Demographic Structure of Pecos Indians: A Model Based on Life Tables

1980 ◽  
Vol 45 (3) ◽  
pp. 518-530 ◽  
Author(s):  
Charles M. Mobley
Keyword(s):  
New Mexico ◽  
Life Expectancy ◽  
Data Base ◽  
Analytical Method ◽  
Life Tables ◽  
Demographic Analysis ◽  
Age At Death ◽  
Demographic Structure ◽  
Model Based ◽  
Death Data

Age-at-death data on over 2,000 burials from two pueblo sites in New Mexico are subjected to demographic analysis. Prior studies are reviewed to illustrate deficiencies and qualifications in the data base and the analytical method. The skeletal assemblage is subdivided into seven samples by chronological phase, and life tables are constructed. Aspects such as mortality and life expectancy are then examined for each phase and a diachronic model of demographic structure is developed for Pecos Indians between A.D. 1150 and 1700.

scholarly journals Dynamics of modal age at death in 1959−2014 in Russia

2019 ◽  
Vol 3 (4) ◽  
pp. 86-96
Author(s):  
Mikhail A. Maksimov
Keyword(s):  
Standard Deviation ◽  
Life Expectancy ◽  
Developed Countries ◽  
Life Tables ◽  
Age At Death ◽  
The Past ◽  
The 1950S ◽  
The Developed Countries ◽  
One Year

Abstract The aim of this paper is to determine the trends of the main indicators of life expectancy in Russia in the 1950s to 2000s. For this purpose, life tables for Russia (former — RSFSR) from 1959 to 2014 for one-year age intervals were analyzed. The main indicators under review are the modal age at death and the standard deviation of life expectancy from the modal value for all ages and the mode. As a result, it is concluded that in Russia the modal age at death and the indicator of life expectancy have stagnated over the past 60 years, and definite trends can be traced only in short periods of time, namely after 2009 when all basic life expectancy indicators were steadily increasing. Life expectancy is far behind those of the developed countries by about half a century.

Life Expectancy in Individuals With Type 2 Diabetes: Implications for Annuities

2009 ◽  
Vol 30 (3) ◽  
pp. 409-414 ◽  
Author(s):  
Hermione C. Price ◽  
Philip M. Clarke ◽  
Alastair M. Gray ◽  
Rury R. Holman
Keyword(s):  
Type 2 Diabetes ◽  
Life Expectancy ◽  
Cholesterol Level ◽  
Total Cholesterol Level ◽  
Insurance Companies ◽  
Age At Death ◽  
Life Annuity ◽  
Uk Government ◽  
The Uk

Background. Insurance companies often offer people with diabetes ‘‘enhanced impaired life annuity’’ at preferential rates, in view of their reduced life expectancy. Objective. To assess the appropriateness of ‘‘enhanced impaired life annuity’’ rates for individuals with type 2 diabetes. Patients. There were 4026 subjects with established type 2 diabetes (but not known cardiovascular or other life-threatening diseases) enrolled into the UK Lipids in Diabetes Study. Measurements. Estimated individual life expectancy using the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model. Results. Subjects were a mean (SD) age of 60.7 (8.6) years, had a blood pressure of 141/83 (17/10) mm Hg, total cholesterol level of 4.5 (0.75) mmol/L, HDL cholesterol level of 1.2 (0.29) mmol/L, with median (interquartile range [IQR]) known diabetes duration of 6 (3—11) years, and HbA1c of 8.0% (7.2—9.0). Sixty-five percent were male, 91% white, 4% Afro-Caribbean, 5% Indian-Asian, and 15% current smokers. The UKPDS Outcomes Model median (IQR) estimated age at death was 76.6 (73.8—79.5) years compared with 81.6 (79.4—83.2) years, estimated using the UK Government Actuary’s Department data for a general population of the same age and gender structure. The median (IQR) difference was 4.3 (2.8—6.1) years, a remaining life expectancy reduction of almost one quarter. The highest value annuity identified, which commences payments immediately for a 60-year-old man with insulin-treated type 2 diabetes investing 100,000, did not reflect this difference, offering 7.4K per year compared with 7.0K per year if not diabetic. Conclusions. The UK Government Actuary’s Department data overestimate likely age at death in individuals with type 2 diabetes, and at present, ‘‘enhanced impaired life annuity’’ rates do not provide equity for people with type 2 diabetes. Using a diabetes-specific model to estimate life expectancy could provide valuable information to the annuity industry and permit more equitable annuity rates for those with type 2 diabetes.

scholarly journals Research on TANH Material Constitutive Model Based on Analytical Method

2020 ◽  
Vol 56 (9) ◽  
pp. 252
Author(s):  
HAO Xiaole ◽  
YUE Caixu ◽  
CHEN Zhitao ◽  
LIU Xianli ◽  
LIANGS Y ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Analytical Method ◽  
Material Constitutive Model ◽  
Model Based

scholarly journals Income inequality in disability-free life expectancy in Denmark

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
H Brønnum-Hansen ◽  
E Foverskov ◽  
I Andersen
Keyword(s):  
Income Inequality ◽  
Life Expectancy ◽  
Social Inequality ◽  
Socioeconomic Position ◽  
Life Tables ◽  
Register Data ◽  
Disposable Income ◽  
Inequality In Health ◽  
Disability Free Life Expectancy ◽  
The Difference

Abstract Background The state old-age pension in Denmark is adjusted in line with the projected increasing life expectancy without taking social inequality in health and life expectancy into account. The purpose of the study was to estimate income disparities in life expectancy and disability-free life expectancy (DFLE) at age 50. Methods By linking nationwide register data on income and mortality each individual at any age was divided into equivalised disposable income quartiles and life tables were constructed for each quartile. Data from the Danish Survey of Health, Ageing and Retirement in Europe (SHARE) was linked to register data providing access to information on respondents equivalised disposable income. Finally, data from the life tables were combined with prevalence on activity limitations by income quartiles from SHARE to estimate DFLE by Sullivan’s method. Differences in DFLE were investigated and decomposed into contributions from mortality and disability effects. Results A clear social gradient was seen for life expectancy as well as DFLE. Thus, life expectancy at age 50 differed between the highest and lowest income quartile by 8.0 years for men and 5.0 years for women. The difference in DFLE was 11.8 and 10.3 years for men and women, respectively. For men the mortality effect from the decomposition contributed by 4.1 years to the difference of 11.8 years in DFLE and 3.9 years to the difference in expected years with disability of 3.8 years while the disability effect contributed by 7.7 years. Conclusions The study quantifies social inequality in health in Denmark. Although income inequality in life expectancy and DFLE can partly be explained by loss of income due to chronic diseases, one would expect a welfare state to provide better financial security for citizens with health problems. Furthermore, the marked social disparity when approaching retirement age is questioning the fairness of implementing a pension scheme independently of socioeconomic position. Key messages Disability-free life expectancy differs between income quartiles by more than 10 years. Pension age follows the projected increasing life expectancy independently of socioeconomic position. This seems unfair.

scholarly journals Why has mortality in England and Wales been increasing? An iterative demographic analysis

2017 ◽  
Vol 110 (4) ◽  
pp. 153-162 ◽  
Author(s):  
Lucinda Hiam ◽  
Danny Dorling ◽  
Dominic Harrison ◽  
Martin McKee
Keyword(s):  
Life Expectancy ◽  
Social Care ◽  
Demographic Analysis ◽  
England And Wales ◽  
Life Expectancy At Birth ◽  
Health And Social Care ◽  
Diagnosis Of Dementia ◽  
Small Decline ◽  
Post War

Objectives To understand why mortality increased in England and Wales in 2015. Design Iterative demographic analysis. Setting England and Wales Participants Population of England and Wales. Main outcome measures Causes and ages at death contributing to life expectancy changes between 2013 and 2015. Results The long-term decline in age-standardised mortality in England and Wales was reversed in 2011. Although there was a small fall in mortality rates between 2013 and 2014, in 2015 we then saw one of the largest increases in deaths in the post-war period. Nonetheless, mortality in 2015 was higher than in any year since 2008. A small decline in life expectancy at birth between 2013 and 2015 was not significant but declines in life expectancy at ages over 60 were. The largest contributors to the observed changes in life expectancy were in those aged over 85 years, with dementias making the greatest contributions in both sexes. However, changes in coding practices and diagnosis of dementia demands caution in interpreting this finding. Conclusions The long-term decline in mortality in England and Wales has reversed, with approximately 30,000 extra deaths compared to what would be expected if the average age-specific death rates in 2006–2014 had continued. These excess deaths are largely in the older population, who are most dependent on health and social care. The major contributor, based on reported causes of death, was dementia but caution was advised in this interpretation. The role of the health and social care system is explored in an accompanying paper.

Accounts and Models for Spatial Demographic Analysis 3: Rates and Life Tables

1975 ◽  
Vol 7 (2) ◽  
pp. 199-231 ◽  
Author(s):  
P H Rees ◽  
A G Wilson
Keyword(s):  
Life Table ◽  
Fundamental Matrix ◽  
Life Tables ◽  
Population Projection ◽  
Age Group ◽  
Demographic Analysis ◽  
Demographic Rate ◽  
Group Life ◽  
Single Region ◽  
Lexis Diagram

The paper begins by distinguishing, with the aid of the Lexis diagram that plots age against time, three kinds of demographic rate: age-group rates, period rates, and life-table rates. There are single-region and multiregion versions of those rates. In order to measure multiregional life-table rates, life-table accounts are developed together with an accounts based model that estimates the full accounts matrix from available data. These multiregional rates are then used to construct multiregional life tables akin to those recently proposed by Rogers. It is shown that the calculations involved in measuring the survivorship probabilities of the life table can be succinctly summarized in Stone's fundamental matrix. The detailed connections between life-table accounts and age-group accounts are explored, and the possibility of age-group life tables raised. The conclusion is reached that the age-group accounts are the appropriate ones for generating rates for use in population projection models, and that the life-table accounts are the appropriate ones for generating rates for use in actuarial calculations.

A Method for Using Life Tables to Estimate Lifetime Risk for Prostate Cancer Death

2010 ◽  
Vol 8 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Hyung L. Kim ◽  
Marvin R. Puymon ◽  
Maochun Qin ◽  
Khurshid Guru ◽  
James L. Mohler
Keyword(s):  
Prostate Cancer ◽  
Life Expectancy ◽  
Gleason Score ◽  
Life Tables ◽  
Lifetime Risk ◽  
Cancer Death ◽  
Social Security Administration ◽  
Curative Therapy ◽  
Prostate Cancer Death ◽  
Estimate Lifetime Risk

Prostate cancer can have a long and indolent course, and management without curative therapy should be considered in select patients. When counseling patients, a useful way to convey the risk for death from competing causes is to estimate their lifetime risk for dying from prostate cancer. Double-decrement life tables were constructed to calculate age-specific death rates using the death probabilities from the Social Security Administration life tables and Gleason score–specific mortality rates reported from pre-PSA cohort study. The lifetime risk for prostate cancer death was calculated. Life tables provided life expectancy and risk for prostate cancer death based on age at diagnosis. For example, 60-year-old patient with a Gleason score 6, 7, or 8 tumor had an overall life expectancy of 14.4, 10.2, or 6.6 years, respectively. The risk for prostate cancer death during the expected years of life was 33%, 49%, or 57%, respectively. If a 10-year lead-time bias was assumed for PSA detection, the risks for death from prostate cancer decreased to 16%, 26%, or 37%, respectively. If the patient was in the bottom quartile for overall health and disease was detected by prostate examination, the risk for death from prostate cancer was 21%, 32%, or 40%, respectively. A Web-based tool for performing these calculations is available at http://www.roswellpark.org/Patient_Care/Specialized_Services/Prostate_Cancer_Estimator.html. Life tables can be created to estimate overall life expectancy and risk for prostate cancer death, and to assist with decision-making when considering management without curative therapy.

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