Measuring Healthy Life Expectancy

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Measuring Healthy Life Expectancy   
Leicester Nuffield Research Unit

• Carol Jagger
• Professor of Epidemiology

Westminster Economic Forum 2006-7 Measurement
and Data Performance
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• Dennis Rudgewick didnt need to worry, hed
  just got 75k out of the ESRC for a project on
  trends in old age morbidity. Hed decided to
  specialize in old age a long time ago. It wasnt
  a sexy subject, but it did have a future, as
  everyone had it to look forward to and there was
  a lot more of it around these days.
• (Ann Oakley, Overheads)

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Outline

• Context for healthy life expectancy
• What is the best measure of health?
• X-sectional versus longitudinal data
• Future potential for healthy life expectancy?

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LE at birth and age 65 (UK)
? 2.6 years men 1.7 years women
? 2.0 years men 1.2 years women
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LE at birth (Europe)
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Living longer but healthier?

• Keeping the sick and frail alive
• expansion of morbidity (Kramer, 1980).  
• Delaying onset and progression
• compression of morbidity (Fries, 1980, 1989).
• Somewhere in between more disability but less
  severe
• dynamic equilibrium (Manton, 1982).

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WHO model of health transition (1984)
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Quality or quantity of life?

• Health expectancy
• partitions years of life at a particular age into
  years healthy and unhealthy
• adds information on quality
• is used to
• monitor population health over time
• compare countries (EU Healthy Life Years)
• compare regions within countries
• compare different social groups within a
  population (education, social class)

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What is the best measure?
Health Expectancy Healthy LE Disability free
LE Disease free LE (self rated health)
DFLE DemFLE
HLE
Cog imp-free LE Active LE (ADL)
Many measures of health many health
expectancies!
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Example 1

• Regional variations in health expectancies from
  the MRC Cognitive Function and Ageing Study

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MRC CFAS

• Five centres
• stratified random sample aged 65
• includes those in institutions
• 13004 interviewed at baseline in 1991
• 2, 6 (Cambridge only) and 10 year follow-ups
• death information from ONS

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Regional variations in HALE

• Cross-sectional analysis (baseline)
• Regional life tables (1991-3)
• Health measures from CFAS
• Self-rated health
• Functional impairment (ADL)
• Cognitive impairment (MMSE)

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LE by region women
Source MRC CFAS
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of life at age 65 spent healthy by region women
Source MRC CFAS
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What is the best measure?

• Depends on the question
• Need a range of severity
• dynamic equilibrium
• Performance versus self-report
• cultural differences
• Cross-national comparability
• translation issues

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Cross-sectional versus longitudinal data
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X-sectional versus longitudinal data

• The simplest method of calculating a health
  expectancy is Sullivans method (Sullivan 1971)
  with
• prevalence of the health state from a
  cross-sectional survey
• a standard life table for the same period
• Multi-state life tables require longitudinal data
  on transitions between health states and death

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HE with cross-sectional data
Mortality data
Age specific prevalence of ill-health (e.g.
disability)
Life table
Life expectancy
LE free of disability
LE with disability
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HE with longitudinal data
Baseline
Follow-up
No disability
No disability
Disability
Disability
Dead
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X-sectional versus longitudinal

• Cross-sectional
• easiest for trends
• - life tables not available for subgroups
• Longitudinal
• explicitly estimates incidence and recovery
  providing better future forecasts
• - cost, attrition

Not either/or but must include institutional
population
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Example 2

• Social inequalities in disability-free life
  expectancy from the MRC Cognitive Function and
  Ageing Study

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Social inequalities at age 65
1.6 yrs
2.7 yrs
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Mobility transitions OR ( 95 CI)
MEN
WOMEN

• 10,11 yrs education
• 0-9 yrs education

adjusted for age, gender, comorbidity
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Example 3

• Burden of disease on disability-free life
  expectancy from the MRC Cognitive Function and
  Ageing Study

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Change in LE at age 65
Arthritis
Cog imp
WOMEN
CHD
Stroke
Arthritis
MEN
15.6 years without v 10.9 with stroke at baseline
Cog imp
CHD
Stroke
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Change in mildDFLE at age 65
Arthritis
Cog imp
WOMEN
CHD
Stroke
Arthritis
MEN
Gains in DFLE greater than gains in LE
Cog imp
CHD
Stroke
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Change in modDFLE at age 65
More gains without arthritis when mild disability
included
Arthritis
Cog imp
WOMEN
CHD
Stroke
Arthritis
MEN
Cog imp
CHD
Stroke
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Future potential of HLE

• Are social and regional inequalities widening?
• effect of greater access to education in new
  cohorts
• Diseases more or less disabling?
• saving lives v reducing disability
• Living longer healthier?
• new cohorts with more ethnic minority elders

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Issues

• Must have total population including those in
  institutions
• Cultural differences in self-report?
• Accurate translation to underlying concepts for
  cross national comparability

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Conclusion

• HLE raises awareness that we need to focus on
  alleviating disability as much as saving lives

Life is not just being alive, but being well.
(Martial, Epigrammata)
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Measuring Healthy Life Expectancy   
Leicester Nuffield Research Unit

• Carol Jagger
• Professor of Epidemiology
• (cxj_at_le.ac.uk)

Westminster Economic Forum 2006-7 Measurement
and Data Performance