200

• Average 1400 patients per clinic receive intervention or control: about 200,000 patients

• Likely total cost: £100,000

Cost per patient: £2 per patient included

In 2012, four Health Informatics Research Centres were

awarded by a consortium of 10 United Kingdom

funders led by the Medical Research Council

Our Story

Farr London

Farr Scotland

Farr at Swansea, Wales

Farr N8 Manchester

Strengthening health informatics research

• MRC coordinated 10-partner £19m call for e-health informatics research centres across the UK

Cutting edge research using data linkage

capacity building

• Additional £20m capital to create Farr Institute

• UK Health Informatics Research Network

Coordinate training, share good practice and

develop methodologies

Engage with the public, collaborate with industry

and the NHS

“To harness health data for patient and public benefit

by setting the international standard in the use of

electronic patient records and related data for large-

scale research.”

Our Vision

Basic discoverie

s

Proof of concept

(Experimental medicine)

Clinical Trials

Quality and

outcomes research

Health gain

What are the aims of Farr London?

= research along the translational pathway

+

Farr Tools: Informatics methods

Farr People: Capacity development

Farr Curated data: Research-ready cohorts with 10m person years now

Reverse translation

Bringing together people

Inter-disciplinary: genomics, biostatistics, epidemiology, bioinformatics,

health informatics, computer science, social science etc.

Inter-institutional

William Farr

“Diseases are more

easily prevented than

cured and the first

step to their

prevention is the

discovery of their

existing causes”

•

• Photo, quote

• And a

William Farr’s grand challenge

Health records ‘An arsenal

that the genius of English

healers cannot fail to turn to

account’

William Farr 1874

supplement to 35th annual report

of the Registrar General,

What is needed?

• Expertise

• Novel methods and approaches

• Ensuring high data quality

• Confidentiality and security of data

An expectation by patients/citizens, clinicians and policy makers that research and evaluation is a normal - in fact a necessary - part of health care and policy