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Title: Approaches to Measuring Population Health Ian McDowell November, 2005


1
Approaches to Measuring Population HealthIan
McDowellNovember, 2005
  • Mortality-based summary measures
  • Combined disability mortality methods
  • Conceptual rationale for summary measures
  • Environmental indicators
  • Global indicators

POP 8910
2
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

3
Classifying Population Health Measures by their
Purpose
  • Descriptive measures
  • Current health status (e.g., health surveys)
  • Evaluative measures (e.g., to assess outcomes of
    health policies)
  • Analytic measures include an implicit time
    dimension
  • Predictive methods (risk assessment projections
    of disease burden) look forward
  • Explanatory measures (income inequality or social
    cohesion) look backwards.

4
These purposes may correspond to different types
of research (shown in the ellipses)
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.
5
Classifying Population Health Measures by their
Focus
  • Aggregate measures combine data from individual
    people, 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. Annual
    Reviews of Public Health 1995 1661-81.

6
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

7
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 Morgensterns categories of
measures used to evaluate the intervention
and to the presumed etiological sequence
8
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

9
Aggregate MeasuresMortality-Based Indicators
  • Life expectancy
  • Expected years of life lost
  • Potential years of life lost

10
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

11
Expectancies and Gaps
  • 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
    disease-specific (e.g., life yrs lost due to
    cancer)

12
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

13
Gaps Expected Years of Life Lost
  • Uses population life expectancy at the
    individuals age of death
  • Problems different countries may have different
    life expectancies. Its 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

14
Potential Years of Life Lost (PYLL)
  • PYLL ? ( normal age at death actual age at
    death). Doesnt 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

15
3. Aggregate Measures that Combine Mortality
Morbidity
  • Health expectancies
  • Health gaps

16
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

17
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., Womens Health
    Questionnaire)

18
Survivorship Functions for Health States
Survivors
Deaths
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.
Age
19
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
    health-adjusted life expectancy, then healthy
    life expectancy.

20
A Simple PresentationLife Expectancy and
Disability-Free Life Expectancy, Canada, 1986-1991
Years
Life Expectancy from birth Disability-Free Life
Expectancy (DFLE)
M F M
F
1986 1991
21
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

22
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

23
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.

24
Polytomous Curves Showing Quality of Survival
Survivors
Deaths
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.
Age
25
Health Expectancy by Income Level and Sex,
Canada, 1978 (Wilkins)
Years
Severely disabled Restricted Minor
limitations Healthy
Low
High
Income Quintiles
Males Females
26
Relationship between Life Expectancy, Health
Expectancy and Health-Adjusted Life Expectancy
Life Expectancy
Health-Adjusted Life Expectancy
Healthy Life Expectancy
By down-weighting the various levels of
disability, the HALE falls between LE and HLE
27
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 19868(1)41-46

28
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

29
Complementarity of Health Expectancies and Health
Gaps
Gaps
Age
Expectancies
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
30
4. When do we Use Each Type of Measure?
  • Towards a Functional Classification

31
Recall our Classification of Measures
  • Descriptive measures
  • Current health status
  • Evaluative measures
  • Analytic measures
  • Predictive methods that look forward
  • Explanatory measures that look backwards.

32
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

33
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 side-effects

34
Sensitivity of a Measurement
Metaphor of the combs
Descriptive
Evaluative
35
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
36
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)

37
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

38
5. Environmental Measures
  • Compositional vs. Contextual Measures

39
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)

40
Contextual
  • Neighbourhood type, quality amenities,
    transportation
  • Employment opportunities
  • Access to care
  • Environmental quality pollution levels air,
    water, noise
  • Climate
  • Equity

41
6. Global Measures
  • Income inequalities,
  • Health inequalities.

42
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

43
Canadian Social Health Index
Composite Indicator, including 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
Source Human Resources Development
CanadaApplied Research Bulletin 199736-8
44
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.

45
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

46
Gini Coefficient Measure of Income Inequality
  • L(s) lies below line of equality when income
    inequality favours the rich
  • Gini coefficient is twice the area between the
    curve and the line of equality

of income
100
L(s)
0
100
of population
47
Standardized Index of Health Inequality
  • 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)

Cum of ill-health
100
L(s)
L(s)
100
0
Cum. of population ordered by income
48
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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