scholarly journals Life Expectancy in Germany Based on the 2011 Census: Was the Healthy Migrant Effect Merely an Artefact?

2016 ◽  
Vol 41 (2) ◽  
Author(s):  
Felix Zur Nieden ◽  
Bettina Sommer
Keyword(s):  
Life Expectancy ◽  
Life Table ◽  
Census Data ◽  
Population Data ◽  
Life Tables ◽  
German Population ◽  
Healthy Migrant Effect ◽  
Foreign Population ◽  
Life Expectancy At Birth ◽  
Do So

The Federal Statistical Office’s 2010/12 general life table is the first to provide results on life expectancy based on census data for reunified Germany. This article therefore examines the question of how the revisions of the population figures from the 2011 census affected the measured life expectancy. To do so, we analysed both the official life tables based on the old intercensal population updates before the census and those based on the population data from the 2011 census. The method used to calculate the census-adjusted 2010/12 general life table was also transferred to separate life tables drawn up for the German and the foreign population. In this way, findings on the so-called “healthy migrant effect” can be discussed, ruling out possible errors in the intercensal population updates. These errors had previously been cited as the main causes for a distinctly longer life expectancy among the foreign population compared with the German population. As expected, a census-based calculation for the total population and for the German population resulted in only minor revisions to the life expectancy figures. The use of the census results does, however, distinctly alter the life expectancy of foreign women and men. An advantage of over 5 years in life expectancy at birth, measured on the basis of the old population data, needs to be revised to about 2.9 years for men and 2.1 years for women based on the 2011 census. The healthy migrant effect therefore cannot be traced back solely to data artefacts from the old intercensal population updates – even with revised data, the foreign population shows marked survival advantages.

Analysis of Mortality Data due to Infectious Diseases: Life Table Methods Complementary to Direct Rates

1988 ◽  
Vol 27 (03) ◽  
pp. 137-141
Author(s):  
M. A. A. Moussa ◽  
M. M. Khogali ◽  
T. N. Sugathan
Keyword(s):  
Life Expectancy ◽  
Infectious Diseases ◽  
Life Table ◽  
Census Data ◽  
Total Mortality ◽  
Life Tables ◽  
Mortality Data ◽  
Men And Women ◽  
Male And Female ◽  
Life Expectancy At Birth

SummaryLife table methods are employed complementary to standard rates to analyse Kuwaiti mortality data due to infectious diseases. The procedure comprises total mortality, multiple-decrement, cause—elimination and cause—delay life tables. To improve reliability of estimated age-specific death rates, the numerator was based on the three-year average of deaths (1981-83), while the denominator was the mid 1982 population projected from the 1980 and 1985 population censuses. To overcome the difficulty of age heaping, both mortality and census data were graduated using the natural cubic spline approach. Proportional mortality was maximum in intestinal infectious diseases particularly in the rural Jahra Governorate. Infectious diseases caused 29.4 and 37.1% of male and female deaths respectively in infancy and early childhood. The male and female life expectancy at birth were 67 and 72 years, respectively.The multiple-decrement life tables showed that 3,346 men and 2,986 women out of the birth cohort (100,000) will ultimately die from infectious diseases. The average number of years lost due to infectious diseases were 0.75 years in both men and women. Relating this loss to the affected (saved) subpopulation only, large gains in life expectancy occur (22.3 and 25.2 years in men and women respectively).

scholarly journals A new model for estimating district life expectancy at birth in India, with special reference to Assam state

2014 ◽  
Vol 41 (1-2) ◽  
pp. 180
Author(s):  
Rajan Sarma ◽  
Labananda Choudhury
Keyword(s):  
Life Expectancy ◽  
South Asian ◽  
Census Data ◽  
Life Tables ◽  
Important Indicator ◽  
Registration System ◽  
Life Expectancy At Birth ◽  
Sample Registration System ◽  
Using Data ◽  
The Relationship

Life expectancy at birth (e0) is considered as an important indicator of the mortality level of a population. In India, direct estimation of e0 is not possible due to incomplete death registration. The Sample Registration System (SRS) of India provides information on e0 only for the 16 major states. Estimates of e0 for the districts are not available. Using data from the Coale-Demeny West model life tables, United Nations South Asian model life tables, and SRS life tables of India and its major states, the paper shows that the relationship between life expectancy at age one (e0) and the probability of surviving to age one (l1) is linear, and the relationship between e0 and l1 is quadratic. From the quadratic relationship between e0 and l1, an attempt is made to estimate e0 for some selected districts of India for 2001 and 2010, using estimated l1 from 2001 census data and Annual Health Survey (2010–11) data.

scholarly journals The life table paradox: has Brazil already overcome it?

2021 ◽  
Vol 38 ◽  
pp. 1-23
Author(s):  
Filipe Costa de Souza
Keyword(s):  
Infant Mortality ◽  
Life Expectancy ◽  
Life Table ◽  
National Level ◽  
Decomposition Analysis ◽  
Life Tables ◽  
Total Change ◽  
Life Expectancy At Birth ◽  
The Difference ◽  
Over Time

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.

scholarly journals On Mathematical Equalities and Inequalities in the Life Table: Something Old and Something New

2021 ◽  
Author(s):  
David A. Swanson ◽  
Lucky M. Tedrow
Keyword(s):  
Life Expectancy ◽  
Life Table ◽  
Life Tables ◽  
Entire Population ◽  
Maximum Lifespan ◽  
Life Expectancy At Birth ◽  
Inequality Theorem ◽  
New Inequalities

AbstractThis paper discusses known mathematical equalities and inequalities found within life tables and proceeds to identify two new inequalities. The first (theorem 1) is that at any given age x, the sum of mean years lived and mean years remaining exceeds life expectancy at birth when age is greater than zero and less than the maximum lifespan. The second inequality (theorem 2) applies to the entire population and shows that the sum of mean years lived and mean years remaining exceeds life expectancy at birth. Illustrations of the two inequalities are provided as well as a discussion.

scholarly journals Potential gain in life expectancy by gender after elimination of a specific cause of death in urban India

2020 ◽  
Vol 7 (5) ◽  
pp. 1848
Author(s):  
Bal Kishan Gulati ◽  
Damodar Sahu ◽  
Anil Kumar ◽  
M. V. Vardhana Rao
Keyword(s):  
Life Expectancy ◽  
Life Table ◽  
Cause Of Death ◽  
Metabolic Diseases ◽  
Circulatory System ◽  
Average Life Span ◽  
Maximum Gain ◽  
Life Expectancy At Birth ◽  
Potential Gain ◽  
Urban India

Background: Life expectancy is a statistical measure to depict average life span a person is expected to live at a given age under given age-specific mortality rates. Cause-elimination life table measures potential gain in life expectancy after elimination of a specific disease. The present study aims to estimate potential gain in life expectancy by gender in urban India after complete and partial elimination of ten leading causes of deaths using secondary data of medical certification of cause of death (MCCD) for the year 2015.Methods: Life table method was used for estimating potential gain after eliminating diseases to the tune of 25%, 50%, 75% and 100%.Results: Maximum gain in life expectancy at birth estimated from complete elimination of diseases of the circulatory system (11.1 years in males versus 13.1 years in females); followed by certain infectious and parasitic diseases (2.2  versus 2.1 years); diseases of the respiratory system (2.2 versus 2.1); injury, poisoning and certain other consequences of external causes (1.1 versus 0.7); neoplasms (0.9 versus 1.0); endocrine, nutritional and metabolic diseases (0.8 versus 0.9); diseases of the digestive system (0.8 versus 0.4); diseases of the genitourinary system (0.6 versus 0.6); diseases of the nervous system (0.4 versus 0.4); and diseases of blood & blood forming organs and certain disorders involving the immune mechanism (0.2 versus 0.3 years).Conclusions: Elimination of the circulatory diseases resulted into maximum gain in life expectancy. These findings may have implications in setting up health goals, allocating resources and launching tailor-made health programmes.

scholarly journals The Linear Link: Deriving Age-Specific Death Rates from Life Expectancy

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 109
Author(s):  
Marius Pascariu ◽  
Ugofilippo Basellini ◽  
José Aburto ◽  
Vladimir Canudas-Romo
Keyword(s):  
Life Expectancy ◽  
R Package ◽  
Value Of Life ◽  
Life Tables ◽  
Human Longevity ◽  
Death Rates ◽  
New Model ◽  
Life Expectancy At Birth ◽  
Age Patterns ◽  
Full Life

The prediction of human longevity levels in the future by direct forecasting of life expectancy offers numerous advantages, compared to methods based on extrapolation of age-specific death rates. However, the reconstruction of accurate life tables starting from a given level of life expectancy at birth, or any other age, is not straightforward. Model life tables have been extensively used for estimating age patterns of mortality in poor-data countries. We propose a new model inspired by indirect estimation techniques applied in demography, which can be used to estimate full life tables at any point in time, based on a given value of life expectancy at birth. Our model relies on the existing high correlations between levels of life expectancy and death rates across ages. The methods presented in this paper are implemented in a publicly available R package.

scholarly journals Hierarchical multistate models from population data: An application to parity statuses

2016 ◽  
Author(s):  
Robert Schoen
Keyword(s):  
Life Table ◽  
Hierarchical Models ◽  
Census Data ◽  
Population Data ◽  
Demographic Analysis ◽  
Multistate Models ◽  
American Women ◽  
Number Of Children ◽  
The Past

Hierarchical models are characterized by having N living states connected by N–1 rates of transfer. Demographic measures for such models can be calculated directly from counts of the number of persons in each state at two nearby points in time. Exploiting the ability of population stocks to determine the flows in hierarchical models expands the range of demographic analysis. The value of such analyses is illustrated by an application to childbearing, where the states of interest reflect the number of children a woman has born. Using Census data on the distribution of women by age and parity, a parity status life table for U.S. Women, 2005-2010, is constructed. That analysis shows that nearly a quarter of American women are likely to remain childless, with a 0-3 child pattern replacing the 2-4 child pattern of the past.

scholarly journals Hierarchical multistate models from population data: an application to parity statuses

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2535 ◽  
Author(s):  
Robert Schoen
Keyword(s):  
Life Table ◽  
Hierarchical Models ◽  
Census Data ◽  
Population Data ◽  
Demographic Analysis ◽  
Multistate Models ◽  
American Women ◽  
Number Of Children ◽  
The Past

Hierarchical models are characterized by havingNliving states connected byN− 1 rates of transfer. Demographic measures for such models can be calculated directly from counts of the number of persons in each state at two nearby points in time. Exploiting the ability of population stocks to determine the flows in hierarchical models expands the range of demographic analysis. The value of such analyses is illustrated by an application to childbearing, where the states of interest reflect the number of children a woman has born. Using Census data on the distribution of women by age and parity, a parity status life table for US Women, 2005–2010, is constructed. That analysis shows that nearly a quarter of American women are likely to remain childless, with a 0–3 child pattern replacing the 2–4 child pattern of the past.

3. A New Approach to Estimating Life Tables with Covariates and Constructing Interval Estimates of Life Table Quantities

2005 ◽  
Vol 35 (1) ◽  
pp. 177-225 ◽  
Author(s):  
Scott M. Lynch ◽  
J. Scott Brown
Keyword(s):  
Life Table ◽  
Transition Probabilities ◽  
Population Data ◽  
Life Tables ◽  
Mcmc Methods ◽  
Model Parameters ◽  
Term Care ◽  
Interval Estimates ◽  
Health And Nutrition ◽  
Care Survey

Extant approaches to constructing life tables generally rely on the use of population data, and differences between groups defined by discrete characteristics are examined by disaggregating the data before estimation. When sample data are used, few researchers have attempted to include covariates directly in the process of estimation, and fewer still have attempted to construct interval estimates for state expectancies when covariates are used. In this paper, we present a Bayesian approach that is useful for producing interval estimates for single-decrement, multiple-decrement, and multistate life tables. The method involves (1) estimating a hazard or survival model using Bayesian Markov chain Monte Carlo (MCMC) methods to produce a sample from the posterior distribution for the parameters of the model; (2) generating distributions of transition probabilities for selected values of covariates using the sample of model parameters; (3) using these distributions of transition probabilities as inputs for life table construction; and (4) summarizing the distribution of life table quantities. We illustrate the method on data simulated from the Berkeley Mortality Database, data from the National Health and Nutrition Examination Survey (and follow-ups), and data from the National Long Term Care Survey, and we show how the results can be used for hypothesis testing.

scholarly journals Life Table Analysis of a Small Sample of Santal Population Living in a Rural Locality of West Bengal, India

2014 ◽  
Vol 77 (2) ◽  
pp. 233-248
Author(s):  
Arupendra Mozumdar ◽  
Bhubon Mohan Das ◽  
Subrata K. Roy
Keyword(s):  
Life Expectancy ◽  
Life Table ◽  
West Bengal ◽  
Small Sample ◽  
Optimum Number ◽  
Small Populations ◽  
Marginal Populations ◽  
Acceptable Error ◽  
Closed Population ◽  
Life Expectancy At Birth

Abstract Life table calculation of small populations, especially of marginal populations, is difficult due to a small number of death records and lack of a systematic birth and death registry. The present study aimed to calculate a life table of a small sample of Santal population from Beliatore area of the Bankura district, West Bengal, India, using the recall method. The data on birth and death events were collected using house-to-house interviewing and cross-checking the data with reference to the significant events of the area and the family. The life table was calculated from age specific death rate of a closed population retrospectively estimated for 10 years. The calculated life expectancy at birth of the study population was 63.9 years with a standard error of 3.15 years. The finding agrees with the life expectancy of the other larger populations of the region, although calculated using conventional methods. The method needs to be evaluated to get the optimum number of death events required for calculating the life table with an acceptable error level. The study will be helpful for comparisons of overall health status of small populations with respect to time and space.

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