Deviation in the Age Structure of Mortality as an Indicator of COVID-19 Pandemic Severity

Author:

Chandra Siddharth1,Chandra Madhur1

Affiliation:

1. Siddharth Chandra is with the Asian Studies Center and James Madison College, Michigan State University, East Lansing. Madhur Chandra is with the Ingham County Health Department, Lansing, MI, and the Department of Epidemiology and Biostatistics, Michigan State University.

Abstract

Objectives. To test whether distortions in the age distribution of deaths can track pandemic activity. Methods. We compared weekly distributions of all-cause deaths by age during the COVID-19 pandemic in the United States from March to December 2020 with corresponding prepandemic weekly baseline distributions derived from data for 2015 to 2019. We measured distortions via Kolmogorov–Smirnov (K-S) and χ2 goodness-of-fit statistics as well as deaths among individuals aged 65 years or older as a percentage of total deaths (PERC65+). We computed bivariate correlations between these measures and the number of recorded COVID-19 deaths for the corresponding weeks. Results. Elevated COVID-19-associated fatalities were accompanied by greater distortions in the age structure of mortality. Distortions in the age distribution of weekly US COVID-19 deaths in 2020 relative to earlier years were highly correlated with COVID fatalities (K-S: r = 0.71, P < .001; χ2: r = 0.90, P < .001; PERC65+: r = 0.85, P < .001). Conclusions. A population-representative sample of age-at-death data can serve as a useful means of pandemic activity surveillance when precise cause-of-death data are incomplete, inaccurate, or unavailable, as is often the case in low-resource environments. (Am J Public Health. 2022;112(1):165–168. https://doi.org/10.2105/AJPH.2021.306567 )

Publisher

American Public Health Association

Subject

Public Health, Environmental and Occupational Health

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