What 273 Years of Mortality Data Reveal About Population Resilience

Imagine a Swedish public-health analyst scrolling through centuries of mortality records—from 1751 to today. Wars, pandemics, antibiotics, and aging societies have all left their mark. Yet when the analyst plots the data, a familiar pattern emerges: mortality at midlife rises in a straight line on a logarithmic scale. The slope and intercept may shift across eras, but their relationship stays curiously consistent.

That enduring relationship is the Strehler-Mildvan correlation (SM correlation)—a statistical heartbeat of population health that, remarkably, has persisted for nearly 300 years.

The Problem: How Do We Detect Long-Term Health Change?

Public-health leaders often monitor short-term signals: excess deaths, disease incidence, and hospital utilization. But the deeper story—how a population’s overall vitality evolves—can hide beneath year-to-year fluctuations.

Josef Dolejs, from the University of Hradec Králové in Czechia, wanted to know whether a simple mathematical relationship could act as a long-term diagnostic of population health.

Building on Benjamin Gompertz’s 1825 model—where mortality intensity rises exponentially with age—the SM correlation links two key parameters:

• the baseline mortality (intercept), and
• the rate of aging (slope).

Over time, these two move in tandem: as living conditions and medicine improve, baseline mortality drops while the slope steepens. In geometric terms, successive mortality lines “rotate” around a common point of intersection, signaling a stable equilibrium of population vitality.

The Study: Three Centuries, Three Countries

Using open data from the Human Mortality Database, Dolejs examined long-term mortality patterns in Sweden (1751–2023), France (1816–2021), and the Netherlands (1850–2021).

Each dataset was modeled using the Gompertz equation across ages 40 to 95, producing two parameters per five-year period. When plotted against each other, those points form the SM correlation line.

Key quantitative results:

• Sweden: slope = −102.7 years, R² = 0.989
• Netherlands: −106.5 years, R² = 0.985
• France: −117.7 years, R² = 0.942 (lowest fit)

Despite cultural, medical, and political differences, all three countries showed a consistent long-term SM relationship—evidence that mortality dynamics follow similar rules across populations.

When History Breaks the Pattern

The model isn’t unbreakable. Dolejs found clear disruptions corresponding to major societal shocks:

• 1915–1919 (World War I): all three countries showed negative deviations from the correlation.
• 1965–1979: another shared disturbance, likely tied to global economic, environmental, and behavioral shifts in post-industrial societies.

Yet the key insight is resilience: once external pressures subside, populations tend to return to the SM equilibrium. Even the COVID-19 years (2020–2023)—despite mortality spikes—did not break the underlying trend.

Interpreting the Mechanism

The original theory by Strehler and Mildvan (1960) framed this relationship around “vitality”—the latent capacity to resist damage and recover from stress.

• When public health improves (e.g., vaccination campaigns, clean water, antibiotics), baseline mortality falls.
• But as more people survive into old age, the aging slope steepens—more deaths occur in the oldest cohorts.

In other words, our success at reducing early death re-shapes the curve of aging itself.

This pattern is not just theoretical. It has been observed in animals under controlled conditions and validated in historical data spanning centuries.

What This Means in Practice

For public-health agencies

• Use the SM correlation as a long-term health indicator, revealing whether population vitality is improving or under stress.
• Track deviations as early signals of systemic shock (e.g., pandemics, economic collapse).

For policy and planning

• Integrate SM-based metrics with life-expectancy and disability-adjusted life years to capture both length and quality of life.
• Use historical comparisons to test policy impact: Are today’s health gains sustainable or temporary perturbations?

For researchers

• Apply the SM framework to emerging chronic diseases and neurodegenerative conditions to detect shifts in mortality composition.
• Combine mortality data with biomarkers and social determinants to quantify “resilience” at the population level.

For community stakeholders

• Translate this evidence into narratives about societal resilience and recovery, helping communities see how policy and prevention efforts echo over generations.

What’s Next & Barriers

Future applications

• Linking SM metrics to real-time surveillance systems could help flag health system strain before excess mortality appears.
• Machine-learning models could incorporate SM parameters to forecast population aging and health-system demand.

Barriers

• Mortality data lag and inconsistent international reporting may limit real-time use.
• Translating statistical patterns into actionable policy requires training and clear communication to non-technical audiences.
• Political and financial cycles favor short-term metrics, not century-long trends.

Why This Matters Now

As health systems grapple with aging populations, climate stress, and economic volatility, the SM correlation offers a rare long-view lens. It shows that human societies can bend but rarely break their trajectory toward longer lives—and that restoring equilibrium after crisis is a measure of collective resilience.

“When the system is exposed to strong external influences like wars or epidemics, it eventually returns to an equilibrium state,” Dolejs notes. That return itself is a story of public health success.

Conversation Starters

• How might your agency use long-term mortality patterns to plan for future aging trends?
• Which recent events in your region might have temporarily “bent” the SM correlation?
• What would it take to treat resilience as a measurable public-health outcome?

📈 Bottom Line

The Strehler-Mildvan correlation is more than an equation—it’s a centuries-long mirror of how communities survive, recover, and adapt. By recognizing its patterns, public-health leaders gain a powerful tool for seeing not just where we are, but how far we’ve come—and how we might steer toward a healthier future.