Separating the signal from the noise: Evidence for deceleration in old-age death rates

TL;DR

This study evaluates various mortality models at old ages using high-quality data, finding that models allowing for deceleration fit better and introducing an R package for further analysis.

Contribution

It empirically compares nine mortality models at old ages, highlighting the effectiveness of models with deceleration and providing a new R tool for researchers.

Findings

Models with decelerating death rates fit data better.

The Log-Quadratic model most consistently predicts old-age mortality.

Model fit varies by country and sex, influenced by regional and cultural factors.

Abstract

Widespread population aging has made it critical to understand death rates at old ages. However, studying mortality at old ages is challenging because the data are sparse: numbers of survivors and deaths get smaller and smaller with age. We show how to address this challenge by using principled model selection techniques to empirically evaluate theoretical mortality models. We test nine different theoretical models of old-age death rates by fitting them to 360 high-quality datasets on cohort mortality above age 80. Models that allow for the possibility of decelerating death rates tend to fit better than models that assume exponentially increasing death rates. No single model is capable of universally explaining observed old-age mortality patterns, but the Log-Quadratic model most consistently predicts well. Patterns of model fit differ by country and sex; we discuss possible mechanisms, including sample size, period effects, and regional or cultural factors that may be important keys to understanding patterns of old-age mortality. We introduce a freely available R package that enables researchers to extend our analysis to other models, age ranges, and data sources.