Summary Measures of Population Health

Report
Approaches to Measuring
Population Health
Ian McDowell
November, 2005
1. Mortality-based summary measures
2. Combined disability & mortality methods
3. Conceptual rationale for summary
measures
4. Environmental indicators
5. Global indicators
POP 8910
1. Why do we need measures
of population health?
Governments wish to monitor health of citizens
–
–
–
–
–
–
–
To set priorities for health services & policies
To evaluate social and health policies
To compare health of different regions
To identify pressing health needs
To draw attention to inequalities in health
Highlight balance between length and quality of life
Numerical index desirable: a “GNP of Health”
Classifying Population Health
Measures by their Purpose
1. Descriptive measures:
i. Current health status (e.g., health surveys)
ii. Evaluative measures (e.g., to assess
outcomes of health policies)
2. Analytic measures include an implicit time
dimension:
iii. Predictive methods (risk assessment;
projections of disease burden) look forward;
iv. Explanatory measures (income inequality or
social cohesion) look backwards.
These purposes may correspond to
different types of research (shown in the
ellipses)
Descriptive
(measures of
current health status)
Health
Services
Research
Evidence-based
policy
Evaluative
(process & outcome
measures)
Evidence-based
medicine
Etiologic
epidemiology
Analytic
(etiology & determinants)
Predictive
(projection &
risk estimation)
Note: the figure is
intended to show the
typical blend of
methods you might
use in a particular
type of study: HSR
would use
descriptive and
analytic, for
example.
Classifying Population Health
Measures by their Focus
1. Aggregate measures combine data from individual people,
summarized at regional or national levels. E.g., rates of smoking or
lung cancer.
2. Environmental indicators record physical or social characteristics of
the place in which people live and cover factors external to the
individual, such as air or water quality, or the number of community
associations that exist in a neighborhood. These can have
analogues at the individual level.
3. Global indicators have no obvious analogue at the individual level.
Examples include contextual indicators such as the existence of
healthy public policy; laws restricting smoking in public places, or
social equity in access to care; social cohesion, etc.
Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods.
Annual Reviews of Public Health 1995; 16:61-81.
Linking the focus of a measure to its
application
• Aggregate measures are typically used in
descriptive studies; they focus on the individuals
within the population, i.e. idiographic.
They measure health in the population
• Environmental measures can be used in
descriptive, analytic or explanatory studies
• Global measures mainly used in analytic studies;
focus on generating theory (nomothetic studies).
They could measure health of the population
Linking the target of a population
intervention to the type of measure
Interventions can target people, environmental factors, or policy in general
These
correspond to
Morgenstern’s
categories of
measures
used to
evaluate the
intervention…
Individual
(+ aggregate)
Individual
Human
Lifestyles
biology
Individual
outcomes
Environmental
Environmental
Physical
environment
Social
environment
Risk
factors
Health
care policies
Population
determinants
Policy
Global
Class of
Indicators
General
policies
Levels of Intervention
Etiological
sequence
…and to
the
presumed
etiological
sequence
History of changing approaches
to measuring population health
• Originally based on mortality rates. IMR is often
used to describe level of development of a country
• With declining mortality, people with chronic
disease survive; morbidity & disability gain
importance
• Concern with quality of life, not mere survival
• To compare populations at different stages of
economic development, it may be desirable to
combine mortality and morbidity in a single,
composite index
2. Aggregate Measures:
Mortality-Based Indicators
Life expectancy
Expected years of life lost
Potential years of life lost
Life Expectancy
• Summary of all age-specific mortality rates
• Estimates hypothetical length of life of a
cohort born in a particular year
– This assumes that current mortality rates will
continue
Expectancies and Gaps
100%
G
80%
60%
40%
E
20%
0%
0 10 20 30 40 50 60 70 80 90 100
• From a typical survival
curve, we can either
consider the life
expectancy (“E”), or the
gap (“G”) between current
life expectancy and some
ideal.
• Expectancies are generic;
gaps can be diseasespecific (e.g., life yrs lost
due to cancer)
Classifying Health Gaps
• Gaps: Compare population health to some
target. = Difference between time lived in
health states less than ideal health, and the
specified target
• The implied norm or target can be arbitrary,
but must be explicit and the same for all
populations being compared. The precise
value does not matter
Gaps: Expected Years of Life Lost
• Uses population life expectancy at the
individual’s age of death
– Problems: different countries may have different life
expectancies. It’s overall mortality, so cannot identify
impact of a disease.
• Standard Expected Years of Life Lost
– Reference is to an “ideal” life expectancy
• E.g., Japan (82 years for women)
• Area between survivorship curve and the chosen norm
Potential Years of Life Lost (PYLL)
• PYLL =  ( “normal age at death” – actual age
at death). Doesn’t much matter what age is
chosen as reference; typically 75
• Attempts to represent impact of a disease on
the population: death at a young age is a
greater loss than death of an elderly person
• Focuses attention on conditions that kill
younger people (accidents; cancers)
• All-causes or cause-specific
3. Aggregate Measures that
Combine Mortality & Morbidity
Health expectancies
Health gaps
Composite Measures
• Aim to represent overall health of a population
• Composite measures combine morbidity and
mortality into a health index. (An index is a
numerical summary of several indicators of
health)
• Mortality data typically derived from life tables;
morbidity indicators from health surveys, e.g.
• Self-rated health
• Disability or activity limitations
• A generic health index
Sidebar: Different Types of Morbidity
Scales for Use in Composite Measures
• Generic instruments cover a wide range of
health topics, e.g. reflecting the WHO definition.
These can be health profiles (e.g., Sickness
Impact Profile, SF-36) or “health indexes” (e.g.,
Health Utilities Index, EuroQol)
• Specific instruments
– Disease-specific (e.g., Arthritis Impact Measurement
Scale)
– Age-specific (e.g., Child Behavior Checklist)
– Gender-specific (e.g., Women’s Health
Questionnaire)
Survivorship Functions for Health States
Survivors
Deaths
100%
G
80%
60%
40%
H
This diagram extends the earlier
one by recognizing that not all
survivors are perfectly healthy.
The lower area ‘H’ shows the
proportion of people in good health
(however defined); it shows
healthy life expectancy. The top
curve shows deaths; intermediate
area represents levels of disability.
Area ‘G’ again represents the health
gap. The question arises whether
the people with a disability ought
to be counted with H or with G.
20%
0%
0 10 20 30 40 50 60 70 80 90 100
Age
More details on the combined
indicators
• From the previous chart:
– We can still read from the bottom, and talk of
“health expectancies,” or from the top, and
create gap indexes: years of life lost, etc.
– The value of a life lived in less than perfect
health is less than a healthy life-year. This is
“health-adjusted life expectancy”
– The indicators will fall in a descending
sequence: overall life expectancy, then healthadjusted life expectancy, then healthy life
expectancy.
A Simple Presentation:
Life Expectancy and Disability-Free Life
Expectancy, Canada, 1986-1991
Years
90
Life Expectancy
from birth
80
70
Disability-Free
Life Expectancy
(‘DFLE’)
60
50
40
30
20
10
0
M
F
1986
M
1991
F
Health expectancies
• Generic term: any expectation of life in
various states of health. Includes other,
more specific terms, such as Disability
Free Life Expectancy
• Two main classes:
– Dichotomous rating: two health states
– Health state valuations for a range of levels
I. Dichotomous expectancies
• Here full health is rated 1, and any state of poor
health (mild, moderate, severe disability) is
rated 0.
• This leads to Disability-free life expectancy
(DFLE): weight of 1 for “no disability” and 0 for
all other states.
• = Expectation of life with no disability, or Healthy
Life Expectancy (HLE)
• Very sensitive to threshold of disability chosen
II. Polytomous states and valuations
• These incorporate many levels of disability into life
expectancy estimates and count time spent with each
level of disability.
• Polytomous model (three or more health states
defined: weights assigned to each; generally 0 to 1.0.
These may be added together and compared across
diseases)
• = Health-adjusted life expectancy (HALE)
• First calculated for Canada by Wilkins. Four levels of
severity & arbitrary weights.
• Recent work uses utility weights. E.g. from Health
Utilities Index, Quality of Well-Being Scale,
EUROQoL, etc.
Polytomous Curves Showing
Quality of Survival
Survivors
Deaths
100%
G
80%
60%
40%
H
20%
0%
0 10 20 30 40 50 60 70 80 90 100
Age
This diagram illustrates several
classes of disability, each
having a separate severity
weighting.
The area ‘H’ again includes
healthy people, but the
definition may have changed.
The top curve shows deaths;
intermediate curves represent
various levels of disability.
Health Expectancy by Income Level and
Sex, Canada, 1978 (Wilkins)
Years
80
Severely disabled
70
Restricted
60
Minor limitations
50
40
Healthy
30
20
10
0
Low 1
2
3
4
5
1
2
3
4
Income Quintiles
Males
Females
5 High
Relationship between Life Expectancy, Health
Expectancy and Health-Adjusted Life
Expectancy
Life
Expectancy
Healthy
Life
Expectancy
Health-Adjusted
Life Expectancy
By down-weighting the
various levels of disability,
the HALE falls between
LE and HLE
Some HALE Results for Canada
• Wolfson & Wilkins at Statistics Canada used data from
the National Population Health Survey to calculate
HALEs, using the “Health Utilities Index” to weight
different levels of imperfect health
• The difference between LE and HALE is 11% for men,
and 15% for women, because women live longer and
suffer more chronic disease at older ages
• They recalculated HALEs, deleting certain types of
disability, and found that sensory problems (eyesight,
hearing) were the major contributor in Canada to lost
years. Vision problem have a very minor effect on health
status, but are very common… Pain was the second
largest cause
• They also showed that less educated people both live
shorter lives, and also experience more disability
• Source: Wolfson MC. Health Reports 1986;8(1):41-46
Gap Measures: QALYs & DALYs
• Gap measures can also use a weighting for
intermediate health states. This is necessary to
combine time lost due to ill health with time lost
due to premature mortality
• Quality Adjusted Life Years (QALYs) lost
– Common outcome measurement in clinical trials,
program evaluation
– Record extra years of life provided by therapy and
quality of that life
– Typically use utility scale running from 0 to 1
• DALYS (disability-adjusted life years) lost
Complementarity of Health
Expectancies and Health Gaps
SLE
LE
Gaps
HALE
Age
HLE
Expectancies
Birth
LE SEYLL
SURVIVAL
HALE HALY
POLYTOMOUS
HLE
?
DICHOTOMOUS
LE = Life Expectancy; SLE = Standard LE; HALE = Health-Adjusted LE;
HLE = Healthy LE; SEYLL = Standard Expected Years of Life Lost
HALY = Health-Adjusted Life Years Lost
4. When do we Use Each
Type of Measure?
Towards a Functional
Classification
Recall our Classification of
Measures:
1. Descriptive measures:
i. Current health status
ii. Evaluative measures
2. Analytic measures:
iii. Predictive methods that look forward;
iv. Explanatory measures that look backwards.
Characteristics of Descriptive
Measures
•
•
•
•
•
Intuitively simple – cover themes of
interest to people in general (“quality of
life”, etc)
Reflect values; possible political influence
Time frame = present
Emphasis on modifiable themes
Goal = to make broad classifications
Characteristics of Evaluative
Measures
•
•
•
•
•
Fine-grained: select indicators that sample
densely from relevant level of severity
Need to be sensitive to change produced
by particular intervention
Content tailored to intervention; usually not
comprehensive
Common emphasis on summary score
But should also cover potential sideeffects
Sensitivity of a Measurement:
Metaphor of the combs
Descriptive
Evaluative
Match the Instrument to the Application
Population
Monitoring
Outcomes
Research
Patient
Management
4
4
4
3
3
3
2
2
2
1
1
1
Source: John Ware, October 2000
Characteristics of Predictive
Measures
•
•
•
•
Content can be selective rather than
comprehensive
Items not necessarily modifiable, or even
very important
If derived from discriminant analysis, likely
to be parsimonious
Focus on algorithmic scoring and
interpretation (e.g., either x or y, plus z in
the absence of w)
Characteristics of Explanatory
Measures
•
•
•
•
Can combine various types of measures &
classifications, ranging from distal to
proximal
Based on a conceptual model, rather than
empirically based
There can therefore be rival explanatory
approaches
Content not necessarily modifiable factors,
but these would be desirable
5. Environmental Measures
Compositional vs. Contextual
Measures
Compositional
• Demographics; age, ethnic composition,
lone parents, dependency ratios, etc
• Population resources: wealth, educational
levels, etc
• Community: social cohesion, watch
programs, participation (voting, donations,
etc)
Contextual
• Neighbourhood type, quality; amenities,
transportation
• Employment opportunities
• Access to care
• Environmental quality: pollution levels: air,
water, noise
• Climate
• Equity
6. Global Measures
Income inequalities,
Health inequalities.
Some Examples of Global
Measures
• Social solidarity; sense of identity; artistic output;
public interest in health issues, etc.
• Indicators of societal support: the “safety net”
• Quality of social institutions for health (health
protection laws, etc.)
• Social cohesion, neighbourhood quality, social
capital
Canadian Social Health Index
Composite Indicator, including:
700
600
500
400
300
200
100
0
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
Homicides
Alcohol-related fatalities
Affordable housing
Income equity
Child poverty
Child abuse
IMR
Teen suicide
Drug abuse
High school drop-out rate
Unemployment
Avg. weekly earnings
Seniors’ poverty rate
Uninsured health costs
for seniors
GDP
Social Index
Source: Human Resources Development Canada
Applied Research Bulletin 1997;3:6-8
Distributional Measures:
Health Inequalities (I)
• Index of Dissimilarity:
Absolute number or
percentage of all cases that must be redistributed to
obtain the same mortality rate for all SES groups.
• Index of Dissimilarity in Length of Life:
The absolute number or proportion of person-years of
life that should be redistributed among SES strata to
achieve equal length of life in all.
Measures of Health Inequalities (II)
• Relative Index of Inequality: Ratio of morbidity
or mortality rates between those at bottom of SES range
to those at top. This is estimated using regression and
corrects for other factors.
• Slope Index of Inequality: Expresses health
inequality between top and bottom of social hierarchy in
terms of rate differences rather than rate ratios
Gini Coefficient: Measure of
Income Inequality
• L(s) lies below line
of equality when
income inequality
favours the rich
% of
income
100
L(s)
0
% of population
100
• Gini coefficient is
twice the area
between the curve
and the line of
equality
Standardized Index of Health
Inequality
Cum % of
ill-health
100
L(s)
L*(s)
100
Cum.
%
of
population
0
ordered by income
• L(s) lies above line of
equality when ill-health
is concentrated among
poor.
• L*(s) is indirectly
standardized curve
indicating unavoidable
inequality (e.g., due to
age-sex distribution)
• Inequality favours rich if
L(s) lies above L*(s)
Measures of Impact of Interventions
to Reduce Inequalities
• Population attributable risk: The
reduction in health gap that would occur if
everyone experienced the rates in the highest
socioeconomic group
• Population attributable life lost
index: The absolute or proportional
increase in life expectancy if everyone
experienced the life expectancy of the highest
SES group

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