Transcript Population Health Index -- CIHI Slides

Making Progress in Health
and Health Care
 how do we know we are making progress?
 need to distinguish two broad domains:
 progress in population health
 progress in health care services
Michael Wolfson, Statistics Canada
Denise Lievesley, UK NHS and ISI
(please use “normal view” or “notes page” to see speaking text)
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World’s Two Most Widely Used
“Health” Indicators
 Life Expectancy ( + other indicators based on
mortality rates, e.g. infant mortality)
 good as far as it goes; clearly fundamental
 but leaves out how healthy people are while alive
 Health Care Spending as % of GDP
 very poor indicator
 is more spending better or worse?
 focuses on inputs to health care, rather than results
 We can and should do better for our most
basic measures of progress in health and
health care
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How do we know we are making
progress in population health?
 currently, a plethora of indicators
 often a failure to distinguish “health” from
 antecedents, e.g. risk factors like smoking,
 correlates, e.g. bio-medical parameters like blood
pressure, and
 sequalae, e.g. social participation like work, mortality
 simple idea: HALE = health-adjusted life
expectancy
 builds on already very widely use measure, life
expectancy
 progress ≡ “adding years to life” and/or “adding life to
years”
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Basic Definitions
 LE = area under survival curve
 HALE = “weighted” area under survival curve
 where “weights” are levels of individual health
status, ranging between zero (dead) and one (fully
healthy)
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UK LE and HALE (Simpler Method)
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Measuring Functional Health
Status in a Population
 examples: McMaster Health Utility Index,
Euroqol EQ-5D, WHO World Health Survey
 define a set of health domains
 develop a parsimonious set of survey questions
to elicit levels of functioning for each domain,
and collect data for a representative sample
 Budapest Initiative
 apply a systematic method for eliciting values
for various health states for another, typically
smaller, sample
 estimate a “valuation function”
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Changes in Life Expectancy (LE) and
Health-Adjusted Life Expectancy (HALE)
by Cause, Canada
LE
2.5
2
2.4
1.5
1
HALE
0.5
0.5
1
1
0
0.8
0.5
Colorectal cancer
0.3
0.3
0.4
0
Melanoma
0
0
Osteoarthritis
Mental disorders
Men
Women
0.5
0.5
0.4
0.3
0.5
0.4
0.2
0.1
1
0.9
(Source: Manuel et al, ICES and Health Canada, NPHS)
OECD Istanbul June 2007
2.2
0.9
0.7
0.7
Diabetes
0.1
2.5
0.5
Breast cancer
COPD
0.4
0.4
0.7
0.6
Stroke
0.4
0.3
Women
2
1.5
Lung cancer
0.7
0.6
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1.5
IHD
1.8
Men
0
0
2.4
1.1
Progress in Levels and in
Differences – Health Inequality
 old (statistical) adage: “beware of the mean”
 HALE is fundamental for measuring overall
progress in population health – analogous to
“size of the pie” in income analysis
 but HALE itself says nothing about “how the pie
is divided” – about the distribution of health
within a population
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The Concept of Health Inequality
 concept of health inequality is different
 income inequality is “univariate”
 e.g. what share of income goes to the top 1%;
how many individuals are living on less than
$1 per day?
 health inequality is “bivariate”, i.e. about
correlations, especially systematic
associations with socio-economic status
 e.g. how does health (HALE) vary from one
region in a country to another;
 how steep is the gradient – i.e. how much does
health status improve as we move up the
social ladder within a country
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Life Expectancy (LE) and Health-Adjusted
Life Expectancy (HALE), Canada 2001
90
HALE
85
LE
80
75
70
65
60
55
50
male
at birth
female
male
female
bottom
at age 65
middle
males
at birth
top
bottom
middle
females
at birth
income terciles (thirds)
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top
An Almost Familiar World Map
www.worldmapper.org; cartogram algorithm: Mark Newman
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Area Proportional to Population
www.worldmapper.org; cartogram algorithm: Mark Newman
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Area Proportional to GDP 2002
www.worldmapper.org; cartogram algorithm: Mark Newman
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Area Proportional to HIV
(prevalence ages 15 – 49)
www.worldmapper.org; cartogram algorithm: Mark Newman
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Area Proportional to “Unhealthy Life”
(LE – HALE, based on WHO estimates)
www.worldmapper.org; cartogram algorithm: Mark Newman
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National Income and Health, Correlated ?
HALE
(Sources: HALE – WHO; GDP – World Bank)
80
70
60
50
40
30
20
10
0
0
5,000
10,000
15,000
20,000
25,000
30,000
GDP per capita, US $ at PPPs, 2002
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35,000
40,000
How do we know we are making
progress in health care?
 this is a far more popular question than
progress in population health, but also not
nearly so fundamental
 simple reason: there is far more to the
determinants of health than health care – e.g.
poverty, lifestyle, hierarchy
 progress in health care ≡ { health care
interventions  improved health of
individuals treated }
 n.b. most interventions are not well evaluated
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Definition - Health Outcome
health
intervention
health status
“before”
health status
“after”
other
factors
health outcome  change in health status
attributable to a health intervention
(for an individual)
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How NOT to Know Whether We are
Making Progress in Health Care
 try to use SNA (System of National
Accounts) concepts to measure
health care “outputs”
 try to apply macro-economic
concepts of aggregate productivity
to the health care sector
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SNA Approach: Treat Public Sector
Activities the Same as the Private Sector
 Define (i.e. make up) “Outputs”
“Profits”
???
Outputs
Inputs
Commercial
Sector
Industries
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Public
Sector
Why the SNA Approach
is Problematic
 “outputs” do not exist naturally in publicly
provided health care
 we certainly can count “activities”, like numbers of
vaccinations (probably all useful) and numbers of
coronary procedures (see later slide!)
 but outcomes of interventions should clearly be the
objective of systematic and routine measurement
 productivity is obviously important
 but high “productivity” in doing useless or iatrogenic
activities is bad
 remember the three “E’s”: efficacy, effectiveness,
and efficiency; no point measuring efficiency unless
we know efficacy and effectiveness
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n.b. virtually no differences in one year survival; but
no data on differences in health-related QoL
(Tu et al on Coronary Surgery)
e.g. almost
17x, with no
benefits?
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Heart Attack Patients in Large Health Regions – Treatment
and 30 Day Mortality Rates (%) – 1995/96 to 2003/04
20
30 Day Mortality Rate
1995/96
2003/04
15
10
5
0
0
10
20
30
40
50
Percent Revascularized within 30 Days
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60
70
What Does this Graph Tell Us?
 we may be missing important data
 treatments – e.g. nothing on thrombolysis, post
AMI medication and rehabilitation
 Framingham risk factors – smoking, obesity,
physical activity
 other risk factors – income, chronic stress
 (n.b. age, sex and comorbidity included)
 health care is driven by opinions
 clinical judgment is not well-informed by rigorous
and systematic evaluation
 health system managers have no empirical bases
for judging the effectiveness of their activities
 aggregate SNA style measures of
“productivity” miss the real issues
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Concluding Comments
 need to measure both progress in population
health and in health care
 for population health: HALE is fundamental
 for health care: outcomes are fundamental
 for both: a common metric for measuring
individual health status is essential – propose
Budapest Initiative short form questions (along
with items covering many other facets of health)
 using basic health information principles
 incentive compatibility – providers of crucial
health information should have a stake…
 empowerment – information should enable both
general public and providers (as well as health
system managers) to improve outcomes / quality
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