Evidence map›Paper›PMID 40817293›Full record

ArticleJournal of racial and ethnic health disparities2026

Disaggregating Middle Eastern and North African Populations in Mortality Records to Better Understand COVID-19 Inequalities in Virginia.

Elyas Bakhtiari

Abstract read
In one paragraph

Article in Journal of racial and ethnic health disparities, 2026. 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

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

1 author.

Elyas BakhtiariDepartment of Sociology, William and Mary, Williamsburg, VA, USA. ebakhtiari@wm.edu.ORCID 0000-0001-5766-5298

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many ethno-racial minority populations had disproportionately high rates of COVID-19 infection and mortality during the initial years of the pandemic, but it is currently unclear whether Middle Eastern and North African populations in the USA experienced similar inequalities. This study disaggregates the MENA population in Virginia death records using a long short-term memory machine learning model that can probabilistically identify ethnically-unique names. The results suggest that MENA populations had higher overall excess mortality and a higher percentage of COVID-19 deaths among total deaths, when compared to the non-Hispanic White population of Virginia. The paper highlights the importance of disaggregating the MENA population in health inequalities research and offers a new method that improves upon previous name-based disaggregation methods.

Indexed as

COVID-19Health Status DisparitiesAfrica, NorthernHumansMachine LearningMiddle Eastern and North AfricansMiddle Eastern PeopleNorth African PeopleVirginiaCovid-19Health inequalitiesMachine learningMiddle Eastern and North African

Identifiers

PMID40817293
PMCPMC13546347

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.