Comparative Effectiveness Research
f Children
for
Child
with
ith Chronic
Ch
i Conditions:
C diti
A
Canadian Perspective
Astrid Guttmann MDCM, MSc
Division of Paediatric Medicine, Hospital for Sick Children
Department of Paediatrics
Paediatrics, Faculty of Medicine
Medicine, University of Toronto
Institute for Clinical Evaluative Sciences
Department of Health Policy, Management and Evaluation, University of Toronto
Enhancing the effectiveness of health care
for Ontarians through research
Disclosure
• Astrid Guttmann has no conflicts of
interests to disclose
Main Messages
•
•
•
•
•
Nott all
N
ll important
i
t t questions
ti
will
ill be
b on the
th “top
“t 20”
CER or policymaker list
 Need to work to get children’s issues on the
radar
“Complex”
Complex interventions may not lend
themselves to pragmatic trials but “new” ones
are ideal candidates
Observational research will have a role
 but depend
p
on best practice
p
existing
g
 need strong methods
Admin
d
data is
s a sta
starting
t g po
pointt for
o many
a y quest
questions
o s
As in US researcher capacity a BIG issue
General Landscape of CER* in Canada
• Provinces have healthcare jurisdiction
• Ontario as an example
• Ministry of Health


Medical Advisory Secretariat
• Inform policy/funding decisions on new interventions
or “investments” in disease groups
• Systematic reviews and commission field studies
Ontario Drug Benefit Plan
• Funding decisions on drugs to be covered
* We used to call this hsr…
Enhancing the effectiveness of health care
for Ontarians through research
Ontario landscape cont’d
• Research institutes – ICES
Funded by MOH but independent
 Most administrative data sets housed
and linked – for use for research and
informing policy
 Universal healthcare system –
population-based

Enhancing the effectiveness of health care
for Ontarians through research
Examples
E
l off research/policy
h/ li
collaborations
•
•
Prospective studies with funding
decisions riding on results
Implantable cardiac defibrillator

Field study, admin data component
• Wound Care Trial (home care)***

Stepped
pp wedge
g design
g
***
• Ideal CER
Randomized design
 Population
Population-based
based
 Knowledge user (policymaker) involved
in design integrated knowledge
translation
 Funded
F d d by
b knowledge
k
l d user with
ith
complete researcher independence

Oth forms
Other
f
off CER – observational
b
ti
l
studies
• Evaluations of current
policies/interventions
Drugs – post marketing surveillance
 Cardiac stents

• Mixed
Mi d methods
th d

Capitalizing on the “natural experiment”
of variations in care provision
• Order sets in ED asthma care for children
Advantages in Canada
• Strong relationships between MOH
•
•
and
d researchers
h
–>
> evaluation
l ti
Potential to “control” intervention
Administrative data for use within
privacy legislation
population based
 relatively complete across sectors
 Ability to add other data

Challenges
• R words – randomize, research
• If policies “rolled” out rather than
•
•
y hard to scale back
“trialed” – very
New data collection almost always
needed
Observational – confounding by
indication – need STRONG
METHODS (propensity scores, IV)
CER A
Activities
ti iti around
d Child
Children with
ith
Chronic Conditions
• Focus on validating disease definitions in
administrative data



Asthma To et al, 2005 Pediatr Allergy Immunol
Diabetes Guttmann et al, 2009 Pediatric Diabetes
Inflammatory bowel disease Benchimol et al 2009 Gut
• Aligning with current governmental
priorities


Technology dependent
Complex Chronic Conditions
Top “CER” issues in Ontario
• Models of care

Care coordination for children with
p
care needs
complex
• Medically fragile
• Complex
p
obesity
y
• Multi-diagnosis mental health

Transition care for youth to adult care
Mean age at death, 1950-2003
Age ((yrs)
50
45
5
All CA
Spina bifida
CHD
Down's
40
35
30
25
20
15
10
5
0
1950
1960 of health
1970
Enhancing
the effectiveness
care
1980
1990
2000
for Ontarians through research
R.Wilkins Health Canada 2007
Transition
T
iti to
t Adult
Ad lt Care
C
for
f
Youth
Y th
with Chronic Conditions
• Multi-facetted issue
• Care
Physician
 Multi-disciplinary team

• Financing

Devices,
e ces, ot
other
e se
services
ces
• Timing
• Lack of evidence on best practice

Freed et al 2006
Main transition program services
• Disease-specific model
• Subspecialty model
• Adolescent health model
• Primary Care model
Ontario Pediatric Diabetes Network
Methods
• Survey of modes of transition
• Administrative health data for outcomes
•
•
(admissions, eye care)
Compared outcomes post- to pretransition
Assumed that allocation to diabetes
centre based on location and within
patient analysis minimized confounding…
Youth with DM by mode of transition
Method of Transition
N
%
Change of MD and DM team
969
64
Change of MD only
383
25
No change
g
29
2
Change of DM Team only
65
4
Change of MD, no DM team follow up
61
4
Relative
R
l ti risk
i k off admissions
d i i
for
f diabetes
di b t
complications after transition to adult care
Method of Transition
Relative Risk (95% CI)
New MD and team
1.00
New MD only
0.79 (0.52-1.18)
No change
0.09 (0.003-0.71)
New Team only
0 23 (0
0.23
(0.005-0.79)
005 0 79)
Future Directions
• Collecting more granular data on
•
transition programs – add other
p
provinces
Evaluate network itself

O l Canadian
Only
C
di province
i
with
ith one
• ?improved access to specialized care,
b tt outcomes
better
t
Current or Planned Work
• Insulin pumps for children and youth with
T1DMm
• Care coordination for children with
complex
p
conditions
• Best practice for improving ED care for
children with asthma -- needs a trial
• Best care for IBD – including new data on
disease severity +/- genotypes
• Whatever
Wh t
is
i mostt policy
li relevant
l
t and
d
where there is researcher interest
Main Messages
•
Not all important questions will be on the “top 20”
CER or policymaker list

•
•
“Complex” interventions may not lend
themselves to pragmatic trials but “new” ones
are ideal candidates
Observational research will have a role


•
•
Need to work to get children’s issues on the radar
but depend on best practice existing
need strong
g methods
Admin data is a starting point for many questions
As in US researcher capacity a BIG issue