Evidence map›Paper›PMID 38375671›Full record

ArticleAmerican journal of epidemiology2024

Understanding racial/ethnic disparities in COVID-19 mortality using a novel metric: COVID excess mortality percentage.

Andy Ye Yuan, Vladimir Atanasov, Natalia Barreto, Lorenzo Franchi, Jeff Whittle, Benjamin Weston, John Meurer, Qian Eric Luo, Bernard Black

Abstract readComparative Study
In one paragraph

Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Vladimir Atanasov
Natalia Barreto
Lorenzo Franchi
Jeff Whittle
John Meurer
Qian Eric Luo
Bernard Black

Funding

Clinical and Translational Science AwardUL1TR001436 · NCATS · MEDICAL COLLEGE OF WISCONSIN · PI FREED, JULIE K · 2015 to 2025
$47.5M
NCATS NIH HHS UL1 TR001436NIH HHS 3 UL1 TR001436-06S1
6 · The paper itself

Abstract

Prior research on racial/ethnic disparities in COVID-19 mortality has often not considered to what extent they reflect COVID-19-specific factors, versus preexisting health differences. This study examines how racial/ethnic disparities in COVID-19 mortality vary with age, sex, and time period over April-December 2020 in the United States, using mortality from other natural causes as a proxy for underlying health. We study a novel measure, the COVID excess mortality percentage (CEMP), defined as the COVID-19 mortality rate divided by the non-COVID natural mortality rate, converted to a percentage, where the CEMP denominator controls (albeit imperfectly) for differences in population health. Disparities measured using CEMP deviate substantially from those in prior research. In particular, we find very high disparities (up to 12:1) in CEMP rates for Hispanics versus Whites, particularly for nonelderly men. Asians also have elevated CEMP rates versus Whites, which were obscured in prior work by lower overall Asian mortality. Native Americans and Blacks have significant disparities compared with White populations, but CEMP ratios to Whites are lower than ratios reported in other work. This is because the higher COVID-19 mortality for Blacks and Native Americans comes partly from higher general mortality risk and partly from COVID-specific risk.

Indexed as

COVID-19Health Status DisparitiesAdolescentAdultAgedAged, 80 and overAmerican Indian or Alaska NativeAsianBlack or African AmericanChildChild, PreschoolEthnicityFemaleHispanic or LatinoHumansInfantCOVID-19mortalityracial/ethnic disparities

Identifiers

PMID38375671
PMCPMC11145910

What OpenQuestion holds

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Registered trials

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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.