Devising PoPStat: A Metric Bridging Population Pyramids with Global Disease Mortality

TL;DR

This paper introduces PoPStat, a new metric that quantifies the relationship between population pyramid deviations and disease mortality, outperforming traditional indicators in explaining disease-specific death rates across countries.

Contribution

The study presents two novel metrics, PoPDivergence and PoPStat, linking population pyramid deviations with disease mortality, and demonstrates their effectiveness using global data.

Findings

PoPStat outperforms traditional demographic indicators in explaining disease mortality.

Different diseases are associated with specific population pyramid shapes.

PoPStat offers insights into demographic influences on health and epidemiological transition models.

Abstract

Understanding the relationship between population dynamics and disease-specific mortality is central to evidence-based health policy. This study introduces two novel metrics, PoPDivergence and PoPStat, one to quantify the difference between population pyramids and the other to assess the strength and nature of their association with the mortality of a given disease. PoPDivergence, based on Kullback-Leibler divergence, measures deviations between a countrys population pyramid and a reference pyramid. PoPStat is the correlation between these deviations and the log form of disease-specific mortality rates. The reference population is selected by a brute-force optimization that maximizes this correlation. Utilizing mortality data from the Global Burden of Disease 2021 and population statistics from the United Nations, we applied these metrics to 371 diseases across 204 countries. Results reveal that PoPStat outperforms traditional indicators such as median age, GDP per capita, and Human Development Index in explaining the mortality of most diseases. Noncommunicable diseases (NCDs) like neurological disorders and cancers, communicable diseases (CDs) like neglected tropical diseases, and maternal and neonatal diseases were tightly bound to the underlying demographic attributes whereas NCDs like diabetes, CDs like respiratory infections and injuries including self-harm and interpersonal violence were weakly associated with population pyramid shapes. Notably, except for diabetes, the NCD mortality burden was shared by constrictive population pyramids, while mortality of communicable diseases, maternal and neonatal causes and injuries were largely borne by expansive pyramids. Therefore, PoPStat provides insights into demographic determinants of health and empirical support for models on epidemiological transition. Code and scripts: https://github.com/Buddhi19/DevisingPoPStat.git