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Introduction
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Transition to Acting
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Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
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Professor Liam Smeeth: Big Data, 30 June 2014
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
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
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
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
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
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
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
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
33.
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
40.
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
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