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Life expectancy at birth at the year of the first crossing with life expectancy at age one, total population by country
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The single most used demographic measure to describe population health is life expectancy at birth, but life expectancies at ages other than zero are also used in the study of human longevity. Our intuition tells us that the longest life expectancy is that of a newborn. However, historically, the expectation of life at age one (e1) has exceeded the...
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... countries included in the HMD have been excluded from Figure 6a, but a comparison using results from Table 1 and Figure 4 is instructive. The life expectancy crossing occurred between the levels of 65 to 75 years for countries included in the HMD. For countries in Figure 6 experiencing the crossing in recent years, this occurs in some cases at levels of life expectancy higher than 75 years. The interplay between levels of mortality below and above age one have caused these high life expectancies at the crossing. For example, in Figure 4 Southern Europe had the highest levels of life expectancy at the crossing. In these countries, life expectancy at age one had reached high levels as a consequence of high survivorship once the first year of life had passed. Infant mortality had also reduced, but it had to reach very low values to trigger the crossing. Similar circumstances were likely present in countries, such as those in Figure 6, where the crossing was experienced at levels of life expectancies above age ...
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... countries included in the HMD have been excluded from Figure 6a, but a comparison using results from Table 1 and Figure 4 is instructive. The life expectancy crossing occurred between the levels of 65 to 75 years for countries included in the HMD. For countries in Figure 6 experiencing the crossing in recent years, this occurs in some cases at levels of life expectancy higher than 75 years. The interplay between levels of mortality below and above age one have caused these high life expectancies at the crossing. For example, in Figure 4 Southern Europe had the highest levels of life expectancy at the crossing. In these countries, life expectancy at age one had reached high levels as a consequence of high survivorship once the first year of life had passed. Infant mortality had also reduced, but it had to reach very low values to trigger the crossing. Similar circumstances were likely present in countries, such as those in Figure 6, where the crossing was experienced at levels of life expectancies above age ...
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... clear clusters of life expectancy at the time of the crossing observed in Figure 4, are not replicated for infant mortality. For example, Danish females have the highest infant mortality of any of the females' values, even when their life expectancy was at a middle level at the crossing. Clearly mortality at ages other than the first year of life have a role in determining the time of the crossing. To further explain the crossing in life expectancies we look in detail at the interplay between the components of equation ...
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... illustration of the crossover in life expectancy observed in the HMD data suggests that socio-cultural, economic, and political factors that influence the intermediate factors that shape the mortality patterns in each country have traversed borders. In other words, the timing and mortality level at which populations progress from an imbalanced life table to a balanced one depend on the specific characteristics of each country, but also on regional characteristics. For example, the trends in mortality in the Baltic countries, Latvia and Lithuania, are very similar ( Kasmel et al. 2004). Therefore, it is not surprising to find similar gaps between the female and male timing for the crossing in life expectancies (Table 1 and Figure 4). However, it is surprising to find these countries together with Bulgaria and Ukraine with the extreme of over 14 years difference between the female and male timing, while other eastern European countries only differ by a couple of years. Sex differentials in infant mortality vary widely across countries (Fuse and Crenshaw 2006). However, in the context of the Baltic countries this is probably not the only factor. The great disparity between females and males comes from a combination of infant mortality and high levels of adult male mortality. Furthermore, differentials among subpopulations within each country could also drive some of these results (Leinsalu, Vågerö, and Kunst 2004). It should be noted that in Estonia, the third Baltic country, females were close to a crossing in the 1970s, similar to the situation for the other two countries, and thus a similar gender gap in the timing of the crossing might have been ...
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... industrialized countries, Table 1 and Figure 4 show the timing of the crossing of e 0 and e 1 . There is considerable variation in the timing of the crossover. Table 1 also shows the life expectancy and the infant mortality level at that time. http://www.demographic-research.org 1955 1960 1965 1970 1975 1980 1985 1990 1995 The first crossing in the world occurred in 1957 for Icelandic females, for males in the same country this was not observed until 1967. The time of the male crossover is later than that of females for all analyzed countries except Slovenia. However, the variation in time is large with one year difference for Portugal, Russia and Spain, and 14 to 18 years difference in Latvia, Lithuania, Bulgaria and Ukraine. The crossings in life expectancies occurred in the range of infant mortality of 11.6 to 14.5 deaths per thousand live births for females and of 13.6 to 17.5 for males. The level of life expectancy varied between the values of 71.8 to 78.0 years for females and 58.9 to 72.3 years for males. The larger variation in levels of life expectancy among males than among females (13.4 versus 6.2 years respectively) is due to the very low life expectancy at the crossing for Russia. Figure 4 shows a clear regional clustering of the crossing of the life expectancies. The Nordic countries, together with Switzerland and the Netherlands, were among the first to experience a balanced life table. In the Eastern European region by contrast, the first crossings are not observed until the 1980s. Another unexpected finding is the regular patterns in levels of life expectancy at which the crossing occurs: Eastern Europe at low levels, Scandinavia, Western and Central Europe at middle levels, and finally Southern Europe at high ...
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... industrialized countries, Table 1 and Figure 4 show the timing of the crossing of e 0 and e 1 . There is considerable variation in the timing of the crossover. Table 1 also shows the life expectancy and the infant mortality level at that time. http://www.demographic-research.org 1955 1960 1965 1970 1975 1980 1985 1990 1995 The first crossing in the world occurred in 1957 for Icelandic females, for males in the same country this was not observed until 1967. The time of the male crossover is later than that of females for all analyzed countries except Slovenia. However, the variation in time is large with one year difference for Portugal, Russia and Spain, and 14 to 18 years difference in Latvia, Lithuania, Bulgaria and Ukraine. The crossings in life expectancies occurred in the range of infant mortality of 11.6 to 14.5 deaths per thousand live births for females and of 13.6 to 17.5 for males. The level of life expectancy varied between the values of 71.8 to 78.0 years for females and 58.9 to 72.3 years for males. The larger variation in levels of life expectancy among males than among females (13.4 versus 6.2 years respectively) is due to the very low life expectancy at the crossing for Russia. Figure 4 shows a clear regional clustering of the crossing of the life expectancies. The Nordic countries, together with Switzerland and the Netherlands, were among the first to experience a balanced life table. In the Eastern European region by contrast, the first crossings are not observed until the 1980s. Another unexpected finding is the regular patterns in levels of life expectancy at which the crossing occurs: Eastern Europe at low levels, Scandinavia, Western and Central Europe at middle levels, and finally Southern Europe at high ...
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... between life expectancies at birth and at ages above infancy have also occurred over time. Similar to equation 1, it is possible to obtain relations between life expectancies at birth and at other older ages. For example, the crossing with life expectancy at age five occurs at the time when the inverse of under five mortality is equal to life expectancy at age five, i.e. Figure 8 includes the timing of the crossing between e 0 and e 5 for the HMD countries with available data and the child mortality level at the time of the crossing. Figure 8 is analogous to timing of the crossing between life expectancies at birth and age one presented in Figure 4. Several countries from the HMD have mortality series that start after the crossing between e 0 and e 5 . For this reason we left out of Figure 8: Belarus, Chile, Estonia, Germany (ex-GDR and ex-FRG), Northern Ireland, Lithuania, Luxembourg, Latvia, Russia, Slovenia, Taiwan, Ukraine and the USA. The New Zealand (total population) was also taken out for this reason, although the Non-Maori population from this country experienced the crossing in 1911. In Figure 8 it is more difficult to see regional patterns, even though Eastern and Southern Europe achieve the crossing much later than Northern Europe. Portugal is the last to achieve the equality in equation A6 among the countries with available data in the Human Mortality Database (2010). The range of values of life expectancy at the time of the crossing is wider than those observed in Table 1. For the total population, values of life expectancy are found between 46 and 69 years, but the distribution is much wider among males (22 years) than for females (10 years) (not ...
Citations
... Let the function describing the number of survivors at age x and at time t in a life table be denoted as l(x, t). Life expectancy at age x and at time t is calculated in terms of the survival function as [39]: ...
Background: Mortality estimates at the subnational level are of urgent need in India for the formulation of policies
and programmes at the district level. This is the first-ever study which used survey data for the estimation of life
expectancy at birth (e0) for the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21) for the
total, male and female population in India.
Methods: This study calculated annual age-specific mortality rates from NFHS-4 and NFHS-5 for India and all 36
states for the total, male and female population. This paper constructed the abridged life tables and estimated life
expectancy at birth (e0) and further estimated the model parameters for all 36 states. This study linked state-specific
parameters to the respective districts for the estimation of life expectancy at birth (e0)for 640 districts from NFHS-4
and 707 districts from NFHS-5 for the total, male and female population in India.
Results: Findings at the state level showed that there were similarities between the estimated and calculated e0 in
most of the states. The results of this article observed that the highest e0 varies in the ranges of 70 to 90 years among
the districts of the southern region. e0 falls below 70 years among most of the central and eastern region districts.
In the northern region districts e0 lies in the range of 70 years to 75 years. The estimates of life expectancy at birth
(e0) shows the noticeable variations at the state and district levels for the person, male, and female populations from
the NFHS (2015-16) and NFHS (2019-21). In the absence of age-specific mortality data at the district level in India,
this study used the indirect estimation method of relating state-specific model parameters with the IMR of their
respective districts and estimated e0 across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5
(2019-21). The findings of this study have similarities with the state-level estimations of e0 from both data sources of
SRS and NFHS and found the highest e0 in the southern region and the lowest e0 in the eastern and central region
districts.
Conclusions: In the lack of e0 estimates at the district level in India, this study could be beneficial in providing timely
life expectancy estimates from the survey data. The findings clearly shows variations in the district level e0. The
districts from the southern region show the highest e0 and districts from the central and eastern region has lower e0.
Females have higher e0 as compared to the male population in most of the districts in India.
Keywords: Subnational mortality, Age Pattern, Age at death, Life expectancy at birth, District
... Let the function describing the number of survivors at age x and at time t in a life table be denoted as l(x, t). Life expectancy at age x and at time t is calculated in terms of the survival function as [39]: ...
Background
Mortality estimates at the subnational level are of urgent need in India for the formulation of policies and programmes at the district level. This is the first-ever study which used survey data for the estimation of life expectancy at birth (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{e}}_{0}$$\end{document}) for the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21) for the total, male and female population in India.
Methods
This study calculated annual age-specific mortality rates from NFHS-4 and NFHS-5 for India and all 36 states for the total, male and female population. This paper constructed the abridged life tables and estimated life expectancy at birth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({e_0})$$\end{document} and further estimated the model parameters for all 36 states. This study linked state-specific parameters to the respective districts for the estimation of life expectancy at birth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({e_0})$$\end{document}for 640 districts from NFHS-4 and 707 districts from NFHS-5 for the total, male and female population in India.
Results
Findings at the state level showed that there were similarities between the estimated and calculated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} in most of the states. The results of this article observed that the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} varies in the ranges of 70 to 90 years among the districts of the southern region. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} falls below 70 years among most of the central and eastern region districts. In the northern region districts \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} lies in the range of 70 years to 75 years. The estimates of life expectancy at birth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({e_0})$$\end{document} shows the noticeable variations at the state and district levels for the person, male, and female populations from the NFHS (2015-16) and NFHS (2019-21). In the absence of age-specific mortality data at the district level in India, this study used the indirect estimation method of relating state-specific model parameters with the IMR of their respective districts and estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21). The findings of this study have similarities with the state-level estimations of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} from both data sources of SRS and NFHS and found the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} in the southern region and the lowest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} in the eastern and central region districts.
Conclusions
In the lack of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} estimates at the district level in India, this study could be beneficial in providing timely life expectancy estimates from the survey data. The findings clearly shows variations in the district level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document}. The districts from the southern region show the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} and districts from the central and eastern region has lower \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document}. Females have higher \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${e_0}$$\end{document} as compared to the male population in most of the districts in India.
... Life expectancy at birth is the single most used demographic measure of population health and of the well-being of a population (Canudas-Romo & Becker, 2011). It can be defined as the average number of years lived by newborns who would be exposed throughout their lives to the conditions observed during a particular period. ...
Life expectancy and infant mortality rates are two important indicators of the well-being of a population. However, it is not possible to compute these indicators for specific Indigenous populations in Canada from vital statistics databases because information about the identity of individuals is lacking. We use alternative data sources, linkages between the long-form census questionnaire and the Canadian Vital Statistics, namely the Canadian Census Health and Environment Cohorts and the Canadian Birth Census Cohorts, to compute life expectancy at birth and infant mortality rates among Indigenous populations. We describe the data, explain the methodology, and analyze the results to demonstrate the usefulness of these databases for the regular reporting of these health outcomes and monitoring of trends.
... But lower life expectancy at birth is observed than that of age one or more than one because of high infant and child mortality. This imbalance in life table disappears only when the crossover occurs and it happens when the inverse of the infant mortality becomes equal to the life expectancy at age one (Rabbi, 2013;Canudas-Romo and Becker, 2011). A review of mortality trends in Bangladesh inevitably induces controversies about data sources and methods of estimation, and the mortality is one of the components of population dynamics in any society or country or world. ...
The aim of this study is to construct a life table for female population of Bangladesh in 2001 by Widowhood Method. And then age specific death rates (ASDRs) and crude death rate (CDR) are estimated from the constructed life table. Moreover, some mathematical models are fitted to adult mortality and ASDRs for female population of Bangladesh. For this, the distribution of male population by current marital status due to age in 2001 census is utilized as raw materials in this study. The model validation technique, cross-validity prediction power (CVPP) is applied to verify the validation of the model. In this study, it is estimated that the life expectancy at birth and CDR for female population are 64.37 and 9.37 respectively. These estimates are compared to previous estimates and observed that life expectancy at birth is increasing and CDR is decreasing with time which indicate the health situation of Bangladesh is more improved than earlier time. It is also observed that logistic model has been fitted to adult mortality. Moreover, exponential model for ASDRs from age 0 to 5-9, reciprocal of simple linear model for ASDRs from age 10-14 to 40-44 and exponential model for ASDRs from age 45-49 to 85+ for female population of Bangladesh have been fitted and it is found that all the fitted models are well according to shrinkage coefficients as well as F-test. The proportions of variation of these models are more than 99%.
... One of the features of the demographic transition is the increase in life expectancy at birth. In this transition period, unbalanced life tables may emerge for developing countries (Canudas-Romo and Becker, 2011;Rabbi, 2013). ...
Humanity's quest for immortality has necessitated the struggle for longevity. The way to live better and longer is health. At this point, sports emerge as a tool for a healthy and long life. The aim of this study is to reveal the relationship between life expectancy at birth and the life span of successful athletes. The sample of the study consisted of all athletes who received medals on behalf of Turkey in the Olympics and died until 2021. The expected life span of the athletes at the date of birth was estimated with the help of the exponential equations created in Excel. In the light of these obtained data, graphs were created comparing the ages of death of the athletes with the life expectancy at birth in Turkey, Europe, Asia, Eurasia and the World. As a result, we can say that life expectancy at birth in Turkey, where the sample group is located, is generally low when compared to Europe, Asia, Eurasia and the World, of which Turkey is a part. However, life expectancy at birth of athletes was found to be above the life expectancy in Turkey, Europe, Asia, Eurasia and the World, considering the age of death. This result indicates that sports can be used as a tool that makes a positive contribution to life expectancy.
... Due to the focus of the paper, the first point that should be highlighted about the use of life tables to compute the SSF is the fact that, even though retirement age could (and probably is) a non-integer age, IBGE life tables only provide life expectancies for integer ages, and the retirement age needed to be rounded down to the nearest integer age so that the life expectancy could be properly obtained. However, since life expectancy is typically a monotonically decreasing function of age (Canudas-Romo and Becker, 2011), the reduction of the retirement age to the nearest integer age could cause a reduction on the SSF and, potentially, on retiree's income. In this sense, since life expectancy can be computed for any age directly from the parameters of the ΓGM fitted model (Castellares et al., 2020), then the SSF could be calculated more accurately, which would benefit retirees. ...
Automatic Adjustment Mechanisms (AAM) are legal instruments that help social security systems respond to demographic and economic changes. In Brazil, the Social Security Factor (SSF) was introduced in the late 1990s as an AAM to link retirement benefits to life expectancy at the retirement age, with the hope of promoting contributory justice and discouraging early retirement. Recent research has highlighted the limitations of right-censored life tables, such as those used in Brazil. It has recommended using the gamma-Gompertz-Makeham (GGM) model to estimate adult and old-age mortality. This study investigated the impact of right-censoring on the SSF by comparing the official SSF and other social security metrics with a counterfactual scenario computed based on fitted GGM models. The results indicate that from 2004 to 2012, official life tables may have negatively impacted retirees' income, particularly for those who delayed their retirement. Furthermore, the GGM-fitted models' life expectancies had more stable paths over time, which could have helped with long-term planning. This study's findings are significant for policymakers as they highlight the importance of using appropriate mortality metrics in AAMs to ensure accurate retirement benefit payments. They also underscore the need to consider the potential impacts of seemingly innocuous hypotheses on public action outcomes. Overall, this study provides valuable insights for public planners and policymakers looking to enhance the effectiveness and fairness of social security systems.
... Although it reduced sharply over the years, still the life expectancy at birth is lower than age 1 or 2 years (Figs 1 and 2). Canudas-Romo and Becker quantified the effect of infant mortality on life expectancy at birth and implied that the effect of infant mortality will be minimized when the life expectancy at birth and age 1 will be same [23]. From the obtained forecast of e 0 and e 1 , we plot the difference of e 0 and e 1 in Fig 7. From the obtained difference across time, life expectancy at birth will be slightly larger than life expectancy at age 1 from 2033 for men and 2027 for women. ...
Mortality forecasts are essential part for policymaking in any aging society. In recent years, methods to model and forecast mortality have improved considerably. Among them, Lee-Carter method is one of the most influential method. In this paper, Lee-Carter method is applied to forecast mortality and life expectancy of Bangladesh. A functional data analysis approach is used to decompose the smoothed log-mortality rates in Lee-Carter framework for higher goodness-of-fit of the models and for longer forecast horizons. Bangladesh has been experiencing a mortality transition and has gained life expectancy in last few decades. The fitted model here showed higher pace of mortality decline for women in Bangladesh than that of men. The forecasts showed continuation of mortality improvement in long run and by 2060 life expectancy at birth is expected to reach over 80 years for both sexes in Bangladesh. The study also predicts the effect of reduction in infant mortality on the life expectancy in Bangladesh.
... LEB is generally considered as the basis for determining mortality levels of different age groups in a population, however, life expectancies at ages other than zero are also used for studying human longevity, in historical populations and most developing countries, the incidence of high rate of infant and early childhood mortality results in lower values of 0 e than at other ages, and in such populations those surviving the hazards of early childhood have a higher life expectancy than new born and the highest life expectancy occurs not at birth but at a later stage (Romo and Becker [12]). ...
... Romo and Becker [12] obtained the following relationship among LEB, survivorship probability at age one and life expectancies at age 1: ...
... Dubey, Ram and Ram (2015) affirm that, ideally, life expectancy should be a monotonically decreasing function of age, so its maximum could be attained at age zero. However, according to Canudas-Romo and Becker (2011) this characteristic only started to be observed in the most developed nations in the second half of the twentieth century, more precisely, in 1957, for Icelandic females. ...
... According to Rowland (2003), this counterintuitive situation is known as the paradox of the life table, and it is still observed in populations in the middle (or earlier stages) of the demographic transition. Therefore, the world can be divided in countries/regions that have already overcome the paradox and those that have not BECKER, 2011). ...
... Readers interested in understanding how those projections were performed and learning details about the construction of the abridged life tables will benefit from the reading of IBGE (2013bIBGE ( , 2013cIBGE ( , 2018b. -Romo and Becker (2011) proved that the crossover between e 0 and e x occur when e 0 = e x = 1/ x m 0 , and, in particular, since imbalanced life tables are typically characterized by the fact that e 0 < e 1 , then the age-specific death rate between exact ages 0 and 1, 1 m 0 , is a fundamental metric in studying the life table paradox. Following Canudas-Romo and Becker (2011, p.118), we also refer to 1 m 0 as infant mortality (IM). ...
Ideally, life expectancy should be a decreasing function of age. When this fact is not observed, this situation is known as the life table paradox. This paper investigated the timing (and health metrics at the time) in which Brazil and its Federation Units (FU) overcame (or are expected to overcome) this paradox. The data were gathered from the Brazilian Institute of Geography and Statistics and contained 3,416 sex-specific abridged life tables, from 2000 to 2060. At national level, females and males overcame the paradox in 2016 and 2018, respectively. However, when the FU were examined separately, much heterogeneity was observed. Through the decomposition analysis of the change over time in the difference between life expectancy at birth and at age one, we found that Brazil and most of its FU are expected to have both changes declining over time and the total change is expected to be decreasing and greater than zero. Nevertheless, for some Northeastern states the total change is expected to pass from a positive to a negative value; and for two Northern states the total change is expected to be neither decreasing nor increasing. In a public planning perspective, we understand that achieving balancing in the life tables is a goal to be pursued, especially because having an imbalanced table means that life expectancy at birth is still strongly influenced by high levels of infant mortality. Therefore, this knowledge could help planners to properly define strategies to accelerate the balancing process and revert unequal scenarios.
... Some studies examine the geographical features of the distribution of population by life expectancy (Shaw et al., 2005;Bayati et al., 2013). Others have addressed the causes of death in various socio-demographic groups (Canudas-Romo & Becker, 2011;Chou & Chen, 2019). The spatial variability of life expectancy largely depends on the economic, institutional, social, demographic, environmental development of countries and regions, as well as on the health behaviour. ...
The article is aimed at studying the effects of social, economic, demographic, behavioural and environmental factors on the life expectancy of rural people in different types of regions. Using cluster analysis, we identified four relatively homogeneous groups of Russian regions in terms of life expectancy. The impact of socio-economic, demographic and environmental indicators on life expectancy of the rural population was assessed using regression models. We identified regions with low life expectancy for the rural population, and factors that have negative effect on life expectancy at birth. The main ones were alcohol abuse, high unemployment and emissions of pollutants into the air. The regression analysis showed that investments aimed at the development of health care, provision of social services and improvement of residential premises contributed to an increase in life expectancy. Significant factors in regions with high life expectancy were a lower number of recorded crimes per 100,000 of the population and a decrease in high unemployment, as well as an increase in educational expenses. In the group of regions where life expectancy of the rural population was approaching the average level in Russia, an important factor was also an increase in the level of education. We conclude that a regionally differentiated approach is necessary when introducing social policy changes, and measures aimed at increasing the life expectancy of the rural population should take into account the distinctive differences in socioeconomic development of the various regions of Russia.
