Evidence map›Paper›PMID 41438258›Full record

ArticleAgeing and longevity research2025

The Compensation Effect of Mortality: A Global Analysis of Human Populations.

Natalia S Gavrilova, Leonid A Gavrilov

Abstract read
In one paragraph

Article in Ageing and longevity research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Natalia S GavrilovaNORC at the University of Chicago, 1155 E 60th Street, Chicago, IL60637, USA.
Leonid A GavrilovNORC at the University of Chicago, 1155 E 60th Street, Chicago, IL60637, USA.

Funding

Biodemography of Old-Age MortalityR21AG054849 · NIA · NATIONAL OPINION RESEARCH CENTER · PI GAVRILOVA, NATALIA S · 2017 to 2018
$428k
NIA NIH HHS R21 AG054849
6 · The paper itself

Abstract

The compensation effect of mortality (CEM) refers to the convergence of mortality rates at advanced ages across different human populations. This phenomenon occurs when higher values of the Gompertz slope parameter α (actuarial aging rate) are offset (compensated) by lower values of the intercept parameter R (initial mortality rate). The age at which this convergence occurs is known as the species-specific lifespan (SSLS). The primary aim of this study is to estimate SSLS in contemporary human populations of different world regions using both parametric and nonparametric approaches. We analyzed United Nations period abridged life tables for 251 countries and regions, spanning the period from 1980 to 2020. Both parametric and nonparametric methods of SSLS estimation produced comparable results. For industrialized countries with high-quality vital statistics, SSLS estimates ranged from 95 to 100 years-consistent with estimates made more than three decades ago. This suggests that the convergence point of CEM has remained stable over time, despite substantial declines in mortality at younger ages. High SSLS estimates were also observed in regions of Eastern and South-Eastern Asia. In contrast, other world regions showed lower SSLS values, ranging from 75 to 90 years. Due to the CEM, efforts to extend lifespan are typically accompanied by a paradoxical increase in the actuarial aging rate (Gompertz slope), making significant extensions of life expectancy at older ages challenging. The CEM thus remains a key constraint to radical life extension in humans. Importantly, the compensation effect of mortality appears to be a general regularity, observed consistently across world populations examined.

Indexed as

life extensionlongevitymortalityspecies-specific lifespan

Identifiers

PMID41438258
PMCPMC12721371

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.