Longevity and life expectancy

Abstract

The increase in life expectancy at all ages during the last two centuries is in need of a quantitative model capable of resuming the whole process under a single concept and simple mathematics. The basic hypothesis was that through improved hygiene, medicine, and life-style, the stumbling blocks to the full expression of longevity were progressively removed. The mathematics of learning processes was then applied to the secular evolution of life expectancy at various ages. The hypothesis proved very fertile . Logistic equations fit long strings of statistical data, covering the evolution of life expectancy at various ages, for both sexes, and in many European nations for almost two centuries. These life expectancy increases seem to move progressively to a common asymptote of about 79 years for men and about 84 years for women . It is suggested that these values are taken as a definition of longevity, presumably explicitating a coding in DNA. The evolution of life expectancy during the last couple of centuries appears to follow consistent paths precisely mapped with simple mathematics. This opens the way to more integrated and holistic theories . . © 1997 Elsevier Science Inc.

The Relative Importance of Globalization and Public Expenditure on Life Expectancy in Europe: An Approach Based on MARS Methodology

“…In the beginning, higher public health expenditure could be associated with significant gains in health status of population, even though this association is not applicable in high-expenditure countries [ 130 ]. Moreover, the authors of [ 131 ] concluded that there is a point of saturation at which increases in public health expenditure do not necessarily imply increases in life expectancy. This may merely reflect an inflated administration, expensive technologies, poor comparative effectiveness or personal financial advantages for interest groups or individuals [ 42 ].…”

Bayesian Probabilistic Projections of Life Expectancy for All Countries

“…Marchetti et al (1996) have shown that a sum of logistic functions can be used to model not only the adoption and substitution of competing technological innovations (Fisher & Pry, 1971; Meyer, 1994; Meyer et al, 1999), but also the social diffusion, learning and adoption of new ideas, norms, attitudes and behaviors associated with the fertility and mortality transitions (Marchetti et al, 1996; Marchetti, 1997; Potter et al, 2010) or nuptiality (Hernes, 1972; Goldstein & Kenney, 2001; Li & Wu, 2008). …”

How long might the younger-old live: A predictive model

“…Perks [21] observed that the mortality curve can be better fitted by a logistic function. Marchetti et al [22] and Marchetti [23] also use logistic models to represent mortality changes over time at all ages. Other studies have shown that mortality at higher ages can be modelled closely using logistic or similar models [24][25][26][27][28][29].…”