Literature DB >> 6648150

Parameters of mortality in human populations with widely varying life spans.

W Siler.   

Abstract

A three-component, competing-risk mortality model, developed for animal survival data, fits human life table data for all ages over a range of mean life spans from 16 to 74 years. The competing risks are a novel exponentially-decreasing hazard, dominant during immaturity; a constant hazard, dominant during adulthood; and an exponentially increasing Gompertzian hazard, dominant during senescence. By fitting the model to a specific life table using non-linear techniques, estimates of the five model parameters and their standard errors obtain; the proportion of deaths expected from each hazard alone may then be calculated. Preliminary analysis of 13 life tables indicates that for human populations under heavy stress, with very short mean life spans of about 20 years, the three hazard components account for roughly equal numbers of deaths; for modern populations, with mean life spans of about 75 years, nearly all deaths are due to the hazard of senescence. Factor analysis of the correlation matrix of parameter values for the 13 populations shows a two-factor structure. One factor involves only the multiplicative constants (initial values) of the three hazards, but not the hazard rates of change; the second factor involves only the parameter of the immaturity hazard and the rate of acceleration of the senescence hazard, but not the constant hazard nor the multiplicative constant (initial value) of the senescence hazard.

Entities:  

Mesh:

Year:  1983        PMID: 6648150     DOI: 10.1002/sim.4780020309

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  12 in total

1.  The shape of mortality curves: an analysis of counties in England and Wales, 1911.

Authors:  J Anson
Journal:  Eur J Popul       Date:  1993

2.  Selectivity of black death mortality with respect to preexisting health.

Authors:  Sharon N DeWitte; James W Wood
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-28       Impact factor: 11.205

3.  The Age Pattern of Increases in Mortality Affected by HIV: Bayesian Fit of the Heligman-Pollard Model to Data from the Agincourt HDSS Field Site in Rural Northeast South Africa.

Authors:  David J Sharrow; Samuel J Clark; Mark A Collinson; Kathleen Kahn; Stephen M Tollman
Journal:  Demogr Res       Date:  2013-12-03

4.  Forecasting mortality: a parameterized time series approach.

Authors:  R McNown; A Rogers
Journal:  Demography       Date:  1989-11

5.  Kin competition, natal dispersal and the moulding of senescence by natural selection.

Authors:  Ophélie Ronce; Daniel Promislow
Journal:  Proc Biol Sci       Date:  2010-06-30       Impact factor: 5.349

6.  The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span.

Authors:  A I Yashin; K G Arbeev; I Akushevich; A Kulminski; S V Ukraintseva; E Stallard; K C Land
Journal:  Phys Life Rev       Date:  2012-05-17       Impact factor: 11.025

7.  How Has the Lower Boundary of Human Mortality Evolved, and Has It Already Stopped Decreasing?

Authors:  Marcus Ebeling
Journal:  Demography       Date:  2018-10

8.  SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA.

Authors:  Bailey K Fosdick; Peter D Hoff
Journal:  Ann Appl Stat       Date:  2014       Impact factor: 2.083

9.  Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data.

Authors:  Wilfred D Stein; William Doug Figg; William Dahut; Aryeh D Stein; Moshe B Hoshen; Doug Price; Susan E Bates; Tito Fojo
Journal:  Oncologist       Date:  2008-10-06

Review 10.  Demographic uniformitarianism: the theoretical basis of prehistoric demographic research and its cross-disciplinary challenges.

Authors:  Jennifer C French; Andrew T Chamberlain
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-11-30       Impact factor: 6.237

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.