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Health Care Stories are Good for You
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Health Care Stories are good for you
Sharon Manson Singer, EvidenceNetwork.caSteve Buist, Hamilton Spectator
Jennifer Verma, CHSRF
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Introduce EvidenceNetwork.ca Talk about the Hierarchy of Evidence What makes a good health story? A bad
one? Top ten questions to ask of health experts
about their research Where to go for data How to assess the quality of data Last word to you the audience
Overview
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EvidenceNetwork.ca links journalists with health policy experts to provide access to credible, evidence-based information.
What does EvidenceNetwork.ca do?
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EvidenceNetwork.ca is a non-partisan, web-based project funded by the Canadian
Institutes of Health Research and the Manitoba Health Research Council to make the
latest evidence on controversial health policy issues available to the media.
What is EvidenceNetwork.ca?
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The Canadian Health Accord is scheduled for renegotiation in 2014. Canadians will have to make decisions about many complex health policy issues, including;
• Aging population impact • Rising drug costs • Health care accessibility • Private sector financing/delivery • User fees • Sustainability of the healthcare system
EvidenceNetwork.ca is committed to working with the media to build a healthy dialogue around Canadian healthcare.
Why do we do it?
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Hierarchy of Evidence – Top Tier
Type of Study Expected Results
Systematic Review and meta-analysis
Use upper tier studies in a synthesis of research findings
Strongest evidence – only as good as the underlying studies
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Hierarchy of Evidence – Upper Tier
Type of Study How good is it?
Randomized experiments
Natural experiments
Well designed with sufficient sample size
High quality source of exogenous variation generating comparison group
Well designed pre and post measures
Analytical techniques are appropriate
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Hierarchy of Evidence –Middle Tier
Type of Study How good is it?
Some control in the assignment of treatment
Correlational studies
Limited source of exogenous variables or some control of selection or process
Well designed pre and post measures
Appropriate data with large sample
Reasonable approach to estimating counterfactuals
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Hierarchy of Evidence – Lower Tier
Type of Study How good is it?
Studies without a comparison group
Participant Satisfaction
Expert Opinions
Credible case selection with explicit causal logic model
Quality outcome measures
Collect feedback from participants on quality of intervention
Respected individuals or organizations with explicit rationale for opinion
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Hierarchy of Evidence – Lower Tier
Type of Study How good is it?
Exploratory case studies
Less credible or explicit case selection criteria, theory of change or outcome measure(s)
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Examples of bad science, dubious science or no
scienceSteve Buist, Investigations Editor
The Hamilton Spectator
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Examples of bad science, dubious science or no
scienceLuigi Di Bella and the “miracle” cure for cancer
(Pulling for the underdog . . .)
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Examples of bad science, dubious science or no
scienceAutism and the MMR vaccine
(Science is a lot easier if you just make it up . . .)
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Examples of bad science, dubious science or no
scienceMultiple sclerosis and “liberation therapy”
(The underdog tale, with a modern-day social media twist . . .)
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Examples of bad science, dubious science or no
scienceClimate change and the human touch
(What makes for a balanced story . . .)
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10 questions to consider when writing about
science
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10 questions to consider when writing about
science1. Who is conducting the science?
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10 questions to consider when writing about
science1. Who is conducting the science?2. Who is paying for the research?
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10 questions to consider when writing about
science1. Who is conducting the science?2. Who is paying for the research?
3. Who is paying the researcher?
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10 questions to consider when writing about
science1. Who is conducting the science?2. Who is paying for the research?
3. Who is paying the researcher?4. Where are the results being published?
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10 questions to consider when writing about
science1. Who is conducting the science?2. Who is paying for the research?
3. Who is paying the researcher?4. Where are the results being published?5. What was the population being tested?
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10 questions to consider when writing about
science6. What was the sample size?
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10 questions to consider when writing about
science6. What was the sample size?
7. How significant are the results?
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10 questions to consider when writing about
science6. What was the sample size?
7. How significant are the results?8. What do other people think, and do those people have their own conflicts of interest?
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10 questions to consider when writing about
science9. How do these results fit into the context of what’s already known?
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10 questions to consider when writing about
science9. How do these results fit into the context
of what’s already known?10. Are there opposing viewpoints and how
much weight should those viewpoints be given?
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Making measures meaningful:Finding and interpreting
healthcare dataJenn Verma, Director,
Collaboration for Innovation and Improvement
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mythbusters USING EVIDENCE TO DEBUNK COMMON MISCONCEPTIONS IN CANADIAN HEALTHCARE
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Data, data…everywhere
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THE LATEST RESEARCH SHOWS THAT WE REALLY SHOULD DO SOMETHING
WITH ALL THIS RESEARCH
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In a review of World Health Organization (WHO) and World Bank recommendations on five topics (contracting, healthcare financing, HHR, tuberculosis control and tobacco control): ◦ 2/8 publications cited systematic reviews;◦5/14 WHO and 2/7 World Bank recommendations
were consistent with both the direction and nature of effect claims from systematic reviews.
Hoffman SJ, Lavis JN, Bennett S. 2009. The use of research evidence in two international organizations’ recommendations about health systems. Healthcare Policy
9(1): 66-86.
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High-quality and
performance health
systems
Effective
Accessible
Safe
Equitable
Patient-centredEfficient
Integrated
Appropriately
resourced
Focused on
Population Health
35
Adapte
d w
ith p
erm
issi
on f
rom
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ualit
y O
nta
rio
(20
11
)
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F/P/T Jurisdictions
P/T Ministries of Health
P/T Health Regions
Statistics Canada
Public Health Agency of Canada
Health Canada
Provincial Health Quality Councils
NL Centre for Health Information
NB Health Council
Commissaire à la santé et au bien-être du Québec
Health Quality Ontario
MB Institute of Patient Safety
Health Quality Council SK
Health Quality Council AB
BC Patient Safety and Quality Council
Subject Matter Experts
Canadian Institute for Health
Information
Institute for Clinical Evaluative Sciences
(ON)
Canadian Stroke Network
Canadian Cardiovascular
Outcomes Research Team
POWER Study (ON)
Cardiac Care Network (ON)
Collaboration for Excellence in
Healthcare Quality
Alberta Diabetes Surveillance System
Non-jurisdictional Organizations
Canadian Institute for Health
Information
Health Council of Canada
Canadian Patient Safety Institute
Canadian Partnership Against
Cancer
Accreditation Canada
Fraser Institute
Ontario Hospital Association
Heart and Stroke Foundation
Canadian Diabetes Association
Cancer Care Ontario
Select International
Reporting Initiatives
National Quality Forum (US)
Agency for Health Research and Quality (US)
NHS Indicators for Quality
Improvement (UK)
Australian Commission on
Quality and Safety in Health Care
Quality and Efficiency in
Swedish Health Care
OECD
RAND
36Adapted with permission from the Health Council of Canada (2011)
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Health Indicators provide a Dashboard for Health and Healthcare
They can let you know that things are running smoothly.
They can alert you to problems that may need attention.
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rising BMI (Body Mass Index) doesn’t explain the root cause of weight gain.
In 2009, Canadians received 121 CT scans per 1000 people. There were also 8 MRI units per million population (vs. 12 MRI units per million as the OECD average). OECD (2011) reports Canada is “lagging behind,” but there is no agreed-upon benchmark.
In 2009, Canada had 2.4 physicians per 1000 population (vs. 3.1 OECD avg), but…◦ We have more physicians than ever before – Is this about supply or
distribution and deployment? ◦ We also have more nurses per 1000 people (9.4 in Canada vs. 8.4
OECD avg)39
Interpreting Data…
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Interpreting Data Comparing apples-to-apples?
◦ Age standardization◦ Risk adjustment
Measuring intangibles◦ e.g., quality of life◦ Composite indicators
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“Numbers can’t ‘talk’ but they can tell you as much as your human
sources can. But just like with human sources, you have to ask” (Niles, 2007).
Niles R. 2007. Statistics every writer should know: A simple guide to understanding basic statistics, for journalists and other writers who might not know math. http://nilesonline.com/stats/
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Useful Links
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Support Tools for Policy Making http://www.chsrf.ca/PublicationsAndResources/
ResearchReports/Support_Tools_for_Policy-Making.aspx
Mythbusters http://www.chsrf.ca/PublicationsAndResources/Mythbusters.aspx
What If? http://www.chsrf.ca/Programs/HealthcareFinancingInnovationAndTransformation/WhatIf.aspx
CHSRF’s Quality of Healthcare in Canada: A Chartbook (2010)CIHI’s Making Sense of Health Indicators (2011)HCC’s A Citizen’s Guide to Health Indicators (2011)CIHI’s Making Sense of Health Rankings (2008)OECD’s Health Data 2011: How Does Canada Compare? (2011)