Distributing the Span of Lives: An Illustrative Analysis
Flowing Data has developed an innovative visualization tool that provides an insightful perspective on life expectancy. The tool, which utilizes death statistics from the Centers for Disease Control and Prevention (CDC) and the Social Security Administration, calculates the likelihood of surviving each year of life based on real-world data.
Instead of reporting a single average life expectancy, the tool generates a distribution of potential ages at death for a group of individuals sharing the same age and gender. For instance, the visualization for 19-year-old women in 2025 simulates numerous lifetimes, each represented by a dot moving along a curve depicting the real-world probability of survival to the next year. Each year, the dot represents a person's chance of dying, mimicking the roll of a dice weighted according to real-world survival odds. When a simulated death occurs, the dot falls and is recorded as a tick on the x-axis. The scattering of these ticks illustrates the distribution of expected death ages for 19-year-old women in 2025.
To create such a visualization, data from both the CDC and the Social Security Administration is collected. The CDC's National Vital Statistics System (NVSS) offers mortality statistics, including deaths by age, which can be used to estimate life expectancy and mortality rates for various age groups. Additionally, the Social Security Administration provides actuarial tables, like the Social Security Actuarial Life Table, which offer life expectancy data that can be used to estimate mortality rates and life expectancy for different cohorts.
The collected data is then analyzed to calculate mortality rates and life expectancies for each age group. Life tables are constructed to display the probability of survival or death at each age, enabling the understanding of the range of potential ages at death for a specific cohort. The visualization then showcases the distribution of life expectancy for a cohort starting at a particular age, survival curves illustrating proportion of individuals surviving to each age, and quantile plots highlighting the range of possible ages at death.
This visualization helps users understand the distribution of ages at death for a cohort starting at the same age, offering insights into how life expectancy varies within a group and how different factors might influence mortality rates. The tool can be implemented using Python libraries like Matplotlib or Seaborn, providing a simplified yet powerful method for visualizing life expectancy data.
Scientists in the field of health-and-wellness utilizing data from trusted sources, such as the Centers for Disease Control and Prevention (CDC) and the Social Security Administration, can leverage Flowing Data's innovative visualization tool to analyze life expectancy trends. With this tool, they can access a distribution of potential ages at death for specific age groups and genders, providing a more nuanced understanding of life expectancy beyond a single average.
