Results

Baudisch, A. & Polizzi, A. (2025)

This paper connects two different ways of studying when women have children. The traditional approach views childbirth from the mother's perspective, where having a baby is an uncertain event that can happen multiple times. In contrast, the newer "Born Once, Die Once" (B1D1) framework views birth from the child's perspective—just as every person dies only once, every child is also born only once. This shift in perspective allows researchers to use the same analytical tools for studying both fertility and mortality patterns at the population level. The paper demonstrates that these two frameworks (conventional and B1D1) are mathematically related and introduces new ways to analyze age patterns of fertility that don't require individual birth histories. Using over a century of Swedish data, the research shows that in modern low-mortality populations, both approaches produce nearly identical results, making the new tools readily applicable to contemporary fertility research. This work opens new possibilities for studying fertility trends, improving fertility forecasts, and jointly analyzing birth and death patterns using a common methodological approach.

Baudisch, A. & Aburto, J.M. (2024)

When properly scaled, life expectancy and life years lost at death sum to exactly one—a surprisingly simple mathematical relationship with profound societal implications. By decomposing lifespan into two components—"pace" (environmental mortality) and "shape" (aging processes)—the research reveals that in modern populations, over 95% of survival prospects depend on aging rather than external conditions, compared to roughly 50–50 in the 1880s. This framework offers new tools for improving mortality forecasting, essential for planning pension systems, healthcare infrastructure, and social policies. It also identifies a ratio that successfully detected the major mortality transition around 1950, suggesting it could serve as an early warning system for future demographic shifts. Ultimately, this relationship enhances our ability to predict how long people will live and how populations will age, supporting more informed decisions on retirement ages, healthcare resources, and the sustainability of social programs.

Mogi, R. & Yoda, S. (2024)

This paper examines whether the well-documented trend of delayed childbirth applies equally to all women or varies by how many children they ultimately have. Using data from 17 European countries and Canada, the research reveals a striking pattern: women who have more children tend to have each child at younger ages with less variation in timing, while the delay and increasing diversity in childbirth timing is concentrated among women with fewer children. For instance, among women born in the 1960s who had only one child, the timing of that birth became much more varied and delayed compared to earlier cohorts, while women who had four or more children showed relatively stable patterns across generations. These insights help policymakers design family support systems, workplace leave policies, and reproductive health services.

Patricio, S. & Missov, T. (2024)

This paper revisits the classic Makeham mortality model, which separates deaths related to aging from those caused by external or random events. It shows that Makeham models can be expressed as mixtures—that is, as combinations of probability distributions representing premature and senescent deaths. This mathematical reformulation allows for a clearer estimation of how these two mortality components evolve across ages and populations. Using data from France, Italy, Japan, and Sweden from 1947 to 2020, the study tracks how the threshold age separating premature and senescent mortality has shifted over time. The results show that senescent mortality has been postponed by roughly two to four months per year in most populations, while premature mortality has declined. By framing Makeham models as mixture models, the paper provides a more interpretable and flexible way to analyze mortality dynamics and the changing balance between aging-related and external causes of death.

Bapteste et.al (2023)

This paper argues that traditional evolutionary theories of aging overlook a critical factor: the complex web of interactions shaping how organisms age. While classic theories focus on individual organisms, the authors show that aging is profoundly influenced by microbiomes, symbiotic relationships, social structures, and even parasites that manipulate their hosts' aging—termed "age-distorters." For instance, the human gut microbiome contributes to inflammaging, while queen ants live 30 years versus weeks for genetically identical workers due to social structure. This perspective has important societal implications: understanding that aging results from interactions across multiple species and biological scales could revolutionize how we approach age-related diseases and healthy longevity, suggesting that manipulating these ecological relationships—not just individual genes—may be key to promoting healthier human aging.