Evidence map›Paper›PMID 33442558›Full record

ArticleArXiv2021

Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities.

Lucas Böttcher, Maria R D'Orsogna, Tom Chou

Abstract readPreprint
In one paragraph

Article in ArXiv, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Lucas BöttcherDept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766.
Maria R D'OrsognaDept. of Mathematics, California State University at Northridge, Los Angeles, CA 91330-8313.
Tom ChouDept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766.

Funding

Tracking clonal dynamics during hematopoiesis: mechanistic insight via modeling and data analysisR01HL146552 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHOU, TOM · 2019 to 2022
$907k
NHLBI NIH HHS R01 HL146552
6 · The paper itself

Abstract

Factors such as non-uniform definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US is 13$\%$ higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find negligible or negative excess deaths for part and all of 2020 for Denmark, Germany, and Norway.

Identifiers

PMID33442558
PMCPMC7805454

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.