Deep #disadvantage in #mortality on the frontlines of the #COVID19 #pandemic

 

Abstract

This study presents new evidence on the temporal and spatial impact of the COVID-19 pandemic on mortality among especially vulnerable New Yorkers. Using burial records from Hart Island—the City’s potter’s field—we study the distribution of unclaimed deaths over time and across boroughs in 2020 compared to pre-pandemic levels. We show that the Hart Island deaths began deviating from their historical pattern in early March 2020 and peaked five weeks later at 22 deaths for every death in the same week in 2019 (20:1 adjusted). COVID-19 excess death rates were more than twice as high in the Bronx compared to other boroughs. Citywide, we estimate that 10% of all COVID-related excess deaths during the initial outbreak (March–August 2020) were unclaimed. These findings suggest the pandemic greatly magnified existing inequalities in the City and, more broadly, illustrate the especially devastating impact of COVID-19 on economically and socially vulnerable populations.

Source: 

Link: https://www.nature.com/articles/s41598-026-41219-6

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Clustering #Countries on #Development Indicators Reveals Structure Relevant for #H5N1 #Mortality Analysis

 

Abstract

Infectious diseases are often observed to have different epidemiology in different countries, which arises due to various factors including those that are ecological, socioeconomic, and healthcare-related. Such variability can sometimes be best captured through looking at groups of countries that are similar within-group but variable between-group. In this study we use statistical learning methods to generate data-driven disease-centric groupings of countries rather than those developed for administrative or political reasons by e.g. the WHO, World Bank, and the United Nations. In particular, we apply hierarchical clustering to group countries based on shared disease-relevant characteristics for zoonotic H5N1 influenza. Using statistical methods such as classification and regression trees (CART)-based imputation and dynamic tree cutting, the analysis accounts for missing data and identifies epidemiologically (rather than politically or economically) meaningful clusters. Applying health metric relevant indicators, we cluster the countries of the world and using a Bayesian approach compute CFRs of zoonotic H5N1 influenza before comparing across clusters. We find that countries with stronger healthcare systems and lower poverty rates tend to have lower and more stable CFRs, whereas resource-limited settings face higher fatality risks.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

MKA was supported by the Schlumberger Foundation Faculty for the Future. TH was supported by the Wellcome Trust (227438/Z/23/Z) and the Medical Research Council (UKRI483).

Source: MedRxIV, https://www.medrxiv.org/content/10.1101/2025.11.08.25339808v1

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