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Comparison of HLY from different health surveys. 4th Meeting of the Task Force on Health Expectancies Luxembourg, 4 th June 2007 Bianca Cox. Introduction. EHEMU Information System : Health expectancies for 25 European countries Demographic data Eurostat + national statistical centers

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comparison of hly from different health surveys

Comparison of HLY from different health surveys

4th Meeting of the Task Force on Health Expectancies

Luxembourg, 4th June 2007

Bianca Cox

introduction
Introduction
  • EHEMU Information System :

Health expectancies for 25 European countries

      • Demographic data

Eurostat + national statistical centers

      • Health data

Eurostat, DG SANCO, Mannheim University

 from different European surveys

introduction2
Introduction
  • Are the health expectancies for a given country comparable between the surveys?
  • Surveys carried out in the same year (2004):

ESS, SHARE, SILC

 Common health indicators :

      • Self-Percieved Health (SPH)
      • Activity Limitation (AL)
slide5
Data
  • Health expectancies

comparison WITHIN country :

same demographic data

  • Raw health data (prevalences)
      • Self-Percieved Health : good, fair, bad 
      • Activity Limitation : without, moderate, severe
    • Average over 5-year age groups:

50-54, 55-59,…, 85+

analyses
Analyses
  • Health expectancies : graphical exploration
  • Prevalences : logistic regression

 Separate model for each health state and gender

    • Overall models :
      • Main effects : survey, agegr, country
      • Interactions : survey*agegr, survey*country, agegr*country
    • Country-specific models :
      • Main effects : survey, agegr
      • Interactions : survey*agegr

 Odds ratio estimates of one survey vs the other for agegroups 50, 65 and 85+

results graphical exploration health expectancies
Results : Graphical exploration health expectancies
  • Common trend in some countries :
    • Self-Perceived Health :
      • Good : SHARE > ESS > SILC
      • Bad : SHARE < ESS < SILC
    • Activity Limitation :
      • Without : ESS > SILC > SHARE
      • Severe : ESS < SHARE < SILC
  • Confidence intervals of health expectancies :

SILC << SHARE < ESS

results logistic models on prevalences
Results : Logistic models on prevalences
  • Overall models :
    • Survey*agegr, survey*country, agegr*country
      • SPH: sign. in all 6 models
      • AL: sign. in 4 models (not in ♀models for “without” & “severe”)
    •  Effect of survey different for different age groups and countries
  • Country-specific models :
    • Odds ratio estimates of one survey vs the other for age groups 50, 65 and 85+
results logistic models on sph prevalences
Results : Logistic models on SPH prevalences

+ or - odds ratio > 1 or odds ratio < 1

A & B number of models with a significant odds ratio

C number of models with a odds ratio > 1

results distribution of odds ratio estimates for sph
Results : Distribution of odds ratio estimates for SPH

Good SPH males

SH > ES > SI

Good SPH females

Bad SPH males

SH (<) ES < SI

Bad SPH females

results logistic models on al prevalences
Results : Logistic models on AL prevalences

+ or - odds ratio > 1or odds ratio < 1

A & B number of models with a significant odds ratio

C number of models with a odds ratio > 1

slide14

Results : Distribution of odds ratio estimates for AL

Without AL males

ES > SI > SH

Without AL females

Severe AL males

ES < SH < SI

Severe AL females

discussion
Discussion
  • Significant effect of survey, different for different age groups and countries
  • Still : odds ratios show common trend across age groups and countries
    • Good SPH : SH > ES > SI
    • Bad SPH : SH (<) ES < SI
    • Without AL : ES > SI > SH
    • Severe AL : ES < SH < SI
  • Significance of odds ratios increases with age but also the variability in the distribution of the odds ratio estimates

 chance effects due to smaller sample sizes

discussion1
Discussion
  • Possible reasons for differences between surveys :
    • Different sampling designs :

Sr = Simple random sampling

Systrs = Systematic random sampling

Stratrs = Stratified random sampling

Ms = Multi-stage sampling

Final sampling units: P = Persons

H = Households

A = Addresses

T = Telephone numbers

D = Dwellings

But : Sampling design depends more on country than on survey

 Survey design often constrained by what is available in the country

discussion2
Discussion
  • Possible reasons for differences between surveys :
    • Different selection bias : e.g. participation / item response
    • Different sample sizes : SILC > SHARE > ESS
    • Different position in questionnaire
    • Differences in wording:
      • SPH : “ How is your health in general ? ”

ESS & SILC general questionnaires : “health” further specified (f.e. including mental health) in footnote or between brackets

 Also in country-specific questionnaires or spoken out by interviewer ??

discussion3
Discussion
  • AL :

- ESS : “ Are you hampered in your daily activities in any way by any longstanding illness, or disability, infirmity or mental health problem ? ”

- SHARE & SILC : “ For the past six months at least, to what extent have you been limited because of a health problem in activities people usually do ? ”

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