Using 'Big Data' to Estimate Benefits and Harms of Healthcare Interventions

Transcription

1 Using 'Big Data' to Estimate Benefits and Harms of Healthcare Interventions Experience with ICES and CNODES DAVID HENRY, PROFESSOR OF HEALTH SYSTEMS DATA, UNIVERSITY OF TORONTO, SENIOR SCIENTIST, INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES TORONTO, CANADA DISCLOSURES: NO COMPETING INTERESTS

2 New National Security Agency facility (Utah) Volume Velocity Variability Data capacity in the ze-abytes range Data processing at 100 petaflops or 100,000 trillion calcula9ons/sec 2

3 A lot of data terabyte (TB) $100 petabyte (PB) $100K exabyte (EB) $100M ze-abyte (ZB) $100B yo-abyte (YB) $100T 3

4 Health applications of Big Data 4

5 BIG HEALTH DATA (Donald Stuss OBI) 5

6 Wide data - large population health data-sets Many data are collected routinely transactional, tracking, quality assurance, mandatory reporting Linkage at the level of the individual is increasingly available Matching, linkage, de-identification and encryption processes have improved There have been significant improvements in the analysis of such data 6

7 Provider/ Facilities Physicians Hospitals Complex care Long-term care homes Home care Real-time (IKN) *HOBIC *Peritoneal Dialysis Health Services (IKN) Physician claims In-pt hospital discharge abstracts Emergency and ambulatory care abstracts Prescription drug claims (65 and over) Home care claims Rehab claims Long-term care visits In-patient mental health data People & Geography (IKN) People in Ontario since 1985 Unique individual anonymous # IKN Postal Code Conversion/ Geographical Population Estimates Canada Census Profiles Death register IKN=unique algorithm based on Ontario health card number Special Collections (IKN) Registries: cancer, stoke, CCN, *Birth outcomes) Federal immigration register Corrections *First Nation Metis Bio-informatics data Cell phone records, laboratory data, Clinical trial data Developmental Disabilities Primary Collected Data And Surveys (IKN) Derived chronic conditions (IKN) (using linked data) Diabetes Hypertension COPD CHF AMI Asthma IBD Registries DES/ICD Project Data- Set Presentation Title 7

8 COMMON GROUND Examples of using large population data-sets to measure harms of medication Presentation Title 8

9 Comparison of rofecoxib and diclofenac Major cardiovascular events RRR 1.0 ( 99% CI ) (18 studies) McGettigan and Henry 2011 PLOS Medicine 9 9

10 Hazards of pooling mortality data from a large number of small RCTs 10

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12 Canadian Network for Observational Drug Effect Studies (CNODES) Team grant investigators from 7 provinces BC, AB, SK, MN, ON, QC and NS Additional data purchased from UK (CPRD) and USA (Medicare). Total insured population ~ 44 million Funded by Health Canada and Canadian Institutes for Health Research Drug safety queries originate from Health Canada and provincial health plans Analyses are carried out in distributed fashion Turn around 4-8 months 12

13 CNODES: Measures to reduce bias Standardised protocols Inception cohorts, advanced analyses Pre-specified outcomes and analyses Project team leads lodge results with methods team lead without seeing results from other provinces Methods team led has sole responsibility for combining data to generate pan-canadian estimates Conflict of interest policies

14 Distributed Network (CNODES): High dose statins and acute renal injury Dormuth et al BMJ 2013;346:880 14

15 CNODES: PPIs and pneumonia *Adjusted RR by high dimensional propensity scores >4.3 million NSAID users; 98,000 started a PPI on the same day

16 CONTESTED GROUND Examples of using large population data-sets to measure benefits of interventions Presentation Title 16

17 Considerations 1. Increasing access to big data-sets 2. Data-sets are more comprehensive 3. Improving methods for analysis 4. Increasing acceptance and move away from rigid hierarchy of evidence 5. Cochrane Collaboration is extending the risk of bias tool to non-randomized studies 6. GRADE working group reviewing the incorporation of non-randomized studies Presentation Title 17

18 ConducYng RCTs within large administrayve data- bases Presentation Title 18

19 CIHR/SPOR Strategic Patient Orientated Research Co-funded by CIHR and the Ministries of Health, Research and Innovation in Ontario 2. In Ontario the SUPPORT network comprises 12 major research groupings (next slide) 3. One focus: PHRI and ICES will collaborate to create a data platform to enable a range of clinical, health systems and policy trials 4. Linked de-identified data will be made available securely to researchers across Ontario and later across Canada Presentation Title 19

20 Centres/Networks of the Ontario SPOR SUPPORT UNIT 20 20

21 Linking comprehensive registries to administrayve data Non- randomised data 21

22 Risk of the composite outcome (admission to hospital for heart failure, acute myocardial infarction or stroke), by angiotensin-receptor blocker used among older adult patients with diabetes. Antoniou T et al. CMAJ 2013;185: by Canadian Institute Medical for Clinical Association Evaluative Sciences

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24 Conclusions 1. Big Health Data are increasingly important in understanding benefits and harms of medical interventions 2. A possible hierarchy (high to low): 1. Randomized trials linked to administrative data/her 2. Studies using comprehensive (CED) clinical registry data linked to administrative data 3. Studies comparing two medications with the same indication from the same class (e.g. statin A vs statin B) 4. Studies comparing two medication from different classes (e.g. ACE vs. BB) with same indications 5. Studies of surgical vs. medical management (or surgical vs null treatment) Presentation Title 24

25 But We must remember that the big revolution in health has already occurred But lots of people don t benefit Providing access to those in need will deliver greater benefits than any new intervention Presentation Title 25

26 All cause mortality aged years M 66% reducyon since 1975 F 60% reducyon since 1975 M 45% reducyon since 1975 F 32% reducyon since 1975 Gary Whitlock h-p:// trends.org/