The Deviant Dynamics Of Death In Heterogeneous Populations

The members of most populations gradually die off or drop out: people die, machines wear out, residents move out, etc. In many such "aging" populations, some members are more likely to "die" than others. Standard analytical methods largely ignore this heterogeneity; the methods assume that all members of a population at a given age face the same probability of death. This paper presents some mathematical methods for studying how the behavior over time of a heterogeneous population deviates from the behavior of the individuals that make up the population. The methods yield some startling results: individuals age faster than populations, eliminating a cause of death can decrease life expectancy, a population can suffer a higher death rate than another population even though its members have lower death rates, population death rates can be increasing even though its members' death rates are decreasing.

[1]  N. Keyfitz,et al.  Mortality in a heterogeneous population , 1979 .

[2]  A. G. Arbous,et al.  Accident statistics and the concept of accident-proneness , 1951 .

[3]  Jane Menken,et al.  Mathematical Models of Conception and Birth , 1974 .

[4]  P. Brémaud Point Processes and Queues , 1981 .

[5]  D. D. McFarland Intragenerational Social Mobility as a Markov Process: Including a Time- Stationary Mark-Ovian Model that Explains Observed Declines in Mobility Rates Over Time , 1970 .

[6]  K. Manton,et al.  Methods for evaluating the heterogeneity of aging processes in human populations using vital statistics data: explaining the black/white mortality crossover by a model of mortality selection. , 1981, Human biology.

[7]  R B Ginsberg,et al.  Stochastic Models of Residential and Geographic Mobility for Heterogeneous Populations , 1973 .

[8]  C. Harris,et al.  Life Distributions Derived from Stochastic Hazard Functions , 1968 .

[9]  K G Manton,et al.  Maximum likelihood estimation of a stochastic compartment model of cancer latency: lung cancer mortality among white females in the U.S. , 1979, Computers and biomedical research, an international journal.

[10]  F. F. Stephan,et al.  The Industrial Mobility of Labor as a Probability Process by I. Blumen, M. Kogan, Ph. J. McCarthy. Cornell Studies in Industrial and Labor Relations, Vol. VI. Ithaca, Cornell University, 1955, XII p. 163 p., $ 2.00. , 1956, Bulletin de l'Institut de recherches économiques et sociales.

[11]  P. I. Kitsul,et al.  The One-Year - Five-Year Migration Problem , 1980 .

[12]  B. Singer,et al.  Social mobility models for heterogeneous populations , 1973 .

[13]  N. Weatherby,et al.  Causes of death which contribute to the mortality crossover effect. , 1978, Social biology.

[14]  Donald L. Snyder,et al.  Random point processes , 1975 .

[15]  J. Heckman,et al.  Population heterogeneity in demographic models. , 1982 .

[16]  E. Kay,et al.  Methods for statistical analysis of reliability and life data , 1974 .

[17]  M A Woodbury,et al.  A random-walk model of human mortality and aging. , 1977, Theoretical population biology.

[18]  R. Potter,et al.  Predicting the time required to conceive , 1964 .

[19]  W. Arthur The economics of risks to life. , 1979, The American economic review.

[20]  O. Lundberg On random processes and their application to sickness and accident statistics , 1940 .

[21]  S. Spilerman,et al.  Extensions of the Mover-Stayer Model , 1972, American Journal of Sociology.

[22]  Mayr,et al.  Evolution and the diversity of life , 1942 .

[23]  R. Zeckhauser,et al.  Long-term effects of interventions to improve survival in mixed populations. , 1980, Journal of chronic diseases.

[24]  Richard J. Zeckhauser,et al.  Where Now for Saving Lives , 1976 .

[25]  N. Keyfitz Multidimensionality in Population Analysis , 1980 .

[26]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Statistics of random processes , 1977 .

[27]  M. Hannan,et al.  Social Dynamics: Models and Methods. , 1986 .