Improvements in Age-Specific Mortality at the Oldest Ages

TL;DR

This paper introduces a new two-step method to accurately estimate age-specific mortality improvement rates at ages 85+ across European countries from 1950 to 2019, aiding better planning and forecasting.

Contribution

It proposes a novel Bayesian smoothing procedure and a piecewise linear modeling approach to improve estimation of mortality improvements at very old ages.

Findings

Identified non-uniform mortality improvements across ages and time.

Compared four smoothing methods and selected the best fit for estimating ASDR differences.

Provided a framework for short-term mortality trend forecasting.

Abstract

Age-specific mortality improvements are non-uniform, neither across ages nor across time. We propose a two-step procedure to estimate the rates of mortality improvement (RMI) in age-specific death rates (ASDR) at ages 85 and above for ten European countries from 1950 to 2019. In the first step, we smooth the raw death counts and estimate ASDR using four different methods: one parametric (gamma-Gompertz-Makeham), two non-parametric (P-splines and PCLM), and a novel Bayesian procedure to handle fluctuations resulting from ages with zero death counts. We compare the goodness of fit of the four smoothing methods and calculate the year-to-year ASDR differences according to the best-fitting one. We fit a piecewise linear function to these differences in the second step. The slope in each linear segment captures the average RMI in the respective year range. For each age, we calculate the goodness of fit in the last linear segment to assess how informative the estimated RMI of current mortality change is. The estimated rates of mortality improvement or deterioration (RMI) can be used to make short-term social, health, and social planning, as well as more precise mortality forecasts.