The Rhythm of Aging: Stability and Drift in the Individual Rate of Senescence

Abstract

Human aging is marked by a steady rise in the risk of dying with age-a process demographers call senescence. Over the past century, life expectancy has risen dramatically, but is this because we are aging slower, or simply starting it later? Vaupel hypothesizes that the pace at which individuals age may be constant, with gains in longevity coming from the delayed onset of senescence rather than its slowing down. We test this idea using a new framework that decomposes the pace of senescence into three components: a biological baseline, a long-term trend, and the cumulative impact of period shocks. Applying this to cohort mortality data above age 80 from 12 countries, we find that once period shocks are accounted for, there is no statistical evidence of a long-term trend, consistent with Vaupel's hypothesis. Analyses using lower starting ages yield the same qualitative conclusion. Rather than indicating a change in the process that drives senescence, these variations are consistent with echoes of shared historical events. These results suggest that while longevity has shifted, the rhythm of human aging may be conserved.

Figures (16)

• Figure 1: Posterior estimates of the rate of aging ($b$, left), the drift ($\beta$, center), and the variance of the period effect ($\sigma_{\text{rw}}$, right) for males (blue) and females (red) across 12 countries. Points indicate posterior modes, and horizontal bars represent 95% credible interval. The drift estimates are tightly centered around zero, and the period effect is larger in countries affected by major period shocks, such as France, Italy, and Japan.
• Figure 2: Posterior predictive QQ-plots comparing observed and simulated quantiles of cohort death counts across 12 countries, separately for females (top) and males (bottom). Colored lines show posterior means by country; the black line represents the 45-degree identity line. The close alignment indicates that the model accurately captures the distribution of the observed data across its full range.
• Figure S1: Estimated $b$ across Danish female cohorts and cohort-to-cohort log-difference. The log-difference series is stationary, as confirmed by unit-root and stationarity tests (ADF and PP: $p \leq 0.01$; KPSS: $p \geq 0.10$)
• Figure S2: Random walk decomposition for Australia.
• Figure S3: Random walk decomposition for Canada.