Abstract:
Global mortality models often depend
on simple aging metrics that ignore the structural
diversity in populations. To tackle this issue, we
evaluated three metrics: median age, percentage
aged 65+, and the new PoPDivergence, against mor
tality from the top five global causes of death in
2021 across 178 countries. PoPDivergence, which
is based on Kullback-Leibler divergence, identifies
differences in population pyramids from optimized
references. Using Spearman correlations, Levene’s
test, and Kruskal-Wallis analysis, we found that
PoPDivergence predicts COVID-19 mortality more
accurately in younger regions, such as the East
ern Mediterranean. In contrast, traditional metrics
better capture chronic disease mortality in aging
regions like Europe and in younger areas like Africa,
where non-communicable diseases are increasing. No
metric significantly predicted lower respiratory in
fection mortality. Our study presents a new, statis
tically validated framework for selecting age struc
ture metrics suited to specific diseases and regional
contexts, which offers important improvements for
global health modeling and policy development.
