Transcript Summary Measures of Population Health

Summary Measures of
Population Health
Ian McDowell
November, 2002
1. Rationale for summary measures
2. Mortality-based measures
3. Combined disability & mortality
methods
1. Why do we need measures
of population health?
• We 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,
2.
3.
summarized at regional or national levels. E.g., rates of smoking or
lung cancer.
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.
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. Annu Rev Public
Health 1995; 16:61-81.
Linking the focus of a measure to its
application
• Aggregate measures are typically used in
descriptive studies; 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 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. Then . . .
• 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:
(i) Mortality-Based Indicators
Life expectancy
Expected years of life lost
Potential years of life lost
Expectancies versus Gaps
• From a typical survival
100%
G
80%
60%
40%
E
20%
curve, we can either
consider the life
expectancy (“E”), or the
gap (“G”) between current
life expectancy and some
ideal (here, the outer
rectangle).
• Expectancies are generic;
0%
0 10 20 30 40 50 60 70 80 90 100
gaps can be diseasespecific
Life Expectancy
• Summarizes 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
Gaps: Expected Years of Life Lost
• Can use population life expectancy at the
individual’s age of death
– Problem: different regions may have different life
expectancies. Cannot identify impact of a disease.
• Or: 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 PYLL
3. Aggregate Measures:
(ii) Indicators that Combine
Mortality & Morbidity
Health expectancies
Health gaps
Composite Measures
• Composite measures combine morbidity and
mortality into a health index. An index is a
numerical summary of several indicators of health
• Aims to represent overall health of a population
• Mortality data typically derived from life tables;
morbidity indicators from health surveys, e.g.
• Self-rated health
• Disability or activity limitations
• A formal health index
Survivorship Functions for Health States
Survivors
This diagram illustrates the
composite health of a population.
Deaths
100%
C
80%
60%
40%
A
20%
0%
0 10 20 30 40 50 60 70 80 90 100
Age
The lower area ‘A’ shows the
proportion of people in good health
(however defined); it shows
healthy life expectancy. The top
curve shows deaths; intermediate
curves represent various levels of
disability.
Area ‘C’ represents the deficit of
this population compared to an
arbitrary ideal; this refers back to
the notion of health gaps.
More details on the combined
indicators
• From the previous chart:
– You can read from the bottom, and talk of “health
expectancies.”
– Or you can read from the top, and focus on the gap
between current state and the ideal (these were
discussed earlier)
– The bands in the middle indicate that the value of a life
lived in less than perfect health is less than a healthy
life-year. These are “health-adjusted life expectancies”
– The indicators will fall in a descending sequence: overall
life expectancy, then health-adjusted life expectancy,
then healthy life expectancy.
2.1 Health expectancies
• Generic term: any expectation of life in
various states of health. Includes other,
more specific terms
• Two main classes:
– Dichotomous weighting for health states
– Health state valuations for a number of
categories
Dichotomous expectancies
• Here full health is rated 1, and any state of poor
health (mild, moderate, or 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
Illustrating dichotomous weights:
Life Expectancy and Disability-Free Life
Expectancy, Canada, 1986-1991
Years
90
Life expectancy
From birth
80
70
Disability-free
Life expectancy
60
50
40
30
20
10
0
M
F
1986
M
1991
F
Polytomous states and valuations
• A refinement is to incorporate many levels of
disability and to count time spent with each level of
disability. = Polytomous model (three or more health
states defined)
• Weights are assigned to each state; generally 0 to
1.0. These may be added together and compared
across diseases
• This forms the Health-Adjusted Life Expectancy
(HALE) index
• First calculated for Canada by Wilkins. He used four
levels of severity & arbitrary weights.
• Recent work uses utility weights. E.g. from Health
Utilities Index, Quality of Well-Being Scale, EUROQoL,
etc.
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
Health Expectancies and Health
Gaps
SLE
Gaps
LE
HA LE
Age
HL E
Expectancies
Bi rth
LE
SE YLL
SUR VIV AL
HA LE
HA LY
PO LYT OM OU S
HL E
?
DIC HO TO M O US
LE = Life Expectancy; SLE = Standard LE; HALE = Health-Adjusted LE;
HLE = Healthy LE; SEYLL = Standard Expected Years of Life Lost
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 is arbitrary and
must be explicit, but as long as same for all
populations being compared, does not
matter
Examples of Health Gap Measures
• Gap measures 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)
– 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)
4. Environmental and Global
Measures
Environment,
Income inequalities,
Health inequalities.
Some Examples of Environmental
Indicators
• Environmental indicators of health status
(water, air quality, etc.)
• Indicators of social interactions: changing
patterns of crime; volunteerism
• Scope of, and access to, social & mental
health institutions
• Urban environmental quality; housing,
hospitals, etc.
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
Gini Coefficient: Measure of Income
Inequality
• L(s) lies below line
% of
income
100
of equality when
income inequality
favours the rich
• Gini coefficient is
L(s)
0
% of population
100
twice the area
between the curve
and the line of
equality
Measures of 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
Measures of Impact of Interventions
to Reduce Inequalities
• Population attributable risk: The
reduction in mortality 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
Standardized Index of Health
Inequality
• L(s) lies above line of
Cum % of
ill-health
100
equality when ill-health
is concentrated among
poor.
L(s)
L*(s)
• L*(s) is indirectly
Line of
equality
100
Cum.
%
of
population
0
ordered by income
standardized curve
indicating unavoidable
inequality (e.g., due to
age-sex distribution)
• Inequality favours rich if
L(s) lies above L*(s)