Big data and better health outcomes, the journey to the Ministry of Health virtual information centre, viewed from a research perspective. Presented by Professor Tony Blakely, University of Otago, Wellington, at HINZ 2014, 12 November 2014, 8.30am, Plenary Room

1. Big data and better health outcomes,
the journey to the virtual health
information network.
Researcher perspective
Burden of Disease Epidemiology, Equity
and Cost-Effectiveness Programme
1
Professor Tony Blakely

2. Structure
1. Examples of research using NHI-linked health datasets:
– BODE3: Costing from HealthTracker; tobacco tax simulation
– PREDICT and VIEW (Jackson, University of Auckland)
– Pharmaco-epidemiology (Parkin, Otago – Dunedin)
– Cardiovascular Genomics (Cameron, Otago – Christchurch)
– Personalised Cancer Medicine (Guillford , Otago – Dunedin)
2. Vision: moving from bespoke record linkage to a data
platform. Virtual Health Information Network
3. Options analysis
2

3. HealthTracker – linked health data
• hospital costs paid by the Ministry or DHBs (case mix cost
weights)
• outpatient costs (contracted purchase units)
• GP visits (average capitation cost only)
• general medical subsidy for GP visits outside enrolled PHO
• emergency department triage level contracted purchase unit
cost for event
• community pharmacy, and more recently hospital pharmacy
costs (excluding non-subs medications)
• lab tests funded by Vote:Health.
3

4. HealthTracker colon cancer costs
4
Females age 62.5 yrs by time pre/post-diagnosis
$20,000
$15,000
$10,000
$5,000
$-
6-11
mth
1-5
mth
<1
mth
< 1
mth
1-5
mth
6-11
mth
12-23
mth
24+
mth
6-11
mth
1-5
mth
<1
mth
Pre-Diagnosis Post-Diagnosis, & not within yr of
death
Pre-Death from
cancer
Cost per person month

5. Simulation modelling - How we do it?
Data inputs:
- baseline, much of which NHI-linked
- Intervention (e.g. price elasticity for
tobacco tax)
Simulation Models:
- Markov, discrete event, multistate
lifetable
Cost-effectiveness
INPUTS
MODEL
OUTPUTS

6. Tobacco tax: Timing of health gains
6
5
4
3
2
1
0
QALYs gained (Thousands)
Year
Non-Māori, QALYs
gained,
undiscounted
Māori, QALYs
gained,
undiscounted
Non-Māori, QALYs
gained,
discounted 3%
Māori, QALYs
gained,
discounted 3%
PRELIMINARY RESULTS – will change a little with pending improvements. Not for citation 6

7. Tobacco tax: Timing of cost savings
100
80
60
40
20
0
Net health cost savings (NZD, Millions)
-20
-40
Year
Non-Māori, Health
system costs
averted,
undiscounted
Māori, Health
system costs
averted,
undiscounted
Non-Māori, Health
system costs
averted,
discounted 3%
Māori, Health
system costs
averted,
discounted 3%
PRELIMINARY RESULTS – will change a little with pending improvements. Not for citation 7

8. VIEW (Vascular Informatics using Epidemiology & the Web):
better risk predictionbetter risk managementreduced inequalities
Past projects:
2000-2010
Current programme:
2011-2016
Future programme:
2017-
PREDICT PHO
Cohort
Hospital VIEW ANZACs Cohort (ongoing)
(now includes every ACS admission in NZ)
National & Regional routine
health databases
Comprehensive
National
vascular ‘VIEW’
of all adult
NZ’ders
PHO VIEW PREDICT Cohort (ongoing)
N=400,000 in 2014
(includes approx 1/3 NZ PHOs patients
Establish Patient VIEW
all linkage uses encrypted ‘eNHIs’ (portal) Cohort

9. Simvastatin dose and rhabdomyolysis
(Pharmacoepidemiology: Lianne Parkin et al, Otago
Dunedin)
• Case-control study nested in cohort of
N=313,552 ‘NHI’ people who initiated new
episode of simvastatin
• Internationally:
– Largest study of muscle injury in a general population
of simvastatin users
– First to estimate risks for 20mg & 40mg simvastatin
• National relevance:
– Simvastatin first line statin in national CVD guidelines
• Result: OR Odds ratio for current use 40mg vs
20mg: 5.3 (95% CI 1.9 – 15.0)

10. Cardiovascular Genomics
Vicky Cameron, Christchurch
1000s of Patients
(Stratified by disease type)
Samples - 1000s of Healthy Controls
• Blood
• DNA
• RNA
• Urine
• Cell Lines
Genome-wide
NGS
3 billion bases,
x100 coverage
Gene
Expression
Arrays
20,000 genes
DNA
Methylation
450,000 probes
per sample
MicroRNA
arrays
2000 human
microRNAs
Proteomics
Mass spec
4000
proteins
Clinical Characteristics and Outcomes
1000s of variables, and time points
Clinical Characteristics and Outcomes
1000s of variables, and time points
Integrated Bioinformatics
(Hardware, software and expertise)
New Risk Profiling and Treatment Strategies
Enhanced
patient care
pathways
Enhanced
prevention
strategies

11. Personalised cancer medicine – Parry Guilford, Dunedin
International goal: translate human genome and transcriptome
data into better methods to personalise cancer treatment
• Because of the extreme heterogeneity of human cancer at the
genetic level, treatment of each patient effectively becomes a
trial where n=1
• Personalised cancer medicine will ultimately be achieved by
acquiring and integrating the genomic, transcriptomic and clinical
data of each patient and submitting it to open, international
databases. These databases will ensure that the treatment of
every patient leads iteratively to the better treatment of the next
patient
New Zealand’s role
• NZ is well positioned to contribute to this global effort through
unique ability to provide integrated, national-level clinical and
genomic data

12. Structure
1. Examples of research using NHI-linked health datasets:
– BODE3: Costing from HealthTracker; tobacco tax simulation
– PREDICT and VIEW (Jackson, University of Auckland)
– Pharmaco-epidemiology (Parkin, Otago – Dunedin)
– Cardiovascular Genomics (Cameron, Otago – Christchurch)
– Personalised Cancer Medicine (Guillford , Otago – Dunedin)
2. Vision: moving from bespoke record linkage to a data
platform. Virtual Health Information Network
3. Options analysis
12

13. From bespoke linkage to a linkage platform
• We have done well in NZ with separate bespoke linkage
projects. But we could do better in the future.
• Limitations of bespoke linkage (from Frank Sullivan: SHIP)
– Major staffing and training overhead
– No Auditability
– Code reuse limited
– Un-scalable
– Poor Reliability and Reproducibility
– Turn round >1 month
– Poor Bug Tracking
– No automated versioning
13

14. Vision: Cancer Collections Framework
Hewlett Packard Report for NZHIS and NSU, 2006
14
Cancer Collections:
5 Year Vision
Information/Reporting
Year 1 Year 3 Year 5
System
Implementation and
roll out of Front end
Reporting tool
Link to accessible
aggregated data: PHI;
PHO & DHB
performance indicators
Data Collection/Structure Data Access
Current
State
Links created
between
Mortality and
NZCR
Increase data
accessed from
current systems (no
new data collection).
Set standard
reports for Key
Answers using
existing reporting
tools
Use current reporting/
data extraction channels
(e.g. PHO Performance
Indicators site).
Add Collection
from NNPAC -
“count”
Explore
links to
palliative
care data
Develop front-end
Reporting tools
within existing NZHIS
reporting channels
Some additional data
access to authorised
users for systems
providing key data
First phase of NCMD
implemented (2 cancer
specialities/tumour sites)
Trial of Information
Laboratory for users to
access linked data from
cancer collections and
related datasets
A menu of pre
-
structured
reports made available. There
Is also some ad-hoc report
ability for some datasets
Facilitate national view by
using NCMD with links from
NMDS, NZCR and Mortality
Link to PHO
enhanced data
capture
Explore data
from new
sources eg
Private clinics,
community
services
NCMD Business
Case developed
and approved
Year 3
Year 1
Year 1
Add clinical
information from
NNPAC and
community
Year 2
Year 2
Links created
between BSA
and NZCR
Automate links to
improve speed of
access
Establish a process to increase
cancer related capture eg
palliative care, primary care,
private, by working with other
directorates and developments
within the Ministry
New NCSP
Information
System with
automated links
Links created
between
NCSP and
NZCR
Year 1
Links between
NZCR and
Mortality are
automated

15. Vision: Cancer Collections Framework
Hewlett Packard Report for NZHIS and NSU, 2006
15
User-friendly
interface for
Information search
and reporting
Data Laboratory:
Able to undertake
complex adhoc
analysis in a supported
or facilitated, real or
virtual environment Set of standard
reports readily
available (recent
data) with limited
ability to generate
ad - hoc reports.
Datawarehouse for high-speed
integrated analysis and
sophisticated research
Loosely linked
System with
search capability
Reports available
to answer key
critical questions
based on data 2+
years old
On - line guide to
answering key
questions with links
to particular reports
Front end search
and compilation
tool with links to
key databases
Widespread
Authorised Data
Access
(Authorised) on - line
access through to
base data (that meets
standard alignment
requirements)
On - line access to
filtered/pre - formatted
data and pre -
structured reports
(real - time/recent data)
On - line
access to
standard
reports only
Data held in national collections
linked, with the ability to add data
fields where extraction or linking
is not onerous and is “ value-adding
”
All information linked
by NHI through
cancer continuum for
individuals diagnosed
with cancer
No change to
current data
structures – some
additional data
collected
Uniform Data
Collection/Structure

16. Making vision happen challenging
• Still not there, many committees later
• Researcher/clinician/manager enthusiasm, hits reality of:
– Data dictionaries and definitions
– Systems of collecting the data – Who? How? When?
– Reliability and validity of data
– Fitting it in with existing data
– Cost
– Linking it with biological samples and trial networks
– Demonstrating value
– Privacy, confidentiality and ethics
16

17. Challenge: Champions
• Census-mortality and census-cancer linkage would not
have happened (as soon) without:
– Vision and championing of the then Government Statistician
– An emerging researcher looking for a PhD
• HealthTracker would not have happened (as soon)
without drive of staff within Ministry
17

18. Challenge: Capacity
• Capacity needed to assemble and maintain big data…
• …. but also to make good use of it:
– Provision to likely users
– Users capable of using it well, e.g.:
• Longitudinal data analyses
• Comparative effectiveness research, econometric and epidemiological
skills
• Funding
18

19. Challenge: Cost
• New Zealand is a small country:
– May cost just as much to run a birth cohort study in New
Zealand as Australia to achieve internal validity (e.g. sample
size)….
– …. or put another way New Zealand does not have economies
of scale.
• Any scaling up has to be done involving Government,
multiple universities
19

20. Opportunities
NHI
E.g. HealthTracker, virtual access to data, joining in clinical data, etc.
20

21. Vision
• Turn NHI linked data into information:
– Extend what we currently do
– Facilitate new wave of research with a dedicated ‘centre of
excellence’ that:
• Maintains and curates data in form for research
• Provides easy access for researchers
• Perhaps follows the model, or sits under/alongside, the IDI
• Provides analytical expertise that researchers/DHBs/MoH buy time from
– Enables deeper appended data e.g.:
• Biobanks, clinical and primary care collections
• A network of MoH IT (lead=Osborne) and academics
(lead=Blakely) is currently meeting periodically to explore ‘where
to next’. Virtual Health Information Network.
21

22. Structure
1. Examples of research using NHI-linked health datasets:
– BODE3: Costing from HealthTracker; tobacco tax simulation
– PREDICT and VIEW (Jackson, University of Auckland)
– Pharmaco-epidemiology (Parkin, Otago – Dunedin)
– Cardiovascular Genomics (Cameron, Otago – Christchurch)
– Personalised Cancer Medicine (Guillford , Otago – Dunedin)
2. Vision: moving from bespoke record linkage to a data
platform. Virtual Health Information Network
3. Options analysis (My perspective only, up for debate)
22

23. Option 1: Current model
• Researchers approach Ministry and other providers for
data to link as required.
• Analysis:
– Last century approach
– Lots of duplication
– Poor capacity
– Inefficient
– Will not put NZ research at forefront internationally
– Will not satisfy genomics and personalised medicine
• Reject
23

24. Option 2: MoH funded group
• In-house project in MoH to created living linked NHI
system of data
– i.e. HealthTracker, but maintained and resourced with
additional datasets included (e.g. regional registries)
• Analysis:
– Too risky – Ministry staff likely to be pulled to other work
– Not co-owned by researchers
• Reject
24

25. Option 3: Research Group in 1 University
• Researcher (e.g. me) secures something like CoRE
funding to build and maintain linked platform:
– i.e. HealthTracker, but maintained and resource with additional
datasets included (e.g. regional registries)
• Analysis:
– Will not secure buy-in of researchers in other Universities
– Perhaps not as likely to be used by Ministry and DHB staff for
routine analyses and reporting
• Reject
25

26. Option 4: Institute/Network co-governed by
multiple Universities and MoH
• Follow SHIP (Scotland) and Ontario precedents, perhaps
alongside SNZ Integrated Data Infrastructure
• Analysis:
– Should get more buy in from multiple stakeholders
– Requires serious funding (much of it shifted), governance, etc
• Investigate further
• Options within option:
– Centre versus network; core staffing for routine work and
‘servicing’; researchers buy time/services in step packages
26

27. Summary
• New Zealand has amazing health data
• New Zealand is a small country – one niche of
international research excellence we can occupy is to
really harness our NHI-linked data
• Such an initiative could/should also service DHB- and
Ministry-required analyses and reporting, without
duplication and doubt
• Now is a good time to put wheels under this vision:
– Ministry 5 year plan; National Science Challenges
27

28. Big data and better health outcomes,
the journey to the virtual health
information network.
Researcher perspective
Burden of Disease Epidemiology, Equity
and Cost-Effectiveness Programme
28
Professor Tony Blakely