- The Farr Institute was established in 2012 with £19 million in funding to conduct health informatics research using large electronic health databases. It includes centers in London, Scotland, Wales, and Manchester. - Examples are given of research using big data that would not otherwise be possible, such as studies of MMR vaccination and autism and the relationship between BMI and cancer risk. - Challenges of randomized trials include recruitment, generalizability, and costs, and electronic health records may help address these challenges through trials like one testing text message reminders for flu vaccines.

1. 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

2. Plan
• Big data and e health
• Examples of research
• Data quality
• The Farr Institute

3. UK Government: big data
Universities and Science Minister
Chancellor of the Exchequer
Prime Minister
Health Minister

4. Big data:
is it something new?
Two answers:
• No
• Yes

5. 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

6. 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

7. Examples

8. Measles mumps rubella vaccination
and autism
• 1998 Lancet paper: MMR vaccination might cause
autism
• MMR vaccine coverage fell internationally
• Measles outbreaks occurred

9. MMR coverage by time of 2nd birthday, England
NHS Immunisation Statistics, HSCIC
Study raises
concerns

10. 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)

11. 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

12. 72.0
74.0
76.0
78.0
80.0
82.0
84.0
86.0
88.0
90.0
92.0
94.0%MMRcoverage
Autism risk
published
MMR coverage by time of 2nd birthday, England
NHS Immunisation Statistics, HSCIC
Our study
published

13. • 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

14. 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

15. Bhaskaran K et al Lancet in press

16. Body mass index and cancer: a cohort study
of 5.2 million people
Bhaskaran K Lancet in press

17. Different causes
Bhaskaran K et al Lancet in press

18. Data quality:
myocardial infarction as an example

19. 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

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

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

22. 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

23. Electronic health data and evaluation

24. • 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

25. • 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

26. Can electronic health records help with randomised
trials?
• recruitment
• generalisable
• outcomes
• costs
 incorporate evaluation into everyday care?
Electronic health data and evaluation

27. What to do in the
absence of evidence?

31. What to do in the
absence of evidence?
randomise

32. 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

34. 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

35. • 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%

36. SMS text message reminders
• Widely used by practices
• Effective for appointment
reminders
• High mobile phone usage (93% for
age <60, 70% for age 60+)

37. 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”

38. 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

39. 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

41. 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

42. 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

43. “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

44. 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

45. Bringing together people
Inter-disciplinary: genomics, biostatistics, epidemiology, bioinformatics,
health informatics, computer science, social science etc.
Inter-institutional

46. William Farr
“Diseases are more
easily prevented than
cured and the first
step to their
prevention is the
discovery of their
existing causes”

47. •
• 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,

48. 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