Exploring Patterns of Human Mortality and Aging: A Reliability Theory Viewpoint

Abstract

The most important manifestation of aging is an increased risk of death with advancing age, a mortality pattern characterized by empirical regularities known as mortality laws. We highlight three significant ones: the Gompertz law, compensation effect of mortality (CEM), and late-life mortality deceleration and describe new developments in this area. It is predicted that CEM should result in declining relative variability of mortality at older ages. The quiescent phase hypothesis of negligible actuarial aging at younger adult ages is tested and refuted by analyzing mortality of the most recent birth cohorts. To comprehend the aging mechanisms, it is crucial to explain the observed empirical mortality patterns. As an illustrative example of data-directed modeling and the insights it provides, we briefly describe two different reliability models applied to human mortality patterns. The explanation of aging using a reliability theory approach aligns with evolutionary theories of aging, including idea of chronic phenoptosis. This alignment stems from their focus on elucidating the process of organismal deterioration itself, rather than addressing the reasons why organisms are not designed for perpetual existence. This article is a part of a special issue of the journal that commemorates the legacy of the eminent Russian scientist Vladimir Petrovich Skulachev (1935-2023) and his bold ideas about evolution of biological aging and phenoptosis.

Keywords: Gompertz model; aging; compensation effect of mortality; evolutionary models of aging; mortality; mortality deceleration; reliability theory of aging.

Fig. 1.

Mortality of Norwegian women in 2010 in semi-log scale and its linear fit according to the Gompertz law. Data source:Human Mortality Database (www.mortality.org).

Fig. 2.

Mortality deceleration for cohort of U. S. women born in 1886. Data source: Human Mortality Database (www.mortality.org).

Fig. 3.

Mortality for selected Swedish female birth cohorts. Data source: Human Mortality Database (www.mortality.org).

Fig. 4.

Fitting mortality of Norwegian women born in 1920 by binomial law and competing Gompertz (a) and Weibull (b) laws.Data source: Human Mortality Database (www.mortality.org).