Evidence map›Paper›PMID 33323745›Full record

ArticleEpidemiology (Cambridge, Mass.)2021

Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.

Katie Labgold, Sarah Hamid, Sarita Shah, Neel R Gandhi, Allison Chamberlain, Fazle Khan, Shamimul Khan, Sasha Smith, Steve Williams, Timothy L Lash and 1 more

Open access · greenAbstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 pooled it
2.1field-weighted citation impact, top 9% of its field
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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Observational
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  10. Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.IEEE transactions on knowledge and data engineering · 2025
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Katie LabgoldFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Sarah HamidFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Sarita ShahFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Neel R GandhiFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Allison ChamberlainFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Fazle KhanFulton County Board of Health.
Shamimul KhanFulton County Board of Health.
Sasha SmithFulton County Board of Health.
Steve WilliamsFulton County Board of Health.
Timothy L LashFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Lindsay J CollinFrom the Department of Epidemiology, Rollins School of Public Health, Emory University.
Emory University · USDeKalb County Board of Health · USHuntsman Cancer Institute · US

Funding

Household Air Pollution and Health: A Multi-Country LPG Intervention TrialUM1HL134590 · NHLBI · EMORY UNIVERSITY · PI CHECKLEY, WILLIAM, CLASEN, THOMAS F · 2016 to 2021
$31.1M
Mentorship in Patient-Oriented Research in Tuberculosis, HIV and Global HealthK24AI114444 · NIAID · EMORY UNIVERSITY · PI GANDHI, NEEL RAJNIKANT · 2014 to 2023
$1.8M
NRSA Training CoreTL1TR002540 · NCATS · UNIVERSITY OF UTAH · PI FAGERLIN, ANGELA, FAIRFAX, KEKE CELESTE · 2018 to 2022
$1.6M
Improving Reproducibility by Incorporating UncertaintyR01LM013049 · NLM · EMORY UNIVERSITY · PI LASH, TIMOTHY L. · 2019 to 2022
$1.1M
Biologic and Patient Variation Affecting Breast Cancer Treatment EfficacyF31CA239566 · NCI · EMORY UNIVERSITY · PI COLLIN, LINDSAY JANE · 2019 to 2020
$53k
NCATS NIH HHS TL1 TR002540NCI NIH HHS F31 CA239566NHLBI NIH HHS UM1 HL134590NIAID NIH HHS K24 AI114444NLM NIH HHS R01 LM013049
6 · The paper itself

Abstract

backgroundBlack, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data.

methodsWe quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification.

resultsThe ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons.

conclusionsThese results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.

Indexed as

AsianBlack or African AmericanCOVID-19Data CollectionGeorgiaHealth Status DisparitiesHispanic or LatinoHospitalizationHumansIndigenous PeoplesMortalityNative Hawaiian or Pacific IslanderSARS-CoV-2Statistics as TopicUnited StatesWhite People

Identifiers

PMID33323745
PMCPMC8641438
OpenAlexW3111489730

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.