Approaches to Measuring Population Health Ian McDowell November, 2005

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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
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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

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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.

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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.
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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.

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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

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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
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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

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Aggregate MeasuresMortality-Based Indicators

• Life expectancy
• Expected years of life lost
• Potential years of life lost

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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

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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)

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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

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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

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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

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3. Aggregate Measures that Combine Mortality
Morbidity

• Health expectancies
• Health gaps

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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

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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)

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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
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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.

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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
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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

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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

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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.

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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
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Health Expectancy by Income Level and Sex,
Canada, 1978 (Wilkins)
Years
Severely disabled Restricted Minor
limitations Healthy
Low
High
Income Quintiles
Males Females
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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
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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

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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

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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
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4. When do we Use Each Type of Measure?

• Towards a Functional Classification

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Recall our Classification of Measures

• Descriptive measures
• Current health status
• Evaluative measures
• Analytic measures
• Predictive methods that look forward
• Explanatory measures that look backwards.

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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

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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

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Sensitivity of a Measurement
Metaphor of the combs
Descriptive
Evaluative
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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
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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)

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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

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5. Environmental Measures

• Compositional vs. Contextual Measures

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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)

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Contextual

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

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6. Global Measures

• Income inequalities,
• Health inequalities.

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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

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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
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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.

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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

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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
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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
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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