Based on the harmonized data of the first wave of "Generations and Gender Survey" the comparative analysis of childbearing out of marriage and partner union in Estonia, Russia and France was carried out. Two hypotheses have been formulated. The first one suggests that probability of first childbirth outside union among Russian and Estonian women is equal in generations whose first reproductive events were during the common Soviet period. The second one applies to similarity of women’s behavior concerning the first birth outside union among Estonians and French women after 1991. The obtained results have shown that in the generations of women born in the 1950s-1960s in the three countries under consideration there were no significant distinctions in risks of out-of-union first birth. However, the analysis shows that Estonians from generations of the 1930s and 1940s had significantly less chance of having a child out of union, particularly in young ages under 20 years, than French and Russian women in the same cohorts. But the chance of women born during the 1970s to give birth to a child out of union in Estonia and in Russia is much higher, then in France. The observed increase of out-of-union births among Estonian women could be due to the big flow of Russians in the 1940s-1959s, and the significant differences in contraceptive practices between native Estonian and migrant Russian women in Estonia.
This study proposes a new decomposition method that permits a difference in an aggregate measure at a final time point to be split into additive components corresponding to the initial differences in the event rates of the measure and differences in trends in these underlying event rates. For instance, when studying divergence in life expectancy, this method allows researchers to more easily contrast age-specific mortality trends between populations by controlling for initial age-specific mortality differences. Two approaches are assessed: (1) an additive change method that uses logic similar to cause-of-death decomposition, and (2) a contour decomposition method that extends the stepwise replacement algorithm along an age-period demographic contour. The two approaches produce similar results, but the contour method is more widely applicable. We provide a full description of the contour replacement method and examples of its application to life expectancy and lifetime disparity differences between the United States and England and Wales in the period 1980–2010.
We aimed to explore whether mortality data are consistent with the view that aging is accelerated for people with a history of incarceration compared to the general population, using data on mortality rates and life expectancy for persons in Ontario, Canada.
We obtained data from the Ontario Ministry of Community Safety and Correctional Services on all adults admitted to provincial correctional facilities in Ontario in 2000, and linked these data with death records from provincial vital statistics between January 1, 2000 and December 31, 2012. We used life table methods to calculate mortality rates and life expectancies for this cohort by sex and 5-year age group. We similarly generated population comparison rates using publicly available data for the general population of Ontario in 2006 as the midpoint of the follow up period. We compared these mortality indices between the 2000 Ontario prison cohort and the general population by age group and sex.
The difference in all-cause mortality rates between the 2000 Ontario prison cohort and the general population was greatest for younger adults, with the prison cohort experiencing rates of death that would be expected for persons at least 15 years older at ages 20 to 44 for men and ages 20 to 59 for women. Life expectancy in the 2000 Ontario prison cohort was most similar to life expectancy of persons five years older in the general population at age intervals 20 to 45 in men and 20 to 30 in women.
For most of adulthood, life expectancy and mortality rates are worse for adults with a history of incarceration than for the general population in Ontario, Canada. However, the association between mortality and incarceration status is modified by age, with the greatest relative burden of mortality experienced by younger persons with a history of incarceration and modified by sex, with worse relative mortality in women. Future research should explore the association between incarceration status and markers of aging including mortality, morbidity and physical appearance.
After several decades of negative trends and short-term fluctuations, life expectancy has been increasing in Russia since 2004. Between 2003 and 2014, the length of life rose by 6.6 years among males and by 4.6 years among females. While positive trends in life expectancy are observed in all regions of Russia, these trends are unfolding differently in different regions. First, regions entered the phase of life expectancy growth at different points in time. Second, the age- and cause-specific components of the gains in life expectancy and the number of years added vary noticeably. In this paper, we apply decomposition techniques—specifically, the stepwise replacement algorithm—to examine the age- and cause-specific components of the changes in inter-regional disparities during the current period of health improvement. The absolute inter-regional disparities in length of life, measured by the population-weighted standard deviation, decreased slightly between 2003 and 2014, from 3.3 to 3.2 years for males, and from 2.0 to 1.8 years for females. The decomposition of these small changes by ages and causes of death shows that these shifts were the result of diverse effects of mortality convergence at young and middle ages, and of mortality divergence at older ages. With respect to causes of death, the convergence is mainly attributable to external causes, while the inter-regional divergence of trends is largely determined by cardiovascular diseases. The two major cities, Moscow and Saint Petersburg, are currently pioneering mortality improvements in Russia and are making the largest contributions to the inter-regional divergence.
Since the mid-2000s, after a few decades of negative trends and fluctuations, Russia has experienced the longest and most stable period of life expectancy increasing for the entire period of observation which was determined not only by a decrease in mortality at the middle ages, but also at the old ages. This period has been marked by a very fast increase in life expectancy of Muscovites. The paper shows that the mortality at old ages in Moscow systematically deviates from the patterns observed in economically developed countries with reliable mortality statistics. We assume that experience of these countries is applicable to regions of Russia. Based on this assumption the adjusted estimates of life expectancy at old ages in Moscow and Russia were calculated, as well as effect of the underestimation of mortality over age 80 on life expectancy at birth and at the age of retirement.
This article is devoted to the study of the differences in mortality rates between population groups with different educational levels in Russia after 1979. It also assesses the contribution of changes in the mortality rates within different educational groups and the educational structure of the population to changes in the life expectancy of the entire population. This work is based on state statistics covering the breakdown of the population and the deceased by levels of education. The distribution of the population by age, sex and levels of education was calculated on the basis of the censuses of 1979, 1989 and 2002, and the micro-censuses of 1994 and 2015. Similar data on the deceased for 1979 and 1989 are contained in tables of vital statistics provided by statistical offices as annual reports. Data for 1998 and 2015, was obtained by further development of anonymous micro-data on the number of deceased collected by the Rosstat. The study showed that the change in the educational structure of the population contributed most to the increase in life expectancy of both men and women at the ages of 30 to 69 in 1979 – 2015. Another positive contribution was made by the decrease in mortality in all age groups of the population with higher education, and at the age of 50 and older in groups with secondary education as well
Significance. During the recent decades, Russia has experienced significant shifts in mortality. Considering the heterogeneity of the Russian regions, it is important to analyze how those shifts were experienced at the regional level and how they influenced the inter-regional mortality inequality.
Purpose. To evaluate how the inter-regional mortality inequality in Russia changed during the period 1989-2016; to measure contribution of different age groups and causes of death into the inter-regional mortality inequality in Russia and changes in those contributions over time.
Methods. In the current study, we used gap in life expectancy between the two groups of regions as the main measure of inter-regional inequality. The first group included regions with the highest levels of life expectancy inhabited altogether by 15% of the total Russian population. The second group included regions with the lowest levels of life expectancy, which also altogether accounted for 15% of the total Russian population. The contribution of different age groups and causes of death into the gap in life expectancy between the two groups was estimated with the stepwise replacement decomposition technique.
Results. Life expectancy inequality between regions increased substantially in the 1990s. The recent period of mortality improvements in Russia did not result in any significant changes in the inter-regional inequality. In 2016, the difference between the two 15%-groups of regions performing the best and the worst in terms of life expectancy amounted to 9.6 years in males. Mortality inequality in ages 15-39 contributed 2.6 years to this gap, in ages 40-64 – 4.2 years, and in ages over 65 – 2.9 years. In females the gap amounted to 5.3 years; 1.2 years, 2.0 years, and 2.0 years contributed by age intervals 15-39, 40-64, and 65 years and over respectively. Among the causes of death, the major contribution in inequality is attributable to external causes of death at the young and middle ages and circulatory diseases at the middle and elderly ages.
Conclusions. Decreased contribution of external causes of death in the young and middle ages, and circulatory diseases in the middle age had a positive effect on the inter-regional inequality in life expectancy after 2005 contributing to its decline. At the same time, mortality decline in the elderly contributed to divergency in life expectancy across regions. Those positive changes in the elderly mortality towards reduction, which are associated with the beginning of cardiovascular revolution in Russia, did not equally affect the Russian regions.