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Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
Professor Liam Smeeth: Big Data, 30 June 2014
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Professor Liam Smeeth: Big Data, 30 June 2014

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In this slideshow, Liam Smeeth, Deputy Director and Head of Department of Non-Communicable Disease Epidemiology of the London School of Hygiene and Tropical Medicine discusses big data, e-health and …

In this slideshow, Liam Smeeth, Deputy Director and Head of Department of Non-Communicable Disease Epidemiology of the London School of Hygiene and Tropical Medicine discusses big data, e-health and the Farr Institute.

Liam Smeeth spoke at the Nuffield Trust event: The future of the hospital, in June 2014.

Published in: Health & Medicine
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  • 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?
  • 28. What to do in the absence of evidence? randomise
  • 29. 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
  • 30. 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
  • 31. • 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%
  • 32. SMS text message reminders • Widely used by practices • Effective for appointment reminders • High mobile phone usage (93% for age <60, 70% for age 60+)
  • 33. 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”
  • 34. 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
  • 35. 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
  • 36. 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
  • 37. 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
  • 38. “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
  • 39. 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
  • 40. Bringing together people Inter-disciplinary: genomics, biostatistics, epidemiology, bioinformatics, health informatics, computer science, social science etc. Inter-institutional
  • 41. William Farr “Diseases are more easily prevented than cured and the first step to their prevention is the discovery of their existing causes”
  • 42. • • 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,
  • 43. 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

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