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Graziella Caselli Dipartimento di Scienze Sociali, Economiche, Attuariali e Demografiche

Increasing longevity and decreasing gender mortality differentials: new perspectives from a study on Italian cohorts. Graziella Caselli Dipartimento di Scienze Sociali, Economiche, Attuariali e Demografiche graziella.caselli@uniroma1.it Marco Marsili

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Graziella Caselli Dipartimento di Scienze Sociali, Economiche, Attuariali e Demografiche

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  1. Increasing longevity and decreasing gender mortality differentials: newperspectives from a study on Italian cohorts Graziella Caselli Dipartimento di Scienze Sociali, Economiche, Attuariali e Demografiche graziella.caselli@uniroma1.it Marco Marsili Direzione Centrale Statistiche e Indagini sulle Istituzioni Sociali marsili@istat.it Joint Eurostat-UNECE Work Session on Demographic Projections Lisbon (Portugal), 28-30 April 2010

  2. Outline 1. More long-lived, less different 4. Adults and elderly: what causes of death have been, or could be, responsible for their low mortality and their increasing longevity? 2. Data and method 3. Cohort mortality models: why elderly today are different from elderly in the past and in the future? 5. Are women losing some of their advantage or men recouping their disadvantage? 6. Some conclusions

  3. Life expectancy at birth by sex and gender differences from 1886 to 2007 1979 ΔG = 6.9 2007 ΔG = 5.3 1886 e0=35.5 M and W More long-lived…

  4. Trends of gender differences in life expectancy at birth, at age 65 and 80, from 1886 to 2007 More long-livedANDless different Years

  5. Age specific Sex ratios – over male mortality – in the years 1886, 1979 and 2007 The leading ages of a new mortality model 40-69 years 1-14 years

  6. How should we interpret the reduction of the female advantage in adulthood? • A particularly fortunate period for men? • A problem in survival trends of women? • Which causes of death are responsible for?

  7. As we know, different life histories influence the final outcome, anticipating or postponing the age at death. Studies of mortality that start from macro-data claim that the different mortality histories of the cohorts are the result of different life experiences. Analysing mortality models by age and by cause for succeeding cohorts may be helpful in better understanding the characteristics of the last thirty years in the history of mortality in Italy. Completing some cohort mortality histories may help us see in which direction the recent mortality trendsmight be going.

  8. The aim of this presentation is to analyze the developing characteristics of the mortality of the cohorts that entered adult age (45-64 years) at the end of the 1970s and that have become elderly more recently. The intention is to compare their mortality histories – total and by cause – with those of adults of today, who will be the elderly of tomorrow. Predictions will be necessary to complete the mortality histories of these cohorts, considering the cause of death too. A cohort perspective will be adopted to studylongevity,BUT PARTICULARLY to analyze the changes of gender survival differences

  9. Data Mortality rates and/or probabilities by Sex, Leadingcauses of death, Age(0-100), Period and Cohort SOURCES: From 1861 to 1973 - Department of Demography - Rome (Human mortality database) From 1974 to 2007 - ISTAT Cohorts up to 1907 EXTINCT Cohorts from 1908 to 1965 PARTIALLY OBSERVED

  10. Leading causes of death and corresponding codes in IX ICD Rev. Harmonized database in time according to IX ICD REVISION REFERENCES: Caselli G., Long Term Trends in European Mortality, Studies on Medical and Population Subjects, N. 56, OPCS, London. Caselli G., Health transition and cause specific mortality, in. The decline of mortality in Europe (Edited by R. Schofield, D. Reher and A. Bideau), Clarendon Press, Oxford; Caselli G., National differences in the Health transition in Europe, Historical Methods, Vol. 29, n. 3;

  11. THE PROJECTION MODEL To project the risk of death, a model taking account of age, period and cohort components of mortality (APC model) was used. That is: Parameters to be estimated

  12. PROJECTION STRATEGY Projections carried out for each cause of death and sex. The sum of the projected rates represents the overall mortality (“by cause” approach). Approach = deterministic - single variant Single Age = 0,1,2,….,100 Jump-off year = 2008 Last projected year = 2065 Last fully projected cohort = 1965 We mainly focus our study on cohorts from 1865 to 1965

  13. Schema for identifying some interesting cohorts, from those of adult age (45-64) in 1967, now extinct, to those who were adult in 2007, who will be extinct in 2037-2047. The cohorts to be followed at the various ages are those aged 45-64 on the blue diagonal

  14. For a synthesis of the main results we will refer to the intermediate cohorts of the various groups, and in particular, the cohorts born in the years 1912, 1922, 1932, 1942 and 1952, also considering the cohorts of 1865 and 1890, now extinct, and the one born in 1965, whose history of mortality in adult and old age is projected from the age 42 years and beyond.

  15. Life expectancy at birth by sex and cohort, 1865-1965, Men and Women Cohort 1902 M= 42.1 W= 49.8 Cohort 1965 M=81.3 W=87.6 Cohort 1912 M= 51.4 W= 56.2 Cohort 1917 M= 44.3 W= 49.4

  16. Contributions by age (30+) of the leading causes of death to differences in life expectancy at birth between two selected cohorts, MEN

  17. Contributions by age (30+) of the leading causes of death to differences in life expectancy at birth between two selected cohorts, WOMEN

  18. Contributions by age (30+) of the leading causes of death to differences in life expectancy at birth between cohorts 1932-1952, MEN and WOMEN MEN Adult in the Past vs Adult Today Elderly Today vs Elderly Tomorrow WOMEN

  19. Life expectancy at birth by sex and gender differences - Cohorts 1865-1965 W-M Cohort 1902=7.7 W-M Cohort 1965=6.3 W-M Cohort 1917=5.1 Deep change in gender differences trend as a result of cohort dynamics in life expectancy at birth

  20. Life expectancy at birth by sex and gender differences – Cohort and Period COHORTS 1865-1965 Cohorts aged 45-64 in 1979, showing an increase of gender differences, are those born in 1915-1934 PERIOD 1925-2025

  21. Sex ratios, observed and projected by age and for some cohorts Differences in life expectancy at birth 1865=1.4 1890=3.7 1912=4.8 1922=6.9 1932=7.4 1952=6.8 1965=6.3 The leading adult ages of cohort mortality model 45-64 years

  22. SMR’s for ages 45-64 years by Circulatory diseases and Cancers, MEN and WOMEN (per 1000)

  23. SMR’s for ages 45-64, 65-79 and 80+ years by Circulatory diseases and Cancers, MEN and WOMEN (per 1000) Cohort circulatory mortality at ages 80+ showing the same trend by gender. Observed cancer mortality trend at ages 80+ still increasing for men 45-64 years 65-79 years 80+ years

  24. Contributions by age (45+) of the leading causes of death to increase or decrease gender differences between two selected cohorts in life expectancy at birth POSITIVE BAR: contribution to increasing the distance from male life expectancy at birth NEGATIVE BAR: contribution to bridging the distance from female life expectancy at birth

  25. Conclusions • Making projections by cohort has the advantage of starting from a mortality history, partially already observed, and so limiting predictions to just one part of the whole story. • Cohort analysis allow us to see the final result of a whole history of survival and so to interpret some of the differences that can be seen between cohorts as the effects of having experienced different life histories. • Important modifications of the longevity between cohorts and between genders, and, above all, a rapid bridging of the gap between men and women. • Gender gaps in survival are often the result of a life history that penalized men (World War I and II) with adoption of dangerous life styles such as cigarette smoking. At the same time, for years Italian women, who had been marginalized from the world of work and protected by a traditional culture, were protected from more harmful life styles and so were able to gain more years of life, gradually increasing the gap from men.

  26. Conclusions / 2 • In other countries the reduction in the gender gap for the most recent cohorts was caused by a worsening in female survival due to the new life styles of women, which became more and more similar, negatively, to those of men. This was not true in Italy. • In conclusion, we would like to interpret the GRADUAL CLOSENING of male and female survival as the result of a FEMINIZING OF MALE BEHAVIOUR. We might conclude that Italian males in the younger generations seem to have understood that they need to study women if they want to live longer, hoping that Italian women do not imitate the men of the previous generations! • Men in the most recent cohorts, by contrast, reduce some risks of illness and death that are typically male. Greater care for their bodies, for example, leads them directly or indirectly to follow the path of prevention and to detect in advance some illnesses.

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