Evidence map›Paper›PMID 34528022›Full record

ArticleLancet regional health. Americas2021

A longitudinal study of convergence between Black and White COVID-19 mortality: A county fixed effects approach.

Ralph Lawton, Kevin Zheng, Daniel Zheng, Erich Huang

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

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0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  10. Spatial prediction of COVID-19 pandemic dynamics in the United States.ISPRS international journal of geo-information · 2022
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Ralph LawtonDepartment of Economics, Duke University, 213 Social Sciences, 419 Chapel Drive, Durham, North Carolina, US.
Kevin ZhengDepartment of Biostatistics and Bioinformatics, Duke University Medical Center, 2424 Erwin Road, Suite 1102 Hock Plaza, Box 2721, Durham, North Carolina, US.
Daniel ZhengUniversity of Maryland, Baltimore County, 1000 Hilltop Cir, Baltimore, Maryland, US.
Erich HuangDepartment of Biostatistics and Bioinformatics, Duke University Medical Center, 2424 Erwin Road, Suite 1102 Hock Plaza, Box 2721, Durham, North Carolina, US.

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
NCATS NIH HHS UL1 TR002553
6 · The paper itself

Abstract

backgroundNon-Hispanic Black populations have suffered much greater per capita COVID-19 mortality than White populations. Previous work has shown that rates of Black and White mortality have converged over time. Understanding of COVID-19 disparities over time is complicated by geographic changes in prevalence, and some prior research has claimed that regional shifts in COVID-19 prevalence may explain the convergence.

methodsUsing county-level COVID-19 mortality data stratified by race, we investigate the trajectory of Black and White per capita mortality from June 2020-January 2021. We use a county fixed-effects model to estimate changes within counties, then extend our models to leverage county-level variation in prevalence to study the effects of prevalence versus time trajectories in mortality disparities.

findingsOver this period, cumulative mortality rose by 61% and 90% for Black and White populations respectively, decreasing the mortality ratio by 0.4 (25.8%). These trends persisted when a county-level fixed-effects model was applied. Results revealed that county-level changes in prevalence nearly fully explain changes in mortality disparities over time.

interpretationResults suggest mechanisms underpinning convergence in Black/White mortality are not driven by fixed county-level characteristics or changes in the regional dispersion of COVID-19, but instead by changes within counties. Further, declines in the Black/White mortality ratio over time appear primarily linked to county-level changes in COVID-19 prevalence rather than other county-level factors that may vary with time. Research into COVID-19 disparities should focus on mechanisms that operate within-counties and are consistent with a prevalence-disparity relationship.

fundingThis work was supported by the National Center for Advancing Translational Sciences [E.H.: UL1TR002553].

Indexed as

COVID-19DemographyDisparitiesLongitudinalMortalityRace

Identifiers

PMID34528022
PMCPMC8426155

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LicenceCC BY-NC-ND
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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.