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Monitoring Population Ageing in the EU through trends in Disability-Free Life Expectancy, 1995 – 2003. Carol Jagger EHEMU team. 18 th World Congress of Gerontology, Rio June 2005. Monitoring population ageing.
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Monitoring Population Ageing in the EUthrough trends in Disability-Free Life Expectancy, 1995 – 2003 Carol Jagger EHEMU team 18th World Congress of Gerontology, Rio June 2005
Monitoring population ageing • Most countries are seeing year on year increase in life expectancy at birth and at older ages • Are we exchanging longer life for poorer health (expansion of morbidity scenario) or are the extra years spent in good health (compression of morbidity)? • Do these trends hold for all countries, all social groups, men and women? • Health expectancies provide the answer as they extend the notion of life expectancy to different health dimensions, thus adding quality to quantity of life lived
Purpose • To explore compression or expansion of disability and gender differences through cross-national comparisons of disability-free life expectancy (DFLE) at birth and age 65 among 14 EU countries between 1995 and 2003 • To prepare case for new EU structural indicator Healthy Life Years
Data and methods Estimation of DFLE and 95% CI, using Sullivan method • age specific probability of death: Eurostat life tables • age specific disability prevalence: European Community Household Panel 1995-2001 question ‘Are you hampered in your daily activities by any physical or mental health problem, illness or disability? ‘ • Some interpolation for odd missing values and extrapolation of trends for 2002-3
Distribution of LE and DFLE at birth EU(14), 1995-2003 Women Men LE
Distribution of LE and DFLE at birth EU(14), 1995-2003 Women Men LE DFLE
Distribution of LE and DFLE at birth EU(14), 1995-2003 • By 2003 LE at birth in the EU14 ranged from 74.2 (Portugal) to 78 (Sweden) years for men and 80.1 (Denmark) to 83.2 years (France) for women, following a steady increase from 1995. • Compared to LE, trends in DFLE were more variable although gender differences were smaller • Between 1995-2003 the gain in total years for men exceeded the gain in years free of disability • In women there was only a slight improvement, on average, in life expectancy with a similar gain in disability-free life years.
Trends in proportion of life spent disability-free at age 65 Men = gain of 5% + = gain or loss of less than 5% = loss of 5% +
Trends in proportion of life spent disability-free at age 65 Women = gain of 5% + = gain or loss of less than 5% = loss of 5% +
Men Austria, Belgium, Finland, Germany Italy, Spain France, Greece, Ireland, Netherlands, Portugal Denmark, Sweden, UK Women Italy,Sweden Austria, Belgium, Denmark, Finland, France, Spain, UK Germany, Greece, Ireland, Netherlands, Portugal Trends in the proportion of life spent disability-free at age 65
Trends in DFLE using the ECHP • Life expectancy: • Small variation in LE between these 14 MS • Increase between 1995-2003 • Disability Free Life Expectancy and %DFLE/LE • Large variation in DFLE between these 14 MS • Diverging trends over 1995-2003: reduction / stagnation / increase in the proportion of life with reported disability at age 65 while LE increases • Gender differences in trends
Trends in DFLE using the ECHP Conclusions • Population aging has a different impact in the 14 Member States in Europe: - different levels of reported disability (larger dispersion than LE) - variation in the magnitude of the gender difference - different trends over time • Need to improve cross-national comparisons in self-reported disability to ensure differences are not an artefact: - improved harmonisation of the instruments - using different levels of severity - documenting differences in reporting - documenting differences in selection in the panel
Monitoring Population Ageing in the EUthrough trends in Disability-Free Life Expectancy, 1995 – 2003 Carol Jagger EHEMU team 18th World Congress of Gerontology, Rio June 2005
Data and Methods • Problems in both • mortality and the panel data • 1) Data base • Probable data errors • Replacement with other sources • Missing • 2) Interruption of data collection • No data for 2002 and 2003 • Solutions • 1) Data base • Linear imputation of age pecific probabilities (death and disability) • Shift of the prevalence trend to the ECHP level • Imputation of data according to observed trends • 2) Interruption of data collection • Linear extrapolation of the disability prevalence