Big data, e health and the Farr Institute

Transcription

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 Prime Minister Universities and Science Minister Health Minister Chancellor of the Exchequer

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

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 size (95% CI) Madsen et al autism 0.92 ( ) Madsen et al ASD 0.83 ( ) DeStefano et al 0.93 ( ) MRC study Current study 0.86 ( ) Combined 0.87 (0.76 to 1.001) Effect Decreased risk Increased risk Smeeth et al, Lancet 2004;354;963-9

12 % MMR coverage Autism risk published Our study published MMR coverage by time of 2 nd birthday, England NHS Immunisation Statistics, HSCIC

13 Body mass index and cancer 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

14 Age-standardised prevalence of overweight and obesity ages 20 years, by sex, 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 Incidence Herrett E et al. BMJ 2013;346:bmj.f2350

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 Challenges for randomised trials 1 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

25 Challenges for randomised trials 2 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 Loss to follow-up: leading to bias Campbell MK et al Health Technol Assess 2007 Costs: up to $10,000 per participant not unusual

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

27 What to do in the absence of evidence?

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31 What to do in the absence of evidence? randomise

32 Is there an absence of clear evidence? 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 Results included in the evidence base

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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 Influenza vaccine uptake Vaccine uptake, 2011/12 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 % 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 to book. The London medical practice

38 Practices invited to trial Consenting practices randomised Standard care group: 60 practices 600,000 people Seasonal flu campaign as planned Text messaging group: 60 practices 600,000 people SMS reminder to patients under 65 at risk 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

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41 Our Story In 2012, four Health Informatics Research Centres were awarded by a consortium of 10 United Kingdom funders led by the Medical Research Council

42 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 Farr London Farr Scotland Farr at Swansea, Wales Farr N8 Manchester

43 Our Vision 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 largescale research.

44 What are the aims of Farr London? = research along the translational pathway Reverse translation + Basic discoverie s Proof of concept (Experimental medicine) Clinical Trials Quality and outcomes research Health gain Farr Tools: Informatics methods Farr People: Capacity development Farr Curated data: Research-ready cohorts with 10m person years now

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

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

47 William Farr s grand challenge Photo, quote And a Health records An arsenal that the genius of English healers cannot fail to turn to account William Farr 1874 supplement to 35 th 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